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Chapter 21. Regional Context
Coordinating Lead Authors
Bruce Hewitson (South Africa), Anthony C. Janetos (USA)
Lead Authors
Timothy R. Carter (Finland), Filippo Giorgi (Italy), Richard G. Jones (UK), Won-Tae Kwon (Republic of Korea),
Linda O. Mearns (USA), E. Lisa F. Schipper (Sweden), Maarten K. van Aalst (Netherlands)
Contributing Authors
Eren Bilir (USA), Monalisa Chatterjee (USA / India), Grace Redmond (UK), Carol McSweeney (UK), Katharine
Mach (USA), Vanessa Schweizer (USA), Luke Wirth (USA), Claire van Wyk (South Africa)
Review Editors
Thomas Downing (USA), Phil Duffy (USA)
Volunteer Chapter Scientist
Kristin Kuntz-Duriseti (USA)
Contents
Executive Summary
21.1. Introduction
21.2. Defining Regional Context
21.2.1. Decision-Making Context
21.2.2. Defining Regions
21.2.3. Introduction to Methods and Information
21.3. Synthesis of Key Regional Issues
21.3.1. Vulnerabilities and Impacts
21.3.1.1. Observed Impacts
21.3.1.2. Future Impacts and Vulnerability
21.3.2. Adaptation
21.3.2.1. Similarities and Differences in Regions
21.3.2.2. Adaptation Examples in Multiple Regions
21.3.2.3. Adaptation Examples in Single Regions
21.3.3. Climate System
21.3.3.1. Global context
21.3.3.2. Dynamically and Statistically Downscaled Climate Projections
21.3.3.3. Projected Changes in Hydroclimatic Regimes, Major Modes of Variability, and Regional
Circulations
21.3.3.4. Projected Changes in Extreme Climate Events
21.3.3.5. Projected Changes in Sea Level
21.3.3.6. Projected Changes in Air Quality
21.4. Cross-Regional Phenomena
21.4.1. Trade and Financial Flows
21.4.1.1. International Trade and Emissions
21.4.1.2. Trade and Financial Flows as Factors Influencing Vulnerability
21.4.1.3. Sensitivity of International Trade to Climate
21.4.2. Human Migration
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21.4.3. Migration of Natural Ecosystems
21.5. Analysis and Reliability of Approaches to Regional Impacts, Adaptation, and Vulnerability Studies
21.5.1. Analyses of Vulnerability and Adaptive Capacity
21.5.1.1. Indicators and Indices
21.5.1.2. Hotspots
21.5.2. Impacts Analyses
21.5.3. Development and Application of Baseline and Scenario Information
21.5.3.1. Baseline Information: Context, Current Status, and Recent Advances
21.5.3.2. Development of Projections and Scenarios
21.5.3.3. Credibility of Projections and Scenarios
21.6. Knowledge Gaps and Research Needs
References
Chapter Boxes
21-1. A New Framework of Global Scenarios for Regional Assessment
21-2. Summary Regional Climate Projection Information
21-3. Developing Regional Climate Information Relevant to Political and Economic Regions
21-4. Synthesis of Projected Changes in Extremes Related to Temperature and Precipitation
Frequently Asked Questions
21.1: How does this report stand alongside previous assessments for informing regional adaptation?
21.2: Do local and regional impacts of climate change affect other parts of the world?
21.3: What regional information should I take into account for climate risk management for the 20 year time
horizon?
21.4: Is the highest resolution climate projection the best to use for performing impacts assessments?
Executive Summary
There has been an evolution in the treatment of regional aspects of climate change in IPCC reports from a
patchwork of case examples in early assessments towards recent attempts at a more systematic coverage of
regional issues at continental and sub-continental scales (21.2.2). Key topics requiring a regional treatment
include: changes in the climate itself and in other aspects of the climate system (such as the cryosphere, oceans, sea
level and atmospheric composition), climate change impacts on natural resource sectors and on human activities and
infrastructure, factors determining adaptive capacity for adjusting to these impacts, emissions of greenhouse gases
and aerosols and their cycling through the Earth system, and human responses to climate change through mitigation
and adaptation.
A good understanding of decision-making contexts is essential to define the type and scale of information on
climate change related risks required from physical climate science and impacts, adaptation and vulnerability
assessments (21.2.1) (high confidence). This is a general issue for all impacts, adaptation and vulnerability
assessments, but is especially important in the context of regional issues. Many studies still rely on global datasets,
models and assessment methods to inform regional decisions. However, tailored regional approaches are often more
effective in accounting for variations in trans-national, national and local decision-making contexts, as well as across
different groups of stakeholders and sectors. There is a growing body of literature offering guidance on how to
provide the most relevant climate risk information to suit specific decision-making scales and processes.
A greater range of regional scale climate information is now available which provides a more coherent
picture of past and future regional changes with associated uncertainties (21.3.3). More targeted analyses of
reference and projected climate information for impact assessment studies have been carried out. Leading messages
include:
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Significant improvements have been made in the amount and quality of climate data that are available for
establishing baseline reference states of climate-sensitive systems (21.5.3.1). These include new and
improved observational datasets, rescue and digitisation of historical datasets, and a range of improved
global reconstructions of weather sequences.
A larger set of global and regional (both dynamical and statistical) model projections allow a better
characterization of ranges of plausible climate futures than in the AR4 (21.3.3), and more methods are
available to produce regional probabilistic projections of changes for use in IAV assessment work (21.5.3).
Better process understanding would strengthen the emerging messages on future climate change where
there remains significant regional variation in their reliability (21.3.3).
Confidence in past climate trends has different regional variability and in many regions there is higher
confidence in future changes, often due to a lack of evidence on observed changes (21.3, Box 21-4).
In spite of improvements, the available information is limited by the lack of comprehensive observations of
regional climate, or analyses of these, and different levels of confidence in projected climate change (high
confidence). Some trends that are of particular significance for regional impacts and adaptation include (21.3.3.1;
WG I SPM):
The globally averaged combined land and ocean surface temperature data show a warming of 0.85 [0.65 to
1.06] °C, over the period 1880 2012. There is regional variation in the global trend, but overall the entire
globe has warmed during the period 1901-2012. (WGI SPM) Future warming is very likely to be larger
over land areas than over oceans. (WGI SPM)
Averaged over mid-latitude land areas, precipitation has increased since 1901(medium confidence before
and high confidence after 1951), but for other regions there is low confidence in the assessment of
precipitation trends (WGI SPM).
There are likely more land regions where the number of heavy precipitation events has increased than
where it has decreased. The frequency or intensity of heavy precipitation events has likely increased in
North America and Europe. In other continents, confidence in changes in heavy precipitation events is at
most medium. The frequency and intensity of drought has likely increased in the Mediterranean and West
Africa and likely decreased in central North America and north-west Australia.
The annual mean Arctic sea ice extent decreased over the period 1979 2012 with a rate that was very likely
in the range 3.5 to 4.1% per decade. Climate models indicate a nearly ice-free Arctic Ocean in September
before mid-century is likely under the high forcing scenario RCP8.5 (medium confidence).
The average rate of ice loss from glaciers worldwide, excluding those near the Greenland and Antarctic ice
sheets, was very likely 275 [140 to 410] Gt yr-1 over the period 1993-2009. By the end of the 21st century,
the volume of glaciers (excluding those near the Antarctic ice sheet), is projected to decrease by 15 to 55%
for RCP2.6, and by 35 to 85% for RCP8.5, relative to 1986-2005 (medium confidence).
The rate of global mean sea-level rise during the 21st century is very likely to exceed the rate observed
during 1971 2010, under all RCP scenarios (21.3.3.5; WG I SPM). By the end of the 21st century it is very
likely that sea level will rise in more than about 95% of the ocean area, with about 70% of the global
coastlines projected to experience a sea level change within 20% of the global mean change. Sea-level rise
along coasts will also be a function of local and regional conditions, including land subsidence or uplift and
patterns of development near the coast.
There is substantial regional variation in observations and projections of climate change impacts, both
because the impacts themselves vary, and because of unequal research attention (21.3.1). Evidence linking
observed impacts on biological, physical and (increasingly) human systems to recent and ongoing regional
temperature and (in some cases) precipitation changes have become more compelling since the AR4. This is due
both to the greater availability of statistically robust, calibrated satellite records, and to improved reporting from
monitoring sites in hitherto under-represented regions, though the disparity still remains large between data rich and
data-poor regions. Regional variations in physical impacts such as vegetation changes, sea-level rise, and ocean
acidification are increasingly well documented, though their consequences for ecosystems and humans are less well
studied or understood. Projections of future impacts rely primarily on a diverse suite of biophysical, economic and
integrated models operating from global- to site-scales, though some physical experiments are also conducted to
study processes in altered environments. New research initiatives are beginning to exploit the diversity of impact
model projections, through cross-scale model inter-comparison exercises.
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There are large variations in the degree to which adaptation processes, practices and policy have been studied
and implemented in different regions (21.3.2) (high confidence). Europe and Australia have had extensive
research programs on climate change adaptation, while research in Africa and Asia has been dominated by
international partners and relies heavily on case studies of community-based adaptation. National adaptation
strategies are common in Europe, and adaptation plans are in place in some cities in Europe, the Americas and
Australasia, with agriculture, water and land use management the primary sectors of activity. However, it is still the
case that implementation lags behind planning in most regions of the world.
Contested definitions and alternative approaches to describing regional vulnerability to climate change pose
problems for interpreting vulnerability indicators (21.3.1.2; 21.5.1.1). There are numerous studies that use
indicators to define aspects of vulnerability, quantifying these across regional units (e.g. by country or municipality),
often weighting and merging them into vulnerability indices and presenting them regionally as maps. However,
methods of constructing indices are subjective, often lack transparency and can be difficult to interpret. Moreover,
indices commonly combine indicators reflecting current conditions (e.g. of socio-economic capacity) with other
indicators describing projected changes (e.g. of future climate or population), and have failed to reflect the dynamic
nature of the different indicator variables.
Hotspots draw attention, from various perspectives and often controversially, to locations judged to be
especially vulnerable to climate change (21.5.1.2). Identifying hotspots is an approach that has been used to
indicate locations that stand out in terms of impacts, vulnerability or adaptive capacity (or combinations of these).
The approach exists in many fields and the meaning and use of the term hotspots differs, though their purpose is
generally to set priorities for policy action or for further research. Hotspots can be very effective as communication
tools, but may also suffer from methodological weaknesses. They are often subjectively defined, relationships
between indicator variables may be poorly understood and they can be highly scale-dependent. In part due to these
ambiguities, there has been controversy surrounding the growing use of hotspots in decision making, particularly in
relation to prioritising regions for climate change funding.
Cross-regional phenomena can be crucial for understanding the ramifications of climate change at regional
scales, and its impacts and policies of response (21.4) (high confidence). These include global trade and
international financial transactions, which are linked to climate change as a direct or indirect cause of anthropogenic
emissions, as a predisposing factor for regional vulnerability, through their sensitivity to climate trends and extreme
climate events, and as an instrument for implementing mitigation and adaptation policies. Migration is also a cross-
regional phenomenon, whether of people or of ecosystems, both requiring trans-boundary consideration of their
causes, implications and possible interventions to alleviate human suffering and promote biodiversity.
Downscaling of global climate reconstructions and models has advanced to bring the climate data to a closer
match for the temporal and spatial resolution requirements for assessing many regional impacts, and the
application of downscaled climate data has expanded substantially since AR4 (21.3.3; 21.5.3). This information
remains weakly coordinated, and current results indicate that high resolution downscaled reconstructions of the
current climate can have significant errors. The increase in downscaled data sets has not narrowed the uncertainty
range. Integrating these data with historical change and process based understanding remains an important
challenge.
Characterization of uncertainty in climate change research on regional scales has advanced well beyond
quantifying uncertainties in regional climate projections alone, to incorporating uncertainties in simulations
of future impacts as well as considering uncertainties in projections of societal vulnerability (21.3.3, 21.5.3,
21.5.1. 21.5.2). In particular, intercomparison studies are now examining the uncertainties in impacts models (e.g.,
AgMIP and ISIMIP) and combining them with uncertainties in regional climate projections. Some results indicate
that a larger portion of the uncertainty in estimates of future impacts can be attributed to the impact models applied
rather than to the climate projections assumed. In addition, the deeper uncertainties associated with aspects of
defining societal vulnerability to climate change related to the alternative approaches to defining vulnerability are
becoming appreciated. As yet there has been little research actively to quantify these uncertainties or to combine
them with physical impact and climate uncertainties.
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Studies of multiple stressors and assessments of potential global and regional futures using scenarios with
multiple, non-climate elements are becoming increasingly common (21.5.3.2; 21.5.3.3). Non-climatic factors
relevant to assessing a system s vulnerability generally involve a complex mix of influences such as environmental
changes (e.g. in air, water and soil quality, sea level, resource depletion), land use and land cover changes, and
socio-economic changes (e.g. in population, income, technology, education, equity, governance). All of these non-
climate factors have important regional variations. There is significant variation in vulnerability due to variability in
these factors.
21.1. Introduction
This chapter serves as an introduction to Part B of this volume. It provides context for an assessment of regional
aspects of climate change in different parts of the world, which are presented in the following nine chapters. While
the main focus of those chapters is on the regional dimensions of impacts, adaptation and vulnerability, this chapter
also offers links to regional aspects of the physical climate reported by Working Group I and of mitigation analysis
reported by Working Group III. The chapter frames the discussion of both global and regional issues in a decision-
making context. This context identifies different scales of decisions that are made (e.g. global, international,
regional, national, subnational, local) and the different economic or impact sectors that are often the objects of
decision-making (e.g. agriculture, water resources, energy).
Within this framing, the chapter then provides three levels of synthesis. First there is an evaluation of the state of
knowledge of changes in the physical climate system, and associated impacts and vulnerabilities, and the degree of
confidence that we have in understanding those on a regional basis as relevant to decision-making. Second, the
regional context of the sectoral findings presented in Part A of this volume is discussed. Third, there is an analysis of
the regional variation revealed in subsequent chapters of Part B. In so doing, the goal is to examine how the chapters
reflect differences or similarities in how decision-making is being addressed by policy and informed by research in
different regions of the world, and whether there is commonality of experience among regions that could be useful
for enhancing decisions in the future.
Having analyzed similarities and differences among IPCC regions, the chapter then discusses trans-regional and
cross-regional issues that affect both human systems (e.g. trade and financial flows) and natural systems (e.g.
ecosystem migration). Finally, the chapter evaluates methods of assessing regional vulnerabilities and adaptation,
impact analyses, and the development and application of baselines and scenarios of the future. These evaluations
provide guidance for understanding how such methods might ultimately be enhanced, so that the confidence in
research about possible future conditions and consequences might ultimately improve.
21.2. Defining Regional Context
The climate system may be global in extent, but its manifestations through atmospheric processes, ocean
circulation, bioclimatic zones, daily weather and longer-term climate trends are regional or local in their
occurrence, character and implications. Moreover, the decisions that are or could be taken on the basis of climate
change science play out on a range of scales, and the relevance and limitations of information on both biophysical
impacts and social vulnerability differ strongly from global- to local-scale, and from one region to another. Explicit
recognition of geographical diversity is therefore important for any scientific assessment of anthropogenic climate
change. The following sections emphasize some of the crucial regional issues to be pursued in Part B of this report.
21.2.1. Decision-Making Context
A good understanding of decision-making contexts is essential to define the type and resolution and characteristics
of information on climate change related risks required from physical climate science and impacts, adaptation and
vulnerability assessments (e.g. IPCC, 2012a). This is a general issue for all impacts, adaptation and vulnerability
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assessments (cf. the chapters in Part A), but is especially important in the context of regional issues. Many studies
still rely on global datasets, models and assessment methods to inform regional decisions. However, tailored
regional approaches are often more effective in accounting for variations in trans-national, national and local
decision-making contexts, as well as across different groups of stakeholders and sectors. There is a growing body of
literature offering guidance on how to provide the most relevant climate risk information to suit specific decision-
making scales and processes (e.g., Willows and Connell, 2003; ADB, 2005; Kandlikar et al., 2011).
Table 21-1 illustrates the range of actors involved in decision-making to be informed by climate information at
different scales in different sectors, ranging from international policy makers and agencies, to national and local
government departments, to civil society organizations and the private sector at all levels, all the way to
communities and individual households. The table illustrates how policy makers face a dual challenge in achieving
policy integration vertically, through multiple levels of governance, and horizontally, across different sectors
(policy coherence).
[INSERT TABLE 21-1 HERE
Table 21-1: Dimensions of the institutions and actors involved in climate change decision-making, including
example entries referred to in chapters of this volume. Vertical integration can occur within as well as between
levels. Decison-making domains are illustrative. Modified and extended from Mickwitz (2009).]
Many climate change risk assessments have traditionally been undertaken either in the context of international
climate policy-making (especially the United National Framework Convention on Climate Change UNFCCC), or
by (or for) national governments (e.g. CCRA, 2012; SEI, 2009; GCAP, 2011; Roshydromet, 2008). In those cases,
climate risk information commonly assumes a central role in the decision-making, for instance to inform mitigation
policy, or for plans or projects designed specifically to adapt to a changing climate. In recent years, increasing
attention has been paid to more sector- or project-specific risk assessments, intended to guide planning and practice
by a range of actors (e.g. Liu et al., 2008; Rosenzweig et al., 2011). In those contexts, climate may often be
considered as only one contributor among a much wider set of considerations for a particular decision. In such cases,
there is uncertainty about not only the future climate, but also many other aspects of the system at risk. Moreover,
while analysts will seek the best available climate risk information to inform the relative costs and benefits of the
options available to manage that risk, they will also need to consider the various constraints to action faced by the
actors involved.
Some of these decision-making contexts, such as the design of large infrastructure projects, may require rigorous
quantitative information to feed formal evaluations, often including cost-benefit analysis (e.g.
PriceWaterHouseCoopers, 2010; and see chapter 17). Others, especially at local level, such as decision-making in
traditional communities, are often made more intuitively, with a much greater role for a wide range of social and
cultural aspects. These may benefit much more from experience-based approaches, participatory risk assessments or
story-telling to evaluate future implications of possible decisions (e.g. Van Aalst et al., 2008, World Bank, 2010).
Multi-criteria analysis, scenario planning, and flexible decision paths offer options for taking action when faced with
large uncertainties or incomplete information, and can help bridge adaptation strategies across scales (in particular
between the national and local level). In most cases, an understanding of the context in which the risk plays out, and
the alternative options that may be considered to manage it, are not an afterthought, but a defining feature of an
appropriate climate risk analysis, which requires a much closer interplay between decision-makers and providers of
climate risk information than often occurs in practice (e.g., Cardona et al., 2012; Hellmuth et al., 2010; Mendler de
Suarez et al., 2012).
The different decision-making contexts also determine the types of climate information required, including the
climate variables of interest and the geographic and time scales on which they need to be provided. Many climate
change impact assessments have traditionally focused on changes over longer time horizons (often out to 2100,
though recently studies have begun to concentrate more on mid-century or earlier). In contrast, most decisions taken
today have a planning horizon ranging from a few months to about two decades (e.g. Wilby et al., 2009). For many
such shorter-term decisions, recent climate variability and observed trends are commonly regarded as sufficient to
inform adaptation (e.g. Hallegatte, 2009). However, in so doing, there is often scope to make better use of observed
climatological information as well as seasonal and maybe also decadal climate forecasts (e.g. Wang et al., 2009;
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Ziervogel et al., 2010; Mehta et al., 2011; Kirtman et al., 2014., HLT, 2011). For longer-term decisions, such as
decisions with irreversible long-term implications and investments with a long investment horizon and substantial
vulnerability to changing climate conditions, longer-term climate risk information is needed (e.g. Reeder and
Ranger, 2010). However, while that longer-term information is often used simply to plan for a best-guess scenario to
optimize for most probable conditions, there is increasing attention for informing concerns about maladaptation
(Barnett and O'Neill, 2010) and sequencing of potential adaptation options in a wider range of possible outcomes,
requiring a stronger focus on ranges of possible outcomes and guidance on managing uncertainties, especially at
regional, national and sub-national levels (Hall et al., 2012; Gersonius et al., 2013).
Section 21.3 summarizes different approaches that have been applied at different scales looking at vulnerabilities
and impacts, adaptation, and climate science in a regional context, paying special attention to information contained
in the regional chapters.
21.2.2. Defining Regions
There has been an evolution in the treatment of regional aspects of climate change in IPCC reports (Table 21-2)
from a patchwork of case examples in the First Assessment Report (FAR) and its supplements, through to attempts
at a more systematic coverage of regional issues following a request from governments, beginning with the Special
Report on the Regional Impacts of Climate Change in 1998. That report distilled information from the Second
Assessment Report (SAR) for ten continental scale regions, and the subsequent Third (TAR) and Fourth (AR4)
assessments each contained comparable chapters on impacts, adaptation and vulnerability in the Working Group
(WG) II volumes. WG I and III reports have also addressed regional issues in various chapters, using different
methods of mapping, statistical aggregation and spatial averaging to provide regional information.
[INSERT TABLE 21-2 HERE
Table 21-2: Selected examples of regional treatment in previous IPCC Assessment Reports and Special Reports
(SR). Major assessments are subdivided by the three Working Group reports, each described by generic titles.]
Part B of this WG II Fifth Assessment (AR5) is the first to address regional issues treated in all three WGs. It
comprises chapters on the six major continental land regions, Polar Regions, Small Islands and The Ocean. These
are depicted in Figure 21-1.
[INSERT FIGURE 21-1 HERE
Figure 21-1: Specification of the world regions described in chapters 22-30 of this volume.]
Some of the main topics benefiting from a regional treatment are:
Changes in climate, typically represented over sub-continental regions, a scale at which global climate
models simulate well the pattern of observed surface temperatures, though more modestly the pattern of
precipitation (Flato et al., 2014). While maps are widely used to represent climatic patterns, regional
aggregation of this (typically gridded) information is still required to summarise the processes and trends
they depict. Examples, including information on climate extremes, are presented elsewhere in this chapter,
with systematic coverage of all regions provided in supplementary material. Selected time series plots of
temperature and precipitation change from an atlas of global and regional climate projections
accompanying the WG I report (Collins et al., 2014b) can also be found in several regional chapters of this
volume. In Figure 21-1, the sub-continental regions used for summarising climate information are overlaid
on a map of the nine regions treated in Part B.
Changes in other aspects of the climate system, such as the cryosphere, oceans, sea level, and atmospheric
composition. A regional treatment of these phenomena is often extremely important to gauge real risks, for
example when regional changes in land movements and local ocean currents counter or reinforce global sea
level rise (Nicholls et al., 2013).
Climate change impacts on natural resource sectors, such as agriculture, forestry, ecosystems, water
resources and fisheries, and on human activities and infrastructure, often with regional treatment according
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to biogeographical characteristics (e.g. biomes, climatic zones, physiographic features such as mountains,
river basins, coastlines or deltas, or combinations of these).
Adaptive capacity, which is a measure of society's ability to adjust to the potential impacts of climate
change, sometimes characterised in relation to social vulnerability (Füssel, 2010), and sometimes
represented in regional statistics through the use of socio-economic indicators.
Emissions of greenhouse gases and aerosols and their cycling through the Earth system (Blanco et al.,
2014; Ciais et al., 2014).
Human responses to climate change through mitigation and adaptation, which can require both global and
regional approaches (e.g. Stavins et al., 2014; Agrawala et al., 2014; Somanathan et al., 2014; and see
chapters 14 to 16).
Detailed examples of these elements will be referred to throughout this chapter and the regional chapters that follow.
Some of the more important international political groupings that are pertinent to the climate change issue are
described and catalogued in Supplementary material (section SM21.1). Table SM21-1 in section SM21.1 lists UN
member states and other territories, their status in September 2013 with respect to some illustrative groupings of
potential relevance for international climate change policy making, and the regional chapters in which they are
considered in this report.
Finally, new global socioeconomic and environmental scenarios for climate change research have emerged since the
AR4 that are richer, more diverse and offer a higher level of regional detail than previous scenarios taken from the
IPCC Special Report on Emissions Scenarios (SRES). These are introduced in Box 21-1.
_____ START BOX 21-1 HERE _____
Box 21-1. A New Framework of Global Scenarios for Regional Assessment
The major socio-economic driving factors of future emissions and their effects on the global climate system were
characterized in the TAR and AR4 using scenarios derived from the IPCC Special Report on Emissions Scenarios
(SRES IPCC, 2000). However, these scenarios are becoming outdated in terms of their data and projections, and
their scope is too narrow to serve contemporary user needs (Ebi et al., 2013). More recently a new approach to
developing climate and socio-economic scenarios has been adopted in which concentration trajectories for
atmospheric greenhouse gases and aerosols were developed first (Representative Concentration Pathways or RCPs
Moss et al., 2010), thereby allowing climate modeling work to proceed much earlier in the process than for SRES.
Different possible Shared Socio-economic Pathways (SSPs), intended for shared use among different climate change
research communities, were to be determined later, recognizing that more than one socio-economic pathway can
lead to the same concentrations of greenhouse gases and aerosols (Kriegler et al., 2012).
Four different RCPs were developed, corresponding to four different levels of radiative forcing of the atmosphere by
2100 relative to pre-industrial levels, expressed in units of Wm-2: RCP 8.5, 6.0, 4.5, and 2.6 (van Vuuren et al.,
2012). These embrace the range of scenarios found in the literature, and all except RCP 8.5 also include explicit
stabilization strategies, which were missing from the SRES set. An approximate mapping of the SRES scenarios
onto the RCPs on the basis of a resemblance in radiative forcing by 2100 is presented in chapter 1 (this volume),
pairing RCP 8.5 with SRES A2 and RCP 4.5 with B1 and noting that RCP 6.0 lies between B1 and B2. No SRES
scenarios result in forcing as low as RCP 2.6, though mitigation scenarios developed from initial SRES trajectories
have been applied in a few climate model experiments (e.g. the E1 scenario Johns et al., 2011).
In addition, five SSPs have been proposed, representing a wide range of possible development pathways (van
Vuuren et al., 2013). An inverse approach is applied, whereby the SSPs are constructed in terms of outcomes most
relevant to IAV and mitigation analysis, depicted as challenges to mitigation and adaptation (Chapter 20, Figure 20-
3). Narrative storylines for the SSPs have been outlined and preliminary quantifications of the socio-economic
variables are underway (O'Neill et al., 2013). Priority has been given to a set of basic SSPs with the minimum detail
and comprehensiveness needed to provide inputs to IAV and integrated assessment models, primarily at global or
large regional scales. Building on the basic SSPs, a second stage will construct extended SSPs, designed for finer-
scale regional and sectoral applications (O'Neill et al., 2013).
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An overall scenario architecture has been designed for integrating RCPs and SSPs (Ebi et al., 2013; van Vuuren et
al., 2013), for considering mitigation and adaptation policies using Shared Policy Assumptions (SPAs Kriegler et
al., 2013) and for providing relevant socio-economic information at the scales required for IAV analysis (van
Ruijven et al., 2013). Additional information on these scenarios can be found in chapter 1 of this report (section
1.1.3) and elsewhere in the assessment (Blanco et al., 2014.; Collins et al., 2014a.; Kunreuther et al., 2014).
However, due to the time lags that still exist between the generation of RCP-based climate change projections in
CMIP5 (the Coupled Model Intercomparison Project Taylor et al., 2012) and the development of SSPs, few of the
IAV studies assessed in this report actively use these scenarios. Instead, most of the scenario-related studies in the
assessed literature still rely on the SRES.
_____ END BOX 21-1 HERE _____
21.2.3. Introduction to Methods and Information
There has been significant confusion and debate about the definitions of key terms (Janssen and Ostrom, 2006), such
as vulnerability (Adger, 2006), adaptation (Stafford Smith et al., 2011), adaptive capacity (Smit and Wandel, 2006)
and resilience (Klein et al., 2003). One explanation is that the terms are not independent concepts, but defined by
each other, thus making it impossible to remove the confusion around the definitions (Hinkel, 2011). The differences
in the definitions relate to the different entry points for looking at climate change risk (IPCC, 2012).
Table 21-3 shows two ways to think about vulnerability, demonstrating that different objectives (e.g., improving
well-being and livelihoods or reducing climate change impacts) lead to different sets of questions being asked. This
results in the selection of different methods to arrive at the answers. The two approaches portrayed in the middle and
right hand columns of Table 21-3 have also been characterised in terms of top-down (middle column) and bottom-up
(right column) perspectives, with the former identifying physical vulnerability and the latter social vulnerability
(Dessai and Hulme, 2004). In the middle column, the climate change impacts are the starting point for the analysis,
revealing that people and/or ecosystems are vulnerable to climate change. This approach commonly applies global-
scale scenario information and seeks to refine this to the region of interest through downscaling procedures. For the
approach illustrated on the right, the development context is the starting point (i.e., social vulnerability), commonly
focusing on local scales, on top of which climate change occurs. The task is then to identify what changes are
needed in the broader-scale development pathways in order to reduce vulnerability to climate change. Another
difference is a contrast in time-frames, where a climate change focused approach tends to look to the future to see
how to adjust to expected changes, whereas a vulnerability focused approach is centred on addressing the drivers of
current vulnerability. A similar approach is described by McGray et al. (2009).
[INSERT TABLE 21-3 HERE
Table 21-3: Two possible entry points for thinking about vulnerability to climate change (illustrative and adapted
from Füssel 2007).]
The information assessed in this chapter stems from different entry points, framings and conceptual frameworks for
thinking about risk. They merge social and natural science perspectives with transdisciplinary ones. There is no
single "best" conceptual model: the approaches change as scientific thinking evolves. The IPCC itself is an example
of this: IPCC SREX (IPCC, 2012) presented an approach that has been adjusted and adapted in Chapter 19 of this
volume. Chapter 2 describes other conceptual models for decision making in the context of risk. While this diversity
in approaches enriches our understanding of climate change, it can also create difficulties in comparisons. For
instance, findings that are described as vulnerabilities in some studies may be classified as impacts in others; lack of
adaptive capacity in one setting might be described as social vulnerability in another.
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21.3. Synthesis of Key Regional Issues
This section presents information on vulnerabilities and impacts, adaptation, and climate science in a regional
context. To illustrate how these different elements play out in actual decision-making contexts, Table 21-4 presents
examples drawn from the regional and thematic chapters, which illustrate how information about vulnerability and
exposure, and climate science at different scales, inform adaptation (implemented in policy and practice as part of a
wider decision-making context). These show that decision-making is informed by a combination of different types
of information. However, this section is organized by the three constituent elements: vulnerabilities and impacts;
adaptation; and climate science.
[INSERT TABLE 21-4 HERE
Table 21-4: Illustrative examples of adaptation experience, as well as approaches to reduce vulnerability and
enhance resilience. Adaptation actions can be influenced by climate variability, extremes, and change, and by
exposure and vulnerability at the scale of risk management. Many examples and case studies demonstrate
complexity at the level of communities or specific regions within a country. It is at this spatial scale that complex
interactions between vulnerabilities, inequalities, and climate change come to the fore. At the same time, place-
based examples illustrate how larger-level drivers and stressors shape differential risks and livelihood trajectories,
often mediated by institutions.]
The following two sub-sections offer a brief synopsis of the approaches being reported in the different regional
chapters on impacts and vulnerability studies (21.3.1) and adaptation studies (21.3.2), aiming to particularly
highlight similarities and differences among regions. Table 21-5 serves as a rough template for organising this
discussion which is limited to the literature that has been assessed by the regional chapters. It is organized according
to the broad research approach applied, distinguishing impacts and vulnerability approaches from adaptation
approaches; and according to scales of application ranging from global to local.
[INSERT TABLE 21-5 HERE
Table 21-5: Dimensions of assessments of impacts and vulnerability and of adaptation drawn upon to serve different
target fields (cf. Table 21-1). Scales refer to the level of aggregation at which study results are presented. Entries are
illustrations of different types of study approaches reported and evaluated in this volume, with references given both
to the original studies and to the chapters in which they are cited. Aspects of some of the studies in this table are also
alluded to in Section 21.5.]
Section 21.3.3 then provides an analysis of advances in understanding of the physical climate system for the
different regions covered in chapters 22-30, introducing new regional information to complement the large-scale and
process-oriented findings presented by Working Group I. Understanding the reliability of this information is of
crucial importance. In the context of IAV studies it is relevant to a very wide range of scales and it comes with a
similarly wide range of reliabilities. Using a similar classification of spatial scales to that presented in Table 21-5,
Table 21-6 provides a summary assessment of the reliability of information on two basic climate variables of
relevance, surface temperature and precipitation. It is drawn from the extensive assessment and supporting literature
from the IPCC SREX (IPCC, 2012) and the AR5 WG1 reports. Some discussion of relevant methodologies and
related issues and results are also presented in section 21.5.
[INSERT TABLE 21-6 HERE
Table 21-6: Reliability of climate information on temperature and precipitation over a range of spatial and temporal
scales. Reliability is assigned to one of seven broad categories from Very High (VH) to Medium (M) through to
Very Low (VL).]
Table 21-6 shows there are significant variations in reliability with finer scales information generally less reliable
given the need for a greater density of observations and/or for models to maintain accuracy at high resolutions. The
reliability of information on past climate depends on the availability and quality of observations which is higher for
temperature than precipitation as observations of temperature are easier to make and generally more representative
of surrounding areas than is the case for precipitation. Future climate change reliability depends on the performance
of the models used for the projections in simulating the processes that lead to these changes. Again, information on
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temperature is generally more reliable due to the models demonstrated ability to simulate the relevant processes
when reproducing past changes. The significant geographical variations, in the case of the observations, result from
issues with availability and/or quality of data in many regions, especially for precipitation. For future climate
change, data availability is less of an issue with the advent of large ensembles of climate model projections but
quality is a significant problem in some regions where the models perform poorly and there is little confidence that
processes driving the projected changes are accurately captured. A framework for summary information on model
projections of future climate change placed this in the context of observed changes is presented in Box 21-2.
_____ START BOX 21-2 HERE _____
Box 21-2. Summary Regional Climate Projection Information
Summary figures on observed and projected changes in temperature and precipitation are presented in the following
regional chapters. These provide some context to the risks associated with climate change vulnerability and impacts
and the decision-making on adaptations being planned and implemented in response to these risks. Figure 21-2
provides an example for Africa. The information is identical to that displayed in Box CC-RC.
[INSERT FIGURE 21-2 HERE
Figure 21-2: Observed and projected changes in annual average temperature and precipitation. (Top panel, left)
Observed temperature trends from 1901-2012 determined by linear regression. [WGI AR5 Figures SPM.1 and 2.21]
(Bottom panel, left) Observed precipitation change from 1951-2010 determined by linear regression. [WGI AR5
Figure SPM.2] For observed temperature and precipitation, trends have been calculated where sufficient data
permits a robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data
availability in the first and last 10% of the time period). Other areas are white. Solid colors indicate areas where
change is significant at the 10% level. Diagonal lines indicate areas where change is not significant. (Top and
bottom panel, right) CMIP5 multi-model mean projections of annual average temperature changes and average
percent change in annual mean precipitation for 2046-2065 and 2081-2100 under RCP2.6 and 8.5. Solid colors
indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline
variability, and >90% of models agree on sign of change. Colors with white dots indicate areas with strong
agreement, where >66% of models show change greater than the baseline variability and >66% of models agree on
sign of change. Gray indicates areas with divergent changes, where >66% of models show change greater than the
baseline variability, but <66% agree on sign of change. Colors with diagonal lines indicate areas with little or no
change, less than the baseline variability in >66% of models. (There may be significant change at shorter timescales
such as seasons, months, or days.). Analysis uses model data and methods building from WGI AR5 Figure SPM.8.
See alsoAnnex I of WGI AR5. [Boxes 21-3 and CC-RC]]
These figures provide a very broad overview of the projected regional climate changes but in dealing with only
annual averages they are not able to convey any information about projected changes on seasonal timescales or
shorter, such as for extremes. In addition, they are derived solely from the CMIP5 general circulation models
(GCMs) and do not display any information derived from CMIP3 data which are widely used in many of the studies
assessed within the AR5 WG2 report. To provide additional context two additional sets of figures are presented here
and in Box 21-4 that display temperature and precipitation changes at the seasonal and daily timescales respectively.
Figure 21-3 displays projected seasonal and annual changes averaged over the regions defined in the IPCC SREX
report, Managing the risks of extreme events and disasters to advance climate change adaptation (IPCC 2012), for
Central and South America for the four RCP scenarios and three of the SRES scenarios. The temperature and
precipitation changes for the period 2071-2100 compared to a baseline of 1961-1990 are plotted for the four
standard three months seasons with the changes from each CMIP3 or CMIP5 represented by a symbol. Symbols
showing the CMIP3 model projections are all grey but differ in shape depending on the driving SRES concentrations
scenario and those showing the CMIP5 projections differ in colour depending on the driving RCP
emissions/concentrations scenario (see figure legend for details and Box 21-1 for more information on the SRES and
RCP scenarios). The thirty year periods were chosen for consistency with the figures displayed in Box 21-4 (Figures
21-7 and 21-8) showing changes in daily temperatures and precipitation. Figures presenting similar information for
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the SREX regions contained in the other inhabited continents are presented in supplementary figures SM21-1 to
SM21-7.
[INSERT FIGURE 21-3 HERE
Figure 21-3: Regional average change in seasonal and annual mean temperature and precipitation over five sub-
regions covering South and Central America for the period 2071-2100 relative to 1961-90 in GCM projections from
35 CMIP5 ensemble under four RCP scenarios (van Vuuren et al., 2011) compared with GCM projections from 22
CMIP3 ensemble under three SRES scenarios (IPCC, 2000); see Table 21-1 for details of the relationship between
the SRES and RCP scenarios. Regional averages are based on SREX region definitions (see Figure 21-3).
Temperature changes are given in C and precipitation changes in mm/day with axes scaled relative to the maximum
changes projected across the range of models. The models which generated the data displayed are listed in
supplementary material Table SM21-3.]
_____ END BOX 21-2 HERE _____
21.3.1. Vulnerabilities and Impacts
21.3.1.1. Observed Impacts
The evidence linking observed impacts on biological, physical and (increasingly) human systems to recent and
ongoing regional climate changes has become more compelling since the AR4 (see chapter 18). One reason for this
is the improved reporting of published studies from hitherto under-represented regions of the world, especially in the
tropics (Rosenzweig and Neofotis, 2013). That said, the disparity is still large between the copious evidence being
presented from Europe and North America, as well as good quality data emerging from Australasia, polar regions,
many ocean areas and some parts of Asia and South America, compared to the much sparser coverage of studies
from Africa, large parts of Asia, central and South America and many small islands. On the other hand, as the time
series of well-calibrated satellite observations become longer in duration, and hence statistically more robust, these
are increasingly providing a near global coverage of changes in surface characteristics such as vegetation,
hydrology, and snow and ice conditions that can usefully complement or substitute for surface observations (see
Table 21-4 and chapter 18 for examples). Changes in climate variables other than temperature, such as precipitation.
evapotranspiration and CO2 concentration, are also being related to observed impacts in a growing number of studies
(Rosenzweig and Neofotis, 2013; and see examples from Australia in chapter 25, Table 25-3 and south eastern
South America in chapter 27, Figure 27-7).
Other regional differences in observed changes worth pointing out include trends in relative sea level, which is
rising on average globally (Church et al., 2014), but displays large regional variations in magnitude, or even sign,
due to a combination of influences ranging from El Nino/La Nina cycles to local tectonic activity (Nicholls et al.,
2013), making general conclusions about ongoing and future risks of sea level change very difficult to draw across
diverse regional groupings such as small islands (see chapter 29). There are also regional variations in another
ongoing effect of rising CO2 concentration ocean acidification, with a greater pH decrease at high latitudes
consistent with the generally lower buffer capacities of the high latitude oceans compared to lower latitudes (Rhein
et al., 2014, section 3.8.2). Calcifying organisms are expected to show responses to these trends in future, but key
uncertainties remain at organismal to ecosystem levels (chapter 30, Box CC-OA).
21.3.1.2. Future Impacts and Vulnerability
21.3.1.2.1. Impact models
The long-term monitoring of environmental variables, as well as serving a critical role in the detection and
attribution of observed impacts, also provides basic calibration material used for the development and testing of
impact models. These include process-based or statistical models used to simulate the biophysical impacts of climate
on outcomes such as crop yield, forest productivity, river runoff, coastal inundation or human mortality and
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morbidity (see chapters 2-7; 11). They also encompass various types of economic models that can be applied to
evaluate the costs incurred by biophysical impacts (see, for example, chapters 10 and 17). There are also integrated
assessment models (IAMs), Earth System Models, and other more loosely linked integrated model frameworks that
represent multiple systems and processes (e.g. energy, emissions, climate, land use change, biophysical impacts,
economic effects, global trade) and the various interactions and feedbacks between them. For examples of these, see
chapter 17, section 17.6.3 and Flato et al. (2014).
21.3.1.2.2. Vulnerability mapping
A second approach to projecting potential future impacts is to construct vulnerability maps. These usually combine
information on three components: exposure to a hazard (commonly defined by the magnitude of climate change,
sensitivity to that hazard), the magnitude of response for a given level of climate change, and adaptive capacity
(describing the social and economic means to withstand the impacts of climate change (IPCC, 2001)). Key
indicators are selected to represent each of the three components, which are sometimes combined into a single index
of vulnerability. Indicators are usually measured quantities taken from statistical sources (e.g. income, population),
or have been modelled separately (e.g. key climate variables). Vulnerability indices have received close scrutiny in
several recent reviews (Füssel, 2010; Hinkel, 2011; Malone and Engle, 2011; Preston et al., 2011; Kienberger et al.,
2012), and a number of global studies have been critiqued by Füssel (2010).
A variant of vulnerability mapping is risk mapping (e.g., Tran et al., 2009; Ogden et al., 2008). This commonly
identifies a single indicator of hazard (e.g. a level of flood expected with a given return period), which can be
mapped accurately to define those regions at risk from such an event (e.g. in a flood plain). Combined with
information on changing return periods of such events under a changing climate would enable some estimate of
altered risk to be determined.
21.3.1.2.3. Experiments
A final approach for gaining insights on potential future impacts, concerns physical experiments designed to
simulate future altered environments of climate (e.g. temperature, humidity and moisture), and atmospheric
composition (e.g. CO2, surface ozone and sulphur dioxide concentrations ). These are typically conducted to study
responses of crop plants, trees and natural vegetation, using open top chambers, greenhouses or free air gas release
systems (e.g., Craufurd et al., 2013), or responses of aquatic organisms such as plankton, macrophytes or fish, using
experimental water enclosures known as mesocosms (e.g., Sommer et al., 2007; Lassen et al., 2010).
21.3.1.2.4. Scale issues
Impact models operate at a range of spatial and temporal resolutions, and while their outputs are sometimes
presented as fine resolution maps, key model findings are rarely produced at the finest resolution of the simulations
(i.e. they are commonly aggregated to political or topographic units of interest to the target audience, e.g. watershed,
municipality, national or even global). Aggregation of data to coarse-scale units is also essential for allowing
comparison of outputs from models operating at different resolutions, but it also means that sometimes quite useful
detail may be overlooked when model outputs are presented at the scale of the coarsest common denominator.
Conversely, if outputs from impact models are required as inputs to other models, the outputs may need to be
harmonized to a finer grid than the original data. In such cases, downscaling methods are commonly applied. This
was the case, for example, when providing spatially explicit projections of future land use from different IAMs
(Hurtt et al., 2011) for climate modellers to apply in the CMIP5 process (Collins et al., 2014a). It is also a common
procedure used in matching climate model outputs to impact models designed to be applied locally (e.g. over a river
basin or an urban area see section 21.3.3.2).
Even if the same metrics are being used to compare aggregate model results (e.g. developed versus developing
country income under a given future scenario) estimates may have been obtained using completely different types of
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models operating at different resolutions. Moreover, many models that have a large-scale coverage (e.g. continental
or global) may nonetheless simulate processes at a relatively fine spatial resolution, offering a potentially useful
source of spatially explicit information that is unfamiliar to analysts working in specific regions, who may defer to
models more commonly applied at the regional scale. Examples include comparison of hydrological models with a
global and regional scope (Todd et al., 2011) and bioclimatic models of vascular plant distributions with a European
and local scope (Trivedi et al., 2008). Vulnerability mapping exercises can also be undermined by the inappropriate
merging of indicator datasets that resolve information to a different level of precision (e.g. Tzanopoulos et al.,
2013). There is scope for considerably enhanced cross-scale model intercomparison work in the future, and projects
such as AgMIP (Rosenzweig et al., 2013) and ISI-MIP (Schiermeier, 2012, see section 21.5) have provision for
just such exercises.
21.3.2. Adaptation
This section draws on material from the regional chapters (22-30) as well as the examples described in Table 21-4.
Material from chapters 14-17 is also considered. See also Table 16.4 for a synthesis from the perspective of
adaptation constrains and limits.
21.3.2.1. Similarities and Differences in Regions
As described in the regional chapters, a large portion of adaptation knowledge is based on conclusions drawn from
case studies in specific locations, the conceptual findings are typically being applied globally (chapters 14-17). ). It
is this empirical knowledge on adaptation that guides understandings in the different regions. This is especially the
case for developing regions. Thus, regional approaches to adaptation vary in their degree of generality. One of the
most striking differences between regions in terms of adaptation is the extent to which it has been studied and
implemented. Australia and Europe have invested heavily in research on adaptation since the AR4, and the result is
a rich body of literature published by local scientists. The ability to advance in adaptation knowledge may be related
to the amount and quality of reliable climate information, the lack of which has been identified as a constraint to
developing adaptation measures in Africa (22.4.2). Many case studies, especially of community-based adaptation,
stem from Asia, Africa, Central and South America and Small Islands but the majority of this work has been
undertaken and authored by international non-governmental organisations, as well as by other non-local researchers.
In Africa, most planned adaptation work is considered to be pilot, and seen as part of learning about adaptation,
although there has been significant progress since the AR4 (22.4.4.2).
Most regional chapters report lags in policy work on adaptation (see also 16.5.2). While most European countries
have adaptation strategies, few have been implemented (23.1.2). Lack of implementation of plans is also the case for
Africa (22.4). In North (26.8.4.1.2) and Central and South America (27.5.3.2), adaptation plans are in place for some
cities. In Australasia, there are few adaptation plans (25.4.2). In the Arctic, they are in their infancy (28.4). At the
same time, civil society and local communities have the opportunity to play a role in decision making about
adaptation in Europe and Asia (23.7.2, 24.4.6.5). In Africa, social learning and collective action are used to promote
adaptation (22.4.5.3). Adaptation is observed as mostly autonomous (spontaneous) in Africa, although socio-
ecological changes are creating constraints for autonomous adaptation (22.4.5.4). There is a disconnect in most parts
of Africa between policy and planning levels, and the majority of work is still autonomous and unsupported (22.4.1).
In the case of UNFCCC-supported activities, such as National Adaptation Programmes of Action, few projects from
the African (22.4.4.2) least developed countries have been funded, thus limiting the effectiveness of these
investments. Several chapters (Africa, Europe, North America, Central and South America and Small Islands)
explicitly point out that climate change is only one of multiple factors that affect societies and ecosystems and drives
vulnerability or challenges adaptation (22.4.2, 23.10.1, 26.8.3.1, 27.3.1.2, 29.6.3). For example, North America
reports that for water resources, most adaptation actions are no-regrets , meaning that they have benefits beyond
just adaptation to climate change (26.3.4). In Australasia, the limited role of socio-economic information in
vulnerability assessments restricts confidence regarding the conclusions about future vulnerability and adaptive
capacity (25.3.2).
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Some chapters (Polar Regions, North America, Australasia) emphasise the challenges faced by indigenous peoples
and communities in dealing with climate change (28.4.1, 26.8.2.2, 25.8.2). Although they are described as having
some degree of adaptive capacity to deal with climate variability, shifts in lifestyles combined with a loss of
traditional knowledge leave many groups more vulnerable to climate change (28.2.4.2). Also, traditional responses
have been found to be maladaptive because they are unable to adjust to the rate of change, or the broader context in
which the change is taking place, as seen in the Arctic (28.4.1). In response to changing environmental conditions,
people are taking on maladaptive behaviour for instance, by going further to hunt because of changed fish-stocks
and thus exposing themselves to greater risk, or changing to different species and depleting stocks (28.4.1). Limits to
traditional approaches for responding to changing conditions have also been observed in several Small Island States
(29.8).
Most populated regions have experience with adaptation strategies in agriculture, where exposure to the impacts of
climate variability over centuries provides a starting point for making adjustments to new changes in climate. Water
and land use management strategies stand out in the literature in common across all of the main continental regions.
The link between adaptation and development is explicit in Africa, where livelihood diversification has been key to
reducing vulnerability (22.4.5.2). At the same time, there is evidence that many short-term development initiatives
have been responsible for increasing vulnerability (22.4.4.2). Other chapters mention constraints or barriers to
adaptation in their regions. For example, the low priority accorded to adaptation in parts of Asia, compared to more
pressing issues of employment and education, is attributed in part to a lack of awareness of the potential impacts of
climate change and the need to adapt, a feature common to many regions (22.5.4). All developing regions cite
insufficient financial resources for implementing adaptation as a significant limitation.
21.3.2.2. Adaptation Examples in Multiple Regions
Across regions, similar responses to climate variability and change can be noted. Heat waves are an interesting
example (Table 21-4), as early warning systems are gaining use for helping people reduce exposure to heat waves.
At the global scale, the length and frequency of warm spells, including heat waves, has increased since 1950
(medium confidence), and over most land areas on a regional scale, more frequent and/or longer heat waves or warm
spells are likely by 2016-2035 and very likely by 2081-2100 (IPCC, 2014d). Warning systems are now planned and
implemented in Europe, the US, Canada, Asia and Australia.
Use of mangroves to reduce flood risks and protect coastal areas from storm surges is a measure promoted in Asia,
Africa, the Pacific and South America (Table 21-4). Often, mangroves have been cut down to provide coastal
access, so there is a need to restore and rehabilitate them. This is an example that is considered low-regrets because
it brings multiple benefits to communities besides protecting them from storm surges, such as providing food
security and enhancing ecosystem services. Mangrove forests also store carbon, offering synergies with mitigation.
In several African countries, as well as in India, index-based insurance for agriculture has been used to address food
insecurity and loss of crops resulting from more hot and fewer cold nights, an increase in heavy precipitation events
and longer warm spells (Table 21-4). A predetermined weather threshold typically associated with high loss triggers
an insurance pay-out. The mechanism shares risk across communities and can help encourage adaptive responses
and foster risk awareness and risk reduction. However, limited availability of accurate weather data mean that
establishing which weather conditions causes losses can be challenging. Furthermore, if there are losses but not
enough to trigger payout, farmers may lose trust in the mechanism.
21.3.2.3. Adaptation Examples in Single Regions
Although conditions are distinct in each region and location, practical lessons can often be drawn from looking at
examples of adaptation in different locations. Experience with similar approaches in different regions offers
additional lessons that can be useful when deciding whether an approach is appropriate.
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Community-based adaptation is happening and being planned in many developing regions, especially in locations
that are particularly poor. In small islands, where a significant increase in the occurrence of future sea-level
extremes by 2050 and 2100 is anticipated, traditional technologies and skills may still be relevant for adapting
(Table 21-4). In the Solomon Islands, relevant traditional practices include elevating concrete floors to keep them
dry during heavy precipitation events and building low aerodynamic houses with palm leaves as roofing to avoid
hazards from flying debris during cyclones, supported by perceptions that traditional construction methods are more
resilient to extreme weather. In Fiji after cyclone Ami in 2003, mutual support and risk sharing formed a central
pillar for community-based adaptation, with unaffected households fishing to support those with damaged homes.
Participatory consultations across stakeholders and sectors within communities and capacity building taking into
account traditional practices can be vital to the success of adaptation initiatives in island communities, such as in Fiji
or Samoa. These actions provide more than just the immediate benefits; they empower people to feel in control of
their situations.
In Europe, several governments have made ambitious efforts to address risks of inland and coastal flooding due to
higher precipitation and sea level rise during the coming century (Table 21-4). Efforts include a multitude of
options. One of the key ingredients is strong political leadership or government champions. In the Netherlands,
government recommendations include soft measures preserving land from development to accommodate increased
river inundation; raising the level of lakes to ensure continuous freshwater supply; restoring natural estuary and tidal
regimes; maintaining coastal protection through beach nourishment; and ensuring necessary political-administrative,
legal, and financial resources. The British government has also developed extensive adaptation plans to adjust and
improve flood defenses and restrict development in flood risk areas in order to protect London from future storm
surges and river flooding. They undertook a multi-stage process, engaging stakeholders and using multi-criteria
analysis. Pathways have been analyzed for different adaptation options and decisions, depending on eventual sea
level rise, with ongoing monitoring of the drivers of risk informing decisions.
In Australia, farmers and industries are responding to experienced and expected changes in temperature, rainfall, and
water availability by relocating parts of their operations, such as for rice, wine, or peanuts, or changing land use
completely (Table 21-4). In South Australia, for instance, there has been some switching from grazing to cropping.
The response is transformational adaptation, and can have positive or negative implications for communities in both
origin and destination regions. This type of adaptation requires a greater level of commitment, access to more
resources and greater integration across decision-making levels because it spans regions, livelihoods and economic
sectors.
21.3.3. Climate System
This section places the regional chapters in a broader context of regional climate information, particularly regarding
cross regional aspects, but does not provide a detailed region-by-region assessment. Boxes 21-2 and 21-4 introduce
examples of regional information for continental/sub-continental regions but other regional definitions are often
relevant (see Box 21-3). The focus in this section is on the summary of new and emerging knowledge since the AR4
relevant to vulnerability, impacts and adaptation research, with emphasis on material deriving from dynamical and
statistical downscaling work which is often of greater relevance for VIA applications than the coarser resolution
global climate model data. In a regional context, the AR5 WG1 Chapter 14 (Regional Phenomena) is particularly
relevant for the projections and evaluation of confidence in models ability to simulate temperature, precipitation
and phenomena, together with an assessed implication for the general level of confidence in projections for 2080-
2099 of regional temperature and precipitation (See WG1, Ch 14, Table 14.2).
_____ START BOX 21-3 HERE _____
Box 21-3. Developing Regional Climate Information Relevant to Political and Economic Regions
In many world regions, countries form political and/or economic groupings that coordinate activities to further the
interests of the constituent nations and their peoples. For example, the Intergovernmental Authority on Development
(IGAD) of the countries of the Greater Horn of Africa recognizes that the region is prone to extreme climate events
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such as droughts and floods that have severe negative impacts on key socio-economic sectors in all its countries. In
response it has set up the IGAD Climate Prediction and Applications Centre (ICPAC). ICPAC provides and
supports application of early warning and related climate information for the management of climate-related risks
(for more details see http://www.icpac.net/). In addition it coordinates the development and dissemination of
seasonal climate forecasts for the IGAD countries as part of a WMO-sponsored Regional Climate Outlook Forum
process (Ogallo et al., 2008) which perform the same function in many regions. A more recent WMO initiative, the
Global Framework for Climate Services (Hewitt et al., 2012), aims to build on these and other global, regional and
national activities and institutions to develop climate information services for all nations.
As socio-economic factors are important contributors to both the vulnerability and adaptability of human and natural
systems, it clearly makes sense to summarise and assess available climate and climate change information for these
regions, as this will be relevant to policy decisions taken within these groupings on their responses to climate
change. For example, Figure 22-2 in Chapter 22 illustrates the presentation of observed and projected climate
changes of two summary statistics for 5 political/economic regions covering much of Africa. It conveys several
important pieces of information: the models are able to reproduce the observed trends in temperature; they simulate
significantly lower temperatures without the anthropogenic forcings and significantly higher future temperatures
under typical emissions paths; for most regions the models project that future variations in the annual average will
be similar to those simulated for the past. However, for a more comprehensive understanding additional information
needs to be included on other important aspects of climate, e.g. extremes (see Box 21-4).
_____ END BOX 21-3 HERE _____
21.3.3.1. Global context
21.3.3.1.1. Observed changes
Temperature and precipitation
New estimates of global surface air temperatures give a warming of about 0.89 C (0.69 C 1.08 C) for the period
of 1901-2012 and about 0.72 C (0.49 C 0.79 C) for the period 1951-2012 (WGI Chapter 2, Section 2.4.3).
Positive annual temperature trends are found over most land areas, particularly since 1981. Over the period 1981-
2012, relatively large trends have occurred over areas of Europe, the Sahara and middle East, central and northern
Asia, north-eastern North America (WGI Chapter 2, Section 2.4.3).
For precipitation, the Northern Hemisphere mid to high latitudes show a likely increasing trend (medium confidence
prior to 1950, high confidence afterwards) (WGI Chapter 2, Section 2.5.1). Observed precipitation trends show a
high degree of spatial and temporal variability, with both positive and negative values (WGI Chapter 2, Section 2.5).
The human influence on warming since the middle of the 20th century is likely over every continental region, except
Antarctica (WGI Chapter 10, Section 10.3.1), while the attribution of changes in hydrological variables is less
confident (WGI Chapter 10, Section 10.3.2).
Cryosphere
New data have become available since the AR4 to evaluate changes in the cryosphere (WGI Chapter 4, Section 4.1)
showing that the retreat of annual Arctic sea ice extent has continued, at a very likely rate of 3.5-4.1% per decade
during the period 1979-2012. The perennial sea ice extent (sea ice area at summer minimum) decreased at a rate of
11.5 +/- 2.1% per decade (very likely) over the same period 1979-2012 (WGI Chapter 4, Section 4.2.2). The
thickness, concentration and volume of arctic ice have also decreased. Conversely, the total annual extent of
Antarctic ice has increased slightly (very likely 1.2-1.8% per decade between 1979 and 2011), with strong regional
differences (WGI Chapter 4, Section 4.2.3).
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Almost all glaciers worldwide have continued to shrink since the AR4, with varying rates across regions (WG1
Chapter 4, Sections 4.3.1, 4.3.3). In particular, during the last decade most ice loss has been observed from glaciers
in Alaska, the Canadian Arctic, the Southern Andes, the Asian mountains and the periphery of the Greenland Ice
Sheet. Several hundred glaciers globally have completely disappeared in the last 30 years (WG1 Chapter 4, 4.3.3).
Because of better techniques and more data, confidence has increased in the measurements of Greenland and
Antarctica ice sheets. These indicate that parts of the Antarctic and Greenland ice sheets have been losing mass over
the last two decades (high confidence), mostly due to changes in ice flow in Antarctica, and a mix of changes in ice
flow and increases in snow/ice melt in Greenland. Ice shelves in the Antarctic Peninsula are continuing a long-term
trend of thinning and partial collapse started some decades ago (WGI Chapter 4, Sections 4.4.2, 4.4.3, 4.4.5).
21.3.3.1.2. Near-term and long-term climate projections
The uncertainty in near term CMIP5 projections is dominated by internal variability of the climate system (see
Glossary entry on Climate Variability), initial ocean conditions and inter-model response, particularly at smaller
spatial and temporal scales (Hawkins and Sutton 2009, 2011). In the medium and long term, emission profiles may
affect the climate response. Global warming of 0.3-0.7C is likely for the period of 2016-2035 compared to 1986-
2005 based on the CMIP5 multi model ensemble, and spatial patterns of near term warming are generally consistent
with the AR4 (WGI Chapter 11, Section 11.3.6). For precipitation (2016-2035 vs. 1986-2005), zonal mean
precipitation will very likely increase in high and some of the mid-latitudes, and will more likely than not decrease in
the subtropics (WGI Chapter 11, Section 11.3.2). Results from multi-decadal near term prediction experiments (up
to 2035) with initialized ocean state show that there is some evidence of predictability of yearly to decadal
temperature averages both globally and for some geographical regions (WGI Chapter 11, Section 11.2.3).
Moving to long term projections (up to 2100), analyses of the CMIP5 ensemble have shown that, in general, the
mean temperature and precipitation regional change patterns are similar to those found for CMIP3, with a pattern
correlation between CMIP5 and CMIP3 ensemble mean late 21st century change greater than 0.9 for temperature and
greater than 0.8 for precipitation (WGI Chapter 12, Section 12.4). Given the increased comprehensiveness and
higher resolution of the CMIP5 models this adds an element of robustness to the projected regional change patterns.
Some of the main characteristics of the projected late 21st century regional temperature and precipitation changes
derived from the CMIP5 ensemble can be broadly summarized as follows (from WGI Chapter 12 and the WGI
Atlas) with further details provided in Box 21-2 and accompanying supplementary material.
Temperature
Regions that exhibit relatively high projected temperature changes (often greater than the global mean by 50% or
more) are high latitude Northern Hemisphere land areas and the Arctic, especially in December-January-February,
and Central North America, portions of the Amazon, the Mediterranean, and Central Asia in June-July-August
(Figure 21-4).
[INSERT FIGURE 21-4 HERE
Figure 21-4: CMIP5 ensemble median ratio of local:global average temperature change in the period 2071-2100
relative to 1961-90 under the RCP8.5 emissions/concentrations scenario. The values are displayed on a common
2.5x3.75 grid onto which each models data were regridded and they were calculated as follows: 1) for each model
the local change was calculated between 1961 and 1990 at each grid cell, and is divided by the global average
change in that model projection over the same period; 2) the median ratio value across all models at each grid cell is
identified and shown. Data used are from the 35 CMIP5 models for which monthly projections were available under
RCP8.5 which are listed in supplementary Table 21-3. Overplotted polygons indicate the SREX regions (IPCC,
2012) used to define the sub-regions used to summarise information in Chapters 21 and some of the subsequent
regional chapters.]
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Precipitation
Changes in precipitation are regionally highly variable, with different areas projected to experience positive or
negative changes (Box 21-2). By the end of the century in the RCP8.5 scenario, the high latitudes will very likely
experience greater amounts of precipitation, some mid-latitude arid and semi-arid regions will likely experience
drying, while some moist mid-latitude regions will likely experience increased precipitation (WG1 Chapter 12,
Section 12.4.5).
Studies have also attempted to obtain regional information based on pattern scaling techniques in which regional
temperature and precipitation changes are derived as a function of global temperature change (e.g. Giorgi 2008;
Watterson 2008; Watterson and Whetton 2011; Watterson 2011 Ishizaki et al. 2012, 2013a, 2013b). Figure 21-5
from Harris et al. (2013) provides an example of Probability Density Functions (PDFs) of temperature and
precipitation change over sub-continental scale regions obtained using a Bayesian method complemented by pattern
scaling and performance-based model weighting.
[INSERT FIGURE 21-5 HERE
Figure 21-5: Evolution of the 5%, 17%, 33%, 50%, 66%, 83% and 95% percentiles of the distribution functions for
annual surface air temperature changes (upper panel) and annual precipitation (lower panel) for the Giorgi-Francisco
(2000) regions and the globe with the SRES A1B forcing scenario combining results from a perturbed physics
ensemble and the CMIP3 ensemble. Twenty year means relative to the 1961-1990 baseline are plotted in decadal
steps using a common y-axis scale. The 5%, 50% and 95% percentile values for the period 2080-2099 are displayed
for each region. (From Harris et al. 2012).]
21.3.3.2. Dynamically and Statistically Downscaled Climate Projections
Dynamical and statistical downscaling techniques have been increasingly applied to produce regional climate
change projections, often as part of multi-model intercomparison projects (Görgen et al, 2010). A large number of
RCM-based climate projections for the European region were produced as part of the European projects
PRUDENCE (Christensen et al. 2007; Deque et al. 2007) and ENSEMBLES (Hewitt 2005; Deque and Somot
2010). High resolution projections (grid interval of ~12 km) were also produced as part of Euro-CORDEX (Jacob et
al 2013). All these studies provide a generally consistent picture of seasonally and latitudinally varying patterns of
change, which Giorgi and Coppola (2007) summarized with the term European Climate Change Oscillation
(ECO) . The ECO consists of a dipole pattern of precipitation change, with decreased precipitation to the south
(Mediterranean) and increased to the north (Northern Europe) following a latitudinal/seasonal oscillation. As a
result, the Mediterranean region is projected to be much drier and hotter than today in the warm seasons (Giorgi and
Lionello 2008), and central/northern Europe much warmer and wetter in the cold seasons (Kjellstrom and
Ruosteenoja, 2007). An increase of interannual variability of precipitation and summer temperature is also projected
throughout Europe, with a decrease in winter temperature variability over Northern Europe (Schar et al. 2004;
Giorgi and Coppola 2007; Lenderink et al. 2007). This leads to broader seasonal anomaly distributions and a higher
frequency and intensity of extreme hot and dry summers (e.g. Schar et al. 2004; Seneviratne et al. 2006; Beniston et
al. 2007; Coppola and Giorgi 2010), for which a substantial contribution is given by land-atmosphere feedbacks
(Seneviratne et al. 2006; Fischer et al. 2007; Seneviratne et al. 2010; Hirschi et al. 2011; Jaeger and Seneviratne
2011). The broad patterns of change in regional model simulations generally follow those of the driving global
models (Christensen and Christensen 2007; Deque et al. 2007; Zanis et al. 2009), however fine scale differences
related to local topographical, land use and coastline features are produced (e.g. Gao et al. 2006; Coppola and Giorgi
2010; Tolika et al. 2012).
As part of the ENSEMBLES and AMMA projects, multiple RCMs were run for the period 1990-2050 (A1B
scenario) over domains encompassing the West Africa region with lateral boundary conditions from different
GCMs. The RCM-simulated West Africa monsoon showed a wide range of response in the projections, even when
the models were driven by the same GCMs (Paeth et al. 2011) (Figure 21-6). Although at least some of the response
patterns may be within the natural variability, this result suggests that for Africa, and probably more generally the
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tropical regions, local processes and how they are represented in models play a key factor in determining the
precipitation change signal, leading to a relatively high uncertainty (Engelbrecht et al. 2009; Haensler et al. 2011;
Mariotti et al. 2011; Diallo et al. 2012). Statistical downscaling techniques have also been applied to the Africa
region (Hewitson and Crane 2006; Lumsden et al. 2009; Paeth and Diederich 2010; Goergen et al., 2010; Benestad
2011). In this regard, methodological developments since the AR4 have been limited (see, for example reviews in
Brown et al. 2008; Paeth et al. 2011) and activities have focused more on the applications (e.g. Mukheibir 2007;
Gerbaux et al. 2009) for regional specific activities in the context of IAV work.
[INSERT FIGURE 21-6 HERE
Figure 21-6: Linear changes (i.e. changes obtained by fitting the time series at each grid point with straight lines) of
annual precipitation during the 2001-2050 period from 10 individual RCM experiments and the MME mean under
the A1B emission scenario. The top middle panels also account for projected land cover changes (see Paeth et al.
2011 for further explanation). Note that the REMO trends in both panels arise from a three-member ensemble
whereas all other RCMs are represented by one single simulation. Trends statistically significant at the 95% level
are marked by black dots. From Paeth et al. (2011).]
Several RCM and time-slice high resolution GCM experiments have been conducted or analyzed for the South
America continent (Nunez et al. 2009; Menendez et al. 2010; Sorensson et al. 2010; Marengo et al. 2009, 2010;
Cabre et al. 2010; Kitoh et al. 2011). Overall, these studies revealed varied patterns of temperature and precipitation
change, depending on the global and regional models used, however a consistent change found in many of these
studies was an increase in both precipitation intensity and extremes, especially in areas where mean precipitation
was projected to also increase. The Central American region has emerged as a prominent climate change hot-spot
since the AR4, especially in terms of a consistent decrease of precipitation projected by most models, particularly in
June-July (Rauscher et al 2008, 2011). Regional model studies focusing specifically on Central America projections
are however still too sparse to provide robust conclusions (e.g. Campbell et al. 2010).
Since the AR4 there has been considerable attention to producing higher resolution climate change projections over
North America based on RCMs and high resolution global time slices (e.g. Salathe et al. 2008, 2010; Dominguez et
al. 2010; Subin et al. 2011), in particular as part of the North American Regional Climate Change Assessment
Program (NARCCAP Mearns et al. 2009; 2012; 2013). Results indicate variations (and thus uncertainty) in future
climate based on the different RCMs, even when driven by the same GCM in certain subdomains (De Elia and Cote
2010; Mearns et al., 2013; Bukovsky et al. 2013). However, in the NARCCAP suite of simulations there were also
some important commonalities in the climate changes produced by the RCMs. For example, they produced larger
and more consistent decreases in precipitation throughout the Great Plains in summer than did the driving GCMs or
the full suite of CMIP3 GCM simulations as well as larger increases in precipitation in the northern part of the
domain in winter. In the realm of statistical downscaling and spatial disaggregation, considerable efforts have been
devoted to applying different statistical models for the entire US and parts of Canada (e.g., Maurer et al. 2007;
Hayhoe et al. 2010; Schoof et al., 2010).
Numerous high resolution RCM projections have been carried out over the East Asia continent. While some of these
find increases in monsoon precipitation over South Asia in agreement with the driving GCMs (Kumar et al. 2013)
others also produce results that are not in line with those from GCMs. For example, both Ashfaq et al. (2009) and
Gao et al. (2011) found in high resolution RCM experiments (20 and 25 km grid spacing, respectively) decreases in
monsoon precipitation over areas of India and China in which the driving GCMs projected an increase in monsoon
rain. Other high resolution (20 km grid spacing) projections include a series of double nested RCM scenario runs for
the Korea peninsula (Im et al. 2007a; 2008a,b; 2010; 2011; Im and Ahn, 2011) indicating a complex fine scale
structure of the climate change signal in response to local topographical forcing. Finally, very high resolution
simulations were also performed. Using a 5-km mesh non-hydrostatic RCM nested within a 20-km mesh AGCM,
Kitoh et al. (2009) and Kanada et al. (2012) projected a significant increase in intense daily precipitation around
western Japan during the late Baiu season.
Finally, a range of RCM, variable resolution and statistical downscaling 21st century projections have been
conducted over the Australian continent or some of its sub-regions (Nunez and Mc Gregor 2007; Watterson et al.
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2008; Song et al. 2008, Timbal et al. 2008; Yin et al. 2010, Grose et al 2012a,b; Bennett et al 2012), showing that a
local fine scale modulation of the large scale climate signal occurs in response to topographical and coastal forcings.
21.3.3.3. Projected Changes in Hydroclimatic Regimes, Major Modes of Variability, and Regional Circulations
By modifying the Earth s energy and water budgets, climate change may possibly lead to significant changes in
hydroclimatic regimes and major modes of climate variability (Trenberth et al. 2003). For example, Giorgi et al.
(2011) defined an index of hydroclimatic intensity (HY-INT) incorporating a combined measure of precipitation
intensity and mean dry spell length. Based on an analysis of observations, global and regional climate model
simulations, they found that a ubiquitous global and regional increase in HY-INT was a strong hydroclimatic
signature in model projections consistent with observations for the late decades of the 20th century. This suggests
that global warming may lead to a hydroclimatic regime shift towards more intense and less frequent precipitation
events, which would increase the risk of both flood and drought associated with global warming.
ENSO is a regional mode of variability that substantially affects human and natural systems (Mc Phaden et al.
2006). Although model projections indicate that ENSO remains a major mode of tropical variability in the future,
there is little evidence to indicate changes forced by GHG warming which are outside the natural modulation of
ENSO occurrences (WGI Chapter 14, Sections 14.4, 14.8).
The North Atlantic Oscillation (NAO) is a major mode of variability for the northern Hemisphere mid-latitude
climate. Model projections indicate that the NAO phase is likely to become slightly more positive (WGI Chapter 14
ES) due to GHG forcing, but the NAO will be dominated by its large natural fluctuations. Model projections
indicate that the Southern Annular Mode (SAM), a major mode of variability for the southern hemisphere, is likely
going to weaken as ozone concentrations recover through the mid-21st century (WGI Chapter 14, Sections 14.5,
14.8).
Regional circulations, such as the monsoon, are expected to change. The global monsoon precipitation, aggregated
over all monsoon systems, is likely to strengthen in the 21st century with increases in its area and intensity, while the
monsoon circulation weakens. Different regional monsoon systems, however, exhibit different responses to GHG
forcing in the 21st century (WGI Chapter 14, Section 14.2.1).
21.3.3.4. Projected Changes in Extreme Climate Events
CMIP5 projections confirm results from the CMIP3; a decrease in the frequency of cold days and nights, an increase
in the frequency of warm days and nights, an increase in the duration of heat waves and an increase in the frequency
and intensity of high precipitation events, both in the near term and far future (IPCC (2012), 3.3.2, 3.4.4; WGI
Chapter 12, 12.4.5). Increases in intensity of precipitation (and thus risk of flood) and summer drought occurrence
over some mid-continental land areas is a robust signature of global warming, both in observations for recent
decades and in model projections (Trenberth 2011; WG1 Chapter 12, 12.4.5). For tropical cyclones there is still little
confidence in past trends and near term projections (Seneviratne et al 2012). Globally, tropical cyclone frequency is
projected to either not change or decrease and, overall, wind speed and precipitation is likely to increase though
basin scale specific conclusions are still unclear (Knutson et al. 2010). A summary of observed and projections
extremes along with some statistics on CMIP5 projections of changes in daily temperature and precipitation
extremes over the main continents and the SREX regions (Figure 21-4) are introduced in Box 21-4 and
accompanying supplementary material.
_____ START BOX 21-4 HERE _____
Box 21-4. Synthesis of Projected Changes in Extremes Related to Temperature and Precipitation
The IPCC report, Managing the risks of extreme events and disasters to advance climate change adaptation (IPCC
2012), or SREX for short, provides an in depth assessment of observed and projected changes in climate extremes.
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Due to the relevance of this material for assessing risks associated with climate change vulnerability and impacts
and responses to these risks, summary information is presented here both drawing from and building on the material
in the SREX report, including additional analyses of CMIP5 data (only CMIP3 data were used in SREX).
Summaries of SREX findings relevant to three continents Latin America (Cameron et al., 2012), Asia and Africa
(available from http://cdkn.org/srex/) have been developed using material from SREX Chapter 3. A synthesis of this
material for all SREX regions, along with additional material from WG1 AR5, is presented in Table 21-7. This
demonstrates that in many areas of the world there is higher confidence in future changes in extreme events than
there is in past trends, often due to a lack of evidence on observed changes.
[INSERT TABLE 21-7 HERE
Table 21-7: An assessment of observed and projected future changes in temperature and precipitation extremes over
26 sub-continental regions as defined in the SREX report (IPCC 2012; see also Figure 21-4 and Table SM21.2).
Confidence levels are indicated by colour coding of the symbols. Likelihood terms are given only for high
confidence statements and are specified in the text. Observed trends in temperature and precipitation extremes,
including dryness, are generally calculated from 1950, using the period 1961-1990 as a baseline (see Box 3-1 of
IPCC (2012a)). The future changes are derived from global and regional climate model projections of the climate of
2071-2100 compared with 1961-1990 or 2080-2100 compared with 1980-2000. Table entries are summaries of
information in Tables 3-2 and 3-3 of IPCC (2012a) supplemented with or superseded by material from Chapters 2
(section 2.6 and Table 2-13) and 14 (section 14.4) of the IPCC AR5 WG1 report and Table 25-1 of the IPCC WG2
report. The source(s) of information for each entry are indicated by the superscripts a (Table 3-2 of IPCC, 2012a), b
(Table 3-3 of IPCC, 2012a), c (Chapter 2 (section 2.6 and Table 2-13) IPCC AR5 WG1 report), d (Chapter 14
(section 14.4) of the IPCC AR5 WG1 report) and e (Table 25-1 of the IPCC WG2 report).]
In the SREX report, the only coordinated global multi-model ensemble information available was from the CMIP3.
In order to provide information consistent with the projections assessed elsewhere in WG1 and 2, changes in daily
temperature and precipitation projected by the CMIP5 models are presented here for two example indices, the 90th
percentiles of the daily maximum temperature and daily precipitation amounts on wet days. Changes in these indices
were calculated over 30 year periods (1961-1990 for the baseline and two future periods, 2041-2070 and 2071-2100)
and the analysis was focused on the less extreme daily events to reduce problems with the number needed to be
sampled to generate robust statistics (Kendon et. al, 2008). Projected changes were calculated for RCPs 4.5 and 8.5
and the results are displayed as a map for a given continental region and also regional averages over the SREX
regions within that continent. Two examples are provided, for temperature changes over N America (Figure 21-7)
and precipitation changes over Asia (Figure 21-8). A full set can be found in supplementary Figures SM21-8 to
SM21-19.
[INSERT FIGURE 21-7 HERE
Figure 21-7: The frequency of 'warm days' (defined here as the 90th percentile daily maximum temperature during a
baseline period of 1961-1990) projected for the 2071-2100 period by 26 CMIP5 GCMs for North America. Map:
Ensemble median frequency of 'warm days' during 2071-2100 under RCP8.5. Graphs: Box-and-whisker plots
indicate the range of regionally-averaged 'hot-day' frequency by 2041-2070 and 2071-2100 under RCPs 4.5 and 8.5
across the 26 CMIP5 models for each SREX sub-regions in North America. Boxes represent inter-quartile range and
whiskers indicate full range of projections across the ensemble. The baseline frequency of warm days of 10% is
represented on the graphs by the dashed line. A full list of CMIP5 models for which data is shown here can be found
in supplementary material Table SM21-4.]
[INSERT FIGURE 21-8 HERE
Figure 21-8: The frequency of 'very wet days' (defined here as the 90th percentile of daily precipitation on wet days
during a baseline period of 1961-1990 with wet days defined as days with 1mm of precipitation or more) projected
for the 2071-2100 period by 26 CMIP5 GCMs for Asia. Map: Ensemble median frequency of 'very wet days' during
2071-2100 under RCP8.5. Graphs: Box-and-whisker plots indicate the range of regionally-averaged 'very wet day'
frequency by 2041-2070 and 2071-2100 under RCPs 4.5 and 8.5 across the 26 CMIP5 models for each SREX sub-
regions in Asia Boxes represent inter-quartile range and whiskers indicate full range of projections across the
ensemble. The baseline frequency of Very wet days of 10% is represented on the graphs by the dashed line. A full
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list of CMIP5 models for which data is shown here can be found in supplementary material Table SM21-4. (Note
the WMO Expert Team on Climate Change Detection Indices defines very wet days threshold as the 95%-ile daily
precipitation event.]
_____ END BOX 21-4 HERE _____
21.3.3.5. Projected Changes in Sea Level
Projections of regional sea level changes, both based on the CMIP3 and CMIP5 models, indicate a large regional
variability of sea level rise (even more than 100% of the global mean sea level rise) in response to different regional
processes (WGI Chapter 13, Section 13.6.5). However, by the end of the 21st century it is very likely that over about
95% of the oceans will undergo sea level rise, with about 70% of coastlines experiencing a sea level rise within 20%
of the global value and most regions experiencing sea level fall being located near current and former glaciers and
ice sheets (WGI Chapter 13, Section 13.6.5). Some preliminary analysis of the CMIP5 ensembles indicates areas of
maximum steric sea level rise in the Northern Atlantic, the northwestern Pacific off the East Asia coasts, the eastern
coastal oceanic regions of the Bay of Bengal and the western coastal regions of the Arabian Sea (WGI Chapter 13,
Section 13.6.5).
21.3.3.6. Projected Changes in Air Quality
Since the AR4 more studies have become available addressing the issue of the effects of both climate and emission
changes on air quality. Most of these studies focused on the continental United States and Europe, and utilized both
global and regional climate and air quality models run in off-line or coupled mode. Regional modeling studies over
the United States or some of its sub-regions include, for example, those of Hogrefe et al. (2004), Knowlton et al.
(2004), Steiner et al. (2006), Dawson et al. (2006), Lin et al. (2008), Zhang et al. (2008), Weaver et al. (2009), while
examples of global modeling studies include Murazaki and Hess (2006), Stevenson et al. (2006), Shindell et al.
(2006), Doherty et al. (2006). Weaver et al. (2009) provide a synthesis of simulated effects of climate change on
ozone concentrations in the U.S. using an ensemble of regional and global climate and air quality models, indicating
a predominant increase in near-surface ozone concentrations, particularly in the Eastern U.S. (Figure 21-9) mostly
tied to higher temperatures and corresponding biogenic emissions. An even greater increase was found in the
frequency and intensity of extreme ozone concentration events, which are the most dangerous for human health.
Examples of regional studies of air quality changes in response to climate change over Europe include Langner et al.
(2005), Forkel and Knocke (2006) , Szopa and Hauglustaine (2007), and Meleux et al. (2007), Kruger et al (2008),
Engardt et al. (2009), Carvalho et al. (2010), Andersson and Engardt (2010), Athanassiadou et al. (2010), Katragkou
et al. (2010, 2011), Zanis et al. (2011), Huszar et al. (2011), Juda-Rezler et al. (2012). All these studies indicated the
potential of large increases in near surface summer ozone concentrations especially in Central and Southern Europe
due to much warmer and drier projected summer seasons.
[INSERT FIGURE 21-9 HERE
Figure 21-9 Mean (top panels) and standard deviation (bottom panels) in future-minus-present (2050s minus 1990s)
MDA8 summer ozone concentrations across (left-hand panels) all seven experiments (five regional and 2 global)
and for comparison purposes (right hand panels), not including the WSU experiment (which simulated July only
conditions). The different experiments use different pollutant emission and SRES GHG emission scenarios. The
pollutant emissions are the same in the present and future simulations (from Weaver et al., 2009).]
21.4. Cross-Regional Phenomena
Thus far, this chapter has covered climate change-related issues that have a regional expression in one part of the
world or another. In principle, these issues can be studied and described, in situ, in the regions in which they occur.
However, there is a separate class of issues that transcends regional boundaries and demands a different treatment.
In order to understand such cross-regional phenomena, knowledge is required of critical but geographically remote
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associations and of dynamic cross-boundary flows. The following sections consider some examples of these
phenomena, focusing on trade and financial flows and migration. Though these issues are treated in more detail in
Part A of this report, they are restated here in Part B to stress the importance of a global perspective in appreciating
climate change challenges and potential solutions at the regional scale.
21.4.1. Trade and Financial Flows
Global trade and international financial transactions are the motors of modern global economic activity. Their role as
key instruments for implementing mitigation and adaptation policies is explored in detail in chapters 14-17 and in
the Working Group III report Stavins et al., 2014; Gupta et al., 2014). They are also inextricably linked to climate
change (WTO-UNEP, 2009) through a number of other interrelated pathways that are expanded here: (i) as a direct
or indirect cause of anthropogenic emissions (e.g., Peters et al., 2011), (ii) as contributory factors for regional
vulnerability to the impacts of climate change (e.g. Leichenko and O'Brien, 2008), and (iii) through their sensitivity
to climate trends and extreme climate events (e.g., Nelson et al., 2009a; Headey, 2011).
21.4.1.1. International Trade and Emissions
The contemporary world is highly dependent on trading relationships between countries in the import and export of
raw materials, food and fibre commodities and manufactured goods. Bulk transport of these products, whether by
air, sea or over land, is now a significant contributor to emissions of greenhouse gases and aerosols (Stavins et al.,
2014). Furthermore, the relocation of manufacturing has transferred net emissions via international trade from
developed to developing countries (see Figure 21-10), and most developed countries have increased their
consumption-based emissions faster than their domestic (territorial) emissions (Peters et al., 2011). This regional
transfer of emissions is commonly referred to in climate policy negotiations as "carbon leakage" (Barker et al.,
2007), though only a very small portion of this can be attributed to climate policy ("strong carbon leakage"), a
substantial majority being due to the effect of non-climate policies on international trade ("weak carbon leakage"
Peters, 2010). A particular example of strong carbon leakage concerns the conversion of land use from the
production of food to bioenergy crops. These crops sequester carbon otherwise extracted from the ground as fossil
fuels, but in the process displace demand for food production to land in other regions, often inducing land clearance
and hence an increase in emissions (Searchinger et al., 2008), though the empirical basis for this latter assertion is
disputed (see Kline and Dale, 2008).
[INSERT FIGURE 21-10 HERE
Figure 21-10: Growth rates from 1990-2008 of international trade, its embodied CO2 emissions and net emissions
transfers from Annex B and non-Annex B countries compared to other global macrovariables, all indexed to 1990
(Peters et al., 2011). Annex B and non-Annex B Parties to the UNFCCC are listed in the supplementary material.]
21.4.1.2. Trade and Financial Flows as Factors Influencing Vulnerability
The increasingly international nature of trade and financial flows (commonly referred to as globalisation), while
offering potential benefits for economic development and competitiveness in developing countries, also presents
high exposure to climate-related risks for some of the populations already most vulnerable to climate change
(Leichenko and O'Brien, 2008). Examples of these risks, explored further in Chapters 7-9, 12, 13 and 19 of Part A,
include:
Severe impacts of food price spikes in many developing countries (including food riots and increased
incidence of child malnutrition) such as occurred in 2008 following shortfalls in staple cereals, due to a
coincidence of regional weather extremes (e.g. drought) in producer countries, the reallocation of food
crops by some major exporters for use as biofuels (an outcome of climate policy see previous section)
and market speculation (Ziervogel and Ericksen, 2010). Prices subsequently fell back as the world
economy went into recession, but spiked again in early 2011 for many of the same reasons (Trostle et al.,
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2011), with some commentators predicting a period of rising and volatile prices due to increasing demand
and competition from biofuels (Godfray et al., 2010).
A growing dependence of the rural poor on supplementary income from seasonal urban employment by
family members and/or on international financial remittances from migrant workers (Davies et al., 2009).
These workers are commonly the first to lose their jobs in times of economic recession, which
automatically decreases the resilience of recipient communities in the event of adverse climate
conditions.On the other hand, schemes to provide more effective communication with the diaspora in times
of severe weather and other extreme events can provide rapid access to resources to aid recovery and
reduce vulnerability (Downing, 2012).
Some aspects of international disaster relief, especially the provision of emergency food aid over protracted
periods, has been cited as an impediment to enhancing adaptive capacity to cope with climate-related
hazards in many developing countries (Schipper and Pelling, 2006). Here, international intervention, while
well-intentioned to relieve short-term stress, may actually be counter-productive in regard to the building of
long-term resilience.
21.4.1.3. Sensitivity of International Trade to Climate
Climate trends and extreme climate events can have significant implications for regional resource exploitation and
international trade flows. The clearest example of an anticipated, potentially major impact of climate change
concerns the opening of Arctic shipping routes as well as exploitation of mineral resources in the exclusive
economic zones (EEZs) of Canada, Greenland/Denmark, Norway, Russia and the USA (Figure 21-11, and see
chapter 28, section 28.3.4). For instance, the CCSM4 climate and sea ice model has been used to provide projections
under RCP4.5, RCP6.0 and RCP8.5 forcing (see Box 21-1) of future accessibility for shipping to the sea ice hazard
zone of the Arctic marine environment defined by the International Maritime Organization (Stephenson et al., 2013
Figure 21-11 (central map). Results suggest that moderately ice-strengthened ships (Polar Class 6), which are
estimated under baseline (1980-1999) conditions to be able to access annually about 36 % of the IMO zone, would
increase this access to 45-48 % by 2011-2030, 58-69% by 2046-2065 and 68-93% by 2080-2099, with almost
complete accessibility projected for summer (90-98% in July-October) by the end of the century (Stephenson et al.,
2013). The robustness of those findings was confirmed using seven sea ice models in an analysis of optimal sea
routes in peak season (September) for 2050-2069 under RCP 4.5 and RCP8.5 forcing (Smith and Stephenson, 2013).
All studies imply increased access to the three major cross Arctic routes: the Northwest Passage, Northern Sea
Route (which is part of the Northeast Passage), and Trans-Polar Route (Figure 21-11), which could represent
significant distance savings for trans-continental shipping currently using routes via the Panama and Suez Canals
(Stephenson et al., 2011). Indeed, in 2009 two ice-hardened cargo vessels the Beluga Fraternity and Beluga
Foresight became the first to successfully traverse the Northeast Passage from South Korea to the Netherlands, a
reduction of 5,500 km and 10 days compared to their traditional 20,000 km route via the Suez Canal, translating into
an estimated saving of some $300,000 per ship, including the cost of standby icebreaker assistance (Smith, 2009;
Det Norsk Veritas, 2010). A projection using an earlier version of the CCSM sea ice model under the SRES A1B
scenario, but offering similar results (with forcing by mid-century lying just below RCP8.5 chapter 1, Figure 1-
5a), is presented in Figure 21-11 (peripheral maps), which also portrays winter transportation routes on frozen
ground. These routes are heavily relied upon for supplying remote communities and for activities such as forestry
and, in contrast to the shipping routes, are projected to decline in many regions.
[INSERT FIGURE 21-11 HERE
Figure 21-11: Central map: Marine exclusive environmental zones (EEZs dashed lines) of Canada,
Greenland/Denmark, Norway, Russia, and the USA, and location of the Northwest Passage, Northern Sea Route,
Trans-Polar Route, and international high seas within the IMO Guidelines Boundary for Arctic shipping (thick black
border). After Stephenson et al. (2013). Peripheral monthly maps: Projected change in accessibility of maritime and
land-based transportation by mid-century (2045-2059 relative to 2000-2014) using the Arctic Transport
Accessibility Model and CCSM3 climate and sea ice estimates assuming an SRES A1B scenario. Dark blue areas
denote new maritime access to Polar Class 6 vessels (light icebreaker); white areas remain inaccessible. Red delimits
areas of lost winter road potential for ground vehicles exceeding 2 metric tonnes (Stephenson et al., 2011).]
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A second illustration of how the risk of adverse climate changes may have contributed to anticipatory adaptive
actions affecting countries in other regions of the world and potentially influencing commodity markets, relates to
the purchase or renting of large tracts of productive land in parts of Africa, Latin America, Central Asia and
Southeast Asia by countries in Europe, Africa, the Gulf and South and East Asia (De Schutter, 2009; Cotula et al.,
2011; Zoomers, 2011). While there is clearly a profit motive in many of these purchases (i.e., cheap and fertile land
and the opportunity to cultivate high value food or biofuel crops), there is also a concern that domestic agricultural
production in some countries will be unable to keep pace with rapid growth in domestic demand and changing
dietary preferences, especially in agricultural regions affected by frequent shortfalls due to droughts, floods and
cyclones (Cotula et al., 2011), or threatened by sea-level rise (Zoomers, 2011). Land acquisition on such a large
scale raises a number of ethical issues relating to local access to food and the appropriate and sustainable
management of the land (Deininger and Byerlee, 2012). These issues have led the UN Special Rapporteur on the
right to food to recommend a list of eleven principles for ensuring informed participation of local communities,
adequate benefit sharing and the respect of human rights (De Schutter, 2009). This issue is elaborated with respect to
livelihoods and poverty in chapter 13, section 13.4.3.4, and land dispossession is categorised a key risk in chapter
19, section 19.6.2.
Extreme climate phenomena that may be harbingers of similar and more frequent events in a warmer world, already
exact devastating consequences in some regions that extend well beyond country boundaries. A recent event that
disrupted international trade and commodity flows was the severe 2010/2011 flooding in eastern Australia (Giles,
2011; Queensland Floods Commission of Inquiry, 2012; and see chapter 25, Box 25-8), which combined with
damaging cyclones in Queensland and Western Australia curtailed numerous mining operations and damaged
transportation networks, leading to declines in both thermal and metallurgical coal exports (by 31% and 19%,
respectively, relative to the previous quarter ABARES, 2011) with a sharp rise in their monthly price between
November 2010 and January 2011 (Index Mundi, 2012). The severe weather was the primary factor contributing to a
fall in Australian GDP of 1.2% during January-March 2011 compared with a rise of 0.7% in the preceding three-
month period (Australian Bureau of Statistics, 2011). Other examples of how extreme climate events can affect
international trade are reported by Oh and Reuveny (2010) and Handmer et al. (2012).
21.4.2. Human Migration
There has been considerable debate in recent years around the postulate that anthropogenic climate change and
environmental degradation could lead to mass migration (Perch-Nielsen et al., 2008; Feng et al., 2010; Warner,
2010; Black et al., 2011; Government Office for Science, 2011; Assan and Rosenfeld, 2012). The issue is treated at
length in Chapters 9, 12 and 19 of Part A, so only a few aspects are touched on here, to highlight the growing
significance of migration in all regions of the world. Four possible pathways through which climate change could
affect migration are suggested by Martin (2009):
1) Intensification of natural disasters
2) Increased warming and drought that affects agricultural production and access to clean water
3) Sea-level rise, which makes coastal areas and some island states increasingly uninhabitable
4) Competition over natural resources, leading to conflict and displacement of inhabitants.
Abundant historical evidence exists to suggest that changes in climatic conditions have been a contributory factor in
migration, including large population displacements in the wake of severe events such as Hurricane Katrina in New
Orleans, Louisiana in 2005 (Cutter et al., 2012), Hurricane Mitch in Central America in 1998 and the northern
Ethiopian famines of the 1980s (McLeman and Smit, 2006). Other examples are provided in Chapter 12, Table 12-3.
However, the evidence is not clear cut (Black, 2001), with counter examples also available of migration being
limited due to economic hardship (e.g. during the Sahel drought of the mid-1980s in Mali Findley, 1994).
The spatial dimension of climate-related migration is most commonly internal to nations (e.g. from affected regions
to safer zones Naik, 2009). In this context it is also worth pointing out that internal migration for other
(predominantly economic) reasons may actually expose populations to increased climate risk. For instance, there are
large cities in developing countries in low elevation coastal zones that are vulnerable to sea-level rise. Increased
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migration to these cities could exacerbate the problems with the migrants themselves being especially vulnerable
(Nordas and Gleditsch, 2007; UNFPA, 2007).
Migration can also be international, though this is less common in response to extreme weather events, and where it
does happen it usually occurs along well established routes. For example, emigration following Hurricane Mitch
tripled from Honduras and increased from Nicaragua by 40%, mainly to the southern States of the US (already a
traditional destination for migrants), and was aided by a relaxation of temporary residency requirements by the
United States (Naik, 2009).
The causal chains and links between climate change and migration are complex and can be difficult to demonstrate
(e.g., Perch-Nielsen et al., 2008; Piguet, 2010; Tänzler et al., 2010; ADB, 2012; Oliver-Smith, 2012; Chapter 12,
section 12.4; Chapter 9, section 9.3.3.3.1; Chapter 19, section 19.4.2.1), though useful insights can be gained from
studying past abandonment of settlements (McLeman, 2011). Thus projecting future climate-related migration
remains a challenging research topic (Feng et al., 2010). There are also psychological, symbolic, cultural and
emotional aspects to place attachment, which are well documented from other non-climate causes of forced
migration, and are also applicable to cases of managed coastal retreat due to sea-level rise (e.g., Agyeman et al.,
2009).
Forced migration appears to be an emerging issue requiring more scrutiny by governments in organising
development co-operation, and to be factored into international policy making as well as international refugee
policies. For example, it has been suggested that the National Adaptation Plans of Action (NAPAs) under the
UNFCCC, by ignoring transboundary issues (such as water scarcity), and propounding nationally-orientated
adaptation actions (e.g. upstream river management, to the detriment of downstream users in neighbouring
countries), could potentially be a trigger for conflict, with its inevitable human consequences. Currently there is no
category in the United Nations High Commission for Refugees classification system for environmental refugees, but
it is possible that this group of refugees will increase in the future and their needs and rights will need to be taken
into consideration (Brown, 2008). The Nansen Initiative, put forward jointly by Norway and Switzerland at a 2011
ministerial meeting, pledges "to cooperate with interested states and relevant actors, including UNHCR, to obtain a
better understanding of cross-border movements provoked by new factors such as climate change, identify best
practices and develop a consensus on how best to protect and assist those affected ", and may eventually result in a
soft law or policy framework (Kolmannskog, 2012). However, migration should not always be regarded as a
problem; in those circumstances where it contributes to adaptation (e.g. through remittances) it can be part of the
solution (Laczko and Aghazarm, 2009).
21.4.3. Migration of Natural Ecosystems
One of the more obvious consequences of climate change is the displacement of biogeographical zones and the
natural migration of species (see Chapters 4, 6 and 19). General warming of the climate can be expected to result in
migration of ecosystems towards higher latitudes and upward into higher elevations (Chapter 4, section 4.3.2.5) or
downward to cooler depths in marine environments (Chapter 6, section 6.3.2.1). Species shifts are already occurring
in response to recent climate changes in many parts of the world (Rosenzweig et al., 2008), with average poleward
shifts in species range boundaries of 6 km per decade being reported (Parmesan et al., 2011).
Study of the estimated shifts of climatic zones alone can provide insights into the types of climatic regimes to
anticipate under projected future anthropogenic climate change. By grouping different combinations and levels of
climatic variables it is possible not only to track the shifts in the zones in which they occur, but also to identify
newly emerging combinations of conditions not found at the present day as well as combinations that may not
survive global climate change (known respectively as novel and disappearing climates Williams et al., 2007; and
see Chapter 19, section 19.5.1). These analyses can help define what types of climatic niches may be available in the
future and where they will be located. Such a spatial analogue approach can delimit those regions that might
currently or potentially (in the future) be susceptible to invasion by undesirable aquatic (e.g., EPA, 2008) or
terrestrial (e.g., Mainka and Howard, 2010) alien species or alternatively might be candidates for targetting
translocation (assisted colonisation) of species endangered in their native habitats (e.g., Brooker et al., 2011;
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Thomas, 2011). However, there are many questions about the viability of such actions, including genetic
implications (e.g., Weeks et al., 2011), inadvertent transport of pests or pathogens with the introduced stock (e.g.,
Brooker et al., 2011) and risk of invasiveness (e.g., Mueller and Hellmann, 2008).
The ability of species to migrate with climate change must next be judged, in the first instance, against the rate at
which the climatic zones shift over space (e.g. Loarie et al., 2009; Burrows et al., 2011; Diffenbaugh and Field,
2013; Chapter 4, section 4.3.2.5). For projecting potential future species shifts, this is the most straightforward part
of the calculation. In contrast, the ecological capacity of species to migrate is a highly complex function of factors,
including their ability to:
Reproduce, propagate or disperse
Compete for resources
Adapt to different soils, terrain, water quality and daylength
Overcome physical barriers (e.g. mountains, water/land obstacles)
Contend with obstacles imposed by human activity (e.g. land use, pollution or dams).
Conservation policy under a changing climate is largely a matter of promoting the natural adaptation of ecosystems,
if this is even feasible for many species given the rapidity of projected climate change. Studies stress the risks of
potential mismatching in responses of co-dependent species to climate change (e.g., Schweiger et al., 2012) as well
as the importance of maintaining species diversity as insurance for the provision of basic ecosystem services (e.g.,
Traill et al., 2010; Isbell et al., 2011). Four priorities have been identified for conservation stakeholders to apply to
climate change planning and adaptation (Heller and Zavaleta, 2009): (i) regional institutional coordination for
reserve planning and management and to improve landscape connectivity; (ii) a broadening of spatial and temporal
perspectives in management activities and practice, and actions to enhance system resilience; (iii) mainstreaming of
climate change into all conservation planning and actions; and (iv) holistic treatment of multiple threats and global
change drivers, also accounting for human communities and cultures. The regional aspects of conservation planning
transcend political boundaries, again arguing for a regional (rather than exclusively national) approach to adaptation
policy. This issue is elaborated in Chapters 4 (section 4.4.2) and 19 (section 19.4.2.3).
21.5. Analysis and Reliability of Approaches to Regional Impacts, Adaptation, and Vulnerability Studies
Assessing climate vulnerability or options for adapting to climate impacts in human and natural systems requires an
understanding of all factors influencing the system and how change may be effected within the system or applied to
one or more of the external influencing factors. This will require, in general, a wide range of climate and non-
climate information and methods to apply this to enhance the adaptive capacity of the system. There are both areas
of commonality across and differences between regions in the information and methods and these are explored in
this section. It initially focuses on advances in methods to study vulnerability and adaptive capacity and to assess
impacts (studies of practical adaptation and the processes of adaptation decision-making are treated in detail in
Chapters 14-17 and so not addressed here). This is followed by assessments of new information on, and thinking
related to: (a) baseline and recent trends in factors needed to assess vulnerability and define impacts baselines; and
(b) future scenarios used to assess impacts, changes in vulnerability and adaptive capacity; and then assessment of
the credibility of the various types of information presented.
21.5.1. Analyses of Vulnerability and Adaptive Capacity
Multiple approaches exist for assessing vulnerability and for exploring adaptive capacity (Schipper et al 2010,
UNFCCC 2008). The choice of method is influenced by objectives and starting point (see Table 21-3) as well as the
type of information available. Qualitative assessments usually draw on different methods and inputs from
quantitative assessments. Qualitative information cannot always be translated to quantitative information, or vice
versa, yet both approaches can sometimes be used to answer the same questions. Indicators, indices and mapping are
the most common ways to aggregate the resulting vulnerability and adaptive capacity information to compare across
regions (section 21.5.1.1) or to identify "hotspots" (section 21.5.1.2). .
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21.5.1.1. Indicators and Indices
Several attempts have been made to develop vulnerability indicators and indices (Birkmann 2011; Chen et al. 2011;
Barr et al 2010; Cardona, 2007; Luers et al 2003; Lawrence et al., 2003, Villa and McLeod, 2002, Downing et al.
2001, Atkins et al., 2000; Moss et al. 2001). Representation on a map or through an index is a common way to
depict global vulnerability information and requires quantification of selected variables in order to measure them
against a selected baseline, even though quantification of some qualitative information may not be possible (Hinkel,
2011; Edwards et al, 2007; Luers et al 2003). Vulnerability is differentiated according to factors such as gender, age,
livelihood or access to social networks, among many other factors (Cardona et al 2012; Wisner et al 2005), which
may not be represented accurately through some indicators. One approach used to create regional comparisons is to
use indices, which are composites of several indicators thought to contribute to vulnerability, each normalised and
sometimes weighted so they can be combined (Rygel et al 2006, Adger et al 2004). The approach has been critiqued
extensively because the weights assigned the indicators depends on expert opinion which can result in different
regions appearing more or less vulnerable, as Füssel (2010) found in reviewing global vulnerability maps based on
different indices.
Vulnerability indices developed to date have failed to reflect the dynamic nature of component indicator variables.
This is illustrated by the (in)ability to characterise how the selected indicators contribute to determining
vulnerability over time. Importantly, the relative importance of the indicator may change from season-to-season (e.g.
access to irrigation water) or may gradually or rapidly become obsolete. Hinkel s (2011) review of literature on
vulnerability indicators suggests that vulnerability has been confused as a proxy for unsustainable or insufficient
development so that simple measurements are seen as sufficient to tell a story about vulnerability. Hinkel (2011)
suggests that the simplification of information to create vulnerability indicators is what limits their utility.
Indicator systems have also been developed to improve understanding of adaptive capacity. These are used both to
measure adaptive capacity and identify entry points for enhancing it (Adaptation Sub-Committee, 2011;
Lioubimtseva and Henebry, 2009; Swanson et al., 2007; Adger and Vincent, 2005; Eriksen and Kelly, 2007). For
example, the Global Adaptation Index, developed by the Global Adaptation Alliance (GAIN, n.d.) uses a national
approach to assess vulnerability to climate change and other global challenges and compare this with a country s
"Readiness to improve resilience" (GAIN, n.d.) to assist public and private sectors to prioritise financial investments
in adaptation activities.
21.5.1.2. Hotspots
A special case of the use of indicators concerns the identification of hotspots, a term original used in the context of
biodiversity, where a biodiversity hotspot is a biologically diverse region typically under threat from human
activity, climate change or other drivers (Myers, 1988). The term typically relates to a geographical location, which
emerges as a concern when multiple layers of information are compiled to define it. In climate change analysis,
hotspots are used to indicate locations that stand out in terms of impacts, vulnerability or adaptive capacity (or all
three). Examples of hotspot mapping include how climate change can influence disease risk (de Wet et al., 2001),
extinctions of endemic species (Malcolm et al., 2006), and disaster risk (Dilley, 2006). Hotspots analysis is used to
serve various purposes, such as setting priorities for policy action, identifying focal regions for further research (de
Sherbinin, 2013; Dilley, 2006, Ericksen et al., 2011, see www.climatehotmap.org), or, increasingly, helping
distinguish priority locations for funding. Examples of the latter purpose include guiding the allocation of global
resources to pre-empt, or combat, disease emergence (Jones et al 2008) or funding for disaster risk management
(Arnold et al 2005). Because identifying hotspots raises important methodological issues about the limitation of
using indicators to integrate quantitative impacts with qualitative dimensions of vulnerability, their use to compare
regions leads to a subjective ranking of locations as having priority for climate change investment. This can be
controversial and considered politically-motivated (Klein 2009).
Certain locations are considered hotspots because of their regional or global importance. These can be defined by
population size and growth rate, contributions to regional or global economies, productive significance (e.g., food
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production) as well as by disaster frequency and magnitude, and projected climate change impacts. The choice of
variables may result in different locations being identified as hotspots (Füssel, 2009). For example, the Consultative
Group on International Agricultural Research (CGIAR) Research Program on Climate Change Agriculture and Food
Security (CCAFS) mapped hotspots of food insecurity and climate change in the tropics (Ericksen et al, 2011) using
stunted growth as a proxy for food security, but other variables could also have been selected. Scale matters in
representing hotspots and they will look different on a global scale than on a finer scale (Arnold et al., 2006).
The rationale for identifying such hotspots is that they may gradually evolve into locations of conflict or disaster,
where a combination of factors lead to the degradation of resources and social fabric. Climate change hotspots have
been defined as locations where impacts of climate change are "well pronounced and well documented" (UCS
2011). A climate change hotspot can describe (a) a region for which potential climate change impacts on the
environment or different activity sectors can be particularly pronounced or (b) a region whose climate is especially
responsive to global change (Giorgi 2006). An example of the former is given by Fraser et al (2013), combining
hydrological modelling with quantitatively modelled adaptive capacity (defined as the inverse of sensitivity to
drought) to identify vulnerability hotspots for wheat and maize. Examples of the latter are given by Giorgi (2006),
Diffenbaugh et al. (2008), Giorgi and Bi (2009), Xu et al. (2009), Diffenbaugh and Scherer (2011) and Diffenbaugh
and Giorgi (2012) who used different regional climate change indices, including changes in mean and interannual
variability of temperature and precipitation and metrics of seasonal extremes, to identify the Mediterranean Basin,
Central America, Central and West Africa, the Northern high latitude regions, the Amazon, the southwestern United
States, Southeast Asia and the Tibetan Plateau as prominent hot-spots.
21.5.2. Impacts Analyses
In recent years, there has been increased scrutiny of the methods and tools applied in impact assessment, especially
quantitative models that are used to project the biophysical and socio-economic impacts of future climate change
(see chapter 2, 2.3.2.1), but also encompassing qualitative methods, including studies of indigenous knowledge
(Galloway McLean, 2010, and see chapter 12, 12.3.3). In an advance from previous assessments, different types of
impact models are now being applied for the first time in many regions of the world. This is largely due to
burgeoning international development support for climate change vulnerability and adaptation studies (Fankhauser,
2010). It is also related to a surge of interest in regional economic assessments in the wake of the Stern review
(Stern, 2007) as well as to the evolution of climate models into Earth system models that incorporate a more realistic
representation of land surface processes (Flato et al., 2014) and their increased application to study hydrological
(chapter 3, 3.4.1), ecophysiological (chapter 4, 4.3.3) and cryospheric (Vaughan et al., 2014) impacts.
Potential impacts have been simulated for single as well as multiple sectors, at spatial scales ranging from site or
household to global, and over a range of temporal scales and time horizons (Table 21-5). A majority of impact
studies still follow the conventional approach where future impacts are modelled based on a set of assumptions
(scenarios) about future climate and socioeconomic conditions (see 21.2.3, left hand side of Table 21-3),. However,
an increasing number are being undertaken that follow a "socio-institutional" approach to adaptation planning
(Downing, 2012), right hand side of Table 21-3, which emphasises the importance of adaptive flexibility and climate
resilience given the often intractable, "deep" uncertainties implicit in many projections of future change (e.g.,
Donley et al., 2012; Garrett et al., 2013; Gersonius et al., 2013).
Impact modelling studies also commonly treat aspects of adaptation, either explicitly as modelled options or
implicitly as built-in autonomous responses (Dickinson, 2007; White et al., 2011). Furthermore, as an anthropogenic
signature is attributed to ongoing climate changes in many regions (Bindoff et al., 2014), and with growing evidence
that these changes are having impacts on natural and human systems in many more regions than reported in the AR4
(chapter 18, Rosenzweig and Neofotis, 2013), it is now possible in some regions and sectors to test impact models
projections against observed impacts of recent climate change (e.g. Araújo et al., 2005; Barnett et al., 2008; Lobell
et al., 2011). This is also an essential element in the attribution of observed impacts (Chapter 18, 18.3, 18.4, 18.5).
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Uncertainties in and Reliability of Impacts Analyses
Literature on uncertainty in impacts analyses has focused mainly on the uncertainties in impacts that result from the
uncertainties in future climate (Mearns et al., 2001; Carter et al., 2007), and this literature continues to grow since
AR4, particularly in the realm of agriculture and water resources (e.g., Wetterhall et al., 20011; Ferrise et al., 2011;
Ficklin et al., 2012; Littell et al., 2011, Osborne et al., 2013), but also in other areas such as flood risk (Ward et al.,
2013). Furthermore, research has advanced to establish which future climate uncertainties are most important to the
resultant uncertainties about crop yields (e.g., Lobell and Burke, 2008) and to apply future resource uncertainties to
adaptation studies (Howden et al., 2007). Use of multiple global or regional model scenarios is now found in many
more studies (e.g. Gosling et al., 2011; Bae et al., 2011; Arnell, 2011; Olsson et al., 2011) and the use of
probabilistic quantification of climate uncertainties has produced estimates of probabilities of changes in future
resources such as agriculture and water (e.g., Watterson and Whetton, 2011; Tebaldi and Lobell, 2008). Some
studies have developed probability distributions of future impacts by combining results from multiple climate
projections and, sometimes, different emissions scenarios, making different assumptions about the relative weight to
give to each scenario (Brekke et al., 2009). Nobrega et al. (2011) apply 6 different GCMs and 4 different SRES
emissions scenarios to study the impacts of climate change on water resources in the Rio Grande Basin in Brazil and
found that choice of GCM was the major source of uncertainty in terms of river discharge.
With an ever-increasing number of impacts projections appearing in the literature and the unprecedented rate and
magnitude of climate change projected for many regions, some authors have begun to question both the robustness
of the impacts models being applied (e.g., Heikkinen et al., 2006; Fitzpatrick and Hargrove, 2009; Watkiss, 2011a)
as well as the methods used to represent key uncertainties in impacts projections (e.g., Arnell, 2011; Rötter et al.,
2011; White et al., 2011). This is being addressed through several prominent international research efforts,
Agricultural Model Intercomparison and Improvement Project, involving crop and economic models at different
scales (AgMIP Rosenzweig et al., 2013), the Carbon Cycle Model Intercomparison Project (C4MIP
Friedlingstein et al., 2006; Sitch et al., 2008; Arora et al., 2013) and the Water Model Intercomparison Project
(WaterMIP Haddeland et al., 2011). Modelling groups from these projects are also participating in the Inter-
Sectoral Impact Model Intercomparison Project, initially focusing on intercomparing global impact models for
agriculture, ecosystems, water resources, health and coasts under RCP- and SSP-based scenarios (see Box 21-1)
with regional models being considered in a second phase of work (ISI-MIP Schiermeier, 2012). AgMIP results for
27wheat models run at contrasting sites worldwide indicate that projections of yield to the mid-21st century are more
sensitive to crop model differences than to global climate model scenario differences (Asseng et al., 2013; Carter,
2013). WaterMIP s analysis of runoff and evapotranspiration from five global hydrologic and six land surface
models indicate substantial differences in the models estimates in these key parameters (Haddelenad et al., 2011).
Finally, as in climate modelling, researchers are now applying multiple impact model and perturbed parameter
ensemble approaches to future projections (e.g., Araújo and New, 2007; Jiang et al., 2007; Palosuo et al., 2011),
usually in combination with ensemble climate projections treated discretely (e.g., New et al., 2007; Graux et al.,
2013; Tao and Zhang, 2013) or probabilistically (e.g., Luo et al., 2007; Fronzek et al., 2009, 2011; Brgesen and
Olesen, 2011; Ferrise et al., 2011; Wetterhall et al., 2011).
These new impact MIPs, and similar initiatives, have the common purpose of mobilising the research community to
address some long-recognised but pervasive problems encountered in impact modelling. A sample of recent papers
illustrate the variety of issues being highlighted, e.g. forest model typology and comparison (Medlyn et al., 2011),
crop pest and disease modelling and evaluation (Sutherst et al., 2011; Garrett et al., 2013), modelling responses to
extreme weather events (Lobell et al., 2010; Asseng et al., 2013), field experimentation for model calibration and
testing (Long et al., 2006; Craufurd et al., 2013) and data quality considerations for model input and calibration
(Lobell, 2013). Greater attention is also being paid to methods of economic evaluation of the costs of impacts and
adaptation at scales ranging from global (e.g., UNFCCC, 2007; Nelson et al., 2009b; Parry et al., 2009; Fankhauser,
2010; Füssel, 2010; Patt et al., 2010), through regional (e.g., EEA, 2007; World Bank, 2010; Ciscar et al., 2011;
Watkiss, 2011b), to national (SEI, 2009; GCAP, 2011) and local level (e.g., Perrels et al., 2010).
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21.5.3. Development and Application of Baseline and Scenario Information
21.5.3.1. Baseline Information: Context, Current Status, and Recent Advances
This section deals with defining baseline information for assessing climate change impacts, adaptation and
vulnerability The baseline refers to a reference state or behaviour of a system, e.g. current biodiversity of an
ecosystem, or a reference state of factors (e.g. agricultural activity, climate) which influence that system (see
Glossary entry). For example, the UNFCCC defines the pre-industrial baseline climate, prior to atmospheric
composition changes from its baseline pre-industrial state, as a reference for measuring global average temperature
rises. A baseline may be used to characterise average conditions and/or variability during a reference period, or may
allude to a single point in time, such as a reference year. It may provide information on physical factors such as
climate, sea level or atmospheric composition, or on a range of non-climate factors, such as technological, land -use
or socio-economic conditions. In many cases a baseline needs to capture much of system's variability to enable
assessment of its vulnerability or to test whether significant changes have taken place. Thus the information used to
establish this baseline must account for the variability of the factors influencing the system. In the case of climate
factors often this requires 30 years of data (e.g. Jones et al., 1997) and sometimes substantially more (e.g. Kendon et
al., 2008). Also temporal and spatial properties of systems will influence the information required. Many depend on
high resolution information, for example urban drainage systems (high spatial scales) or temperature sensitive
organisms (sub-daily time-scales). This section assesses methods to derive relevant climatic and non-climatic
information and its reliability.
21.5.3.1.1. Climate baselines and their credibility
Observed weather data are generally used as climate baselines, e.g. with an impacts model to form a relevant
impacts baseline, though downscaled climate model data are now being used as well. For example Bell et al. (2012)
use dynamically and statistically downscaled hourly rainfall data with a 1km river flow model to generate realistic
high resolution baseline river flows. These were then compared with future river flows derived used corresponding
downscaled future climate projections to generate projected impacts representing realistic responses to the imposed
climate perturbations. This use of high resolution data was important to ensure that changes in climate variability the
system was sensitive to were taken into account (see also Hawkins et al., 2013). Underscoring the importance of
including the full spectrum of climate variability when assessing climate impacts, Kay and Jones (2012) showed a
greater range of projected changes in UK river flows resulted when using high time-resolution (daily rather than
monthly) climate data.
Thus to develop the baseline of a climate-sensitive system it is important to have a good description of the baseline
climate, thus including information on its variability on timescales of days to decades. This has motivated significant
efforts to enhance the quality, length and homogeneity of, and make available, observed climate records (also
important for monitoring, detecting and attributing observed climate change Hartmann et al., 2014; Rhein et al.,
2014; Vaughan et al., 2014; Masson-Delmotte et al., 2014; Bindoff et al., 2014). This has included generating new
datasets such as APHRODITE (a gridded rain-gauge based dataset for Asia, Yatagai, et al., 2012), coordinated
analyses of regional climate indices and extremes by CLIVAR s ETCCDI (see e.g. Zhang et al., 2011) and data
rescue work typified by the ACRE initiative (Allan et al., 2011) resulting in analysis and digitization of many daily
or sub-daily weather records from all over the world. Also, estimates of uncertainty in the observations are either
being directly calculated, e.g. for the HadCRUT4 near-surface temperature record (Morice et al., 2012), or can be
generated from multiple datasets, e.g. for precipitation using datasets such as GPCC (Rudolf et al., 2011), TRMM
(Huffman et al., 2010) and APHRODITE, Yatagi et al.; 2012.
Significant progress has also been made in developing improved and new global reanalyses. These use climate
models constrained by long time-series of observations from across the globe to reconstruct the temporal evolution
of weather patterns during the period of the observations. An important new development has been the use of
digitized surface pressure data from ACRE by the 20th Century Reanalysis (20CR) project (Compo et al., 2011)
covering 1871 to the present day. 20CR provides the basis for estimating historical climate variability from the sub-
daily to the multi-decadal timescale (Figure 21-12) at any location. It can be used directly, or via downscaling, to
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develop estimates of the baseline sensitivity of a system to climate and addressing related issues such as establishing
links between historical climate events and their impacts. Other advances in reanalyses (http://reanalyses.org) have
focused on developing higher quality reconstructions for the recent past. They include a new European Centre for
Medium Range Weather Forecasts Reanalyses (ERA) dataset, ERA-Interim (Dee et al., 2011) and the NASA
Modern Era Reanalysis for Research and Applications (MERRA Rienecker et al., 2011), 1979-present, the NCEP
Climate Forecast System Reanalysis (CFSR), 1979-Jan 2010 (Saha et al., 2010) and regional reanalyses such as the
North American Regional Reanalysis (NARR) (Mesinger et al., 2006) and EURO4M (http://www.euro4m.eu/).
[INSERT FIGURE 21-12 HERE
Figure 21-12: Time series of seasonally averaged climate indices representing three modes of large-scale climate
variability: (a) the tropical September to January Pacific Walker Circulation (PWC); (b) the December to March
North Atlantic Oscillation (NAO); (c) the December to March Pacific North America (PNA) pattern. Indices (as
defined in Brönnimann etal. (2009) are calculated (with respect to the overlapping 1989 1999 period) from various
observed, reanalysis and model sources: statistical reconstructions of the PWC, the PNA and the NAO, see
Brönnimann et al. (2009) for details, (all cyan); 20CR (pink); NCEP NCAR reanalyses (NNR; dark blue); ERA-40
(green); ERA-Interim (orange). The black line and grey shading represent the ensemble mean and spread from a
climate model ensemble with a lower boundary condition of observed seas-surface temperatures and sea-ice from
the HadISST dataset (Rayner et al. 2003), see Brönnimann etal. (2009) for details. The model results provide a
measure of the predictability of these modes of variability from sea-surface temperature and sea-ice alone and
demonstrate that the reanalyses have significantly higher skill in reproduces these modes of variability.]
In many regions high temporal and spatial resolution baseline climate information is not available (e.g. Washington
et al. 2006, World Weather Watch 2005). Recent reanalyses may provide globally complete and temporally detailed
reconstructions of the climate of the recent past but generally lack the spatial resolution or have significant biases
(Thorne and Vose, 2010; Dee et al., 2011; Cerezo-Mota et al., 2011). Downscaling the reanalyses can be used with
available observations to estimate the error in the resulting reconstructions which can often be significant (Duryan et
al., 2010; Mearns et al., 2012). Advances in this area are expected through the WCRP-sponsored Coordinated
Regional Downscaling Experiment (CORDEX) project (http://wcrp.ipsl.jussieu.fr/SF_RCD_CORDEX.html; Giorgi
et al., 2009) which includes downscaling ERA-Interim over all land and enclosed sea areas (e.g. Nikulin et al. 2012).
21.5.3.1.2. Non-climatic baselines and their credibility
Climate-sensitive systems can be influenced by many non-climatic factors, so information on the baseline state of
these factors is also commonly required (Carter et al., 2001; Carter et al., 2007). Examples of physical non-climatic
factors include availability of irrigation systems, effectiveness of disease prevention or flood protection. Examples
of socio-economic factors include levels of social, educational and economic development, political/governance
background and available technology. Significant work has been undertaken to collect and make this information
available. Local and national governments and international agencies (e.g. UN agencies, World Bank) have been
collecting data (http://data.worldbank.org/data-catalog) on the human-related factors for many decades and similarly
information on technological developments is widely available. Often these factors are evolving quickly and the
baseline is taken as the reference state at a particular point in time rather than aggregated over a longer period. In the
case of the physical factors, information on many of these have been refined and updated as they are critical inputs
to deriving the climate forcings in the Representative Concentration Pathways (RCPs, van Vuuren et al., 2011) used
in CMIP5 (Taylor et al., 2012). This includes updated information on land-use change (Hurtt et al., 2011),
atmospheric composition (Meinshausen et al., 2011) and aerosols (Grainer et al., 2011, Lamarque et al., 2011).
The importance of establishing an appropriate physical baseline is illustrated in a study of potential climate change
impacts on flow in the River Thames in the UK over a 126 year period. No long-term trend is seen in annual
maximum flows despite increases in temperature and a major change in the seasonal partitioning of rainfall, winter
rainfall becoming larger than summer (Marsh 2004). An investigation of the physical environment found that it had
been significantly modified as part of river management activities with increases in channel capacity of 30% over 70
years leading to fewer floods. Thus establishing a baseline for river channel capacity explained the current reduced
vulnerability of the Thames to flooding. In a study of the potential for crop adaptation (Challinor et al., 2009), the
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relevant non-climatic factor identified was technological. Detailed field studies demonstrated that the current
germplasm included varieties with a wide range of tolerance to higher temperatures (Badigannavar et al., 2002).
This established an agricultural technology baseline, current crop properties, which demonstrated the potential to
reduce vulnerability in the system to compensate for the projected climate change impact.
21.5.3.2. Development of Projections and Scenarios
Since the AR4 there have been several new developments in the realm of scenarios and projections: 1) a new
approach to the construction of global scenarios for use in climate change analysis, initiated with the development of
representative concentration pathways (RCPs) (see Box 21.1 for a full description); 2) the development and
application of a greater number of higher resolution climate scenarios (21.3.3.2); and 3) further use of multiple
scenario elements as opposed to use of climate change scenarios only and greater focus on multiple stressors.
21.5.3.2.1. Application of high-resolution future climate information
There are now many examples of the generation and application of high resolution climate scenarios for assessing
impacts and adaptation planning. These provide information at resolutions relevant for many impacts and adaptation
studies but also, particularly with regard to dynamical downscaling, account for higher resolution forcings, such as
complex topography (e.g., Salathé et al., 2010) or more detailed land-atmosphere feedbacks such as in West Africa
(Taylor et al. 2011). In an analysis of climate impacts including possible adaptations in the Pacific North West of
North America (Miles et al., 2010) application of two dynamically downscaled scenarios was particularly useful for
the assessment of effects of climate change on storm water infrastructure (Rosenberg et al., 2010). More widely in
North America results from NARCCAP have been used to assess impacts of climate change on available wind
energy (Pryor and Barthelmie, 2011), road safety (Hambly et al., 2012), hydrology (Burger et al., 2011; Shrestha et
al., 2011), forest drought (Williams et al., 2012), and human health (Li et al., 2012).
Several European-led projects have generated and applied high resolution climate scenarios to investigate the
impacts of climate change over Europe for agriculture, river flooding, human health, and tourism (Christensen et al.
2012) and on energy demand, forest fire risk, wind storms damage, crop yields and water resources (Morse et al.,
2009). The UK developed new UK Climate Projections in 2009 (UKCP09) combining the CMIP3, a perturbed
physics GCM and a regional climate model ensemble to develop probabilities of changes in temperature and
precipitation at a 25 km resolution (Murphy et al., 2009) to determine probabilities of different impacts of climate
change and possible adaptations. In general, with all of this work a range of different techniques have been used
with little assessment or guidance on the relative merits of each.
21.5.3.2.2. Use of multiple scenario elements and focus on multiple stressors
Many more impacts and adaptation studies now use multiple scenario elements, and focus on multiple stressors as
opposed to climate change scenarios and effects alone (e.g., 3.3.2, 4.2.4, and 7.1.2). Good examples of use of
multiple scenario elements involve studies of climate change and human health considering additional factors such
as urban heat island (e.g., Knowlton et al., 2008; Rosenzweig et al., 2009), population increase and expanded urban
areas (McCarthy et al., 2010) and population and socio-economic conditions (Watkiss and Hunt, 2012). As these
studies are often undertaken at small scales, local scale information on relevant factors may be inconsistent with
larger scale scenario elements used in quantifying other stressors. In recognition of this, efforts have been or are
being made to downscale the large-scale scenario elements, e.g. the SRES scenarios were downscaled for Europe
(van Vuuren and O Neill, 2006) and economic activity information has been downscaled to 0.5 degree grids in some
regions (Gaffin et al., 2004; Grübler et al., 2007; van Vuuren et al., 2010) However, this information is far from
comprehensive and has not yet been examined carefully in the impacts and vulnerability literature (van Ruijven et
al., 2013).
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Typical non-climate stressors include changes in population, migration, land use, economic factors, technological
development, social capital, air pollution, and governance structures. They can have independent, synergistic, or
antagonistic effects and their importance varies regionally. Land-use and socio-economic changes are stressors of
equal importance to climate change for some studies in Latin America (27.2.2.1), numerous changes in addition to
climate strongly affect ocean ecosystem health (6.6.1) and in Asia rapid urbanization, industrialization and economic
development are identified as major stressors expected to be compounded by climate change in (24.4-7). Most
multiple stressor studies are regional or local in scope. For example Ziervogel and Taylor (2008) examined two
different villages in South Africa and found that a suite of stressors are present such as high unemployment, health
status (e.g., increased concern about AIDs), and access to education with climate change concerns present only in
the context of other impacts such as availability of water. In a study on the Great Lakes region, additional stressors
included land use change, population increase, and point source pollution (Danz et al., 2007). Mawdesly et al.
(2009) considered wildlife management and biodiversity conservation and noted that reducing pressure from other
stressors can maximize flexibility for adaptation to climate change. This increased focus on multiple stressors
obviously increases the need for a much wider range of data and wider range of projections for the wide range of
stressors, across multiple spatial scales.
21.5.3.3. Credibility of Projections and Scenarios
21.5.3.3.1. Credibility of regional climate projections
Obtaining robust regional projections of climate change (i.e. at least a clear indication of the direction of change),
requires combining projections with detailed analysis and understanding of the drivers of the changes. The most
successful example of this is the application of the attribution of observed global and regional temperature changes
using global models incorporating known natural and anthropogenic climate forcing factors (Flato et al., 2014,
section 10.3). The ability of GCMs to reproduce the observed variations in temperature, the quantification of the
influence of the different forcings factors and how well these influences are captured in the models provide
confidence that models capture correctly the physical processes driving the changes. This can also provide
confidence in projections of precipitation when physically linked to changes in temperature (Rowell and Jones,
2006; Kendon et al. 2010). It is important, especially with precipitation where regional change may appear to differ
in direction from one model to another, to distinguish when changes are significant (Tebaldi et al. 2011, Collins et
al., 2014b, Box 12.1). Significant future projections of opposite direction are found with neither possibility able to
be excluded on the basis of our physical understanding of the drivers of these changes. For example, McSweeney et
al. (2012) found that in an ensemble of GCM projections over south-east Asia, all models simulated the important
monsoon processes and rainfall well but projected both positive and negative changes in monsoon precipitation and
significantly different patterns of change.
Model trends or projections may also be inconsistent with trends in available observations and in these cases, their
projections are less credible. For example, the magnitude of the significant drying trend seen in the Sahel from the
1960s to the 1990s is not captured by models driven by observed sea-surface temperatures (SSTs) (e.g. Held et al.
2005) despite statistical analysis demonstrating the role of SSTs in driving Sahel rainfall variability. Thus our
understanding of the system and its drivers, and their representation in the models, is incomplete, which complicates
the interpretation of future projected changes in this region (e.g. Biasutti et al., 2008, Druyan, 2010). It implies that
other processes are important and so research is required to identify these and ensure they are correctly represented
in the models, without which projections of rainfall changes over this region cannot be considered reliable.
21.5.3.3.1. Credibility regarding socioeconomic scenario elements
Cash et al. (2003) distinguish three criteria for linking scientific knowledge to policy action; credibility (scientific
adequacy of a policy-relevant study), salience (relevance of a study s findings to the needs of decision-makers), and
legitimacy (the perception that the study is respectful of divergent values and beliefs). Studies examining the
performance of scenarios in climate change research across all three of these criteria are rare, but a general
conclusion has been that much less attention is paid to salience and legitimacy (Hulme and Dessai 2008, Garb et al.
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2008, Girod et al. 2009). Recognising this a new framework for global scenarios has been developed (Box 21-1),
providing researchers greater freedom than hitherto for customizing information provided by global scenarios. These
innovations may pose challenges for scientific credibility, and it is unclear how difficult it will be to bring
independently developed climate and socioeconomic projections together as scenarios in an internally consistent
manner, especially when some of these may include fine-scale regional detail (O Neill and Schweizer, 2011; O'Neill
et al., 2013).
Due to the common practice for scenario development of using narrative descriptions of alternative futures as the
inspiration for socioeconomic simulations (the Story and Simulation approach Alcamo 2008) it has been suggested
that the exclusion of some details in socioeconomic scenario studies can affect the internal consistency and therefore
the overall credibility of a study (e.g. Lloyd and Schweizer, 2013; Schweizer and Kriegler, 2012). Storylines can
offer a point of entry for multi-scalar scenario analyses (Rounsevell and Metzger, 2010), and such sub-global
scenario studies have been on the rise (Kok et al. 2011, Preston et al. 2011, Sietz et al. 2011, van Ruijven et al.,
2013).
Environmental scenario exercises crossing geographical scales suggests that linkages between scenarios at different
scales can be hard or soft (Zurek and Henrichs 2007), where downscaling (van Vuuren et al. 2010) would be an
example of a hard linkage while other similarities between scenarios would be soft linkages. How to apply flexible
interpretations of scientific adequacy and maintain scenario credibility is relatively unexplored, and there is thus a
need for studies to document best practices in this respect.
21.6. Knowledge Gaps and Research Needs
Understanding of the regional nature of climate change, its impacts, regional and cross-regional vulnerabilities, and
options for adaptation is still at a rudimentary level. There are both fundamental and methodological research issues
in the physical sciences concerned with the projection of regional changes in the climate system and the potential
impacts of those changes on various resource sectors and natural systems. Of equal importance, there are also
fundamental gaps in our understanding of the determinants of vulnerability and adaptive capacity, thus presenting
methodological challenges for projecting how societal vulnerability might evolve as the climate system changes.
While development of new scenarios is a part of the underlying research agenda, they will inevitably be limited
without further progress in our knowledge of the determinants of vulnerability.
Table 21-8 summarizes major research gaps in the physical, ecological, and social sciences that impede the scientific
communities progress in understanding the regional context of climate changes, their consequences, and societies
responses.
[INSERT TABLE 21-8 HERE]
Table 21-8: Leading knowledge gaps and related research needs.]
Frequently Asked Questions
FAQ 21.1: How does this report stand alongside previous assessments for informing regional adaptation?
[to be inserted in Section 21.3]
The five major Working Group II Assessment Reports produced since 1990 all share a common focus that addresses
the environmental and socioeconomic implications of climate change. In a general sense, the earlier assessments are
still valid, but the assessments have become much more complete over time, evolving from making very simple,
general statements about sectoral impacts, through greater concern with regions regarding observed and projected
impacts and associated vulnerabilities, through to an enhanced emphasis on sustainability and equity, with a deeper
examination of adaptation options. Finally, in the current report there is a much improved appreciation of the context
for regional adaptation and a more explicit treatment of the challenges of decision-making within a risk management
framework.
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Obviously one can learn about the latest understanding of regional impacts, vulnerability and adaptation in the
context of climate change by looking at the most recent report. This builds on the information presented in previous
reports by reporting developments in key topics. New and emergent findings are given prominence, as these may
present fresh challenges for decision-makers. Differences with the previous reports are also highlighted whether
reinforcing, contradicting or offering new perspectives on earlier findings as these too may have a bearing on past
and present decisions. Following its introduction in the Third Assessment Report (TAR), uncertainty language has
been available to convey the level of confidence in key conclusions, thus offering an opportunity for calibrated
comparison across successive reports. Regional aspects have been addressed in dedicated chapters for major world
regions, first defined following the Second Assessment and used with minor variations in the three subsequent
assessments. These comprise the continental regions of Africa, Europe, Asia, Australasia, North America, Central
and South America, Polar Regions and Small Islands , with a new chapter on The Oceans added for the present
assessment.
FAQ 21.2: Do local and regional impacts of climate change affect other parts of the world?
[to be inserted in Section 21.3.1]
Local and regional impacts of climate change, both adverse and beneficial, may indeed have significant
ramifications in other parts of the world. Climate change is a global phenomenon, but often expresses itself in local
and regional shocks and trends impacting vulnerable systems and communities. These impacts often materialize in
the same place as the shock or trend, but also much farther afield, sometimes in completely different parts of the
world. Regional interdependencies include both the global physical climate system as well as economic, social and
political systems that are becoming increasingly globalised.
In the physical climate system, some geophysical impacts can have large-scale repercussions well beyond the
regions in which they occur. A well-known example of this is the melting of land-based ice, which is contributing to
sea-level rise (and adding to the effects of thermal expansion of the oceans), with implications for low-lying areas
far beyond the polar and mountain regions where the melting is taking place.
Other local impacts can have wider socio-economic and geopolitical consequences. For instance, extreme
weather events in one region may impact production of commodities that are traded internationally, contributing to
shortages of supply and hence increased prices to consumers, influencing financial markets and disrupting food
security worldwide, with social unrest a possible outcome of food shortages. Another example, in response to
longer-term trends is the potential prospect of large-scale migration due to climate change. While hotly contested,
this link is already seen in the context of natural disasters, and could become an issue of increasing importance to
national and international policy makers. A third example is the shrinkage of Arctic sea ice, opening Arctic shipping
routes as well as providing access to valuable mineral resources in the exclusive economic zones of countries
bordering the Arctic, with all the associated risks and opportunities. Other examples involving both risks and
opportunities include changes of investment flows to regions where future climate change impacts may be beneficial
for productivity
Finally, some impacts that are entirely local and may have little or no direct effect outside the regions in which
they occur still threaten values of global significance, and thus trigger international concern. Examples include
humanitarian relief in response to local disasters or conservation of locally threatened and globally valued
biodiversity.
FAQ 21.3: What regional information should I take into account for climate risk management for the 20 year
time horizon? [to be inserted in Section 21.3.2]
The fundamental information required for climate risk management is to understand the climate events that put the
system being studied at risk and what is the likelihood of these arising. The starting point for assembling this
information is a good knowledge of the climate of the recent past including any trends in aspects of these events
(e.g. their frequency or intensity). It is also be important to consider that many aspects of the climate are changing,
to understand how the future projected changes may influence the characteristics of these events and that these
changes will, in general, be regionally variable. However, it should be noted that over the coming 20 years the
magnitude of projected changes may not be sufficient to have a large influence the frequency and intensity of these
events. Finally, it is also essential to understand which other factors influence the vulnerability of the system. These
may be important determinants in managing the risks and also if they are changing at faster rates than the climate
then changes in the latter become a secondary issue.
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For managing climate risks over a 20-year time horizon it is essential to identify the climate variables which the
system at risk is vulnerable to. It could be a simple event such as extreme precipitation or a tropical cyclone or a
more complex sequence of a late onset of the monsoon coupled with prolonged dry spells within the rainy season.
The current vulnerability of the system can then be estimated from historical climate data on these variables
including any information on trends in the variables. These historical data would give a good estimate of the
vulnerability assuming the record was long enough to provide a large sample of the relevant climate variables and
that the reasons for any trends, e.g. clearly resulting from climate change, were understood. It should be noted that in
many regions sufficiently long historical records of the relevant climate variables are often not available.
It is also important to recognize that many aspect of the climate of the next 20 years will be different from the
past. Temperatures are continuing to rise with consequent increases in evaporation and atmospheric humidity and
reductions in snow amount and snow season length in many regions. Average precipitation is changing in many
regions with both increases and decreases and there is a general tendency for increases in extreme precipitation
observed over land areas. There is a general consensus amongst climate projections that further increases in heavy
precipitation will be seen as the climate continues to warm and more regions will see significant increases or
decreases in average precipitation. In all cases the models project a range of changes for all these variables which
are generally different for different regions.
Many of these changes may often be relatively small compared to their natural variations but it is the influence
of these changes on the specific climate variables which the system is at risk from that is important. Thus
information needs to be derived from the projected climate changes on how the characteristics of these variables,
e.g. the likelihood of their occurrence or magnitude, will change over the coming 20 years. These projected future
characteristics in some cases may be indistinguishable from those historically observed but in other cases some or all
models will project significant changes. In the latter situation, the effect of the projected climate changes will then
result in a range of changes in either the frequency or magnitude of the climate event, or both. The climate risk
management strategy would then need to adapt to accounting for either a greater range or changed magnitude of
risk. This implies that in these cases a careful analysis of the implications of projected changes for the specific
temporal and spatial characteristic of the climate variables relevant to the system at risk is required.
FAQ 21.4: Is the highest resolution climate projection the best to use for performing impacts assessments?
[to be inserted in Section 21.5.3.3]
A common perception is that higher resolution (i.e., more spatial detail) equates to more useable and robust
information. Unfortunately data does not equal information, and more high resolution data does not necessarily
translate to more or better information. Hence, while high resolution global climate models (GCMs) and many
downscaling methods can provide high resolution data, and add value in, for example, regions of complex
topography, it is not a given that there will be more value in the final climate change message. This partially
depends upon how the higher resolution data were obtained. For example, simple approaches such as spatial
interpolation or adding climate changes from GCMs to observed data fields do increase the spatial resolution but add
no new information on high resolution climate change. Nonetheless, these data sets are useful for running impacts
models. Many impacts settings are somewhat tuned to a certain resolution, such as the nested size categorizations of
hydrologic basins down to watershed size, commonly used in hydrologic modeling. Using dynamical or statistical
downscaling methods will add a new high resolution component, providing extra confidence that sub-GCM scale
processes are being represented more accurately. However, , there are new errors associated with the additional
method applied which need to be considered. More importantly, if downscaling is applied to only one or two GCMs
then the resulting high resolution scenarios will not span the full range of projected changes that a large GCM
ensemble would indicate are plausible futures. Spanning that full range is important in being able to properly sample
the uncertainty of the climate as it applies in an impacts context. Thus for many applications, such as understanding
the full envelope of possible impacts resulting from our current best estimates of regional climate change, lower
resolution data may be more informative. At the end of the day, no one data set is best, and it is through the
integration of multiple sources of information that robust understanding of change is developed. What is important
in many climate change impacts contexts is appropriately sampling the full range of known uncertainties, regardless
of spatial resolution. It is through the integration of multiple sources of information that robust understanding of
change is developed.
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Cross-Chapter Box
Box CC-RC. Regional Climate Summary Figures
[Noah Diffenbaugh (USA), Dáithí Stone (Canada / South Africa / USA), Peter Thorne (USA / Norway / UK), Filippo Giorgi (Italy), Bruce
Hewitson (South Africa), Richard Jones (UK), Geert Jan van Oldenborgh (Netherlands)]
Information about the likelihood of regional climate change, assessed by WGI, is foundational for the Working
Group II assessment of climate-related risks. To help communicate this assessment, the regional chapters of WGII
present a coordinated set of regional climate figures, which summarize observed and projected change in annual
average temperature and precipitation during the near-term and the longer-term for RCP2.6 and RCP8.5. These
WGII regional climate summary figures use the same temperature and precipitation fields that are assessed in WGI
Chapter 2 and WGI Chapter 12, with spatial boundaries, uncertainty metrics, and data classes tuned to support the
WGII assessment of climate-related risks and options for risk management. Additional details on regional climate
and regional climate processes can be found in WGI Chapter 14 and WGI Annex 1.
The WGII maps of observed annual temperature and precipitation use the same source data, calculations of data
sufficiency, and calculations of trend significance as WGI Chapter 2 and WGI Figures SPM.1 and SPM.2. (A full
description of the observational data selection and significance testing can be found in WGI Box 2.2.) Observed
trends are determined by linear regression over the 1901-2012 period of MLOST for annual temperature, and over
the 1951-2010 period of GPCC for annual precipitation. Data points on the maps are classified into three categories,
reflecting the categories used in WGI Figures SPM.1 and SPM.2:
1) Solid colors indicate areas where (i) sufficient data exist to permit a robust estimate of the trend (i.e., only
for grid boxes with greater than 70% complete records and more than 20% data availability in the first and
last 10% of the time period), and (ii) the trend is significant at the 10% level (after accounting for
autocorrelation effects on significance testing).
2) Diagonal lines indicate areas where sufficient data exist to permit a robust estimate of the trend, but the
trend is not significant at the 10% level.
3) White indicates areas where there are not sufficient data to permit a robust estimate of the trend.
The WGII maps of projected annual temperature and precipitation are based on the climate model simulations from
Phase 5 of the Coupled Model Intercomparison Project (CMIP5) (Taylor et al., 2012), which also form the basis for
the figures presented in WGI (including WGI Chapter 12, Chapter 14, and Annex I). The CMIP5 archive includes
output from atmosphere-ocean general circulation models (AOGCMs), AOGCMs with coupled vegetation and/or
carbon cycle components, and AOGCMs with coupled atmospheric chemistry components. The number of models
from which output is available, and the number of realizations of each model, varies between the different CMIP5
experiments.
The WGII regional climate maps use the same source data as WGI Chapter 12 (e.g., Box 12.1 Figure 1), including
the WGI multi-model mean values; the WGI individual model values; the WGI measure of baseline ( internal )
variability; and the WGI time periods for the reference (1986-2005), mid-21st-century (2046-2065), and late-21st-
century (2081-2100) periods. The full description of the selection of models, the selection of realizations, the
definition of internal variability, and the interpolation to a common grid can be found in WGI Chapter 12 and Annex
1.
In contrast to Phase 3 of the Coupled Model Intercomparison Project (CMIP3) (Meehl et al., 2007), which used the
IPCC SRES emission scenarios (IPCC, 2000), CMIP5 uses the Representative Concentration Pathways (RCPs) (van
Vuuren et al., 2011) to characterize possible trajectories of climate forcing over the 21st century. The WGII regional
climate projection maps include RCP2.6 and RCP8.5, which represent the high and low end of the RCP range at the
end of the 21st century. Projected changes in global mean temperature are similar across the RCPs over the next few
decades (Figure RC-1; WGI Fig. 12.5). During this near-term era of committed climate change, risks will evolve as
socioeconomic trends interact with the changing climate. In addition, societal responses, particularly adaptations,
will influence near-term outcomes. In the second half of the 21st century and beyond, the magnitude of global
temperature increase diverges across the RCPs (Figure RC-1; WGI Fig. 12.5). For this longer-term era of climate
options, near-term and ongoing mitigation and adaptation, as well as development pathways, will determine the risks
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of climate change. The benefits of mitigation and adaptation thereby occur over different timeframes, and present-
day choices thus affect the risks of climate change throughout the 21st century.
[INSERT FIGURE RC-1 HERE
Figure RC-1: Observed and projected changes in global annual average temperature. Values are expressed relative
to 1986-2005. Black lines show the GISTEMP, NCDC-MLOST, and HadCRUT4.2 estimates from observational
measurements. Colored shading denotes the +/-1.64 standard deviation range based on simulations from 32 models
for RCP2.6 (blue) and 39 models for RCP8.5 (red). Blue and red lines denote the scenario mean for RCP2.6 and
RCP8.5, respectively.]
The projection maps plot differences in annual average temperature and precipitation between the future and
reference periods (Figure RC-2 and Figure RC-3), categorized into four classes. The classes are constructed based
on the IPCC uncertainty guidance, providing a quantitative basis for assigning likelihood (Mastrandrea et al., 2010),
with likely defined as 66-100% and very likely defined as 90-100%.
The classifications in the WGII regional climate projection figures are based on two aspects of likelihood (e.g., WGI
Box 12.1 and Knutti et al. (2010)). The first is the likelihood that projected changes exceed differences arising from
internal climate variability (e.g., Tebaldi et al. (2011)). The second is agreement among models on the sign of
change (e.g., Christensen et al. (2007) and IPCC (2012)).
The four classifications of projected change depicted in the WGII regional climate maps are:
1) Solid colors indicate areas with very strong agreement, where the multi-model mean change is greater than
twice the baseline variability, and >90% of models agree on sign of change. These criteria (and the areas that
fall into this category) are identical to the highest-confidence category in WGI Box 12.1. This category
supersedes other categories in the WGII regional climate maps.
2) Colors with white dots indicate areas with strong agreement, where >66% of models show change greater
than the baseline variability, and >66% of models agree on sign of change.
3) Gray indicates areas with divergent changes, where >66% of models show change greater than the baseline
variability, but <66% agree on sign of change.
4) Colors with diagonal lines indicate areas with little or no change, where >66% of models show change less
than the baseline variability. It should be noted that areas that fall in this category for the annual average
could still exhibit significant change at seasonal, monthly and/or daily timescales.
[INSERT FIGURE RC-2 HERE
Figure RC-2: Observed and projected changes in annual average temperature. (A) Observed temperature trends from
1901-2012 are determined by linear regression. Trends have been calculated where sufficient data permit a robust
estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data availability in the
first and last 10% of the time period). Other areas are white. Solid colors indicate areas where change is significant
at the 10% level (after accounting for autocorrelation effects on significance testing). Diagonal lines indicate areas
where change is not significant. Observed data are from WGI AR5 Figures SPM.1 and 2.21. The range of grid-point
values is -0.53 to +2.50°C over period. (B) CMIP5 multi-model mean projections of annual average temperature
changes for 2046-2065 and 2081-2100 under RCP2.6 and RCP8.5, relative to 1986-2005. Solid colors indicate areas
with very strong agreement, where the multi-model mean change is greater than twice the baseline variability and
>90% of models agree on the sign of change. Colors with white dots indicate areas with strong agreement, where
>66% of models show change greater than the baseline variability and >66% of models agree on the sign of change.
Gray indicates areas with divergent changes, where >66% of models show change greater than the baseline
variability, but <66% agree on the sign of change. Colors with diagonal lines indicate areas with little or no change,
where >66% of models show change less than the baseline variability (although there may be significant change at
shorter timescales such as seasons, months, or days). Analysis uses model data from WGI AR5 Figure SPM.8, Box
12.1, and Annex I. The range of grid-point values for the multi-model mean is: +0.19 to +4.08C for mid-21st
century of RCP2.6; +0.06 to +3.85C for late-21st century of RCP2.6; +0.70 to +7.04C for mid-21st century of
RCP8.5; and +1.38 to +11.71°C for late-21st century of RCP8.5.]
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[INSERT FIGURE RC-3 HERE
Figure RC-3: Observed and projected changes in annual average precipitation. (A) Observed precipitation trends
from 1951-2010 are determined by linear regression. Trends have been calculated where sufficient data permit a
robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data
availability in the first and last 10% of the time period). Other areas are white. Solid colors indicate areas where
change is significant at the 10% level (after accounting for autocorrelation effects on significance testing). Diagonal
lines indicate areas where change is not significant. Observed data are from WGI AR5 Figures SPM.2. The range of
grid-point values is -185 to +111 mm/year/decade. (B) CMIP5 multi-model mean projections of annual average
precipitation changes for 2046-2065 and 2081-2100 under RCP2.6 and RCP8.5, relative to 1986-2005. Solid colors
indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline
variability and >90% of models agree on the sign of change. Colors with white dots indicate areas with strong
agreement, where >66% of models show change greater than the baseline variability and >66% of models agree on
the sign of change. Gray indicates areas with divergent changes, where >66% of models show change greater than
the baseline variability, but <66% agree on the sign of change. Colors with diagonal lines indicate areas with little or
no change, where >66% of models show change less than the baseline variability (although there may be significant
change at shorter timescales such as seasons, months, or days). Analysis uses model data from WGI AR5 Figure
SPM.8, Box 12.1, and Annex I. The range of grid-point values for the multi-model mean is: -10 to +24% for mid-
21st century of RCP2.6; -9 to +22% for late-21st century of RCP2.6; -19 to +57% for mid-21st century of RCP8.5;
and -34 to +112% for late-21st century of RCP8.5.]
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Table 21-1: Dimensions of the institutions and actors involved in climate change decision-making, including
example entries referred to in chapters of this volume. Vertical integration can occur within as well as between
levels. Decison-making domains are illustrative. Modified and extended from Mickwitz (2009).
Domain: Economy Energy Food/fibre Technology Environment ...
Coherent policies and decision-making
Level:
Multi-level organisation and governance
IMF/WB IEA FAO WIPO UNFCCC
WTO NGOs WTO NGOs CBD
Global MDGs CLOS
(fisheries) Montreal Protocol
NGOs NGOs NGOs
MFIs/MDBs OPEC AFTA Multi- nationals R&D CLRTAP
Trans- BFIs Electric
grid
COMESA EU
Innovation
Union MRC
national
OECD/EU operators MERCOSUR LVBC
CLOS
(transport) Oil/gas
distributor EU CAP/CFP EU Directives
Ministry/Gov.
Ministry/Gov.
Ministry/Gov.
Ministry/Gov.
Ministry/Gov.
Dept./Agency Dept./Agency Dept./Agency Dept./Agency
Dept./Agency
National
Banks Energy
provider Tariffs, Quotas,
Education/R&D/ Environmental law
Taxation Energy
regulator Regulations Innovation
State/Province/ State/Province/ State/Province/ State/Province/ State/Province/
Sub- County/City
County/City
County/City
County/City
County/City
national
Taxation Public/private Extension service Incentives,
Science
Protected
areas
energy
provider Land use
planning parks
Regional offices
Micro- finance, Renewables
Farmer, Forester,
Entrepreneur, Environmentalist,
Co- operative,
Producer,
Voter,
Fisher,
Invester,
Voter, Landowner,
Voter,
Local Employer, Voter,
Consumer Landowner,
Consumer Consumer
Consumer Voter,
Consumer
Acronyms: IMF International Monetary Fund; WB World Bank; WTO World Trade Organization; MDGs Millennium Development
Goals, NGO Non-governmental Organization; MDBs Multilateral Development Banks; MFIs Multilateral Financial Institutions; BFIs
Bilateral Finance Institutions; ECD Organisation for Economic Co-operation and Development; EU European Union; CLOS United Nations
Convention on the Law of the Sea; IEA International Energy Agency; OPEC Organization of the Petroleum Exporting Countries; FAO
Food and Agriculture Organization of the United Nations; AFTA Association of Southeast Asian Nations (ASEAN) Free Trade Area;
COMESA Common Market for Eastern and Southern Africa; MERCOSUR Mercado Común del Sur (Southern Common Market); CAP/CFP
Common Agricultural Policy/Common Fisheries Policy; WIPO World Intellectual Property Organization, UNFCCC United Nations
Framework Convention on Climate Change; CBD Convention on Biological Diversity; CLRTAP Convention on Long-range Transboundary
Air Pollution (Europe, N. America, C. Asia); MRC Mekong River Commission For Sustainable Development; Lake Victoria Basin
Commission.
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Table 21-2: Selected examples of regional treatment in previous IPCC Assessment Reports and Special Reports
(SR). Major assessments are subdivided by the three Working Group reports, each described by generic titles.
IPCC report Year Treatment of regions
[references]
First Assessment 1990 Climate: Climate projections for 2030 in 5 sub-continental regions; Observations averaged for
Report (FAR) northern/southern hemisphere, by selected regions and by 20° latitude x 60° longitude grid boxes
[1, 2, 3] Impacts: Agriculture by continent (7 regions); Ecosystem impacts for 4 biomes; water resources for
case study regions; Oceans and Coastal Zones treated separately
Responses: Emissions scenarios by 5 economic groupings; Energy and Industry by 9 regions;
Coastal Zone and Wetlands by 20 world regions
Supplements to FAR 1992 Climate: IS92 emissions scenarios by 7 world regions
[4, 5] Impacts: Agriculture by continent (6 regions); Ocean Ecology by 3 latitude zones; Questionnaire to
governments on current activities on impacts by 6 WMO regions
SR: Climate Change 1994 Evaluation of IS92 emissions scenarios by 4 world regions: OECD, USSR/E. Europe,
1994 [6] China/Centrally Planned Asia and Other.
Second Assessment 1995 Climate: Gridded proportional circle maps for observed climate trends (5° latitude/ longitude);
Report (SAR) climate projections for 7 sub-continental regions
[7, 8, 9] Impacts, Adaptations, Mitigation: Energy production statistics by 10 world regions;
Forests, Wood Production and Management by three zones: Tropical, Temperate, Boreal; separate
chapters by physiographic types: Deserts, Mountain Regions, Wetlands, Cryosphere, Oceans, and
Coastal Zones and small islands; country case studies, Agriculture by 8 continental-scale regions;
Energy supply by 8 world regions
Economic and Social Dimensions: Social Costs and Response Options by 6 economic regions
SR: Regional Impacts 1998 10 continental-scale regions: Africa, Arctic and Antarctic, Australasia, Europe, Latin America,
[10] Middle East and Arid Asia, North America, Small Island States, Temperate Asia, Tropical Asia.
Subdivisions applied in some regions; Vegetation shifts mapped by 9 biomes; Baseline (1990)
Socio-Economic data provided by country and for all regions except polar.
SR: Land-Use Change 1998 9 Biomes; 15 land-use categories; National and Regional case studies.
and Forestry [11]
SR: Aviation [12] 1999 Observed and projected emissions by 22 regional air routes; Inventories by 5 economic regions
SR: Technology 2000 Country case studies; Indicators of technology transfer by 6-7 economic regions
Transfer [13]
SR: Emissions 2000 4 SRES world regions defined in common across integrated assessment models; 11 sub-regions;
Scenarios [14] Driving Factors by 6 continental regions
Third Assessment 2001 Climate: gridded observations of Climate trends; 20 example Glaciers; 9 Biomes for Carbon Cycle;
Report (TAR) Circulation Regimes for model evaluation; 23 "Giorgi" regions for regional climate projections
[15, 16, 17] Impacts, adaptation and vulnerability: Example projections from 32 "modified-Giorgi" regions;
Basins by continent; 5 Coastal types; Urban/Rural Settlements; Insurance by economic regions; 8
continental-scale regions equivalent to 1998 Special report but with single chapter for Asia;
Subdivisions used for each region (Africa, Asia and Latin America by climate zones; North
America by 6 core regions and 3 border regions)
Mitigation: Country examples; Developed (Annex I) and Developing (non-Annex I); Various
economic regions; Policies, Measures and Instruments by 4 blocs: OECD, Economies in Transition,
China and Centrally Planned Asia, and Rest of the World.
SR: Ozone Layer [18] 2005 Various economic regions/countries depending on sources and uses of chemicals;
SR: Carbon Capture 2005 CO2 sources by 9 economic regions; potential storage facilities: by geological formation, by oil/gas
and Storage [19] wells, by ocean depth,; costs, by 4 economic groupings
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Fourth Assessment 2007 Climate: Land-use types for surface forcing of climate; Observations by 19 "Giorgi" regions;
Report (AR4) Modes of variability for Model Evaluation; Attribution of climate change by 22 "Giorgi-type"
[20, 21, 22] regions and by 6 ocean regions; Climate statistics for 30 "Giorgi-type" regions; PDFs of projections
for 26 regions; summary graphs for 8 continental regions
Impacts, adaptation and vulnerability: Studies reporting observed impacts by 7 IPCC regions;
comparison of TAR and AR4 climate projections for 32 Giorgi regions; Ecosystems by 11 biomes;
Agriculture by latitudinal zone; Examples of Coastal mega-Deltas; Industry and settlement by
continetal region; 8 continental regions, as in TAR, but Small Islands not Small Island States; Sub-
regional summary maps for each region, using physiographic, biogeographic or geographic
definitions; Example vulnerability maps at sub-national scale and globally by country.
Mitigation: 17 global economic regions for GDP; Energy supply by continent, by economic
regions, by 3 UNFCCC groupings; Trends in CO2 emissions (and projections) , waste and carbon
balance by economic regions.
SR: Renewable Energy 2012 Global maps showing potential resources for renewable energy: land suitability for bioenergy
Sources and Climate production, global irradiance for solar, geoethermal, hydropower, ocean waves/tidal range, wind);
Change Mitigation [23] Various economic/continental regions: installed capacity (realised vs. potential), types of
technologies, investment cost, cost effectiveness, various scenario-based projections; Country
comparisons of deployment and uptake of technologies, share of energy market.
SR: Managing the 2012 Trends in observed (tables) and projected (maps and tables) climate extremes (Tmax, Tmin, heat
Risks of Extreme Events waves, heavy precipitation and dryness) by 26 sub-continental regions covering most land areas of
and Disasters to the globe; Attribution studies of return periods of extreme temperatures for 15 "Giorgi-type"
Advance Climate regions; Gridded global maps of projected extremes of temperature, precipitation, windspeed, dry
Change Adaptation spells and soil moisture anomalies; Continental-scale estimates of projected changes in impacts of
[24] extremes (floods, cyclones, coastal inundation) as well as frequencies of observed climate extremes
and their estimated costs); Distinctions drawn between local, country and international/global actors
with respect to risk management and its financing.
Fifth Assessment 2014 Climate: Gridded global maps of observed changes in climate; Cryosphere observations from 19
Report (AR5) glacierized regions and 3 Arctic permafrost zones; Paleoclimatic reconstructions for 7 continental
[25, 26, 27] regions; CO2 fluxes for 11 land and 10 ocean regions; Observed aerosol concentrations for 6
continental regions and projections for 9 regions; Detection and attribution of changes in mean and
extreme climate for 7 continental and 8 ocean regions; Climate model evaluation and multi-model
projections of extremes for 26 sub-continental regions; Maps and time series of seasonal and annual
multi-model simulated climate changes for 19 sub-continental regions and global over 1900-2100.
Impacts, adaptation and vulnerability Part A Global and sectoral aspects: Gridded global maps
of water resources, species distributions, ocean productivity; Global map of 51 ocean biomes;
Detection and attribution of observed impacts, key risks and vulnerabilities and adaptation
synthesis by IPCC regions. Part B Regional aspects: Nine continental-scale regions, eight as in
AR4 plus the ocean; Sub-regions in Africa (5), Europe (5), Asia (6), Central and South America (5
or 7); Polar (2); Small Islands (4), Oceans (7); Other disaggregation by gridded maps or countries.
Mitigation: Economic statistics by development (3 or 5 categories) or by income; 5 country
groupings (plus international transport) for emission-related scenario analysis (RCP5: OECD 1990
countries, Reforming Economies, Latin America and Caribbean, Middle East and Africa, Asia)
with further disaggregation to 10 regions (RCP10) for regional development; Land use regions for
forest (13) and agriculture (11); Most other analyses by example countries.
1. IPCC (1990c); 2. IPCC (1990a); 3. IPCC (1990b); 4. IPCC (1992b); 5. IPCC (1992a); 6. IPCC (1994) 7. IPCC (1996c); 8. IPCC (1996b); 9.
IPCC (1996a); 10. IPCC (1998b); 11. IPCC (1998a); 12. IPCC (1999); 13. IPCC(2000a), 14. IPCC (2000b); 15. IPCC (2001c); 16. IPCC
(2001a); 17. IPCC (2001b); 18. IPCC/TEAP (2005); 19. IPCC (2005); 20. IPCC (2007c); 21. IPCC (2007a); 22. IPCC (2007b); 23. IPCC
(2012b); 24. IPCC (2012a); 25. IPCC (2014b); 26. This assessment; 27. IPCC (2014a).
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Table 21-3: Two possible entry points for thinking about vulnerability to climate change (illustrative and adapted
from Füssel 2007).
Context Climate change impacts perspective Vulnerability perspective
Root problem Climate change Social vulnerability
Policy context Climate change mitigation, Social adaptation, sustainable
compensation, technical adaptation development
Illustrative policy question What are the benefits of climate change How can the vulnerability of societies to
mitigation? climatic hazards be reduced?
Illustrative research What are the expected net impacts of Why are some groups more affected by
question climate change in different regions? climatic hazards than others?
Vulnerability and adaptive Adaptive capacity determines Vulnerability determines adaptive
capacity vulnerability capacity
Reference for adaptive Adaptation to future climate change Adaptation to current climate variability
capacity
Starting point of analysis Scenarios of future climate change Current vulnerability to climatic
variabilty
Analytical function Descriptive, positivist Explanatory, normative
Main discipline Natural science Social science
Meaning of vulnerability Expected net damage for a given level Susceptibility to climate change and
of global climate change variability as determined by
socioeconomic factors
Vulnerability approach Integrated, risk-hazard Political economy
Reference IPCC (2001a) Adger (1999)
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Table 21-4: Illustrative examples of adaptation experience, as well as approaches to reduce vulnerability and
enhance resilience. Adaptation actions can be influenced by climate variability, extremes, and change, and by
exposure and vulnerability at the scale of risk management. Many examples and case studies demonstrate
complexity at the level of communities or specific regions within a country. It is at this spatial scale that complex
interactions between vulnerabilities, inequalities, and climate change come to the fore. At the same time, place-
based examples illustrate how larger-level drivers and stressors shape differential risks and livelihood trajectories,
often mediated by institutions.
Early warning systems for heat
EXPOSURE AND VULNERABILITY:
Factors affecting exposure and vulnerability include age, pre-existing health status, level of outdoor activity, socioeconomic
factors including poverty and social isolation, access to and use of cooling, physiological and behavioral adaptation of the
population, urban heat island effects, and urban infrastructure. [8.2.3-4, 11.3.3-4, 11.4.1, 11.7, 13.2.1, 19.3.2, 23.5.1, 25.3, 25.8.1,
SREX Table SPM.1]
CLIMATE INFORMATION AT THE GLOBAL SCALE:
Observed: Very likely decrease in the number of cold days and nights and increase in the number of warm days and nights, on
the global scale between 1951 and 2010. [WGI AR5 2.6.1]
Medium confidence that the length and frequency of warm spells, including heat waves, has increased globally since 1950. [WGI
AR5 2.6.1]
Projected: Virtually certain that, in most places, there will be more hot and fewer cold temperature extremes as global mean
temperatures increase, for events defined as extremes on both daily and seasonal timescales. [WGI AR5 12.4.3]
CLIMATE INFORMATION AT THE REGIONAL SCALE:
Observed: Likely that heat wave frequency has increased since 1950 in large parts of Europe, Asia, and Australia. [WGI AR5
2.6.1]
Medium confidence in overall increase in heat waves and warm spells in North America since 1960. Insufficient evidence for
assessment or spatially varying trends in heat waves or warm spells for South America and most of Africa. [SREX Table 3-2;
WGI AR5 2.6.1]
Projected: Likely that, by the end of the 21st century under RCP8.5 in most land regions, a current 20-year high temperature
event will at least double its frequency and in many regions occur every two years or annually, while a current 20-year low
temperature event will become exceedingly rare. [WGI AR5 12.4.3]
Very likely more frequent and/or longer heat waves or warm spells over most land areas. [WGI AR5 12.4.3]
DESCRIPTION:
Heat-health early warning systems are instruments to prevent negative health impacts during heat waves. Weather forecasts are
used to predict situations associated with increased mortality or morbidity. Components of effective heatwave and health warning
systems include identifying weather situations that adversely affect human health, monitoring weather forecasts, communicating
heatwave and prevention responses, targeting notifications to vulnerable populations, and evaluating and revising the system to
increase effectiveness in a changing climate. Warning systems for heat waves have been planned and implemented broadly, for
example in Europe, the United States, Asia, and Australia.
[11.7.3, 24.4.6, 25.8.1, 26.6, Box 25-6]
BROADER CONTEXT:
Heat health warning systems can be combined with other elements of a health protection plan, for example building capacity to
support communities most at risk, supporting and funding health services, and distributing public health information.
In Africa, Asia, and elsewhere, early warning systems have been used to provide warning of and reduce a variety of risks,
related to famine and food insecurity; flooding and other weather-related hazards; exposure to air pollution from fire; and vector-
borne and food-borne disease outbreaks.
[7.5.1, 11.7,15.4.2, 22.4.5, 24.4.6, 25.8.1, 26.6.3, Box 25-6]
Mangrove restoration to reduce flood risks and protect shorelines from storm surge
EXPOSURE AND VULNERABILITY :
Loss of mangroves increases exposure of coastlines to storm surge, coastal erosion, saline intrusion, and tropical cyclones.
Exposed infrastructure, livelihoods, and people are vulnerable to associated damage. Areas with development in the coastal zone,
such as on small islands, can be particularly vulnerable. [5.4.3, 5.5.6, 29.7.2, Box CC-EA]
CLIMATE INFORMATION AT THE GLOBAL SCALE:
Observed: Likely increase in the magnitude of extreme high sea level events since 1970, mostly explained by rising mean sea
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level. [WGI AR5 3.7.5]
Low confidence in long-term (centennial) changes in tropical cyclone activity, after accounting for past changes in observing
capabilities. [WGI AR5 2.6.3]
Projected: Very likely significant increase in the occurrence of future sea level extremes by 2050 and 2100. [WGI AR5 13.7.2]
In the 21st century, likely that the global frequency of tropical cyclones will either decrease or remain essentially unchanged.
Likely increase in both global mean tropical cyclone maximum wind speed and rainfall rates. [WGI AR5 14.6]
CLIMATE INFORMATION AT THE REGIONAL SCALE:
Observed: Change in sea level relative to the land (relative sea level) can be significantly different from the global mean sea
level change because of changes in the distribution of water in the ocean and vertical movement of the land. [WGI AR5 3.7.3]
Projected: Low confidence in region-specific projections of storminess and associated storm surges. [WGI AR5 13.7.2]
Projections of regional changes in sea level reach values of up to 30% above the global mean value in the Southern Ocean and
around North America, and between 10% to 20% above the global mean value in equatorial regions. [WGI AR5 13.6.5]
More likely than not substantial increase in the frequency of the most intense tropical cyclones in the western North Pacific and
North Atlantic. [WGI AR5 14.6]
DESCRIPTION:
Mangrove restoration and rehabilitation has occurred in a number of locations (e.g., Vietnam, Djibouti, and Brazil) to reduce
coastal flooding risks and protect shorelines from storm surge. Restored mangroves have been shown to attenuate wave height
and thus reduce wave damage and erosion. They protect aquaculture industry from storm damage and reduce saltwater intrusion.
[2.4.3, 5.5.4, 8.3.3, 22.4.5, 27.3.3]
BROADER CONTEXT:
Considered a low-regrets option benefiting sustainable development, livelihood improvement, and human well-being through
improvements for food security and reduced risks from flooding, saline intrusion, wave damage, and erosion. Restoration and
rehabilitation of mangroves, as well as of wetlands or deltas, is ecosystem-based adaptation that enhances ecosystem services.
Synergies with mitigation given that mangrove forests represent large stores of carbon.
Well-integrated ecosystem-based adaptation can be more cost effective and sustainable than non-integrated physical engineering
approaches.
[5.5, 8.4.2, 14.3.1, 24.6, 29.3.1, 29.7.2, 30.6.1-2, Table 5-4, Box CC-EA]
Community-based adaptation and traditional practices in small island contexts
EXPOSURE AND VULNERABILITY:
With small land area, often low elevation coasts, and concentration of human communities and infrastructure in coastal zones,
small islands are particularly vulnerable to rising sea levels and impacts such as inundation, saltwater intrusion, and shoreline
change. [29.3.1, 29.3.3, 29.6.1-2, 29.7.2]
CLIMATE INFORMATION AT THE GLOBAL SCALE:
Observed: Likely increase in the magnitude of extreme high sea level events since 1970, mostly explained by rising mean sea
level. [WGI AR5 3.7.5]
Low confidence in long-term (centennial) changes in tropical cyclone activity, after accounting for past changes in observing
capabilities. [WGI AR5 2.6.3]
Since 1950 the number of heavy precipitation events over land has likely increased in more regions than it has decreased. [WGI
AR5 2.6.2]
Projected: Very likely significant increase in the occurrence of future sea level extremes by 2050 and 2100. [WGI AR5 13.7.2]
In the 21st century, likely that the global frequency of tropical cyclones will either decrease or remain essentially unchanged.
Likely increase in both global mean tropical cyclone maximum wind speed and rainfall rates. [WGI AR5 14.6]
Globally, for short-duration precipitation events, likely shift to more intense individual storms and fewer weak storms. [WGI AR5
12.4.5]
CLIMATE INFORMATION AT THE REGIONAL SCALE:
Observed: Change in sea level relative to the land (relative sea level) can be significantly different from the global mean sea
level change because of changes in the distribution of water in the ocean and vertical movement of the land. [WGI AR5 3.7.3]
Projected: Low confidence in region-specific projections of storminess and associated storm surges. [WGI AR5 13.7.2]
Projections of regional changes in sea level reach values of up to 30% above the global mean value in the Southern Ocean and
around North America, and between 10% to 20% above the global mean value in equatorial regions. [WGI AR5 13.6.5]
More likely than not substantial increase in the frequency of the most intense tropical cyclones in the western North Pacific and
North Atlantic. [WGI AR5 14.6]
DESCRIPTION:
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Traditional technologies and skills can be relevant for climate adaptation in small island contexts. In the Solomon Islands,
relevant traditional practices include elevating concrete floors to keep them dry during heavy precipitation events and building
low aerodynamic houses with palm leaves as roofing to avoid hazards from flying debris during cyclones, supported by
perceptions that traditional construction methods are more resilient to extreme weather. In Fiji after cyclone Ami in 2003, mutual
support and risk sharing formed a central pillar for community-based adaptation, with unaffected households fishing to support
those with damaged homes. Participatory consultations across stakeholders and sectors within communities and capacity building
taking into account traditional practices can be vital to the success of adaptation initiatives in island communities, such as in Fiji
or Samoa. [29.6.2]
BROADER CONTEXT:
Perceptions of self-efficacy and adaptive capacity in addressing climate stress can be important in determining resilience and
identifying useful solutions.
The relevance of community-based adaptation principles to island communities, as a facilitating factor in adaptation planning
and implementation, has been highlighted, for example with focus on empowerment and learning-by-doing, while addressing
local priorities and building on local knowledge and capacity. Community-based adaptation can include measures that cut across
sectors and technological, social, and institutional processes, recognizing that technology by itself is only one component of
successful adaptation.
[5.5.4, 29.6.2]
Adaptive approaches to flood defense in Europe
EXPOSURE AND VULNERABILITY :
Increased exposure of persons and property in flood risk areas has contributed to increased damages from flood events over
recent decades.
[5.4.3-4, 5.5.5, 23.3.1, Box 5-1]
CLIMATE INFORMATION AT THE GLOBAL SCALE:
Observed: Likely increase in the magnitude of extreme high sea level events since 1970, mostly explained by rising mean sea
level. [WGI AR5 3.7.5]
Since 1950 the number of heavy precipitation events over land has likely increased in more regions than it has decreased. [WGI
AR5 2.6.2]
Projected: Very likely that the time-mean rate of global mean sea level rise during the 21st century will exceed the rate observed
during 1971-2010 for all RCP scenarios. [WGI AR5 13.5.1]
Globally, for short-duration precipitation events, likely shift to more intense individual storms and fewer weak storms. [WGI AR5
12.4.5]
CLIMATE INFORMATION AT THE REGIONAL SCALE:
Observed: Likely increase in the frequency or intensity of heavy precipitation in Europe, with some seasonal and/or regional
variations. [WGI AR5 2.6.2] Increase in heavy precipitation in winter since the 1950s in some areas of northern Europe (medium
confidence). Increase in heavy precipitation since the 1950s in some parts of west-central Europe and European Russia,
especially in winter (medium confidence). [SREX Table 3-2]
Increasing mean sea level with regional variations, except in the Baltic sea where the relative sea level is decreasing due to
vertical crustal motion. [5.3.2, 23.2.2]
Projected: Over most of the mid-latitude land-masses, extreme precipitation events will very likely be more intense and more
frequent in a warmer world. [WGI AR5 12.4.5]
Overall precipitation increase in northern Europe and decrease in southern Europe (medium confidence). [23.2.2]
Increased extreme precipitation in northern Europe during all seasons, particularly winter, and in central Europe except in
summer (high confidence). [23.2.2; SREX Table 3-3]
DESCRIPTION:
Several governments have made ambitious efforts to address flood risk and sea level rise over the coming century. In the
Netherlands, government recommendations include soft measures preserving land from development to accommodate
increased river inundation, maintaining coastal protection through beach nourishment, and ensuring necessary political-
administrative, legal, and financial resources. Through a multi-stage process, the British government has also developed
extensive adaptation plans to adjust and improve flood defenses in order to protect London from future storm surges and river
flooding. Pathways have been analyzed for different adaptation options and decisions, depending on eventual sea level rise, with
ongoing monitoring of the drivers of risk informing decisions. [5.5.4, 23.7.1, Box 5-1]
BROADER CONTEXT:
The Dutch plan is considered a paradigm shift, addressing coastal protection by working with nature and providing room for
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river.
The British plan incorporates iterative, adaptive decisions depending on the eventual sea level rise with numerous and diverse
measures possible over the next 50-100 years to reduce risk to acceptable levels.
In cities in Europe and elsewhere, the importance of strong political leadership or government champions in driving successful
adaptation action has been noted.
[5.5.3-4, 8.4.3, 23.7.1-2, 23.7.4, Boxes 5-1 and 26-3]
Index-based insurance for agriculture in Africa
EXPOSURE AND VULNERABILITY:
Susceptibility to food insecurity and depletion of farmers' productive assets following crop failure. Low prevalence of insurance
due to absent or poorly developed insurance markets or to amount of premium payments. The most marginalized and resource-
poor especially may have limited ability to afford insurance premiums. [10.7.6, 13.3.2, Box 22-1]
CLIMATE INFORMATION AT THE GLOBAL SCALE:
Observed: Very likely decrease in the number of cold days and nights and increase in the number of warm days and nights, on
the global scale between 1951 and 2010. [WGI AR5 2.6.1]
Medium confidence that the length and frequency of warm spells, including heat waves, has increased globally since 1950. [WGI
AR5 2.6.1]
Since 1950 the number of heavy precipitation events over land has likely increased in more regions than it has decreased. [WGI
AR5 2.6.2]
Low confidence in a global-scale observed trend in drought or dryness (lack of rainfall). [WGI AR5 2.6.2]
Projected: Virtually certain that, in most places, there will be more hot and fewer cold temperature extremes as global mean
temperatures increase, for events defined as extremes on both daily and seasonal timescales. [WGI AR5 12.4.3]
Regional to global-scale projected decreases in soil moisture and increased risk of agricultural drought are likely in presently dry
regions, and are projected with medium confidence by the end of this century under the RCP8.5 scenario. [WGI AR5 12.4.5]
Globally, for short-duration precipitation events, likely shift to more intense individual storms and fewer weak storms. [WGI AR5
12.4.5]
CLIMATE INFORMATION AT THE REGIONAL SCALE:
Observed: Medium confidence in increase in frequency of warm days and decrease in frequency of cold days and nights in
southern Africa. [SREX Table 3-2]
Medium confidence in increase in frequency of warm nights in northern and southern Africa. [SREX Table 3-2]
Projected: Likely surface drying in southern Africa by the end of this century under RCP8.5 (high confidence). [WGI AR5
12.4.5]
Likely increase in warm days and nights and decrease in cold days and nights in all regions of Africa (high confidence). Increase
in warm days largest in summer and fall (medium confidence). [Table SREX 3-3]
Likely more frequent and/or longer heat waves and warm spells in Africa (high confidence). [Table SREX 3-3]
DESCRIPTION:
A recently introduced mechanism that has been piloted in a number of rural locations, including in Malawi, Sudan, and Ethiopia,
as well as in India. When physical conditions reach a particular predetermined threshold where significant losses are expected to
occur--weather conditions such as excessively high or low cumulative rainfall or temperature peaks--the insurance pays out.
[9.4.2, 13.3.2, 15.4.4, Box 22-1]
BROADER CONTEXT:
Index-based weather insurance is considered well-suited to the agricultural sector in developing countries.
The mechanism allows risk to be shared across communities, with costs spread over time, while overcoming obstacles to
traditional agricultural and disaster insurance markets. It can be integrated with other strategies such as micro-finance and social
protection programs.
Risk-based premiums can help encourage adaptive responses and foster risk awareness and risk reduction by providing financial
incentives to policyholders to reduce their risk profile.
Challenges can be associated with limited availability of accurate weather data and difficulties in establishing which weather
conditions cause losses. Basis risk (i.e., farmers suffer losses but no payout is triggered based on weather data) can promote
distrust. There can also be difficulty in scaling up pilot schemes.
Insurance for work programs can enable cash-poor farmers to work for insurance premiums by engaging in community-
identified disaster risk reduction projects.
[10.7.4-6, 13.3.2, 15.4.4, Table 10-7, Box 22-1, Box 25-7]
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Relocation of agricultural industries in Australia
EXPOSURE AND VULNERABILITY :
Crops sensitive to changing patterns of temperature, rainfall, and water availability. [7.3, 7.5.2]
CLIMATE INFORMATION AT THE GLOBAL SCALE:
Observed: Very likely decrease in the number of cold days and nights and increase in the number of warm days and nights, on
the global scale between 1951 and 2010. [WGI AR5 2.6.1]
Medium confidence that the length and frequency of warm spells, including heat waves, has increased globally since 1950. [WGI
AR5 2.6.1]
Medium confidence in precipitation change over global land areas since 1950. [WGI AR5 2.5.1]
Since 1950 the number of heavy precipitation events over land has likely increased in more regions than it has decreased. [WGI
AR5 2.6.2]
Low confidence in a global-scale observed trend in drought or dryness (lack of rainfall). [WGI AR5 2.6.2]
Projected: Virtually certain that, in most places, there will be more hot and fewer cold temperature extremes as global mean
temperatures increase, for events defined as extremes on both daily and seasonal timescales. [WGI AR5 12.4.3]
Virtually certain increase in global precipitation as global mean surface temperature increases. [WGI AR5 12.4.1]
Regional to global-scale projected decreases in soil moisture and increased risk of agricultural drought are likely in presently dry
regions, and are projected with medium confidence by the end of this century under the RCP8.5 scenario. [WGI AR5 12.4.5]
Globally, for short-duration precipitation events, likely shift to more intense individual storms and fewer weak storms. [WGI AR5
12.4.5]
CLIMATE INFORMATION AT THE REGIONAL SCALE:
Observed: Cool extremes rarer and hot extremes more frequent and intense over Australia and New Zealand, since 1950 (high
confidence). [Table 25-1]
Likely increase in heat wave frequency since 1950 in large parts of Australia. [WGI AR5 2.6.1]
Late autumn/winter decreases in precipitation in Southwestern Australia since the 1970s and Southeastern Australia since the
mid-1990s, and annual increases in precipitation in Northwestern Australia since the 1950s (very high confidence). [Table 25-1]
Mixed or insignificant trends in annual daily precipitation extremes, but a tendency to significant increase in annual intensity of
heavy precipitation in recent decades for sub-daily events in Australia (high confidence). [Table 25-1]
Projected: Hot days and nights more frequent and cold days and nights less frequent during the 21st century in Australia and
New Zealand (high confidence). [Table 25-1]
Annual decline in precipitation over southwestern Australia (high confidence) and elsewhere in southern Australia (medium
confidence). Reductions strongest in the winter half-year (high confidence). [Table 25-1]
Increase in most regions in the intensity of rare daily rainfall extremes and in sub-daily extremes (medium confidence) in
Australia and New Zealand. [Table 25-1]
Drought occurrence to increase in Southern Australia (medium confidence). [Table 25-1]
Snow depth and snow area to decline in Australia (very high confidence). [Table 25-1]
Freshwater resources projected to decline in far southeastern and far southwest Australia (high confidence). [25.5.2]
DESCRIPTION:
Industries and individual farmers are relocating parts of their operations, for example for rice, wine, or peanuts in Australia, or
are changing land use in situ in response to recent climate change or expectations of future change. For example, there has been
some switching from grazing to cropping in South Australia. Adaptive movement of crops has also occurred elsewhere. [7.5.1,
25.7.2, Table 9-7, Box 25-5]
BROADER CONTEXT:
Considered transformational adaptation in response to impacts of climate change.
Positive or negative implications for the wider communities in origin and destination regions.
[25.7.2, Box 25-5]
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Table 21-5: Dimensions of assessments of impacts and vulnerability and of adaptation drawn upon to serve different
target fields (cf. Table 21-1). Scales refer to the level of aggregation at which study results are presented. Entries are
illustrations of different types of study approaches reported and evaluated in this volume, with references given both
to the original studies and to the chapters in which they are cited. Aspects of some of the studies in this table are also
alluded to in Section 21.5.
Approach/field: Impacts/vulnerability Adaptation Target field
Scale:
Resource availability1,2,3 Adaptation costs4,5,6,7,12 - Policy negotiations
Impact costs4,5,6,7 - Development aid
Global
Vulnerability/risk mapping8,9,10 - Disaster planning
Hotspots analysis11 - Capacity building
Observed impacts13,14,15 Adaptation costs5 - Capacity building
Continental/ Future biophysical impacts16,17 Modelled adaptation19 - International law
biome Impact costs5,16 - Policy negotiations
Vulnerability/risk mapping18 - Regional development
Observed impacts20,21,22 Observed adaptation26 - National adaptation
Future impacts/risks23,24 Adaptation assessment24,27 plan/strategy
National/
Vulnerability assessment24 - Nat. Communication
state/province
Impact costs25 - Legal requirement
- Regulation
Hazard/risk mapping28 Adaptation cost28 - Spatial planning
Municipality/
Pest/disease risk mapping29 Urban adaptation30,31 - Extension services
basin/patch/
Urban risks/vulnerabilities30 - Water utilities
delta/farm
- Private sector
Site/field/tree/ Field experiments32 Coping studies33,34 - Individual actors
floodplain/ Economic modelling35 - Local planners
household Agent-based modelling36
Notes for Table 21-5
1
Global terrestrial water balance, in the Water Model Intercomparison Project (Haddeland et al., 2011), see chapter 3
2
Global dynamic vegetation model intercomparison (Sitch et al., 2008), see chapter 4
3
Impacts on agriculture, coasts, water resources, ecosystems and health in the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP
Schiermeier, 2012), see chapter 19
4
UNFCCC study to estimate the aggregate cost of adaptation (UNFCCC, 2007), which is critiqued by Parry (2009) and Fankhauser (2010)
5
The Economics of Adaptation to Climate Change study (World Bank, 2010)
6
Chapter 17 provides a thorough evaluation of global modelling studies (see also chapters 14 and 16)
7
Impacts on agriculture and costs of adaptation (e.g. Nelson et al., 2009), see chapter 7
8
Can we avoid dangerous climate change? (AVOID) programme and Quantifying and Understanding the Earth System (QUEST) Global-scale
impacts of climate change (GSI) project (Arnell et al., 2013), see chapter 19
9
OECD project on Cities and Climate Change (Hanson et al., 2011), see chapters 5, 23, 24 and 26
10
For critical reviews of global vulnerability studies, see Füssel (2010) and Preston et al.(2011)
11
A discussion of hotspots can be found in section 21.5.1.2
12
Adaptation costs for climate change-related human health impacts (Ebi, 2008), see chapter 17
13
Satellite monitoring of sea ice over polar regions (Comiso and Nishio, 2008), see also Vaughan et al. (2014) and chapters 18 and 28
14
Satellite monitoring of vegetation growth (e.g., Piao et al., 2011) and phenology (e.g., Heumann et al., 2007), see chapters 4 and 18
15
Meta-analysis of range shifts in terrestrial organisms (e.g., Chen et al., 2011), see chapters 4 and 18
16
Physical and economic impacts of future climate change in Europe (Ciscar et al., 2011), see chapter 23
17
Impacts on crop yields in West Africa (Roudier et al., 2011), see chapter 22
18
Climate change integrated methodology for cross-sectoral adaptation and vulnerability in Europe (CLIMSAVE) project (Harrison et al., 2012),
see chapter 23
19
Modelling agricultural management under climate change in sub-Saharan Africa (Waha et al., 2013)
20
Satellite monitoring of lake levels in China (Wang et al., 2013)
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21
Satellite monitoring of rice phenology in India (Singh et al., 2006), see chapter 18
22
UK Climate Change Risk Assessment (CCRA, 2012), see chapter 23
23
United States Global Change Research Program second (Karl et al., 2009) and third (in review) national climate change impact assessments,
see chapter 26
24
The Global Environment Facility (GEF)-funded Assessments of Impacts and Adaptations to Climate Change (AIACC) program addressed
impacts and vulnerability (Leary et al., 2008b) and adaptation (Leary et al., 2008a) in developing countries, see chapter 27
25
Economics of Climate Change national studies in Kenya and Tanzania (SEI, 2009; GCAP, 2011), see chapter 22
26
Sowing dates of various crops in Finland (Kaukoranta and Hakala, 2008), and see chapter 18
27
Finnish Climate Change Adaptation Research Programme (ISTO) Synthesis Report (Ruuhela, 2012)
28
Urban flood risk and adaptation cost, Finland (Perrels et al., 2010)
29
See Garrett (2013) for a specific example of a risk analysis, or Sutherst (2011) for a review and see chapter 25
30
New York City coastal adaptation (Rosenzweig et al., 2011), see chapters 8 and 26
31
Bangkok Assessment Report of Climate Change (BMA/GLF/UNEP, 2009), see chapters 8 and 24
32
Field, chamber and laboratory plant response experiments (e.g., Long et al., 2006; Hyvönen et al., 2007; Wittig et al., 2009; Craufurd et al.,
2013), see chapters 4 and 7
33
Farming response to irrigation water scarcity in China (Liu et al., 2008) and see chapter 13
34
Farmers' mechanisms for coping with hurricanes in Jamaica (Campbell and Beckford, 2009) and see chapter 29
35
Modelling micro-insurance of subsistence farmers for drought losses in Ethiopia (Meze-Hausken et al., 2009), see chapter 14
36
Simulating adaptive behaviour of farming communities in the Philippines (Acosta-Michlik and Espaldon, 2008), see chapter 24
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Table 21-6: Reliability of climate information on temperature and precipitation over a range of spatial and temporal
scales. Reliability is assigned to one of seven broad categories from Very High (VH) to Medium (M) through to
Very Low (VL).
Scale Temporal Annual Seasonal-Monthly Daily
Spatial Variable Temp Precip Temp Precip Temp Precip
Era
Past VH H VH H N/A N/A
Global
Future VH direction H direction VH direction H direction
N/A N/A
change H amount MH amount H amount MH amount
VH-H
VH-H depends H-L depends on VH-H depends H-L depends on H-L depends
depends on
Past on observation observation on observation observation on observation
Regional, observation
availability availability availability availability availability
Large availability
river basin H-L depends on H-L depends on H-L depends
Future VH direction VH direction VH direction
capture of capture of on capture of
change H amount MH amount MH amount
processes processes processes
VH-H
VH-H depends H-L depends on VH-H depends H-L depends on H-VL depends
depends on
Past on observation observation on observation observation on observation
observation
National, availability availability availability availability availability
availability
State
H-L depends on H-L depends on H-VL depends
Future VH direction VH direction H direction
capture of capture of on capture of
change MH amount MH amount MH amount
processes processes processes
VH-M depends H-VL depends VH-M depends H-VL depends H-ML depends H-VL depends
Past on observation on observation on observation on observation on observation on observation
City, availability availability availability availability availability availability
County H-VL depends H-VL depends M-VL depends
Future H direction H direction H direction
on capture of on capture of on capture of
change MH amount MH amount M amount
processes processes processes
VH-ML depends H-VL depends VH-ML depends H-VL depends H-ML depends H-VL depends
Past on observation on observation on observation on observation on observation on observation
Village, availability availability availability availability availability availability
Site/field
H-VL depends H-VL depends M-VL depends
Future H direction H direction H direction
on capture of on capture of on capture of
change MH amount MH amount M amount
processes processes processes
"
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Table 21-7: An assessment of observed and projected future changes in temperature and precipitation extremes over 26 sub-continental regions as defined in the
SREX report (IPCC 2012; see also Figure 21.4 and Table SM21.2). Confidence levels are indicated by colour coding of the symbols. Likelihood terms are given
only for high confidence statements and are specified in the text. Observed trends in temperature and precipitation extremes, including dryness, are generally
calculated from 1950, using the period 1961-1990 as a baseline (see Box 3.1 chapter 3 of IPCC (2012a)). The future changes are derived from global and
regional climate model projections of the climate of 2071-2100 compared with 1961-1990 or 2080-2100 compared with 1980-2000. Table entries are summaries
of information in Tables 3.2 and 3.3 of IPCC (2012a) supplemented with or superseded by material from Chapters 2 (section 2.6 and Table 2.13) and 14 (section
14.4) of the IPCC AR5 WG1 report and Table 25-1 of the IPCC WG2 report. The source(s) of information for each entry are indicated by the superscripts a
(Table 3.2 of IPCC, 2012a), b (Table 3.3 of IPCC, 2012a), c (Chapter 2 (section 2.6 and Table 2.13) IPCC AR5 WG1 report), d (Chapter 14 (section 14.4) of the
IPCC AR5 WG1 report) and e (Table 25-1 of the IPCC WG2 report).
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Table 21-8: Leading knowledge gaps and related research needs.
Knowledge Gap Research need
1 There is no clear understanding of how to integrate the Research is needed to distinguish the relative stochastic and
diversity of climate change projections data. The full deterministic sources of variability and change as a function
associated uncertainty is weakly characterized and of scale, variable, and application. The need is to develop
quantifying how much of an observed or simulated climate further and build on physical understanding of the drivers of
change is due to internal variability or external forcings is climate variability and change and how to represent these
difficult in many situations. Collectively this results in data realistically within models to understand the source of the
products with differing time and space resolution, differing spread and any contradictions in the regional projections at
dependencies and assumptions and that can have conflicting scales relevant to users and then to provide guidance on a
messages. At present individual products are plausible and likely range of outcomes within which the true response
mostly defensible in-so-far as they have physical basis would be expected to lie. Similarly, there is a need is to
within the assumptions of the method. However, at articulate the real inherent uncertainty within climate
decision-relevant scales understanding where (or whether) projection data and to understand when climate information
the true outcome will lie within the range of the products is useful at the scales of need. This also requires stronger
collectively is often not possible and thus the products are dialogues with users of climate information to inform
often not strongly actionable. choices of variables and ways to characterize envelopes of
risk and uncertainties.
2. The growth of multi-model, multi-method, and multi- Methodological and conceptual advances are needed to
generational data for climate projections creates confusion facilitate the synthesis of diverse data sets on different
for the IAV community. The lack of a clear approach to scales from methods with different assumptions, and to
handling this diversity leads to choosing one or other subset, integrate these into cohesive and defensible understanding
and where one choice may substantially alter the IAV of projected regional change.
conclusion compared to a different subset.
3 The attributes of regional climate change through which The research need is to be able to demonstrate how to
impacts are manifest, such as the intensity, persistence, unpack the regional projections into terms relevant for
distribution, recurrence, and frequency of weather events is impacts and adaptation. For example, how is the shape of
poorly understood. The information conveyed to the the distribution of weather events changing (not just the
adaptation community is dominated by aggregates in time extremes), or how stable are the critical global
and space (e.g. SREX regional averages, or time averages), teleconnection patterns that contribute to the variability of a
which hide the important attributes underlying these region?
aggregated changes. In part this is a consequence of (1)
above.
4 The historical record for many regions, especially those The research need is to integrate the multiplicity of
regions most vulnerable to climate change, is poor to the historical data as represented by the raw observations
extent that the historical record is at best an estimate with processed gridded products (e.g. CRU and GPCP), satellite
unknown uncertainty. This severely undermines the data, and reanalysis data sets. Involving national scientists
development of regional change analysis, limits the with their inherent local knowledge and rescue and
evaluation of model skill, and presents a weak baseline digitization of the many national archives still inaccessible
against which to assess change signals or to develop to the wider research community would significantly
impacts, adaptation or vulnerability baselines. enhance this research activity.
5 Impact model sensitivity studies and inter-comparison Intensified efforts are needed to refine, test and inter-
exercises are beginning to reveal fundamental flaws and compare impact models over a wider range of sectors and
omissions in some impact models in the representation of environments than hitherto. These should be supported,
key processes that are expected to be important under where applicable, by targeted field, chamber and laboratory
projected climate changes. For example, high temperature experiments under controlled atmospheric composition and
constraints, and CO2 and drought effects on agricultural climate conditions, to improve understanding of key
yields are poorly represented in many crop models. physical, biological and chemical processes operating in
changed environments. Such experiments are needed across
a range of terrestrial and aquatic biogeographical zones in
different regions of the world
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6 New global scenarios are under development, based on Extended SSPs for major sub-continental regions of the
climate projections for different representative world, including variables that define aspects of adaptive
concentration pathways (RCPs) and socio-economic capacity and guidance on how to combine RCP-based
scenarios based on shared socio-economic pathways (SSPs). regional climate projections with regional SSPs and SPAs to
However, there is currently little or no guidance on how form plausible regional scenarios for application in IAV
these projections are to be accessed or applied in IAV analysis.
studies. Moreover, as yet, quantitative SSPs are available
only for large regions (basic SSPs), and regional SSPs that
are consistent with the global SSPs (extended SSPs) along
with scenarios that include mitigation and adaptation
policies (shared policy assumptions SPAs) have not yet
been developed.,
7 The determinants and regional variability of vulnerability, Case studies and underlying theory of these features of
exposure and adaptive capacity are not well understood, and societies, and documentation of the effectiveness of actions
methods for projecting changes in them are under- taken are needed in conjunction with methods development
developed. Furthermore, given these lacks of understanding for projections. More attention placed on determining their
uncertainties of these three elements are poorly uncertainties, in national and regional assessments.
characterized and quantified
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Figure 21-1: Specification of the world regions described in chapters 22-30 of this volume.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-2: Observed and projected changes in annual average temperature and precipitation. (Top panel, left)
Observed temperature trends from 1901-2012 determined by linear regression. [WGI AR5 Figures SPM.1 and 2.21]
(Bottom panel, left) Observed precipitation change from 1951-2010 determined by linear regression. [WGI AR5
Figure SPM.2] For observed temperature and precipitation, trends have been calculated where sufficient data
permits a robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data
availability in the first and last 10% of the time period). Other areas are white. Solid colors indicate areas where
change is significant at the 10% level. Diagonal lines indicate areas where change is not significant. (Top and
bottom panel, right) CMIP5 multi-model mean projections of annual average temperature changes and average
percent change in annual mean precipitation for 2046-2065 and 2081-2100 under RCP2.6 and 8.5. Solid colors
indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline
variability, and >90% of models agree on sign of change. Colors with white dots indicate areas with strong
agreement, where >66% of models show change greater than the baseline variability and >66% of models agree on
sign of change. Gray indicates areas with divergent changes, where >66% of models show change greater than the
baseline variability, but <66% agree on sign of change. Colors with diagonal lines indicate areas with little or no
change, less than the baseline variability in >66% of models. (There may be significant change at shorter timescales
such as seasons, months, or days.). Analysis uses model data and methods building from WGI AR5 Figure SPM.8.
See alsoAnnex I of WGI AR5. [Boxes 21-3 and CC-RC]
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Figure 21-3: Regional average change in seasonal and annual mean temperature and precipitation over five sub-
regions covering South and Central America for the period 2071-2100 relative to 1961-90 in GCM projections from
35 CMIP5 ensemble under four RCP scenarios (van Vuuren et al., 2011) compared with GCM projections from 22
CMIP3 ensemble under three SRES scenarios (IPCC, 2000); see Table 21-1 for details of the relationship between
the SRES and RCP scenarios. Regional averages are based on SREX region definitions (see Figure 21-3).
Temperature changes are given in C and precipitation changes in mm/day with axes scaled relative to the maximum
changes projected across the range of models. The models which generated the data displayed are listed in
supplementary material Table SM21-3.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-4: CMIP5 ensemble median ratio of local:global average temperature change in the period 2071-2100
relative to 1961-90 under the RCP8.5 emissions/concentrations scenario. The values are displayed on a common
2.5x3.75 grid onto which each models data were regridded and they were calculated as follows: 1) for each model
the local change was calculated between 1961 and 1990 at each grid cell, and is divided by the global average
change in that model projection over the same period; 2) the median ratio value across all models at each grid cell is
identified and shown. Data used are from the 35 CMIP5 models for which monthly projections were available under
RCP8.5 which are listed in supplementary Table 21-3. Overplotted polygons indicate the SREX regions (IPCC,
2012) used to define the sub-regions used to summarise information in Chapters 21 and some of the subsequent
regional chapters. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-5: Evolution of the 5%, 17%, 33%, 50%, 66%, 83% and 95% percentiles of the distribution functions for
annual surface air temperature changes (upper panel) and annual precipitation (lower panel) for the Giorgi-Francisco
(2000) regions and the globe with the SRES A1B forcing scenario combining results from a perturbed physics
ensemble and the CMIP3 ensemble. Twenty year means relative to the 1961-1990 baseline are plotted in decadal
steps using a common y-axis scale. The 5%, 50% and 95% percentile values for the period 2080-2099 are displayed
for each region. (From Harris et al. 2012).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-6: Linear changes (i.e. changes obtained by fitting the time series at each grid point with straight lines) of
annual precipitation during the 2001-2050 period from 10 individual RCM experiments and the MME mean under
the A1B emission scenario. The top middle panels also account for projected land cover changes (see Paeth et al.
2011 for further explanation). Note that the REMO trends in both panels arise from a three-member ensemble
whereas all other RCMs are represented by one single simulation. Trends statistically significant at the 95% level
are marked by black dots. From Paeth et al. (2011).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-7: The frequency of 'warm days' (defined here as the 90th percentile daily maximum temperature during a
baseline period of 1961-1990) projected for the 2071-2100 period by 26 CMIP5 GCMs for North America. Map:
Ensemble median frequency of 'warm days' during 2071-2100 under RCP8.5. Graphs: Box-and-whisker plots
indicate the range of regionally-averaged 'hot-day' frequency by 2041-2070 and 2071-2100 under RCPs 4.5 and 8.5
across the 26 CMIP5 models for each SREX sub-regions in North America. Boxes represent inter-quartile range and
whiskers indicate full range of projections across the ensemble. The baseline frequency of warm days of 10% is
represented on the graphs by the dashed line. A full list of CMIP5 models for which data is shown here can be found
in supplementary material Table SM21-4.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-8: The frequency of 'very wet days' (defined here as the 90th percentile of daily precipitation on wet days
during a baseline period of 1961-1990 with wet days defined as days with 1mm of precipitation or more) projected
for the 2071-2100 period by 26 CMIP5 GCMs for Asia. Map: Ensemble median frequency of 'very wet days' during
2071-2100 under RCP8.5. Graphs: Box-and-whisker plots indicate the range of regionally-averaged 'very wet day'
frequency by 2041-2070 and 2071-2100 under RCPs 4.5 and 8.5 across the 26 CMIP5 models for each SREX sub-
regions in Asia Boxes represent inter-quartile range and whiskers indicate full range of projections across the
ensemble. The baseline frequency of Very wet days of 10% is represented on the graphs by the dashed line. A full
list of CMIP5 models for which data is shown here can be found in supplementary material Table SM21-4. (Note
the WMO Expert Team on Climate Change Detection Indices defines very wet days threshold as the 95%-ile daily
precipitation event. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-9: Mean (top panels) and standard deviation (bottom panels) in future-minus-present (2050s minus 1990s)
MDA8 summer ozone concentrations across (left-hand panels) all seven experiments (five regional and 2 global)
and for comparison purposes (right hand panels), not including the WSU experiment (which simulated July only
conditions). The different experiments use different pollutant emission and SRES GHG emission scenarios. The
pollutant emissions are the same in the present and future simulations (from Weaver et al., 2009).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-10: Growth rates from 1990-2008 of international trade, its embodied CO2 emissions and net emissions
transfers from Annex B and non-Annex B countries compared to other global macrovariables, all indexed to 1990
(Peters et al., 2011). Annex B and non-Annex B Parties to the UNFCCC are listed in the supplementary material.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-11: Central map: Marine exclusive environmental zones (EEZs dashed lines) of Canada,
Greenland/Denmark, Norway, Russia, and the USA, and location of the Northwest Passage, Northern Sea Route,
Trans-Polar Route, and international high seas within the IMO Guidelines Boundary for Arctic shipping (thick black
border). After Stephenson et al. (2013). Peripheral monthly maps: Projected change in accessibility of maritime and
land-based transportation by mid-century (2045-2059 relative to 2000-2014) using the Arctic Transport
Accessibility Model and CCSM3 climate and sea ice estimates assuming an SRES A1B scenario. Dark blue areas
denote new maritime access to Polar Class 6 vessels (light icebreaker); white areas remain inaccessible. Red delimits
areas of lost winter road potential for ground vehicles exceeding 2 metric tonnes (Stephenson et al., 2011).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 21-12: Time series of seasonally averaged climate indices representing three modes of large-scale climate
variability: (a) the tropical September to January Pacific Walker Circulation (PWC); (b) the December to March
North Atlantic Oscillation (NAO); (c) the December to March Pacific North America (PNA) pattern. Indices (as
defined in Brönnimann etal. (2009) are calculated (with respect to the overlapping 1989 1999 period) from various
observed, reanalysis and model sources: statistical reconstructions of the PWC, the PNA and the NAO, see
Brönnimann et al. (2009) for details, (all cyan); 20CR (pink); NCEP NCAR reanalyses (NNR; dark blue); ERA-40
(green); ERA-Interim (orange). The black line and grey shading represent the ensemble mean and spread from a
climate model ensemble with a lower boundary condition of observed seas-surface temperatures and sea-ice from
the HadISST dataset (Rayner et al. 2003), see Brönnimann etal. (2009) for details. The model results provide a
measure of the predictability of these modes of variability from sea-surface temperature and sea-ice alone and
demonstrate that the reanalyses have significantly higher skill in reproduces these modes of variability.
[Illustration to be redrawn to conform to IPCC publication specifications.]
Figure RC-1: Observed and projected changes in global annual average temperature. Values are expressed relative
to 1986-2005. Black lines show the GISTEMP, NCDC-MLOST, and HadCRUT4.2 estimates from observational
measurements. Colored shading denotes the +/-1.64 standard deviation range based on simulations from 32 models
for RCP2.6 (blue) and 39 models for RCP8.5 (red). Blue and red lines denote the scenario mean for RCP2.6 and
RCP8.5, respectively. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure RC-2: Observed and projected changes in annual average temperature. (A) Observed temperature trends from
1901-2012 are determined by linear regression. Trends have been calculated where sufficient data permit a robust
estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data availability in the
first and last 10% of the time period). Other areas are white. Solid colors indicate areas where change is significant
at the 10% level (after accounting for autocorrelation effects on significance testing). Diagonal lines indicate areas
where change is not significant. Observed data are from WGI AR5 Figures SPM.1 and 2.21. The range of grid-point
values is -0.53 to +2.50°C over period. (B) CMIP5 multi-model mean projections of annual average temperature
changes for 2046-2065 and 2081-2100 under RCP2.6 and RCP8.5, relative to 1986-2005. Solid colors indicate areas
with very strong agreement, where the multi-model mean change is greater than twice the baseline variability and
>90% of models agree on the sign of change. Colors with white dots indicate areas with strong agreement, where
>66% of models show change greater than the baseline variability and >66% of models agree on the sign of change.
Gray indicates areas with divergent changes, where >66% of models show change greater than the baseline
variability, but <66% agree on the sign of change. Colors with diagonal lines indicate areas with little or no change,
where >66% of models show change less than the baseline variability (although there may be significant change at
shorter timescales such as seasons, months, or days). Analysis uses model data from WGI AR5 Figure SPM.8, Box
12.1, and Annex I. The range of grid-point values for the multi-model mean is: +0.19 to +4.08C for mid-21st
century of RCP2.6; +0.06 to +3.85C for late-21st century of RCP2.6; +0.70 to +7.04C for mid-21st century of
RCP8.5; and +1.38 to +11.71°C for late-21st century of RCP8.5.
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Figure RC-3: Observed and projected changes in annual average precipitation. (A) Observed precipitation trends
from 1951-2010 are determined by linear regression. Trends have been calculated where sufficient data permit a
robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data
availability in the first and last 10% of the time period). Other areas are white. Solid colors indicate areas where
change is significant at the 10% level (after accounting for autocorrelation effects on significance testing). Diagonal
lines indicate areas where change is not significant. Observed data are from WGI AR5 Figures SPM.2. The range of
grid-point values is -185 to +111 mm/year/decade. (B) CMIP5 multi-model mean projections of annual average
precipitation changes for 2046-2065 and 2081-2100 under RCP2.6 and RCP8.5, relative to 1986-2005. Solid colors
indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline
variability and >90% of models agree on the sign of change. Colors with white dots indicate areas with strong
agreement, where >66% of models show change greater than the baseline variability and >66% of models agree on
the sign of change. Gray indicates areas with divergent changes, where >66% of models show change greater than
the baseline variability, but <66% agree on the sign of change. Colors with diagonal lines indicate areas with little or
no change, where >66% of models show change less than the baseline variability (although there may be significant
change at shorter timescales such as seasons, months, or days). Analysis uses model data from WGI AR5 Figure
SPM.8, Box 12.1, and Annex I. The range of grid-point values for the multi-model mean is: -10 to +24% for mid-
21st century of RCP2.6; -9 to +22% for late-21st century of RCP2.6; -19 to +57% for mid-21st century of RCP8.5;
and -34 to +112% for late-21st century of RCP8.5.
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Chapter 22. Africa
Coordinating Lead Authors
Isabelle Niang (Senegal), Oliver C. Ruppel (Namibia)
Lead Authors
Mohamed Abdrabo (Egypt), Ama Essel (Ghana), Christopher Lennard (South Africa), Jonathan Padgham (USA),
Penny Urquhart (South Africa)
Contributing Authors
Ibidun Adelekan (Nigeria), Sally Archibald (South Africa), Armineh Barkhordarian (Germany), Jane Battersby
(South Africa), Michael Balinga (Cameroon), Eren Bilir (USA), Marshall Burke (USA), Mohammed Chahed
(Tunisia), Monalisa Chatterjee (USA / India), Chineke Theo Chidiezie (Nigeria), Katrien Descheemaeker
(Netherlands), Houria Djoudi (Algeria), Kristie L. Ebi (USA), Papa Demba Fall (Senegal), Ricardo Fuentes
(Mexico), Rebecca Garland (South Africa), Fatou Gaye (The Gambia), Karim Hilmi (Morocco), Emiloa Gbobaniyi
(Nigeria), Patrick Gonzalez (USA), Blane Harvey (UK), Mary Hayden (USA), Andreas Hemp (Germany), Guy
Jobbins (UK), Jennifer Johnson (USA), David Lobell (USA), Bruno Locatelli (France), Eva Ludi (UK), Lars Otto
Naess (UK), Mzime R. Ndebele-Murisa (Zimbabwe), Aminata Ndiaye (Senegal), Andrew Newsham (UK), Sirra
Njai (The Gambia), Johnson Nkem (Cameroon), Jane Mukarugwiza Olwoch (South Africa), Pieter Pauw
(Netherlands), Emilia Pramova (Bulgaria), Marie-Louise Rakotondrafara (Madagascar), Clionadh Raleigh (Ireland),
Debra Roberts (South Africa), Carla Roncoli (USA), Aissa Toure Sarr (Senegal), Michael Henry Schleyer (South
Africa), Lena Schulte-Uebbing (Germany), Roland Schulze (South Africa), Hussen Seid (Ethiopia), Sheona
Shackleton (South Africa), Mxolisi Shongwe (Swaziland), Dáithí Stone (Canada / South Africa / USA), David
Thomas (UK), Okoro Ugochukwu (Nigeria), Dike Victor (Nigeria), Katharine Vincent (South Africa), Koko Warner
(Germany), Sidat Yaffa (The Gambia)
Review Editors
Pauline Dube (Botswana), Neil Leary (USA)
Volunteer Chapter Scientist
Lena Schulte-Uebbing (Germany)
Contents
Executive Summary
22.1. Introduction
22.1.1. Structure of the Regions
22.1.2. Major Conclusions from Previous Assessments
22.1.2.1. Regional Special Report and Assessment Reports
22.1.2.2. Special Report on Managing the Risks of Extreme Events and Disasters to Advance
Climate Change Adaptation (SREX)
22.2. Observed Climate Trends and Future Projections
22.2.1. Temperature
22.2.1.1. Observed Trends
22.2.1.2. Projected Trends
22.2.2. Precipitation
22.2.2.1. Observed Changes
22.2.2.2. Projected Changes
22.2.3. Observed and Projected Changes in Extreme Temperature and Rainfall
22.3. Vulnerability and Impacts
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22.3.1. Socioeconomic and Environmental Context Influencing Vulnerability and Adaptive Capacity
22.3.2. Ecosystems
22.3.2.1. Terrestrial Ecosystems
22.3.2.2. Freshwater Ecosystems
22.3.2.3. Coastal and Ocean Systems
22.3.3. Water Resources
22.3.4. Agriculture and Food Security
22.3.4.1. Crops
22.3.4.2. Livestock
22.3.4.3. Agricultural Pests, Diseases, and Weeds
22.3.4.4. Fisheries
22.3.4.5. Food Security
22.3.5. Health
22.3.5.1. Introduction
22.3.5.2. Food- and Water-Borne Diseases
22.3.5.3. Nutrition
22.3.5.4. Vector-Borne Diseases and Other Climate-Sensitive Health Outcomes
22.3.6. Urbanization
22.4. Adaptation
22.4.1. Introduction
22.4.2. Adaptation Needs, Gaps, and Adaptive Capacity
22.4.3. Adaptation, Equity, and Sustainable Development
22.4.4. Experiences in Building the Governance System for Adaptation, and Lessons Learned
22.4.4.1. Introduction
22.4.4.2. Regional and National Adaptation Planning and Implementation
22.4.4.3. Institutional Frameworks for Adaptation
22.4.4.4. Sub-National Adaptation Governance
22.4.4.5. Community-Based Adaptation and Local Institutions
22.4.4.6. Adaptation Decisionmaking and Monitoring
22.4.5. Experiences with Adaptation Measures in Africa and Lessons Learned
22.4.5.1. Overview
22.4.5.2. Climate Risk Reduction, Risk Transfer, and Livelihood Diversification
22.4.5.3. Adaptation as a Participatory Learning Process
22.4.5.4. Knowledge Development and Sharing
22.4.5.5. Communication, Education, and Capacity Development
22.4.5.6. Ecosystem Services, Biodiversity, and Natural Resource Management
22.4.5.7. Technological and Infrastructural Adaptation Responses
22.4.5.8. Maladaptation Risks
22.4.6. Barriers and Limits to Adaptation in Africa
22.5. Key Risks for Africa
22.6. Emerging Issues
22.6.1. Human Security
22.6.1.1 Violent Conflict
22.6.1.2 Migration
22.6.2. Integrated Adaptation / Mitigation Approaches
22.6.3. Biofuels and Land Use
22.6.4. Climate Finance and Management
22.7. Research Gaps
References
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Chapter Boxes
22-1. Experience with Index-Based Weather Insurance in Africa
22-2. African Success Story: Integrating Trees into Annual Cropping Systems
Frequently Asked Questions
22.1: How could climate change impact food security in Africa?
22.2: What role does climate change play with regard to violent conflict in Africa?
Executive Summary
Evidence of warming over land regions across Africa, consistent with anthropogenic climate change, has
increased (high confidence). Decadal analyses of temperatures strongly point to an increased warming trend across
the continent over the last 50-100 years. [22.2.1.1]
Mean annual temperature rise over Africa, relative to the late 20th Century mean annual temperature, is
likely to exceed 2° C in the A1B and A2 scenarios by the end of this century (medium confidence). Warming
projections under medium scenarios indicate that extensive areas of Africa will exceed 2° C by the last two decades
of this century relative to the late 20th Century mean annual temperature and all of Africa under high emission
scenarios. Under a high RCP, that exceedence could occur by mid-century across much of Africa and reach between
3 and 6° C by the end of the century. It is likely that land temperatures over Africa will rise faster than the global
land average, particularly in the more arid regions, and that the rate of increase in minimum temperatures will
exceed that of maximum temperatures. [22.2.1.2]
A reduction in precipitation is likely over Northern Africa and the south-western parts of South Africa by the
end of the 21st Century under the A1B and A2 scenarios (medium to high confidence). Projected rainfall change
over sub-Sarahan Africa in the mid- and late 21st Century is uncertain. In regions of high or complex topography
such as the Ethopian Highlands, downscaled projections indicate likely increases in rainfall and extreme rainfall by
the end of the 21st Century. [22.2.2.2, 22.2.3]
African ecosystems are already being affected by climate change, and futre impacts are expected to be
substantial (high confidence). There is emerging evidence on shifting ranges of some species and ecosystems due
to elevated CO2 and climate change, beyond the effects of land-use change and other non-climate stressors (high
confidence). Ocean ecosystems, in particular coral reefs, will be affected by ocean acidification and warming as well
as changes in ocean upwellings, thus negatively affecting economic sectors such as fisheries (medium confidence).
[22.3.2, Table 22-3]
Climate change will amplify existing stress on water availability in Africa (high confidence). Water resources
are subjected to high hydro-climatic variability over space and time, and are a key constraint on the continent s
continued economic development. The impacts of climate change will be superimposed onto already water-stressed
catchments with complex land uses, engineered water systems, and a strong historical socio-political and economic
footprint. Strategies that integrate land and water management, and disaster risk reduction, within a framework of
emerging climate change risks would bolster resilient development in the face of projected impacts of climate
change. [22.3.2.2, 22.3.3]
Climate change will interact with non-climate drivers and stressors to exacerbate vulnerability of agricultural
systems, particularly in semi-arid areas (high confidence). Increasing temperatures and changes in precipitation
are very likely to reduce cereal crop productivity. This will have strong adverse effects on food security. New
evidence is also emerging that high-value perennial crops could also be adversely affected by temperature rise
(medium confidence). Pest, weed and disease pressure on crops and livestock is expected to increase as a result of
climate change combined with other factors (low confidence). Moreover, new challenges to food security are
emerging as a result of strong urbanization trends on the continent and increasingly globalized food chains, which
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require better understanding of the multi-stressor context of food and livelihood security in both urban and rural
contexts in Africa. [22.3.4, 22.3.4.3, 22.3.4.5]
Progress has been achieved on managing risks to food production from current climate variability and near-
term climate change but these will not be sufficient to address long-term impacts of climate change (high
confidence). Livelihood-based approaches for managing risks to food production from multiple stressors, including
rainfall variability, have increased substantially in Africa since the IPCC s Fourth Assessment Report (AR4). While
these efforts can improve the resiliency of agricultural systems in Africa over the near term, current adaptations will
be insufficient for managing risks from long-term climate change, which will be variable across regions and farming
system types. Nonetheless, processes such as collaborative, participatory research that includes scientists and
farmers, strengthening of communication systems for anticipating and responding to climate risks, and increased
flexibility in livelihood options, which serve to strengthen coping strategies in agriculture for near-term risks from
climate variability, provide potential pathways for strengthening adaptive capacities for climate change. [22.4.5.4,
22.4.5.7, 22.4.6, 22.6.2]
Climate change may increase the burden of a range of climate-relevant health outcomes (medium confidence).
Climate change is a multiplier of existing health vulnerabilities (high confidence) including insufficient access
to safe water and improved sanitation, food insecurity, and limited access to health care and education.
[22.3.5.1] Detection and attribution of trends is difficult because of the complexity of disease transmission, with
many drivers other than weather and climate, and short and often incomplete datasets. Evidence is growing that
highland areas, especially in East Africa, could experience increased malaria epidemics due to climate change
(medium evidence, very high agreement). The strong seasonality of meningococcal meningitis and associations with
weather and climate variability suggest the disease burden could be negatively affected by climate change (medium
evidence and high agreement). The frequency of leishmaniasis epidemics in sub-Saharan Africa is changing, with
spatial spread to peri-urban areas and to adjacent geographic regions, with possible contributions from changing
rainfall patterns (low confidence). Climate change is projected to increase the burden of malnutrition (medium
confidence), with the highest toll expected in children. [22.3.5.3]
In all regions of the continent, national governments are initiating governance systems for adaptation and
responding to climate change, but evolving institutional frameworks cannot yet effectively co-ordinate the
range of adaptation initiatives being implemented (high confidence). Progress on national and sub-national
policies and strategies has initiated the mainstreaming of adaptation into sectoral planning. [22.4.4] However,
incomplete, under-resourced and fragmented institutional frameworks and overall low levels of adaptive capacity,
especially competency at local government level, to manage complex socio-ecological change translate into a
largely ad hoc and project-level approach, which is often donor-driven. [22.4.2, 22.4.4.3, 22.4.4.4] Overall adaptive
capacity is considered to be low. [22.4.2] Disaster risk reduction, social protection, technological and infrastructural
adaptation, ecosystem-based approaches and livelihood diversification are reducing vulnerability, but largely in
isolated initiatives. [22.4.5] and most adaptation remain autonomous and reactive to short-term motivations. [22.4.3,
22.4.4.5]
Conservation agriculture provides a viable means for strengthening resilience in agroecosystems and
livelihoods that also advance adaptation goals (high confidence). A wide array of conservation agriculture
practices, including agroforestry and farmer-managed natural tree regeneration, conservation tillage, contouring and
terracing, and mulching are being increasingly adopted in Africa. These practices strengthen resilience of the land
base to extreme events and broaden sources of livelihoods, both of which have strongly positive implications for
climate risk management and adaptation. Moreover, conservation agriculture has direct adaptation-mitigation co-
benefits. Addressing constraints to broader adoption of these practices, such as land tenure/usufruct stability, access
to peer-to-peer learning, gender-oriented extension and credit and markets, as well as identification of perverse
policy incentives would help to enable larger scale transformation of agricultural landscapes. [22.4.5.6, 22.4.5.7,
22.4.6, 22.6.2]
Despite implementation limitations, Africa s adaptation experiences nonetheless highlight valuable lessons for
enhancing and scaling up the adaptation response, including principles for good practice and integrated
approaches to adaptation (high confidence). Five common principles for adaptation and building adaptive
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capacity can be distilled: (i) supporting autonomous adaptation through policy that recognises the multiple stressor
nature of vulnerable livelihoods; (ii) increasing attention to the cultural, ethical, and rights considerations of
adaptation by increasing the participation of women, youth and poor and vulnerable people in adaptation policy and
implementation; [22.4.5] (iii) combining soft path options and flexible and iterative learning approaches with
technological and infrastructural approaches and blending scientific, local and indigenous knowledge when
developing adaptation strategies; (iv) focusing on building resilience and implementing low-regrets adaptation with
development synergies, in the face of future climate and socio-economic uncertainties; and (v) building adaptive
management and social and institutional learning into adaptation processes at all levels. [22.4] Ecosystem-based
approaches and pro-poor integrated adaptation-mitigation initiatives hold promise for a more sustainable and
system-orientedapproach to adaptation, as does promoting equity goals, key for future resilience, through
emphasising gender aspects and highly vulnerable groups such as children. . [22.4.2, 22.4.5.6, 22.6.2, Table 22-5]
Strengthened inter-linkages between adaptation and development pathways and a focus on building resilience
would help to counter the current adaptation deficit and reduce future maladaptation risks (high confidence).
[22.4.3] Development strategies are currently not able to counter current climate risks, as highlighted by the impacts
of recent extreme events; national policies that disregard cultural, traditional and context-specific factors can act as
barriers to local adaptation; and there is increased knowledge of maladaptation risks from narrowly conceived
development interventions and sectoral adaptation strategies that decrease resilience in other sectors or ecosystems.
[22.4.4, 22.4.6] Given multiple uncertainties in the African context, successful adaptation will depend upon building
resilience. [22.4, 22.5, 22.6] Options for pro-poor adaptation/resilient livelihoods include improved social
protection, social services and safety nets; better water and land governance and tenure security over land and vital
assets; enhanced water storage, water harvesting and post-harvest services; strengthened civil society and greater
involvement in planning; and more attention to urban and peri-urban areas heavily affected by migration of poor
people. [22.4.2, 22.4.4, 22.4.5, 22.4.6]
Growing understanding of the multiple interlinked constraints on increasing adaptive capacity is beginning
to indicate potential limits to adaptation in Africa (medium confidence). Climate change combined with other
external changes (environmental, social, political, technological) may overwhelm the ability of people to cope and
adapt, especially if the root causes of poverty and vulnerability are not addressed. Evidence is growing for the
effectiveness of flexible and diverse development systems that are designed to reduce vulnerability, spread risk, and
build adaptive capacity. These points indicate the benefits of new development trajectories that place climate
resilience, ecosystem stability, equity and justice at the centre of development efforts. [22.4.6]
There is increased evidence of the significant financial resources, technological support and investment in
institutional and capacity development needed to address climate risk, build adaptive capacity and implement
robust adaptation strategies (high confidence). Funding and technology transfer and support isto both address
Africa s current adaptation deficit and to protect rural and urban livelihoods, societies and economies from climate
change impacts at different local scales. [22.4,] [22.6.4] Strengthening institutional capacities and governance
mechanisms to enhance the ability of national governments and scientific institutions in Africa to absorb and
effectively manage large amounts of funds allocated for adaptation, will assure the effectiveness of adaptation
initiatives (medium confidence). [22.6.4]
Climate change and climate variability have the potential to exacerbate or multiply existing threats to human
security including food, health and economic insecurity, all being of particular concern for Africa (medium
confidence). [22.6.1, 22.6.1.1] Many of these threats are known drivers of conflict (high confidence). Causality
between climate change and violent conflict is difficult to establish due to the presence of these and other
interconnected causes, including country-specific sociopolitical, economic and cultural factors. For example, the
degradation of natural resources as a result of both overexploitation and climate change will contribute to increased
conflicts over the distribution of these resources. [22.6.1.1] Many of the interacting social, demographic and
economic drivers of observed urbanization and migration in Africa are sensitive to climate change impacts.
[22.6.1.2]
A wide range of data and research gaps constrain decisionmaking in processes to reduce vulnerability, build
resilience and plan and implement adaptation strategies at different levels in Africa (high confidence).
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Overarching data and research gaps identified include data management and monitoring of climate parameters and
development of climate change scenarios; monitoring systems to address climate change impacts in the different
sectors; research and improved methodologies to assess and quantify the impact of climate change on different
sectors and systems; and socio-economic consequences of the loss of ecosystems, of economic activities, of certain
mitigation choices, such as biofuels, and of adaptation strategies. [22.7]
Of nine climate-related key regional risks identified for Africa, eight pose medium or higher risk even with
highly adapted systems, while only one key risk assessed can be potentially reduced with high adaptation to
below a medium risk level, for the end of the 21st century under 2°C global mean temperature increase above
pre-industrial levels (medium confidence). Key regional risks relating to shifts in biome distribution, loss of coral
reefs, reduced crop productivity, adverse effects on livestock, vector- and water-borne diseases, undernutrition, and
migration are assessed as either medium or high for the present under current adaptation, reflecting Africa s existing
adaptation deficit. [22.3.1, 22.3.2, 22.3.4, 22.3.5, 22.6.1.2] The assessment of significant residual impacts in a 2°C
world at the end of the 21st century suggests that even under high levels of adaptation, there could be very high
levels of risk for Africa. At a global mean temperature increase of 4°C, risks for Africa s food security (see key risks
on livestock and crop production) are assessed as very high, with limited potential for risk reduction through
adaptation. [22.3.4, 22.4.5, 22.5, Table 22-6]
22.1. Introduction
Africa as a whole is one of the most vulnerable continents due to its high exposure and low adaptive capacity
Climate, ecology and political boundaries in Africa vary across the continent. Since the African Union, together with
its Regional Economic Communities (RECs), are encharged of the adaptation policies we have used these divisions
for regional assessment within the chapter.
22.1.1. Structure of the Regions
The African continent (including Madagascar) is the world s second largest and most populous continent
(1,031,084,000 in 2010) behind Asia (UN DESA, 2013). The continent is organized at the regional level under the
African Union (AU).1 The AU s Assembly of Heads of State and Government has officially recognized eight
Regional Economic Communities (RECs) (Ruppel, 2009). Except for the Sahrawi Arab Democratic Republic,2 all
AU member states are affiliated with one or more of these RECs. These RECs include the Arab Maghreb Union
(AMU), with 5 countries in Northern Africa; the Community of Sahel-Saharan States (CEN-SAD), grouping 27
countries; the Common Market for Eastern and Southern Africa (COMESA), grouping 19 countries in Eastern and
Southern Africa; the East African Community (EAC), with 5 countries; the Economic Community of Central
African States (ECCAS), with 10 countries; the Economic Community of West African States (ECOWAS), with 15
countries; the Intergovernmental Authority on Development (IGAD) with 8 countries; and the Southern African
Development Community (SADC), with 15 countries. The regional subdivision of African countries into REC s is a
structure used by the AU and the New Partnership for Africa (NEPAD).
[FOOTNOTE 1: Due to the controversies regarding the Sahrawi Arab Democratic Republic, Morocco withdrew
from the Organization of African Unity (OAU) in protest in 1984 and, since South Africa's admittance in 1994,
remains the only African nation not within what is now the AU.]
[FOOTNOTE 2: Although the Sahrawi Arab Democratic Republic has been a full member of the OAU since 1984
and remains a member of the AU, the republic is not generally recognized as a sovereign state and has no
representation in the UN.]
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22.1.2. Major Conclusions from Previous Assessments
22.1.2.1. Regional Special Report and Assessment Reports
Refer to Table 22-1 for a brief summary of conclusions from previous IPCC assessments.
[INSERT TABLE 22-1 HERE
Table 22-1: Major conclusions from previous IPCC assessments.]
22.1.2.2. Special Report on Managing the Risks of Extreme Events and Disasters
to Advance Climate Change Adaptation (SREX)
The Special Report of the IPCC on managing the risks of extreme events and disasters to advance climate change
adaptation (IPCC, 2012) is of particular relevance to the African continent. There is low to medium confidence in
historical extreme temperature and heavy rainfall trends over most of Africa because of partial lack of data,
literature and lack of consistency of reported patterns in the literature (Seneviratne et al., 2012). However, most
regions within Africa for which data is available have recorded an increase in extreme temperatures (Seneviratne et
al., 2012). For projected temperature extreme there is high confidence that heat waves and warm spell durations will
increase, suggesting an increased persistence of hot days (90th percentile) toward the end of the century (Tebaldi et
al., 2006; Orlowsky and Seneviratne, 2012). There is high confidence for projected shorter extreme maximum
temperature return periods across the B1, A1B and A2 scenarios for the near and far future as well as a reduction of
the number of cold extremes (Seneviratne et al., 2012). In East and southern Africa, there is medium confidence that
droughts will intensify in the 21st Century in some seasons, due to reduced precipitation and/or increased
evapotranspiration. There is low confidence in projected increases of heavy precipitation over most of Africa except
over East Africa where there is a high confidence in a projected increase in heavy precipitation (Seneviratne et al.,
2012).
22.2. Observed Climate Trends and Future Projections
22.2.1. Temperature
22.2.1.1. Observed Trends
Near surface temperatures have increased by 0.5°C or more during the last 50-100 years over most parts of Africa
with minimum temperatures warming more rapidly than maximum temperatures (Hulme et al., 2001; Jones and
Moberg, 2003; Kruger and Shongwe, 2004; Schreck and Semazzi, 2004; New et al., 2006; IPCC, 2007; Rosenzweig
et al., 2007; Trentberth et al., 2007; Christy et al., 2009; Collins 2011; Grab and Craparo, 2011; Hoffman et al.,
2011; Mohamed, 2011; Stern et al. 2011; Funk et al., 2012; Nicholson et al., 2013). Near surface air temperature
anomalies in Africa were significantly higher for the period 1995 2010 compared to the period 1979 1994 (Collins,
2011). Figure 22-1 shows that it is very likely that mean annual temperature has increased over the past century over
most of the African continent, with the exception of areas of the interior of the continent where the data coverage
has been determined to be insufficient to draw conclusions about temperature trends (Figure 22-1, Box CC-RC).
There is strong evidence of an anthropogenic signal in continent-wide temperature increases in the 20th century
(WGI 10.3.1; Stott, 2003; Min and Hense, 2007, Stott et al., 2010; Stott et al., 2011).
In recent decades North African annual and seasonal observed trends in mean near surface temperature indicates an
overall warming that is significantly beyond the range of changes due to natural (internal) variability (Barkhordarian
et al., 2012a). During the warm seasons (March-April-May, June-July-August) an increase in near surface
temperature is shown over north Algeria and Morocco which is very unlikely due to natural variability or natural
forcing alone (Barkhordarian et al., 2012b). The region has also experienced positive trends in annual minimum and
maximum temperature (Vizy and Cook, 2012).
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Over West Africa and the Sahel near surface temperatures have increased over the last 50 years. Using indices
developed by the Expert Team on Climate Change Detection and Indices (ETCCDI), New et al. (2006) show the
number of cold days and cold nights have decreased and the number of warm days and warm nights have increased
between 1961 and 2000. Many of these trends are statistically significant at the 90% level and they find similar
trends in extreme temperature indices. Collins (2011) shows statistically significant warming of between 0.5-0.8
degrees between 1970 and 2010 over the region using remotely sensed data with a greater magnitude of change in
the latter 20 years of the period compared to the former.
The equatorial and southern parts of eastern Africa have experienced a significant increase in temperature since the
beginning of the early 1980s (Anyah and Qiu, 2012). Similarly, recent reports from the Famine Early Warning
Systems Network (FEWS NET) indicate that there has been an increase in seasonal mean temperature in many areas
of Ethiopia, Kenya, South Sudan, and Uganda over the last 50 years (Funk et al., 2011, 2012). In addition, warming
of the near surface temperature and an increase in the frequency of extreme warm events has been observed for
countries bordering the western Indian Ocean between 1961 and 2008 (Vincent et al., 2011b).
In recent decades, most of southern Africa has also experienced upward trends in annual mean, maximum, and
minimum temperature over large extents of the subregion during the last half of the 20th century, with the most
significant warming occurring during the last two decades (Zhou et al., 2010; Collins, 2011; Kruger and Sekele,
2012). Minimum temperatures have increased more rapidly relative to maximum temperatures over inland southern
Africa (New et al., 2006).
22.2.1.2. Projected Trends
Temperatures in Africa are projected to rise faster than the global average increase during the 21st Century
(Christensen et al., 2007; Joshi et al. 2011; Sanderson et al., 2011; James and Washington, 2013). Global average
near-surface air temperature are projected to move beyond 20th Century simulated variability by 2069 (+/-18 years)
under RCP4.5 and by 2047 (+/-14 years) under RCP8.5 (Mora et al. 2013). However, in the tropics, especially
tropical West Africa, these unprecedented climates are projected to occur one to two decades earlier the global
average because the relatively small natural climate variability in this region generates narrow climate bounds that
can be easily surpassed by relatively small climate changes. Figure 22-1 shows projected temperature increases
based on the CMIP5 ensemble. Increases in mean annual temperature over all land areas are very likely in the mid-
and late-21st-century periods for RCP2.6 and RCP8.5 (Figure 22-1, Box CC-RC). Ensemble-mean changes in mean
annual temperature exceed 2C above the late-20th-century baseline over most land areas of the continent in the mid-
21st-century for RCP8.5, and exceed 4C over most land areas in the late-21st-century for RCP8.5. Changes in mean
annual temperature for RCP8.5 follow a pattern of larger changes in magnitude over northern and southern Africa,
with (relatively) smaller changes in magnitude over central Africa. The ensemble-mean changes are less than 2C
above the late-20th-century baseline in both the mid- and late-21st-century for RCP2.6.
Over North Africa under the A1B scenario, both annual minimum and maximum temperature are likely to increase
in the future, with greater increase in minimum temperature (Vizy and Cook, 2012). The faster increase in minimum
temperature is consistent with greater warming at night, resulting in a decrease in the future extreme temperature
range (Vizy and Cook, 2012). Higher temperature increases are projected during boreal summer by CMIP5 GCMs
(WGI Annex 1). A strengthening of the North African thermal low in 21st century is associated with a surface
temperature increase (Paeth et al., 2009; Patricola and Cook, 2010; Barkhordarian et al., 2012a; Cook and Vizy,
2012).
Temperature projections over West Africa for the end of the 21st Century from both the CMIP3 GCMs (A2 and A1B
scenarios) and CMIP5 GCMs (RCP4.5 and RCP8.5) range between 3-6°C above the late 20th Century baseline
(Meehl et al. 2007; Fontaine et al. 2011; Diallo et al., 2012; Monrie et al. 2012; Figure 22-1; Figure 22-2). Regional
downscalings over the region produce a similar range of projected change (Patricola and Cook, 2010; Mariotti et al.,
2011; Patricola and Cook, 2011; Vizy et al., 2012). Diffenbaugh and Giorgi (2012) identify the Sahel and tropical
West Africa as a hotspot of climate change for both RCP4.5 and RCP8.5 pathways and unprecedented climates are
projected to occur earliest (late 2030s to early 2040s) in these regions (Mora et al., 2013).
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Climate model projections under the A2 and B1 scenarios over Ethiopia show warming in all four seasons across the
country, which may cause a higher frequency of heat waves as well as higher rates of evaporation (Conway and
Schipper, 2011). Projected maximum and minimum temperatures over equatorial eastern Africa show a significant
increase in the number of days warmer than 2°C above the 1981 2000 average by the middle and end of the 21st
century under the A1B and A2 scenarios (Anyah and Qiu, 2012). Elshamy et al. (2009) show a temperature increase
over the upper Blue Nile of between 2°C and 5°C at the end of the 21st Century under the A1B scenario compared to
a 1961-1990 baseline.
Mean land surface warming in Southern Africa is likely to exceed the global mean land surface temperature increase
in all seasons (Sillmann and Roeckner, 2008; Watterson, 2009; Mariotti et al., 2011; James and Washington, 2013;
Orlowsky and Seneviratne, 2012). Furthermore, towards the end of the 21st Century the projected warming of
between 3.4-4.2C above the 1981-2000 average under the A2 scenario far exceeds natural climate variability
(Moise and Hudson, 2008). High warming rates are projected over the semi-arid southwestern parts of the subregion
covering northwestern South Africa, Botswana, and Namibia (WGI Annex 1; Moise and Hudson, 2008; Engelbrecht
et al., 2009; Watterson, 2009; Shongwe et al., 2009). Observed and simulated variations in past and projected future
annual average temperature over five African regions (UMA, SADC, ECCAS, ECOWAS, and COMESA) are
captured in Figure 22-2 which indicates the projected temperature rise is very likely to exceed the 1986-2005
baseline by between 3 and 6 °C across these regions by the end of this century under RCP8.5.
[INSERT FIGURE 22-1 HERE
Figure 22-1: Observed and simulated variations in past and projected future annual average precipitation and
temperature. Observed differences in the Climate Research Unit, University of East Anglia data (CRU) are shown
between the 1986-2005 and 1906-1925 periods, with white indicating areas where the difference between the 1986-
2005 and 1906-1925 periods is less than twice the standard deviation of the 20 20-year periods beginning in the
years 1906 through 1925. For CMIP5, white indicates areas where <66% of models exhibit a change greater than
twice the baseline standard deviation of the respective model s 20 20-year periods ending in years 1986 through
2005. Grey indicates areas where >66% of models exhibit a change greater than twice the respective model baseline
standard deviation, but <66% of models agree on the sign of change. Colors with circles indicate the ensemble-mean
change in areas where >66% of models exhibit a change greater than twice the respective model baseline standard
deviation and >66% of models agree on the sign of change. Colors without circles indicate areas where >90% of
models exhibit a change greater than twice the respective model baseline standard deviation and >90% of models
agree on the sign of change. The realizations from each model are first averaged to create baseline-period and
future-period mean and standard deviation for each model, from which the multi-model mean and the individual
model signal-to-noise ratios are calculated. The baseline period is 1986-2005. The late-21st Century period is 2081-
2100. The mid-21st century period is 2046-2065.]
[INSERT FIGURE 22-2 HERE
Figure 22-2: Observed and simulated variations in past and projected future annual average temperature over EAC-
IGAD-Egypt, ECCAS, ECOWAS, SADC and UMA. Black lines show various estimates from observational
measurements. Shading denotes the 5-95 percentile range of climate model simulations driven with "historical"
changes in anthropogenic and natural drivers (63 simulations), historical changes in "natural" drivers only (34), the
"RCP2.6" emissions scenario (63), and the "RCP8.5" (63). Data are anomalies from the 1986-2005 average of the
individual observational data (for the observational time series) or of the corresponding historical all-forcing
simulations. Further details are given in Box 21-3.]
22.2.2. Precipitation
22.2.2.1. Observed Changes
Most areas of the African continent lack sufficient observational data to draw conclusions about trends in annual
precipitation over the past century (Figure 22-1, Box CC-RC). Additionally, in many regions of the continent
discrepancies exist between different observed precipitation datasets (Nikulin et al. 2012; Sylla et al., 2012; Kim et
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al. 2013; Kalognomou et al., 2013). Areas where there are sufficient data include very likely decreases in annual
precipitation over the past century over parts of the western and eastern Sahel region in northern Africa, along with
very likely increases over parts of eastern and southern Africa.
Over the last few decades the northern regions of North Africa (north of the Atlas Mountains and along the
Mediterranean coast of Algeria and Tunisia) have experienced a strong decrease in the amount of precipitation
received in winter and early spring (Barkhordarian et al., 2013). The observed record also indicates greater than 330
dry days (with less than 1 mm/day rainfall) per year over the 1997 - 2008 time period (Vizy and Cook, 2012).
However, in autumn (September, October, November) observations show a positive trend in precipitation in some
parts of North Algeria and Morocco (Barkhordarian et al., 2013). The Sahara Desert, which receives less than 25
mm/year, shows little seasonal change (Liebmann et al., 2012).
Rainfall over the Sahel has experienced an overall reduction over the course of the 20th Century with a recovery
toward the last 20 years of the century (WGI 14.3.7.1; Nicholson et al., 2000; Lebel and Ali, 2009; Ackerley et al.,
2011; Mohamed, 2011; Biasutti, 2013). The occurrence of a large number of droughts in the Sahel during the 1970s
and 1980s is well documented and understood (Biasutti and Giannini, 2006; Biasutti et al., 2008; Greene et al.,
2009). The recovery of the rains may be due to natural variability (Mohino et al., 2011) or a forced response to
increased greenhouse gases (Haarsma et al., 2005; Biasutti, 2013) or reduced aerosols (Ackerley et al., 2011).
Precipitation in eastern Africa shows a high degree of temporal and spatial variability dominated by a variety of
physical processes (Rosell and Holmer, 2007; Hession and Moore, 2011). Williams and Funk (2011) and Funk et al.
(2008) indicate that over the last three decades rainfall has decreased over eastern Africa between March and
May/June. The suggested physical link to the decrease in rainfall is the rapid warming of Indian Ocean, which
causes an increase in convection and precipitation over the tropical Indian Ocean and thus contributes to increased
subsidence over eastern Africa and a decrease in rainfall during March to May/June (Funk et al., 2008; Williams and
Funk, 2011). Similarly, Lyon and DeWitt (2012) show a decline in the March May seasonal rainfall over eastern
Africa. Summer (June September) monsoonal precipitation has declined throughout much of the Great Horn of
Africa over the last 60 years [during the 1948 2009 period; Williams et al., (2012)] as a result of the changing sea
level pressure (SLP) gradient between Sudan and the southern coast of the Mediterranean Sea and the southern
tropical Indian Ocean region (Williams et al., 2012).
Over Southern Africa a reduction in late austral summer precipitation has been reported over its western parts,
extending from Namibia, through Angola, and toward the Congo during the second half of the 20th Century
(Hoerling et al., 2006; New et al., 2006). The drying is associated with an upward trend in tropical Indian Ocean Sea
Surface Temperatures (SSTs). Modest downward trends in rainfall are found in Botswana, Zimbabwe, and western
South Africa. Apart from changes in total or mean summer rainfall, certain intra-seasonal characteristics of seasonal
rainfall such as onset, duration, dry spell frequencies, and rainfall intensity as well as delay of rainfall onset have
changed (Tadross et al., 2005; Thomas et al., 2007; Tadross et al., 2009; Kniveton et al., 2009). An increasing
frequency of dry spells is accompanied by an increasing trend in daily rainfall intensity which has implications for
run-off characteristics (New et al., 2006).
22.2.2.2. Projected Changes
Precipitation projections are more uncertain than temperature projections (Rowell, 2012) and exhibit higher spatial
and seasonal dependence than temperature projections (Orlowsky and Seneviratne, 2012). The CMIP5 ensemble
projects very likely decreases in mean annual precipitation over the Mediterranean region of northern Africa in the
mid- and late-21st century periods for RCP8.5 (Figure 22-1, Box CC-RC). CMIP5 also projects very likely decreases
in mean annual precipitation over areas of southern Africa beginning in the mid-21st-century for RCP8.5 and
expanding substantially in the late-21st-century for RCP8.5. In contrast, CMIP5 projects likely increases in mean
annual precipitation over areas of central and eastern Africa beginning the mid-21st-century for RCP8.5. Most areas
of the African continent do not exhibit changes in mean annual precipitation that exceed the baseline variability in
more than 66% of the models in either the mid- or late-21st-century periods for RCP2.6. Observed and simulated
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variations in past and projected future annual average precipitation over five African regions (UMA, ECCAS,
ECOWAS, SADC and COMESA) are captured in Figure 22-2.
A reduction in rainfall over northern Africa is very likely by the end of the 21st Century. The annual and seasonal
drying/warming signal over the Northern African region (including North of Morocco, Algeria, Libya, Egypt and
Tunisia) is a consistent feature in the global (Giorgi and Lionello, 2008; Barkhordarian et al., 2013) and the regional
(Lionello and Giorgi, 2007; Gao and Giorgi, 2008; Paeth et al., 2009; Patricola and Cook, 2010) climate change
projections for the 21st Century under the A1B and A2 scenarios. Furthermore, over the northern basin of Tunisia,
climate models under the A1B scenario project a significant decrease in the median, and 10th and 90th percentiles
values of precipitation in winter and spring seasons (Bargaoui et al., 2013).
West African precipitation projections in the CMIP-3 and CMIP-5 archives show inter-model variation in both the
amplitude and direction of change that is partially attributed to the inability of GCMs to resolve convective rainfall
(WGI 14.8.7; Biasutti et al., 2008; Druyan, 2011; Fontaine et al., 2011; Roehrig et al., 2013). Many CMIP-5 models
indicate a wetter core rainfall season with a small delay to rainy season by the end of the 21st Century ( WGI 14.8.7;
Biasutti, 2013). However, regional climate models (RCMs) can alter the sign of rainfall change of the driving GCM
especially in regions of high or complex topography (WGI 14.3.7.1; WGI 9.6.4; Sylla et al., 2012; Cook and Vizy,
2013; Saeed et al. 2013). There is therefore low to medium confidence in the robustness of projected regional
precipitation change until a larger body of regional results become available through, for example, the coordinated
regional downscaling experiment, CORDEX (Giorgi et al., 2009, Jones et al., 2011, Hewitson et al., 2012).
An assessment of 12 CMIP-3 GCMs over eastern Africa suggest that by the end of the 21st Century there will be a
wetter climate with more intense wet seasons and less severe droughts during October-November-December (OND)
and March-April-May (MAM) (WGI 14.8.7; Moise and Hudson, 2008; Shongwe et al., 2011). These results indicate
a reversal of historical trend in these months (Williams and Funk, 2011; Funk et al. 2008). Lyon and DeWitt (2012)
ascribe this reversal to recent cooling in the eastern equatorial Pacific that offsets the equatorial Pacific SST
warming projected by CMIP3 GCMs in future scenarios. However, GCM projections over Ethiopia indicate a wide
range of rainfall spatial pattern changes (Conway and Schipper, 2011) and in some regions GCMs do not agree on
the direction of precipitation change, e.g. in the upper Blue Nile basin in the late 21st Century (Elshamy et al., 2009).
Regional climate model studies suggest drying over most parts of Uganda, Kenya, and South Sudan in August and
September by the end of the 21st Century as a result of a weakening Somali jet and Indian monsoon (Patricola and
Cook, 2011). Cook and Vizy (2013) indicate truncated boreal spring rains in the mid-21st Century over eastern
Ethiopia, Somalia, Tanzania and southern Kenya while the boreal fall season is lengthened in the southern Kenya
and Tanzania (Nakaegawa et al., 2012). These regional studies highlight the importance of resolving both regional
scale atmospheric processes and local effects like land surface on rainfall simulation across the region (WGI 14.8.7).
Over southern Africa CMIP3 GCM projections show a drying signal in the annual mean over the climatologically
dry southwest, extending northeastward from the desert areas in Namibia and Botswana (Moise and Hudson, 2008;
James and Washington, 2013; Orlowsky and Seneviratne, 2012). This pattern is replicated by CMIP5 GCMs (see
Figure 22-1). During the austral summer months, dry conditions are projected in the southwest while downscaled
projections indicate wetter conditions in the southeast of South Africa and the Drakensberg mountain range
(Hewitson and Crane, 2006; Engelbrecht et al., 2009). Consistent with the AR4, drier winters are projected over a
large area in southern Africa by the end of the century as a result of the poleward displacement of mid-latitude storm
tracks (WGI 14.8.7; Moise and Hudson, 2008; Engelbrecht et al., 2009; Shongwe et al., 2009; Seth et al., 2011;
James and Washington, 2013). Rainfall decreases are also projected during austral spring months, implying a delay
in the onset of seasonal rains over a large part of the summer rainfall region of Southern Africa (Shongwe et al.,
2009; Seth et al., 2011). The sign, magnitude and spatial extent of projected precipitation changes are dependent on
the coupled general circulation model (CGCM) employed, due primarily to parameterization schemes used and their
interaction with model dynamics (Hewitson and Crane, 2006; Rocha et al., 2008). Changes in the parameterization
schemes of a single regional climate model produced opposite rainfall biases over the region (Crétat et al., 2012) so
multiple ensemble downscalings, such as those being produced through CORDEX, are important to more fully
describe the uncertainty associated with projected rainfall changes across the African continent (WGI 9.6.5; Laprise
et al., 2013).
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22.2.3. Observed and Projected Changes in Extreme Temperature and Rainfall
In Northern Africa the north western Sahara experienced 40-50 heat wave days per year during the 1989-2009 time
period (Vizy and Cook, 2012). There is a projected increase in this number of heat wave days over the 21st century
(Patricola and Cook, 2010; Vizy and Cook, 2012).
Over West Africa there is low to medium confidence in projected changes of heavy precipitation by the end of the
21st Century based on CMIP-3 GCMs (Seneviratne et al., 2012). Regional model studies suggest an increase in the
number of extreme rainfall days over West Africa and the Sahel during May and July (Vizy and Cook, 2012) and
more intense and more frequent occurrences of extreme rainfall over the Guinea Highlands and Cameroun
Mountains (Sylla et al., 2012; Haensler et al., 2013. The ability of regional climate models to resolve complex
topography captures the amplifying role of topography in producing extreme rainfall that GCMs cannot.
Extreme precipitation changes over Eastern Africa such as droughts and heavy rainfall have been experienced more
frequently during the last 30-60 years (Funk et al., 2008; Williams and Funk, 2011; Shongwe et al., 2011; Lyon and
DeWitt, 2012). A continued warming in the Indian Pacific warm pool has been shown to contribute to more
frequent East African droughts over the past 30 years during the spring and summer seasons (Williams and Funk,
2011). It is unclear whether these changes are due to anthropogenic influences or multidecadal natural variability
(Lyon and DeWitt, 2012; Lyon et al., 2013). Projected increases in heavy precipitation over the region have been
reported with high certainty in the SREX (Seneviratne et al., 2012) and Vizy and Cook (2012) indicate an increase
in the number of extreme wet days by the mid-20th Century.
Over southern Africa an increase in extreme warm ETCCDI indices (hot days, hot nights, hottest days) and a
decrease in extreme cold indices (cold days and cold nights) in recent decades is consistent with the general
warming trend (New et al., 2006; Tebaldi et al., 2006; Aguilar et al. 2009; Kruger and Sekele, 2012). The
probability of austral summer heat waves over South Africa increased over the last two decades of the 20th century
compared to 1961 to 1980 (Lyon, 2009). Enhanced heat wave probabilities are associated with deficient rainfall
conditions that tend to occur during El Nino events. The southwestern regions are projected to be at a high risk to
severe droughts during the 21st Century and beyond (Hoerling et al., 2006; Shongwe et al., 2011). Large
uncertainties surround projected changes in tropical cyclone landfall from the southwest Indian Ocean that have
resulted in intense floods during the 20th Century. Future precipitation projections show changes in the scale of the
rainfall probability distribution, indicating that extremes of both signs may become more frequent in the future (Kay
and Washington, 2008).
22.3. Vulnerability and Impacts
This section highlights Africa s vulnerability to climate change, as well as the main observed and potential impacts
on natural resources, ecosystems, and economic sectors. Figure 22-3 summarizes the main conclusions regarding
observed changes in regional climate and their relation to anthropogenic climate change (described in 22.2) as well
as regarding observed changes in natural and human systems and their relation to observed regional climate change
(described in this section). Confidence in detection and attribution of anthropogenically-driven climate change is
highest for temperature measures. In many regions of Africa, evidence is constrained by limited monitoring.
However, impacts of observed precipitation changes are amongst the observed impacts with the highest assessment
of confidence, implying that some of the potentially more significant impacts of anthropogenic climate change for
Africa are of a nature that challenges detection and attribution analysis (18.5.1).
[INSERT FIGURE 22-3 HERE
Left: Confidence in detection and in attribution of observed climate change over Africa to anthropogenic emissions.
All detection assessments are against a reference of no change, while all attribution assessments concern a major
role of anthropogenic emissions in the observed changes. See 22.2, and SREX-3, and WGI AR5 10 for details.
Right: Confidence in detection and in attribution of the impacts of observed regional climate change on various
African systems. All detection assessments are against a reference of no change, except "Kenyan Highlands malaria"
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(changes due to vaccination, drug resistance, demography, and livelihoods), "Great Lakes fisheries" (changes due to
fisheries management and land use) and Adapting South African farmers (economic changes). Attribution is to a
major role or a minor role of observed climate change, as indicated. See 22.2.2, 22.3.2.1, 22.3.2.2, 2.3.3, 22.4.2,
22.3.4.4, 22.3.5.4, 22.4.5.7 and Tables 18-5, 18-6, 18-7, and 18-9 for details. Assessments follow the methods
outlined in 18.2.]
22.3.1. Socioeconomic and Environmental Context Influencing Vulnerability and Adaptive Capacity
Equitable socioeconomic development in Africa may strengthen its resilience to various external shocks, including
climate change. In 2009, the Human Rights Council adopted Resolution 10/43 which noted the effects of climate
change on the enjoyment of human rights, and reaffirmed the potential of human rights obligations and
commitments to inform and strengthen international and national policy making.
[FOOTNOTE 3: U.N. Doc. A/HRC/10/L.11.]
The impacts of climate change on human rights have been explicitly recognised by the African Commission on
Human and Peoples Rights (hereafter African Commission) in its Resolution on Climate Change and Human
Rights and the Need to Study its Impact in Africa (ACHPR/Res 153 XLV09). The 1981 African (Banjul) Charter on
Human and Peoples Rights (hereafter African Charter) protects the right of peoples to a general satisfactory
environment favorable to their development (Article 24). The recognition of this right and the progressive
jurisprudence by the African Commission in environmental matters underline the relevance of potential linkages
between climate change and human rights (Ruppel, 2012).
The link between climate change and humans is not only associated with human rights. Rather, strong links exist
between climate change and the MDGs: climate change may adversely affect progress toward attaining the MDGs,
as climate change can increase the pressure not only economic activities, such as agriculture (22.3.4) and fishing
(22.3.4.4) but also adversely affect urban areas located in coastal zones (22.3.6). Slow progress in attaining most
MDGs may, meanwhile, reduce the resilience and adaptive capabilities of African individuals, communities, states,
and nations (ECA et al., 2009, 2012; UNDP et al., 2011).
The African continent has made significant progress on some MDGs; however, not all MDGs have been achieved,
yet with high levels of spatial and group disparities. Additionally, progress on all MDG indicators is skewed in favor
of higher-income groups and urban populations, which means further marginalization of already excluded groups
(UN et al., 2008; AfDB et al., 2010; World Bank and IMF, 2010). As a whole, the continent is experiencing a
number of demographic and economic constraints, with the population having more than doubled since 1980;
exceeding one billion in 2010 and expected to reach three billion by the year 2050, should fertility remain constant
(Muchena et al., 2005; Fermont et al., 2008; UN DESA, 2011). The global economic crisis is adding additional
constraints on economic development efforts leading to increased loss of livelihood and widespread poverty (Moyo,
2009; Easterly, 2009; Adesina, 2010). The percent of the population below the poverty line has decreased from
56.5% in 1990 to 47.5% in 2008 (excluding North Africa); however a significant proportion of the population living
below the poverty line remains chronically poor (ECA et al., 2012). Although poverty in rural areas in Sub-Saharan
Africa has declined from 64.9% in 1998 to 61.6% in 2008, it is still double the prevailing average in developing
countries in other regions (IFAD, 2010).
Agriculture, which is the main economic activity in terms of employment share, is 98% rain fed in the sub-Saharan
region (FAO, 2002). 4 Stagnant agricultural yields, relative to the region s population growth, have led to a fall in per
capita food availability since the 1970s (UN et al., 2008).5 Such stagnation was reversed with an improved
performance of the agricultural sector in sub Saharan Africa during the 2000 2010. However, most of this
improvement was the result of countries recovering from the poor performance of the 1980s and 1990s along with
favorable domestic prices (Nin-Pratt et al., 2012).
In addition, recent increases in global food prices aggravate food insecurity among the urban poor, increasing the
risk of malnutrition and its consequences (UN et al., 2008). For example, it was estimated that the global rise in food
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prices has contributed to the deaths of an additional 30,000 to 50,000 children suffering from malnutrition in 2009 in
sub-Saharan Africa (Friedman and Schady, 2009) see Table 22.2. This situation may be complicated further by
changes in rainfall variability and extreme weather events affecting the agriculture sector (Yabi and Afouda, 2012).
In response, the New Partnership for Africa s Development (NEPAD) was founded in 2001, for Africans to take the
lead in efforts to achieve the development vision espoused in the AU Constitutive Act as well as the MDGs and to
support regional integration as a mechanism for inclusive growth and development in Africa (NEPAD et al., 2012).
Furthermore, the Comprehensive Africa Agriculture Development Program (CAADP), which works under the
umbrella of NEPAD, was established in 2003 to help African countries reach a higher path of economic growth
through agriculture-led development. For this to happen, it focuses on four pillars for action: land and water
management, market access, food supply and hunger, and agricultural research (NEPAD, 2010).
[INSERT TABLE 22-2 HERE
Table 22-2: Under-nourishment in Africa, by number and % of total population.]
[FOOTNOTE 4: However, mining and energy sectors, where active, are undergoing expansion, stimulating growth
and adding potentially to state revenues but are also highly vulnerable to global recession. overall, the limited
production and export structures of the continent are likely to maintain its historical vulnerability to external shocks
(ECA and AUC, 2011).]
[FOOTNOTE 5: Lack of extension services for farmers in Africa can also contribute to low utilization and spread of
innovations and technologies that can help mitigate climate change.]
Africa has made much progress in the achievement of universal primary education; however, the results are
unevenly distributed. Nevertheless, a considerable number of children, especially girls from poor backgrounds and
rural communities, still do not have access to primary education (UN et al., 2008).
From the livelihood perspective, African women are vulnerable to the impacts of climate change because they
shoulder an enormous but imprecisely recorded portion of the responsibility for subsistence agriculture, the
productivity of which can be expected to be adversely affected by climate change and over-exploited soil (Viatte et
al., 2009; see also 22.4.2 and Table 22-5).6 Global financial crises, such as the one experienced in 2007/2008, as
well as downturn economic trends at national level, may cause job losses in the formal sector and men may compete
for jobs in the informal sector that were previously undertaken by women, making them more vulnerable (AfDB et
al., 2010).
[FOOTNOTE 6: For instance, 84% of women in sub-Saharan Africa, compared with 69.5% of men, are engaged in
such jobs. In Northern Africa, even though informal or self-employment is less predominant, the gender gap is stark,
with much higher proportion of women compared to men are in the more vulnerable informal and self-employed
status (56.7% of women compared with 34.9% of men) (UN DESA, 2011).]
Significant efforts have been made to improve access to safe drinking water and sanitation in Africa, with access to
safe drinking water increasing from 56 to 65% between 1990 and 2008 (UNDP et al., 2011), with sub-Saharan
Africa nearly doubling the number of people using an improved drinking water source from 252 to 492 million
over the same period (UN, 2011). Despite such progress, significant disparities in access to safe water and
sanitation, between not only urban and rural but also between large- and medium- and small-sized cities, still exist
(UNDP et al., 2011). Use of improved sanitation facilities, meanwhile, is generally low in Africa, reaching 41% in
2010 compared to 36% in 1990 (UNDP et al., 2011).
22.3.2. Ecosystems
It is recognized that interactions between the different drivers of ecosystem structure, composition, and function are
complex, which makes the prediction of the impacts of climate change more difficult (see Chapter 4). In AR4, the
chapter on Africa indicated that extensive pressure is exerted on different ecosystems by human activities
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(deforestation, forest degradation, biomass utilisation for energy) as well as processes inducing changes such as fires
or desertification (see WGII AR4 9.2.2.7). Even if the trend is toward better preservation of ecosystems and a
decrease in degradation (like deforestation), pressures linked, for example, to agriculture and food security, energy
demand, and urbanization are increasing, putting these ecosystems at risk. This chapter emphasizes new information
since AR4 regarding the vulnerability to and impacts of climate change for some terrestrial, fresh water and
coastal/ocean ecosystems.
22.3.2.1. Terrestrial Ecosystems
Changes are occurring in the distribution and dynamics of all types of terrestrial ecosystems in Africa, including
deserts, grasslands and shrublands, savannas and woodlands, and forests (high confidence) (see also 4.3.2.5). Since
AR4, three primary trends have been observed at the continental scale. The first is a small overall expansion of
desert and contraction of the total vegetated area (Brink and Eva, 2009) (low confidence). The second is a large
increase in the extent of human influence within the vegetated area, accompanied by a decrease in the extent of
natural vegetation (Brink and Eva, 2009; Potapov et al., 2012; Mayaux et al., 2013) (high confidence). The third is a
complex set of shifts in the spatial distribution of the remaining natural vegetation types, with net decreases in
woody vegetation in western Africa (Vincke et al., 2010; Ruelland et al., 2011; Gonzalez et al., 2012) and net
increases in woody vegetation in central, eastern, and southern Africa (Wigley et al., 2009; Wigley et al., 2010;
Buitenwerf et al., 2012; Mitchard and Flintrop, 2013) (high confidence).
Overall, the primary driver of these changes is anthropogenic land use change, particularly the expansion of
agriculture, livestock grazing, and fuelwood harvesting (Brink and Eva, 2009; Kutsch et al., 2011; Bond and
Midgley, 2012; Gonzalez et al., 2012) (high confidence). Natural climate variability, anthropogenic climate change,
and interactions between these drivers and anthropogenic land use change have important additional and interacting
effects (Foden et al., 2007; Touchan et al., 2008; Brink and Eva, 2009; Bond and Midgley, 2012; Gonzalez et al.,
2012) (high confidence). Due to these interactions, it has been difficult to determine the role of climate change in
isolation from the other drivers (Malhi et al., 2013). In general, while there are already many examples of changes in
terrestrial ecosystems that are consistent with a climate change signal and have been detected with high confidence,
attribution to climate change has tended to be characterized by low confidence [see Table 22-3]. New observations
and approaches are improving confidence in attribution (e.g., Buitenwerf et al., 2012, Gonzalez et al., 2012,
Pettorelli et al., 2012, Otto et al., 2013).
There is high agreement that continuing changes in precipitation, temperature, and CO2 associated with climate
change are very likely to drive important future changes in terrestrial ecosystems throughout Africa (high
confidence) [see examples in 4.3.3.1, 4.3.3.2]. Modeling studies focusing on vegetation responses to climate have
projected a variety of biome shifts, primarily related to the extent of woody vegetation (Delire et al., 2008; Gonzalez
et al., 2010; Bergengren et al., 2011; Zelazowski et al., 2011; Midgley, 2013). For an example of such projections,
see Figure 22-4. However, substantial uncertainties are inherent in these projections because vegetation across much
of the continent is not deterministically driven by climate alone (high confidence). Advances in understanding how
vegetation dynamics are affected by fire, grazing, and the interaction of fire and grazing with climate are expected to
enable more sophisticated representations of these processes in coupled models (Scheiter and Higgins, 2009; Staver
et al., 2011a; Staver et al., 2011b). Improvements in forecasting vegetation responses to climate change should
reduce the uncertainties that are currently associated with vegetation feedbacks to climate forcing, as well as the
uncertainties about impacts on water resources, agriculture, and health (Alo and Wang, 2008; Sitch et al., 2008) [see
4.5].
[INSERT FIGURE 22-4 HERE
Figure 22-4: Left Projected biome change from the periods 1961-1990 to 2071-2100 using the MC1 Dynamic
Vegetation Model. Change is indicated if any of nine combinations of three GCMs (CSIRO Mk3, HadCM3,
MIROC 3.2 medres) and three emissions scenarios (B1, A1B, A2) project change and is thus a worst-case scenario.
Colours represent the future biome predicted. Right Vulnerability of ecosystems to biome shifts based on historical
climate (1901-2002) and projected vegetation (2071-2100), where all nine GCM-emissions scenario combinations
agree on the projected biome change. Source: Gonzalez et al. (2010).]
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22.3.2.2. Freshwater Ecosystems
Freshwater ecosystems in Africa are at risk from anthropogenic land use change, over-extraction of water and
diversions from rivers and lakes, and increased pollution and sedimentation loading in water bodies (Vörösmarty et
al., 2005; Vié et al., 2009; Darwall et al., 2011). Climate change is also beginning to affect freshwater ecosystems
(see also Chapter 3.5.2.4, as evident by elevated water temperatures reported in surface waters of Lakes Kariba,
Kivu, Tanganyika, Victoria, and Malawi (Odada et al., 2006; Verburg and Hecky, 2009; Marshall et al., 2009;
Hecky et al., 2010; Magadza, 2010; Tierney et al., 2010; Olaka et al., 2010; Magadza, 2011; Ndebele-Murisa, 2011;
Woltering et al., 2011; Osborne, 2012; Ndebele-Murisa et al., 2012) (medium confidence).
Small variations in climate cause wide fluctuations in the thermal dynamics of freshwaters (Odada et al., 2006;
Stenuite et al., 2007; Verburg and Hecky, 2009; Moss, 2010; Olaka et al., 2010). Thermal stratification in the
regions lakes, for instance, isolates nutrients from the euphotic zone, and is strongly linked to hydrodynamic and
climatic conditions (Sarmento et al., 2006; Ndebele-Murisa et al., 2010). Moderate warming may be contributing to
reduced lake water inflows and therefore nutrients, which subsequently destabilizes plankton dynamics and thereby
adversely affects food resources for higher trophic levels of mainly planktivorous fish (low confidence) (Magadza,
2008; Verburg and Hecky, 2009; Magadza, 2010; Ndebele-Murisa et al., 2011). However, the interacting drivers of
fisheries decline in African lakes are uncertain, given the extent to which other factors, such as overfishing,
pollution, and invasive species also impact lake ecosystems and fisheries production (Phoon et al., 2004; Sarvala et
al., 2006; Verburg et al., 2007; Tumbare, 2008; Hecky et al., 2010; Marshall, 2012).
22.3.2.3. Coastal and Ocean Systems
Coastal and ocean systems are important for the economies and livelihoods of African countries, and climate change
will increase challenges from existing stressors, such as overexploitation of resources, habitat degradation, loss of
biodiversity, salinization, pollution, and coastal erosion (Arthurton et al., 2006; UNEP and IOC-UNESCO, 2009;
Diop et al., 2011). Coastal systems will experience impacts through sea level rise. They will also experience impacts
through high sea levels combined with storm swells, for example as observed in Durban in March 2007, when a
storm swell up to 14 m due to winds generated by a cyclone combined with a high astronomic tide at 2.2 m, leading
to damages estimated at US$ 100 million (Mather and Stretch, 2012). Other climate change impacts (such as
flooding of river deltas or an increased migration toward coastal towns due to increased drought induced by climate
change (Rain et al., 2011) will also affect coastal zones.
Some South African sea bird species have moved farther south over recent decades, but land use change may also
have contributed to this migration (Hockey and Midgley, 2009; Hockey et al., 2011). However, it is considered that
South African seabirds could be a valuable signal for climate change, particularly of the changes induced on prey
species related to changes in physical oceanography, if we are able to separate the influences of climate parameters
from other environmental ones (Crawford and Altwegg, 2009).
Upwellings, including Eastern Boundary Upwelling Ecosystems (EUBEs) and Equatorial Upwelling Systems
(EUSs) are the most biologically active systems in the Oceans (Box CC-UP). In addition to equatorial upwelling, the
primary upwelling systems that affect Africa are the Benguela and Canary currents along the Atlantic coast (both
EBUEs).The waters of the Benguela current have not shown warming over the period 1950-2009 (30.5.5.1.2),
whereas most observations suggest that the Canary current has warmed since the early 1980s, and there is medium
evidence and agreement that primary production in the Canary current has decreased over the past two decades
(30.5.5.1.1). Changing temperatures in the Canary current has resulted in changes to important fisheries species
(e.g., Mauritanian waters have become increasingly suitable for Sardinella aurita) (30.5.5.1.1). Upwellings are areas
of naturally low pH and high CO2 concentrations, and, consequently, may be vulnerable to ocean acidification and
its impacts (Box CC-OA, Box CC-UP, 30.5.5). Warming is projected to continue in the Canary current, and the
synergies between this increase in water temperature and ocean acidification could influence a number of biological
processes (30.5.5.2). Regarding the Benguela current upwelling, there is medium agreement despite limited evidence
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that the Benguela system will experience changes in upwelling intensity as a result of climate change (30.5.5.1.2).
There is considerable debate as to whether or not climate change will drive an intensification of upwelling (e.g.
Bakun et al., 2010; Narayan et al., 2010) in all regions. Discussion of the various hypotheses for how climate
change may affect coastal upwelling is presented in Box 30-1.
Ocean acidification (OA) is the term used to describe the process whereby increased CO2 in the atmosphere, upon
absorption, causes lowering of the pH of seawater (CC-OA). Projections indicate that severe impairment of reef
accretion by organisms such as corals (Hoegh-Guldberg et al., 2007) and coralline algae (Kuffner et al., 2008) are
substantial potential impacts of ocean acidification, and the combined effects of global warming and ocean
acidification have been further demonstrated to lower both coral reef productivity (Anthony et al., 2008) and
resilience (Anthony et al., 2011). These effects will have consequences for reef biodiversity, ecology, and ecosystem
services (6.3.1, 6.3.2, 6.3.5, 6.4.1, 30.3.2, Box CC-CR).
Coral vulnerability to heat anomalies is high in the Western Indian Ocean (30.5.6.1.2). Corals in the southwestern
Indian Ocean (Comoros, Madagascar, Mauritius, Mayotte, Réunion and Rodrigues) appeared to be more resilient
than those in eastern locations (30.5.6.1.2). Social adaptive capacity to cope with such change varies, and societal
responses (such as closures to fishing) can have a positive impact on reef recovery, as observed in Tanzania
(McClanahan et al., 2009). In Africa, fisheries mainly depend on either coral reefs (on the eastern coast) or coastal
upwelling (on the western coast). These two ecosystems will be affected by climate change through ocean
acidification, a rise in sea surface temperatures, and changes in upwelling (see Box CC-OA, Box CC-CR, Box CC-
UP).
[INSERT TABLE 22-3
Table 22-3: Examples of detected changes in species, natural ecosystems, and managed ecosystems in Africa that
are both consistent with a climate change signal and published since the AR4. Confidence in detection of change is
based on the length of study, and the type, amount and quality of data in relation to the natural variability in the
particular species or system. Confidence in the role of climate being a major driver of the change is based on the
extent to which the detected change is consistent with that expected under climate change, and to which
other confounding or interacting non-climate factors have been considered and been found insufficient to explain the
observed change.]
22.3.3. Water Resources
Knowledge has advanced since the AR4 regarding current drivers of water resource abundance in Africa, and in
understanding of potential future impacts on water resources from climate change and other drivers. However,
inadequate observational data in Africa remains a systemic limitation with respect to fully estimating future
freshwater availability (Neumann et al., 2007; Batisani, 2011). Detection of and attribution to climate change are
difficult given that surface and groundwater hydrology are governed by multiple, interacting drivers and factors,
such as land use change, water withdrawals, and natural climate variability (see also Chapter 3.2.1 and Box CC-
WE). There is poor understanding in Africa of how climate change will affect water quality. This is an important
knowledge gap.
A growing body of literature generated since the AR4 suggests that climate change in Africa will have an overall
modest effect on future water scarcity relative to other drivers, such as population growth, urbanization, agricultural
growth, and land use change (high confidence) (Alcamo et al., 2007; Carter and Parker, 2009; MacDonald et al.,
2009; Taylor et al., 2009; Calow and MacDonald, 2009; Abouabdillah et al., 2010; Beck and Bernauer, 2011;
Droogers et al., 2012; Notter et al., 2012; Tshimanga and Hughes, 2012). However, broad-scale assumptions about
drivers of future water shortages can mask significant sub-regional variability of climate impacts, particularly in
water-stressed regions that are projected to become drier, such as northern Africa and parts of southern Africa. For
example, rainfed agriculture in northern Africa is highly dependent on winter precipitation and would be negatively
impacted if total precipitation and the frequency of wet days declines across North Africa as has been indicated in
recent studies (Born et al., 2008; Driouech et al., 2010; Abouabdillah et al., 2010; García-Ruiz et al., 2011).
Similarly, climate model predictions based on average rainfall years do not adequately capture interannual and
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interdecadal variability that can positively or negatively influence surface water runoff (Beck and Bernauer, 2011;
Notter et al., 2012; Wolski et al., 2012). Key challenges for estimating future water abundance in Africa lie in better
understanding relationships between evapotranspiration, soil moisture, and land use change dynamics under varying
temperature and precipitation projections (Goulden et al., 2009a) and to understand how compound risks such as
heat waves and seasonal rainfall variability might interact in the future to impact water resources.
Several studies from Africa point to a future decrease in water abundance due to a range of drivers and stresses,
including climate change in Southern and northern Africa (medium confidence). For example, all countries within
the Zambezi River Basin could contend with increasing water shortages (A2 scenario) although non-climate drivers
(e.g., population and economic growth, expansion of irrigated agriculture, and water transfers) are expected to have
a strong influence on future water availability in this basin (Beck and Bernauer, 2011). In Zimbabwe, climate
change is estimated to increase water shortages for downstream users dependent on the Rozva dam (Ncube et al.,
2011). Water shortages are also estimated for the Okavango Delta, from both climate change and increased water
withdrawals for irrigation (Murray-Hudson et al., 2006; Milzow et al., 2010; Wolski et al., 2012), and the Breede
River in South Africa (Steynor et al., 2009). For North Africa, Droogers et al. (2012) estimated that in 2050 climate
change will account for 22% of future water shortages in the region while 78% of increased future water shortages
can be attributed to socioeconomic factors. Abouabdillah et al. (2010) estimated that higher temperatures and
declining rainfall (A2 and B1 scenarios) would reduce water resources in Tunisia. Reduced snowpack in the Atlas
Mountains from a combination of warming and reduced precipitation, combined with more rapid springtime melting
is expected to reduce supplies of seasonal meltwater for lowland areas of Morocco (García-Ruiz et al., 2011).
In Eastern Africa, potential climate change impacts on the Nile Basin are of particular concern given the basin s
geopolitical and socioeconomic importance. Reduced flows in the Blue Nile are estimated by late century due to a
combination of climate change (higher temperatures and declining precipitation) and upstream water development
for irrigation and hydropower (Elshamy et al., 2009; McCartney and Menker Girma, 2012). Beyene et al. (2010)
estimated that streamflow in the Nile River will increase in the medium term (2010 2039) but will decline in the
latter half of this century (A2 and B1 scenarios) as a result of both declining rainfall and increased evaporative
demand, with subsequent diminution of water allocation for irrigated agriculture downstream from the High Aswan
Dam. Kingston and Taylor (2010) reached a similar conclusion about an initial increase followed by a decline in
surface water discharge in the Upper Nile Basin in Uganda. Seasonal runoff volumes in the Lake Tana Basin are
estimated to decrease by the 2080s under the A2 and B2 scenarios (Abdo et al., 2009), while Taye et al. (2011)
reported inconclusive findings as to changes in runoff in this basin. The Mara, Nyando, and Tana rivers in Eastern
Africa, are projected to have increased flow in the second half of this century (Taye et al., 2011; Dessu and Melesse,
2012; Nakaegawa et al., 2012)).
Estimating the influence of climate change on water resources in West Africa is limited by the significant climate
model uncertainties with regards to the region s future precipitation. For example, Itiveh and Bigg (2008) estimate
higher future rainfall in the Niger River Basin (A1, A2 and B1 scenarios), whereas Oguntunde and Abiodun (2013)
report a strong seasonal component with reduced precipitation in the basin during the rainy season and increased
precipitation during the dry season (A1B scenario). The Volta Basin is projected to experience a slight mean
increase in precipitation (Kunstmann et al., 2008), and the Bani River Basin in Mali is estimated to experience
substantial reductions in runoff (A2 scenario) due to reduced rainfall (Ruelland et al., 2012). The impact of climate
change on total runoff in the Congo Basin is estimated to be minimal (A2 scenario) (Tshimanga and Hughes, 2012).
Continental wide studies (e.g. De Wit and Stankiewicz, 2006) indicate that surface drainage in dry areas is more
sensitive to, and will be more adversely affected by, reduced rainfall than would surface drainage in wetter areas that
experience comparable rainfall reductions.
The overall impact of climate change on groundwater resources in Africa is expected to be relatively small in
comparison with impacts from non-climatic drivers such as population growth, urbanization, increased reliance on
irrigation to meet food demand, and land use change (Calow and MacDonald, 2009; Carter and Parker, 2009;
MacDonald et al., 2009; and Taylor et al., 2009). Climate change impacts on groundwater will vary across climatic
zones. (See also Chapter 3.4.6). An analysis by MacDonald et al. (2009) indicated that changes in rainfall would not
be expected to impact the recharge of deep aquifers in areas receiving below 200 mm rainfall per year, where
recharge is negligible due to low rainfall. Groundwater recharge may also not be significantly affected by climate
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change in areas that receive more than 500 mm per year, where sufficient recharge would remain even if rainfall
diminished, assuming current groundwater extraction rates. By contrast, areas receiving between 200 to 500 mm per
year, including the Sahel, the Horn of Africa, and southern Africa, may experience a decline in groundwater
recharge with climate change to the extent that prolonged drought and other precipitation anomalies becomes more
frequent with climate change, particularly in shallow aquifers, which respond more quickly to seasonal and yearly
changes in rainfall than do deep aquifers (Barthel et al., 2009).
Coastal aquifers are additionally vulnerable to climate change because of high rates of groundwater extraction,
which leads to saltwater intrusion in aquifers, coupled with increased saltwater ingression resulting from sea level
rise (Moustadraf et al., 2008; Bouchaou et al., 2008; Al-Gamal and Dodo, 2009; Kerrou et al., 2010). Some studies
have shown additional impacts of sea level rise on aquifer salinization with salinity potentially reaching very high
levels (Carneiro et al., 2010; Niang et al., 2010; Research Institute for Groundwater, 2011). Although these effects
are expected to be localized, in some cases they will occur in densely populated areas (Niang et al., 2010). The
profitability of irrigated agriculture in Morocco is expected to decline (under both B1 and A1B scenarios) due to
increased pumping of groundwater and increased salinization risk for aquifers (Heidecke and Heckelei, 2010).
The capacity of groundwater delivery systems to meet demand may take on increasing importance with climate
change (Calow and MacDonald, 2009). For example, where groundwater pumping and delivery infrastructure is
poor, and the number of point sources limited, prolonged pumping can lead to periodic drawdowns and increased
failure of water delivery systems or increased saline intrusion (Moustadraf et al., 2008). To the extent that drought
conditions become more prevalent in Africa with climate change, stress on groundwater delivery infrastructures will
increase.
Future development of groundwater resources to address direct and indirect impacts of climate change, population
growth, industrialization, and expansion of irrigated agriculture, will require much more knowledge of groundwater
resources and aquifer recharge potentials than currently exists in Africa. Observational data on groundwater
resources in Africa are extremely limited and significant effort needs to be expended to assess groundwater recharge
potential across the continent (Taylor et al., 2009). A preliminary analysis by MacDonald et al. (2012) indicates that
total groundwater storage in Africa is 0.66 million km3, which is more than 100 times the annual renewable
freshwater resources, and 20 times the freshwater stored in African lakes. However, borehole yields are variable
and in many places water yields are relatively low. Detailed analysis of groundwater conditions for water resource
planning would need to consider these constraints.
22.3.4. Agriculture and Food Security
Africa s food production systems are among the world s most vulnerable because of extensive reliance on rainfed
crop production, high intra- and inter-seasonal climate variability, recurrent droughts and floods that affect both
crops and livestock, and persistent poverty that limits the capacity to adapt (Boko et al., 2007). In the near term,
better managing risks associated with climate variability may help to build adaptive capacities for climate change
(Washington et al., 2006; Cooper et al., 2008; Funk et al., 2008). However, agriculture in Africa will face
significant challenges in adapting to climate changes projected to occur by mid-century, as negative effects of high
temperatures become increasingly prominent under an A1B scenario (Battisti and Naylor, 2009; Burke et al.,
2009a), thus increasing the likelihood of diminished yield potential of major crops in Africa (Schlenker and Lobell,
2010; Sultan et al., 2013). Changes in growing season length are possible, with a tendency towards reduced growing
season length (Thornton et al., 2011), though with potential for some areas to experience longer growing seasons
(Cook and Vizy, 2012). The composition of farming systems from mixed crop-livestock to more livestock
dominated food production may occur as a result of reduced growing season length for annual crops and increases in
the frequency and prevalence of failed seasons (Jones and Thornton, 2009; Thornton et al., 2010). Transition zones,
where livestock keeping is projected to replace crop cultivation by 2050, include the West African Sahel and coastal
and mid-altitude areas in eastern and southeastern Africa (Jones and Thornton, 2009), areas that currently support 35
million people and are chronically food insecure.
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22.3.4.1. Crops
Climate change is very likely to have an overall negative effect on yields of major cereal crops across Africa, with
strong regional variability in the degree of yield reduction (see also Chapter 7.3.2.1) (Lobell et al., 2008; Liu et al.,
2008; Walker and Schulze, 2008; Thornton et al., 2009a; Lobell et al., 2011; Roudier et al., 2011; Berg et al., 2013)
(high confidence). One exception is in eastern Africa where maize production could benefit from warming at sites
above roughly 1,700 m in elevation (A1FI scenario) (Thornton et al., 2009a), although the majority of current maize
production occurs at lower elevations thereby implying a potential change in the distribution of maize cropping.
Maize-based systems, particularly in southern Africa, are among the most vulnerable to climate change (Lobell et
al., 2008). Estimated yield losses at mid-century range from 18% for southern Africa (Zinyengere et al., 2013) to
22% aggregated across SSA, with yield losses for South Africa and Zimbabwe in excess of 30% (Schlenker and
Lobell, 2010). Simulations that combine all regions south of the Sahara suggest consistently negative effects of
climate change on major cereal crops in Africa, ranging from 2% for sorghum to 35% for wheat by 2050 under an
A2 scenario (Nelson et al., 2009). Studies in North Africa by Eid et al., 2007; Hegazy et al., 2008; Drine, 2011;
Mougou et al., 2011 also indicate a high vulnerability of wheat production to projected warming trends. In West
Africa, temperature increases above 2° C (relative to a 1961-1990 baseline) are estimated to counteract positive
effects on millet and sorghum yields of increased precipitation (for B1, A1B and A2 scenarios) (Figure 22.5), with
negative effects stronger in the savannah than in the Sahel, and with modern cereal varieties compared with
traditional ones (Sultan et al., 2013).
Several recent studies since the AR4 indicate that climate change will have variable impacts on non-cereal crops,
with both production losses and gains possible (low confidence). Cassava yields in eastern Africa are estimated to
moderately increase up to the 2030s assuming CO2 fertilization and under a range of low to high emissions
scenarios (Liu et al., 2008), findings that were similar to Lobell et al. (2008). Suitability for growing cassava is
estimated to increase with the greatest improvement in suitability in eastern and central Africa (A1B scenario)
(Jarvis et al., 2012). However, Schlenker and Lobell (2010) estimated negative impacts from climate change on
cassava at mid-century, although with impacts estimated to be less than those for cereal crops. Given cassava s
hardiness to higher temperatures and sporadic rainfall relative to many cereal crops, it may provide a potential
option for crop substitution of cereals as an adaptation response to climate change (Rosenthal and Ort, 2012; Jarvis
et al., 2012). Bean yields in Eastern Africa are estimated to experience yield reductions by the 2030s under an
intermediate emissions scenario (A1B) (Jarvis et al., 2012) and by the 2050s under low (B1) and high (A1FI)
emissions scenarios (Thornton et al., 2011). For peanuts, some studies indicate a positive effect from climate change
(A2 and B2 scenarios) (Tingem and Rivington, 2009) and others a negative one (Lobell et al., 2008; Schlenker and
Lobell, 2010). Bambara groundnuts (Vigna subterranea) are estimated to benefit from moderate climate change
(Tingem and Rivington, 2009) (A2 and B2 scenarios) although the effect could be highly variable across varieties
(Berchie et al., 2012). Banana and plantain production could decline in West Africa and lowland areas of East
Africa, whereas in highland areas of East Africa it could increase with temperature rise (Ramirez et al., 2011). Much
more research is needed to better establish climate change impacts on these two crops.
Suitable agro-climatic zones for growing economically important perennial crops are estimated to significantly
diminish, largely due to the effects of rising temperatures (Läderach et al., 2010; Eitzinger et al., 2011a; Läderach et
al., 2011a; Eitzinger et al., 2011b; Läderach et al., 2011b; Läderach et al., 2011c). Under an A2 scenario, by mid-
century suitable agro-climatic zones that are currently classified as very good to good for perennial crops may
become more marginal, and what are currently marginally suitable zones may become unsuitable; the constriction of
crop suitability could be severe in some cases (Table 22-4). Movement of perennial crops to higher altitudes would
serve to mitigate the loss of suitability at lower altitudes but this option is limited. Loss of productivity of high-value
crops such as tea, coffee and cocoa would have detrimental impacts on export earnings.
[INSERT TABLE 22-4 HERE
Table 22-4. Projected changes in agro-climatic suitability for perennial crops in Africa by mid-century under an A2
scenario.]
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[INSERT FIGURE 22-5 HERE
Figure 22-5: The effect of rainfall and temperature changes on mean crop yield. Mean crop yield change (%) relative
to the 1961 90 baseline for 7 temperatures (x-axis) and 5 rainfall (y-axis) scenarios. Results are shown as the
average over the 35 stations across West Africa and the 6 cultivars of sorghum and millet. White triangles and
circles are the projected anomalies computed by several CMIP3 GCMs and three IPCC emission scenarios (B1,
A1B, A2) for 2071 90 and 2031 50, respectively. Projections from CMIP5 GCMs and three RCPs (4.5, 6.0 and 8.5)
are represented by grey triangles and circles. Models and scenarios names are displayed in figure S2 (available
atstacks.iop.org/ERL/8/014040/mmedia). Past observed climate anomalies from CRU data are also projected by
computing 10-year averages (e.g. '1940' is for 1941 50). All mean yield changes are significant at a 5% level except
boxes with a diagonal line. Source: Sultan et al., 2013.].
22.3.4.2. Livestock
Livestock systems in Africa face multiple stressors that can interact with climate change and variability to amplify
the vulnerability of livestock-keeping communities. These stressors include rangeland degradation, increased
variability in access to water, fragmentation of grazing areas, sedentarization, changes in land tenure from
communal towards private ownership, in-migration of non-pastoralists into grazing areas, lack of opportunities to
diversify livelihoods, conflict and political crisis, weak social safety nets, and insecure access to land, markets, and
other resources (Solomon et al., 2007; Smucker and Wisner, 2008; Galvin, 2009; Thornton et al., 2009b; Dougill et
al., 2010; Ifejika Speranza, 2010). (See also Chapter 7.3.2.4.)
Loss of livestock under prolonged drought conditions is a critical risk given the extensive rangeland in Africa that is
prone to drought. Regions that are projected to become drier with climate change, such as Northern and Southern
Africa, are of particular concern (Solomon et al., 2007; Masike and Urich, 2008; Thornton et al., 2009b; Dougill et
al., 2010; Freier et al., 2012; Schilling et al., 2012). Adequate provision of water for livestock production could
become more difficult under climate change. For example, Masike and Urich (2009) estimated that the cost of
supplying livestock water from boreholes in Botswana will increase by 23% by 2050 under an A2 scenario due to
increased hours of groundwater pumping needed to meet livestock water demands under warmer and drier
conditions. Although small in comparison to the water needed for feed production, drinking water provision for
livestock is critical, and can have a strong impact on overall resource use efficiency in warm environments (Peden et
al., 2009; van Breugel et al., 2010; Descheemaeker et al., 2010; Descheemaeker et al., 2011). Livestock production
will be indirectly affected by water scarcity through its impact on crop production and subsequently the availability
of crop residues for livestock feeding. Thornton et al. (2010) estimated that maize stover availability per head of
cattle will decrease in several East African countries by 2050.
The extent to which increased heat stress associated with climate change will affect livestock productivity has not
been well established, particularly in the tropics and sub-tropics (Thornton et al., 2009b), although a few studies
point to the possibility that keeping heat-tolerant livestock will become more prevalent in response to warming
trends. For example, higher temperatures in lowland areas of Africa could result in reduced stocking of dairy cows
in favor of cattle (Kabubo-Mariara, 2008), a shift from cattle to sheep and goats (Kabubo-Mariara, 2008; Seo and
Mendelsohn, 2008), and decreasing reliance on poultry (Seo and Mendelsohn, 2008). Livestock keeping in highland
areas of East Africa, which is currently cold-limited, would potentially benefit from increased temperatures
(Thornton et al., 2010). Lunde and Lindtjrn (2013) challenge a finding in the AR4 that there is direct
proportionality between range-fed livestock numbers and changes in annual precipitation in Africa. Their analysis
indicates that this relationship may hold in dry environments but not in humid ones.
22.3.4.3. Agricultural Pests, Diseases, and Weeds
Since the AR4, understanding of how climate change will potentially affect crop and livestock pests and diseases
and agricultural weeds in Africa is beginning to emerge. Climate change in interaction with other environmental and
production factors could intensify damage to crops from pests, weeds and diseases (7.3.2.3).
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Warming in highland regions of eastern Africa could lead to range expansion of crop pests into cold-limited areas
(low confidence). For example, in highland arabica coffee-producing areas of eastern Africa, warming trends may
result in the coffee berry borer (Hypothenemus hampei) becoming a serious threat in coffee-growing regions of
Ethiopia, Kenya, Uganda, Rwanda, and Burundi (Jaramillo et al., 2011). Temperature increases in highland banana-
producing areas of eastern Africa enhance the risk of altitudinal range expansion of the highly destructive burrowing
nematode, Radopholus similis (Nicholls et al., 2008); however, no detailed studies have assessed this risk. Ramirez
et al. (2011) estimated that increasing minimum temperatures by 2020 would expand the suitable range of Black
Leaf Streak disease (Mycosphaerella fijiensis M.) of banana in Angola and Guinea.
Climate change may also affect the distribution of economically important pests in lowland and dryland areas of
Africa (low confidence). Under A2A and B2A for 2020, Cotter et al. (2012) estimated that changes in temperature,
rainfall, and seasonality will result in more suitable habitats for Striga hermonthica in central Africa, whereas the
Sahel region may become less suitable for this weed. Striga weed infestations are a major cause of cereal yield
reduction in Sub-Saharan Africa. Climate change could also lead to an overall decrease in the suitable range of
major cassava pests whitefly, cassava brown streak virus, cassava mosaic geminivirus, and cassava mealybug
(Jarvis et al., 2012), although southeast Africa and Madagascar is estimated to experience increased suitability for
cassava pests (Bellotti et al., 2012).
In the case of livestock, Olwoch et al. (2008) estimated that the distribution of the main tick vector species
(Rhipicephalus appendiculatus) of East Coast fever disease in cattle could be altered by a 2°C temperature increase
over mean annual temperatures throughout the 1990s, and changes in mean precipitation resulting in the climatically
suitable range of the tick shifting southward. However, a number of environmental and socio-economic factors (e.g.,
habitat destruction, land use and cover change, and host density) in addition to climatic ones influence tick
distribution and need to be considered in assigning causality (Rogers and Randolph, 2006).
22.3.4.4. Fisheries
Fisheries are an important source of food security in Africa. Capture fisheries (marine and inland) and aquaculture
combined contribute over one-third of Africa s animal protein intake (Welcomme, 2011), while in some coastal
countries fish contribute up to two-thirds of total animal protein intake (Allison et al., 2009). Demand for fish is
projected to increase substantially in Africa over the next few decades (De Silva and Soto, 2009). To meet fish food
demand by 2020, De Silva and Soto (2009) estimated that aquaculture production in Africa would have to increase
nearly 500%.
The vulnerability of national economies to climate change impacts on fisheries can be linked to exposure to the
physical effects of climate change, the sensitivity of the country to impacts on fisheries, and adaptive capacity within
the country (Allison et al., 2009). In an analysis of fisheries in 132 countries Allison et al. (2009) estimated that two-
thirds of the most vulnerable countries were in Africa. Among these countries, the most vulnerable were Angola,
DR Congo, Mauritania and Senegal, due to the importance of fisheries to the poor and the close link between
climate variability and fisheries production. Coastal countries of West Africa will experience a significant negative
impact from climate change. Lam et al. (2012) projected that by 2050 (under an A1B scenario) the annual landed
value of fish for that region is estimated to decline by 21%, resulting in a nearly 50% decline in fisheries-related
employment and a total annual loss of US$ 311 million to the region s economy.
22.3.4.5. Food Security
Food security in Africa faces multiple threats stemming from entrenched poverty, environmental degradation, rapid
urbanization, high population growth rates, and climate change and variability. The intertwined issues of markets
and food security have emerged as an important issue in Africa and elsewhere in the developing world since the
AR4. Price spikes for globally traded food commodities in 2007 2008 and food price volatility and higher overall
food prices in subsequent years have undercut recent gains in food security across Africa (Brown et al., 2009;
Hadley et al., 2011; Mason et al., 2011; Tawodzera, 2011; Alem and Söderbom, 2012; Levine, 2012). Among the
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most affected groups are the urban poor, who typically allocate more than half of their income to food purchases
(Cohen and Garrett, 2010; Crush and Frayne, 2010). The proportion of smallholder farmers that are net food buyers
of staple grains exceeds 50% in Mozambique, Kenya and Ethiopia (Jayne et al., 2006), thus food security of rural
producers is also sensitive to food spikes, particularly in the case of female-headed households, which generally
have fewer assets than male-headed households (Kumar and Quisumbing, 2011). Although the recent spike in global
food prices can be attributed to a convergence of several factors, the intensification of climate change impacts could
become more important in the future in terms of exerting upward pressure on food prices of basic cereals (Nelson et
al., 2009; Hertel et al., 2010), which would have serious implications for Africa s food security. As the recent wave
of food price crises demonstrates, factors in other regions profoundly impact food security in Africa. Much more
research is needed to understand better the potential interactions between climate change and other key drivers of
food prices that act at national, regional, and global scales. (See also Chapter 7.2.2.)
Africa is undergoing both rapid urbanization and subsequent transformation of its food systems to accommodate
changes in food processing and marketing as well as in food consumption patterns. Considering the increasing
reliance on purchased food in urban areas, approaches for addressing the impacts of climate change on food security
will need to encompass a food systems approach (production as well as processing, transport, storage, and
preparation) that moves food from production to consumption (Battersby, 2012). Weaknesses in the food system
may be exacerbated by climate change in the region as high temperatures increase spoilage and the potential for
increased flooding places food transportation infrastructure at higher risk of damage. In this respect, high post-
harvest losses in Africa resulting in a large part from inadequate transport and storage infrastructure (Godfray et al.,
2010; Parfitt et al., 2010) are an important concern.
22.3.5. Health
22.3.5.1. Introduction
Africa currently experiences high burdens of health outcomes whose incidence and geographic range could be
affected by changing temperature and precipitation patterns, including malnutrition, diarrheal diseases, and malaria
and other vector-borne diseases, with most of the impact on women and children (WHO 2013a). In 2010, there were
451,000 to 813,000 deaths from malaria in Africa, continuing a slow decline since approximately 2004 (WHO,
2012). There are insufficient data series to assess trends in incidence in most affected countries in Africa. Parasite
prevalence rates in children less than 5 years of age are highest in poorer populations and rural areas; factors
increasing vulnerability include living in housing with little mosquito protection and limited access to health care
facilities offering effective diagnostic testing and treatment. Of the 3.6 million annual childhood deaths in Africa,
11% are due to diarrheal diseases (Liu et al., 2012).
Drivers of these and other climate-relevant health outcomes include inadequate human and financial resources,
inadequate public health and health care systems, insufficient access to safe water and improved sanitation, food
insecurity, and poor governance. Although progress has been made on improving safe water and sanitation
coverage, sub-Saharan Africa still has the lowest coverage, highlighting high vulnerability to the health risks of
climate change (UNICEF and WHO, 2008; UNICEF and WHO, 2012). Vulnerabilities also arise from policies and
measures implemented in other sectors, including adaptation and mitigation options. Collaboration between sectors
is essential. For example, the construction of the Akosombo dam in the 1960s to create Lake Volta in Ghana was
associated with a subsequent increase in the prevalence of schistosomiasis (Scott et al., 1982).
22.3.5.2. Food- and Water-Borne Diseases
Cholera is primarily associated with poor sanitation, poor governance, and poverty, with associations with weather
and climate variability suggesting possible changes in incidence and geographic range with climate change (Rodó et
al., 2002; Koelle et al., 2005; Olago et al., 2007; Murray et al., 2012). The frequency and duration of cholera
outbreaks are associated with heavy rainfall in Ghana, Senegal, other coastal West African countries, and South
Africa, with a possible association with the El Nino-Southern Oscillation (ENSO) (de Magny et al., 2007;
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Mendelsohn and Dawson, 2008; de Magny et al., 2012). In Zanzibar, Tanzania, and Zambia, an increase in
temperature or rainfall increases the number of cholera cases (Luque Fernández et al., 2009; Reyburn et al., 2011).
The worst outbreak of cholera in recent African history occurred in Zimbabwe from August 2008 to June 2009. The
epidemic was associated with the rainy season and caused more than 92,000 cases and 4,000 deaths. Contamination
of water sources spread the disease (Mason, 2009). Poor governance, poor infrastructure, limited human resources,
and underlying population susceptibility (high burden of malnutrition) contributed to the severity and extent of the
outbreak (Murray et al., 2012). Other mechanisms for increases in cholera incidence have been described in Chapter
11 (11.5.2.1). As discussed above in section 22.2 there are projected increases in precipitation in areas in Africa for
example West Africa where cholera is already endemic. This possibly will lead to more frequent cholera outbreaks
in the sub-regions affected. However, further research is needed to quantify the climatic impacts.
22.3.5.3. Nutrition
Malnutrition: Detailed spatial analyses of climate and health dynamics among children in Mali and Kenya suggest
associations between livelihoods and measures of malnutrition, and between weather variables and stunting (Grace
et al., 2012; Jankowska et al., 2012). Projections of climate and demographic change to 2025 for Mali (based on
2010-2039 climatology from the Famine Early Warning System Network FCLIM method), suggest approximately
250,000 children will suffer stunting, nearly 200,000 will be malnourished, and over 100,000 will become anemic,
assuming constant morbidity levels; the authors conclude that climate change will cause a statistically significant
proportion of stunted children (Jankowska et al., 2012).
Using a process-driven approach, (Lloyd et al., 2011) projected future child malnutrition (as measured by severe
stunting) in 2050 for four regions in sub-Saharan Africa, taking into consideration food and nonfood
(socioeconomic) causes, and using regional scenario data based on the A2 scenario. Current baseline prevalence
rates of severe stunting were 12-20%. Considering only future socioeconomic change, the prevalence of severe
stunting in 2050 would be 7-17% (e.g. a net decline). However, including climate change, the prevalence of severe
stunting would be 9-22%, or an increase of 31-55%in the relative percent of children severely stunted. Western sub-
Saharan Africa was projected to experience a decline in severe stunting from 16% at present to 9% in 2050 when
considering socioeconomic and climate change. Projected changes for central, south, and east sub-Saharan Africa
are close to current prevalence rates, indicating climate change would counteract the beneficial consequences of
socioeconomic development. Local economic activity and food accessibility can reduce the incidence of
malnutrition (Funk et al., 2008; Rowhani et al., 2011).
22.3.5.4. Vector-Borne Diseases and Other Climate-Sensitive Health Outcomes
A wide range of vector-borne diseases contribute to premature morbidity and mortality in Africa, including malaria,
leishmaniasis, Rift Valley fever, as well as tick- and rodent-borne diseases.
Malaria: Weather and climate are among the environmental, social, and economic determinants of the geographic
range and incidence of malaria (Reiter 2008). The association between temperature and malaria varies regionally,
(Chaves and Koenraadt, 2010; Paaijmans et al., 2010a; Alonso et al., 2011; Gilioli and Mariani, 2011). Malaria
transmission peaks at 25°C and declines above 28°C (Lunde et al., 2013; Mordecai et al., 2013). Total precipitation,
rainfall patterns, temperature variability, and the water temperature of breeding sites are expected to alter disease
susceptibility (Bomblies and Eltahir, 2010; Paaijmans et al., 2010b; Afrane et al., 2012; Blanford et al., 2013; Lyons
et al., 2013). ENSO events also may contribute to malaria epidemics (Mabaso et al., 2007; Ototo et al., 2011). The
complexity of the malaria transmission cycle makes it difficult to determine whether the distribution of the pathogen
and vector are already changing due to climate change. Other factors like the Indian Ocean Dipole have been
proposed to affect malaria incidence (Hashizume et al., 2009, Chaves et al., 2012, Hashizume et al., 2012).
Climate change is expected to affect the geographic range and incidence of malaria, particularly along the current
edges of its distribution, with contractions and expansions, and increasing and decreasing incidence (Yé et al., 2007;
Peterson, 2009; Parham and Michael, 2010; Paaijmans et al., 2010b; Alonso et al., 2011; Egbendewe-Mondzozo et
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al., 2011; Chaves et al., 2012; Paaijmans et al., 2012; Parham et al., 2012; Ermert et al., 2012), depending on other
drivers, such as public health interventions, factors influencing the geographic range and reproductive potential of
malaria vectors, land use change (e.g., deforestation), and drug resistance, as well as the interactions of these drivers
with weather and climate patterns (Chaves et al., 2008; Kelly-Hope et al., 2009; Paaijmans et al., 2009; Saugeon et
al., 2009; Artzy-Randrup et al., 2010; Dondorp et al., 2010; Gething et al., 2010; Jackson et al., 2010; Kulkarni et
al., 2010; Loha and Lindtjrn, 2010; Tonnang et al., 2010; Stern et al., 2011; Caminade et al., 2011; Omumbo et al.,
2011; Afrane et al., 2012; Edlund et al., 2012; Githeko et al., 2012; Himeidan and Kweka, 2012; Jima et al., 2012;
Lyons et al., 2012; Ermert et al., 2012; Stryker and Bomblies, 2012; Mordecai et al., 2013). Movement of the
parasite into new regions is associated with epidemics with high morbidity and mortality. Because various
Anopheles species are adapted to different climatic conditions, changing weather and climate patterns could affect
species composition differentially, which could, in turn affect malaria transmission (Afrane et al., 2012; Lyons et
al., 2013).
Consensus is growing that highland areas, especially in East Africa, will experience increased malaria epidemics,
with areas above 2,000 m, with temperatures currently too low to support malaria transmission, particularly affected
(Pascual et al., 2006; Peterson, 2009; Gething et al., 2010; Lou and Zhao, 2010; Paaijmans et al., 2010a; Ermert et
al., 2012). Reasons for different projections across models include use of different scenarios; use of global versus
regional climate models (Ermert et al., 2012); the need for finer-scale and higher-resolution models of the sharp
climate variations with altitude (Bouma et al., 2011); and the extent to malaria transmission and the drivers of its
geographic range and incidence of malaria respond to and interact with climate change.
Leishmaniasis: Directly or indirectly, climate change may increase the incidence and geographic range of
leishmaniasis, a highly neglected disease that has recently become a significant health problem in northern Africa
(Postigo, 2010), with a rising concern in western Africa because of co-infection with HIV (Kimutai et al., 2006).
The epidemiology of the disease appears to be changing (Dondji, 2001; Yiougo et al., 2007; WHO, 2009; Postigo,
2010). During the 20th century, zoonotic cutaneous leishmaniasis emerged as an epidemic disease in Algeria,
Morocco, and Tunisia, and is now endemic (Salah et al., 2007; Aoun et al., 2008; Rhajaoui, 2011; Toumi et al.,
2012; Bounoua et al., 2013). Previously an urban disease in Algeria, leishmaniasis now has a peri-urban distribution
linked to changes in the distribution of the rodent host and of the vector since the early 1990s (Aoun et al., 2008).
Cutaneous leishmaniasis has expanded its range from its historical focus at Biskra, Algeria into the semi-arid steppe,
with an associated upward trend in reported cases. In Morocco, sporadic cases of leishmania major (vector
Phlebotomus papatasi) appeared early in the 20th century; since that time there have been occasional epidemics of
up to 2,000 cases, interspersed with long periods with few or no cases (Rhajaoui, 2011). Outbreaks of zoonotic
cutaneous leishmaniasis have become more frequent in Tunisia (where it emerged as an epidemic disease in 1991)
(Salah et al., 2007; Toumi et al., 2012). The disease has since spread to adjacent areas in West Africa and East
Africa (Dondji, 2001; Yiougo et al., 2007; WHO, 2009). Disease incidence is associated with rainfall and minimum
temperature (Toumi et al., 2012; Bounoua et al., 2013). Relationships between decadal shifts over 1990-2009 in
northwest Algeria and northeast Morocco in the number of cases and climate indicators suggested increased
minimum temperatures created conditions suitable for endemicity (Bounoua et al., 2013). Environmental
modifications, such as construction of dams, can change the temperature and humidity of the soil and thus affect
vegetation that may result in changes in the composition and density of sandfly species and rodent vectors. More
research however, is needed to quantity the climate related impacts because there are multiple underlying factors.
Rift Vally fever (RVF): RVF epidemics in the Horn of Africa are associated with altered rainfall patterns.
Additional climate variability and change could further increase its incidence and spread. Rift Valley fever is
endemic in numerous African countries, with sporadic repeated epidemics. Epidemics in 2006 2007 in the Horn of
Africa (Nguku et al., 2007; WHO, 2007; Adam et al., 2010; Andriamandimby et al., 2010; Hightower et al., 2012)
and southern Africa were associated with heavy rainfall (Chevalier et al., 2011), strengthening earlier analyses by
Anyamba et al. (2009) showing that RVF epizootics and epidemics are closely linked to the occurrence of the warm
phase of ENSO and La Nina events (Linthicum et al., 1999; Anyamba et al., 2012) and elevated Indian Ocean
temperatures. These conditions lead to heavy rainfall and flooding of habitats suitable for the production of the
immature Aedes and Culex mosquitoes that serve as the primary RVF virus vectors in East Africa. Flooding of
mosquito habitats also may introduce the virus into domestic animal populations.
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Ticks and tick-borne diseases: Changing weather patterns could expand the distribution of ticks causing animal
disease, particularly in East and South Africa. Ticks carry theileriosis (East Coast Fever), which causes anemia and
skin damage that expose cattle to secondary infections. Habitat destruction, land use and cover change, and host
density also affect tick distribution (Rogers and Randolph, 2006). Using a climate envelope and a species prediction
model, Olwoch et al. (2007) projected that by 2020s, under the A2 scenario, East Africa and South Africa would be
particularly vulnerable to climate-related changes in tick distributions and tick-borne diseases: more than 50% of the
30 Rhipicephalus species examined showed significant range expansion and shifts. More than 70% of this range
expansion was found in tick species of economic importance.
Schistosomiasis: Worldwide, approximately 243 million people required treatment for schistosomiasis in 2011, of
which 90% lived in underdeveloped areas of Africa (WHO, 2013b). Water resource development, such as irrigation
dams recommended for adaptation in agriculture, can amplify the risk of schistosomiasis (Huang and Manderson,
1992; Hunter et al., 1993; Jobin, 1999). Migration and sanitation play a significant role in the spread of
schistosomiasis from rural areas to urban environments (Babiker et al., 1985; WHO, 2013b). Temperature and
precipitation patterns may play a role in transmission (Odongo-Aginya et al., 2008; Mutuku et al., 2011; Huang et
al., 2011). Projections for the period 2070-2099, under A2 and B2 emission scenarios, suggest that although the
geographic areas suitable for transmission will increase with climate change, snail regions are expected to contract
and/or move to cooler areas; these results highlight the importance of understanding how climate change could alter
snail habitats when projecting future human schistosomiasis prevelance under different scenarios (Stensgaard et al.
2011).
Meningococcal meningitis: There is a strong environmental relationship between the seasonal cycle of
meningococcal meningitis and climate, including a relationship between the seasonal pattern of the Harmattan dusty
winds and onset of disease. Transmission of meningitis occurs throughout Africa in the dry season and coincides
with periods of very low humidity and wind-driven dusty conditions, ending with the onset of the rains (Molesworth
et al., 2003). Research corroborates earlier hypothesized relationships between weather and meningitis (Yaka et al.,
2008; Palmgren, 2009; Roberts, 2010; Dukiæ et al., 2012; Agier et al., 2013). In the northern region of Ghana,
exposure to smoke from cooking fires increased the risk of contracting meningococcal meningitis (Hodgson et al.,
2001). This increased risk suggests that exposure to elevated concentrations of air pollutants, such as carbon
monoxide (CO) and particulate matter, may be linked to illness. More research is needed to clarify the possible
impact of climate change on atmospheric concentrations of aerosols and particulates that can impact human health
and any associations between meningitis and these aerosols and particles. The relationship between the environment
and the location of the epidemics suggest connections between epidemics and regional climate variability
(Molesworth et al., 2003; Sultan et al., 2005; Thomson et al., 2006) which may allow for early warning systems for
predicting the location and onset of epidemics.
Hantavirus: Novel hantaviruses with unknown pathogenic potential have been identified in some insectivores
(shrews and a mole) in Africa (Klempa, 2009), with suggestions that weather and climate, among other drivers,
could affect natural reservoirs and their geographic range, and thus alter species composition in ways that could be
epidemiologically important (Klempa, 2009).
Other health issues: Research into other health issues has begun. It has been noted that any increase in food
insecurity due to climate change would be expected to further compromise the poor nutrition of people living with
HIV/AIDs (Drimie and Gillespie, 2010). Laboratory studies suggest that the geographic range of the tsetse fly
(Glossina species), the vector of human and animal trypanosomiasis in Africa, may be reduced with climate change
(Terblanche et al., 2008). More studies are needed to clarify the role of climate change on HIV and other disease
vectors.
Heat waves and high ambient temperatures: Heat waves and heat-related health effects are only beginning to attract
attention in Africa. High ambient temperatures are associated with increased mortality in Ghana, Burkina Faso, and
Nairobi with associations varying by age, gender, and cause of death (Azongo et al., 2012; Diboulo et al., 2012;
Egondi et al., 2012). Children are particularly at risk. Heat-related health effects also may be of concern in West and
southern Africa (Dapi et al., 2010; Mathee et al., 2010). Chapter 11 (11.4.1) assesses the literature on the health
impacts of heat waves and high ambient temperatures. Low ambient temperatures are associated with mortality in
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Nairobi and Tanzania (Egondi et al., 2012; Mrema et al., 2012). Chapter 11 discusses the relationship between heat
and work capacity loss. This is an important issue for Africa because of the number of workers engaged in
agriculture.
Air quality: Climate change is anticipated to affect the sources of air pollutants as well as the ability of pollutants to
be dispersed in the atmosphere (Denman et al., 2007). Assessments of the impacts of projected climate change on
atmospheric concentrations of aerosols and particules that can adversely affect human health indicate that changes in
surface temperature, land cover, and lightning may alter natural sources of ozone precursor gases and consequently
ozone levels over Africa (Stevenson et al., 2005; Brasseur et al., 2006; Zeng et al., 2008). However, insufficient
climate and emissions data for Africa prevent a more comprehensive assessment and further research is needed to
better understand the implications of climate change on air quality in Africa.
22.3.6. Urbanization
The urban population in Africa is projected to triple by2050, increasing by 0.8 billion (UN DESA, 2010). African
countries are experiencing some of the world s highest urbanization rates (UN-Habitat, 2008). Many of Africa s
evolving cities are unplanned and have been associated with growth of informal settlements, inadequate housing and
basic services, and urban poverty (Yuen and Kumssa, 2011).7
[FOOTNOTE 7: However, community-driven upgrading may contribute to reducing the vulnerability of such
informal areas (for more details see Chapter 8).]
Climate change could affect the size and characteristics of rural and urban human settlements in Africa because the
scale and type of rural-urban migration are partially driven by climate change (UN-Habitat and UNEP, 2010; Yuen
and Kumssa, 2011). The majority of migration flows observed in response to environmental change are within
country boundaries (Jäger et al., 2009; Tacoli, 2009). For large urban centers located on mega-deltas (e.g.,
Alexandria in Egypt in the Nile delta, and Benin City, Port Harcourt, and Aba in Nigeria in the Niger delta),
urbanization through migration may also lead to increasing numbers of people vulnerable to coastal climate change
impacts (Seto, 2011). Floods are exerting considerable impacts on cities and smaller urban centers in many African
nations for example, heavy rains in East Africa in 2002 caused floods and mudslides, which forced tens of
thousands to leave their homes in Rwanda, Kenya, Burundi, Tanzania and Uganda, and the very serious floods in
Port Harcourt and Addis Ababa in 2006 (Douglas et al., 2008).
Additionally sea level rise along coastal zones including coastal settlements could disrupt economic activities such
as tourism and fisheries (Naidu et al., 2006; Kebede et al., 2012, Kebede and Nicholls, 2012). More than a quarter of
Africa s population lives within 100 km of the coast and more than half of Africa s total population living in low-
elevation coastal zones is urban, accounting for 11.5% of the total urban population of the continent (UN-Habitat,
2008).
In eastern Africa, an assessment of the impact of coastal flooding due to sea level rise in Kenya found that, by 2030,
10,000 to 86,000 people would be affected, with associated economic costs ranging between US$ 7 million to US$
58 million (SEI, 2009). Detailed assessments of damages arising from extreme events have also been made for some
coastal cities, including Mombasa and Dar-es-Salaam. In Mombasa, by 2030 the population and assets at risk of 1 in
100-year return period extreme water levels is estimated to be between 170,700 to 266,300 inhabitants, while
economic assets at risk are between US$ 0.68 billion and 1.06 billion (Kebede et al., 2012). In Dar-es-Salaam, the
population and economic assets at risk of 1 in 100-year return period extreme water levels by 2030 range between
30,300 and 110,000 inhabitants and US$ 35.6 million to US$ 404.1 million (Kebede and Nicholls, 2012). For both
city assessments, the breadth of these ranges encompasses three different population growth scenarios and four
different sea-level rise scenarios (low (B1), medium (A1B), high (A1FI), and Rahmstorf (based on Rahmstorf,
2007)); these four sea-level rise scenarios were also the basis for the broader assessment of the coast of Kenya (SEI,
2009). The scale of the damages projected in the city-specific studies highlights the risks of extremes in the context
of projected sea-level rise.
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In southern Africa, urban climate change risk assessments have been made at the regional scale (Theron and
Rossouw, 2008) as well as at the city level for Durban, Cape Town, and the uMhlathuze local municipality. For
these cities, risk assessments have focused on a broad range of sectors, including business and tourism; air quality,
heath, and food security; infrastructure and services; biodiversity; and water resources (Naidu et al., 2006;
Cartwright, 2008; Zitholele Consulting, 2009).
Assessments for western Africa (Appeaning Addo et al., 2008; Niang et al., 2010) and northern Africa (Snoussi et
al., 2009; World Bank, 2011) share similarities with those for eastern and southern Africa. For instance, it was
suggested that by the end of the 21st century, about 23 %, 42 %, and 49 % of the total area of coastal governorates of
the Nile Delta would be susceptible to inundation under the A1FI, Rahmstorf and Pfeffer scenarios of SLR. It was
also suggested that a considerable proportion of these areas (ranging between 32% and 54 %) are currently either
wetland or undeveloped areas (Hassaan and Abdrabo, 2013). Another study, assessing the economic impacts of sea
level rise on the Nile Delta, suggested that losses in terms of housing and road would range between 1 and 2 billion
EGP in 2030 and between 2 and 16 billion EGP in 2060 under the A1FI and B1 emissions scenarios as well as
current sea level rise trends (Smith et al., 2013).
African cities and towns represent highly vulnerable locations to the impacts of climate change and climate
variability (Boko et al., 2007; Diagne, 2007; Dossou and Gléhouenou-Dossou, 2007; Douglas et al., 2008;
Adelekan, 2010; Kithiia, 2011). Rapid rates of urbanization represent a burden on the economies of African urban
areas, due to the massive investments needed to create job opportunities and provide infrastructure and services.
Basic infrastructure services are not keeping up with urban growth, which has resulted in a decline in the coverage
of many services, compared to 1990 levels (Banerjee et al., 2007). Squatter and poor areas typically lack provisions
to reduce flood risks or to manage floods when they happen (Douglas et al., 2008).
African small- and medium-sized cities have limited adaptive capacity to deal not only with future climate impacts
but also with the current range of climate variability (Satterthwaite et al., 2009; UN-Habitat, 2011); for more details
see Chapter 5 and Chapter 8). African cities, despite frequently having more services compared to rural areas (e.g.,
piped water, sanitation, schools and healthcare) that lead to human life spans above their respective national
averages, show a shortfall in infrastructure due to low quality and short lifespan which may be of particular concern,
when climate change impacts are taken into consideration (Satterthwaite et al., 2009). It is not possible, however,
to climate-proof infrastructure that is not there (Satterthwaite et al., 2009). At the same time, hard infrastructural
responses such as seawalls and channelized drainage lines are costly and can be maladaptive (Dossou and
Gléhouenou-Dossou, 2007; Douglas et al., 2008; Kithiia and Lyth, 2011).
High levels of vulnerability and low adaptive capacity results from structural factors, particularly local governments
with poor capacities and resources (Kithiia, 2011). Weak local government creates and exacerbates problems
including the lack of appropriate regulatory structures and mandates; poor or no planning; lack of or poor data; lack
of disaster risk reduction strategies; poor servicing and infrastructure (particularly waste management and drainage);
uncontrolled settlement of high-risk areas such as floodplains, wetlands, and coastlines; ecosystem degradation
competing development priorities and timelines; and a lack of coordination among government agencies (AMCEN
and UNEP, 2006; Diagne, 2007; Dossou and Gléhouenou-Dossou, 2007; Mukheibir and Ziervogel, 2007; Douglas
et al., 2008; Roberts, 2008; Adelekan, 2010; Kithiia and Dowling, 2010; Kithiia, 2011).
22.4. Adaptation
22.4.1. Introduction
Since 2007, Africa has gained experience in conceptualizing, planning and beginning to implement and support
adaptation activities, from local to national levels and across a growing range of sectors (22.4.4, 22.4.5). However,
across the continent, most of the adaptation to climate variability and change is reactive in response to short-term
motivations, is occurring autonomously at the individual / household level, and lacks support from government
stakeholders and policies (Vermuelen et al., 2008; Ziervogel et al., 2008; Berrang-Ford et al., 2011). A complex web
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of interacting barriers to local-level adaptation, manifesting from national to local scales, both constrains and
highlights potential limits to adaptation (22.4.6).
22.4.2. Adaptation Needs, Gaps, and Adaptive Capacity
Africa s urgent adaptation needs stem from the continent s foremost sensitivity and vulnerability to climate change,
together with its low levels of adaptive capacity (Ludi et al., 2012; 22.3). While overall adaptive capacity is
considered low in Africa due to economic, demographic, health, education, infrastructure, governance and natural
factors, levels vary within countries and across sub-regions, with some indication of higher adaptive capacity in
North Africa and some other countries; individual or household level adaptive capacity depends, in addition to
functional institutions and access to assets, on the ability of people to make informed decisions to respond to
climatic and other changes (Vincent, 2007; Ludi et al., 2012).
Inherent adaptation-related strengths in Africa include the continent s wealth in natural resources, well-developed
social networks, and longstanding traditional mechanisms of managing variability through, for example, crop and
livelihood diversification, migration and small-scale enterprises, all of which are underpinned by local or indigenous
knowledge systems for sustainable resource management (Eyong, 2007; Nyong et al., 2007; UNFCCC, 2007;
Cooper et al., 2008; Macchi et al., 2008; Nielsen, 2010; Castro et al., 2012;). However, it is uncertain to what extent
these strategies will be capable of dealing with future changes, among them climate change and its interaction with
other development processes (Leary et al., 2008b; Paavola, 2008; van Aalst et al., 2008; Conway, 2009; Jones,
2012, section 22.4.6). Since Africa is extensively exposed to a range of multiple stressors (22.3) that interact in
complex ways ith longer term climate change, adaptation needs are broad, encompassing institutional, social,
physical and infrastructure needs, ecosystem services and environmental needs, and financial and capacity needs.
Making climate change information more reliable and accessible is one of the most pressing and cross-cutting
adaptation needs, but providing information is insufficient to guarantee adaptation, which requires behavioural
change (22.4.5.5, 22.4.6). As noted in the AR4 and emphasised in subsequent literature, monitoring networks in
Africa are insufficient and characterised by sparse coverage and short and fragmented digitised records, which
makes modelling difficult (Boko et al., 2007; Goulden et al., 2009b; Ziervogel and Zermoglio, 2009; Jalloh et al.,
2011a). Adding to this is the shortage of relevant information and skills, in particular for downscaling climate
models and using scenario outputs for development and adaptation planning, which is exacerbated by under-
resourcing of Meteorological Agencies and a lack of in-country expertise on climate science; and the capacity of
civil society and government organisations to access, interpret and use climate information for planning and
decisionmaking (Ziervogel and Zermoglio, 2009; Brown et al., 2010; Ndegwa et al., 2010; Dinku et al., 2011; Jalloh
et al., 2011a).
Given its economic dependence on natural resources, most research on strengthening adaptive capacity in Africa is
focused on agriculture, forestry or fisheries-based livelihoods (Collier et al., 2008; Berrang-Ford et al., 2011). The
rural emphasis is now being expanded through a growing focus on requirements for enhancing peri-urban and urban
adaptive capacity (Lwasa, 2010; Ricci, 2012). Many African countries have prioritised the following knowledge
needs: vulnerability and impact assessments with greater continuity in countries; country-specific socio-economic
scenarios and greater knowledge on costs and benefits of different adaptation measures; comprehensive programmes
that promote adaptation through a more holistic development approach, including integrated programmes on
desertification, water management and irrigation; promoting sustainable agricultural practices and the use of
appropriate technologies and innovations to address shorter growing seasons, extreme temperatures, droughts, and
floods; developing alternative sources of energy; and approaches to deal with water shortages, food security and loss
of livelihoods (UNFCCC, 2007; Bryan et al., 2009; Eriksen and Silva, 2009; Chikozho, 2010; Gbetibouo et al.,
2010b; Jalloh et al., 2011b; Sissoko et al., 2011; AAP, 2012). The literature, however, stresses the vast variety of
contexts that shape adaptation and adaptive capacity - even when people are faced with the same climatic changes
and livelihood stressors, responses vary greatly (Cooper et al., 2008; Vermuelen et al., 2008; Ziervogel et al., 2008;
Gbetibouo, 2009; Westerhoff & Smit, 2009)
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Despite significant data and vulnerability assessment gaps, the literature highlights that delayed action on adaptation
due to this would not be in the best interests of building resilience commensurate with the urgent needs (UNFCCC,
2007; Jobbins, 2011). See section 22.6.4 for a discussion of adaptation costs and climate finance.
22.4.3. Adaptation, Equity, and Sustainable Development
Multiple uncertainties in the African context mean that successful adaptation will depend upon developing resilience
in the face of uncertainty (high confidence) (Adger et al., 2011; Conway, 2011; Ludi et al., 2012). The limited
ability of developmental strategies to counter current climate risks, in some cases due to significant implementation
challenges related to complex cultural, political and insitutional factors, has led to an adaptation deficit, which
reinforces the desirability for strong inter-linkages between adaptation and development, and for low-regrets
adaptation strategies (see AR5 Glossary) that produce developmental co-benefits (high confidence) (Bauer and
Scholz, 2010; Smith et al., 2011).
Research has highlighted that no single adaptation strategy exists to meet the needs of all communities and contexts
in Africa (high confidence) (22.4.4, 22.4.5). In recognition of the socioeconomic dimensions of vulnerability (Bauer
and Scholz, 2010), the previous focus on technological solutions to directly address specific impacts is now evolving
toward a broader view that highlights the importance of building resilience, through social, institutional, policy,
knowledge, and informational approaches (ADF, 2010; Chambwera and Anderson, 2011), as well as on linking the
diverse range of adaptation options to the multiple livelihood vulnerability risks faced by many people in Africa
(Tschakert and Dietrich, 2010), and on taking into account local norms and practices in adaptation strategies (Nyong
et al., 2007; Ifejika Speranza et al., 2010; section 22.4.5.4). Moreover, effective adaptation responses necessitate
differentiated and targeted actions from the local to national levels, given the differentiated social impacts based on
gender, age, disability, ethnicity, geographical location, livelihood, and migrant status (Tanner and Mitchell, 2008;
IPCC, 2012). Additional attention to equity and social justice aspects in adaptation efforts in Africa, including the
differential distribution of adaptation benefits and costs, would serve to enhance adaptive capacity (Burton et al.,
2002; Brooks et al., 2005; Thomas and Twyman, 2005; Madzwamuse, 2010); nevertheless, some valuable
experience has been gained recently on gender-equitable adaptation, human rights based-approaches, and
involvement of vulnerable or marginalized groups such as indigenous peoples and children, aged and disabled
people, internally displaced persons and refugees (ADF, 2010; UNICEF, 2010; Levine et al., 2011; UNICEF, 2011;
Romero González et al., 2011; IDS, 2012; Tanner and Seballos, 2012) (Table 22-5). See also CC-GC on Gender and
Climate Change.
[INSERT TABLE 22-5 HERE
Table 22-5: Cross-cutting approaches for equity and social justice in adaptation.]
22.4.4. Experiences in Building the Governance System for Adaptation, and Lessons Learned
22.4.4.1. Introduction
Section 22.4.4 assesses progress made in developing policy, planning and institutional systems for climate
adaptation at regional, national and sub-national levels in Africa, with some assessment of implementation. This
includes an assessment of community-based adaptation, as an important local level response, and a consideration of
adaptation decisionmaking and monitoring.
22.4.4.2. Regional and National Adaptation Planning and Implementation
Regional policies and strategies for adaptation, as well as transboundary adaptation, are still in their infancy. Early
examples include the Climate Change Strategies and Action Plans being developed by the Southern African
Development Community and the Lake Victoria Basin Committee, as well as efforts being made by six highly
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forested Congo basin countries to co-ordinate conservation and sustainable forest management of the Central
African forest ecosystem, and obtain payments for ecosystem services (Harmeling et al., 2011; AfDB, 2012).
At the national level, African countries have initiated comprehensive planning processes for adaptation by
developing National Adaptation Programmes of Action (NAPAs), in the case of the Least Developed Countries, or
National Climate Change Response Strategies (NCCRS); implementation is, however, lagging and integration with
economic and development planning is limited but growing (high confidence). Prioritized adaptation measures in the
NAPAs tend to focus narrowly on agriculture, food security, water resources, forestry, and disaster management;
and onprojects, technical solutions, education and capacity development, with little integration with economic
planning and poverty reduction processes (Madzwamuse, 2010; Mamouda, 2011; Pramova et al., 2012). Only a
small percentage of the NAPA activities have been funded to date, although additional funding is in the pipeline
(Prowse et al., 2009; Madzwamuse, 2010; Mamouda, 2011; Romero González et al., 2011).
Subsequent to the NAPAs and early experience with the NCCRS, there is some evidence of evolution to a more
integrated, multi-level and multi-sector approach to adaptation planning (medium confidence). Examples include
Ethiopia s Programme of Adaptation to Climate Change, which includes sectoral, regional, national and local
community levels (Hunde, 2012); Lesotho s co-ordinated policy framework involving all ministries and
stakeholders (Corsi et al., 2012); and Mali s experience with a methodology for integrating adaptation into multiple
sectors (Fröde et al., 2013). Cross-sectoral adaptation planning and risk management is occurring through
mainstreaming initiatives like the twenty country Africa Adaptation Programme (AAP), initiated in 2008 (UNDP,
2009; Siegel, 2011). Examples of the more programmatic approach of national climate resilient development
strategies include Rwanda s National Strategy on Climate Change and Low Carbon Development, under
development in 2012, and the Pilot Programmes for Climate Resilience in Niger, Zambia and Mozambique (Climate
Investment Fund, 2009). Inter-sectoral climate risk management approaches can be detected in integrated water
resources management, integrated coastal zone management, disaster risk reduction, and land use planning
initiatives (Boateng, 2006; Koch et al., 2007; Awuor et al., 2008; Cartwright et al., 2008; Kebede and Nicholls,
2011; Kebede et al., 2012), while in South Africa, climate change design principles have been incorporated into
existing systematic biodiversity planning to guide land use planning (Petersen and Holness, 2011).
The move to a more integrated approach to adaptation planning is occurring within efforts to construct enabling
national policy environments for adaptation in many countries. Examples include Namibia s National Policy on
Climate Change; Zambia s National Climate Change Response Strategy and Policy, and South Africa s National
Climate Change Response Policy White Paper. Ten countries were developing new climate change laws or formal
policies at the end of 2012, including the proposed National Coastal Adaptation Law in Gabon (Corsi et al., 2012).
Despite this progress in mainstreaming climate risk in policy and planning, significant disconnects still exist at the
national level, and implementation of a more integrated adaptation response remains tentative (high confidence)
(Koch et al., 2007; Fankhauser and Schmidt-Traub, 2010; Madzwamuse, 2010; Oates et al., 2011; UNDP-UNEP
Poverty-Environment Initiative, 2011a). Legislative and policy frameworks for adaptation remain fragmented,
adaptation policy approaches seldom take into account realities in the political and institutional spheres, and national
policies are often at odds with autonomous local adaptation strategies, which can act as a barrier to adaptation,
especially where cultural, traditional and context-specific factors are ignored (Dube and Sekhwela, 2008; Patt and
Schröter, 2008; Stringer et al., 2009; Bele et al. 2010; Hisali et al., 2011; Kalame et al. 2011; Naess et al., 2011;
Lockwood, 2012; Sonwa et al. 2012; section 22.4.6). While climate resilience is starting to be mainstreamed into
economic planning documents - for example, Zambia s Sixth National Development Plan 2011-2015, and the new
Economic and Social Investment Plan in Niger (Corsi et al., 2012), measures to promote foreign direct investment
and industrial competitiveness can undercut adaptive capacity of poor people (Madzwamuse, 2010), while poor
business environments impede both foreign direct investment and adaptation (Collier et al., 2008). Stakeholders in
climate-sensitive sectors for example, Botswana s tourism industry - have yet to develop and implement
adaptation strategies (Saarinen et al., 2012).
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22.4.4.3. Institutional Frameworks for Adaptation
Global adaptation institutions, both within and outside of the UNFCCC, are critically important for Africa s ability
to move forward on adaptation (14.2.3). Regional institutions focused on specific ecosystems rather than on political
groupings, such as the Commission of Central African Forests (COMIFAC), present an opportunity to strengthen
the institutional framework for adaptation. National frameworks include a number of institutions that cover all
aspects of climate change: most countries have inter-ministerial coordinating bodies and inter-sectoral technical
working groups, while an increasing number now have multi-stakeholder co-ordinating bodies (Harmeling et al,
2011) and are establishing national institutions to serve as conduits for climate finance (Gomez-Echeverri, 2010;
Smith et al., 2011).
Many studies in Africa show that under uncertain climatic futures, replacing hierarchical governance systems that
operate within siloes with more adaptive, integrated, multi-level and flexible governance approaches, and with
inclusive decisionmaking that can operate successfully across multiple scales or adaptive governance and co-
management will enhance adaptive capacity and the effectivess of the adaptation response (Folke et al., 2005;
Olsson et al., 2006; Koch et al., 2007; Berkes, 2009; Pahl-Wostl, 2009; Armitage and Plummer, 2010; Bunce et al.,
2010a; Plummer, 2012). Despite some progress with developing the institutional framework for governing
adaptation, there are significant problems with both transversal and vertical coordination, including institutional
duplication with other inter-sectoral platforms, such for disaster risk reduction; while in fragile states, institutions for
reducing climate risk and promoting adaptation may be extremely weak or almost non-existent (Hartmann and
Sugulle, 2009; Sietz et al., 2011; Simane et al., 2012). Facilitating institutional linkages and co-ordinating responses
across all boundaries of government, private sector and civil society would enhance adaptive capacity (Brown et al.,
2010). Resolving well-documented institutional challenges of natural resource management, including lack of co-
ordination, monitoring and enforcement, is a fundamental step towards more effective climate governance. For
example, concerning groundwater, developing organizational frameworks and strengthening institutional capacities
for more effectively assessing and managing groundwater resources over the long term are critically important
(Nyenje and Batelaan, 2009; Braune and Xu, 2010).
22.4.4.4. Sub-National Adaptation Governance
Since AR4, there has been additional effort on sub-national adaptation planning in African countries, but adaptation
strategies at provincial and municipal levels are mostly still under development, with many local governments
lacking the capacity and resources for the necessary decentralised adaptation response (high confidence). Provinces
in some countries have developed policies and strategies on climate change: for example, Lagos State s 2012
Adaptation Strategy in Nigeria (BNRCC, 2012); mainstreaming adaptation into district development plans in Ghana;
and communal climate resilience plans in Morocco (Corsi et al., 2012). Promising approaches include sub-national
strategies that integrate adaptation and mitigation for low-carbon climate resilient development, as is being done in
Delta State in Nigeria, and in other countries (UNDP, 2011a). In response to the identified institutional weaknesses,
capacity development has been implemented in many cities and towns, including initiatives in Lagos, Nigeria,
Durban and Cape Town in South Africa: notable examples include Maputo s specialized local government unit to
implement climate change response, ecosystem-based adaptation and improved city wetlands; and participatory
skills development in integrating community-based disaster risk reduction and climate adaptation into local
development planning in Ethiopia (Madzwamuse, 2010; ACCRA, 2012; Castán Broto, et al., 2013).
22.4.4.5. Community-Based Adaptation and Local Institutions
Since AR4, there has been progress in Africa in implementing and researching community-based adaptation (high
confidence), with broad agreement that support to local-level adaptation is best achieved by starting with existing
local adaptive capacity, and incorporating and building upon present coping strategies and norms, including
indigenous practices (Dube and Sekhwela, 2007; Archer et al., 2008; Huq, 2011). Community-based adaptation
(CBA) is community initiated, and/or draws upon community knowledge or resources refer to AR5 glossary.
Some relevant initiatives include the Community-based Adaptation in Africa (CBAA) project, which implemented
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community-level pilot projects in eight African countries (Sudan, Tanzania, Uganda, Zambia, Malawi, Kenya,
Zimbabwe, South Africa) through a learning-by-doing approach; the Adaptation Learning Program, implemented in
Ghana, Niger, Kenya and Mozambique (CARE International, 2012); and UNESCO Biosphere Reserves where good
practices were developed in Ethiopia, Kenya, South Africa and Senegal (German Commission for UNESCO, 2011).
See also section 22.4.5.6 on institutions for CBA. The literature includes a wide range of case studies detailing
involvement of local communities in adaptation initiatives and projects facilitated by NGOs and researchers (for
example, Leary et al., 2008a; CCAA, 2011; CARE International, 2012; Chishakwe et al., 2012); these and other
initiatives have generated process-related lessons (22.4.5), with positive assessments of effectiveness in improving
adaptive capacity of African communities, local organisations and researchers (Lafontaine et al., 2012).
The key role for local institutions in enabling community resilience to climate change has been recognised,
particularly with respect to natural resource dependent communities for example, the role of NGOs and CBOs in
catalysing agricultural adaptation or in building resilience through enhanced forest governance and sustainable
management of non-timber forest products; institutions managing access to and tenure of land and other natural
resources, which are vital assets for the rural and peri-urban poor, are particularly crucial for enabling CBA and
enhancing adaptive capacity in Africa (Bryan et al., 2009; Brown et al., 2010; Mogoi et al., 2010). Local studies and
adaptation planning have revealed the following priorities for pro-poor adaptation: social protection, social services
and safety nets; better water and land governance; action research to improve resilience of under-researched food
crops of poor people; enhanced water storage and harvesting; better post-harvest services; strengthened civil society
and greater involvement in planning; and more attention to urban and peri-urban areas heavily affected by migration
of poor people (Moser and Satterthwaite, 2008; Urquhart, 2009; Bizikova et al., 2010).
22.4.4.6. Adaptation Decisionmaking and Monitoring
Emerging patterns in Africa regarding adaptation decision-making, a critical component of adaptive capacity,
include limited inclusive governance at the national level, with greater involvement in local initiatives of vulnerable
and exposed people in assessing and choosing adaptation responses (high confidence). Civil society institutions and
communities have to date played a limited role in formulation of national adaptation policies and strategies,
highlighting the need for governments to widen the political space for citizens and institutions to participate in
decision-making, for both effectiveness and to ensure rights are met (Madzwamuse, 2010; Castro et al., 2012).
Building African leadership for climate change may assist with this (CCAA, 2011; Chandani, 2011; Corsi et al.,
2012). A critical issue is how planning and decisionmaking for adaptation uses scientific evidence and projections,
while also managing the uncertainties within the projections (Conway, 2011; Dodman and Carmin, 2011).
A range of tools has been used in adaptation planning in Africa, including vulnerability assessment (22.4.5), risk
assessment, cost-benefit analysis, cost-effectiveness, multi-criteria analysis, and participatory scenario planning (see
for example Cartwright et al., 2008; Kemp-Benedict and Agyemang-Bonsu, 2008; Njie et al., 2008; Mather and
Stretch, 2012), but further development and uptake of decision tools would facilitate enhanced decisionmaking. A
related point is that monitoring and assessing adaptation is still relatively undeveloped in Africa, with national co-
ordinating systems for collating data and synthesizing lessons not in place. Approaches for assessing adaptation
action at local and regional levels have been developed (see for example Hahn et al., 2009; Gbetibouo et al., 2010a;
Below et al., 2012), while there are positive examples of local monitoring of adaptation at the project level (see for
example Archer et al., 2008; Below et al., 2012). Chapter 2 contains additional discussion of the foundations for
decisionmaking on climate change matters.
22.4.5. Experiences with Adaptation Measures in Africa and Lessons Learned
22.4.5.1. Overview
Section 22.4.5 provides a cross-cutting assessment of experience gained in Africa with a range of adaptation
approaches, encompassing climate risk reduction measures; processes for participatory learning and knowledge
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development and sharing; communication, education and training; ecosystem-based measures; and technological
and infrastructural approaches; concluding with a discussion of maladaptation.
Common priority sectors across countries for implementing adaptation measures since 2008 include agriculture,
food security, forestry, energy, water, and education (Corsi et al., 2012), which reflects a broadening of focus since
the AR4. While there has been little planning focus on regional adaptation (22.4.4.2, 22.4.4.3), the potential for this
has been recognized (UNFCCC, 2007; Sonwa et al., 2009; Niang, 2012).
Attention is increasing on identifying opportunities inherent in the continent s adaptation needs, as well as
delineating key success factors for adaptation. A number of studies identify the opportunity inherent in
implementing relatively low-cost and simple low-regrets adaptation measures that reduce people s vulnerability to
current climate variability, have multiple developmental benefits, and are well-positioned to reduce vulnerability to
longer-term climate change as well (UNFCCC, 2007; Conway and Schipper, 2011; see also Section 22.4.3).
Responding to climate change provides an opportunity to enhance awareness that maintaining ecosystem
functioning underpins human survival and development in a most fundamental way (Shackleton and Shackleton,
2012), and to motivate for new development trajectories (22.4.6). While it is difficult to assess adaptation success,
given temporal and spatial scale issues, and local specificities, Osbahr et al. (2010) highlight the role of social
networks and institutions, social resilience, and innovation as possible key success factors for adaptation in small-
scale farming livelihoods in southern Africa. Kalame et al. (2008) note opportunities for enhancing adaptation
through forest governance reforms to improve community access to forest resources, while Martens et al. (2009)
emphasise the importance of soft path measures for adaptation strategies (see also section 22.4.5.6).
The following discussion of adaptation approaches under discrete headings does not imply that these are mutually
exclusive adaptation initiatives usually employ a range of approaches simultaneously, and indeed, the literature
increasingly recognizes the importance of this for building resilience.
22.4.5.2. Climate Risk Reduction, Risk Transfer, and Livelihood Diversification
Risk reduction strategies used in African countries to offset the impacts of natural hazards on individual households,
communities, and the wider economy include early warning systems, emerging risk transfer schemes, social safety
nets, disaster risk contingency funds and budgeting, livelihood diversification, and migration (World Bank, 2010;
UNISDR, 2011).
Disaster risk reduction (DRR) platforms are being built at national and local levels, with the synergies between DRR
and adaptation to climate change being increasingly recognized in Africa (Westgate, 2010; UNISDR, 2011; Hunde,
2012); however, Conway and Schipper (2011) find that additional effort is needed for a longer-term vulnerability
reduction perspective in disaster management institutions.
Early warning systems (EWS) are gaining prominence as multiple stakeholders strengthen capabilities to assess and
monitor risks and warn communities of a potential crisis, through regional systems such as the Permanent Inter-
States Committee for Drought Control in the Sahel (CILSS) and the Famine Early Warning System Network
(FEWS-NET), as well as national, local and community-based EWS on for example food and agriculture
(Pantuliano and Wekesa, 2008; Sissoko et al., 2011; FAO, 2011). Some of the recent EWS emphasise a gendered
approach, and may incorporate local knowledge systems used for making short-, medium-, and long-term decisions
about farming and livestock-keeping, as in Kenya (UNDP, 2011b). The health sector has employed EWS used to
predict disease for adaptation planning and implementation, such as the prediction of conditions expected to lead to
an outbreak of Rift Valley fever in the Horn of Africa in 2006/2007 (Anyamba et al., 2010). Progress has been made
in prediction of meningitis and in linking climate/weather variability and extremes to the disease (Thomson et al.,
2006; Cuevas et al., 2007).
Local projects often use participatory vulnerability assessment or screening to design adaptation strategies (van
Vliet, 2010; GEF Evaluation Office, 2011; Hambira, 2011), but vulnerability assessment at the local government
level is often lacking, and assessments to develop national adaptation plans and strategies have not always been
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conducted in a participatory fashion (Madzwamuse, 2010). Kienberger (2012) details spatial modelling of social and
economic vulnerability to floods at the district level in Búzi, Mozambique. Lessons from vulnerability analysis
highlight that the highest exposure and risk do not always correlate with vulnerable ecosystems, socially
marginalized groups, and areas with at-risk infrastructure, but may also lie in unexpected segments of the population
(Moench, 2011).
Community-level DRR initiatives include activities that link food security, household resilience, environmental
conservation, asset creation, and infrastructure development objectives and co-benefits (Parry et al., 2009a;
UNISDR, 2011; Frankenberger et al., 2012). Food security and nutrition-related safety nets and social protection
mechanisms can mutually reinforce each other for DRR that promotes adaptation, as in Uganda s Karamoja
Productive Assets Programme (Government of Uganda and WFP 2010; WFP, 2011). Initiatives in Kenya, South
Africa, Swaziland and Tanzania have also sought to deploy local and traditional knowledge for the purposes of
disaster preparedness and risk management (Mwaura, 2008; Galloway McLean, 2010). Haan et al. (2012) highlight
the need for increased donor commitment to the resilience-building agenda within the framework of DRR, based on
lessons from the 2011 famine in Somalia.
Social protection8, a key element of the African Union social policy framework, is being increasingly used in
Ethiopia, Rwanda, Malawi, Mozambique, South Africa, and other countries to buffer against shocks by building
assets and increasing resilience of chronically and transiently poor households; in some cases this surpasses repeated
relief interventions to address slower onset climate shocks, as in Ethiopia s Productive Safety Net Program (Brown
et al., 2007; Heltberg et al. 2009). While social protection is helping with ex post and ex ante DRR and will be
increasingly important for securing livelihoods should climate variability increase, less evidence exists for its
effectiveness against the most extreme climatic shocks associated with higher emissions scenarios, which would
require reducing dependence on climate-sensitive livelihood activities (Davies et al., 2009; Wiseman et al., 2009;
Pelham et al., 2011; Béné et al., 2012). Social protection could further build adaptive capacity if based on improved
understanding of the structural causes of poverty, including political and institutional dimensions (Brown et al.,
2007; Davies et al., 2009; Levine et al., 2011).
[FOOTNOTE 8: Social protection can include social transfers (cash or food), minimum standards such as for child
labor, and social insurance.]
Risk spreading mechanisms used in the African context include kinship networks; community funds; and disaster
relief and insurance, which can provide financial security against extreme events such as droughts, floods, and
tropical cyclones, and concurrently reduce poverty and enhance adaptive capacity9 (Leary et al., 2008a; Linnerooth-
Bayer et al. 2009; Coe and Stern, 2011). Recent developments include the emergence of index-based insurance
contracts (Box 22-1), which pay out not with the actual loss, but with a measurable event that could cause loss.
[FOOTNOTE 9: Climate (or disaster) risk financing instruments include contingency funds; agricultural and
property (private) insurance; sovereign insurance; reallocation of program expenditures; weather derivatives; and
bonds.]
_____ START BOX 22-1 HERE _____
Box 22-1. Experience with Index-Based Weather Insurance in Africa
Malawi s initial experience of dealing with drought risk through index-based weather insurance directly to
smallholders appears positive: 892 farmers purchased the insurance in the first trial period, which was bundled with
a loan for groundnut production inputs (Hellmuth et al., 2009). In the next year, the pilot expanded, with the
addition of maize, taking numbers up to 1,710 farmers and stimulating interest among banks, financiers, and supply
chain participants such as processing and trading companies and input suppliers. A pilot insurance project in
Ethiopia was designed to pay claims to the government based on a drought index that uses a time window between
observed lack of rain and actual materialization of losses. This allows stakeholders to address threats to food
security in ways that prevent the depletion of farmers productive assets, which reduces the future demand for
humanitarian aid by enabling households to produce more food during subsequent seasons (Krishnamurty, 2011).
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Another key innovation in Ethiopia is the insurance for work program that allows cash-poor farmers to work for
their insurance premiums by engaging in community-identified disaster risk reduction products, such as soil
management and improved irrigation (WFP, 2011), which makes insurance affordable to the most marginalized and
resource-poor sectors of society.
_____ END BOX 22-1 HERE _____
The challenges associated with current risk reduction strategies include political and institutional challenges in
translating early warning into early action (Bailey, 2013); communication challenges related to EWS: conveying
useful information in local languages and communicating EWS in remote areas; national-level mistrust of locally
collected data, which are perceived to be inflated to leverage more relief resources (Hellmuth et al., 2007;
Pantuliano and Wekesa, 2008; Cartwright et al., 2008; FAO, 2011); the call for improved user-friendliness of early
warning information, including at smaller spatial scales; the need for increased capacity in National Meteorological
centres (22.4.2); and the need for better linkages between early warning, response, and prevention (Haan et al.,
2012).
Evidence is increasing that livelihood diversification, long used by African households to cope with climate shocks,
can also assist with building resilience for longer term climate change by spreading risk. Over the past 20 years,
households in the Sahel have reduced their vulnerability and increased their wealth through livelihood
diversification, particularly when diversifying out of agriculture (Mertz et al., 2011). Households may employ a
range of strategies, including on-farm diversification or specialization (Sissoko et al., 2011; Tacoli, 2011).
Motsholapheko et al. (2011) show how livelihood diversification is used as an adaptation to flooding in the
Okavango Delta, Botswana, and Badjeck et al. (2010) recommend private and public insurance schemes to help
fishing communities rebuild after extreme events, and education and skills upgrading to enable broader choices
when fishery activities can no longer be sustained. See Chapter 9 for a fuller discussion of the role of livelihood
diversification in adaptation, particularly 9.3.3.1 and 9.3.5.2). Remittances are a longstanding and important means
of reducing risk to climate variability and other household stressors, and of contributing to recovery from climatic
shocks, as further discussed in Chapter 9 (9.3.3.3, 9.3.5.2).
While livelihood diversification is an important adaptation strategy, it may replace formerly sustainable practices
with livelihood activities that have negative environmental impacts (22.4.5.8).
Rural finance and micro-credit can be enabling activities for adaptive response, which are also used by women for
resilience-building activities (e.g., as documented in Sudan by Osman-Elasha et al., 2008). Credit and storage
systems are instrumental in supporting families during the lean period, to prevent the sale of assets to buy food when
market prices are higher (Romero González et al., 2011). Long seen as a fundamental process for most African
families to incorporate choice into their risk profile and adapt to climate variability (Goldstone, 2002; Urdal, 2005;
Reuveny, 2007; Fox and Hoelscher, 2010), there is evidence in some areas of the increased importance of migration
(discussed in section 22.6.1, 8.2, 9.3.3.3, 12.4) and trade for livelihood strategies, as opposed to subsistence
agriculture, as shown by Mertz et al. (2011) for the Sudano-Sahelian region of West Africa.
22.4.5.3. Adaptation as a Participatory Learning Process
Since AR4, there has been more focus on the importance of flexible and iterative learning approaches for effective
adaptation (medium evidence, high agreement). Due to the variety of intersecting social, environmental, and
economic factors that affect societal adaptation, governments, communities, and individuals (Jones et al., 2010;
Jones, 2012), adaptation is increasingly recognized as a complex process involving multiple linked steps at several
scales, rather than a series of simple planned technical interventions (Moser and Ekstrom, 2010). Implementing
adaptation as a participatory learning process enables people to adopt a proactive or anticipatory stance to avoid
learning by shock (Tschakert and Dietrich, 2010).
Iterative and experiential learning allows for flexible adaptation planning, appropriate considering the uncertainty
inherent in climate projections that is compounded by other sources of flux affecting populations in Africa (Suarez
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et al., 2008; Dodman and Carmin, 2011; Huq, 2011; Koelle and Annecke, 2011). Many studies have highlighted the
utility of participatory action research, social and experiential learning, and creating enabling spaces for multi-
stakeholder dialogue for managing uncertainty and unlocking the social and behavioral change required for
adaptation (e.g., Tompkins and Adger, 2003; Ziervogel and Opere, 2010; Bizikova et al., 2010; Tschakert and
Dietrich 2010; CCAA, 2011; Ebi et al., 2011; UNDP-UNEP Poverty-Environment Initiative, 2011b; Thorn, 2011;
Faysse et al., 2012). Transdisciplinary approaches, which hold promise for enhancing linkages between sectors and
thus reducing maladaptation are also starting to be adopted, as for example in the urban context (Evans, 2011).
Learning approaches for adaptation may involve co-production of knowledge such as combining local and
traditional knowledge with scientific knowledge (22.4.5.4).
Adaptive co-management10 holds potential to develop capacity to deal with change (Watkiss et al., 2010; Plummer,
2012); the implications of strategic adaptive management for adaptation in aquatic protected areas in South Africa
are being explored (Kingsford et al., 2011).
[FOOTNOTE 10: Adaptive co-management is understood as a process by which institutional arrangements and
ecological knowledge are tested and revised in a dynamic, ongoing, self-organized process of learning-by-doing
(Folke et al., 2002).]
Caveats and constraints to viewing adaptation as a participatory learning process include the time and resources
required from both local actors and external facilitators, the challenges of multidisciplinary research, the politics of
stakeholder participation and the effects of power imbalances, and the need to consider not only the consensus
approach but also the role of conflicts (Aylett, 2010; Tschakert and Dietrich, 2010; Beardon and Newman, 2011;
Jobbins, 2011; Shankland and Chambote, 2011). Learning throughout the adaptation process necessitates additional
emphasis on ways of sharing experiences between communities and other stakeholders, both horizontally and
vertically (22.4.5.4). Information and communication technologies, including mobile phones, radio, and the internet,
can play a role in facilitating participatory learning processes and helping to overcome some of the challenges
(Harvey et al., 2012).
The increased emphasis on the importance of innovation for successful adaptation, in both rural and urban contexts,
relates to interventions that employ innovative methods, as well as the innovation role of institutions (Tschakert and
Dietrich, 2010; Dodman and Carmin, 2011; Rodima-Taylor, 2012; Scheffran et al., 2012). Scheffran et al. (2012)
demonstrate how migrant social organizations in the western Sahel initiate innovations across regions by
transferring technology and knowledge, as well as remittances and resources. While relevant, high-quality data is
important as a basis for adaptation planning, innovative methods are being used to overcome data gaps, particularly
local climatic data and analysis capability (Tschakert and Dietrich, 2010; GEF Evaluation Office, 2011).
22.4.5.4. Knowledge Development and Sharing
Recent literature has confirmed the positive role of local and traditional knowledge in building resilience and
adaptive capacity, and shaping responses to climatic variability and change in Africa (Nyong et al., 2007; Osbahr et
al., 2007; Goulden et al., 2009b; Ifejika Speranza et al., 2010; Jalloh et al., 2011b; Newsham and Thomas, 2011).
This is particularly so at the community scale, where there may be limited access to, quality of, or ability to use
scientific information. The recent report on extreme events and disasters (IPCC, 2012) supports this view, finding
high agreement and robust evidence of the positive impacts of integrating indigenous and scientific knowledge for
adaptation. Concerns about the future adequacy of local knowledge to respond to climate impacts within the multi-
stressor context include the decline in intergenerational transmission; a perceived decline in the reliability of local
indicators for variability and change, as a result of socio-cultural, environmental, and climate changes (Hitchcock
2009; Jennings and Magrath 2009); and challenges of the emerging and anticipated climatic changes seeming to
overrun indigenous knowledge and coping mechanisms of farmers (Berkes, 2009; Ifejika Speranza et al., 2010;
Jalloh et al., 2011b; section 22.4.6). Based on analysis of the responses to the Sahel droughts during the 1970s and
1980s, Mortimore (2010) argues that local knowledge systems are more dynamic and robust than is often
acknowledged. Linking indigenous and conventional climate observations can add value to climate change
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adaptation within different local communities in Africa (Roncoli et al., 2002; Nyong et al., 2007; Chang'a et al.,
2010; Guthiga and Newsham, 2011).
Choosing specific adaptation actions that are informed by users perceptions and supported by accurate climate
information, relevant to the scale where decisions are made, would be supportive of the largely autonomous
adaptation taking place in Africa (Vogel and O Brien, 2006; Ziervogel et al., 2008; Bryan et al., 2009; Godfrey et
al., 2010). Key problems regarding how science can inform decision making and policy are how best to match
scientific information, for example about uncertainty of change, with decision needs; how to tailor information to
different constituencies; and what criteria to use to assess whether or not information is legitimate to influence
policy and decisionmaking (Vogel et al., 2007; Hirsch Hadorn et al., 2008). Institutional innovation is one solution:
for example, Nigeria established the Science Committee on Climate Change to develop strategies to bridge the gap
between increasing scientific knowledge and policy (Corsi et al., 2012).
There is agreement that culture, or the shaping social norms, values, and rules including those related to ethnicity,
class, gender, health, age, social status, cast, and hierarchy, is of crucial importance for adaptive capacity as a
positive attribute but also as a barrier to successful local adaptation (22.4.6); further research is required in this field,
not least because culture is highly heterogeneous within a society or locality (Adger et al., 2007, 2009; Ensor and
Berger, 2009; Nielsen and Reenberg, 2010; Jones, 2012). Studies show that while it is important to further develop
the evidence base for the effectiveness of traditional knowledge, integrating cultural components such as stories,
myths, and oral history into initiatives to document local and traditional knowledge on adaptive or coping
mechanisms is a key to better understanding how climate vulnerability and adaptation are framed and experienced
(Urquhart, 2009; Beardon and Newman, 2011; Ford et al., 2012). Appropriate and equitable processes of
participation and communication between scientists and local people have been found to prevent misuse or
misappropriation of local and scientific knowledge (Nyong et al., 2007; Orlove et al., 2010; Crane, 2010).
While multi-stakeholder platforms promote collaborative adaptation responses (CARE, 2012), adaptation initiatives
in Africa lack comprehensive, institutionalised and proactive systems for knowledge sharing (GEF Evaluation
Office, 2011; AAP, 2012).
22.4.5.5. Communication, Education, and Capacity Development
Capacity development and awareness raising to enhance understanding of climate impacts and adaptation
competencies and engender behavioural change have been undertaken through civil society-driven approaches or by
institutions, such as regional and national research institutes, international and national programs and non-
governmental organizations (UNFCCC, 2007; Reid et al., 2010; CCAA, 2011; START, 2011; Figueiredo and
Perkins, 2012). Promising examples include youth ambassadors in Lesotho and civil society organizations in
Tanzania (Corsi et al., 2012), and children as effective communicators and advocates for adaptation-related
behavioral and policy change (22.4.3). Progress on inclusion of climate change into formal education is mixed,
occurring within the relatively low priority given to environmental education in most countries (UNFCCC, 2007;
Corsi et al., 2012; Mukute et al., 2012).
Innovative methods used to communicate climate change include participatory video, photo stories, oral history
videos, vernacular drama, radio, television and festivals, with an emphasis on the important rle of the media (Suarez
et al., 2008; Harvey, 2011; Chikapa, 2012; Corsi et al., 2012). Better evidence-based communication processes will
enhance awareness raising of the diverse range of stakeholders at all levels on the different aspects of climate
change (Niang, 2007; Simane et al., 2012). A better understanding of the dimensions of the problem could be
achieved by bringing together multiple users and producers of scientific and local knowledgein a trans-disciplinary
process (Vogel et al., 2007; Hirsch Hadorn et al., 2008; Ziervogel et al., 2008; Koné et al., 2011)
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22.4.5.6. Ecosystem Services, Biodiversity, and Natural Resource Management
Africa s longstanding experiences with natural resource management, biodiversity use, and ecosystem-based
responses such as afforestation, rangeland regeneration, catchment rehabilitation and community-based natural
resource management (CBNRM) can be harnessed to develop effective and ecologically sustainable local adaptation
strategies (high confidence). Relevant specific experiences include using mobile grazing to deal with both spatial
and temporal rainfall variability in the Sahel (Djoudi et al., 2013); reducing the negative impacts of drought and
floods on agricultural and livestock-based livelihoods through forest goods and services in Mali, Tanzania, and
Zambia (Robledo et al., 2012); and ensuring food security and improved livelihoods for indigenous and local
communities in West and Central Africa through the rich diversity of plant and animal genetic resources (Jalloh et
al., 2011b).
Natural resource management (NRM) practices that improve ecosystem resilience can serve as proactive, low
regrets adaptation strategies for vulnerable livelihoods (high confidence). Two relevant widespread dual-benefit
practices, developed to address desertification, are natural regeneration of local trees (Box 22-2) and water
harvesting. Water harvesting practices11 have increased soil organic matter, improved soil structure, and increased
agricultural yields at sites in Burkina Faso, Mali, Niger, and elsewhere, and are used by 60% of farmers in one area
of Burkina Faso (Fatondji et al., 2009; Vohland and Barry, 2009; Barbier et al., 2009; Larwanou and Saadou, 2011).
Although these and other practices serve as adaptations to climate change, revenue generation and other concerns
may outweigh climate change as a motivating factor in their adoption (Mertz et al., 2009; Nielsen and Reenberg,
2010). While destocking of livestock during drought periods may also address desertification and adaptation, the
lack of individual incentives and marketing mechanisms to destock and other cultural barriers inhibit their
widespread adoption in the Sahel (Hein et al., 2009; Nielsen and Reenberg, 2010). Despite these provisos and other
constraints (see for example Nelson and Agrawal, 2008; section 22.4.6 further highlights local-level institutional
constraints), local stakeholder institutions for CBNRM do enable a more flexible response to changing climatic
conditions; CBNRM is also a vehicle for improving links between ecosystem services and poverty reduction, to
enable sustainable adaptation approaches (Shackleton et al., 2010; Chishakwe et al., 2012; Girot et al., 2012). Based
on lessons learned in Botswana, Malawi, Mozambique, Namibia, Tanzania, Zambia and Zimbabwe, Chishakwe et
al. (2012) point out the synergies between CBNRM and adaptation at the community level, notwithstanding
institutional and other constraints experienced with CBNRM.
[FOOTNOTE 11: Water harvesting refers to a collection of traditional practices in which farmers use small planting
pits, half-moon berms, rock bunds along contours, and other structures to capture runoff from episodic rain events
(Kandji et al., 2006).]
_____ START BOX 22-2 HERE _____
Box 22-2. African Success Story: Integrating Trees into Annual Cropping Systems
Recent success stories from smallholder systems in Africa illustrate the potential for transforming degraded
agricultural landscapes into more productive, sustainable and resilient systems by integrating trees into annual
cropping systems. For example, in Zambia and Malawi, an integrated strategy for replenishing soil fertility on
degraded lands, which combines planting of nitrogen-fixing Faidherbia trees with small doses of mineral fertilizers,
has consistently more than doubled yields of maize leading to increased food security and greater income generation
(Garrity et al., 2010). In the Sahel, natural regeneration, or the traditional selection and protection of small trees to
maturity by farmers and herders has, perhaps for centuries, produced extensive parks of Acacia albida (winter thorn)
in Senegal (Lericollais, 1989), Adansonia digitata (baobab) in West and southern Africa (Sanchez et al., 2011), and
Butyrospermum parkii (Shea butter) in Burkina Faso (Gijsbers et al., 1994). Recent natural regeneration efforts have
increased tree density and species richness at locations in Burkina Faso (Raebild et al., 2012) and Niger (Larwanou
and Saadou, 2011), though adoption and success is somewhat dependent on soil type (Haglund et al., 2011;
Larwanou and Saadou, 2011). In southern Niger, farmer-managed natural regeneration of Faidherbia albida and
other field trees, which began in earnest in the late 1980s, has led to large-scale increase in tree cover across 4.8
million ha, and to decreased sensitivity to drought of the production systems, compared to other regions in Niger
(Reij et al., 2009; Tougiani et al., 2009; Sendzimir et al., 2011).
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_____ END BOX 22-2 HERE _____
Differentiation in the literature is growing between hard path and soft path approaches to adaptation (Sovacool,
2011; Kundzewicz, 2011), with soft path , low-regrets approaches, such as using intact wetlands for flood risk
management, often the first line of defence for poor people in Africa; as contrasted with hard path approaches like
embankments and dams for flood control (McCully, 2007; Kundzewicz, 2011). Intact ecosystem services and
biodiversity are recognized as critical components of successful human adaptation to climate change that may be
more effective and incur lower costs than hard or engineered solutions (Abramovitz et al., 2002; Petersen and
Holness, 2011; UNDP-UNEP Poverty-Environment Initiative, 2011a; Girot et al, 2012; Pramova et al., 2012;
Roberts et al., 2012; Box 22-2). This provides a compelling reason for linking biodiversity, developmental, and
social goals, as taken up, for example, in Djibouti s NAPA project on mangrove restoration to reduce salt water
intrusion and coastal production losses due to climate hazards (Pramova et al., 2012).
The emerging global concept of ecosystem-based adaptation (EbA) provides a system-oriented approach for
Africa s longstanding local NRM practices. Despite the evidence from studies cited in this section, scaling-up to
prioritize ecosystem responses and EbA in plans and policy has been slow; a broad understanding that EbA is an
integral component of the developmental agenda, rather than a competing green agenda, would promote this
process. Adaptive environmental governance represents one of the future challenges for the implementation of EbA
strategies in Africa, together with sustainable use of resources, secure access to meet needs under climate change,
and strong local institutions to enable this (Robledo et al., 2012). Ecosystem-based adaptation could be an important
approach to consider for the globally significant Congo Basin forests, particularly given the predominance of
REDD+ approaches for this region that risk neglecting adaptation responses, or may result in maladaptation
(Somorin et al., 2012; Sonwa et al., 2012; sections 22.4.5.8, 22.6.2). Ecosystem-based approaches are further
discussed in WGII Chapter 4, and Box CC-EA.
22.4.5.7. Technological and Infrastructural Adaptation Responses
Since AR4, experience has been gained on technological and infrastructural adaptation in agricultural and water
management responses, for climate-proofing infrastructure, and for improved food storage and management to
reduce post-harvest losses; this has been increasingly in conjunction with soft measures.
There is increased evidence that farmers are changing their production practices in response to increased food
security risks linked to climate change and variability, through both technical and behavioural means. Examples
include planting cereal crop varieties that are better suited to shorter and more variable growing seasons (Akullo et
al., 2007; Thomas et al., 2007; Yesuf et al., 2008; Yaro, 2010; Laube et al., 2012), constructing bunds to more
effectively capture rainwater and reduce soil erosion (Nyssen et al., 2007; Thomas et al., 2007; Reij et al., 2009),
reduced tillage practices and crop residue management to more effectively bridge dry spells (Ngigi et al., 2006;
Marongwe et al., 2011), and adjusting planting dates to match shifts in the timing of rainfall (Abou-Hadid, 2006;
Vincent et al., 2011b).
Conservation agriculture has good potential to both bolster food production and enable better management of
climate risks (high confidence) (Verchot et al., 2007; Thomas, 2008; Syampungani et al., 2010; Thierfelder and
Wall, 2010; Kassam et al., 2012). Such practices, which include conservation/zero tillage, soil incorporation of crop
residues and green manures, building of stone bunds, agroforestry, and afforestation/reforestation of croplands,
reduce runoff and protect soils from erosion, increase rainwater capture and soil water-holding capacity, replenish
soil fertility, and increase carbon storage in agricultural landscapes. Conservation agriculture systems have potential
to lower the costs of tillage and weed control with subsequent increase in net returns, as found in Malawi by Ngwira
et al. (2012).
Expansion of irrigation in sub-Saharan Africa holds significant potential for spurring agricultural growth while also
better managing water deficiency risks associated with climate change (Dillon, 2011; You et al., 2011). Embedding
irrigation expansion within systems-level planning that considers the multi-stressor context in which irrigation
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expansion is occurring can help to ensure that efforts to promote irrigation can be sustained and do not instead
generate a new set of hurdles for producers or engender conflict (van de Giesen et al., 2010; Burney and Naylor,
2012; Laube et al., 2012). Suitable approaches to expand irrigation in Africa include using low-pressure drip
irrigation technologies and construction of small reservoirs, both of which can help to foster diversification toward
irrigated high-value horticultural crops (Karlberg et al., 2007; Woltering et al., 2011; Biazin et al., 2012). If drought
risk increases and rainfall patterns change, adaptation in agricultural water management would be enhanced through
a strategic approach that encompasses overall water use efficiency for both rainfed and irrigated production (Wei et
al., 2009), embeds irrigation expansion efforts within a larger rural development context that includes increased
access to agricultural inputs and markets (You et al., 2011; Burney and Naylor, 2012), and that involves an
integrated suite of options (e.g., plant breeding and improved pest and disease and soil fertility management, and in
situ rainwater harvesting) to increase water productivity (Passioura, 2006; Biazin et al., 2012).
Experience has been gained since the AR4 on adaptation of infrastructure (transportation, buildings, food storage,
coastal), with evidence that this can sometimes be achieved at low cost, and additional implementation of soft
measures such as building codes and zone planning (UNFCCC, 2007; Halsnaes and and Trarup, 2009; Urquhart,
2009; UN-Habitat and UNEP, 2010; AfDB, 2011; Mosha, 2011; Siegel, 2011; Corsi et al., 2012). Examples of
adaptation actions for road and transportation infrastructure include submersible roads in Madagascar and building
dikes to avoid flooding in Djibouti (UNFCCC, 2007; Urquhart, 2009). Infrastructural climate change impact
assessments and enhanced construction and infrastructural standards - such as raising foundations of buildings,
strengthening roads, and increasing storm water drainage capacity - are steps to safeguard buildings in vulnerable
locations or with inadequate construction (UN-Habitat and UNEP, 2010; Mosha, 2011; Corsi et al., 2012).
Mainstreaming adaptation into infrastructure development can be achieved at low cost, as has been shown for flood-
prone roads in Mozambique (Halsnaes and and Trarup, 2009). Integrating climate change considerations into
infrastructure at the design stage is preferable from a cost and feasibility perspective than trying to retrofit
infrastructure (Chigwada, 2005; Siegel, 2011). Softer measures, such as building codes and zone planning are being
implemented and are needed to complement and/or provide strategic guidance for hard infrastructural climate
proofing, for example, the adoption of cyclone-resistant standards for public buildings in Madagascar (AfDB, 2011).
Research in South Africa has recognized that the best option for adaptation in the coastal zone is not to combat
coastal erosion in the long term, but rather to allow progression of the natural processes (Naidu et al., 2006;
Zitholele Consulting, 2009).
Reducing post-harvest losses through improved food storage, food preservation, greater access to processing
facilities, and improved systems of transportation to markets are important means to enhance food security (Brown
et al., 2009; Godfray et al., 2010; Codjoe and Owusu 2011). Low cost farm-level storage options, such as metal silos
(Tefera et al., 2011), and triple-sealed plastic bags (Baoua et al., 2012) are effective for reducing post-harvest losses
from pests and pathogens. Better storage allows farmers greater flexibility in when they sell their grain, with related
income benefits (Brown et al., 2009), and reduces post-harvest infection of grain by aflatoxins, which is widespread
in Africa and increases with drought stress and high humidity during storage (Cotty and Jaime-Garcia, 2007;
Shephard, 2008).
22.4.5.8. Maladaptation Risks
The literature increasingly highlights the need, when designing development or adaptation research, policies and
initiatives, to adopt a longer-term view and to consider the multi-stressor context in which people live, in order to
avoid maladaptation, or outcomes that may serve short-term goals but come with future costs to society (refer to
Glossary). The short-term nature of policy and other interventions, especially if they favor economic growth and
modernization over resilience and human security, may themselves act as stressors or allow people to only react to
short-term climate variability (Bryan et al., 2009; Brooks et al., 2009; Bunce et al., 2010a; Levine et al, 2011). The
political context can also undermine autonomous adaptation and lead to maladaptation; for instance, Smucker and
Wisner (2008) found that political and economic changes in Kenya meant that farmers could no longer use
traditional strategies for coping with climatic shocks and stressors, with the poorest increasingly having to resort to
coping strategies that undermined their long-term livelihood security, also known as erosive coping, such as more
intensive grazing of livestock and shorter crop rotations (van der Geest and Dietz, 2004). In a case from the Simiyu
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wetlands in Tanzania, Hamisi et al. (2012) find that coping and reactive adaptation strategies may lead to
maladaptation for instance, through negative impacts on natural vegetation because of increased intensity of
farming in wetter parts of the floodplain, where farmers have moved to exploit the higher soil water content.
Some diversification strategies, such as charcoal production and artisanal mining, may increase risk through
promoting ecological change and the loss of ecosystem services to fall back on (Paavola, 2008; Adger et al, 2011;
Shackleton and Shackleton, 2012). Studies also highlight risks that traditional adaptive pastoralism systems may be
replaced by maladaptive activities. For example, charcoal production has become a major source of income for 70%
of poor and middle-income pastoralists in some areas of Somaliland, with resultant deforestation (Hartmann and
Sugulle, 2009).
Another example of maladaptation provided in the literature is the potential long-term hydro-dependency risks and
threats to ecosystem health and community resilience as a result of increased dam building in Africa, which may be
underpinned by policies of multi-lateral donors (Avery, 2012; Beilfuss, 2012; Jones et al., 2012). While increased
rainwater storage will assist with buffering dry periods, and hydropower can play a key role in ending energy
poverty, it is important that this is designed to promote environmental and social sustainability; that costs and
benefits are equitably shared; and that water storage and energy generation infrastructure is itself climate proofed.
Additional substantive review of such international development projects would assist in assuring that these do not
result in maladaptation. See WGII Chapter 4 for a discussion of the unwanted consequences of building more and
larger impoundments and increased water abstraction on terrestrial and freshwater ecosystems; health aspects of this
are noted in sections 22.3.5.1 and 22.3.5.4. See section 22.6.2 on avoiding undesirable trade-offs between REDD+
approaches and adaptation that have the potential to result in significant maladaptation.
22.4.6. Barriers and Limits to Adaptation in Africa
A complex web of interacting barriers to local-level adaptation exists that manifests from national to local scales to
constrain adaptation, which includes institutional, political, social, cultural, biophysical, cognitive and behavioral,
and gender-related (high confidence). While relatively few studies from Africa have focused specifically on barriers
and limits to adaptation, perceived and experienced constraints distilled from the literature encompass the resources
needed for adaptation, the factors influencing adaptive capacity, the reasons for not employing particular adaptive
strategies or not responding to climate change signals, and the reasons why some groups or individuals adapt but not
others (Roncoli et al., 2010; Bryan et al., 2011; Nyanga et al., 2011; Ludi et al., 2012).
At the local level, institutional barriers hamper adaptation through elite capture and corruption; poor survival of
institutions without social roots; and lack of attention to the institutional requirements of new technological
interventions (Ludi et al., 2012). Tenure security over land and vital assets is widely accepted as being crucial for
enabling people to make longer-term and forward-looking decisions in the face of uncertainty, such as changing
farming practices, farming systems, or even transforming livelihoods altogether (Bryan et al., 2009; Brown et al.,
2010; Romero González et al., 2011). In addition to unclear land tenure, legislation forbidding ecosystem use is one
of the issues strengthening underlying conflicts over resources in Africa; resolving this would enable ecosystems to
contribute to adaptation beyond short-term coping (Robledo et al., 2012). There is also evidence that innovation may
be suppressed if the dominant culture disapproves of departure from the normal way of doing things (Ludi et al.,
2012; Jones 2012).
Characteristics such as wealth, gender, ethnicity, religion, class, caste, or profession can act as social barriers for
some to adapt successfully or acquire the required adaptive capacities (Ziervogel et al., 2008; Godfrey et al. 2010;
Jones and Boyd, 2011). Based on field research conducted in the Borana area of southern Ethiopia, Debsu (2012)
highlights the complex way in which external interventions may affect local and indigenous institutions by
strengthening some coping and adaptive mechanisms and weakening others. Restrictive institutions can block
attempts to enhance local adaptive capacity by maintaining structural inequities related to gender and ethnic
minorities (Jones, 2012). Constraints faced by women, often through customs and legal barriers, include limited
access to land and natural resources; lack of credit and input in decisionmaking, limited ability to take financial risk,
lack of confidence, limited access to information and new ideas, and under-valuation of women s opinions
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(McFerson, 2010; Peach Brown, 2011, Djoudi and Brockhaus 2011; Jones 2012; Ludi et al., 2012; Goh, 2012;
Codjoe et al. 2012).
Few small-scale farmers across Africa are able to adapt to climatic changes, while others are restricted by a
suite of overlapping barriers (high agreement, robust evidence). Constraints identified in Kenya, South
Africa, Ethiopia, Malawi, Mozambique, Zimbabwe, Zambia and Ghana included poverty and a lack of cash
or credit (financial barriers); limited access to water and land, poor soil quality, land fragmentation, poor
roads, and pests and diseases (biophysical and infrastructural barriers); lack of access to inputs, shortage of
labor, poor quality of seed and inputs attributed to a lack of quality controls by government and corrupt
business practices by traders, insecure tenure, and poor market access (institutional, technological, and
political barriers); and finally a lack of information on agroforestry/afforestation, different crop varieties,
climate change predictions and weather, and adaptation strategies (informational barriers) (Bryan et al. 2009,
2011; Barbier et al., 2009; Clover and Eriksen, 2009; Deressa et al., 2009; Roncoli et al., 2010; Mandleni and
Anim, 2011; Nhemachena and Hassan, 2011; Nyanga et al.,2011; Vincent et al., 2011a).
Recognition is increasing that understanding psychological factors such as mindsets and risk perceptions is
crucial for supporting adaptation (Grothmann and Patt, 2005; Patt and Schröter, 2008; Jones, 2012).
Cognitive barriers to adaptation include alternative explanations of extreme events and weather such as
religion (God s will), the ancestors, and witchcraft, or seeing these changes as out of people s own control
(Byran et al., 2009; Roncoli et al., 2010; Mandleni and Anim, 2011; Artur and Hilhorst, 2012; Jones, 2012;
Mubaya et al. 2012).
Climate uncertainty, high levels of variability, lack of access to appropriate real-time and future climate information,
and poor predictive capacity at a local scale are commonly cited barriers to adaptation from the individual to
national level (Repetto, 2008; Dinku et al., 2011; Jones, 2012; Mather and Stretch, 2012). Despite the cultural and
psychological barriers noted above, several studies have shown that farmers with access to climate information are
more predisposed to adjust their behaviour in response to perceived climate changes (Mubaya et al., 2012).
At a policy level, studies have detected political, institutional and discursive barriers to adaptation. Adaptation
options in southern Africa have been blocked by political and institutional inefficiencies, lack of prioritization of
climate change, and the dominance of other discourses, such as the mitigation discourse in South Africa and short-
term disaster-focused views of climate variability (Madzwamuse, 2010; Bele et al., 2011; Berrang-Ford et al.,
2011; Conway and Schipper, 2011; Kalame et al., 2011; Chevallier, 2012; Toteng, 2012; Leck et al., 2012). Lack
of local participation in policy formulation, the neglect of social and cultural context, and the inadvertent
undermining of local coping and adaptive strategies have also been identified by several commentators as barriers
to appropriate national policies and frameworks that would support local-level adaptation (e.g., Brockhaus and
Djoudi, 2008; Bele et al., 2011; Chevallier, 2012).
Many of these constraints to adaptation are well entrenched and will be far from easy to overcome; some may act as
limits to adaptation for particular social groups (high confidence). Biophysical barriers to adaptation in the arid areas
could present as limits for more vulnerable groups if current climate change trends continue (Leary et al., 2008b;
Sallu et al., 2010; Roncoli et al., 2010). Traditional and autonomous adaptation strategies, particularly in the
drylands, have been constrained by social-ecological change and drivers such as population growth, land
privatization, land degradation, widespread poverty, HIV/AIDS, poorly conceived policies and modernization,
obstacles to mobility and use of indigenous knowledge, as well as erosion of traditional knowledge, to the extent
that it is difficult or no longer possible to respond to climate variability and risk in ways that people did in the past
(Leary et al., 2008b; Dabi et al., 2008; Paavola, 2008; Smucker and Wisner, 2008; Clover and Eriksen, 2009;
Conway, 2009; UNCCD et al., 2009; Bunce et al., 2010b; Quinn et al., 2011; Jones, 2012; section 22.4.5.4). As a
result of these multiple stressors working together, the number of response options has decreased and traditional
coping strategies are no longer sufficient (Dube and Sekhwela, 2008). Studies have shown that most autonomous
adaptation usually involves minor adjustments to current practices (e.g. changes in planting decisions); there are
simply too many barriers to implementing substantial changes that require investment (e.g., agroforestry and
irrigation) (Bryan et al., 2011). Such adaptation strategies would be enhanced through government and private
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sector/NGO support, without which many poor groups in Africa may face real limits to adaptation (Vincent et al.,
2011a; Jones, 2012).
These findings highlight the benefits of transformational change in situations where high levels of vulnerability and
low adaptive capacity detract from the possibility for systems to adapt sustainably. This is in agreement with the
Special Report on Extreme Events, which additionally found high agreement and robust evidence for the importance
of a spectrum of actions ranging from incremental steps to transformational changes in order to reduce climate risks
(IPCC, 2012). In support of such solutions, Moench (2011) has called for distilling common principles for building
adaptive capacity at different stages, and adaptive management and learning are seen as critical approaches for
facilitating transformation (Section 22.4.5.3; IPCC, 2012). Chapter 16 provides further discussion on how
encountering limits to adaptation may trigger transformational change, which can be a means of adapting to hard
limits.
22.5. Key Risks for Africa
Table 22-6 highlights key risks for Africa (see also Table 19-4 and CC-KR), as identified through assessment of the
literature and expert judgement of the author team, with supporting evaluation of evidence and agreement in the
sections of this chapter, as referenced in the caption.
[INSERT TABLE 22-6 HERE
Table 22-6: Key risks from climate change and the potential for risk reduction through mitigation and adaptation in
Africa. Key risks are identified based on assessment of the literature and expert judgments made by authors of the
various WGII AR5 chapters, with supporting evaluation of evidence and agreement in the referenced chapter
sections. Each key risk is characterized as very low, low, medium, high, or very high. Risk levels are presented for
the near-term era of committed climate change (here, for 2030-2040), in which projected levels of global mean
temperature increase do not diverge substantially across emissions scenarios. Risk levels are also presented for the
longer-term era of climate options (here, for 2080-2100), for global mean temperature increase of 2°C and 4°C
above preindustrial levels. For each timeframe, risk levels are estimated for the current state of adaptation and for a
hypothetical highly adapted state. As the assessment considers potential impacts on different physical, biological,
and human systems, risk levels should not necessarily be used to evaluate relative risk across key risks. Relevant
climate variables are indicated by symbols.]
As indicated in Table 22-6, seven of the nine key regional risks are assessed for the present as being either medium
or high under current adaptation levels, reflecting both the severity of multiple relevant stressors and Africa s
existing adaptation deficit. This is the case for risks relating to shifts in biome distribution (22.3.2.1), degradation of
coral reefs (22.3.2.3), reduced crop productivity (22.3.4.1), adverse effects on livestock (22.3.4.2), vector- and
water-borne diseases (22.3.5.2, 22.3.5.4), under nutrition (22.3.5.3), and migration (22.6.1.2). This assessment
indicates that allowing current emissions levels to result in a +4°C world (above pre-industrial levels) by the 2080-
2100 period would have negative impacts on Africa s food security, as even under high adaptation levels, risks of
reduced crop productivity and adverse effects on livestock are assessed as remaining very high. Moreover, our
assessment is that even if high levels of adaptation were achieved, risks of stress on water resources (22.3.3),
degradation of coral reefs (22.3.2.3), and the destructive effects of sea level rise and extreme weather events (22.3.6)
would remain high. However, even under a lower emissions scenario leading to a long-term 2°C warming, all nine
key regional risks are assessed as remaining high or very high under current levels of adaptation. The assessment
indicates that even under high adaptation, residual impacts in a 2°C world would be significant, with only risk
associated with migration rated as being capable of reduction to low under high levels of adaptation. High
adaptation would be enabled by concerted effort and substantial funding; even if this is realized, no risk is assessed
as being capable of reduction to below medium status.
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22.6. Emerging Issues
22.6.1. Human Security
Although the significance of human security cannot be overestimated, the evidence of the impact of climate change
on human security in Africa is disputable (see Chapters 12 and 19). Adverse climate events potentially impact all
aspects of human security, either directly or indirectly (on mapping climate security vulnerability in Africa see
Busby et al., 2013). Food security, water stress, land use and, health security, violent conflict, changing migration
patterns, and human settlements are interrelating issues of discussions between climate change and human security.
Violent conflict and migration are discussed below (for further details see Chapter 12).
22.6.1.1 Violent Conflict
While there seems to be consensus that the environment is only one of several interconnected causes of conflict and
is rarely considered to be the most decisive factor (Kolmannskog, 2010), it remains disputed whether, and if so,
how, the changing climate directly increases the risk of violent conflict in Africa (for more details see also Chapters
12 and 19, in particular 12.5.1; 19.4; Gleditsch, 2012). However, views are emerging that there is a positive
relationship between increases in temperature and increases in human conflict (Hsiang et al., 2013). Some of the
factors which may increase the risk of violent conflict, such as low per capita incomes, economic contraction and
inconsistent state institutions are sensitive to climate change (12.5.1). For the African Sahel States it has been argued
that the propensity for communal conflict across ethnic groups within Africa is influenced by political and economic
vulnerability to climate change (Raleigh, 2010). Evidence on the question of whether, and if so to what extent,
climate change and variability increases the risk of civil war in Africa is contested (Burke et al., 2009b; Buhaug,
2010; Devitt and Tol, 2012). It has been suggested that due to the depletion of natural resources in Africa as a result
of overexploitation and the impact of climate change on environmental degradation, competition for scarce
resources could increase and lead to violent conflict (Kumssa and Jones, 2010). For East Africa it has been
suggested that increased levels of malnutrition are related to armed conflicts (Rowhani et al., 2011). There is some
agreement that rainfall variation has an inconsistent relationship to conflict: both higher and lower anomalous
rainfall is associated with increased communal conflict levels; although dry conditions have a lesser effect (Raleigh
and Kniveton, 2012; Hendrix and Salehyan 2012; Theisen 2012).
22.6.1.2 Migration
Human migration has social, political, demographic, economic and environmental drivers, which may operate
independently or in combination (for more in-depth discussions see Chapters 12.4 and 19.4.2.1; Perch-Nielsen et al.,
2008; Piguet, 2010; Foresight, 2011; Piguet et al., 2011; Black et al., 2011a; Van der Geest, 2011). Many of these
drivers are climate sensitive (Black et al., 2011c; 12.4.1.). People migrate either temporarily or permanently, within
their country or across borders (12.4.1.2; Figure 12-1; Table 12-3; Warner et al., 2010; Kälin and Schrepfer, 2012).
The evidence base in the field of migration in Africa is both varied and patchy. Evidence suggests that migration is a
strategy to adapt to climate change (12.4.2). Mobility is indeed a strategy (not a reaction) to high levels of climatic
variation that is characteristic of Africa (Tacoli, 2011) and the specifics of the response are determined by the
economic context of the specific communities.
Besides low-lying islands and coastal and deltaic regions in general, sub-Saharan Africa is one of the regions that
would particularly be affected by environmentally induced migration (Gemenne, 2011a). Case studies from Somalia
and Burundi emphasize the interaction of climate change, disaster, conflict, displacement, and migration
(Kolmannskog, 2010). In Ghana for example, an African country with few conflicts caused by political, ethnic, or
religious tensions, and thus with migration drivers more likely related to economic and environmental motivators
(Tschakert and Tutu, 2010), some different types of migration flows are considered to have different sensitivity to
climate change (Black et al., 2011a). The floods of the Zambezi River in Mozambique in 2008 have displaced
90,000 people, and it has been observed that along the Zambezi River Valley, with approximately 1 million people
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living in the flood-affected areas, temporary mass displacement is taking on permanent characteristics (Jäger et al.,
2009; Warner et al., 2010).
Different assessments of future trends have recently produced contradictory conclusions (e.g., UN-OCAH and
IDMC, 2009; Naude, 2010; ADB, 2011; Tacoli, 2011, IDMC, 2011). One approach in assessing future migration
potentials, with considerable relevance to the African context, focused on capturing the net effect of environmental
change on aggregate migration through analysis of both its interactions with other migration drivers and the role of
migration within adaptation strategies, rather than identifying specific groups as potential environmental migrants
(Foresight, 2011). Even if Africa s population doubles by 2050 to 2 billion (Lutz and K.C., 2010) and the potential
for displacement rises as a consequence of the impact of extreme weather events, recent analyses (Foresight, 2011;
Black et al., 2011b) show that the picture for future migration is much more complex than previous assessments of a
rise in climate induced migration suggest, and relates to the intersection of multiple drivers with rates of global
growth, levels of governance, and climate change.
The empirical base for major migration consequences is weak (Liller and Van den Broeck, 2011; Black et al.,
2011a; Gemenne, 2011b) and non-existent for international migration patterns (Marchiori et al., 2011). Even across
the same type of extreme weather event, the responses can vary (Findlay, 2011; Gray, 2011 for Kenya and Uganda;
Raleigh, 2011 for the African Sahel States)).
22.6.2. Integrated Adaptation / Mitigation Approaches
Relevant experience gained in Africa since AR4 in implementing integrated adaptation mitigation responses within
a pro-poor orientation that leverages developmental benefits encompasses some participation of farmers and local
communities in carbon offset systems, increasing the use of relevant technologies such as agroforestry and farmer-
assisted tree regeneration (22.4.5.6), and emerging Green Economy policy responses. The recognition that
adaptation and mitigation are complementary elements of the global response to climate change, and not trade-offs,
is gaining traction in Africa (Goklany, 2007; Nyong et al., 2007; UNCCD et al., 2009; Woodfine, 2009; Jalloh et
al., 2011b; Milder et al., 2011).
While the suitability of on- and off-farm techniques for an integrated adaptation-mitigation response depends on
local physical conditions as well as political and institutional factors, sustainable land management techniques are
particularly beneficial for an integrated response in Africa; these include agroforestry, including through farmer-
managed natural regeneration; and conservation agriculture (Woodfine, 2009; Milder et al., 2011; Mutonyi and
Fungo, 2011; section 22.4.5.6; Box 22-2). An emerging area is multiple-benefit initiatives that aim to reduce
poverty, promote adaptation through restoring local ecosystems, and deliver benefits from carbon markets. Brown et
al., (2011) note the example of a community-based project in Humbo, Ethiopia, which is facilitating adaptation and
generating temporary certified emissions reductions under the Clean Development Mechanism, by restoration of
degraded native forests (2,728 ha) through farmer-managed natural regeneration.
The key role of local communities in carbon offset systems through community forestry entails land use flexibility
(Purdon, 2010), but can be constrained by the lack of supportive policy environments for example, for
conservation agriculture (Milder et al., 2011).
The literature highlights the desirability of responding to climate change through integrated adaptation mitigation
approaches, including through spatial planning, in the implementation of REDD+ in Africa, especially given the
significant contribution to food security and livelihoods of forest systems (Bwango et al., 2000; Guariguata et al.,
2008; Nkem et al., 2007; Nasi et al., 2008; Biesbroek et al., 2009; Somorin et al., 2012). However, forests are
mainly used for reactive coping and not anticipatory adaptation; studies show that governments favour mitigation
while local communities prioritise adaptation (Fisher et al., 2010; Somorin et al., 2012). Flexible REDD+ models
that include agriculture and adaptation hold promise for generating co-benefits for poverty reduction, given food
security and adaptation priorities, and help to avoid trade-offs between REDD+ implementation and adaptive
capacities of communities, ecosystems, and nations (Nkem et al., 2008; Thomson et al., 2010; CIFOR, 2011;
Richard et al., 2011; Wertz-Kanounnikoff et al., 2011).
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Integrated adaptation mitigation responses are being considered within the context of the emerging Green Economy
discussions. African leaders agreed in 2011 to develop an African Green Growth Strategy, to build a shared vision
for promoting sustainable low-carbon growth through a linked adaptation mitigation approach, with adaptation seen
as an urgent priority. A national example is the launch of Ethiopia s Climate Resilient Green Economy Facility in
2012 (Corsi et al., 2012).
22.6.3. Biofuels and Land Use
The potential for first-generation biofuel production in Africa, derived from bioethanol from starch sources and
biodiesel production from oilseeds, is significant given the continent s extensive arable lands, labor availability, and
favorable climate for biofuel crop production (Amigun et al., 2011; Arndt et al., 2011; Hanff et al., 2011). While
biofuel production has positive energy security and economic growth implications, the prospect of wide-scale
biofuel production in Africa carries with it significant risks related to about environmental and social sustainability.
Among the concerns are competition for land and water between fuel and food crops, adverse impacts of biofuels on
biodiversity and the environment, contractual and regulatory obligations that expose farmers to legal risks, changes
in land tenure security, and reduced livelihood opportunities for women, pastoralists and migrant farmers who
depend on access to the land resource base (Unruh, 2008; Amigun et al., 2011; German et al., 2011; Schoneveld et
al., 2011).
More research is needed to understand fully the socioeconomic and environmental tradeoffs associated with biofuel
production in Africa. One critical knowledge gap concerns the effect of biofuel production, particularly large-scale
schemes, on land use change and subsequent food and livelihood security. For example, the conversion of marginal
lands to biofuel crop production would impact the ability of users of these lands (pastoralists and in some cases
women who are allocated marginal land for food and medicinal production) to participate in land use and food
production decisions (Amigun et al., 2011; Schoneveld et al., 2011). In addition, biofuel production could
potentially lead to the extension of agriculture into forested areas, either directly through conversion of fallow
vegetation or the opening of mature woodland, or indirectly through use of these lands to offset food crop
displacement (German et al., 2011). Such land use conversion would result in biofuel production reducing terrestrial
carbon storage potential (Vang Rasmussen et al., 2012a; Vang Rasmussen et al., 2012b).
Better agronomic characterization of biofuel crops is another key knowledge gap. For example, little information
exists with respect to the agronomic characteristics of the oilseed crop Jatropha (Jatropha curcas) under conditions
of intensive cultivation across differing growing environments, despite the fact that Jatropha has been widely touted
as an appropriate feedstock for biofuel production in Africa because of its ability to grow in a wide range of climates
and soils. Oilseed yields of Jatropha can be highly variable, and even basic information about yield potential and
water and fertilizer requirements for producing economically significant oilseed yields is scanty (Achten et al.,
2008; Peters and Thielmann, 2008; Hanff et al., 2011). Such knowledge would not only provide a basis for better
crop management but would also help to gain better estimates of the extent of water consumption for biofuel
production in the context of non-biofuel water-use needs across landscapes. Assessments of Jatropha s potential as
an invasive species and its potential allelopathic effects on native vegetation are also needed, in light of the fact that
some countries have designated Jatropha as an invasive species (Achten et al., 2008).
22.6.4. Climate Finance and Management
Recent analyses emphasise the significant financial resources and technological support needed to both address
Africa s current adaptation deficit and to protect rural and urban livelihoods, societies and economies from climate
change impacts at different local scales, with estimates of adaptation costs between US$20-30 billion per annum
over the next couple of decades, up to US$60 billion per annum by 2030 (for example see figure 22.6), although
these figures are likely to be under-estimates, as studies upon which these estimates are based do not always include
the costs of overcoming Africa s current adaptation deficit, may be run for one scenario at a time, and do not factor
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in a range of uncertainties in the planning environment (Parry et al, 2009b; Fankhauser and Schmidt-Traub, 2010;
Watkiss et al, 2010; AfDB, 2011; Dodman and Carmin, 2011; LDC Expert Group, 2011; Smith et al, 2011).
Damages related to climate change may affect economic growth and the ability to trade (Lecocq and Shalizi, 2007;
Ruppel and Ruppel-Schlichting, 2012). Costs of adaptation and negative economic impacts of climate change have
been referred to in sections 22.3.4.4 and 22.3.6; Warner et al. (2012) have highlighted the residual impacts of
climate change that would occur after adaptation, for case studies in Kenya and The Gambia. The following
examples are illustrative of the move to discuss financial implications in the literature.
Scenarios for Tanzania, where agriculture accounts for about half of gross production and employs about 80% of the
labor force (Thurlow and Wobst, 2003), project that changes in the mean and extremes of climate variables, could
increase poverty vulnerability (Ahmed et al., 2011). Scenarios for Namibia based on a computable general
equilibrium model project that annual losses to the economy ascribed to the impacts of climate change on the
country s natural resources could range between 1.0% and 4.8% of GDP (Reid et al., 2008). Ghana s agricultural
and economic sector with cocoa being the single most important export product is particularly vulnerable, since
cocoa is prone to the effects of a changing climate (Black et al., 2011c), which has been central to the country s
debates on development and poverty alleviation strategies (WTO Trade Policy Review, 2008).
The potential for adaptation to reduce the risks associated with sea level rise is substantial for cumulative land loss
and for numbers of people flooded or forced to migrate, with adaptation costs lower than the economic and social
damages expected if nothing is done (Kebede et al., 2010).See Figure 22-6.
[INSERT FIGURE 22-6 HERE
Figure 22-6: Total additional costs of adaptation per year from 2000 to 2100 for Tanzania (including beach
nourishment and sea and river dikes). The values do not consider the existing adaptation deficit (values in $US
2005, without discounting Source: Kebede et al., 2010.]
The Dynamic Interactive Vulnerability Assessment (DIVA) model was used to assess the monetary and non-
monetary impacts of sea level rise on the entire coast (3,461 km) of Tanzania. Under the B1 low-range sea level rise
scenario it was estimated that by 2030, a total area of 3,579 to 7,624 km2 would be lost, mainly through inundation;
with around 234,000 to 1.6 million people per year who could potentially experience flooding. Without adaptation,
residual damages have been estimated at between US$ 26 and 55 million per year (Kebede et al., 2010). Table 22-7
shows the economic impacts of land inundated in Cape Town based on different sea level rise scenarios.
[INSERT TABLE 22-7 HERE
Table 22-7: Land inundated and economic impacts in Cape Town based on a risk assessment (Cartwright, 2008).]
In line with increasing international impetus for adaptation (Persson et al., 2009) the Parties to the UNFCCC agreed
on providing adequate, predictable and sustainable financial resources for adaptation in developing countries, and,
within this context, paid special attention to Africa which is particularly vulnerable to the adverse effects of
climate change (UNFCCC, 2009; UNFCCC, 2011; Berenter, 2012). Doubts remain about how private sector
financing can be effectively mobilized and channeled toward adaptation in developing countries (Atteridge, 2011;
Naidoo et al., 2012). The 2012 Landscape of Climate Finance Report (Buchner et al., 2012) stated that mitigation
activities attracted US$ 350 billion, mostly related to renewable energy and energy efficiency, while adaptation
activities attracted US$ 14 billion. Approximately 30% of the global distributed adaptation finance went to Africa
(Nakhooda et al., 2011) and seems to prioritize the continent (Naidoo et al., 2012). However, it is being questioned,
whether the adaptation funding that is currently delivered does fulfill demonstrated needs (Flam and Skjaerseth,
2009; Denton, 2010 for sub-Saharan Africa Nakhooda et al., 2011).
Effective adaptation requires more than sufficient levels of funding. It requires developing country readiness,
which includes abilities to plan and access finances; the capacity to deliver adaptation projects and programs, and to
monitor, report, and evaluate their effectiveness (Vandeweerd et al., 2012); and also a regulatory framework, which
guarantees e.g. property rights (IPCC AR5 WGIII Draft 2 Chapter 16 p.:27; line 30-33). Particularly serious
challenges are associated with directing finance to the sectors and people most vulnerable to climate change
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(Denton, 2010; Nakhooda et al., 2011; Pauw et al., 2012). The risk of fund mismanagement with regard to climate
finance and adaptation funds needs to be borne in mind. Suggestions to address adequately the level of complexity,
uncertainty, and novelty that surrounds many climate finance issues inter alia include longer-term and integrated
programs rather than isolated projects; building capacity and institutions in African countries (Nakhooda et al.,
2011; Pauw et al., 2012); identifying priorities, processes, and knowledge needs at the local level (Haites, 2011;
Pauw, 2013); and, accordingly, developing grassroots projects (Fankhauser and Burton, 2011).
22.7. Research Gaps
Research has a key role to play in providing information for informed decisionmaking at local to national levels
(Fankhauser, 1997; Ziervogel et al., 2008; Arendse and Crane, 2010). While there is significant activity in African
research institutions, much African research capacity is spent on foreign-led research that may necessarily prioritize
addressing national knowledge gaps about climate change (Madzwamuse, 2010), and African research may lack
merited policy uptake or global recognition as it is often not published in peer-reviewed literature (Denton et al.,
2011).
The following overarching data and research gaps have been identified also see Table 22.8:
Data management and monitoring of climate and hydroclimate parameters and development of climate
change scenarios as well as monitoring systems to address climate change impacts in the different sectors
(for example the impacts of pests and diseases on crops and livestock) and systems;
Research and improved methodologies to assess and quantify the impact of climate change on different
sectors and systems.
Socio-economic consequences of the loss of ecosystems and also of economic activities as well as of
certain choices in terms of mitigation (biofuels and their links with food and livelihood security for
example) and adaptation to climate change;
The links influence of climate change in emerging issues such as migration and urban food security;
Developing tools allowing decision makers to make their decisions based on the complexity of the world
under climate change, taking into consideration gender, age and all regarding the contribution of local
communities.
[INSERT TABLE 22-8 HERE
Table 22-8: Research gaps in different sectors.]
Frequently Asked Questions
FAQ 22.1: How could climate change impact food security in Africa? [to be inserted in Section 22.3.4.5]
Food security is comprised of availability (is enough food produced), access (can people get it, and afford it),
utilization (how local conditions bear on peoples nutritional uptake from food), and stability (is the supply and
access ensured). Strong consensus exists that climate change will have a significantly negative impact on all these
aspects of food security in Africa.
Food availability could be threatened through direct climate impacts on crops and livestock from increased
flooding, drought, shifts in the timing and amount of rainfall, and high temperatures, or indirectly through increased
soil erosion from more frequent heavy storms or through increased pest and disease pressure on crops and livestock
caused by warmer temperatures and other changes in climatic conditions. Food access could be threatened by
climate change impacts on productivity in important cereal-producing regions of the world which, along with other
factors, could raise food prices and erode the ability of the poor in Africa to afford purchased food. Access is also
threatened by extreme events that impair food transport and other food system infrastructure. Climate change could
impact food utilization through increased disease burden that reduces the ability of the human body to absorb
nutrients from food. Warmer and more humid conditions caused by climate change could impact food availability
and utilization through increased risk of spoilage of fresh food and pest and pathogen damage to stored foods
(cereals, pulses, tubers) that reduces both food availability and quality. Stability could be affected by changes in
availability and access that are linked to climatic and other factors.
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FAQ 22.2: What role does climate change play with regard to violent conflict in Africa?
[to be inserted in Section 22.6.1.1]
Wide consensus exists that violent conflicts are based on a variety of interconnected causes, of which the
environment is considered to be one, but rarely the most decisive factor. Whether the changing climate increases the
risk of civil war in Africa remains disputed and little robust research is available to resolve this question. Climate
change impacts that intensify competition for increasingly scarce resources like freshwater and arable land,
especially in the context of population growth, are areas of concern. The degradation of natural resources as a result
of both overexploitation and climate change will contribute to increased conflicts over the distribution of these
resources. In addition to these stressors, however, the outbreak of armed conflict depends on many country-specific
sociopolitical, economic and cultural factors.
Cross-Chapter Boxes
Box CC-GC. Gender and Climate Change
[Jon Barnett (Australia), Marta G. Rivera Ferre (Spain), Petra Tschakert (U.S.A.), Katharine Vincent (South Africa),
Alistair Woodward (New Zealand)]
Gender, along with socio-demographic factors of age, wealth and class, is critical to the ways in which climate
change is experienced. There are significant gender dimensions to impacts, adaptation and vulnerability. This issue
was raised in WGII AR4 and SREX reports (Adger et al., 2007; IPCC, 2012), but for the AR5 there are significant
new findings, based on multiple lines of evidence on how climate change is differentiated by gender, and how
climate change contributes to perpetuating existing gender inequalities. This new research has been undertaken in
every region of the world (e.g. Brouwer et al., 2007; Nightingale, 2009; Buechler, 2009; Nelson and Stathers, 2009;
Dankelman, 2010; MacGregor, 2010; Alston, 2011; Arora-Jonsson, 2011; Resureccion, 2011; Omolo, 2011).
Gender dimensions of vulnerability derive from differential access to the social and environmental resources
required for adaptation. In many rural economies and resource-based livelihood systems, it is well established that
women have poorer access than men to financial resources, land, education, health and other basic rights. Further
drivers of gender inequality stem from social exclusion from decision-making processes and labour markets, making
women in particular less able to cope with and adapt to climate change impacts (Rijkers and Costa, 2012; Djoudi
and Brockhaus, 2011; Paavola, 2008). These gender inequalities manifest themselves in gendered livelihood impacts
and feminisation of responsibilities: whilst both men and women experience increases in productive roles, only
women experience increased reproductive roles (Resureccion, 2011; 9.3.5.1.5, Box 13-1). A study in Australia, for
example, showed how more regular occurrence of drought has put women under increasing pressure to earn off-farm
income, and contribute to more on-farm labor (Alston, 2011). Studies in Tanzania and Malawi demonstrate how
women experience food and nutrition insecurity since food is preferentially distributed among other family members
(Nelson and Stathers, 2009; Kakota et al., 2011).
AR4 assessed a body of literature that focused on women s relatively higher vulnerability to weather-related
disasters in terms of number of deaths (Adger et al., 2007). Additional literature published since that time adds
nuances by showing how socially-constructed gender differences affect exposure to extreme events, leading to
differential patterns of mortality for both men and women (high confidence) [11.3.3, Table 12-3]. Statistical
evidence of patterns of male and female mortality from recorded extreme events in 141 countries between 1981-
2002 found that disasters kill women at an earlier age than men (Neumayer and Plümper, 2007) [Box 13-1]. Reasons
for gendered differences in mortality include various socially- and culturally-determined gender roles. Studies in
Bangladesh, for example, show that women do not learn to swim and so are vulnerable when exposed to flooding
(Röhr, 2006) and that, in Nicaragua, the construction of gender roles means that middle-class women are expected to
stay in the house, even during floods and in risk-prone areas (Bradshaw, 2010). While the differential vulnerability
of women to extreme events has long been understood, there is now increasing evidence to show how gender roles
for men can affect their vulnerability. In particular, men are often expected to be brave and heroic, and engage in
risky life-saving behaviors that increase their likelihood of mortality [Box 13-1]. In Hai Lang district, Vietnam, for
example, more men died than women due to their involvement in search and rescue and protection of fields during
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flooding (Campbell et al., 2009). Women and girls are more likely to become victims of domestic violence after a
disaster, particularly when they are living in emergency accommodation, which has been documented in the U.S.
and Australia (Jenkins and Phillips, 2008; Anastario et al., 2009; Alston, 2011; Whittenbury, 2013; Box 13-1).
Heat stress exhibits gendered differences, reflecting both physiological and social factors (11.3.3). The majority of
studies in European countries show women to be more at risk, but their usually higher physiological vulnerability
can be offset in some circumstances by relatively lower social vulnerability (if they are well connected in supportive
social networks, for example). During the Paris heat wave, unmarried men were at greater risk than unmarried
women, and in Chicago elderly men were at greatest risk, thought to reflect their lack of connectedness in social
support networks which led to higher social vulnerability (Kovats and Hajat, 2008). A multi-city study showed
geographical variations in the relationship between sex and mortality due to heat stress: in Mexico City, women had
a higher risk of mortality than men, although the reverse was true in Santiago and Sao Paulo (Bell et al., 2008).
Recognizing gender differences in vulnerability and adaptation can enable gender-sensitive responses that reduce the
vulnerability of women and men (Alston, 2013). Evaluations of adaptation investments demonstrate that those
approaches that are not sensitive to gender dimensions and other drivers of social inequalities risk reinforcing
existing vulnerabilities (Figueiredo and Perkins, 2012; Arora-Jonsson, 2011; Vincent et al., 2010). Government-
supported interventions to improve production through cash-cropping and non-farm enterprises in rural economies,
for example, typically advantage men over women since cash generation is seen as a male activity in rural areas
(Gladwin et al., 2001;13.3.1). In contrast, rainwater and conservation-based adaptation initiatives may require
additional labor which women cannot necessarily afford to provide (Baiphethi et al., 2008). Encouraging gender-
equitable access to education and strengthening of social capital are among the best means of improving adaptation
of rural women farmers (Below et al., 2012; Goulden et al., 2009; Vincent et al., 2010) and could be used to
complement existing initiatives mentioned above that benefit men. Rights-based approaches to development can
inform adaptation efforts as they focus on addressing the ways in which institutional practices shape access to
resources and control over decision-making processes, including through the social construction of gender and its
intersection with other factors that shape inequalities and vulnerabilities (Tschakert, 2013; Bee et al., 2013;
Tschakert and Machado, 2012; see also 22.4.3 and Table 22-5).
Box CC-GC References
Adger, W.N., S. Agrawala, M.M.Q. Mirza, C. Conde, K. O Brien, J. Pulhin, R. Pulwarty, B. Smit, and K. Takahashi, 2007: Chapter 17:
Assessment of adaptation practices, options, constraints and capacity. In: Climate Change 2007: Synthesis Report. Contribution of Working
Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. [IPCC (ed.)]. IPCC, Geneva,
Switzerland, pp. 719-743.
Alston, M., 2011: Gender and climate change in Australia. Journal of Sociology, 47(1), 53-70.
Alston, M., 2013: Women and adaptation. Wiley Interdisciplinary Reviews: Climate Change, (4)5, 351-358.
Anastario, M., N. Shebab, and L. Lawry, 2009: Increased gender-based violence among women internally displaced in Mississippi 2 years post-
Hurricane Katrina. Disaster Medicine and Public Health Preparedness, 3(1), 18-26.
Arora-Jonsson, S., 2011: Virtue and vulnerability: Discourses on women, gender and climate change. Global Environmental Change, 21, 744-
751.
Baiphethi, M.N., M. Viljoen, and G. Kundhlande, 2008: Rural women and rainwater harvesting and conservation practices: Anecdotal evidence
from the Free State and Eastern Cape. Agenda, 22(78), 163-171.
Bee, B., M. Biermann, and P. Tschakert, 2013: Gender, development, and rights-based approaches: Lessons for climate change adaptation and
adaptive social protection. In: Research, Action and Policy: Addressing the Gendered Impacts of Climate Change. [Alston, M. and K.
Whittenbury(eds.)]. Springer, Netherlands, pp. 95-108.
Bell M.L., M.S. O'Neill, N. Ranjit, V.H. Borja-Aburto, L.A. Cifuentes and N.C. Gouveia, 2008: Vulnerability to heat-related mortality in Latin
America: a case-crossover study in Sao Paulo, Brazil, Santiago, Chile and Mexico City, Mexico. International Journal of Epidemiology
37(4), 796 804.
Below, T.B., K.D. Mutabazi, D. Kirschke, C. Franke, S. Sieber, R. Siebert, and K. Tscherning, 2012: Can farmers adaptation to climate change
be explained by socio-economic household-level variables? Global Environmental Change, 22(1), 223-235.
Bradshaw, S., 2010: Women, poverty, and disasters: Exploring the links through hurricane Mitch in Nicaragua. In: The international handbook
of gender and poverty: concepts, research, policy. [Chant, S. (ed.)]. Edward Elgar Pub, Cheltenham, UK, pp. 627.
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Brouwer, R., S. Akter, L. Brander, and E. Haque, 2007: Socioeconomic vulnerability and adaptation to environmental risk: A case study of
climate change and flooding in Bangladesh. Risk Analysis, 27(2), 313-326.
Campbell, B., S. Mitchell, and M. Blackett, 2009: Responding to Climate Change in Vietnam. Opportunities for Improving Gender Equality.
Oxfam; UNDP, Hanoi, Vietnam, pp. 1-63.
Dankelman, I., 2010: Introduction: Exploring gender, environment, and climate change. In: Gender and climate change: An introduction.
[Dankelman, I. (ed.)]. Earthscan, London, UK, Sterling, VA, USA, pp. 1-20.
Djoudi, H. and M. Brockhaus, 2011: Is adaptation to climate change gender neutral? Lessons from communities dependent on livestock and
forests in northern Mali. International Forestry Review, 13(2), 123-135.
Figueiredo, P. and P.E. Perkins, 2012: Women and water management in times of climate change: participatory and inclusive processes. Journal
of Cleaner Production, (online).
Gladwin, C.H., A.M. Thomson, J.S. Peterson, and A.S. Anderson, 2001: Addressing food security in Africa via multiple livelihood strategies of
women farmers. Food Policy, 26(2), 177-207.
Goulden, M., L.O. Naess, K. Vincent, and W.N. Adger, 2009: Diversification, networks and traditional resource management as adaptations to
climate extremes in rural Africa: opportunities and barriers. In: Adapting to Climate Change: Thresholds, Values and Governance. [Adger,
W.N., I. Lorenzoni, and K. O Brien(eds.)]. Cambridge University Press, Cambridge, pp. 448-464.
IPCC (ed.), 2012: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working
Groups I and II of the Intergovernmental Panel on Climate Change. Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi,
M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley, Cambridge University Press, Cambridge, UK, and
New York, NY, USA, pp. 582.
Jenkins, P. and B. Phillips, 2008: Battered Women, Catastrophe, and the Context of Safety after Hurricane Katrina. NWSA, 20(3), 49-68.
Kakota, T., D. Nyariki, D. Mkwambisi, and W. Kogi-Makau, 2011: Gender vulnerability to climate variability and household food insecurity.
Climate and Development, 3(4), 298-309.
Kovats R, Hajat S., 2008: Heat stress and public health: a critical review. Public Health, 29, 41-55.
MacGregor, S., 2010: Gender and climate change : from impacts to discourses. Journal of the Indian Ocean Region, 6(2), 223-238.
Nelson, V. and T. Stathers, 2009: Resilience, power, culture, and climate: a case study from semi-arid Tanzania, and new research directions.
Gender & Development, 17(1), 81-94.
Neumayer, E. and T. Plümper, 2007: The gendered nature of natural disasters: The impact of catastrophic events on the gender gap in life
expectancy, 1981 2002. Annals of the Association of American Geographers, 97(3), 551-566.
Nightingale, A., 2009: Warming up the climate change debate: A challenge to policy based on adaptation. Journal of Forest and Livelihood,
8(1), 84-89.
Omolo, N., 2011: Gender and climate change-induced conflict in pastoral communities: Case study of Turkana in northwestern Kenya. African
Journal on Conflict Resolution, 10(2), 81-102.
Paavola, J., 2008: Livelihoods, vulnerability and adaptation to climate change in Morogoro, Tanzania. Environmental Science & Policy, 11(7),
642-654.
Resurreccion, B.P., 2011: The Gender and Climate Debate: More of the Same or New Pathways of Thinking and Doing?. In: Asia Security
Initiative Policy Series. RSIS Centre for Non-Traditional Security (NTS) Studies, Singapore, pp. 1-22.
Rijkers, B. and Costa, R., 2012: Gender and Rural Non-Farm Entrepreneurship, Policy research working papers, 6066, World Bank, pp. 68
Röhr, U., 2006: Gender and climate change. Tiempo, 59, 3-7.
Tschakert, P., 2013: From impacts to embodied experiences: tracing political ecology in climate change research, Geografisk Tidsskrift-Danish
Journal of Geography, 112(2), 144-158.
Tschakert, P. and M. Machado, 2012: Gender Justice and Rights in Climate Change Adaptation: Opportunities and Pitfalls., Ethics and Social
Welfare, doi: 10.1080/17496535.2012.704929.
Vincent, K., T. Cull, and E. Archer, 2010: Gendered vulnerability to climate change in Limpopo province, South Africa. In: Gender and Climate
Change: An Introduction. [Dankelman, I. (ed.)]. Earthscan, London, pp. 160-167.
Whittenbury, K., 2013: Climate Change, Women's Health, Wellbeing and Experiences of Gender-Based Violence in Australia. In: Research,
Action and Policy: Addressing the Gendered Impacts of Climate Change. [Alston, M. and K. Whittenbury(eds.)]. Springer, Australia, pp.
207-222.
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Box CC-UP. Uncertain Trends in Major Upwelling Ecosystems
[Salvador E. Lluch-Cota (Mexico), Ove Hoegh-Guldberg (Australia), David Karl (USA), Hans O. Pörtner (Germany), Svein Sundby (Norway),
Jean-Pierre Gatusso (France)]
Upwelling is the vertical transport of cold, dense, nutrient-rich, relatively low-pH and often oxygen-poor waters to
the euphotic zone where light is abundant. These waters trigger high levels of primary production and a high
biomass of benthic and pelagic organisms. The driving forces of upwelling include wind stress and the interaction of
ocean currents with bottom topography. Upwelling intensity also depends on water column stratification. The major
upwelling systems of the Planet, the Equatorial Upwelling System (EUS, 30.5.2, Figure 30.1A) and the Eastern
Boundary Upwelling Ecosystems (EBUE, 30.5.5, Figure 30.1A), represent only 10% of the ocean surface but
contribute nearly 25 % to global fish production (Figure 30.1B, Table S30.1).
Marine ecosystems associated with upwelling systems can be influenced by a range of bottom-up trophic
mechanisms, with upwelling, transport, and chlorophyll concentrations showing strong seasonal and interannual
couplings and variability. These, in turn, influence trophic transfer up the food chain, affecting zooplankton,
foraging fish, seabirds and marine mammals.
There is considerable speculation as to how upwelling systems might change in a warming and acidifying ocean.
Globally, the heat gain of the surface ocean has increased stratification by 4% (WGI 3.2, 3.4.4, 3.8), which means
that more wind energy is needed to bring deep waters to the surface. It is as yet unclear to what extent wind stress
can offset the increased stratification, due to the uncertainty in wind speed trends (WGI, 3.4.4). In the tropics,
observations of reductions in trade winds over several decades contrast more recent evidence indicating their
strengthening since the early 1990s (WGI, 9.4.1.3.4). Observations and modelling efforts in fact show diverging
trends in coastal upwelling at the eastern boundaries of the Pacific and the Atlantic. Bakun (1990) proposed that the
the difference in heat gaining rates between land and ocean causes an increase in the pressure gradient, which results
in increased alongshore winds and leads to intensified offshore transport of surface water through Ekman pumping,
and the upwelling of nutrient rich, cold waters (Figure CC-UP). Some regional records support this hypothesis,
others do not. There is considerable variability in warming and cooling trends over the past decades both within and
among systems making it difficult to predict changes in the intensity of all Eastern Boundary Upwelling Ecosystems
(30.5.5).
Understanding whether upwelling and climate change will impact resident biota in an additive, synergistic or
antagonistic manner is important for projections of how ecological goods and services provided for human society
will change. Even though upwellings may prove more resilient to climate change than other ocean ecosystems
because of their ability to function under extremely variable conditions (Capone and Hutchins, 2013), consequences
of their shifts are highly relevant since these are the most biologically active systems in the ocean. Increased
upwelling would enhance fisheries yields. However, the export of organic material from surface to deeper layers of
the ocean may increase and stimulate its decomposition by microbial activity, thereby enhancing oxygen depletion
and CO2 enrichment in deeper water layers. Once this water returns to the surface through upwelling benthic and
pelagic coastal communities will be exposed to acidified and deoxygenated water which may combine with
anthropogenic impact to negatively affect marine biota and ecosystem structure of the upper ocean (high confidence,
6.3.2, 6.3.3; 30.3.2.2, 30.3.2.3). Extreme hypoxia may result in abnormal mortalities of fishes and invertebrates
(Keller et al., 2010), reduce the fisheries catch potential and impact aquaculture in coastal areas (5.4.3.3, 6.3.7,
30.5.1.1.2, 30.5.5.1.3, Barton et al., 2012). Shifts in upwelling also coincide with an apparent increase in the
frequency of submarine eruptions of methane and hydrogen sulphide gas, caused by enhanced formation and sinking
of phytoplankton biomass to the hypoxic or anoxic sea floor . This combination of factors has been implicated in the
extensive mortality of coastal fishes and invertebrates (Bakun and Weeks, 2004), resulting in significant reductions
in fishing productivity, such as Cape hake (Merluccius capensis), Namibia s most valuable fishery (Hamukuaya et
al., 1998).
Reduced upwelling would also reduce the productivity of important pelagic fisheries, such as for sardines, anchovies
and mackerel, with major consequences for the economies of several countries (6.4.1, Chp 7, Figure 30.1A, B, Table
S30.1). However, under projected scenarios of reduced upward supply of nutrients due to stratification of the open
ocean , upwelling of both nutrients and trace elements may become increasingly important to maintaining upper
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ocean nutrient and trace metal inventories. It has been suggested that upwelling areas may also increase nutrient
content and productivity under enhanced stratification, and that upwelled and partially denitrified waters containing
excess phosphate may select for N2-fixing microorganisms (Deutsch et al., 2007; Deutsch and Weber, 2012), but
field observations of N2 fixation in these regions have not supported these predictions (Fernandez et al., 2011; Franz
et al., 2012). The role of this process in global primary production thus needs to be validated (low confidence).
The central question therefore is whether or not upwelling will intensify, and if so, whether the effects of intensified
upwelling on O2 and CO2 inventories will outweigh its benefits for primary production and associated fisheries and
aquaculture (low confidence). In any case increasing atmospheric CO2 concentrations will equilibrate with upwelling
waters that may cause them to become more corrosive, depending upon pCO2 of the upwelled water, and potentially
increasingly impact the biota of Eastern Boundary Upwelling Ecosystems.
[INSERT FIGURE UP-1 HERE
Figure UP-1: Upper panel: Schematic hypothetic mechanism of increasing coastal wind-driven upwelling at eastern
boundary systems, where differential warming rates between land and ocean results in increased land-ocean pressure
gradients (1) that produce stronger alongshore winds (2) and offshore movement of surface water through Ekman
transport (3), and increased upwelling of deep cold nutrient rich waters to replace it (4). Lower panel: potential
consequences of climate change in upwelling systems. Increasing stratification and uncertainty in wind stress trends
result in uncertain trends in upwelling. Increasing upwelling may result in higher input of nutrients to the euphotic
zone, and increased primary production, which in turn may enhance pelagic fisheries, but also decreased coastal
fisheries due to an augmented exposure of coastal fauna to hypoxic, low pH waters. Decreased upwelling may result
in lower primary production in these systems with direct impacts on pelagic fisheries productivity.]
Box CC-UP References
Bakun, A., 1990: Global climate change and intensification of coastal ocean upwelling, Science, 247(4939), 198-201.
Bakun, A. and S.J. Weeks, 2004: Greenhouse gas buildup, sardines, submarine eruptions and the possibility of abrupt degradation of intense
marine upwelling ecosystems. Ecology Letters, 7(11), 1015-1023.
Barton, A., B. Hales, G.G. Waldbusser, C. Langdon, R.A. Feely, 2012: The Pacific oyster, Crassostrea gigas, shows negative correlation to
naturally elevated carbon dioxide levels: Implications for near-term ocean acidification effects, Limnology and Oceanography, 57(3): 698-
710.
Capone, D.G. and D.A. Hutchins, 2013: Microbial biogeochemistry of coastal upwelling regimes in a changing ocean. Nature geoscience, 711-
717.
Deutsch, C. and T. Weber, 2012: Nutrient ratios as a tracer and driver of ocean biogeochemistry. Annual Review of Marine Science, 4, 113-114.
Deutsch, C., J.L. Sarmiento, D.M. Sigman, N. Gruber and J.P. Dunne, 2007: Spatial coupling of nitrogen inputs and losses in the ocean. Nature,
445(7124), 163-167.
Fernandez, C., L. Farías and O. Ulloa, 2011: Nitrogen fixation in denitrified marine waters. PLoS ONE, 6(6), e20539.
Franz, J., G. Krahmann, G. Lavik, P. Grasse, T. Dittmar and U. Riebesell, 2012: Dynamics and stoichiometry of nutrients and phytoplankton in
waters influenced by the oxygen minimum zone in the eastern tropical Pacific. Deep Sea Research Part I: Oceanographic Research Papers,
62, 20-31.
Hamukuaya, H., M.J. O'Toole and P.M.J. Woodhead, 1998: Observations of severe hypoxia and offshore displacement of Cape hake over the
Namibian shelf in 1994. South African Journal of Marine Science, 19(1), 57-59.
Keller, AA, Simon V, Chan F, Wakefield WW, Clarke ME, et al., 2010: Demersal fish and invertebrate biomass in relation to an offshore
hypoxic zone along the US West Coast. Fisheries Oceanography 19:76 87.
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Box CC-WE: The Water-Energy-Food/Feed/Fiber Nexus as Linked to Climate Change
[Douglas J. Arent (USA), Petra Döll (Germany), Kenneth M. Strzepek (UNU / USA), Blanca Elena Jimenez Cisneros (Mexico), Andy Reisinger
(New Zealand), FerencToth (IAEA / Hungary), Taikan Oki (Japan)]
Water, energy, and food/feed/fiber are linked through numerous interactive pathways and subject to a changing
climate, as depicted in Figure WE-1. The depth and intensity of those linkages vary enormously between countries,
regions and production systems. Energy technologies (e.g. biofuels, hydropower, thermal power plants),
transportation fuels and modes, and food products (from irrigated crops, in particular animal protein produced by
feeding irrigated crops and forages) may require significant amounts of water (Sections 3.7.2, 7.3.2, 10.2, 10.3.4,
22.3.3, 25.7.2; Allan, 2003; King and Weber 2008; McMahon and Price, 2011; Macknick et al., 2012a). In irrigated
agriculture, climate, irrigating procedure, crop choice and yields determine water requirements per unit of produced
crop. In areas where water (and wastewater) must be pumped and/or treated, energy must be provided (Asano et al.,
2006; Khan and Hanjra, 2009; USEPA, 2010; Gerten et al., 2011). While food production, refrigeration, transport
and processing require large amounts of energy (Pelletier et al., 2011), a major link between food and energy as
related to climate change is the competition of bioenergy and food production for land and water (Section 7.3.2, Box
25-10; Diffenbaugh et al., 2012; Skaggs et al., 2012) (robust evidence, high agreement). Food and crop wastes, and
wastewater, may be used as sources of energy, saving not only the consumption of conventional non-renewable
fuels used in their traditional processes, but also the consumption of the water and energy employed for processing
or treatment and disposal (Schievano et al., 2009; Sung et al., 2010; Olson, 2012). Examples of this can be found in
several countries across all income ranges. For example, sugar cane by-products are increasingly used to produce
electricity or for cogeneration (McKendry, 2002; Kim and Dale 2004) for economic benefits, and increasingly as an
option for greenhouse gas mitigation.
[INSERT FIGURE WE-1 HERE
Figure WE-1: The water-energy-food nexus as related to climate change. The interlinkages of supply/demand,
quality and quantity of water, energy and food/feed/fiber with changing climatic conditions have implications for
both adaptation and mitigation strategies.]
Most energy production methods require significant amounts of water, either directly (e.g., crop-based energy
sources and hydropower) or indirectly (e.g., cooling for thermal energy sources or other operations) (Sections 10.2.2
10.3.4, 25.7.4; van Vliet et al., 2012; Davies et al., 2013) (robust evidence, high agreement). Water for biofuels, for
example, under the IEA Alternative Policy Scenario, which has biofuels production increasing to 71 EJ in 2030, has
been reported by Gerbens-Leenes et al. (2012) to drive global consumptive irrigation water use from 0.5% of global
renewable water resources in 2005 to 5.5% in 2030, resulting in increased pressure on freshwater resources, with
potential negative impacts on freshwater ecosystems. Water is also required for mining (Section 25.7.3), processing,
and residue disposal of fossil and nuclear fuels or their byproducts. Water for energy currently ranges from a few
percent in most developing countries to more than 50% of freshwater withdrawals in some developed countries,
depending on the country (Kenny et al., 2009; WEC, 2010). Future water requirements will depend on electricity
demand growth, the portfolio of generation technologies and water management options employed (WEC, 2010;
Sattler et al., 2012) (medium evidence, high agreement). Future water availability for energy production will change
due to climate change (Sections 3.4, 3.5.1, 3.5.2.2) (robust evidence, high agreement).
Water may require significant amounts of energy for lifting, transport and distribution and for its treatment either to
use it or to depollute it. Wastewater and even excess rainfall in cities requires energy to be treated or disposed. Some
non-conventional water sources (wastewater or seawater) are often highly energy intensive. Energy intensities per
m3 of water vary by about a factor of 10 between different sources, e.g. locally produced potable water from
ground/surface water sources vs. desalinated seawater (Box 25-2, Tables 25-6 and 25-7; Macknick et al., 2012b;
Plappally and Lienhard, 2012). Groundwater (35% of total global water withdrawals, with irrigated food production
being the largest user; Döll et al., 2012) is generally more energy intensive than surface water. In India, for example,
19% of total electricity use in 2012 was for agricultural purposes (Central Statistics Office, 2013), with a large share
for groundwater pumping. Pumping from greater depth increases energy demand significantly electricity use
(kWhr/m3 of water) increases by a factor of 3 when going from 35 to 120 m depth (Plappally and Lienhard, 2012).
The reuse of appropriate wastewater for irrigation (reclaiming both water and energy-intense nutrients) may increase
agricultural yields, save energy, and prevent soil erosion (Smit and Nasr, 1992; Jimenez, 1996; Wichelns et al.,
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2007; Raschid-Sally and Jayakody, 2008) (medium confidence). More energy efficient treatment methods enable
poor quality ( black ) wastewater to be treated to quality levels suitable for discharge into water courses, avoiding
additional fresh water and associated energy demands (Keraita et al, 2008). If properly treated to retain nutrients,
such treated water may increase soil productivity, contributing to increased crop yields/food security in regions
unable to afford high power bills or expensive fertilizer (Oron, 1996; Lazarova and Bahri, 2005; Redwood and
Huibers, 2008; Jimenez, 2009) (high confidence).
Linkages among water, energy, food/feed/fiber and climate are also strongly related to land use and management
(Section 4.4.4, Box 25-10) (robust evidence, high agreement). Land degradation often reduces efficiency of water
and energy use (e.g. resulting in higher fertilizer demand and surface runoff), and compromises food security
(Sections 3.7.2, 4.4.4). On the other hand, afforestation activities to sequester carbon have important co-benefits of
reducing soil erosion and providing additional (even if only temporary) habitat (see Box 25-10) but may reduce
renewable water resources. Water abstraction for energy, food or biofuel production or carbon sequestration can also
compete with minimal environmental flows needed to maintain riverine habitats and wetlands, implying a potential
conflict between economic and other valuations and uses of water (Sections 25.4.3 and 25.6.2, Box 25-10) (medium
evidence, high agreement). Only a few reports have begun to evaluate the multiple interactions among energy, food,
land, and water and climate (McCornick et al., 2008; Bazilian et al., 2011; Bierbaum and Matson, 2013), addressing
the issues from a security standpoint and describing early integrated modeling approaches. The interaction among
each of these factors is influenced by the changing climate, which in turn impacts energy and water demand,
bioproductivity and other factors (see Figure WE-1 and Wise et al., 2009), and has implications for security of
supplies of energy, food and water, adaptation and mitigation pathways, air pollution reduction as well as the
implications for health and economic impacts as described throughout this report.
The interconnectivity of food/fiber, water, land use, energy and climate change, including the perhaps not yet well
understood cross-sector impacts, are increasingly important in assessing the implications for adaptation/mitigation
policy decisions. Fuel-food-land use-water-GHG mitigation strategy interactions, particularly related to bioresources
for food/feed, power, or fuel, suggest that combined assessment of water, land type and use requirements, energy
requirements and potential uses and GHG impacts often epitomize the interlinkages. For example, mitigation
scenarios described in the IPCC Special Report on Renewable Energy Sources and Climate Change Mitigation
(IPCC, 2011) indicate up to 300EJ of biomass primary energy by 2050 under increasingly stringent mitigation
scenarios. Such high levels of biomass production, in the absence of technology and process/management/operations
change, would have significant implications for land use, water and energy, as well as food production and pricing.
Consideration of the interlinkages of energy, food/feed/fiber, water, land use and climate change is increasingly
recognized as critical to effective climate resilient pathway decision making (medium evidence, high agreement),
although tools to support local- and regional-scale assessments and decision-support remain very limited.
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Table 22-1: Major conclusions from previous IPCC assessments.
Report Major conclusions Reference
IPCC special Sensitivity of water resources and coastal zones to climatic parameters Zinyowera
report on Identification of climate change as an additional burden on an already stressful situation et al.,
regional climate Major challenges for Africa: Lack of data on energy sources; uncertainties linked to 1997
change climate change scenarios (mainly for precipitation); need for integrated studies; and the
necessary links between science and decision makers
Third Impacts of climate change on and vulnerability of six sectors: water resources; food Desanker
Assessment security; natural resources and biodiversity management; health; human settlements and et al.,
Report (TAR) infrastructure; desertification 2001
Adaptation strategies for each of the sectors
Threats of desertification and droughts to the economy of the continent
Suggestion of adaptation options: mainly linked with better resource management
Identification of research gaps and needs: capacity building; data needs; development
of integrated analysis; consideration of literature in other languages
Fourth Vulnerability of Africa mainly due to its low adaptive capacity Boko et
Assessment Sources of vulnerability mainly socioeconomic causes (demographic growth, al., 2007
Report (AR4) governance, conflicts, etc.)
Impacts of climate change on various sectors: energy, tourism and coastal zones
considered separately
Potential impacts of extreme weather events (droughts and floods)
Adaptation costs
Need for mainstreaming climate change adaptation into national development policies
Two case studies:
1. Food security: climate change could affect the three main components of food
security
2. Traditional Knowledge: African communities have prior experience with climate
variability, although this knowledge will not be sufficient to face climate change
impacts.
Research needs: better knowledge of climate variability; more studies on the impacts of
climate change on water resources, energy, biodiversity, tourism, and health; the links
between different sectors (e.g., between agriculture, land availability, and biofuels);
developing links with the disaster reduction community; increasing interdisciplinary
analysis of climate change; and strengthening institutional capacities
Table 22-2: Under-nourishment in Africa, by number and % of total population.
Undernourished 1990 1992 1999 2001 2004 2006 2007 2009 2010 2012
Million 175 205 210 220 239
(%) of total population 27.3% 25.3% 23.1% 22.6% 22.9%
Source: IFAD et al., 2012
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Table 22-3: Examples of detected changes in species, natural ecosystems, and managed ecosystems in Africa that
are both consistent with a climate change signal and published since the AR4. Confidence in detection of change is
based on the length of study, and the type, amount and quality of data in relation to the natural variability in the
particular species or system. Confidence in the role of climate being a major driver of the change is based on the
extent to which the detected change is consistent with that expected under climate change, and to which
other confounding or interacting non-climate factors have been considered and been found insufficient to explain the
observed change.
Confidence
Type of
Confidence in in the role
change and Time scale of Potential climate
Examples the detection of climate
nature of observations change driver(s)
of change vs. other
evidence
drivers
Changes in Across sub-Saharan Africa, 57% ~ 25 years medium Increasing CO2, low
ecosystem increase in agricultural areas and (1975-2000) changing
types 15% increase in barren (largely precipitation
Robust desert) areas was accompanied by patterns, increasing
evidence 16% decrease in total forest cover temperatures
and 5% decrease in total non-forest
cover (Brink and Eva, 2009)
On Mt. Kilimanjaro, increased ~ 25 years high Increasing Low
vulnerability to anthropogenic fires (1976-2000) temperatures,
has driven 9% decreases in montane decreasing
forest and 83% decreases in precipitation
subalpine forest (Hemp, 2009)
In the Democratic Republic of the ~ 10 years high None proposed low
Congo, total forest cover declined (2000-2010)
by 2.3%, with most losses in
secondary humid forest (Potapov et
al., 2012)
Dieback of seaward edge of ~ 35 years high Sea level rise medium
mangroves in Cameroon at rates up (1975-2010)
to 3 m year-1 (Ellison and Zhou,
2012)
Across western Africa, central ~ 20 years high None proposed low
Africa and Madagascar, net (1990-2010)
deforestation was 0.28% year-1 for
1990-2000 and 0.14% year-1 for
2000-2010 (Mayaux et al., 2013)
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Changes in Surveys of coral reefs in northern ~ 9 years high None proposed low
ecosystem Tanzania indicate relative stability in (1996-2005)
structure the abundance and diversity of
Robust species, despite climate and non-
evidence climate stressors (McClanahan et al.,
2009)
Analysis of sediment cores from ~ 100 years high Increasing Low
Lake Victoria indicates current (1900-2000) temperatures
community structure (i.e., dominated
by cyanobacteria and invasive fish)
was established rapidly, during the
1980s (Hecky et al., 2010)
Long-term declines in density of ~ 20-50 years high Drought stress low
trees and shrubs in the Sahel zone of (Senegal, 1976- induced by
Senegal (Vincke et al., 2010) and 1995; Mali, 1952- decreasing
Mali (Ruelland et al., 2011) 2003) precipitation
Southward shift in the Sahel, Sudan, ~ 40-50 years medium Increasing medium
and Guinean savanna vegetation (density,1954- temperatures,
zones inferred from declines in tree 2002; diversity, decreasing
density in Senegal and declines in 1960-2000) precipitation
tree species richness and changes in
species composition in Mauritania,
Mali, Burkina Faso, Niger, and Chad
(Gonzales et al., 2012)
Long-term increase in shrub and tree ~67 years high Increasing CO2 low
cover across mesic savanna sites (1937-2004)
(700-1000 mm MAP) with
contrasting land-use histories in
South Africa (Wigley et al., 2009;
Wigley et al., 2010)
In long-term field experiments in ~ 30 50 years high In mesic site, Medium
South Africa where disturbance from (1980-2010 for increasing CO2; but
fire and herbivory was controlled, 600 mm MAP site; lack of response in
density of trees and shrubs increased 1954-2004 for 550 semiarid site
in mesic savannas (600 and 750 mm & 750 mm MAP surprising and
MAP) but showed no change in a sites) unexplained
semi-arid savanna (550 mm MAP)
(Buitenwerf et al., 2012)
Changes in A reconstruction of drought history ~ 550 years (1456 high Increasing low
ecosystem in Tunisia and Algeria based on tree 2002) temperatures,
physiology ring records from Cedrus atlantica decreasing
Moderate and Pinus halepensis indicates that a precipitation
evidence 1999-2002 drought was the most
severe since the 15th century
(Touchan et al., 2008)
Across 79 African tropical forest ~ 40 years high Increasing CO2 Medium
plots, above-ground carbon storage (1968-2007)
in live trees increased by 0.63 Mg C
ha-1 yr-1 (Lewis et al., 2009)
Increased stratification and reduced ~ 90 years high Increasing High
nutrient fluxes and primary (1913-2000) temperatures
productivity in Lake Tanganyika
(Verburg and Hecky, 2009)
Recent increases in surface ~ 1,500 years high Increasing High
temperatures and decreases in (500-2000) temperatures
productivity of Lake Tanganyika
exceed the range of natural
variability (Tierney et al., 2010)
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Changes in species The range of Aloe dichotoma, a Namib ~ 100 high Increasing Medium
distributions, Desert tree, is shifting poleward, but years temperatures,
physiology, or extinction along trailing edge exceeds (1904- decreasing
behavior colonization along leading edge (Foden et 2002) precipitation
Moderate evidence al., 2007)
On Tsaratanana Massif, the highest ~ 10 high Increasing Medium
mountain in Madagascar, reptiles and years temperatures
amphibians are moving upslope (1993-
(Raxworthy et al., 2008) 2003)
Pomacentrus damselfish species vary in minutes high Increasing CO2 low
avoidance of predation-related mortality to days
under elevated CO2 (Ferrari et al., 2011) (Nov.-
Dec.
2009)
In greenhouse experiments, growth of ~1-2 high Increasing CO2 medium
seedlings of woody savanna species years
(Acacia karoo and Terminalia sericea)
was enhanced at elevated CO2 (Bond and
Midgley, 2012)
Table 22-4: Projected changes in agro-climatic suitability for perennial crops in Africa by mid-century
under an A2 scenario.
Crop Suitability change Country Source
Coffee Increased suitability at high latitudes; decreased Kenya Läderach et al., 2010
suitability at low latitudes
Tea Decreased suitability Uganda Eitzinger et al.,
Increased suitability at high latitudes; decreased Kenya 2011a,b
suitability at low latitudes
Cocoa Constant or increased suitability at high Ghana, Côte d Ivoire Läderach et al., 2011c
latitudes; decreased suitability at low latitudes
Cashew Increased suitability Ghana, Côte d Ivoire Läderach et al., 2011a
Cotton Decreased suitability Ghana, Côte d Ivoire Läderach et al., 2011b
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Table 22-5: Cross-cutting approaches for equity and social justice in adaptation.
Equitable
Key issues to address for Factors that could
adaptation Opportunities Lessons learned
adaptation cause maladaptation
approach
Gender Lack of empowerment and Employment Women s aptitude for Security of tenure
mainstreamed participation in decision- opportunities not long-term thinking, over land and
adaptation in Africa making (Patt et al., 2009) sufficiently extended trusting and integrating resource access is
Climate impacts increase to women in scientific knowledge, and critical for enabling
women s household roles, adaptation initiatives taking decisions under enhanced adaptive
with risk of girls missing (Madzwamuse, 2010) uncertainty (Patt et al., capacity of women
school to assist (Raworth, Failure to incorporate 2009) (ADF, 2010)
2008; Romero González et power relations in Potential long-term Research on
al., 2011; UNDP, 2011b) adaptation responses increase in women s understanding
Male adaptation strategies (Djoudi and empowerment and social different adaptive
e.g. migration risk Brockhaus, 2011; and economic status strategies of benefit
increasing women s Romero González et (Djoudi and Brockhaus, for women and men
vulnerability (Djoudi and al., 2011) 2011) is needed
Brockhaus, 2011) Women opportunistically
using development
projects for adaptation
(Nielsen, 2010)
Child-centered Children and youth Limits to children s Using approaches that Positive role of
approaches to represent over 60% of agency related to stress agency and children and youth
adaptation Africa s population, yet power imbalances empowerment, and as change agents for
their issues are largely between children and innovative energies of climate adaptation,
absent from adaptation adults, and different youth; build on targeted within appropriate
policy (ADF, 2010) cultural contexts adaptation initiatives, such enabling
Children s differential (Seballos et al., 2011) as child-centred disaster environment
vulnerability to projected risk reduction and Child-sensitive
climate impacts is high, adaptation (ADF, 2010; programmes and
particularly to hunger, Seballos et al., 2011) policies can reduce
malnutrition and disasters risks children face
(UNICEF, 2007) from disasters
(Seballos et al,
2011)
Funding for climate
resilience
programmes will
protect children s
basic rights
(UNICEF, 2010;
UNICEF, 2011)
Human rights-based Common critical rights Lack of recognition Using the HRBA lens to Applying HRBA
approaches (HRBA) issues for local and promotion of their understand climate risk presents a
communities are human rights blocks necessitates risk analysis framework for
land/resource rights, indigenous peoples to probe the root causes of addressing
gender equality, and coping and adaptation differential disaster risk conflicting rights
political voice and fair capacities (UNPFII, vulnerabilities, to enable and interests,
adjudication of grievances 2008) structural, sustainable necessary for
for the poor and excluded responses (Urquhart, building resilience
(Castro et al., 2012) 2014) and equitable
adaptation
responses (Nilsson
and Schnell, 2010)
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Table 22-6: Key risks from climate change and the potential for risk reduction through mitigation and adaptation in
Africa. Key risks are identified based on assessment of the literature and expert judgments made by authors of the
various WGII AR5 chapters, with supporting evaluation of evidence and agreement in the referenced chapter
sections. Each key risk is characterized as very low, low, medium, high, or very high. Risk levels are presented for
the near-term era of committed climate change (here, for 2030-2040), in which projected levels of global mean
temperature increase do not diverge substantially across emissions scenarios. Risk levels are also presented for the
longer-term era of climate options (here, for 2080-2100), for global mean temperature increase of 2°C and 4°C
above preindustrial levels. For each timeframe, risk levels are estimated for the current state of adaptation and for a
hypothetical highly adapted state. As the assessment considers potential impacts on different physical, biological,
and human systems, risk levels should not necessarily be used to evaluate relative risk across key risks. Relevant
climate variables are indicated by symbols.
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Table 22-7: Land inundated and economic impacts in Cape Town based on a risk assessment (Cartwright, 2008).
Sea level rise scenarios Land inundated Economic impacts (for 25 years)
Scenario 1 (+ 2.5 to 6.5 m depending 25.1 km2 (1% of the total CT area) 5.2 billion R (794 million US$)
on the exposure) 95%
Scenario 2 (+ 4.5 m) 85% 60.9 km2 (2% of the total CT area) 23.7 billion R (30.3 billion US$)
Scenario 3 (+6.5 m) 20% 95 km2 (4% of the total CT area) 54.8 billion R
Note: The economic impacts are determined based on the value of properties, losses of touristic revenues and the cost of infrastructure
replacement. The total geographical gross product for Cape Town in 2008 was 165 billion of Rands.
Table 22-8: Research gaps in different sectors.
Key Sectors Gaps observed
Climate Science Research in climate and climate impacts would be greatly enhanced if data custodians and researchers
worked together to use observed station data in scientific studies. Research into regional climate change
and climate impacts relies on observed climate and hydrological data as an evaluative base. These data
are most often recorded by meteorological institutions in each country and sold to support data collection
efforts. However, African researchers are generally excluded from access to these critical data due to the
high costs involved which hinders both climate and climate impacts research.
Downscaling GCM data to the regional scale captures the influence of topography on the regional
climate. Regional climate information is essential for understanding regional climate processes, regional
impacts and potential future changes in these. Additionally, impacts models such as hydrology and crop
models generally require input data at a resolution higher than what GCMs can provide. Regional
downscaling, either statistically or through using regional climate models, can provide information at
these scales and can also change the sign of GCM-projected rainfall change over topographically
complex areas [22.2.2.2].
Ecosystems Monitoring networks for assessing long-term changes to critical ecosystems such as coastal ecosystems,
lakes, mountains, grasslands, forests, wetlands, deserts, and savannas to enhance understanding of long-
term ecological dynamics, feedbacks between climate and ecosystems, the effects of natural
climate variability on ecosystems, the limits of natural climate variability, and the marginal additional
effects of global climate forcing.
Develop the status of protected areas to include climate change effects
Food Systems Socioeconomic and environmental tradeoffs of biofuel production, especially the effect on land use
change and food and livelihood security; better agronomic characterization of biofuel crops to avoid
maladaptive decisions with respect to biofuel production
Vulnerability to and impacts of climate change on food systems (production, transport, processing,
storage, marketing and consumption)
Impacts of climate change on urban food security, and dynamic of rural-urban linkages in vulnerability
and adaptive capacity
Impacts of climate change on food safety and quality
Water resources Characterization of Africa s groundwater resource potential; understanding interactions between non-
climate and climate drivers as related to future groundwater resources.
Impacts of climate change on water quality, and how this links to food and health security
Decision making under uncertainty with respect to water resources given limitations of climate models
for adequately capturing future rainfall projections
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Human security Research to explore and monitor the links between climate change and migration and its potential
and urban areas negative effects on environmental degradation; the potential positive role of migration in climate change
adaptation.
Improved methods and research to analyse the relation between climate change and violent conflict.
Livelihoods and Methodologies for cyclical learning and decision-support to enable anticipatory adaptation in contexts of
poverty high poverty and vulnerability (Tschakert and Dietrich, 2010)
Frameworks to integrate differentiated views of poverty into adaptation and disaster risk reduction, and
to better link these with social protection in different contexts
Ethical and political dimensions of engaging with local and traditional knowledge on climate change
Health Research and improved methodologies (including longitudinal studies) to assess and quantify the impact
of climate change on vector borne, foodborne, waterborne, nutrition, heatstress and indirect impacts on
HIV.
Research to quantify the direct and indirect health impacts of extreme weather events in Africa; injuries,
mental illness; health infrastructure
Frameworks and research platforms to be developed with other sectors to determine how underlying risks
(for example food security) will be addressed to improve health outcomes.
Adaptation Research to develop home-grown and to localize global adaptation technologies to build resilience
Equitable adaptation frameworks to deal with high uncertainty levels and integrate marginalized groups;
and that identify and eliminate multi-level constraints to women s adaptive ability
Multi--tiered approach to building institutional and community capacity to respond to climate risk
Potential changes in economic and social systems under different climate scenarios, to understand the
implications of adaptation and planning choices (Clements et al., 2011)
Principles/determining factors for effective adaptation, including community-based adaptation
Understanding synergies and trade-offs between different adaptation and mitigation approaches
(Chambwera and Anderson, 2011)
Additional national and sub-natinal modeling and analysis of the economic costs of impacts and
adaptation, including of the soft costs of impacts and adaptation
Monitoring adaptation
Other Methods in vulnerability analysis for capturing the complex interactions in systems across scales
Understanding compound impacts from concomitant temperature and precipitation stress, e.g. effect on a
particular threshold of a heatwave occurring during a period of below normal precipitation.
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Figure 22-1: Observed and simulated variations in past and projected future annual average precipitation and
temperature. Observed differences in the Climate Research Unit, University of East Anglia data (CRU) are shown
between the 1986-2005 and 1906-1925 periods, with white indicating areas where the difference between the 1986-
2005 and 1906-1925 periods is less than twice the standard deviation of the 20 20-year periods beginning in the
years 1906 through 1925. For CMIP5, white indicates areas where <66% of models exhibit a change greater than
twice the baseline standard deviation of the respective model s 20 20-year periods ending in years 1986 through
2005. Grey indicates areas where >66% of models exhibit a change greater than twice the respective model baseline
standard deviation, but <66% of models agree on the sign of change. Colors with circles indicate the ensemble-mean
change in areas where >66% of models exhibit a change greater than twice the respective model baseline standard
deviation and >66% of models agree on the sign of change. Colors without circles indicate areas where >90% of
models exhibit a change greater than twice the respective model baseline standard deviation and >90% of models
agree on the sign of change. The realizations from each model are first averaged to create baseline-period and
future-period mean and standard deviation for each model, from which the multi-model mean and the individual
model signal-to-noise ratios are calculated. The baseline period is 1986-2005. The late-21st Century period is 2081-
2100. The mid-21st century period is 2046-2065.
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Figure 22-2: Observed and simulated variations in past and projected future annual average temperature over EAC-
IGAD-Egypt, ECCAS, ECOWAS, SADC and UMA. Black lines show various estimates from observational
measurements. Shading denotes the 5-95 percentile range of climate model simulations driven with "historical"
changes in anthropogenic and natural drivers (63 simulations), historical changes in "natural" drivers only (34), the
"RCP2.6" emissions scenario (63), and the "RCP8.5" (63). Data are anomalies from the 1986-2005 average of the
individual observational data (for the observational time series) or of the corresponding historical all-forcing
simulations. Further details are given in Box 21-3.
[Illustration to be redrawn to conform to IPCC publication specifications.]
Figure 22-3: Left: Confidence in detection and in attribution of observed climate change over Africa to
anthropogenic emissions. All detection assessments are against a reference of no change, while all attribution
assessments concern a major role of anthropogenic emissions in the observed changes. See 22.2, and SREX-3, and
WGI AR5 10 for details. Right: Confidence in detection and in attribution of the impacts of observed regional
climate change on various African systems. All detection assessments are against a reference of no change, except
"Kenyan Highlands malaria" (changes due to vaccination, drug resistance, demography, and livelihoods), "Great
Lakes fisheries" (changes due to fisheries management and land use) and Adapting South African farmers
(economic changes). Attribution is to a major role or a minor role of observed climate change, as indicated. See
22.2.2, 22.3.2.1, 22.3.2.2, 2.3.3, 22.4.2, 22.3.4.4, 22.3.5.4, 22.4.5.7 and Tables 18-5, 18-6, 18-7, and 18-9 for details.
Assessments follow the methods outlined in 18.2.
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Figure 22-4: Left Projected biome change from the periods 1961-1990 to 2071-2100 using the MC1 Dynamic
Vegetation Model. Change is indicated if any of nine combinations of three GCMs (CSIRO Mk3, HadCM3,
MIROC 3.2 medres) and three emissions scenarios (B1, A1B, A2) project change and is thus a worst-case scenario.
Colours represent the future biome predicted. Right Vulnerability of ecosystems to biome shifts based on historical
climate (1901-2002) and projected vegetation (2071-2100), where all nine GCM-emissions scenario combinations
agree on the projected biome change. Source: Gonzalez et al. (2010).
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Figure 22-5: The effect of rainfall and temperature changes on mean crop yield. Mean crop yield change (%) relative
to the 1961 90 baseline for 7 temperatures (x-axis) and 5 rainfall (y-axis) scenarios. Results are shown as the
average over the 35 stations across West Africa and the 6 cultivars of sorghum and millet. White triangles and
circles are the projected anomalies computed by several CMIP3 GCMs and three IPCC emission scenarios (B1,
A1B, A2) for 2071 90 and 2031 50, respectively. Projections from CMIP5 GCMs and three RCPs (4.5, 6.0 and 8.5)
are represented by grey triangles and circles. Models and scenarios names are displayed in figure S2 (available at
stacks.iop.org/ERL/8/014040/mmedia). Past observed climate anomalies from CRU data are also projected by
computing 10-year averages (e.g. '1940' is for 1941 50). All mean yield changes are significant at a 5% level except
boxes with a diagonal line. Source: Sultan et al., 2013.
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Figure 22-6: Total additional costs of adaptation per year from 2000 to 2100 for Tanzania (including beach
nourishment and sea and river dikes). The values do not consider the existing adaptation deficit (values in
$US2005), without discounting. Source: Kebede et al., 2010.
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Figure UP-1: Upper panel: Schematic hypothetic mechanism of increasing coastal wind-driven upwelling at eastern
boundary systems, where differential warming rates between land and ocean results in increased land-ocean pressure
gradients (1) that produce stronger alongshore winds (2) and offshore movement of surface water through Ekman
transport (3), and increased upwelling of deep cold nutrient rich waters to replace it (4). Lower panel: potential
consequences of climate change in upwelling systems. Increasing stratification and uncertainty in wind stress trends
result in uncertain trends in upwelling. Increasing upwelling may result in higher input of nutrients to the euphotic
zone, and increased primary production, which in turn may enhance pelagic fisheries, but also decreased coastal
fisheries due to an augmented exposure of coastal fauna to hypoxic, low pH waters. Decreased upwelling may result
in lower primary production in these systems with direct impacts on pelagic fisheries productivity.
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Figure WE-1: The water-energy-food nexus as related to climate change. The interlinkages of supply/demand,
quality and quantity of water, energy and food/feed/fiber with changing climatic conditions have implications for
both adaptation and mitigation strategies.
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Chapter 23. Europe
Coordinating Lead Authors
Sari Kovats (UK), Riccardo Valentini (Italy)
Lead Authors
Laurens M. Bouwer (Netherlands), Elena Georgopoulou (Greece), Daniela Jacob (Germany), Eric Martin (France),
Mark Rounsevell (UK), Jean-Francois Soussana (France)
Contributing Authors
Martin Beniston (Switzerland), Maria Vincenza Chiriaco (Italy), Philippe Cury (France), Michael Davies (UK),
Paula Harrison (UK), Olaf Jonkeren (Italy), Mark Koetse (Netherlands), Markus Lindner (Finland), Andreas
Matzarakis (Germany), Reinhard Mechler (Germany), Annette Menzel (Germany), Marc Metzger (UK), Luca
Montanarella (Italy), Antonio Navarra (Italy), Juliane Peterson (Germany), Martin Price (UK), Boris Revich
(Russian Federation), Piet Rietveld (Netherlands), Cristina Sabbioni (Italy), Yannis Sarafidis (Greece), Philipp
Schmidt-Thomé (Finland), Vegard Skirbekk (Austria), Donatella Spano (Italy), Jan E. Vermaat (Netherlands), Paul
Watkiss (UK), Meriwether Wilson (UK), Thomasz Zylicz (Poland)
Review Editors
Lucka Kajfez Bogataj (Slovenia), Roman Corobov (Moldova), Ramón Vallejo (Spain)
Contents
Executive Summary
23.1. Introduction
23.1.1. Scope and Route Map of Chapter
23.1.2. Policy Frameworks
23.1.3. Conclusions from Previous Assessments
23.2. Current and Future Trends
23.2.1 Non- Climate Trends
23.2.2. Observed and Projected Climate Change
23.2.2.1. Observed Climate Change
23.2.2.2. Projected Climate Changes
23.2.2.3. Projected Changes in Climate Extremes
23.2.3. Observed and Projected Trends in the Riverflow and Drought
23.3. Implications of Climate Change for Production Systems and Physical Infrastructure
23.3.1. Settlements
23.3.1.1. Coastal Flooding
23.3.1.2. River and Pluvial Flooding
23.3.1.3. Windstorms
23.3.1.4.Mass Movements and Avalanches
23.3.2. Built Environment
23.3.3. Transport
23.3.4. Energy Production, Transmission, and Use
23.3.5. Industry and Manufacturing
23.3.6. Tourism
23.3.7. Insurance and Banking
23.4. Implications of Climate Change for Agriculture, Fisheries, Forestry, and Bioenergy Production
23.4.1. Plant (Food) Production
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23.4.2. Livestock Production
23.4.3. Water Resources and Agriculture
23.4.4. Forestry
23.4.5. Bioenergy Production
23.4.6. Fisheries and Aquaculture
23.5. Implications of Climate Change for Health and Social Welfare
23.5.1. Human Population Health
23.5.2. Critical Infrastructure
23.5.3. Social Impacts
23.5.4. Cultural Heritage and Landscapes
23.6. Implications of Climate Change for the Protection of Environmental Quality and Biological Conservation
23.6.1. Air Quality
23.6.2. Soil Quality and Land Degradation
23.6.3. Water Quality
23.6.4. Terrestrial and Freshwater Ecosystems
23.6.5. Coastal and Marine Ecosystems
23.7. Cross-Sectoral Adaptation Decision-making and Risk Management
23.7.1. Coastal Zone Management
23.7.2. Integrated Water Resource Management
23.7.3. Disaster Risk Reduction and Risk Management
23.7.4. Land Use Planning
23.7.5. Rural Development
23.7.6. Economic Assessments of Adaptation
23.7.7. Barriers and Limits to Adaptation
23.8. Co-Benefits and Unintended Consequences of Adaptation and Mitigation
23.8.1. Production and Infrastructure
23.8.2. Agriculture, Forestry, and Bioenergy
23.8.3. Social and Health Impacts
23.8.4. Environmental Quality and Biological Conservation
23.9. Synthesis of Key Findings
23.9.1. Key Vulnerabilities
23.9.2. Climate Change Impacts Outside Europe and Inter-Regional Implications
23.9.3. Effects of Observed Climate Change in Europe
23.9.4. Key Knowledge Gaps and Research Needs
References
Chapter Boxes
23-1. Assessment of Climate Change Impacts on Ecosystem Services by Sub-Region
23-2. Implications of Climate Change for European Wine and Vineyards
23-3. National and Local Adaptation Strategies
Frequently Asked Questions
23.1: Will I still be able to live on the coast in Europe?
23.2: Will climate change introduce new infectious diseases into Europe?
23.3: Will Europe need to import more food because of climate change?
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Executive Summary
Observed climate trends and future climate projections show regionally varying changes in temperature and
rainfall in Europe [high confidence] [23.2.2], in agreement with AR4 findings, with projected increases in
temperature throughout Europe and increasing precipitation in Northern Europe and decreasing precipitation in
Southern Europe [23.2.2.2]. Climate projections show a marked increase in high temperature extremes [high
confidence], meteorological droughts [medium confidence] [23.2.3] and heavy precipitation events [high confidence]
[23.2.2.3] with variations across Europe, and small or no changes in wind speed extremes [low confidence] except
increases in winter wind speed extremes over Central and Northern Europe [medium confidence] [23.2.2.3].
Observed climate change in Europe has had wide ranging effects throughout the European region including:
the distribution, phenology, and abundance of animal, fish and plant species [high confidence] [23.6.4, Table
23.6]; stagnating wheat yields in some sub-regions [medium confidence, limited evidence] [23.4.1]; and forest
decline in some sub-regions [medium confidence] [23.4.4]. Climate change has affected both human health (from
increased heat waves) [medium confidence] [23.5.1] and animal health (changes in infectious diseases) [high
confidence] 23.4.5]. There is less evidence of impacts on social systems attributable to observed climate change,
except in pastoralist populations [low confidence].
Climate change will increase the likelihood of systemic failures across European countries caused by extreme
climate events affecting multiple sectors [medium confidence] [23.2.2.3, 23.2.3, 23.3, 23.4, 23.5, 23.6, 23.9.1].
Extreme weather events currently have significant impacts in Europe in multiple economic sectors as well as
adverse social and health effects [high confidence] [Table 23.1]. There is limited evidence that resilience to heat
waves and fires has improved in Europe [medium confidence] [23.9.2, 23.5.], while some countries have improved
their flood protection following major flood events [23.9.2, 23.7.3]. Climate change is very likely to increase the
frequency and intensity of heat waves, particularly in Southern Europe [high confidence] [23.2.2] with mostly
adverse implications for health, agriculture, forestry, energy production and use, transport, tourism, labour
productivity, and the built environment [Table 23-1, 23.3.2, 23.3.3, 23.3.4, 23.3.6, 23.4.1, 23.4.2, 23.4.3, 23.4.4,
23.5.1].
The provision of ecosystem services is projected to decline across all service categories in response to climate
change in Southern Europe and Alpine sub-regions [high confidence] [23.9.1, Box 23-1]. Both gains and losses
in the provision of ecosystem services are projected for the other European sub-regions [high confidence], but the
provision of cultural services is projected to decline in the Continental, Northern and Southern sub-regions [low
confidence] [Box 23-1].
Climate change is expected to impede economic activity in Southern Europe more than in other sub-regions
[medium confidence] [Table 23.4, 23.9.3], and may increase future intra-regional disparity [low confidence]
[23.9.3]. There are also important differences in vulnerability within sub-regions, for example, plant species and
some economic sectors are most vulnerable in high mountain areas due to lack of adaptation options [medium
confidence][23.9.1.]. Southern Europe is particularly vulnerable to climate change [high confidence] as multiple
sectors will be adversely affected (tourism, agriculture, forestry, infrastructure, energy, population health) [high
confidence] [23.9] [Box 23-3].
The impacts of sea level rise on populations and infrastructure in coastal regions can be reduced by
adaptation [medium confidence] [23.3.1, 23.5.3]. Populations in urban areas are particularly vulnerable to climate
change impacts due to the high density of people and built infrastructure [medium confidence] [23.3, 23.5.1].
Synthesis of evidence across sectors and sub-regions confirm that there are limits to adaptation from
physical, social, economic and technological factors [high confidence] [23.5]. Adaptation is further impeded
because climate change affects multiple sectors [23.10]. The majority of published assessments are based on climate
projections in the range 1-4 degrees global mean temperature per century. Limited evidence exists regarding the
potential impacts in Europe under high rates of warming (>4 degrees global mean temperature per century) [23.9.1].
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Impacts by Sector
Sea level rise and increases in extreme rainfall are projected to further increase coastal and river flood risk in
Europe and, without adaptive measures, will substantially increase flood damages (people affected and
economic losses) [high confidence] [23.3.1, 23.5.1]. Adaptation can prevent most of the projected damages [high
confidence based on medium evidence, high agreement] but there may be constraints to building flood defences in
some areas [23.3.1, 23.7.1, 23.8.3]. Direct economic river flood damages in Europe have increased over recent
decades [high confidence] but this increase is due to development in flood zones and not due to observed climate
change [23.3.1.2, SREX 4.5]. Some areas in Europe show changes in river flood occurrence related to observed
changes in extreme river discharge [medium confidence] [23.2.3].
Climate change is projected to affect the impacts of hot and cold weather extremes on transport leading to
economic damage and/or adaptation costs, as well as some benefits (e.g. reduction of maintenance costs)
during winter [medium confidence] [23.3.3]. Climate change is projected to reduce severe accidents in road
transport [medium confidence] and adversely affect inland water transport in summer in some rivers (e.g. the Rhine)
after 2050 [medium confidence]. Damages to rail infrastructure from high temperatures may also increase [medium
confidence]. Adaptation through maintenance and operational measures can reduce adverse impacts to some extent.
Climate change is expected to affect future energy production and transmission [23.3.4]. Hydropower
production is likely to decrease in all sub-regions except Scandinavia [high confidence] [23.3.4]. Climate change is
unlikely to affect wind energy production before 2050 [medium confidence] but will have a negative impact in
summer and a varied impact in winter after 2050 [medium confidence]. Climate change is likely to decrease thermal
power production during summer [high confidence] [23.3.4]. Climate change will increase the problems associated
with overheating in buildings [medium confidence] [23.3.2]. Although climate change is very likely to decrease
space heating demand [high confidence], cooling demand will increase [very high confidence] although income
growth mostly drives projected cooling demand up to 2050 [medium confidence] [23.3.4]. More energy efficient
buildings and cooling systems as well as demand-side management will reduce future energy demands [23.3.4].
After 2050, tourism activity is projected to decrease in southern Europe [low confidence] and increase in
Northern and Continental Europe [medium confidence]. No significant impacts on the tourism sector are
projected before 2050 in winter or summer tourism except for ski tourism in low altitude sites and under limited
adaptation [medium confidence] [23.3.6]. Artificial snowmaking may prolong the activity of some ski resorts
[medium confidence] [23.3.6].
Climate change is likely to increase cereal yields in Northern Europe [medium confidence, disagreement] but
decrease yields in Southern Europe [high confidence] [23.4.1]. In Northern Europe, climate change is very likely
to extend the seasonal activity of pests and plant diseases [high confidence] [23.4.1]. Yields of some arable crop
species like wheat have been negatively affected by observed warming in some European countries since 1980s
[medium confidence, limited evidence] [23.4.1] Compared to AR4, new evidence regarding future yields in Northern
Europe, is less consistent regarding the magnitude and sign of change. Climate change may adversely affect dairy
production in Southern Europe because of heat stress in lactating cows [medium confidence] [23.4.2]. Climate
change has contributed to vector-borne disease in ruminants in Europe [high confidence] [23.4.2] and northward
expansion of tick disease vectors [medium confidence] [23.4.2, 23.5.1].
Climate change will increase irrigation needs [high confidence] but future irrigation will be constrained by
reduced runoff, demand from other sectors, and by economic costs [23.4.1, 23.4.3]. By 2050s, irrigation will not
be sufficient to prevent damage from heat waves to crops in some sub-regions [medium confidence]. System costs
will increase under all climate scenarios [high confidence] [23.4.3]. Integrated management of water, also across
countries boundaries, is needed to address future competing demands between agriculture, energy, conservation and
human settlements [23.7.2].
As a result of increased evaporative demand, climate change is likely to significantly reduce water availability
from river abstraction and from groundwater resources [medium confidence], in the context of increased
demand (from agriculture, energy and industry, and domestic use) and cross-sectoral implications which are not
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fully understood [23.4.3, 23.9.1]. Some adaptation is possible through uptake of more water efficient technologies
and water saving strategies [23.4.3, 23.7.2, 23.9.1].
Climate change will change the geographic distribution of wine grape varieties [high confidence] and this will
reduce the value of wine products and the livelihoods of local wine communities in Southern and Continental
Europe [medium confidence] and increase production in Northern Europe [low confidence] [23.4.1, 23.3.5,
23.5.4, Box 23-2]. Some adaptation is possible through technologies and good practice [Box 23-2].
Climate warming will increase forest productivity in northern Europe [medium confidence] [23.4.4], although
damage from pests and diseases in all sub-regions will increase due to climate change [high confidence] [23.4.4].
Wildfire risk in Southern Europe [high confidence] and damages from storms in central Europe [low confidence]
may also increase due to climate change [23.4.4]. Climate change is likely to cause ecological and socio-economic
damages from shifts in forest tree species range (from south-west to north-east) [medium confidence], and in pest
species distributions [low confidence] [23.4.4]. Forest management measures can enhance ecosystem resilience
[medium confidence] [23.4.4].
Observed warming has shifted marine fish species ranges to higher latitudes [high confidence] and reduced
body size in species [medium confidence] [23.4.6]. There is limited and diverging evidence on climate change
impacts on net fisheries economic turnover. Local economic impacts attributable to climate change will depend on
the market value of (high temperature tolerant) invasive species [23.4.6]. Climate change is unlikely to entail
relocation of fishing fleets [high confidence] [23.4.6]. Observed higher water temperatures have adversely affected
both wild and farmed freshwater salmon production in the southern part of their distribution [high confidence]
[23.4.6]. High temperatures may increase the frequency of harmful algal blooms [low confidence] [23.4.6].
Climate change will affect bioenergy cultivation patterns in Europe by shifting northward their potential area
of production [medium confidence] [23.4.5]. Elevated atmospheric CO2 can improve drought tolerance of
bioenergy crop species due to improved plant water use, maintaining high yields in future climate scenarios in
temperate regions [low confidence] [23.4.5].
Climate change is likely to affect human health in Europe. Heat-related deaths and injuries are likely to increase,
particularly in Southern Europe [medium confidence] [23.5.1]. Climate change may change the distribution and
seasonal pattern of some human infections, including those transmitted by arthropods [medium confidence], and
increase the risk of introduction of new infectious diseases [low confidence] [23.5.1].
Climate change and sea level rise may damage European cultural heritage, including buildings, local industries,
landscapes, archaeological sites, and iconic places [medium confidence] and some cultural landscapes may be lost
forever [low confidence] [23.5.4] [Table 23.3].
Climate change may adversely affect background levels of tropospheric ozone [low confidence, limited
evidence, low agreement], assuming no change in emissions, but the implications for future particulate
pollution (which is more health-damaging) are very uncertain [23.6.1]. Higher temperatures may have affected
trends in ground level tropospheric ozone [low confidence] [23.6.1.]. Climate change is likely to decrease surface
water quality due to higher temperatures and changes in precipitation patterns [medium confidence] [23.6.3], and is
likely to increase soil salinity in coastal regions [low confidence] [23.6.2]. Climate change may also increase soil
erosion (from increased extreme events) and reduce soil fertility [low confidence, limited evidence] [23.6.2].
Observed climate change is affecting a wide range of flora and fauna, including plant pests and diseases [high
confidence] [23.4.1, 23.4.4] and the disease vectors and hosts [medium confidence] [23.4.3]. Climate change is
very likely to cause changes in habitats and species, with local extinctions [high confidence] and continental scale
shifts in species distributions [medium confidence] [23.6.4]. The habitat of alpine plants is very likely to be
significantly reduced [high confidence] [23.6.4]. Phenological mismatch will constrain both terrestrial and marine
ecosystem functioning under climate change [high confidence] [23.6.4, 23.6.5], with a reduction in some ecosystem
services [low confidence] [23.6.4, Box 23-1]. The introduction and expansion of invasive species, especially those
with high migration rates, from outside Europe is likely to increase with climate change [medium confidence]
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[23.6.4]. Climate change is likely to entail the loss or displacement of coastal wetlands [high confidence] [23.6.5].
Climate change threatens the effectiveness of European conservation areas [low confidence] [23.6.4], and stresses
the need for habitat connectivity through specific conservation policies [23.6.4].
Adaptation
The capacity to adapt in Europe is high compared to other world regions, but there are important differences
in impacts and in the capacity to respond between and within the European sub-regions. In Europe, adaptation
policy has been developed at international (European Union), national and local government level [23.7], including
the prioritisation of adaptation options. There is limited systematic information on current implementation or
effectiveness of adaptation measures or policies [Box 23-3]. Some adaptation planning has been integrated into
coastal and water management, as well as disaster risk management [23.7.1, 23.7.2, 23.7.3]. There is limited
evidence of adaptation planning in rural development or land-use planning [23.7.4, 23.7.5].
Adaptation will incur a cost, estimated from detailed bottom-up sector-specific studies for coastal defences, energy
production, energy use, and agriculture [23.7.6]. The costs of adapting buildings (houses, schools, hospitals) and
upgrading flood defences increase under all scenarios relative to no climate change [high confidence] [23.3.2]. Some
impacts will be unavoidable due to limits (physical, technological, social, economic or political) [Table 23-3,
23.7.7].
There is also emerging evidence regarding opportunities and unintended consequences of policies, strategies
and measures that address adaptation and/or mitigation goals [23.8]. Some agricultural practices can reduce
GHG emissions and also increase resilience of crops to temperature and rainfall variability [23.8.2]. There is
evidence for unintended consequences of mitigation policies in the built environment (especially dwellings) and
energy sector [medium confidence] [23.8.1]. Low carbon policies in the transport and energy sectors to reduce
emissions are associated with large benefits to human health [23.8.3] [high confidence].
23.1. Introduction
This chapter reviews the scientific evidence published since AR4 on observed and projected impacts of
anthropogenic climate change in Europe and adaptation responses. The geographical scope of this chapter is the
same as in AR4 with the inclusion of Turkey. Thus, the European region includes all countries from Iceland in the
west to Russia (west of the Urals) and the Caspian Sea in the east, and from the northern shores of the
Mediterranean and Black Seas and the Caucasus in the south to the Arctic Ocean in the north. Impacts above the
Arctic Circle are addressed in the Polar Regions Chapter 28 and impacts in the Baltic and Mediterranean Seas are
addressed in the Open Oceans Chapter 30. Impacts in Malta, Cyprus, and other island states in Europe are discussed
in the Small Island Chapter 29.
The European region has been divided into 5 sub-regions (see Figure 23-1): Atlantic, Alpine, Southern, Northern,
and Continental. The sub-regions are derived by aggregating the climate zones developed by (Metzger et al., 2005)
and therefore represent geographical and ecological zones rather than political boundaries. The scientific evidence
has been evaluated to compare impacts across (rather than within) sub-regions, although this is not always possible,
depending on the scientific information available.
[INSERT FIGURE 23-1 HERE
Figure 23-1: Sub-regional classification of the IPCC Europe region. Based on Metzger et al., 2005.]
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23.1.1. Scope and Route Map of Chapter
The chapter is structured around key policy areas. Sections 23.3 to 23.6 summarise the latest scientific evidence on
sensitivity climate, observed impacts and attribution, projected impacts and adaptation options, with respect to four
main categories of impacts:
Production systems and physical infrastructure
Agriculture, fisheries, forestry and bioenergy production
Health protection and social welfare
Protection of environmental quality and biological conservation.
The benefit of assessing evidence in a regional chapter is that impacts across sectors can be described, and
interactions between impacts can be identified. Further, the cross-sectoral decision making required to address
climate change can be reviewed. The chapter also includes sections that were not in AR4. As adaptation and
mitigation policy develops, the evidence for potential co-benefits and unintended consequences of such strategies is
reviewed (Section 23.8). The final section synthesise the key findings with respect to: observed impacts of climate
change, key vulnerabilities and research and knowledge gaps.
The chapter evaluates the scientific evidence in relation to the five sub-regions discussed above. The majority of the
research in the Europe region is for impacts in countries in the European Union due to targeted research funding
through the European Commission and national governments which means that countries in eastern Europe and
Russia are less well represented in this chapter. Further, regional assessments may be reported for the EU15, EU27
or EEA (32) group of countries [Table SM23-1].
23.1.2. Policy Frameworks
Since AR4, there have been significant changes in Europe in responses to climate change. More countries now have
adaptation and mitigation policies in place. An important force for climate policy development in the region is the
European Union (EU). EU Member States have mitigation targets, as well as the overall EU target, with both
sectoral and regional aspects to the commitments.
Adaptation policies and practices have been developed at the international, national and local levels although
research on implementation of such policies is limited. Due to the vast range of policies, strategies and measures it is
not possible to describe them extensively here. However, adaptation in related to cross-sectoral decision-making is
discussed in section 23.7 (see also Box 23-3 on national adaptation policies). The European Climate Adaptation
Platform (Climate-ADAPT) catalogues adaptation actions reported by EU Member States (EC, 2013b). The EU
Adaptation Strategy was adopted in 2013 (EC, 2013a). See Chapter 15 for a more extensive discussion of
institutions and governance in relation to adaptation planning and implementation.
23.1.3. Conclusions from Previous Assessments
AR4 documented a wide range of impacts of observed climate change in Europe (AR4 WG2 Chapter 12). The
SREX confirmed increases in warm days, warm nights and decreases in cold days and cold nights since 1950 (high
confidence, SREX-3.3.1). Extreme precipitation increased in part of the continent, mainly in winter over western-
central Europe and European Russia (medium confidence, SREX-3.3.2). Dryness has increased mainly in Southern
Europe (medium confidence, SREX-3.3.2). Climate change is expected to magnify regional differences within
Europe for agriculture and forestry because water stress was projected to increase over central and southern Europe
(AR4-12.4.1, SREX-3.3.2, SREX-3.5.1). Many climate-related hazard were projected to increase in frequency and
intensity, but with significant variations within the region (AR4-12.4).
The AR4 identified that climate changes would pose challenges to many economic sectors and was expected to alter
the distribution of economic activity within Europe (high confidence). Adaptation measures were evolving from
reactive disaster response to more proactive risk management. A prominent example was the implementation of heat
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health warning systems following the 2003 heat wave event (AR4 WG2 12.6.1, SREX 9.2.1). National adaptation
plans were developed and specific plans were incorporated in European and national policies (AR4 WG2 12.2.3,
12.5) but these were not yet evaluated (AR4 WG2 12.8).
23.2. Current and Future Trends
23.2.1 Non- Climate Trends
European countries are diverse in both demographic and economic trends. Population health and social welfare has
improved everywhere in Europe, with reductions in adult and child mortality rates, but social inequalities both
within and between countries persist (Marmot et al., 2012). Population has increased in most EU27 countries,
primarily due to net immigration (Eurostat, 2011a), although population growth is slow (total and working age
population) (Rees et al., 2012). Ageing of the population is a significant trend in Europe, as in all high income
populations. This will have both economic and social implications, with many regions experiencing a decline in the
labour force (Rees et al., 2012). Since AR4, economic growth has slowed or become negative in many countries,
leading to a reduction in social protection measures and increased unemployment (Eurostat, 2011b). The longer term
implications of the financial crisis in Europe are unclear, although it may lead to a modification of the economic
outlook and affect future social protection policies with implications for adaptation.
Europe is one of the world s largest and most productive suppliers of food and fibre (Easterling et al., 2007) and
agriculture is the most important European land use by area (45% of the total area) (Rounsevell et al., 2006). After
1945, an unprecedented increase in agricultural productivity occurred, but also declines in agricultural land use
areas. This intensification had several negative impacts on the ecological properties of agricultural systems, such as
carbon sequestration, nutrient cycling, soil structure and functioning, water purification and pollination. Pollution
from agriculture has led to eutrophication and declines in water quality in some areas (ELME, 2007). Most scenario
studies suggest that agricultural land areas will continue to decrease in the future (see also (Busch, 2006) for a
discussion). Agriculture accounts for 24 % of total national freshwater abstraction in Europe and more than 80 % in
some southern European countries (EEA, 2009). Economic restructuring in some eastern European countries has led
to a decrease in water abstraction for irrigation, suggesting the potential for future increases in irrigated agriculture
and water use efficiency (EEA, 2009).
Forest in Europe covers approximately 35% of the land area (Eurostat, 2009). The majority of forests now grow
faster than in the early 20th century due to advances in forest management practices, genetic improvement and in
central Europe, the cessation of site-degrading practices such as litter collection for fuel. Increasing temperatures
and CO2 concentrations, nitrogen deposition, and the reduction of air pollution (SO2) have also had a positive effect
on forest growth. Scenario studies suggest that forested areas will increase in Europe in the future on land formerly
used for agriculture (Rounsevell et al., 2006). Soil degradation is already intense in parts of the Mediterranean and
central-eastern Europe and, together with prolonged drought periods and fires, is already contributing to an
increased risk of desertification. Projected risks for future desertification are the highest in these areas (EEA, 2012).
Urban development is projected to increase all over Europe (Reginster and Rounsevell, 2006), but especially rapidly
in Eastern Europe, with the magnitude of these increases depending on population growth, economic growth and
land use planning policy. Although changes in urban land use will be relatively small in area terms, urban
development has major impacts locally on environmental quality. Outdoor air quality has, however, been improving
(ELME, 2007). Peri-urbanisation is an increasing trend in which residents move out of cities to locations with a rural
character, but retain a functional link to cities by commuting to work (Reginster and Rounsevell, 2006)(Rounsevell
and Reay, 2009).
Several European scenario studies have been undertaken to describe European future trends with respect to: socio-
economic development (Mooij de and Tang, 2003), land use change (Letourneau et al., 2012; Verburg et al.,
2010)(Haines-Young et al., 2012), land use and biodiversity (Spangenberg et al., 2011), crop production (Hermans
et al., 2010), demographic change (Davoudi et al., 2010), economic development (Dammers, 2010) and European
policy (Helming et al., 2011)(Lennert and Robert, 2010). Many of these scenarios also account for the effects of
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future climate change (see (Rounsevell and Metzger, 2010) for a review). Long term projections (to the end of the
century) are described under the new Shared Socio-economic Pathway scenarios (SSPs) (Kriegler et al., 2010).
Detailed country and regional scale socio-economic scenarios have also been produced for the Netherlands (WLO,
2006), the UK (UK National Ecosystem Assessment, 2011) and Scotland (Harrison et al., 2013). The probabilistic
representation of socio-economic futures has also been developed for agricultural land use change (Hardacre et al.,
2012). There is little evidence to suggest, however, that probabilistic futures or scenarios more generally are being
used in policy making (Bryson et al., 2010).
23.2.2. Observed and Projected Climate Change
23.2.2.1. Observed Climate Change
The average temperature in Europe has continued to increase with regionally and seasonally different rates of
warming, being greatest in high latitudes in Northern Europe (AR5 WG2 Chapter 28). Since the 1980s, warming has
been strongest over Scandinavia, especially in winter, whereas the Iberian Peninsula warmed mostly in summer
(EEA, 2012; Haylock et al., 2008). The decadal average temperature over land area for 2002-2011 is 1.3°C+/-
0.11°C above the 1850-1899 average, based on HadCRUT3 (Brohan et al., 2006), MLOST (Smith et al., 2008) and
GISS Temp (Hansen et al., 2010). See AR5 WG1 Section 2.4 for a discussion of data and uncertainties and AR5
WG2 Chapter 21for observed regional climate change.
Since 1950, high-temperature extremes (hot days, tropical nights, and heat waves) have become more frequent,
while low-temperature extremes (cold spells, frost days) have become less frequent (AR5 WG1 Chapter 2.6, SREX-
3)(EEA, 2012). The recent cold winters in Northern and Atlantic Europe reflect the high natural variability in the
region (Peterson et al., 2012)(AR5 WG1 section 2.7), and do not contradict the general warming trend. In Eastern
Europe, including the European part of Russia, summer 2010 was exceptionally hot, with an amplitude and spatial
extent that exceeded the previous 2003 heat wave (Barriopedro et al., 2011). Table 23-1 describes the impacts of
major extreme events in Europe in the last decade.
Since 1950, annual precipitation has increased in Northern Europe (up to +70 mm/decade) based on Haylock et al.
(2008), and decreased in parts of Southern Europe (EEA, 2012). Winter snow cover extent has a high inter-annual
variability and a non-significant negative trend over the period 1967-2007 (Henderson and Leathers, 2010).
Regional observed changes in temperature and precipitation extremes are also described in Table 3-2 of SREX and
in Berg et al. (2013). Mean wind speeds have declined over Europe over recent decades (Vautard et al., 2010) with
low confidence due to problematic anemometer data and climate variability (SREX Section 3.3). Bett et al (2013)
did not find any trend in windspeed using the Twentieth Century Reanalysis.
Europe is marked by increasing mean sea level with regional variations, except in the northern Baltic Sea where the
relative sea level decreased due to vertical crustal motion (Albrecht et al., 2011; EEA, 2012; Haigh et al., 2010;
Menendez and WoodWorth, 2010). Extreme sea levels have increased due to mean sea level rise (medium
confidence, SREX Section 3.5, Haigh et al., 2010; Menendez and WoodWorth, 2010). Variability in waves is related
to internal climate variability rather than climate trends (SREX Section 3.5, Charles et al., 2012).
23.2.2.2. Projected Climate Changes
For Europe, sub-regional information from global (AR5 WG1 Chapter 14.8.6; AR5 WG1 Annex 1; AR5 WG2
Chapter 21 supplement) and regional high resolution climate model output (AR5 WG1 Chapter 14.8.6; WG2
Chapter 21, 23) provide more knowledge about the range of possible future climates under the SRES and RCP
emission scenarios. Within the recognized limitations of climate projections (AR5 WG1 Chapter 9; WG2 Chapter
21), new research on inter-model comparisons has provided a more robust range of future climates to assess future
impacts. Since AR4, climate impact assessments are more likely to use a range for the projected changes in
temperature and rainfall. Access to comprehensive and detailed sets of climate projections for decision making exist
in Europe (SREX Section 3.2.1, (Mitchell et al., 2004)(Fronzek et al., 2012; Jacob et al., 2013).
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Climate models show significant agreement for all emission scenarios in warming (magnitude and rate) all over
Europe, with strongest warming projected in Southern Europe in summer, and in Northern Europe in winter
(Kjellström et al., 2011)(Goodess et al., 2009). Even under an average global temperature increase limited to 2°C
compared to pre-industrial times, the climate of Europe is simulated to depart significantly in the next decades from
today s climate (Jacob and Podzun, 2010);(Van der Linden and Mitchell, 2009).
Precipitation signals vary regionally and seasonally. Trends are less clear in Continental Europe, with agreement in
increase in Northern Europe and decrease in Southern Europe (medium confidence) (Kjellström et al., 2011).
Precipitation is projected to decrease in the summer months up to Southern Sweden and increase in winter (Schmidli
et al., 2007) with more rain than snow in mountainous regions (Steger et al., 2013). In Northern Europe, a decrease
of long term mean snow pack (although snow-rich winters will remain) towards the end of the century (Räisänen
and Eklund, 2012) is projected. There is lack of information about past and future changes in hail occurrence in
Europe. Changes in future circulation patterns (Kreienkamp et al., 2010; Ulbrich et al., 2009) and mean wind speed
trends are uncertain in sign (Kjellström et al., 2011)(McInnes et al., 2011).
Regional coupled simulations over the Mediterranean region provide a more realistic characterization of impact
parameters (e.g. snow cover, aridity index, river discharge), which were not revealed by CMIP3 global simulations
(Dell'Aquila et al., 2012).
For 2081-2100 compared to 1986-2005, projected global mean sea level rises (metres) are in the range 0.29-0.55 for
RCP2.6, 0.36-0.63 for RCP4.5, 0.37-0.64 for RCP6.0 and 0.48-0.82 for RCP8.5 (medium confidence, AR5 WG3
Chapter 5). There is a low confidence on projected regional changes (Slangen et al., 2012)(AR5 WG1 13.6). Low
probability/high impact estimates of extreme mean sea-level rise projections derived from the A1FI SRES scenario
for the Netherlands (Katsman et al., 2011) indicate that the mean sea-level could rise globally between 0.55 and 1.15
m, and locally (the Netherlands) by 0.40 to 1.05 m, by 2100. Extreme (very unlikely) scenarios for the UK vary
from 0.9 to 1.9 m by 2100 (Lowe et al., 2009).
23.2.2.3. Projected Changes in Climate Extremes
There will be a marked increase in extremes in Europe, in particular, in heat waves, droughts and heavy precipitation
events (Beniston et al., 2007)(Lenderink and Van Meijgaard, 2008) and AR5 WG2 Chapter 21 Supplement. There is
a general high confidence concerning changes in temperature extremes (toward increased number of warm days,
warm nights and heat waves, SREX Table 3-3). Figure 23-2 (upper panels) shows projected changes in the mean
number of heat waves in May to September for 2071-2100 compared to 1971-2000 for RCP4.5 and RCP8.5 with
large differences depending on the emission scenario. The increase in likelihood of some individual events due to
anthropogenic change has been quantified for the 2003 heat wave (Schär and Jendritzky, 2004), the warm winter of
2006/2007 and warm spring of 2007 (Beniston, 2007).
Changes in extreme precipitation depend on the region, with a high confidence of increased extreme precipitation in
Northern Europe (all seasons) and Continental Europe (except summer). Future projections are regionally and
seasonally different in Southern Europe (SREX Table 3-3). Figure 23-2 (middle panels) shows projected seasonal
changes of heavy precipitation events for 2071-2100 compared to 1971-2000 for RCP4.5 and RCP8.5.
[INSERT FIGURE 23-2 HERE
Figure 23-2: First row: Projected changes in the mean number of heat waves occurring in the months May to
September for the period 2071-2100 compared to 1971-2000 (number per 30 years). Heat waves are defined as
periods of more than 5 consecutive days with daily maximum temperature exceeding the mean maximum
temperature of the May to September season of the control period (1971-2000) by at least 5°C. Second and third
rows: Projected seasonal changes in heavy precipitation defined as the 95th percentile of daily precipitation (only
days with precipitation > 1mm/day are considered) for the period 2071-2100 compared to 1971-2000 (in %) in the
months December to January (DJF) and June to August (JJA). Fourth row: Projected changes in the 95th percentile
of the length of dry spells for the period 2071-2100 compared to 1971-2000 (in days). Dry spells are defined as
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periods of at least 5 consecutive days with daily precipitation below 1mm. Hatched areas indicate regions with
robust (at least 66% of models agree in the sign of change) and/or statistical significant change (significant on a 95%
confidence level using Mann-Whitney-U test). For the eastern parts of Black Sea, Eastern Anatolia and Southeast
Anatolia (Turkey), no regional climate model projections are available. Changes represent the mean over 8 (RCP4.5,
left side) and 9 (RCP8.5, right side) regional model simulations compiled within the EURO-CORDEX initiative.
Adapted from Jacob et al. (2013).]
Projected changes of spatially averaged indices over the European sub-regions (Figure 23-1) are described in the
supplemental information (Table SM23-2).
In winter, small increases in extreme wind speed are projected for Central and Northern Europe [medium
confidence] (AR5 WG2 21.3.3.1.6; SREX Figure 3-8) (Beniston et al., 2007; Haugen and Iversen, 2008; Rauthe et
al., 2010; Rockel and Woth, 2007; Schwierz et al., 2010), connected to changes in storm tracks [medium confidence]
(Pinto et al., 2007a; Pinto et al., 2007b)(Donat et al., 2010)(Pinto et al., 2010). Other parts of Europe and seasons
are less clear in sign with a small decreasing trend in southern Europe [low confidence] (Donat et al., 2011; McInnes
et al., 2011).
Extreme sea level events will increase (high confidence, AR5 WG1 13.7, SREX 3.5.3), mainly dominated by the
global mean sea level increase. Storm surges are expected to vary along the European coasts. Significant increases
are projected in the eastern North Sea (increase of 6-8% of the 99th percentile of the storm surge residual, 2071-2100
compared to 1961-1990, based on the B2, A1B and A2 SRES scenarios) (Debernard and Ryed, 2008) and west of
UK and Ireland (Debernard and Ryed, 2008)(Wang et al., 2008), except South of Ireland (Wang et al., 2008). There
is a medium agreement for the South of North Sea and Dutch coast where trends vary from increasing (Debernard
and Ryed, 2008) to stable (Sterl et al., 2009). There is a low agreement on the trends in storm surge in the Adriatic
sea (Jorda et al., 2012; Lionello et al., 2012; Troccoli et al., 2012b)(Planton et al., 2011).
23.2.3. Observed and Projected Trends in the Riverflow and Drought
Streamflows have decreased in the south and east of Europe and increased in Northern Europe (Stahl et al.,
2010)(Wilson et al., 2010) (AR5 WG2 3.2.3). In general, few changes in flood trends can be attributed to climate
change, partly due to the lack of sufficiently long records (Kundzewicz et al., 2013). European mean and peak
discharges are highly variable (Bouwer et al., 2008); for instance in France, upward trends in low flows were
observed over 1948-1988 and downward trends over 1968-2008 (Giuntoli et al., 2013). Alpine glacier retreat during
the last two decades caused a 13% increase in glacier contribution to August runoff of the four main rivers
originating in the Alps, compared to the long-term average (Huss, 2011). Increases in extreme river discharge (peak
flows) over the past 30-50 years have been observed in parts of Germany (Petrow et al., 2009)(Petrow et al., 2007),
the Meuse river basin (Tu et al., 2005), parts of Central Europe (Villarini et al., 2011), Russia (Semenov, 2011), and
Northeastern France (Renard et al., 2008). Decreases in extreme river discharge have been observed in the Czech
Republic (Yiou et al., 2006), and no change observed in Switzerland (Schmocker-Fackel and Naef, 2010), Germany
(Bormann et al., 2011), and the Nordic countries (Wilson et al., 2010). River regulation possibly partly masks
increasing peak flows in the Rhine (Vorogushyn et al., 2012). One study (Pall et al., 2011) suggested that the UK
2000 flood was partly due to anthropogenic forcing, although another showed a weaker effect (Kay et al., 2011).
Climate change is projected to affect the hydrology of river basins (SREX Chapter 3; AR5 WG2 Chapter 4). The
occurrence of current 100-year return period discharges is projected to increase in Continental Europe, but decrease
in some parts of Northern and Southern Europe by 2100 (Dankers and Feyen, 2008)(Rojas et al., 2012). In contrast,
studies for individual catchments indicate increases in extreme discharges, to varying degrees, in Finland
(Veijalainen et al., 2010), Denmark (Thodsen, 2007), Ireland (Wang et al., 2006)(Steele-Dunne et al., 2008)(Bastola
et al., 2011), the Rhine basin (Görgen et al., 2010; Te Linde et al., 2010a), Meuse basin (Leander et al., 2008)(Ward
et al., 2011), the Danube basin (Dankers et al., 2007), and France (Chauveau et al., 2013; Quintana-Segui et al.,
2011). Although snowmelt floods may decrease, increased autumn and winter rainfall could lead to higher peak
discharges in northern Europe (Lawrence and Hisdal, 2011). Declines in low flows are projected for the UK
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(Christierson et al., 2012), Turkey (Fujihara et al., 2008), France (Chauveau et al., 2013), and rivers fed by Alpine
glaciers (Huss, 2011).
The analysis of trends in droughts is made complex by the different categories or definitions of drought
(meteorological, agricultural, and hydrological) and the lack of long-term observational data (SREX Box 3-3).
Southern Europe shows trends towards more intense and longer meteorological droughts, but they are still
inconsistent (Sousa et al., 2011). Drought trends in all other sub-regions are not statistically significant (SREX
3.5.1). Regional and global climate simulations project (medium confidence) an increase in duration and intensity of
droughts in central and southern Europe and the Mediterranean up until the UK for different definitions of drought
(Feyen and Dankers, 2009; Gao and Giorgi, 2008; Vidal and Wade, 2009)(Koutroulis et al., 2010; Tsanis et al.,
2011) (AR5 WG2 Chapter 21). Even in regions where summer precipitation is expected to increase, soil moisture
and hydrological droughts may become more severe due to increasing evapotranspiration (Wong et al., 2011).
Projected changes in the length of meteorological dry spells show that the increase is large in Southern Europe
(Figure 23-2 fourth row).
23.3. Implications of Climate Change for Production Systems and Physical Infrastructure
23.3.1. Settlements
23.3.1.1. Coastal Flooding
As the risk of extreme sea level events increases with climate change [23.2.3, AR5 WG2 Chapter 5], coastal flood
risk will remain a key challenge for several European cities, port facilities and other infrastructure (Nicholls et al.,
2008)(Hallegatte et al., 2008)(Hallegatte et al., 2011). With no adaptation, coastal flooding in the 2080s is projected
to affect an additional 775,000 and 5.5 million people per year in the EU27 (B2 and A2 scenarios) (Ciscar et al.,
2011). The Atlantic, Northern and Southern European regions are projected to be most affected. Direct costs from
sea level rise in the EU27 without adaptation could reach 17 billion Euros per year by 2100 (Hinkel et al., 2010),
with indirect costs also estimated for land-locked countries (Bosello et al., 2012). Countries with high absolute
damage costs include the Netherlands, Germany, France, Belgium, Denmark, Spain and Italy (Hinkel et al., 2010).
Upgrading coastal defences would substantially reduce impacts and damage costs (Hinkel et al., 2010). However,
the amount of assets and populations that need to protected by coastal defences is increasing, thus, the magnitude of
losses when floods do occur will also increase in the futre (Hallegatte et al. 2013), entailing the need to prepare for
very large flood disasters in the future.
An increase in future flood losses due to climate change have been estimated for Copenhagen (Hallegatte et al.,
2011), the UK coast (Mokrech et al., 2008)(Purvis et al., 2008)(Dawson et al., 2011), the North Sea coast
(Gaslikova et al., 2011), cities including Amsterdam and Rotterdam (Hanson et al., 2011), and the Netherlands
(Aerts et al., 2008). A 1m sea-level rise in Turkey could affect 3 million additional people and put 12 billion USD
capital value at risk, with around 20 billion USD adaptation costs (10% of GNP) (Karaca and Nicholls, 2008). In
Poland, up to 240,000 people would be affected by increasing flood risk on the Baltic coast (Pruszak and Zawadzka,
2008). The increasing cost of insurance and unwillingness of investors to place assets in affected areas is a potential
growth impediment to coastal and island economies (Day et al., 2008).
23.3.1.2. River and Pluvial Flooding
Recent major flood events in Europe include the 2007 floods in the UK (Table 23-1) (Chatterton et al., 2010) and
the 2013 floods in Germany. The observed increase in river flood events and damages in Europe is well documented
(see AR5 WG2 18.4.2.1), however, the main cause is increased exposure of persons and property in flood risk areas
(Barredo, 2009). Since AR4, new studies provide a wider range of estimates of future economic losses from river
flooding attributable to climate change, depending on the modelling approach and climate scenario (Bubeck et al.,
2011). Studies now also quantify risk under changes in population and economic growth, generally indicating this
contribution to be about equal or larger than climate change per se (Feyen et al., 2009; Maaskant et al., 2009; Rojas
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et al., 2013)(Bouwer et al., 2010)(Te Linde et al., 2011). Some regions may see increasing risks, but others may see
decreases or little to no change (Bubeck et al., 2011)(ABI, 2009)(Feyen et al., 2009)(Lugeri et al., 2010)(Mechler et
al., 2010)(Feyen et al., 2012)(Lung et al., 2012). In the EU15, river flooding could affect 250,000-400,000
additional people by the 2080s (SRES A2 and B2 scenarios) and more than doubling annual average damages, with
Central and Northern Europe and the UK most affected (Ciscar, 2009)(Ciscar et al., 2011). When economic growth
is included, economic flood losses in Europe could increase 17-fold under the A1B climate scenario (Rojas et al.,
2013).
Few studies have estimated future damages from inundation in response to an increase in intense rainfall (Hoes,
2006; Willems et al., 2012). Processes that influence flash flood risk include increasing exposure from urban
expansion, and forest fires that lead to erosion and increased surface runoff (Lasda et al., 2010). Some studies have
costed adaptation measures but these may only partly offset anticipated impacts (Zhou et al., 2012).
[INSERT Table 23-1 HERE
Table 23-1: Impacts of climate extremes in the last decade in Europe.]
23.3.1.3. Windstorms
Several studies project an overall increase storm hazard in northwest Europe [23.2.2.3] and in economic and insured
losses [AR5 WG2 Chapter 17.7.3], but natural variations in frequencies are large. There is no evidence that the
observed increase in European storm losses is due to anthropogenic climate change (Barredo, 2010). There is a lack
of information for other storm types, such as tornadoes and thunderstorms.
23.3.1.4.Mass Movements and Avalanches
In the European Alps, the frequency of rock avalanches and large rock slides has apparently increased over the
period 1900-2007 (Fischer et al., 2012). The frequency of landslides may also have increased in some locations
(Lopez Saez et al., 2013). Mass movements are projected to become more frequent with climate change (Huggel et
al., 2010; Stoffel and Huggel, 2012), although several studies indicate a more complex or stabilising response of
mass movements to climate change (Dixon and Brook, 2007; Huggel et al., 2012; Jomelli et al., 2007; Jomelli et al.,
2009; Melchiorre and Frattini, 2012). Some land-use practices have led to conditions favourable to increased
landslide risk, despite climate trends that would result in a decrease of landslide frequency, as reported in Calabria
(Polemio and Petrucci, 2010) and in the Apennines (Wasowski et al., 2010). Snow avalanche frequency changes in
Europe are dominated by climate variability; studies based on avalanche observations (Eckert et al., 2010) or
favourable meteorological conditions (Castebrunet et al., 2012; Teich et al., 2012) show contrasting variations,
depending on the region, elevation, season and orientation.
23.3.2. Built Environment
Built infrastructure in Europe is vulnerable to extreme weather events, including overheating of buildings (houses,
hospitals, schools) during hot weather (Crump et al., 2009; DCLG, 2012). Buildings that were originally designed
for certain thermal conditions will need to function in warmer climates in the future (WHO, 2008). Climate change
in Europe is expected to increase cooling energy demand (23.3.4) (Dolinar et al., 2010), with implications for
mitigation and adaptation policies (23.8.1). A range of adaptive strategies for buildings are available, including
effective thermal mass and solar shading (ARUP, 2008). Climate change may also increase the frequency and
intensity of drought-induced soil subsidence and associated damage to dwellings (Corti et al., 2009).
With respect to the outdoor built environment, there is limited evidence regarding the potential for differential rates
of radiatively-forced climate change in urban compared to rural areas (McCarthy et al., 2010). Climate change may
exacerbate London's nocturnal urban heat island (UHI) (Wilby, 2008), however, the response of different cities may
vary. For example, a study of Paris (Lemonsu et al., 2013) indicated a future reduction in strong urban heat island
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events when increased soil dryness was taken into effect. Modification of the built environment, via enhanced urban
greening, for example, can reduce temperatures in urban areas, with co-benefits for health and wellbeing (23.7.4,
23.8.1).
23.3.3. Transport
Systematic and detailed knowledge on climate change impacts on transport in Europe remains limited (Koetse and
Rietveld, 2009).
On road transport, in line with AR4, more frequent but less severe collisions due to reduced speed are expected in
case of increased precipitation (Brijs et al., 2008)(Kilpeläinen and Summala, 2007). However, lower traffic speed
may cause welfare losses due to additional time spent driving (Sabir et al., 2010). Severe snow and ice-related
accidents will also decrease, but the effect of fewer frost days on total accidents is unclear (Andersson and
Chapman, 2011a)(Andersson and Chapman, 2011b). Severe accidents caused by extreme weather are projected to
decrease by 63-70% in 2040-2070 compared to 2007 as a result of modified climate and expected developments in
vehicle technology and emergency systems (Nokkala et al., 2012).
For rail, consistent with AR4, increased buckling in summer, as occurred in 2003 in the UK, is expected to increase
the average annual cost of heat-related delays in some regions, while the opposite is expected for ice and snow-
related delays (Dobney et al., 2010; Lindgren et al., 2009; Palin et al., 2013). Effects from extreme precipitation, as
well as the net overall regional impact of climate change remain unclear. Efficient adaptation comprises proper
maintenance of track and track bed.
Regarding inland waterways, the case of Rhine shows that for 1-2 oC increases by 2050 more frequent high water
levels are expected in winter, while after 2050 days with low water levels in summer will also increase (Jonkeren et
al., 2011)(Te Linde et al., 2011)(Te Linde, 2007)(Hurkmans et al., 2010). Low water levels will reduce the load
factor of inland ships and consequently increase transport prices, as in the Rhine and Moselle in 2003 (Jonkeren,
2009)(Jonkeren et al., 2007). Adaptation includes modal shifts, increase navigational hours per day under low water
levels, and infrastructure modifications (e.g. canalization of river parts) (Jonkeren et al., 2011; Krekt et al., 2011).
For long range ocean routes, the economic attractiveness of the Northwest Passage and the Northern Sea Route
depends also on passage fees, bunker prices and cost of alternative sea routes (Verny and Grigentin, 2009)(Liu and
Kronbak, 2010)(Lasserre and Pelletier, 2011). Regarding air transport, for Heathrow airport in the UK, future
temperature and wind changes were estimated to cause a small net annual increase but much larger seasonal changes
on the occurrence of delays (Pejovic et al., 2009).
23.3.4. Energy Production, Transmission, and Use
On wind energy, no significant changes are expected before 2050, at least in Northern Europe (Pryor and Schoof,
2010)(Pryor and Barthelmie, 2010)(Seljom et al., 2011)(Barstad et al., 2012; Hueging et al., 2013). After 2050, in
line with AR4, the wind energy potential in Northern, Continental and most of Atlantic Europe may increase during
winter and decrease in summer (Harrison et al., 2008; Hueging et al., 2013)(Nolan et al., 2012; Rockel and Woth,
2007). For Southern Europe, a decrease in both seasons is expected, except for the Aegean Sea and Adriatic coast
where a significant increase during summer is possible (Bloom et al., 2008; Hueging et al., 2013; Najac et al., 2011;
Pa¹ièko et al., 2012).
For hydropower, electricity production in Scandinavia is expected to increase by 5-14% during 2071-2100 compared
to historic or present levels (Golombek et al., 2012) (Haddeland et al., 2011); for 2021-2050, increases by 1-20%
were estimated (Haddeland et al., 2011)(Hamududu and Killingtveit, 2012; Seljom et al., 2011). In Continental, and
part of Alpine Europe, reductions in electricity production by 6-36% were estimated (Schaefli et al., 2007) (Paiva et
al., 2011; Pa¹ièko et al., 2012)(Hendrickx and Sauquet, 2013; Stanzel and Nachtnebel, 2010). For Southern Europe,
production is expected to decrease by 5-15% in 2050 compared to 2005 (Bangash et al., 2013; Hamududu and
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Killingtveit, 2012). Adaptation consists in improved water management, including pump storage if appropriate
(Schaefli et al., 2007)(García-Ruiz et al., 2011).
Biofuel production is discussed in section 23.4.5. There are few studies of impacts on solar energy production.
Crook et al. (2011) estimated an increase of the energy output from photovoltaic panels and especially from
concentrated solar power plants in most of Europe under the A1B scenario.
On thermal power, in line with AR4, van Vliet et al. (2012) estimated a 6-19% decrease of the summer average
usable capacity of power plants by 2031 2060 compared to 1971-2000, while smaller decreases have been also
estimated (Linnerud et al., 2011)(Förster and Lilliestam, 2010). Closed-cooling circuits are efficient adaptation
choices for new plants (Koch and Vögele, 2009). In power transmission, increasing lightning and decreasing snow-
sleet-and blizzard faults for 2050-2080 were estimated for the UK (McColl et al., 2012).
By considering both heating and cooling, under a +3.7 oC scenario by 2100 a decrease of total annual energy
demand in Europe as a whole during 2000-2100 was estimated (Isaac and van Vuuren, 2009). Seasonal changes will
be prominent, especially for electricity (see Figure 23-3), with summer peaks arising also in countries with moderate
summer temperatures (Hekkenberg et al., 2009). Heating degree days are expected to decrease by 11-20% between
2000 and 2050 due solely to climate change (Isaac and van Vuuren, 2009). For cooling, very large percentage
increases up to 2050 are estimated by the same authors for most of Europe as the current penetration of cooling
devices is low; then, increases by 74-118% in 2100 (depending on the region) from 2050 are expected under the
combined effect of climatic and non-climatic drivers. In Southern Europe, cooling degree days by 2060 will
increase, while heating degree days will decrease but with substantial spatial variations (Giannakopoulos et al.,
2009). Consequently, net annual electricity generation cost will increase in most of the Mediterranean and decrease
in the rest of Europe (Eskeland and Mideksa, 2010)(Mirasgedis et al., 2007)(Pilli-Sihlova et al., 2010; Zachariadis,
2010). Future building stock changes and retrofit rates are critical for impact assessment and adaptation (Olonscheck
et al., 2011). Energy efficient buildings and cooling systems, and demand-side management are effective adaptation
options (Artmann et al., 2008; Breesch and Janssens, 2010; Chow and Levermore, 2010; Day et al., 2009; Jenkins et
al., 2008).
[INSERT FIGURE 23-3 HERE
Figure 23-3: Percentage change in electricity demand in Greece attributable to climate change, under a range of
climate scenarios and economic assumptions. Source: Mirasgedis et al., 2007.]
23.3.5. Industry and Manufacturing
Research on the potential effects of climate change in industry is limited. Modifications in future consumption of
food and beverage products have been estimated on the basis of current sensitivity to seasonal temperature
(Mirasgedis et al., 2013). Higher temperatures may favour the growth of food borne pathogens or contaminants
(Jacxsens et al., 2010; Popov Janevska et al., 2010) (see also 23.5.1). The quality of some products, such as wine
(23.4.1, Box 23-2), is also likely to be affected. In other sectors, the cumulative cost of direct climate change
impacts in the Greek mining sector for 2021-2050 has been estimated at 0.245 billion Euros, in 2010 prices
(Damigos, 2012). Adaptation to buildings or work practices are likely to be needed in order to maintain labour
productivity during hot weather (Kjellstrom et al., 2009)(11.6.2.2).
23.3.6. Tourism
In line with AR4, the climate for general tourist activities especially after 2070 is expected to improve significantly
during summer and less during autumn and spring in northern Continental Europe, Finland, southern Scandinavia
and southern England (Amelung and Moreno, 2012)(Amelung et al., 2007)(Nicholls and Amelung, 2008). For the
Mediterranean, climatic conditions for light outdoor tourist activities are expected to deteriorate in summer mainly
after 2050, but improve during spring and autumn (Amelung and Moreno, 2009)(Hein et al., 2009)(Perch-Nielsen et
al., 2010)(Amelung et al., 2007)(Giannakopoulos et al., 2011). Others concluded that before 2030 (or even 2060)
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this region as a whole will not become too hot for beach or urban tourism (Moreno and Amelung, 2009)(Rutty and
Scott, 2010), while surveys showed that beach tourists are deterred mostly by rain (De Freitas et al., 2008; Moreno,
2010).
Thus, from 2050, domestic tourism and tourist arrivals at locations in Northern and parts of Continental Europe may
be enhanced at the expense of Southern locations (Amelung and Moreno, 2012; Bujosa and Roselló, 2012; Hamilton
and Tol, 2007; Hein et al., 2009). The age of tourists, the climate in their home country, local economic and
environmental conditions (e.g. water stress, tourist development) are also critical (Hamilton and Tol, 2007)(Moreno
and Amelung, 2009; Perch-Nielsen et al., 2010)(Eugenio-Martin and Campos-Soria, 2010; Lyons et al., 2009)(Rico-
Amoros et al., 2009).
Tourism in mountainous areas may benefit from improved climatic conditions in summer (Endler et al., 2010;
Endler and Matzarakis, 2011b; Perch-Nielsen et al., 2010; Serquet and Rebetez, 2011). However, in agreement with
AR4, natural snow reliability and thus ski season length will be adversely affected, especially where artificial
snowmaking is limited (OECD, 2007; Steiger, 2011)(Moen and Fredman, 2007). Low-lying areas will be the most
vulnerable (Endler et al., 2010; Endler and Matzarakis, 2011a; Serquet and Rebetez, 2011; Steiger, 2011; Uhlmann
et al., 2009). Tourist response to marginal snow conditions remains largely unknown, while changes in weather
extremes may also be critical (Tervo, 2008). Up to 2050, demographic changes (e.g. population declines in source
countries, ageing populations) may have a higher impact than climate change (Steiger, 2012). Artificial snowmaking
has physical and economic limitations, especially in small sized and low-altitude ski stations (Sauter et al., 2010;
Steiger and Mayer, 2008; Steiger, 2010; Steiger, 2011), and increases water and energy consumption. Shifts to
higher altitudes, operational/ technical measures and year-round tourist activities may not fully compensate for
adverse impacts.
23.3.7. Insurance and Banking
Insurance and banking face problems related to accurate pricing of risks, shortage of capital after large loss events,
and by an increasing burden of losses that can affect markets and insurability, within but also outside the European
region (Botzen et al., 2010a; Botzen et al., 2010b; CEA, 2007)(AR5 WG2 Section 10.7). However, risk transfer
including insurance also holds potential for adaptation by providing incentives to reduce losses (Botzen and van den
Bergh, 2008; CEA, 2009)(Herweijer et al., 2009).
Banking is potentially affected through physical impacts on assets and investments, as well as through regulation
and/or mitigation actions by changing demands regarding sustainability of investments and lending portfolios. Few
banks have adopted climate strategies that also address adaptation (Furrer et al., 2009)(Cogan, 2008).
Windstorm losses are well covered in Europe by building and motor policies, and thus create a large exposure to the
insurance sector. Flood losses in the UK in 2000, 2007 and 2009 have put the insurance market under further
pressure, with increasing need for the government to reduce risk (Ward et al., 2008)(Lamond et al., 2009). Other
risks of concern to the European insurance industry is building subsidence related to drought (Corti et al., 2009), and
hail damage to buildings and agriculture (Kunz et al., 2009; Botzen et al., 2010b; GIA, 2011).
The financial sector can adapt through adjustment of premiums, restricting or reduction of coverage, further risk
spreading, and importantly incentivising risk reduction (Botzen et al., 2010a; Clemo, 2008)(Crichton,
2007)(Crichton, 2006)(Wamsler and Lawson, 2011)(Surminski and Philp, 2010). Public attitudes in Scotland and
the Netherlands would support insurance of private property and public infrastructure damages in the case of
increasing flood risk (Botzen et al., 2009)(Glenk and Fisher, 2010). Government intervention is however often
needed to provide compensation and back-stopping in the event of major losses (Aakre and Rübbelke, 2010; Aakre
et al., 2010). Hochrainer et al. (2010) analysed the performance of the EU Solidarity Fund that supports European
governments in large events, and argue there is a need to increase its focus on risk reduction. Current insurance
approaches present in Europe are likely to remain, as they are tailored to local situations and preferences (Schwarze
et al., 2011).
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23.4. Implications of Climate Change for Agriculture, Fisheries, Forestry, and Bioenergy Production
23.4.1. Plant (Food) Production
In AR4, Alcamo et al. (2007) reported that crop suitability is likely to change throughout Europe. During the 2003
and 2010 summer heat waves, grain-harvest losses reached 20 and 30% in affected regions of Europe and Russia,
respectively (Barriopedro et al., 2011; Ciais et al., 2005) (Table 23-1). Cereals production fell on average by 40% in
the Iberian Peninsula during the intense 2004/2005 drought (EEA, 2010a). Climate-induced variability in wheat
production has increased in recent decades in Southern and Central Europe (Brisson et al., 2010)(Hawkins et al.,
2013)(Ladanyi, 2008), but no consistent reduction has been recorded in the northernmost areas of Europe (Peltonen-
sainio et al., 2010). Country-scale rainfed cereals yields are below agro-climatic potentials (Supit et al., 2010) and
wheat yield increases have levelled off in several countries over 1961-2009 (Olesen et al., 2011). High temperatures
and droughts during grain filling has contributed to the lack of yield increase of winter wheat in France despite
improvements in crop breeding (Brisson et al., 2010; Kristensen et al., 2011). In contrast, in eastern Scotland,
warming has favoured an increase in potato yields since 1960 (Gregory and Marshall, 2012). In north-east Spain,
grape yield was reduced by an increased water deficit in the reproductive stage since the 1960s (Camps and Ramos,
2012).
Insight into the potential effect of climate change on crops requires the combination of a wide range of emission
scenarios, global circulation models (GCM) and impact studies (Trnka et al., 2007)(Soussana et al., 2010). In the
EU27, a 2.5 °C regional temperature increase in the 2080s under the B2 scenario could lead to small changes (on
average +3%) in crop yields, whereas a 5.4 °C regional warming under the A2 scenario could reduce mean yields by
10% according to a study based on regional climate models (Ciscar et al., 2011). An initial benefit from the
increasing CO2 concentration for rainfed crop yields would contrast by the end of the century with yield declines in
most European subregions, although wheat yield could increase under the A2 scenario (Supit et al., 2012, three
GCMs, B1, A2 scenarios). Disease-limited yields of rain fed wheat and maize in the 2030s does not show consistent
trends across two GCMs (Donatelli et al., 2012). For a global temperature increase of 5° C, agroclimatic indices
show an increasing frequency of extremely unfavourable years in European cropping areas (Trnka et al., 2011).
Under the A2 and B2 scenarios, crop production shortfalls, defined as years with production below 50% of its
average climate normal production would double by 2020 and triple by 2070 as compared to a current frequency of
1-3 years per decade in the currently most productive southern European regions of Russia (Alcamo et al., 2007).
The regional distribution of climate change impacts on agricultural production is likely to vary widely (Iglesias et
al., 2012) (Donatelli et al., 2012) (Figure 23-4). Southern Europe would experience the largest yield losses (-25 %
by 2080 under a 5.4 °C warming, (Ciscar et al., 2011) with increased risks of rain fed summer crop failure (Bindi
and Olesen, 2011)(Ferrara et al., 2010)(Ruiz-Ramos et al., 2011). Warmer and drier conditions by 2050 (Trnka et
al., 2010; Trnka et al., 2011) would cause moderate declines in crop yields in Central Europe regions (Ciscar et al.,
2011). In Western Europe, increased heat stress around flowering could cause considerable yield losses in wheat
(Semenov, 2009). For Northern Europe, there is diverging evidence concerning future impacts. Positive yield
changes combined with the expansion of climatically suitable areas could lead to crop production increases (between
2.5 and 5.4°C regional warming) (Bindi and Olesen, 2011)(Bindi and Olesen, 2011). However, increased climatic
variability would limit winter crops expansion (Peltonen-Sainio et al., 2010) and cause at high latitudes high risk of
marked cereal yield loss (Rötter et al., 2011). Spring crops from tropical origin like maize for silage could become
cultivated in Finland by the end of the century (Peltonen-Sainio et al., 2009). Cereal yield reduction from ozone
(Fuhrer, 2009) could reach 6 and 10 % in 2030 for the European Union with the B1 and A2 scenarios, respectively
(Avnery et al., 2011a; Avnery et al., 2011b). Because of limited land availability and soil fertility outside of
Chernozem (black earth) areas, the shift of agriculture to the boreal forest zone would not compensate for crop
losses due to increasing aridity in South European regions of Russia with the best soils (Dronin and Kirilenko,
2011).
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[INSERT FIGURE 23-4 HERE
Figure 23-4: Percentage change in simulated water-limited yield for winter wheat in 2030 with respect to the 2000
baseline for the A1B scenario using ECHAM5 (left column) and HadCM3 (right) GCMs. Upper maps to do not take
adaptation into account. Bottom maps include adaptation. Source: Donatelli et al., 2012.]
With generally warmer and drier conditions, deep rooted weeds (Gilgen et al., 2010b) and weeds with contrasting
physiology, such as C4 species, could pose a more serious threat (Bradley et al., 2010) to crops than shallow rooted
C3 weeds (Stratonovitch, 2012). Arthropod-borne diseases (viruses and phytoplasmas), winter infection root and
stem diseases (phoma stem canker of oilseed rape and eyespot of wheat) (Butterworth et al., 2010)(West et al.,
2012), Fusarium blight (Madgwick et al., 2011), grapevine moth (Caffarra et al., 2012) and a black rot fungus in
fruit trees (Weber, 2009) could create increasing damages in Europe under climate change. However, other
pathogens like cereal stem rots (e.g. Puccinia striiformis) (Luck et al., 2011) and grapevine powdery mildew
(Caffarra et al., 2012) could be limited by increasing temperatures. Increased damages from plant pathogens and
insect pests are projected by 2050 in Nordic countries which have hitherto been protected by cold winters and
geographic isolation (Hakala et al., 2011; Roos et al., 2011). Some pests, like the European corn borer (Trnka et al.,
2007), could also extend their climate niche in Central Europe. Pests and disease management will be affected with
regard to timing, preference and efficacy of chemical and biological measures of control (Kersebaum et al., 2008).
Autonomous adaptation by farmers, through the advancement of sowing and harvesting dates and the use of longer
cycle varieties (Howden et al., 2007; Moriondo et al., 2011; Moriondo et al., 2010; Olesen et al., 2011) could result
in a general improvement of European wheat yields in the 2030s compared to the 2000s (Donatelli et al., 2012)
(Figure 23-4). However, farmer sowing dates seem to advance slower than crop phenology (Menzel et al.,
2006)(Siebert and Ewert, 2012), possibly because earlier sowing is often prevented by lack of soil workability and
frost-induced soil crumbling (Oort et al., 2012). Simulation studies which anticipate on earlier sowing in Europe
may thus be overly optimistic. Further adaptation options include: changes in crop species, fertilization, irrigation,
drainage, land allocation and farming system (Bindi and Olesen, 2011). At the high range of the projected
temperature changes, only plant breeding aimed at increasing yield potential jointly with drought resistance and
adjusted agronomic practices may reduce risks of yield shortfall (Olesen et al., 2011)(Rötter et al., 2011)(Ventrella
et al., 2012). Crop breeding is, however, challenged by temperature and rainfall variability, since: i) breeding has not
yet succeeded in altering crop plant development responses to short-term changes in temperature (Parent and
Tardieu, 2012) and ii) distinct crop drought tolerance traits are required for mild and severe water deficit scenarios
(Tardieu, 2012). Adaptation to increased climatic variability may require an increased use of between and within
species genetic diversity in farming systems (Smith and Olesen, 2010) and the development of insurance products
against weather-related yield variations (Musshoff et al., 2011). Adaptive capacity and long term economic viability
of farming systems may vary given farm structural change induced by climate change (Mandryk et al., 2012);
(Moriondo et al., 2010b). In Southern Europe, the regional welfare loss caused by changes in the agriculture sector
under a high warming scenario (+5.4°C) was estimated at 1% of GDP. Northern Europe was the single sub-region
with welfare gains (+0.7%) from agriculture in this scenario (Ciscar et al., 2011).
23.4.2. Livestock Production
Livestock production is adversely affected by heat (Tubiello et al., 2007)(AR5 WG2 7.2.1.3). With intensive
systems, heat stress reduced dairy production and growth performance of large finishing pigs at daily mean air
temperatures above 18 and 21°C, respectively (André et al., 2011; Renaudeau et al., 2011). High temperature and
air humidity during breeding increased cattle mortality risk by 60% in Italy (Crescio et al., 2010). Adaptation
requires changes in diets and in farm buildings (Renaudeau et al., 2012) as well as targeted genetic improvement
programmes (Hoffmann, 2010).
With grass based livestock systems, model simulations (A1B scenario, ensemble of downscaled GCMs) show by
end of century increases in potential dairy production in Ireland and France, however with higher risks of summer-
autumn production failures in Central Europe and at French sites (Graux et al., 2012; Trnka et al., 2009). Climate
conditions projected for the 2070s in central France (A2 scenario) reduced significantly grassland production in a
four years experiment under elevated CO2 (Cantarel et al., 2013). At the same site, a single experimental summer
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drought altered production during the next two years (Zwicke et al., 2013). Resilience of grassland vegetation
structure was observed to prolonged experimental heating and water manipulation (Grime et al., 2008). However,
weed pressure from tap-rooted forbs was increased after severe experimental summer droughts (Gilgen et al.,
2010a). Mediterranean populations could be used to breed more resilient and better adapted forage plant material for
livestock production (Poirier et al., 2012).
Climate change has affected animal health in Europe [high confidence]. The spread of bluetongue virus in sheep
across Europe has been partly attributed to climate change (Arzt et al., 2010)(Guis et al., 2012) through increased
seasonal activity of the Culicoides vector (Wilson and Mellor, 2009). The distribution of this vector is unlikely to
expand but its abundance could increase in Southern Europe (Acevedo et al., 2010). Ticks, the primary arthropod
vectors of zoonotic diseases in Europe (e.g. Lyme disease and tick-borne encephalitis), have changed distributions
towards higher altitudes and latitudes with climate change (van Dijk et al., 2010)(Petney et al., 2012; Randolph and
Rogers, 2010)(AR5 WG2 23.5). Exposure to fly strike could increase in a warmer climate but adaptation in
husbandry practices would limit impacts on livestock (Wall and Ellse, 2011). The overall risk of incursion of
Crimean-Congo haemorrhagic fever virus in livestock through infected ticks introduced by migratory bird species
would not be increased by climate change (Gale et al., 2012). The probability of introduction and large-scale spread
of Rift Valley Fever in Europe is also very low (Chevalier et al., 2010). Epidemiological surveillance and increased
coordinated regional monitoring and control programmes have the potential to reduce the incidence of vector-borne
animal diseases (Chevalier et al., 2010) (Wilson and Mellor, 2009).
23.4.3. Water Resources and Agriculture
Future projected trends confirm the widening of water resource differences between Northern and Southern Europe
reported in AR4 (Alcamo et al., 2007). In Southern Europe, soil water content will decline, saturation conditions and
drainage will be increasingly rare and restricted to periods in winter and spring, and snow accumulation and melting
will change, especially in the mid-mountain areas (García-Ruiz et al., 2011). Across most of Northern and
Continental Europe, an increase in flood hazards (Falloon and Betts, 2010)(23.3.1) could increase damages to crops
and plant growth, complicate soil workability, and increase yield variability (Olesen et al., 2011). Groundwater
recharge and/or water table level would be significantly reduced by the end of the century under A2 scenario for
river basins located in Southern Italy, Spain, Northern France and Belgium (Ducharne et al., 2010; Goderniaux et
al., 2011; Guardiola-Albert and Jackson, 2011; Senatore et al., 2011). However, non-significant impacts were found
for aquifers in Switzerland and in England (Stoll et al., 2011)(Jackson et al., 2011). Less precipitation in summer
and higher rainfall during winter could increase nitrate leaching (Kersebaum et al., 2008) with negative impacts on
water quality (Bindi and Olesen, 2011). Even with reduced N fertilizer application, groundwater nitrate
concentrations would increase by the end of the century in the Seine river basin (Ducharne et al., 2007). More robust
water management, pricing and recycling policies, in order to secure adequate future water supply and prevent
tensions among users could be required in Southern Europe (García-Ruiz et al., 2011).
Reduced suitability for rainfed agricultural production (Daccache and Lamaddalena, 2010; Daccache et al., 2012;
Henriques et al., 2008; Trnka et al., 2011) will increase water demand for crop irrigation (Savé et al., 2012).
However, increased irrigation may not be a viable option, especially in the Mediterranean area, because of projected
declines in total runoff and groundwater resources (Olesen et al., 2011). In a number of catchments water resources
are already over-licensed and/or over-abstracted (Daccache et al., 2012) and their reliability is threatened by climate
change induced decline in groundwater recharge and to a lesser extent by the increase in potential demand for
irrigation (Ducharne et al., 2010; Majone et al., 2012). To match this demand, irrigation system costs could increase
by 20-27% in Southern Italy (Daccache and Lamaddalena, 2010) and new irrigation infrastructures would be
required in some regions (van der Velde et al., 2010) However, since the economic benefits are expected to be
small, the adoption of irrigation would require changes in institutional and market conditions (Finger et al., 2011).
Moreover, since aquatic and terrestrial ecosystems are affected by agricultural water use (Klve et al., 2011),
irrigation demand restrictions are projected in environmentally focussed future regional scenarios (Henriques et al.,
2008). Earlier sowing dates, increased soil organic matter content, low-energy systems, deficit irrigation and
improved water use efficiency of irrigation systems and crops can be used as adaptation pathways (Daccache and
Lamaddalena, 2010; Gonzalez-Camacho et al., 2008; Lee et al., 2008; Schutze and Schmitz, 2010) especially in
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Southern and south-eastern regions of Europe (Trnka et al., 2009);(Falloon and Betts, 2010). Improved water
management in upstream agricultural areas could mitigate adverse impacts downstream (Klve et al., 2011) and
groundwater recharge could be targeted in areas with poor water-holding soils (Wessolek and Asseng, 2006).
23.4.4. Forestry
Observed and future responses of forests to climate change include changes in growth rates, phenology, composition
of animal and plant communities, increased fire and storm damage, and increased insect and pathogen damage. Tree
mortality and forest decline due to severe drought events were observed in forest populations in Southern Europe
(Affolter et al., 2010; Bigler et al., 2006; Raftoyannis et al., 2008), including Italy (Bertini et al., 2011)(Giuggiola et
al., 2010), Cyprus (ECHOES Country report, 2009), and Greece (Raftoyannis et al., 2008) as well as in Belgium
(Kint et al., 2012), Switzerland (Rigling et al., 2013) and the pre-Alps in France (Allen et al., 2010; Charru et al.,
2010; Rouault et al., 2006). Declines have also been observed in wet forests not normally considered at risk of
drought (Choat et al., 2012). An increase in forest productivity has been observed in Russia (Sirotenko and
Abashina, 2008).
Future projections show that in Northern and Atlantic Europe the increasing atmospheric CO2 and higher
temperatures are expected to increase forest growth and wood production, at least in the short-medium term
(Lindner et al., 2010). On the other hand, in Southern and eastern Europe, increasing drought and disturbance risks
will cause adverse effects and productivity is expected to decline (Hlásny et al., 2011; Keenan et al., 2011; Lavalle
et al., 2009; Lindner et al., 2010; Silva et al., 2012; Sirotenko and Abashina, 2008). By 2100, climate change is
expected to reduce the economic value of European forest land depending on interest rate and climate scenario,
which equates to potential damages of several hundred billion Euros (Hanewinkel et al., 2013).
In Southern Europe, fire frequency and wildfire extent significantly increased after the 1970s compared with
previous decades (Pausas and Fernández-Munoz, 2012) due to fuel accumulation (Koutsias et al., 2012), climate
change (Lavalle et al., 2009) and extreme weather events (Camia and Amatulli, 2009; Carvalho et al., 2011; Hoinka
et al., 2009; Koutsias et al., 2012; Salis et al., 2013) especially in the Mediterranean basin (Marques et al., 2011;
Pausas and Fernández-Munoz, 2012)(Fernandes et al., 2010; Koutsias et al., 2012). The most severe events in
France, Greece, Italy, Portugal, Spain, and Turkey in 2010 were associated with strong winds during a hot dry
period (EEA, 2010c). However, for the Mediterranean region as a whole, the total burned area has decreased since
1985 and the number of wildfires has decreased from 2000 to 2009, with large inter-annual variability (Marques et
al., 2011; San-Miguel-Ayanz et al., 2012; Turco et al., 2013). Megafires, triggered by extreme climate events, had
caused record maxima of burnt areas in some Mediterranean countries during last decades (San-Miguel-Ayanz et al.,
2013).
Future wildfire risk is projected to increase in Southern Europe (Carvalho et al., 2011; Dury et al., 2011; Lindner et
al., 2010; Vilén and Fernandes, 2011), with an increase in the occurrence of high fire danger days (Arca et al., 2012;
Lung et al., 2012) and in fire season length (Pellizzaro et al., 2010). The annual burned area is projected to increase
by a factor of 3 to 5 in Southern Europe compared to the present under the A2 scenario by 2100 (Dury et al., 2011).
In Northern Europe, fires are projected to become less frequent due to increased humidity (Rosan and Hammarlund,
2007). Overall, the projected increase in wildfires is likely to lead to a significant increase in greenhouse gas
emissions due to biomass burning (Chiriaco et al., 2013; Pausas et al., 2008; Vilén and Fernandes, 2011), even if
often difficult to quantify (Chiriaco et al., 2013).
[INSERT FIGURE 23-5 HERE
Figure 23-5: Changes in forest fire risk in Europe for two time periods: baseline (left) and 2041 2070 (right), based
on high-resolution regional climate models and the SRES A1B emission scenario. Source: Lung et al., 2013.]
Wind storm damage to forests in Europe has recently increased (Usbeck et al., 2010). Boreal forests will become
more vulnerable to autumn/early spring storm damage due to expected decrease in period of frozen soil (Gardiner et
al., 2010). Increased storm losses by 8-19% under A1B and B2 scenarios respectively is projected in Western
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Germany for 2060-2100 compared to 1960-2000, with the highest impacts in the mountainous regions (Klaus et al.,
2011; Pinto et al., 2010).
An increase in the incidence of diseases has been observed in many European forests (FAO, 2008b; Marcais and
Desprez-Loustau, 2007). In Continental Europe, some species of fungi benefit from milder winters and others spread
during drought periods from south to north (Drenkhan et al., 2006; Hanso and Drenkhan, 2007). Projected increased
late summer warming events will favour diffusion of bark beetle in Scandinavia, in lowland parts of central Europe
and Austria (Jönsson et al., 2011; Jönsson et al., 2009)(Seidl et al., 2009).
Possible response approaches to the impacts of climate change on forestry include short-term and long-term
strategies that focus on enhancing ecosystem resistance and resilience and responding to potential limits to carbon
accumulation (Millar et al., 2007; Nabuurs et al., 2013). Fragmented small-scale forest ownership can constrain
adaptive capacity (Lindner et al., 2010). Landscape planning and fuel load management may reduce the risk of
wildfires but may be constrained by the higher flammability due to warmer and drier conditions (Moreira et al.,
2011). Strategies to reduce forest mortality include preference of species better adapted to relatively warm
environmental conditions (Resco et al., 2007). The selection of tolerant or resistant families and clones may also
reduce the risk of damage by pests and diseases in pure stands (Jactel et al., 2009).
23.4.5. Bioenergy Production
The potential distribution of temperate oilseeds (e.g. oilseed rape, sunflower), starch crops (e.g. potatoes), cereals
(e.g. barley) and solid biofuel crops (e.g. sorghum, Miscanthus) is projected to increase in Northern Europe by the
2080s, due to increasing temperatures, and to decrease in Southern Europe due to increased drought frequency)
(Tuck et al., 2006). Mediterranean oil and solid biofuel crops, currently restricted to Southern Europe, are likely to
extend further north (Tuck et al., 2006). The physiological responses of bioenergy crops, in particular C3 Salicaceae
trees, to rising atmospheric CO2 concentration may increase drought tolerance due to improved plant water use,
consequently yields in temperate environments may remain high in future climate scenarios (Oliver et al., 2009).
A future increase in the northward extension of the area for short rotation coppice (SRC) cultivation leading to
greenhouse gas neutral is expected (Liberloo et al., 2010). However, the northward expansion of SRC would erode
the European terrestrial carbon sink due to intensive management and high turnover of SRC compared to
conventional forest where usually harvesting is less than annual growth (Liberloo et al., 2010).
23.4.6. Fisheries and Aquaculture
In AR4, Easterling et al. (2007) reported that the recruitment and production of marine fisheries in the North
Atlantic are likely to increase. In European seas, warming causes a displacement to the north and/or in depth of fish
populations (Daufresne et al., 2009) (AR5 WG2 Chapter 6, 23.6.4) which has a direct impact on fisheries (Cheung
et al., 2010; Cheung et al., 2013; Tasker, 2008). For instance, in British waters, the lesser sandeel (Ammodytes
marinus), which is a key link in the food web, shows declining recruitments since 2002 and is projected to further
decline in the future with a warming climate (Heath et al., 2012). In the Baltic Sea, although some new species
would be expected to immigrate because of an expected increase in sea temperature, only a few of these species
would be able to successfully colonize the Baltic because of its low salinity (Mackenzie et al., 2007). In response to
climate change and intensive fishing, widespread reductions in fish body size (Daufresne et al., 2009) and in the
mean size of zooplankton (Beaugrand and Reid, 2012) have been observed over time and these trends further affect
the sustainability of fisheries (Pitois and Fox, 2006)(Beaugrand and Kirby, 2010) [see also Chapter 6]. Aquaculture
can be affected as the areal extent of some habitats that are suitable for aquaculture can be reduced by sea-level rise.
Observed higher water temperatures have adversely affected both wild and farmed freshwater salmon production in
the southern part of the distribution areas (Jonsson and Jonsson, 2009). In addition, ocean acidification may disrupt
the early developmental stages of shellfish (Callaway et al., 2012).
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Numerous studies confirm the amplification through fishing of the effects of climate change on population dynamics
and consequently on fisheries (Planque et al., 2010). The decline of the North Sea cod during the 1980-2000 period
results from the combined effects of overfishing and of an ecosystem regime shift due to climate change (Beaugrand
and Kirby, 2010). Over the next decade, this stock was not restored from its previous collapse (Mieszkowska et al.,
2009)(ICES, 2010). In North Sea and Celtic Seas, the steep decline in boreal species (Henderson, 2007) was
compensated for by the arrival of southern (Lusitanian) species (Engelhard et al., 2011; Lenoir et al., 2011; ter
Hofstede et al., 2010).
Climate change may reinforce parasitic diseases and impose severe risks for aquatic animal health [See Chapter 6].
As water temperatures increase, a number of endemic diseases of both wild and farmed salmonid populations are
likely to become more prevalent and threats associated with exotic pathogens may rise (Marcos-Lopez et al., 2010).
In Iberian Atlantic, the permitted harvesting period for the mussel aquaculture industry was reduced because of
harmful algal blooms resulting from changes in phytoplankton communities linked to a weakening of the Iberian
upwelling (Perez et al., 2010). With freshwater systems, summer heat waves boost the development of harmful
cyanobacterial blooms (Johnk et al., 2008). For oysters in France, toxic algae may be linked to both climate
warming and direct anthropogenic stressors (Buestel et al., 2009).
Fishery management thresholds will have to be reassessed as the ecological basis on which existing thresholds have
been established changes, and new thresholds will have to be developed for immigrant species (Mackenzie et al.,
2007)(Beaugrand and Reid, 2012). These changes may lead to loss of productivity, but also the opening of new
fishing opportunities, depending on the interactions between climate impacts, fishing grounds and fleet types. They
will also affect fishing regulations, the price of fish products and operating costs, which in turn will affect the
economic performance of the fleets (Cheung et al., 2012). Climate change impacts on fisheries profits range from
negative for sardine fishery in the Iberian Atlantic fishing grounds (Perez et al., 2010)(Garza-Gil et al., 2010), to
non-significant for the Bay of Biscay (Le Floc'h et al., 2008) and positive on the Portuguese coast, since most of the
immigrant fish species are marketable (Vinagre et al., 2011). Human social fishing systems dealing with high
variability upwelling systems with rapidly reproducing fish species may have greater capacities to adjust to the
additional stress of climate change than human social fishing systems focused on longer-lived and generally less
variable species (Perry et al., 2011; Perry et al., 2010). Climate change adaptation is being considered for integration
in European maritime and fisheries operational programs (European Commission, 2013).
_____ START BOX 23-1 HERE _____
Box 23-1. Assessment of Climate Change Impacts on Ecosystem Services by Sub-Region
Ecosystems provide a number of vital provisioning, regulating and cultural services for people and society that flow
from the stock of natural capital (Stoate et al., 2009)(Harrison et al., 2010). Provisioning services such as food from
agro-ecosystems or timber from forests derive from intensively managed ecosystems; regulating services underpin
the functioning of the climate and hydrological systems; and, cultural services such as tourism, recreation and
aesthetic value are vital for societal well-being (see section 23.5.4). The table summarises the potential impacts of
climate change on ecosystem services in Europe by sub-region based on an assessment of the published literature
(2004-2013). The direction of change (increasing, decreasing or neutral) is provided, as well as the number of
studies/papers on which the assessment was based (in brackets). Empty cells indicate the absence of appropriate
literature. Unless otherwise stated, impacts assume no adaptation and are assessed for the mid-century (2050s). A
decrease in natural hazard regulation (e.g. for wildfires) implies an increased risk of the hazard occurring.
Biodiversity is included here as a service (for completeness), although it is debated whether biodiversity should be
considered as a service or as part of the natural capital from which services flow. What is agreed, however, is that
biodiversity losses within an ecosystem will have deleterious effects on service provision (Mouillot et al., 2013).
The provision of ecosystem services in Southern Europe is projected to decline across all service categories in
response to climate change [high confidence]. Other European sub-regions are projected to have both losses and
gains in the provision of ecosystem services [high confidence]. The Northern sub-region will have increases in
provisioning services arising from climate change [high confidence]. Except for the Southern sub-region, the effects
of climate change on regulating services are balanced with respect to gains and losses [high confidence]. There are
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fewer studies for cultural services, although these indicate a balance in service provision for the Alpine and Atlantic
regions, with decreases in service provision for the Continental, Northern and Southern sub-regions [low
confidence].
[INSERT BOX 23-1 TABLE HERE]
_____ END BOX 23-1 HERE _____
23.5. Implications of Climate Change for Health and Social Welfare
23.5.1. Human Population Health
Climate change is likely to have a range of health effects in Europe. Further studies since AR4 have confirmed the
effects of heat on mortality and morbidity in European populations and particularly in older people and those with
chronic disease (Corobov et al., 2012; Corobov et al., 2013; Kovats and Hajat, 2008; Aström et al., 2011). With
respect to sub-regional vulnerability, populations in southern Europe appear to be most sensitive to hot weather
(Baccini et al., 2011; D'Ippoliti et al., 2010; Michelozzi et al., 2009; Michelozzi et al., 2009), and also will
experience the highest heat exposures (Figure 23-2). However, populations in Continental (Hertel et al., 2009) and
Northern Europe (Rocklöv and Forsberg, 2010)(Armstrong et al., 2011)(Varakina et al., 2011) are also vulnerable to
heat wave events. Adaptation measures to reduce heat health effects include heat wave plans (Bittner et al., 2013)
which have been shown to reduce heat-related mortality in Italy (Schifano et al., 2012), but evidence of
effectiveness is still very limited (Hajat et al., 2010; Lowe et al., 2011). There is little information about how future
changes in housing and infrastructure (23.3.2) would reduce the regional or local future burden of heat-related
mortality or morbidity. Climate change is likely to increase future heat-related mortality (Baccini et al., 2011;
Ballester et al., 2011; Huang et al., 2011) and morbidity (Aström et al., 2013), although most published risk
assessments do not include consideration of adaptation (Huang et al., 2011). For most countries in Europe, the
current burden of cold-related mortality (Analitis et al., 2008) is greater than the burden of heat mortality. Climate
change is likely to reduce future cold-related mortality (Ballester et al., 2011; HPA, 2012)(AR5 WG2 11.4.1).
Mortality and morbidity associated with flooding is becoming better understood although the surveillance of health
effects of disasters remains inadequate (WHO, 2013). Additional flood mortality due to sea level rise has been
estimated in the Netherlands (Maaskant et al., 2009); and in the UK for river flooding (Hames and Vardoulakis,
2012) but estimates of future mortality due to flooding are highly uncertain. There remains limited evidence
regarding the long term mental health impacts of flood events (Paranjothy et al., 2011; WHO, 2013).
Evidence about future risks from climate change with respect to infectious diseases is still limited (Randolph and
Rogers, 2010; Semenza and Menne, 2009; Semenza et al., 2012). There have been developments in mapping the
current and potential future distribution of important disease vector species in Europe. The Asian tiger mosquito
Aedes albopictus (a vector of dengue and Chikungunya (Queyriaux et al., 2008) is currently present in Southern
Europe (ECDC, 2009) and may extend eastward and northward under climate change (Caminade et al., 2012; Fisher
et al., 2011; Roiz et al., 2011). The risk of introduction of dengue remains very low because it would depend upon
the introduction and expansion of the Ae. Aegypti together with the absence of effective vector control measures
(ECDC, 2012).
Climate change is unlikely to affect the distribution of visceral and cutaneous leishmaniasis (currently present in the
Mediterranean region) in the near term (Ready, 2010). However, in the long term (15-20 years), there is potential for
climate change to facilitate the expansion of either vectors or current parasites northwards (Ready, 2010). The risk
of introduction of exotic Leishmania species was considered very low due to the low competence of current vectors
(Fischer et al., 2010a). The effect of climate change on the risk of imported or locally-transmitted (autochthonous)
malaria in Europe has been assessed in Spain (Sainz-Elipe et al., 2010), France (Linard et al., 2009) and the UK
(Lindsay et al., 2010). Disease re-emergence would depend upon many factors including: the introduction of a large
population of infectious people or mosquitoes, high levels of people-vector contact, resulting from significant
changes in land use, as well as climate change (see chapter 11).
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Since AR4, there is more evidence on implications of climate change on food safety at all stages from production to
consumption (FAO, 2008a; Jacxsens et al., 2010; Popov Janevska et al., 2010). The sensitive of salmonellosis to
temperature has declined in recent years (Lake et al., 2009) and the overall incidence of salmonellosis is declining in
most European countries (Semenza et al., 2012). Climate change may also have affects on food consumption
patterns. Weather affects pre and post harvest mycotoxin production but the implications of climate change are
unclear. Cold regions may become liable to temperate-zone problems concerning contamination ochratoxin A,
patulin and Fusarium toxins (Paterson and Lima, 2010). A control of the environment of storage facilities may avoid
post-harvest problems but at additional cost (Paterson and Lima, 2010).
Other potential consequences concern marine biotoxins in seafood following production of phycotoxins by harmful
algal blooms and the presence of pathogenic bacteria in foods following more frequent extreme weather conditions
(Miraglia et al., 2009). There is little evidence that climate change will affect human exposures to contaminants in
the soil or water (e.g. persistent organic pollutants). Risk modelling is often developed for single exposure agents
(e.g. a pesticide) with known routes of exposure. These are difficult to scale up to the population level. The multiple
mechanisms by climate may affect transmission or contamination routes also makes this very complex (Boxall et al.,
2009).
Adaptation in the health sector has so far been largely limited to the development of heat health warning systems,
but many research gaps regarding effective adaptation options (HPA, 2012). A survey of national infectious disease
experts in Europe identified several institutional changes that needed to be addressed to improve future responses to
climate change risks: ongoing surveillance programs, collaboration with veterinary sector and management of
animal disease outbreaks, national monitoring and control of climate-sensitive infectious diseases, health services
during an infectious disease outbreak and diagnostic support during an epidemic (Semenza et al., 2012).
23.5.2. Critical Infrastructure
Critical national infrastructure is defined as the assets (physical or electronic) that are vital to the continued delivery
and integrity of the essential services upon which a country relies, the loss or compromise of which would lead to
severe economic or social consequences or to loss of life. Extreme weather events, such as floods, heat waves and
wild fires are known to damage critical infrastructure. The UK floods in 2007 led to significant damage to power
and water utilities, and to communications and transport infrastructure (Chatterton et al., 2010) (Table 23-1). Forest
fires can affect transport infrastructure, as well as the destruction of buildings. Major storms in Sweden and Finland
have led to loss of trees, with damage to the power distribution network, leading to electricity blackouts lasting
weeks, as well as the paralysis of services such as rail transport and other public services that depend on grid
electricity.
Health system infrastructure (hospitals, clinics) is vulnerable to extreme events, particularly flooding (Radovic et al.,
2012). The heat waves of 2003 and 2006 had adverse effects on patients and staff in hospitals from overheating of
buildings. Evidence from France and Italy indicate that death rates in in-patients increased significantly during heat
wave events (Ferron et al., 2006; Stafoggia et al., 2008). Further, higher temperatures have had serious implications
for the delivery of healthcare, as well drug storage and transport (Carmichael et al., 2013).
23.5.3. Social Impacts
There is little evidence regarding the implications of climate change for employment and/or livelihoods in Europe.
However, the evidence so far (as reviewed in this chapter) indicates that there are likely to be changes to some
industries (e.g. tourism, agriculture) that may lead to changes in employment opportunities by region and by sector.
Current damages from weather-related disasters (floods and storms) are significant (23.3.1). Disasters have long
lasting effects of the affected populations (Schnitzler et al., 2007). Households are often displaced while their homes
are repaired (Whittle et al., 2010). Little research has been carried out on the impact of extreme weather events such
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as heat waves and flooding on temporary or permanent displacement in Europe. Coastal erosion associated with sea
level rise, storm surges and coastal flooding will require coastal retreat in some of Europe s low lying areas
(Philippart et al., 2011). Managed retreat is also an adaptation option in some coastal areas. Concerns have been
raised about equality of access to adaptation within coastal populations at risk from climate change. For example, a
study in the UK found that vulnerability to climate change in coastal communities is likely to be increased by social
deprivation (Zsamboky et al., 2011).
In the European region, the indigenous populations are present in Arctic regions are considered vulnerable to climate
change impacts on livelihoods and food sources (Arctic Climate Impact Assessment, 2005) [12.3.4, 28.2.4].
Research has focussed on indigenous knowledge, impacts on traditional food sources and community
responses/adaptation (Mustonen and Mustonen, 2011a; Mustonen and Mustonen, 2011b). However, these
communities are also experiencing rapid social, economic and other non-climate-related environmental changes
(such as oil and gas exploration) [see 28.2.4]. There is evidence the climate change has altered the seasonal
behaviour of pastoralist populations, such as the Nenets reindeer herders in northern Russia (Amstislavski et al.,
2013). However, socio-economic factors may be more important than climate change for the future sustainability of
Reindeer husbandry (Rees et al., 2008) [28.2.3.5].
23.5.4. Cultural Heritage and Landscapes
Climate change will affect culturally-valued buildings (Storm et al., 2008) through extreme events and chronic
damage to materials (Brimblecombe et al., 2006; Brimblecombe and Grossi, 2010; Brimblecombe, 2010a;
Brimblecombe, 2010b; Grossi et al., 2011)(Sabbioni et al., 2012). Cultural heritage is a non-renewable resource and
impacts from environmental changes are assessed over long timescales (Brimblecombe and Grossi, 2008)(Bonazza
et al., 2009a; Bonazza et al., 2009b; Brimblecombe and Grossi, 2009; Brimblecombe and Grossi, 2010; Grossi et
al., 2008). Climate change may also affect indoor environments where cultural heritage is preserved (Lankester and
Brimblecombe, 2010) as well as visitor behaviour at heritage sites (Grossi et al., 2010). There is also evidence to
suggest that climate change and sea level rise will affect maritime heritage in the form of shipwrecks and other
submerged archaeology (Björdal, 2012).
Surface recession on marble and compact limestone will be affected by climate change (Bonazza et al., 2009a).
Marble monuments in Southern Europe will continue to experience high levels of thermal stress (Bonazza et al.,
2009b) but warming is likely to reduce frost damage across Europe, except in Northern and Alpine Europe and
permafrost areas (Iceland) (Grossi et al., 2007; Sabbioni et al., 2008). Damage to porous materials due to salt
crystallisation may increase all over Europe (Benavente et al., 2008; Grossi et al., 2011). In Northern and Eastern
Europe, wood structures will need additional protection against rainwater and high winds (Sabbioni et al., 2012).
AR4 concluded that current flood defences would not protect Venice from climate change. Venice now has a flood
forecasting system, and is introducing the MOSE system of flood barriers (Keskitalo, 2010). Recent evidence
suggests, however, that climate change may lead to a decrease in the frequency of extreme storm surges in this area
(Troccoli et al., 2012a).
Europe has many unique rural landscapes, which reflect the cultural heritage that has evolved from centuries of
human intervention, e.g. the cork oak based Montado in Portugal, the Garrigue of southern France, Alpine meadows,
grouse moors in the UK, machair in Scotland, peatlands in Ireland, the polders of Belgium and the Netherlands and
vineyards. Many, if not all, of these cultural landscapes are sensitive to climate change and even small changes in
the climate could have significant impacts (Gifford et al., 2011). Alpine meadows, for example, are culturally
important within Europe, but although there is analysis of the economics (tourism, farming) and functionality (water
run-off, flooding and carbon sequestration) of these landscapes there is very little understanding of how climate
change will affect the cultural aspects on which local communities depend. Because of their societal value, cultural
landscapes are often protected and managed through rural development and environmental policies. The peat-rich
uplands of northern Europe, for example, have begun to consider landscape management as a means of adapting to
the effects of climate change (e.g. the moors for the future partnership in the Peak District National Park, UK). For a
discussion of the cultural implications of climate change for vineyards see Box 23-2.
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_____ START BOX 23-2 HERE _____
Box 23-2. Implications of Climate Change for European Wine and Vineyards
Wine production in Europe accounts for more than 60% of the global total (Goode, 2012) and makes an important
contribution to cultural identity. Apart from impacts on grapevine yield, higher temperatures are also expected to
affect wine quality in some regions and grape varieties by changing the ratio between sugar and acids (Bock et al.,
2011)(Santos et al., 2011)(Duchene et al., 2010). In western and central Europe, projected future changes could
benefit wine quality, but might also demarcate new potential areas for viticulture (Malheiro et al., 2010). Adaptation
measures are already occurring in some vineyards (e.g. vine management, technological measures, production
control and to a smaller extent relocation) (Battaglini et al., 2009; Duarte Alonso and O Neill, 2011; Holland and
Smit, 2010; Malheiro et al., 2010; Moriondo et al., 2011; Santos et al., 2011).Vineyards may be displaced
geographically beyond their traditional boundaries ( terroir linked to soil, climate and traditions) (Metzger and
Rounsevell, 2011), and in principle, wine producers could adapt to this problem by growing grape varieties that are
more suited to warmer climates. Such technical solutions, however, do not account for the unique characteristics of
wine production cultures and consumer perceptions of wine quality that strongly affect the prices paid for the best
wines (Metzger and Rounsevell, 2011)(White et al., 2009). It would become very difficult, for example, to produce
fine wines from the cool-climate Pinot Noir grape within its traditional terroir of Burgundy under many future
climate scenarios, but consumers may not be willing to pay current day prices for red wines produced from other
grape varieties (Metzger and Rounsevell, 2011). An additional barrier to adaptation is that wine is usually produced
within rigid, regionally-specific, regulatory frameworks that often prescribe, amongst other things, what grapes can
be grown where, e.g., the French AOC or the Italian DOC and DOCG designations. Suggestions have been made to
replace these rigid concepts of regional identity with a geographically flexible terroir that ties a historical or
constructed sense of culture to the wine maker and not to the region (White et al., 2009).
_____ END BOX 23-2 HERE _____
23.6. Implications of Climate Change for the Protection of Environmental Quality and Biological
Conservation
Terrestrial and freshwater ecosystems provide a number of vital services for people and society, such as
biodiversity, food, fibre, water resources, carbon sequestration and recreation (Box 23-1).
23.6.1. Air Quality
Climate change will have complex and local effects on pollution chemistry, transport, emissions and deposition.
Outdoor air pollutants have adverse effects on human health, biodiversity, crop yields and cultural heritage. The
main outcomes of concern are both the average (background) levels and peak events for tropospheric ozone,
particulates, sulphur oxides (SOx) and nitrogen oxides (NOx). Future pollutant concentrations in Europe have been
assessed using atmospheric chemistry models, principally for ozone (Forkel and Knoche, 2006; Forkel and Knoche,
2007). Reviews have concluded that GCM/CTM studies find that climate change per se (assuming no change in
future emissions or other factors) is likely to increase summer tropospheric ozone levels (range 1 10 ppb) by 2050s
in polluted areas (that is, where concentrations of precursor nitrogen oxides are higher) (AQEG, 2007; Jacob and
Winner, 2009)[see also 21.4.1.3.2.]. The effect of future climate change alone on future concentrations of
particulates, nitrogen oxides and volatile organic compounds is much more uncertain. Higher temperatures also
affect natural emissions volatile organic compounds (VOCs) which are ozone precursors (Hartikainen et al., 2012).
One study has projected an increase in fire-related air pollution (O3 and PM10) in Southern Europe (Carvalho et al.,
2011).
Overall, the model studies are inconsistent regarding future projections of background level and exceedences.
Recent evidence has shown adverse impacts on agriculture from even low concentrations of ozone, however, there is
more consistent evidence now regarding the threshold for health (mortality) impacts of ozone. Therefore, it is
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unclear whether increases in background levels below health-related thresholds would be associated with an
increased burden of ill health.
Some studies have attributed an observed increase in European ozone levels to observed warming (Meleux et al.,
2007), which appears to be driven by the increase in extreme heat events in 2003, 2006 and 2010 (Solberg et al.,
2008). Peak ozone events were observed during the major heat waves in Europe in multiple countries. Wildfire
events have had an impact on local and regional on air quality (Hodzic et al., 2007; Liu et al., 2009; Miranda et al.,
2009) which have implications for human health (Analitis et al., 2012) (Table 23-1).
23.6.2. Soil Quality and Land Degradation
The current cost of soil erosion, organic matter decline, salinisation, landslides and contamination is estimated to be
EUR 38 billion annually for the EU (JRC-EEA, 2010), in the form of damage to infrastructures, treatment of water
contaminated through the soil, disposal of sediments, depreciation of land and costs related to the ecosystem
functions of soil (JRC-EEA, 2010). Projections show significant reductions in summer soil moisture in the
Mediterranean region, and increases in the north-eastern part of Europe (Calanca et al., 2006). Climate change
impacts on erosion shows diverging evidence under the A2 scenario. In Tuscany, even with a decline in precipitation
volume until 2070, in some month higher erosion rates would occur due to higher rainfall erosivity (Marker et al.,
2008). For two Danish river catchments, assuming a steady-state land use, suspended sediment transport would
increase by 17-27% by 2071-2100 (Thodsen et al., 2008; Thodsen, 2007). In Upper-Austria, with the regional
climate model HadRM3H, a small reduction in average soil losses is projected for croplands in all tillage systems,
however with high uncertainty (Scholz et al., 2008). In Northern Ireland, erosion decreases are generally projected
with downscaled GCMs for a case study hillslope (Mullan et al., 2012).
Adaptive land-use management can reduce the impact of climate change through soil conservation methods like zero
tillage and conversion of arable to grasslands (Klik and Eitzinger, 2010). In central Europe, compared to
conventional tillage, conservation tillage systems reduced modelled soil erosion rates under future climate scenarios
by between 49 and 87% (Scholz et al., 2008). Preserving upland vegetation reduced both erosion and loss of soil
carbon and favoured the delivery of a high quality water resource (House et al., 2011); (McHugh, 2007).
Maintaining soil water retention capacity, e.g. through adaptation measures (Post et al., 2008), contributes to reduce
risks of flooding as soil organic matter absorbs up to twenty times its weight in water.
23.6.3. Water Quality
Climate change may affect water quality in several ways, with implications for food production and forestry
(23.4.3), ecosystem functioning (Box 23-1), human and animal health, and compliance with environmental quality
standards, including those of the Water Framework Directive. Shallower waters will witness a more rapid
temperature increase than deeper waters, since heat is absorbed mainly in the upper water layers and turbulent
mixing is truncated by shallow depth. In parallel, a decrease in saturating oxygen concentrations occurs. Since AR4,
there is further evidence of adverse effects caused by extreme weather events: reductions in dissolved oxygen, algal
blooms (Mooij et al., 2007; Ulén and Weyhenmeyer, 2007) during hot weather, and contamination of surface and
coastal waters with sewage and/or chemicals (pesticides) after rainfall (Boxall et al., 2009). A reduction in rainfall
may lead to low flows which increase concentrations of biological and chemical contaminants. Reduced drainage
can also enhance sedimentation in drainage systems and hence enhance particle-bound phosphorous retention and
reduce phosphorous load to downstream higher order streams (Hellmann and Vermaat, 2012).
Variability in changes in rainfall and run-off, as well as water temperature increases, will lead to differences in water
quality impacts by sub-region. Climate change is projected to increase nutrient loadings: in Northern Europe this is
caused by increased surface runoff; in Southern Europe this is caused by increased evapotranspiration and increased
concentrations due to reduced volumes of receiving lakes (Jeppesen et al., 2011). Local studies generally confirm
this pattern: increased nutrient loads are foreseen in Danish watersheds (Andersen et al., 2006), France (Delpla et
al., 2011) and the UK (Howden et al., 2010; Macleod et al., 2012; Whitehead et al., 2009); AR5 WG2 Chapter
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4.3.2.5). In larger rivers, such as the Meuse, increased summer temperature and drought can lead to more favourable
conditions for algal blooms and reduced dilution capacity of effluent from industry and sewage works (van Vliet and
Zwolsman, 2008).
23.6.4. Terrestrial and Freshwater Ecosystems
Current and future climate changes including CO2 increase are determining negative effects of habitat loss on
species density and diversity (Mantyka-pringle et al., 2012)(Rickebusch et al., 2008). Projected habitat loss is
greater for species at higher elevations (Engler et al., 2011)(Castellari, 2009; Dullinger et al., 2012) and suitable
habitats for Europe's breeding birds are projected to shift nearly 550 km northeast by the end of the century (Huntley
et al., 2007). Aquatic habitats and habitat connectivity in river networks may become increasingly fragmented
(Blaustein et al., 2010; Della Bella et al., 2008; Elzinga et al., 2007; Gómez-Rodríguez et al., 2010; Hartel et al.,
2011; Morán-López et al., 2012)(Harrison et al., 2008)(Clark et al., 2010a)(Clark et al., 2010; Fronzek et al., 2006;
Fronzek et al., 2010; Fronzek et al., 2011; Gallego-Sala et al., 2010). Despite some local successes and increasing
responses, the rate of biodiversity loss does not appear to be slowing (Butchart et al., 2010). The effectiveness of
Natura 2000 areas to respond to climate change has been questioned (Araújo et al., 2011). However, when
considering connectivity related to the spatial properties of the network, the Natura 2000 network appears rather
robust (Mazaris et al., 2013). Several studies now highlight the importance of taking into account climate change
projections in the selection of conservation areas (Araújo et al., 2011; Ellwanger et al., 2011; Filz et al., 2013;
Virkkala et al., 2013).
Observed changes in plant communities in European mountainous regions show a shift of species ranges to higher
altitudes resulting in species richness increase in boreal-temperate mountain regions and decrease in Mediterranean
mountain regions (Pauli et al., 2012)(Gottfried et al., 2012). In Southern Europe, a great reduction in phylogenetic
diversity of plant, bird and mammal assemblages will occur, and gains are expected in regions of high latitude or
altitude for 2020, 2050 and 2080. However, losses will not be offset by gains and a trend towards homogenization
across the continent will be observed (Thuiller et al., 2011)(Alkemade et al., 2011). Large range contractions due to
climate change are projected for several populations of Pinus cembra and Pinus Sylvestris (Casalegno et
al.,2010)(Giuggiola et al., 2010) while for the dominant Mediterranean tree species, Holm oak, a substantial range
expansion is projected under A1B emissions scenario (Cheaib et al., 2012). The human impacts on distribution of
tree species landscape may make them more vulnerable to climate change (del Barrio et al., 2006; Hemery et al.,
2010).
Observed climate changes are altering breeding seasons, timing of spring migration, breeding habitats, latitudinal
distribution and migratory behaviour of birds (Feehan et al., 2009) (Jonzén et al., 2006; Rubolini et al., 2007a;
Rubolini et al., 2007b)(Lemoine et al., 2007a; Lemoine et al., 2007b). A northward shift in bird community
composition has been observed (Devictor et al., 2008). Common species of European birds with the lowest thermal
maxima have showed the sharpest declines between 1980 and 2005 (Jiguet et al., 2010).
Projections for 120 native terrestrial non-volant European mammals suggest that 5-9% are at risk of extinction,
assuming no migration, during the 21st century due to climate change, while 70-78% may be severely threatened
under A1 and B2 climatic scenarios (Levinsky et al., 2007). Those populations not showing a phenological response
to climate change may decline (Moller et al., 2008), such as amphibian and reptile species (Araújo et al., 2006), or
experience ecological mismatches (Saino et al., 2011). Climate change can affect trophic interactions, as co-
occurring species may not react in a similar manner. Novel emergent ecosystems composed of new species
assemblages arising from differential rates of range shifts of species can occur (Keith et al., 2009; Montoya and
Raffaelli, 2010; Schweiger et al., 2012).
Since invasive alien species rarely change their original climatic niches (Petitpierre et al., 2012), climate change can
exacerbate the threat posed by invasive species to biodiversity in Europe (West et al., 2012) amplifying the effects
of introduction of the exotic material such as alien bioenergy crops (EEA, 2012), pest and diseases (Aragon and
Lobo, 2012), tropical planktonic species (Cellamare et al., 2010) and tropical vascular plants (Skeffington and Hall,
2011; Taylor et al., 2012).
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23.6.5. Coastal and Marine Ecosystems
Climate change will affect Europe s coastal and marine ecosystems by altering the biodiversity, functional dynamics
and ecosystem services of coastal wetlands, dunes, inter-tidal and subtidal habitats, offshore shelves, seamounts and
currents (Halpern et al., 2008) through changes in eutrophication, invasive species, species range shifts, changes in
fish stocks and habitat loss (Doney et al., 2011)(EEA, 2010d). The relative magnitude of these changes will vary
temporally and spatially, requiring a range of adaptation strategies that target different policy measures, audiences
and instruments (Philippart et al., 2011)(Airoldi and Bec, 2007).
Europe s northern seas are experiencing greater increases in sea surface temperatures (SSTs) than the southern seas,
with the Baltic, North and Black seas warming at 2-4 times the mean global rate (Philippart et al., 2011)(Belkin,
2009). In the Baltic, decreased sea ice will expose coastal areas to more storms, changing the coastal
geomorphology (BACC, 2008)(HELCOM, 2007). Warming SSTs will influence biodiversity and drive changes in
depth and latitudinal range for intertidal and sub-tidal marine communities, particularly in the North and Celtic seas
(Hawkins et al., 2011)(Sorte et al., 2010)(Wethey et al., 2011).
Warming is affecting food chains and changing phenological rates (Durant et al., 2007). For example, changes in the
timing and location of phytoplankton and zooplankton are affecting North Sea cod larvae (Beaugrand et al.,
2010)(Beaugrand and Kirby, 2010). Temperature changes have affected the distribution of fisheries in all seas over
the past 30 years (Beaugrand and Kirby, 2010)(Hermant et al., 2010). Warmer waters also increase the rate of the
establishment and spread of invasive species, further altering trophic dynamics and the productivity of coastal
marine ecosystems (Molnar et al., 2008)(Rahel and Olden, 2008). Changes in the semi-enclosed seas could be
indicative of future conditions in other coastal-marine ecosystems (Lejeusne et al., 2009). In the Mediterranean,
invasive species have arrived in recent years at the rate of one introduction every 4 to 5 weeks (Streftaris et al.,
2005). While in this case the distribution of endemic species remained stable, most non-native species have spread
northward by an average of 300 km since the 1980s, resulting in an area of spatial overlap with invasive species
replacing natives by nearly 25% in 20 years.
Dune systems will be lost in some places due to coastal erosion from combined storm surge and sea level rise,
requiring restoration (Day et al., 2008)(Ciscar et al., 2011)(Magnan et al., 2009). In the North Sea, the Iberian coast,
and Bay of Biscay, a combination of coastal erosion, infrastructure development and sea defences may lead to
narrower coastal zones ( coastal squeeze ) (EEA, 2010d)(Jackson and McIlvenny, 2011)(OSPAR, 2010).
23.7. Cross-Sectoral Adaptation Decision-making and Risk Management
Studies on impacts and adaptation in Europe generally consider single sectors or outcomes, as described in the
previous sections of this chapter. For adaptation decision-making, more comprehensive approaches are required.
Considerable progress has been made to advance planning and development of adaptation measures, including the
economic analyses (Section 23.7.6) (see Box 23-3), and the developed of climate services (Medri et al., 2012;
WMO, 2011). At the international level, the European Union has started adaptation planning, through information
sharing (Climate-ADAPT platform) and legislation (EC, 2013a). National and local governments are also beginning
to monitor progress on adaptation, including the development of a range of indicators (UK-ASC, 2011).
_____ START BOX 23-3 HERE _____
Box 23-3. National and Local Adaptation Strategies
The increasing number of national (EEA, 2013) and local (Heidrich et al., 2013) adaptation strategies in Europe has
led to research on their evaluation and implementation (Biesbroek et al., 2010b). Many adaptation strategies were
found to be agendas for further research, awareness raising and/or coordination and communication for
implementation (e.g. (Dumollard and Leseur, 2011; Pfenniger et al., 2010). Actual implementation often was limited
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to disaster risk reduction, environmental protection, spatial planning (23.7.4), and coastal zone and water resources
management. The implementation of planned adaptation at the national level was attributed to political will and
good financial and information capacity (Westerhoff et al., 2011). Analysis of seven national adaptation strategies
(Denmark, Finland, France, Germany, Netherlands, Spain, UK) found that that while there is a high political
commitment to adaptation planning and implementation, evaluation of the strategies and actual implementation is
yet to be defined (Biesbroek et al., 2010a; Swart et al., 2009b; Westerhoff et al., 2011). One of the earliest national
adaptation strategies (Finland) has been evaluated, in order to compare identified adaptation measures with those
launched in different sectors. It has found that while good progress has been made on research and identification of
options, few measures have been implemented except in the water resources sector (Ministry of Agriculture and
Forestry, 2009).
At the local government level, adaptation plans are being developed in several cities (EEA, 2013), including London
(GLA, 2010), Madrid, Manchester, Copenhagen, Helsinki, and Rotterdam. Adaptation in general is a low priority
for many European cities, and many plans do not have adaptation priority as the main focus (Carter, 2011). Many
studies are covering sectors sensitive to climate variability, as well as sectors that are currently under pressure from
socioeconomic development. A recent assessment found a lack of cross-sector impact and adaptation linkages as an
important weakness in the city plans (Hunt and Watkiss, 2011). Flexibility in adaptation decision making needs to
be maintained (Hallegatte et al., 2008)(Biesbroek et al., 2010b).
_____ END BOX 23-3 HERE _____
23.7.1. Coastal Zone Management
Coastal zone management and coastal protection plans that integrate adaptation concerns are now being
implemented. Underlying scientific studies increasingly assess effectiveness and costs of specific options (Hilpert et
al., 2007)(Kabat et al., 2009)(Dawson et al., 2011) (23.7.6). Early response measures are needed for floods and
coastal erosion, to ensure that climate change considerations are incorporated into marine strategies, with
mechanisms for regular updating (OSPAR, 2010; UNEP, 2010).
In the Dutch plan for flood protection (Delta Committee, 2008), adaptation to increasing river runoff and sea level
rise plays a prominent role. It also includes synergies with nature conservation and fresh water storage (Kabat et al.,
2009), and links to urban renovation (cost estimates are included in Section 23.7.6). While that plan mostly relies on
large scale measures, new approaches such as small-scale containment of flood risks through compartmentalisation
are also studied (Klijn et al., 2009). The UK government has developed extensive adaptation plans (TE2100) to
adjust and improve flood defences for the protection of London from future storm surges and flooding
(Environmental Agency, 2009). An elaborate analysis has provided insight in the pathways for different adaptation
options and decision-pointss that will depend on the eventual sea-level rise.
23.7.2. Integrated Water Resource Management
Water resources management in Europe has experienced a general shift from hard to soft measures that allow
more flexible responses to environmental change (Pahl-Wostl, 2007). Integrated water resource management
explicitly includes the consideration of environmental and social impacts (Wiering and Arts, 2006). Climate change
has been incorporated into water resources planning in England and Wales (Arnell, 2011)(Charlton and Arnell,
2011)(Wade et al., 2013) and in the Netherlands (de Graaff et al., 2009). The robustness of adaptation strategies for
water management in Europe has been tested in England (Dessai and Hulme, 2007) and Denmark (Haasnoot et al.,
2012; Refsgaard et al., 2013). Other studies have emphasised the search for robust pathways, for instance in the
Netherlands (Haasnoot et al., 2012; Kwadijk et al., 2010). Public participation has also increased in decision
making, e.g. river basin management planning (Huntjens et al., 2010), flood defence plans (e.g. TE2100), and
drought contingency plans (Iglesias et al., 2007). Guidance has been developed on the inclusion of adaptation in
water management (UNECE, 2009) and river basin management plans (EC, 2009b). Adaptation in the water sector
could also be achieved through the EU Water Framework and Flood Directives (Quevauviller, 2011), but a study of
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decision makers, including local basin managers, identified several important barriers to this (Brouwer et al., 2013).
Water allocation between upstream and downstream countries is challenging in regions exposed to prolonged
droughts such as the Euphrates-Tigris river basin, where Turkey plans to more than double water abstraction by
2023(EEA, 2010a).
23.7.3. Disaster Risk Reduction and Risk Management
A series of approaches to disaster risk management are employed in Europe, in response to national and European
policy developments to assess and reduce natural hazard risks. New developments since the AR4 include assessment
and protection efforts in accordance with the EU Floods Directive (European Parliament and Council, 2007), the
mapping of flood risks, improve civil protection response and early warning systems (Ciavola et al., 2011). Most
national policies address hazard assessment and do not include analyses of possible impacts (de Moel et al., 2009).
The effectiveness has been assessed of flood protection (Bouwer et al., 2010) and also non-structural or household
level measures to reduce losses from river flooding (Botzen et al., 2010a)(Dawson et al., 2011). Some studies show
that current plans may be insufficient to cope with increasing risks from climate change, as shown for instance for
the Rhine river basin (Te Linde et al., 2010a; Te Linde et al., 2010b).
Other options that are being explored are the reduction of consequences, response measures, and increasing social
capital (Kuhlicke et al., 2011), as well as options for insuring and transferring losses (Section 23.3.7). The
Netherlands carried out a large-scale analysis and simulation exercise to study the possible emergency and
evacuation response for a worst-case flood event (ten Brinke et al., 2010). Increasing attention is also being paid in
Europe to non-government actions that can reduce possible impacts from extreme events. Terpstra and Gutteling
(2008) found through a survey that individual citizens are willing to assume some responsibility for managing flood
risk, and they are willing to contribute to preparations in order to reduce impacts. Survey evidence is available for
Germany and the Netherlands that, under certain conditions, individuals can be encouraged to adopt loss prevention
measures (Thieken et al., 2006)(Botzen et al., 2009). Small businesses can reduce risks when informed about
possibilities immediately after an event (Wedawatta and Ingirige, 2012).
23.7.4. Land Use Planning
Spatial planning policies can build resilience to the impacts of climate change (Bulkeley, 2010). However, the
integration of adaptation into spatial planning is often limited to a general level of policy formulation that can
sometimes lack concrete instruments and measures for implementation in practice (Mickwitz et al., 2009)(Swart et
al., 2009a). There is evidence to suggest the widespread failure of planning policy to account for future climate
change (Branquart et al., 2008). Furthermore, a lack of institutional frameworks to support adaptation is, potentially,
a major barrier to the governance of adaptation through spatial planning (ESPACE, 2007)[chapter 16]. Climate
change adaptation is often treated as a water management or flooding issue, which omits other important aspects of
the contribution of land use planning to adaptation (Mickwitz et al., 2009)(Wilson, 2006)(Van Nieuwaal et al.,
2009). For example, in the UK, houses were still being built in flood risk areas (2001-2011) due to competing needs
to increase the housing stock (ARUP, 2011).
City governance is also dominated by the issues of climate mitigation and energy consumption rather than in
adapting to climate change (Bulkeley, 2010; Heidrich et al., 2013). Some cities, e.g. Rotterdam, have started to
create climate adaptation plans and this process tends to be driven by the strong political leadership of mayors
(Sanchez-Rodriguez, 2009). The Helsinki Metropolitan Area s Climate Change Adaptation Strategy (HSY, 2010) is
a regional approach focusing on the built environment in the cities of Helsinki, Espoo, Vantaa and Kauniainen, and
their surroundings. It includes approaches for dealing with increasing heat waves, more droughts, milder winters,
increasing (winter) precipitation, heavy rainfall events, river floods, storm surges, drainage water floods and sea
level rise.
Green infrastructure provides both climate adaptation and mitigation benefits as well as offering a range of other
benefits to urban areas, including health improvements, amenity value, inward investment, and the reduction of
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noise and outdoor air pollution. Green infrastructure is an attractive climate adaptation option since it also
contributes to the sustainable development of urban areas (Gill et al., 2007; James et al., 2009). Urban green space
and green roofs can moderate temperature and decrease surface rainwater run-off (Gill et al., 2007). Despite the
benefits however of urban green space, conflict can occur between the use of land for green space and building
developments (Hamin and Gurran, 2009).
European policies for biodiversity (e.g. the European Biodiversity Strategy (EC, 2011)) look to spatial planning to
help protect and safeguard internationally and nationally designated sites, networks and species, as well as locally
valued sites in urban and non-urban areas, and to create new opportunities for biodiversity through the development
process (Wilson, 2008). Conservation planning in response to climate change impacts on species aims to involve
several strategies to better manage isolated habitats, increase colonisation capacity of new climate zones and
optimise conservation networks to establish climate refugia (Vos et al., 2008).
23.7.5. Rural Development
Rural development is one of the key policy areas for Europe, yet there is little or no discussion about the role of
climate change in affecting future rural development. The EU White Paper on adapting to climate change (EC,
2009a) encourages Member States to embed climate change adaptation into the three strands of rural development
aimed at improving competitiveness, the environment, and the quality of life in rural areas. It appears however that
little progress has been made in achieving these objectives.
For example, the EUs Leader programme was designed to help rural actors improve the long-term potential of their
local areas by encouraging the implementation of sustainable development strategies. Many Leader projects address
climate change adaptation, but only as a secondary or in many cases a non-intentional by-product of the primary
rural development goals. The World Bank s community adaptation project has seen a preponderance of proposals
from rural areas in Eastern Europe and Central Asia (Heltberg et al., 2012) suggesting that adaptation based
development needs in Eastern Europe are currently not being met by policy.
23.7.6. Economic Assessments of Adaptation
Compared to studies assessed in AR4 (AR4 WG2 Chapter 17.2.3), costs estimates for Europe are increasingly
derived from bottom-up and sector-specific studies, aimed at costing response measures (Watkiss and Hunt, 2010),
in addition to the economy-wide assessments (Aaheim et al., 2012). The evidence base, however, is still fragmented
and incomplete. The coverage of adaptation costs and benefit estimates is dominated by structural (physical)
protection measures, where effectiveness and cost components can be more easily identified. For energy,
agriculture, infrastructure there is medium coverage of cost and benefit categories. There is a lack of information
regarding adaptation costs in the health and social care sector. Table 23-2 summarises some of the more
comprehensive cost estimates for Europe for sectors at regional and national level. It is stressed that the costing
studies use a range of methods and metrics and relate to different time periods and sectors, which renders robust
comparison difficult. As an example, there are large differences between the cost estimates for coastal and river
protection in the Netherlands and other parts of Europe (Table 23-2), this is due to the objectives for adaptation and
the large differences in the level of acceptable risk. For example, Rojas et al. (2013) assess a 1 in 100 year level of
protection for Europe, while the Netherlands has set standards up to 1 in 4,000 and 10,000 year level return periods.
More detailed treatment of the economics of adaptation is provided in AR5 WG2 Chapter 17.
[INSERT TABLE 23-2 HERE
Table 23-2: Selected published cost estimates for planned adaptation in European countries.]
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23.7.7. Barriers and Limits to Adaptation
The implementation of adaptation options presents a range of opportunities, constraints and limits. Constrains
(barriers) to implementation are financial, technical and political (see discussion in AR5 WG2 16). Some impacts
will be unavoidable due to limits (physical, technological, social, economic or political). Examples of limits in the
European context are described by sector in Table 23-3. For example, the contraints on building or extending flood
defences would include pressure for land, conservation needs, and amenity value of coastal areas (AR5 WG2 5.5.5).
Towards the end of the century, it is likely that adaptation limits are expected to be reached earlier under higher rates
of warming. Opportunities and co-benefits of adaptation are also discussed in section 23.8 below.
[INSERT TABLE 23-3 HERE
Table 23-3: Limits to adaptation to climate change.]
23.8. Co-Benefits and Unintended Consequences of Adaptation and Mitigation
Scientific evidence for decision making is more useful if impacts are considered in the context of impacts on other
sectors and in relation to adaptation, mitigation and other important policy goals. The benefits of adaptation and
mitigation policies can be felt in the near term and in the local population, although benefits relating to greenhouse
gas emissions reduction may not be apparent until the longer term. The benefits of adaptation measures are often
assessed using conventional economic analyses, some of which include non-markets costs and benefits
(externalities)(Watkiss and Hunt, 2010). This section will describe policies, strategies and measures where there is
good evidence regarding mitigation/adaptation costs and benefits. Few studies have quantified directly the trade-
offs/synergies for a given policy.
23.8.1. Production and Infrastructure
Mitigation policies (decarbonisation strategies) are likely to have important implications for dwellings across
Europe. The unintended consequences of mitigation in the housing sector include: changes to household energy
prices and adverse effects from decreased ventilation in dwellings (Mavrogianni et al., 2012)(Davies and Oreszczyn,
2012)(Jenkins et al., 2008; Jenkins, 2009). The location, type and dominant energy use of the building will
determine its overall energy gain or loss to maintain comfort levels. Adaptation measures such as the use of cooling
devices will probably increase a building s energy consumption if no other mitigation measures are applied. The
potential for cooling dwellings without increased energy consumption, and with health benefits is large (Wilkinson
et al., 2009).
When looking at the broader context of urban infrastructures, despite existing efforts to include both adaptation, and
mitigation into sustainable development strategies at city level (e.g. Hague, Rotterdam, Hamburg, Madrid, London,
Manchester), priority on adaptation still remains low (Carter, 2011). There is potential to develop strategies that can
address both mitigation and adaptation solutions, as well as have health and environmental benefits (Milner et al.,
2012). In energy supply, the adverse effect of climate change on water resources in some coastal regions in southern
Europe may further enhance the development of desalination plants as an adaptation measure, possibly increasing
energy consumption and thus greenhouse gases emissions. Coastal flood defence measures may alter vector habits
and have implications for local vector-borne disease transmission (Medlock and Vaux, 2013).
In tourism, adaptation and mitigation may be antagonistic, as in the case of artificial snowmaking in European ski
resorts which requires significant amounts of energy and water (OECD, 2007; Rixen et al., 2011) and the case of
desalination for potable water production which also requires energy. However, depending on the location and size
of the resort, implications are expected to differ and thus need to be investigated on a case-by-case basis. A similar
relationship between adaptation and mitigation may hold for tourist settlements in southern Europe, where expected
temperature increases during the summer may require increased cooling in order to maintain tourist comfort and
thus increase greenhouse gas emissions and operating costs. Furthermore, a change of tourist flows as a result of
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tourists adapting to climate change may affect transport emissions, while mitigation in transport could also lead to a
change in transport prices and thus possibly affect tourist flows.
23.8.2. Agriculture, Forestry, and Bioenergy
Agriculture and forestry face two challenges under climate change, both to reduce emissions and to adapt to a
changing and more variable climate (Smith and Olesen, 2010)(Lavalle et al., 2009). The agriculture sector
contributes to about 10% of the total anthropogenic greenhouse gas (GHG) emissions in the EU27 (EEA, 2010b).
Estimates of European carbon dioxide, methane and nitrous oxide fluxes between 2000 and 2005 suggest that
methane emissions from livestock and nitrous oxide emissions from agriculture are fully compensated for by the
carbon dioxide sink provided by forests and by grassland soils (Schulze et al., 2010). However, projections
following a baseline scenario suggest a significant decline (-25 to -40%) of the forest carbon sink of the EU until
2030 compared to 2010. Using wood for bioenergy results initially in a carbon debt due to reduced storage in
forests, which affects the net GHG balance depending on the energy type that is replaced and the time span
considered (McKechnie et al., 2011). Including additional bioenergy targets of EU member states has an effect on
the development of the European forest carbon sink (and on the carbon stock), which is not accounted for in the EU
emission reduction target (Bottcher et al., 2012).
In arable production systems, adapting to climate change by increasing the resilience of crop yields to heat and to
rainfall variability would have positive impacts on mitigation by reducing soil erosion, as well as soil organic carbon
and nitrogen losses. Improving soil water holding capacity through the addition of crop residues and manure to
arable soils, or by adding diversity to the crop rotations, may contribute both to adaptation and to mitigation (Smith
and Olesen, 2010). There are also synergies and trade-offs between mitigation and adaptation options for soil tillage,
irrigation and livestock breeding (Smith and Olesen, 2010). Reduced tillage (and no-till) may contribute to both
adaptation and mitigation as it tends to reduce soil erosion and run-off (Soane et al., 2012) and fossil-fuel use
(Khaledian et al., 2010), while increasing in some situations soil organic carbon stock (Powlson et al., 2011).
However, increased N2O emission may negate the mitigation effect of reduced tillage (Powlson et al., 2011).
Irrigation may enhance soil carbon sequestration in arable systems (Rosenzweig et al., 2008)(Rosenzweig and
Tubiello, 2007), but increased irrigation under climate change would increase energy use and may reduce water
availability for hydro-power (reduced mitigation potential) (Wreford et al., 2010). In intensive livestock systems,
warmer conditions in the coming decades might trigger the implementation of enhanced cooling and ventilation in
farm buildings (Rosenzweig and Tubiello, 2007), thereby increasing energy use and associated GHG emissions. In
grass-based livestock systems, adaptation by adjusting the mean annual animal stocking density to the herbage
growth potential (Graux et al., 2012) is likely to create a positive feedback on GHG emissions per unit area
(Soussana and Luscher, 2007; Soussana et al., 2010).
Land management options may also create synergies and trade-offs between mitigation and adaptation. Careful
adaptation of forestry and soil management practices will be required to preserve a continental ecosystem carbon
sink in Europe (Schulze et al., 2010) despite the vulnerability of this sink to climatic extremes (Ciais et al., 2005)
and first signs of carbon sink saturation in European forest biomass (Nabuurs et al., 2013). In areas that are
vulnerable to extreme events (e.g. fires, storms, droughts) or with high water demand, the development of bioenergy
production from energy crops and from agricultural residues (De Wit et al., 2011) (Fischer et al., 2010b) could
further increase demands on adaptation (Wreford et al., 2010). Conversely, increased demands on mitigation could
be induced by the potential expansion of agriculture at high latitudes which may release large amounts of carbon and
nitrogen from organic soils (Rosenzweig and Tubiello, 2007).
23.8.3. Social and Health Impacts
Significant research has been undertaken since AR4 on the health co-benefits of mitigation policies (see WGIII
chapters on Housing, Transport and Energy, and WGII chapter 11). Several assessment have quantified benefits in
terms of lives saved by reducing particulate air pollution, and trying to coherent policy objectives for emissions
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reductions in local and global pollution. Policies that improve health from changes in transport and energy can be
said to have a general benefit to population health and resilience (Haines et al., 2009a; Haines et al., 2009b).
Changes to housing and energy policies also have indirect implications for human health. Researches on the benefits
of various housing options (including retrofitting) have been intensively addressed in the context of low energy,
healthy and sustainable housing (see WGIII).
23.8.4. Environmental Quality and Biological Conservation
There are several conservation management approaches that can address both mitigation, adaptation and biodiversity
objectives (Lal et al., 2011). Some infrastructure adaptation strategies, such as desalinisation, sea defences and flood
control infrastructure may have negative effects on both mitigation and biodiversity. However, approaches, such as
forest conservation and urban green space (23.7.4) have multiple benefits and potentially significant effects. There
has been relatively little research about the impacts of future land use demand for bioenergy production, food
production and urbanisation on nature conservation.
Figure 23-6 (Paterson et al., 2008) summarizes the evidence regarding mitigation and adaptation options on
biodiversity assessed from the literature. The figure shows that the options that come closest to being win-win-win
are green rooftops, urban tree planting, forest conservation and low-till cultivation. Other options with clear benefits
are afforestation, forest pest control, increased farmland irrigation and species translocation.
[INSERT FIGURE 23-6 HERE
Figure 23-6: Adaptation and mitigation options and their effects on biodiversity. The horizontal axis ranges from
positive effects on biodiversity (left-hand side) to negative effects (right-hand side). Each mitigation/adaptation
option is located on the biodiversity effect axis (solid bars), including an estimate of the uncertainties associated
with the assessment (error bars). The various options are given vertically with mitigation at the top and adaptation at
the bottom. Options located toward the centre of the vertical axis have benefits for both mitigation and adaptation.
Thus, options located at the centre left of the figure have benefits for mitigation, adaptation and biodiversity and
hence are labelled as win-win-win . Other combinations of benefits and dis-benefits are labelled accordingly, e.g.
win-lose-win, lose-win-lose, etc. Based on Paterson et al., 2009.]
23.9. Synthesis of Key Findings
23.9.1. Key Vulnerabilities
Climate change will have adverse impacts in nearly all sectors and across all sub-regions. Table 23-4 describes the
range of impacts projected in 2050 on infrastructure, settlements, environmental quality and the health and welfare
of the European population. The projected impacts of climate change on ecosystem services (including food
production) are described in Box 23-1. A key finding is that all sub-regions are vulnerable to some impacts from
climate change but that these impacts differ significantly in type between the sub-regions. Impacts in neighbouring
regions (inter-regional) may also redistribute economic activities across the European landscape. The sectors most
likely to be affected by climate change, and therefore with implications for economic activity and population
movement (changes in employment opportunities) include: tourism (23.3.6), agriculture (23.4.1), and forestry
(23.4.4).
[INSERT TABLE 23-4 HERE
Table 23-4: Assessment of climate change impacts by sub-region by 2050, assuming a medium emissions scenario,
and no planned adaptation. Impacts assume economic development, including land use change. Impacts are assessed
for the whole sub-region, although differences in impact within sub-regions are estimated for some impacts.]
The majority of published assessments are based on climate projections in the range 1-4 degrees global mean
temperature per century. Under these scenarios, regions in Europe may experience higher rates of warming (in the
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range 4-6 degrees per century), due to climate variability (Jacob et al. 2013). Limited evidence exists on the
potential impacts in Europe under very high rates of warming (>4 degrees above pre-industrial levels) but these
would lead to large increase in coastal flood risk as well as impacts on global cereal yields and other effects on the
global economy (AR5 WG2 19.5.1).
Many key vulnerabilities are already well known since the AR4, but some new vulnerabilities are emerging based
on the evidence reviewed in this report. The policy/governance context in Europe is extremely important in
determining key vulnerabilities (either reducing or exacerbating vulnerability) since Europe is a highly regulated
region. Further, vulnerability will be strongly affected by changes in the non-climate drivers of change (e.g.
economic, social protection measures, governance, technological drivers).
Extreme events affect multiple sectors and have the potential to cause a systemic impacts from secondary effects
(chapter 19). Past events indicate the vulnerability of transport, energy, agriculture, water resources and health
systems. Resilience to very extreme events varies by sector, and by country (Ludwig et al., 2011; Pitt, 2008; Ulbrich
et al., 2012). Extreme events (heat waves and droughts) have had significant impacts on populations as well impacts
on multiple economic sectors (Table 23-1), and resilience to future heat waves has only been addressed within some
sectors. However, there is surprisingly little evidence regarding the impacts of major extreme events (e.g. Russian
heat wave of 2010) and on responses implemented post-event to increase resilience. Future vulnerability will also be
strongly affected by cross-sectoral (indirect) interactions, e.g. flooding-ecosystems, agriculture-species, agriculture-
cultural landscapes, and so on.
Climate change is likely to have significant impacts on future water availability, and the increased risks of water
restrictions in Southern, Central and Atlantic sub-regions. Studies indicate a significant reduction in water
availability from river abstraction and from groundwater resources, combined to increased demands from a range of
sectors (irrigation, energy and industry, domestic use) and to reduced water drainage and run-off (as a result of
increased evaporative demand) (Ludwig et al., 2011).
Climate change will affect rural landscapes by modifying relative land values, and hence competition, between
different land-uses (Smith et al., 2010). This will occur directly, e.g. through changes in the productivity of crops
and trees [23.4], and indirectly through climate change impacts on the global supply of land-based commodities and
their movement through international trade [23.9.2].
Climate change will have a range of impacts in different European sub-regions. The adaptive capacity of populations
is likely to vary significantly within Europe. Adaptive capacity indicators have been developed based on future
changes in socio-economic indicators and projections (Lung et al., 2012; Metzger et al., 2008)(Acosta et al., 2013).
These studies concluded that the Nordic countries have higher adaptive capacity than most of the Southern European
countries, with countries around the Mediterranean having a lower capacity than the countries around the Baltic Sea
region. Some regions or areas are particularly vulnerable to climate change:
Populations and infrastructure in coastal regions are likely to be adversely affected by sea level rise,
particularly after mid-century [23.3.1, 23.5.3].
Urban areas are also vulnerable due to high density of people and built infrastructure from weather
extremes [23.3, 23.5.1].
High mountains. Due to high impact of climate change on natural hazard, water and snow resources and
lack of migration possibilities for plant species, mountain regions concentrate vulnerabilities in
infrastructure for transport and energy sectors, as well as for tourism, agriculture and biodiversity
Mediterranean region will suffer multiple stresses and systemic failures due to climate changes. Changes in
species composition, increase of alien species, habitat losses and degradation both in land and sea together
with agricultural and forests production losses due to increasing heat waves and droughts exacerbated also
by the competition for water will increase the sub-region vulnerability (Ulbrich et al., 2012).
The following risks have emerged from observations of climate sensitivity and observed adaptation:
Arable crop yields. There is new evidence to suggest that crop yields and production may be more
vulnerable as a result of increasing climate variability. This will limit the potential poleward expansion of
agricultural production. Limits to genetic progress to adapt are increasingly reported.
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New evidence regarding implications during summer on inland waterways (decreased access) and long
range ocean transport (increased access).
Terrestrial and freshwater species are vulnerable from climate-change shifts in habitats. There is new
evidence that species cannot populate new habitat due to habitat fragmentation (urbanization). Observed
migration rates are less than that assumed in modelling studies. There are legal barriers to introducing new
species (e.g. forest species in France). New evidence that phenological mismatch will cause additional
adverse effects on some species.
A positive (and emerging) effect that may reduce vulnerability is that many European governments (and
individual cities) have become aware of the need to adapt to climate change and so are developing and/or
implementing adaptation strategies and measures.
Additional risks have emerged from the assessed literature:
Increased summer energy demand, especially in southern Europe, requires additional power generation
capacity, which will be under-utilised during the rest of the year, entailing higher supply costs.
Housing will be affected, with increased overheating under no adaptation and damage from subsidence and
flooding. Passive cooling measures alone are unlikely to be sufficient to address adaptation in all regions
and types of buildings. Retrofitting current housing stock will be expensive.
An emerging concern is the vulnerability of cultural heritage, including monuments/buildings and cultural
landscapes. Some cultural landscapes will disappear. Grape production is highly sensitive to climate, but
production (of grape varieties) is strongly culturally-dependent and adaptation is potentially limited by the
regulatory context.
Good evidence that climate change will increase distribution and seasonal activity of pests and diseases.
Limited evidence that such effects already occurring. Increased threats to plant and animal health. Public
policies are in place to reduce pesticide use in agriculture use and antibiotics in livestock, and this will
increase vulnerability to the impact of climate change on agriculture and livestock production.
Lack of institutional frameworks is a major barrier to adaptation governance. In particularly, the systematic
failure in land use planning policy to account for climate change.
[INSERT TABLE 23-5 HERE
Table 23-5: Key risks from climate change in Europe and the potential for reducing risk through mitigation and
adaptation. Risk levels are presented in three timeframes: the present, near-term (2030-2040), and longer-term
(2080-2100). For each timeframe, risk levels are estimated for a continuation of current adaptation and for
a hypothetical highly adapted state. For a given key risk, change in risk level through time and across magnitudes of
climate change is illustrated, but because the assessment considers potential impacts on different
physical, biological, and human systems, risk levels should not necessarily be used to evaluate relative risk across
key risks, sectors, or regions. Key risks were identified based on assessment of the literature and expert judgment.]
23.9.2. Climate Change Impacts Outside Europe and Inter-Regional Implications
With increasing globalization, the impacts of climate change outside the European region are likely to have
implications for countries within the region. For example, the Mediterranean region (Southern Europe and non-
European Mediterranean countries) has been considered high vulnerable to climate change (Navarra, 2013). Eastern
European countries have, in general, lower adaptive capacity than Western or Northern European countries. The
high volume of international travel increases Europe s vulnerability to invasive species, including the vectors of
human and animal infectious diseases. The transport of animals and animal products has facilitated the spread of
animal diseases (Conraths and Mettenleiter, 2011). Important exotic vectors that have become established in
Europe include the vector Aedes albopictus (Becker, 2009) (23.5.1).
Another inter-regional implication concerns the changes in the location of commercial fish stocks shared between
countries. Such changes may render existing international agreements regarding the sharing of yield from these
stocks obsolete giving rise to international disputes (Arnason, 2012). For instance, the North Sea mackerel stock has
recently been extending westwards beyond the EU jurisdiction into the Exclusive Economic Zones of Iceland and
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the Faroe Islands, which unilaterally claimed quota for mackerel Territorial disagreements of this type could
increase in the future with climate change.
Although several studies have proposed a role of climate change to increase migration pressures in low and middle
income countries in the future, there is little robust information regarding the role of climate change, environmental
resource depletion and weather disasters in future inter-continental population movements. The effect of climate
change on external migration flows into Europe is highly uncertain (see chapter 12.4.1 for a more complete
discussion). Modelling future migration patterns is complex and so far no robust approaches have been developed.
23.9.3. Effects of Observed Climate Change in Europe
Table 23-6 summarises the evidence with respect to key indicators in Europe for the detection of a trend and the
attribution of that trend to observed changes in climate factors. The attribution of local warming to anthropogenic
climate change is less certain (see Chapter 18 for a full discussion). Further and better quality evidence since 2007
supports the conclusion of AR4 (Europe chapter, Alcamo et al., 2007) that climate change is affecting land,
freshwater and marine ecosystems in Europe. Observed warming has caused advancement in the life cycles of many
animal groups, including frogs spawning, birds nesting and the arrival of migrant birds and butterflies (see WGII
chapter 4 and review by (Feehan et al., 2009). There is further evidence that observed climate change is already
affecting agricultural, forest and fisheries productivity (see 23.4).
The frequency of river flood events, and annual flood and windstorm damages in Europe have increased over recent
decades, but this increase is mainly due to increased exposure and the contribution of observed climate change is
unclear (high confidence based on robust evidence and high agreement)(SREX 4.5.3, (Barredo, 2010).
The observed increase in the frequency of hot days and hot nights (high confidence, WGI) is likely to have
increased heat-related health effects in Europe (medium confidence), and well as a decrease in cold related health
effects (medium confidence) (Christidis et al., 2010). Multiple impacts on health, welfare and economic sectors
were observed due to the major heat wave events of 2003 and 2010 in Europe (Table 23-5) (see Chapter 18 for
discussion on attribution of events).
[INSERT TABLE 23-6 HERE
Table 23-6: Observed changes in key indicators in ecological and human systems attributable to climate factors.]
23.9.4. Key Knowledge Gaps and Research Needs
There is a clear mismatch between the volume of scientific work on climate change since the AR4 and the insights
and understanding required for policy needs, as many categories of impacts are still understudied. Some specific
research needs have been identified:
Little information is available on integrated and cross-sectoral climate change impacts in Europe, as the
impact studies typically describe a single sector [see sections 23.3 to 23.6]. This also includes a lack of
information on cross-sector vulnerabilities, and the indirect effects of climate change impacts and
adaptation responses. This is a major barrier in developing successful evidence-based adaptation strategies
that are cost-effective.
Climate change impact models are difficult to validate [sections 23.3 to 23.6]; proper testing of the
characteristics of baseline impact estimates against baseline information and data would improve their
reliability, or the development of alternative methods where baseline data are not available.
There is little knowledge on the co-benefits and unintended consequences of adaptation options across a
range of sectors [section 23.3 to 23.6].
There is a need to better monitor and evaluate local and national adaptation and mitigation responses to
climate change, in both public and private sectors [23.7; Box 23-3]. This includes policies and strategies
as well as the effectiveness of individual adaptation measures. Evaluation of adaptation strategies, over a
range of time-scales, would better support decision-making. While some means for reporting of national
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actions exist in Europe (e.g. EU Climate-ADAPT), there is no consistent method of monitoring or a
mechanism for information exchange [23.7].
There are now more economic methods and tools available for the costing and valuation of specific
adaptation options, in particular for flood defences, water, energy, and agriculture sectors [23.7.6].
However, for other sectors, such as biodiversity, business and industry, and population health costs, cost
estimates are still lacking or incomplete. The usefulness of this costing information in decision making
need to be evaluated and research can be undertaken to make economic evaluation more relevant to
decision making.
The need for local climate information to inform decision making also needs to be evaluated.
Further research is needed on the effects of climate change on critical infrastructure (including transport,
and water and energy supplies, health services) [23.5.2].
Further research is needed on the role of governance in adaptation (including local and national institutions)
with respect to implementation of measures in the urban environment, including flood defences, over-
heating, and urban planning.
The impacts for Europe from high end scenarios of climate change (above 4°C global average warming,
with higher temperature change in Europe) are yet unknown. This is because such scenarios have only
recently become available, and related impact studies still need to be undertaken for Europe.
More study of the implications of climate change for rural development would inform policy in this area
[23.7.5]. There is also a lack of information on the resilience of cultural landscapes and communities, and
how to manage adaptation, particularly in low technology (productively marginal) landscapes.
More research is needed for the medium and long- term monitoring of forest responses and adaptation to
climate change and on the predictive modelling of wildfire distribution to better address adaptation and
planning policies. There is also a lack of information on the impact of climate changes and climate
extremes on carbon sequestration potential of agricultural and forestry systems [23.4.4].
More research on the impacts of climate change on transport, especially on the vulnerability of road and
rail infrastructure, and on the contribution of climatic and non-climatic parameters in the vulnerability of
air transport (e.g. changes in air traffic volumes, airport capacities, air traffic demand, weather at the
airports of origin, intermediate and final destination) [23.3.3].
Improved monitoring of droughts is needed to support the management of crop production [23.4]. Remote
sensing could be complemented by field experiments that assess the combined effects of elevated CO2 and
extreme heat and drought on crops and pastures.
Research is needed on the resilience of human populations to extreme events (factors which increase
resilience), including responses to flood and heat wave risks. Inequalities and how adaptation policies
may increase or reduce social inequalities [23.5].
Development of improved risk models for vector borne disease (human and animal diseases) in order to
support health planning and surveillance [23.4.2, 23.5.1].
A major barrier to research is lack of access to data, which is variable across regions and countries (especially with
respect to socio-economic data, climate data, forestry, and routine health data). There is a need for long term
monitoring of environmental and social indicators and to ensure open access to data for long-term and sustainable
research programmes. Cross-regional cooperation could also ensure compatibility and consistency of parameters
across the European region.
Frequently Asked Questions
FAQ 23.1: Will I still be able to live on the coast in Europe? [to remain at the end of the chapter]
Coastal areas affected by storm surges will face increased risk both because of the increasing frequency and of
storms and because of higher sea level. Most of this increase in risk will occur after the middle of this century.
Models of the coast line suggest that populations in the north western region of Europe are most affected and many
countries, including the Netherlands, Germany, France, Belgium, Denmark, Spain and Italy, will need to strengthen
their coastal defences. Some countries have already raised their coastal defence standards. The combination of raised
sea defences and coastal erosion may lead to narrower coastal zones in the North Sea, the Iberian coast, and Bay of
Biscay. Adapting dwellings and commercial buildings to occasional flooding is another response to climate change.
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But while adapting buildings in coastal communities and upgrading coastal defences can significantly reduce
adverse impacts of sea level rise and storm surges, they cannot eliminate these risks, especially as sea levels will
continue to rise over time. In some locations, managed retreat is likely to become a necessary response.
FAQ 23.2: Will climate change introduce new infectious diseases into Europe?
[to remain at the end of the chapter]
Many factors play a role in the introduction of infectious diseases into new areas. Factors that determine whether a
disease changes distribution include: importation from international travel of people, vectors or hosts (insects,
agricultural products), changes in vector or host susceptibility, drug resistance, and environmental changes, such as
land use change or climate change. One area of concern that has gained attention is the potential for climate change
to facilitate the spread tropical diseases, such as malaria, into Europe. Malaria was once endemic in Europe. Even
though its mosquito vectors are still present and international travel introduces fresh cases, malaria has not become
established in Europe because infected people are quickly detected and treated. Maintaining good health surveillance
and good health systems are therefore essential to prevent diseases from spreading. When an outbreak has occurred
(i.e. the introduction of a new disease) determining the causes is often difficult. It is likely that a combination of
factors will be important. A suitable climate is a necessary but not a sufficient factor for the introduction of new
infectious diseases.
FAQ 23.3: Will Europe need to import more food because of climate change?
[to remain at the end of the chapter]
Europe is one of the world s largest and most productive suppliers of food, but also imports large amounts of some
agricultural commodities. A reduction in crop yields, particularly wheat in southern Europe, is expected under future
climate scenarios. A shift in cultivation areas of high value crops, such as grapes for wine, may also occur. Loss of
food production may be compensated by increases in other European sub-regions. However, if the capacity of the
European food production system to sustain climate shock events is exceeded, the region would require exceptional
food importation.
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(eds.)]. World Health Organization Regional Office for Europe, Denmark, pp. 1-146.
Wiering, M.A. and B.J.M. Arts, 2006: Discursive shifts in Dutch river management: 'deep' institutional change or
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Table 23-1: Impacts of climate extremes in the last decade in Europe.
Year Region Meteorological Production Systems Agriculture, Health and Environmental Mega-
Characteristics and Physical Fisheries, Social Welfare Quality and fire
Infrastructure, Forestry, Biological
settlements Bioenergy Conservation
2003 Western Hottest summer Damage to road and Grain harvest 35,000 deaths Decline in water Yes
and in at least rail transport systems. losses of 20% in August in quality
Central 500 years Reduced/ interrupted (Ciais, et al. Central and (Daufresne et al.
Europe (Luterbacher et operation of nuclear 2005) Western 2007).
al., 2004) power plants (mostly Europe,
in France). (Robine et al. High outdoor
High transport prices 2008) pollution levels
in Rhine due to low (EEA 2012)
water levels.
2004/ Iberian Hydrological - Grain harvest - -
2005 Peninsula drought losses of 40%
- Portugal (EEA, 2010c)
2007 Southern Hottest summer 1710 buildings burned Approx. Significant Several Yes, -
Europe on record in down or 575,500 mortality protected Greece
Greece since rendered uninhabitable hectares burnt impact: 6 conservation
1891 (Founda & in Greece (JRC, 2008) area (JRC, deaths in sites (Natura
Giannakopoulos 2008) Portugal, 80 2000) were
2009) deaths in destroyed (JRC,
Greece. 2008)
2007 England May July Estimated total losses 78 farms 13 deaths and
and wettest since 4 billion (3 billion flooded. 48,000 flooded
Wales records began in insured losses) Impacts on homes (Pitt,
1766. (Chatterton et al. agriculture 2008). Damage
2010). 50 million costs for health
Failure of pumping (Chatterton et effects, incl.
station led to 20,000 al. 2010) loss of access to
people without water education 287
for 2 weeks million
(Chatterton et
al. 2010)
2010 Western Hottest summer Fire damage Estimated High outdoor Yes
Russia since 1500 to forests 10,000 excess pollution levels
(Barriopedro et (Shvidenko et deaths due to in Moscow
al., 2011) al., 2011). heatwave in (Bondur, 2011,
Reduction in Moscow in July Revich and
crop yields and August Shaposhnikov,
(Coumou and (Revich and 2012)
Rahmstorf, Shaposhnikov,
2012) 2012)
2011 France Hottest and Reduction in snow 8% decline in
driest spring in cover for skiing wheat yield
France since (AGRESTE,
1880 2011)
* Extreme events derived from Coumou and Rahmstorf, 2012.
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Table 23-2: Selected published cost estimates for planned adaptation in European countries.
Region Cost estimate Time period Sectors/Outcomes Reference
Europe 2.6-3.5 billion/a In 2100 Coastal adaptation costs Hinkel et al. 2010
Europe 1.7 billion/a By 2020s Protection from river flood risk Rojas et al., in press
3.4 billion/a By 2050s for EU27
7.9 billion/a By 2080s
Netherlands 1.2 1.6 billion/a up to 2050 Protection from coastal and Delta Committee, 2008
0.9 1.5 billion/a 2050 2100 river flooding
Sweden total of up to 10 2010-2100 Multi sector Swedish Commission
billion on Climate and
Vulnerability, 2007
Italy 0.4-2 billion By 2080s Coastal protection Bosello et al. 2012,
Up to 44 billion Hydrogeological protection Medri et al. 2013.
Greece 0.4-3.3 billion Up to 2100 Coastal protection Bank of Greece, 2011
UK 1.8 billion Until 2035 Maintain and improve Thames EA, 2011
2.2 billion 2035-2050 flood protection
7-8 billion At 2100 Renew and improve Thames
flood protection
New Thames barrier for London
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Table 23-3: Limits to adaptation to climate change.
Area/Location System Adaptation Limits to adaptation measure(s) References
measures
Low altitude/ small- Ski tourism Artificial Climatic, technological and Landauer et al, 2012 ;
size ski resorts snowmaking environmental constraints Steiger, 2010; Steiger,
Economic viability 2011; Steiger and
Social acceptability of charging for Mayer, 2008, Unbehaun
previously free skiing. et al., 2008
Social acceptability of alternatives for
winter sport/leisure.
Thermal power Once-through Closed- circuit High investment cost for retrofitting van Vliet et al., 2012,
plants/ cooling cooling cooling existing plants Koch and Vögele, 2009,
through river intake systems Hoffman et al., 2013
and discharge
Reduced load Increased transport prices (Rhine and Jonkeren, 2009,
factor of inland Moselle market) Jonkeren et al., 2007
Rivers used for Inland ships
freight transport transport Use of smaller Existing barges below optimal size Demirel, 2011
ships (Rhine)
Agriculture, Northern Arable crops Sowing date as Other constraints (e.g. frost) limit Oort, 2012
and Continental agricultural farmer behaviour
Europe. adaptation
Agriculture, Northern Arable crops Irrigation Groundwater availability, competition Olesen et al., 2011
and Continental with other users.
Europe.
Agriculture, High value Change Legislation on cultivar and Box 23-1
Viticulture crops distribution geographical region
Conservation Alpine Extend habitat No technological adaptation option. Engler et al., 2011,
Cultural landscapes meadow/ Dullinger et al., 2012
Conservation of Movement of Extend habitat Landscape barriers and absence of Butchart et al., 2010,
species richness species climate projections in selection of Araújo et al., 2011; Filz
conservation areas.
et al., 2012; Virkkala et
al., 2013
Forests Movement of Introduce new Not socially acceptable, Giuggiola et al., 2010;
species and species Legal barriers to non-native species Hemery et al., 2010;
productivity García-López J.M. and
reduction Alluéa, 2011, Casalegno
et al., 2007
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Table 23-4: Assessment of climate change impacts by sub-region by 2050, assuming a medium emissions scenario,
and no planned adaptation. Impacts assume economic development, including land use change. Impacts are assessed
for the whole sub-region, although differences in impact within sub-regions are estimated for some impacts.
Alpine Southern Northern Continental Atlantic
Energy
1
Wind energy
23.3.4
production
2
Hydropower
23.3.4
generation
Thermal power 23.3.4, 8.2.3.2
production
Energy consumption
23.3.4, 23.8.1
(net annual change)
Transport
Road accidents 3 23.3.3
Rail delays (weather- ? ? ? 23.3.3, 8.3.3.6
4
related)
Load factor of inland
? ? ? 23.3.3
ships
Transport time and
? ? ? 23.3.3, 18.3.3.3.5
cost in ocean routes
Settlements
River flood damages 23.3.1
Coastal flood
n/a
23.3.1
damages
Tourism
Length of ski season ? ? 23.3.6, 3.5.7
Human health
Heat wave mortality
23.5.1
and morbidity
Food safety 23.5.1
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Social and cultural
Impacts
Social costs of floods 23.5.3
Damage on cultural
buildings 23.5.4
Loss of cultural
? 23.5.4
landscapes
Environmental quality
Air quality (ozone ?
23.6.1
background levels)
Air quality ?
23.6.1
(particulates)
Water quality 23.6.3
Key:
Green means a beneficial change
Red means a harmful change
? means no relevant literature found
Confidence levels:
Risks were identified based on assessment of the literature and expert judgment.
Footnotes:
1
Simulations have been performed, but mostly for the period after 2070.
2
The increasing trend is for Norway.
3
The decreasing trend refers mainly to the number of severe accidents.
4
Impacts have been studied and quantified for UK only. The increasing trend stands for summer delays and the decreasing trend for winter
delays.
5
In both seasons, no significant impacts are expected by 2020, while more substantial changes are expected by 2080. For 2050 impacts are
assumed to vary linearly (although this may not be the case).
6
The constant trend stands for the Mediterranean, where some studies estimate no changes due to climate change at least until 2030 or even
2060.
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Table 23-5: Key risks from climate change in Europe and the potential for reducing risk through mitigation and
adaptation. Risk levels are presented in three timeframes: the present, near-term (2030-2040), and longer-term
(2080-2100). For each timeframe, risk levels are estimated for a continuation of current adaptation and for
a hypothetical highly adapted state. For a given key risk, change in risk level through time and across magnitudes of
climate change is illustrated, but because the assessment considers potential impacts on different
physical, biological, and human systems, risk levels should not necessarily be used to evaluate relative risk across
key risks, sectors, or regions. Key risks were identified based on assessment of the literature and expert judgment.
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Table 23-6: Observed changes in key indicators in ecological and human systems attributable to climate factors.
Confidence in
Confidence in attribution to change
Indicator Change in indicator Key references Section
detection in climate factors
[**]
Bio-Physical Systems
Glacier Fast mass loss of 30 Swiss High confidence Medium confidence Huss, 2010 18.3.1.3
retreat glaciers since the 1980s WG1
10.5
Infrastructure
Storm Increase since 1970s High confidence No causal role for Barredo, 2010 23.3.7
losses climate
Hail Increase in parts of Germany Low confidence Low confidence Kunz et al., 2009 23.3.7
losses
Flood Increasing general trend in Medium confidence No causal role for Barredo, 2009; 23.3.1
losses economic losses in Europe climate Barredo et al., 2012
since 1970s; none in some
locations
Agriculture, Fisheries, Forestry, and Bioenergy Production
C3 crop CO2 induced positive High confidence High confidence Amthor, 2001; 7.2.1
yield contribution to yield since (high agreement, (high agreement, Long et al., 2006;
preindustrial for C3 crops robust evidence) robust evidence) McGrath and Lobell,
2011
Wheat Stagnation of wheat yields in High confidence Medium confidence Lobell et al. 2011 ; 23.4.1
yield some countries in recent Brisson et al., 2010;
decades Kristensen et al., 2011
Phenolog Earlier greening, Earlier leaf High confidence High confidence Menzel et al., 2006 4.4.1.1
y leaf emergence and fruit set in (high agreement, (high agreement,
greening temperate and boreal climate, robust evidence) robust evidence)
Phytopla Increased phytoplankton High confidence Medium confidence Beaugrand et al., 6.3.2
nkton productivity in NE. Atlantic, 2002;
productiv decrease in warmer regions, due Edwards and
ity to warming trend and Richardson, 2004
hydroclimatic variations
Ocean Northward movement of High confidence Medium confidence Philippart et al. , 2011 6.3.2
systems species and increased species
richness due to warming trend
Environmental quality and biodiversity
Biodivers Increased number of Medium confidence Medium confidence Walther et al., 2009 4.2.4.6
ity colonization events by alien (high agreement,
plant species in Europe medium evidence)
Migrator Decline over the period 1990- Medium confidence Medium confidence Moller et al., 2008 4.4.1.1
y birds 200 of species that did not (medium agreement,
advance their spring migration medium evidence)
Tree Upward shift in tree line in Medium evidence Medium confidence Gehrig-Fasel et al., 18.3.2.1
species Europe (medium agreement, 2007, Lenoir et al.,
high evidence) 2008
Forest Increase in burnt area High confidence High confidence Camia and Amatulli
fires (high agreement, 2009; Hoinka et al., 23.4.4
robust evidence) 2009; Carvalho et al.,
2010; Salis et al., in
press; Pereira et al.,
2005; Koutsias et al.,
2012
NOTE: The studies included in this table are those with good evidence of a detection of a long term trend in the outcome of
interested, and where there has been an assessment of the attribution of the trend to an observed change in climate factor. It is not
possible to make an attribution to anthropogenic climate change at this scale see chapter 18 for a more complete discussion.
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BOX 23-1 TABLE
Alpine Atlantic Continental Northern Southern
Provisioning services:
No (1) (1)
Food production (1) (1) (1)
(4) (1)
No (1)
Livestock production
(1)
Fibre production (1)
(1)
Bioenergy production (1) (1)
No (1) No (1) No (1)
Fish production (1)
(1) (1) (2)
(5) (1)
(2) (6)
Timber production No (2) No (2) V (2)
No (3) No (1)
(5) (1)
(1)
Non-wood forest products (1)
No (1)
(6) (2) (1) (9)
Sum of effects on No (1)
No (4) No (4) No (2) No (3)
provisioning services (7)
(11) (2) (3) (2)
Regulating services:
Climate regulation (carbon
sequestration)
(4) (4)
(4) (3) (3)
- General/forests No (1) No (1)
No (1) No (1) (1)
(3) (1)
No (1) No (1) No (1)
- Wetland (1)
(1) (1) (1)
No (1) No (1) No (1) No (1)
- Soil carbon stocks (3)
(2) (2) (1) (1)
Pest control (1) (1) (1) (1)
Natural hazard regulation*
- Forest fires / wildfires (1) (2) (1)
- Erosion, avalanche, (2)
landslide (1)
- Flooding (1)
No (1)
- Drought (1)
(1)
Water quality regulation (1) (1)
(2)
(2) (2) (3) (1)
Biodiversity No (1)
(4) (4) (2) (8)
(4)
(9) (6) (6) (8) (4)
Sum of effects on regulating
No (2) No (4) No (2) No (2) No (3)
services
(11) (9) (9) (8) (14)
Cultural services:
Recreation (fishing, nature (1)
(1) (1)
enjoyment) (2)
Tourism (skiing) (1) (1)
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Alpine Atlantic Continental Northern Southern
Asthetic/heritage (landscape
No (1)
character, cultural (1) (1) (1)
(1)
landscapes)
Sum of effects on cultural (1) (1) No (1) (1)
(2)
services (1) (1) (1) (3)
Key:
= decreasing impacts
= increasing impacts
No = neutral effect
(1) = number in brackets refers to the number of studies supporting the change (increasing, decreasing, neutral)
in ecosystem service.
Footnotes:
* A decline in ecosystem services implies an increased risk of the specified natural hazard
^ Entries for biodiversity are those that were found during the literature search for climate change impacts on
ecosystem services. A wider discussion of the impacts of climate change on biodiversity can be found in Section
23.6 and AR5 4.3.4.
References:
Albertson et al., 2010; Bastian, 2013; Bolte et al., 2009; Briner et al., 2012; Canu et al., 2010; Civantos et al., 2012;
Clark et al., 2010a; Forsius et al., 2013; Fuhrer et al., 2006; Garcia-Fayos and Bochet, 2009; Gret-Regamy et al.,
2008; Gret-Regamy et al., 2013; Hemery, 2008; Johnson et al., 2009; Koca et al., 2006; Lindner et al., 2010; Lorz et
al., 2010; Metzger et al., 2008; Milad et al., 2011; Okruszko et al., 2011; Palahi et al., 2008; Rusch, 2012; Schroter
et al., 2005; Seidl et al., 2011; Seidl and Lexer, 2013; Wessel et al., 2004.
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Figure 23-1: Sub-regional classification of the IPCC Europe region. Based on Metzger et al., 2005.
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Figure 23-2: First row: Projected changes in the mean number of heat waves occurring in the months May to
September for the period 2071-2100 compared to 1971-2000 (number per 30 years). Heat waves are defined as
periods of more than 5 consecutive days with daily maximum temperature exceeding the mean maximum
temperature of the May to September season of the control period (1971-2000) by at least 5°C. Second and third
rows: Projected seasonal changes in heavy precipitation defined as the 95th percentile of daily precipitation (only
days with precipitation > 1mm/day are considered) for the period 2071-2100 compared to 1971-2000 (in %) in the
months December to January (DJF) and June to August (JJA). Fourth row: Projected changes in the 95th percentile
of the length of dry spells for the period 2071-2100 compared to 1971-2000 (in days). Dry spells are defined as
periods of at least 5 consecutive days with daily precipitation below 1mm. Hatched areas indicate regions with
robust (at least 66% of models agree in the sign of change) and/or statistical significant change (significant on a 95%
confidence level using Mann-Whitney-U test). For the eastern parts of Black Sea, Eastern Anatolia and Southeast
Anatolia (Turkey), no regional climate model projections are available. Changes represent the mean over 8 (RCP4.5,
left side) and 9 (RCP8.5, right side) regional model simulations compiled within the EURO-CORDEX initiative.
Adapted from Jacob et al. (2013). [Illustration to be redrawn to conform to IPCC publication specifications.]
!"#$%&'()*$+($*,*-./0-0.1$2*3'(2$24*$.+$
-,03'.*$-&'()*$
!"#$%&'()*(+(,-./.$0()*1(2,!3(4'/'5%"(&5'/6(
!"#$%&'()*(+(,-./.$0()*1(-./7'54'/-'(8#&9(2,!3:*;<;(
!"#$%&'()*(+(,-./.$0()*1(-./7'54'/-'(8#&9(2,!3:*=;;(
!"#$%&'(>*(+(,-./.$0(>*1(2,!3(4'/'5%"(&5'/6(
!"#$%&'(>*(+(,-./.$0(>*1(-./7'54'/-'(8#&9(2,!3:*;<;(
Figure 23-3: Percentage change in electricity demand in Greece attributable to climate change, under a range of
climate scenarios and economic assumptions. Source: Mirasgedis et al., 2007.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 23-4: Percentage change in simulated water-limited yield for winter wheat in 2030 with respect to the 2000
baseline for the A1B scenario using ECHAM5 (left column) and HadCM3 (right) GCMs. Upper maps to do not take
adaptation into account. Bottom maps include adaptation. Source: Donatelli et al., 2012.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 23-5: Changes in forest fire risk in Europe for two time periods: baseline (left) and 2041 2070 (right), based
on high-resolution regional climate models and the SRES A1B emission scenario. Source: Lung et al., 2013.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 23-6: Adaptation and mitigation options and their effects on biodiversity. The horizontal axis ranges from
positive effects on biodiversity (left-hand side) to negative effects (right-hand side). Each mitigation/adaptation
option is located on the biodiversity effect axis (solid bars), including an estimate of the uncertainties associated
with the assessment (error bars). The various options are given vertically with mitigation at the top and adaptation at
the bottom. Options located toward the centre of the vertical axis have benefits for both mitigation and adaptation.
Thus, options located at the centre left of the figure have benefits for mitigation, adaptation and biodiversity and
hence are labelled as win-win-win . Other combinations of benefits and dis-benefits are labelled accordingly, e.g.
win-lose-win, lose-win-lose, etc. Based on Paterson et al., 2009.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Chapter 24. Asia
Coordinating Lead Authors
Yasuaki Hijioka (Japan), Erda Lin (China), Joy Jacqueline Pereira (Malaysia)
Lead Authors
Richard T. Corlett (China), Xuefeng Cui (China), Gregory Insarov (Russian Federation), Rodel Lasco (Philippines),
Elisabet Lindgren (Sweden), Akhilesh Surjan (India)
Contributing Authors
Elena M. Aizen (USA), Vladimir B. Aizen (USA), Rawshan Ara Begum (Bangladesh), Kenshi Baba (Japan),
Monalisa Chatterjee (USA / India), J. Graham Cogley (Canada), Noah Diffenbaugh (USA), Li Ding (Singapore),
Qingxian Gao (China), Matthias Garschagen (Germany), Masahiro Hashizume (Japan), Manmohan Kapshe (India),
Andrey G. Kostianoy (Russia), Kathleen McInnes (Australia), Sreeja Nair (India), S.V.R.K. Prabhakar (India),
Yoshiki Saito (Japan), Andreas Schaffer (Singapore), Rajib Shaw (Japan), Dáithí Stone (Canada / South Africa /
USA), Reiner Wassman (Philippines), Thomas J. Wilbanks (USA), Shaohong Wu (China)
Review Editors
Rosa Perez (Philippines), Kazuhiko Takeuchi (Japan)
Volunteer Chapter Scientist
Yuko Onishi (Japan), Wen Wang (China)
Contents
Executive Summary
24.1. Introduction
24.2. Major Conclusions from Previous Assessments
24.3. Observed and Projected Climate Change
24.3.1. Observed Climate Change
24.3.2. Projected Climate Change
24.4. Observed and Projected Impacts, Vulnerabilities, and Adaptation
24.4.1. Freshwater Resources
24.4.1.1. Sub-Regional Diversity
24.4.1.2. Observed Impacts
24.4.1.3. Projected Impacts
24.4.1.4. Vulnerabilities to Key Drivers
24.4.1.5. Adaptation Options
24.4.2. Terrestrial and Inland Water Systems
24.4.2.1. Sub-Regional Diversity
24.4.2.2. Observed Impacts
24.4.2.3. Projected Impacts
24.4.2.4. Vulnerabilities to Key Drivers
24.4.2.5. Adaptation Options
24.4.3. Coastal Systems and Low-Lying Areas
24.4.3.1. Sub-Regional Diversity
24.4.3.2. Observed Impacts
24.4.3.3. Projected Impacts
24.4.3.4. Vulnerabilities to Key Drivers
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24.4.3.5. Adaptation Options
24.4.4. Food Production Systems and Food Security
24.4.4.1. Sub-Regional Diversity
24.4.4.2. Observed Impacts
24.4.4.3. Projected Impacts
24.4.4.4. Vulnerabilities to Key Drivers
24.4.4.5. Adaptation Options
24.4.5. Human Settlements, Industry, and Infrastructure
24.4.5.1. Sub-Regional Diversity
24.4.5.2. Observed Impacts
24.4.5.3. Projected Impacts
24.4.5.4. Vulnerabilities to Key Drivers
24.4.5.5. Adaptation Options
24.4.6. Human Health, Security, Livelihoods, and Poverty
24.4.6.1. Sub-Regional Diversity
24.4.6.2. Observed Impacts
24.4.6.3. Projected Impacts
24.4.6.4. Vulnerabilities to Key Drivers
24.4.6.5. Adaptation Options
24.4.7. Valuation of Impacts and Adaptation
24.5. Adaptation and Managing Risks
24.5.1. Conservation of Natural Resources
24.5.2. Flood Risks and Coastal Inundation
24.5.3. Economic Growth and Equitable Development
24.5.4. Mainstreaming and Institutional Barriers
24.5.5. Role of Higher Education in Adaptation and Risk Management
24.6. Adaptation and Mitigation Interactions
24.7. Intra-regional and Inter-regional Issues
24.7.1. Trans-boundary Pollution
24.7.2. Trade and Economy
24.7.3. Migration and Population Displacement
24.8. Research and Data Gaps
24.9. Case Studies
24.9.1. Transboundary Adaptation Planning and Management - Lower Mekong River Basin
24.9.2. Glaciers of Central Asia
References
Chapter Box
24-1. What's New on Asia in AR5?
Frequently Asked Questions
24.1: What will the projected impact of future climate change be on freshwater resources in Asia?
24.2: How will climate change affect food production and food security in Asia?
24.3: Who is most at risk from climate change in Asia?
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Executive Summary
Warming trends and increasing temperature extremes have been observed across most of the Asian region
over the past century (high confidence) [24.3]. Increasing numbers of warm days and decreasing numbers of cold
days have been observed, with the warming trend continuing into the new millennium. Precipitation trends including
extremes are characterized by strong variability, with both increasing and decreasing trends observed in different
parts and seasons of Asia.
Water scarcity is expected to be a major challenge for most of the region due to increased water demand and
lack of good management (medium confidence) [24.4.3]. Water resources are important in Asia because of the
massive population and vary among regions and seasons. However, there is low confidence in future precipitation
projections at a subregional scale and thus in future freshwater availability in most parts of Asia. Population growth
and increasing demand arising from higher standards of living could worsen water security in many parts in Asia
and affect many people in future. Integrated water management strategies could help adapt to climate change,
including developing water saving technologies, increasing water productivity, and water reuse.
The impacts of climate change on food production and food security in Asia will vary by region with many
regions to experience a decline in productivity (medium confidence) [24.4.4]. This is evident in the case of rice
production. Most models, using a range of GCMs and SRES scenarios, show that higher temperatures will lead to
lower rice yields as a result of shorter growing periods. There are a number of regions that are already near the heat
stress limits for rice. However, CO2 fertilization may at least in part offset yield losses in rice and other crops. In
Central Asia, some areas could be winners (cereal production in northern and eastern Kazakhstan could benefit from
the longer growing season, warmer winters and slight increase in winter precipitation), while others could be losers
(western Turkmenistan and Uzbekistan, where frequent droughts could negatively affect cotton production, increase
water demand for irrigation, and exacerbate desertification). In the Indo-Gangetic Plains of South Asia there could
be a decrease of about 50% in the most favorable and high yielding wheat area due to heat stress at 2x CO2. Sea-
level rise will inundate low lying areas and will especially affect rice growing regions. There are many potential
adaptation strategies being practiced and being proposed but research studies on their effectiveness are still few.
Terrestrial systems in many parts of Asia have responded to recent climate change with shifts in the
phenologies, growth rates, and the distributions of plant species, and permafrost degradation, and the
projected changes in climate during the 21st Century will increase these impacts (high confidence) [24.4.2].
Boreal trees will likely invade treeless arctic vegetation, while evergreen conifers will likely invade deciduous larch
forest. Large changes may also occur in arid and semiarid areas, but uncertainties in precipitation projections make
these more difficult to predict. The rates of vegetation change in the more densely populated parts of Asia may be
reduced by the impact of habitat fragmentation on seed dispersal, while the impacts of projected climate changes on
the vegetation of the lowland tropics are currently poorly understood. Changes in animal distributions have also
been projected, in response to both direct impacts of climate change and indirect impacts though changes in the
availability of suitable habitats.
Coastal and marine systems in Asia are under increasing stress from both climatic and non-climatic drivers
(high confidence) [24.4.3]. It is likely that mean sea-level rise will contribute to upward trends in extreme coastal
high water levels [WG1 Section 3.7.6]. In the Asian Arctic, rising sea-levels are expected to interact with projected
changes in permafrost and the length of the ice-free season to cause increased rates of coastal erosion (high
agreement, medium evidence). Mangroves, salt marshes and seagrass beds may decline unless they can move inland,
while coastal freshwater swamps and marshes will be vulnerable to saltwater intrusion with rising sea-levels.
Widespread damage to coral reefs correlated with episodes of high sea-surface temperature has been reported in
recent decades and there is high confidence that damage to reefs will increase during the 21st century as a result of
both warming and ocean acidification. Marine biodiversity is expected to increase at temperate latitudes as warm-
water species expand their ranges northwards (high confidence), but may decrease in the tropics if thermal tolerance
limits are exceeded (medium confidence).
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Multiple stresses caused by rapid urbanization, industrialization and economic development will be
compounded by climate change (high confidence) [24.4, 24.5, 24.6, 24.7]. Climate change is expected to
adversely affect the sustainable development capabilities of most Asian developing countries by aggravating
pressures on natural resources and the environment. Development of sustainable cities in Asia with fewer fossil fuel
driven vehicles and with more trees and greenery would have a number of co-benefits, including improved public
health.
Extreme climate events will have an increasing impact on human health, security, livelihoods, and poverty,
with the type and magnitude of impact varying across Asia (high confidence) [24.4.6]. More frequent and
intense heat-waves in Asia will increase mortality and morbidity in vulnerable groups. Increases in heavy rain and
temperature will increase the risk of diarrheal diseases, dengue fever and malaria. Increases in floods and droughts
will exacerbate rural poverty in parts of Asia due to negative impacts on the rice crop and resulting increases in food
prices and the cost of living.
Studies of observed climate changes and their impacts are still inadequate for many areas, particularly in
North, Central and West Asia (high confidence) [24.8]. Improved projections for precipitation, and thus water
supply, are most urgently needed. Understanding of climate change impacts on ecosystems in Asia is currently
limited by the incompleteness and inaccessibility of biodiversity information. Major research gaps in the tropics
include the temperature dependence of carbon fixation by tropical trees and the thermal tolerances and acclimation
capacities of both plants and animals. Interactions between climate change and the direct impacts of rising CO2 on
crops and natural ecosystems are also currently poorly understood. More research is needed on impacts,
vulnerability and adaptation in urban settlements, especially cities with populations under 500,000. More generally,
there is a need to develop low-cost adaptation measures appropriate to the least developed parts of the region.
24.1. Introduction
Asia is defined here as the land and territories of 51 countries/regions (see Figure 24-1). It can be broadly divided
into six subregions based on geographical position and coastal peripheries. These are (in alphabetical order) Central
Asia (5 countries), East Asia (7 countries/regions), North Asia (2 countries), South Asia (8 countries), Southeast
Asia (12 countries) and West Asia (17 countries). The population of Asia was reported to be about 4,299 million in
2013, which is about 60% of the world population (UN, 2013). The population density was reportedly about 134 per
square kilometer in 2012 (PRB, 2012). The highest life expectancy at birth is 84 (Japan) and the lowest is 50
(Afghanistan) (CIA, 2013). The GDP per capita ranged from US$620 (Afghanistan for 2011) to US$46,720 (Japan
for 2012) (World Bank, 2013).
[INSERT FIGURE 24-1 HERE
Figure 24-1: The land and territories of 51 countries in Asia.
NOTE: Currently in production and will be brought to specification using the current UN-accepted maps.]
24.2. Major Conclusions from Previous Assessments
Major highlights from previous assessments for Asia include:
Warming trends including higher extremes are strongest over the continental interiors of Asia, and warming
in the period 1979 onwards was strongest over China in winter, and northern and eastern Asia in spring and
autumn (see WGI AR4 Section 3.2.2.7 and SREX Section 3.3.1).
From 1900 to 2005, precipitation increased significantly in northern and central Asia but declined in parts
of southern Asia (see WGI AR4 SPM).
Future climate change is likely to affect water resource scarcity with enhanced climate variability and more
rapid melting of glaciers (see WGII AR4 Section 10.4.2)
Increased risk of extinction for many plant and animal species in Asia is likely as a result of the synergistic
effects of climate change and habitat fragmentation (see WGII AR4 Section 10.4.4).
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Projected sea-level rise is very likely to result in significant losses of coastal ecosystems (see WGII AR4
Sections 10.4.3.2, 10.6.1).
There will be regional differences within Asia in the impacts of climate change on food production (see
WGII AR4 Section 10.4.1.1).
Due to projected sea-level rise, a million or so people along the coasts of South and Southeast Asia will
likely be at risk from flooding (high confidence, see WGII AR4 Section 10.4.3.1).
It is likely that climate change will impinge on sustainable development of most developing countries of
Asia as it compounds the pressures on natural resources and the environment associated with rapid
urbanisation, industrialisation and economic development (see WGII AR4 Section 10.7).
Vulnerabilities of industry, infrastructure, settlements and society to climate change are generally greater in
certain high-risk locations, particularly coastal and riverine areas (see WGII AR4 Section 7.3, 7.4, 7.5).
_____ START BOX 24-1 HERE _____
Box 24-1. What s New on Asia in AR5?
Improved country coverage on observed and future impacts of climate change
Increase in the number of studies reflecting advances in research tools (e.g. more use of remote sensing and
modeling of impacts); with an evaluation of detection and attribution where feasible.
More conclusions have confidence statements, while confidence levels have changed in both directions since
AR4.
Expanded coverage of issues: for example discussion of the Himalayas has been expanded to cover observed
and projected impacts (see Box 3-2), including those on: tourism (see Section10.6.2); livelihood assets such as
water and food (see Sections 9.3.3.1, 13.3.1.1, 18.5.3 and 19.6.3); poverty (see Section 13.3.2.3); culture (see
Sections 12.3.2); flood risks (see Sections 18.3.1.1 and 24.2.1); health risks (see Section 24.4.6.2); and
ecosystems (see Section 24.4.2.2).
_____ END BOX 24-1 HERE _____
24.3. Observed and Projected Climate Change
24.3.1. Observed Climate Change
Temperature. It is very likely that mean annual temperature has increased over the past century over most of the
Asia region, but there are areas of the interior and at high latitudes where the monitoring coverage is insufficient for
the assessment of trends (see WGI AR5 Chapter 2, Figure 24-2). New analyses continue to support the AR4 and
SREX conclusions that it is likely that the numbers of cold days and nights have decreased and the numbers of warm
days and nights have increased across most of Asia since about 1950, and heat wave frequency has increased since
the middle of the 20th century in large parts of Asia (see WGI AR5 Section 2.6.1).
As a part of the polar amplification, large warming trends (>2°C per 50 years) in the second half of the 20th century
were observed in the northern Asian sector (see WGI AR5 Section 14.8.8). Over the period 1901-2009, the warming
trend was particularly strong in the cold season between November and March, with an increase of 2.4°C in the mid-
latitude semi-arid area of Asia (see WGI AR5 Section 14.8.8). Increasing annual mean temperature trends at the
country scale in East and South Asia have been observed during the 20th century (Table 24-SM-1). In West Asia,
upward temperature trends are notable and robust in recent decades (see WGI AR5 Section 14.8.10). Across
Southeast Asia, temperature has been increasing at a rate of 0.14°C to 0.20°C per decade since the 1960s, coupled
with a rising number of hot days and warm nights, and a decline in cooler weather (see WGI AR5 Section 14.8.12).
Precipitation and Monsoons. Most areas of the Asian region lack sufficient observational records to draw
conclusions about trends in annual precipitation over the past century (see WGI AR5 Chapter 2, Figure 24-2, Table
24-SM-2). Precipitation trends, including extremes, are characterized by strong variability, with both increasing and
decreasing trends observed in different parts and seasons of Asia (see WGI AR5 Chapter 14 and Table 24-SM-2). In
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northern Asia, the observations indicate some increasing trends of heavy precipitation events, but in central Asia, no
spatially coherent trends were found (see WGI AR5 Section 14.8.8). Both the East Asian summer and winter
monsoon circulations have experienced an interdecadal scale weakening after the 1970s, due to natural variability of
the coupled climate system, leading to enhanced mean and extreme precipitation along the Yangtze River valley
(30°N), but deficient mean precipitation in North China in summer (see WGI AR5 Section 14.8.9). A weakening of
the East Asian summer monsoon since the 1920s was also found in sea level pressure gradients (low confidence, see
WGI AR5 Section 2.7.4). In West Asia, a weak but non-significant downward trend in mean precipitation was
observed in recent decades, although with an increase in intense weather events (see WGI AR5 Section 14.8.10). In
South Asia, seasonal mean rainfall shows interdecadal variability, noticeably a declining trend with more frequent
deficit monsoons under regional inhomogeneities (see WGI AR5 Section 14.8.11). Over India, the increase in the
number of monsoon break days and the decline in the number of monsoon depressions are consistent with the
overall decrease in seasonal mean rainfall (see WGI AR5 Section 14.8.11). But an increase in extreme rainfall
events occurred at the expense of weaker rainfall events over the central Indian region and in many other areas (see
WGI AR5 Section 14.2.2.1). In South Asia, the frequency of heavy precipitation events is increasing, while light
rain events are decreasing (see WGI AR5 Section 14.8.11). In Southeast Asia, annual total wet-day rainfall has
increased by 22 mm per decade, while rainfall from extreme rain days has increased by 10 mm per decade, but
climate variability and trends differ vastly across the region and between seasons (see WGI AR5 Section 14.4.12,
14.8.12). In Southeast Asia, between 1955 and 2005 the ratio of rainfall in the wet to the dry seasons increased.
While an increasing frequency of extreme events has been reported in the northern parts of Southeast Asia,
decreasing trends in such events are reported in Myanmar (see WGI AR5 Section 14.4.12). In Peninsular Malaya
during the southwest monsoon season, total rainfall and the frequency of wet days decreased, but rainfall intensity
increased in much of the region. On the other hand, during the northeast monsoon, total rainfall, the frequency of
extreme rainfall events, and rainfall intensity all increased over the peninsula (see WGI AR5 Section 14.4.12).
Tropical and Extratropical Cyclones. Significant trends in tropical cyclones making landfall are not found on
shorter timescales. Time series of cyclone indices show weak upward trends in the western North Pacific since the
late 1970s, but interpretation of longer-term trends is constrained by data quality concerns (see WGI AR5 Section
2.6.3). A decrease in extratropical cyclone activity and intensity over the last 50 years has been reported for northern
Eurasia (60°N-40°N), including lower latitudes in East Asia (see WGI AR5 Section 2.6.4).
Surface Wind Speeds. Over land in China, including the Tibetan region, a weakening of the seasonal and annual
mean winds, as well as the maximums, is reported from around the 1960s or 1970s to the early 2000s (low
confidence, see WGI AR5 Section 2.7.2).
Oceans. A warming maximum is observed at 25°N-65°N with signals extending to 700 m depth and is consistent
with poleward displacement of the mean temperature field (see WGI AR5 Section 3.2.2). The pH measurements
between 1983 and 2008 in the western North Pacific showed a -0.0018 +/- 0.0002 yr-1 decline in winter and -0.0013 +/-
0.0005 yr-1 decline in summer (see WGI AR5 Section 3.8.2). Over the period 1993-2010, large rates of sea-level rise
in the western tropical Pacific were reported, corresponding to an increase in the strength of the trade winds in the
central and eastern tropical Pacific (see WGI AR5 Section 13.6.1). Spatial variation in trends in Asian regional sea
level may also be specific to a particular sea or ocean basin. For example, a rise of 5.4 +/- 0.3 mm yr 1 in the Sea of
Japan from 1993 to 2001 is nearly two times the GMSL trend, with more than 80% of this rise being thermosteric,
and regional changes of sea level in the Indian Ocean that have emerged since the 1960s are driven by changing
surface winds associated with a combined enhancement of Hadley and Walker cells (see WGI AR5 Section 13.6.1).
24.3.2. Projected Climate Change
The AR4 assessed that warming is very likely in the 21st century (Christensen et al., 2007), and that assessment still
holds for all land areas of Asia in the mid- and late-21st-century, based on the CMIP5 simulations under all four RCP
scenarios (Figures 24-2, 24-SM-1, and Table 24-SM-3). Ensemble-mean changes in mean annual temperature
exceed 2C above the late-20th-century baseline over most land areas in the mid-21st-century under RCP8.5, and
range from greater than 3C over South and Southeast Asia to greater than 6C over high latitudes in the late-21st-
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century. The ensemble-mean changes are less than 2C above the late-20th-century baseline in both the mid- and
late-21st-century under RCP2.6, with the exception of changes between 2C and 3C over the highest latitudes.
Projections of future annual precipitation change are qualitatively similar to those assessed in the AR4 (Christensen
et al., 2007) (Figure 24-2). Precipitation increases are very likely at higher latitudes by the mid-21st-century under
the RCP8.5 scenario, and over eastern and southern areas by the late-21st-century. Under the RCP2.6 scenario,
increases are likely at high latitudes by the mid-21st century, while it is likely that changes at low latitudes will not
substantially exceed natural variability.
[INSERT FIGURE 24-2 HERE
Figure 24-2: Observed and projected changes in annual average temperature and precipitation in Asia.]
Tropical and Extra-Tropical Cyclones. The future influence of climate change on tropical cyclones is likely to vary
by region, but there is low confidence in region-specific projections of frequency and intensity. However, better
process understanding and model agreement in specific regions indicate that precipitation will likely be more
extreme near the centers of tropical cyclones making landfall in West, East, South and Southeast Asia (see WGI
AR5 Sections 14.6, 14.8.9, 14.8.10, 14.8.11, 14.8.12). There is medium confidence that a projected poleward shift in
the North Pacific storm track of extratropical cyclones is more likely than not. There is low confidence in the
magnitude of regional storm track changes and the impact of such changes on regional surface climate (see WGI
AR5 Section 14.6)
Monsoons. Future increases in precipitation extremes related to the monsoon are very likely in East, South and
Southeast Asia (see WGI AR5 Sections 14.2.1, 14.8.9, 14.8.11, 14.8.12). More than 85% of CMIP5 models show an
increase in mean precipitation in the East Asian summer monsoons, while more than 95% of models project an
increase in heavy precipitation events (see WGI AR5 Section 14.2.2 and Figure 14.4). All models and all scenarios
project an increase in both the mean and extreme precipitation in the Indian summer monsoon (see WGI AR5
Section 14.2.2 and SAS in Figure 14.4). In these two regions, the interannual standard deviation of seasonal mean
precipitation also increases (see WGI AR5 Section 14.2.2).
Oceans. The ocean in subtropical and tropical regions will warm in all RCP scenarios and will show the strongest
warming signal at the surface (see WGI AR5 Section 12.4.7 and Figure 12.12). Negligible change or a decrease in
mean significant wave heights are projected for the trade and monsoon wind regions of the Indian Ocean (see WGI
AR5 Section 13.7.3).
24.4. Observed and Projected Impacts, Vulnerabilities, and Adaptation
The key observed and projected climate change impacts in Asia are summarized based on subsections 24.4.1 to
24.4.6 (Tables 24-1, 24-SM-4 and 24-SM-5).
[INSERT TABLE 24-1 HERE
Table 24-1: Key risks from climate change and the potential for risk reduction through mitigation and adaptation in
Asia.]
24.4.1 Freshwater Resources
24.4.1.1. Sub-Regional Diversity
Freshwater resources are very important in Asia because of the massive population and heavy economic dependence
on agriculture, but water availability is highly uneven and requires assessment on the subregional scale because of
Asia s huge range of climates (Pfister et al., 2009). Adequate water supply is one of the major challenges in many
regions (Vörösmarty et al., 2010), particularly Central Asia. Growing demand for water is driven by soaring
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populations, increasing per-capita domestic use, due to urbanization and thriving economic growth, and increasing
use of irrigation.
24.4.1.2. Observed Impacts
The impact of changes in climate, particularly precipitation, on water resources varies cross Asia (Table 24-SM-4).
There is medium confidence that water scarcity in northern China has been exacerbated by decreasing precipitation,
doubling population, and expanding water withdrawal from 1951 to 2000 (Xu et al., 2010). There is no evidence
that suggests significant changes of groundwater in the Kherlen River Basin in Mongolia over the past half century
(Brutsaert and Sugita, 2008). Apart from water availability, there is medium confidence that climate change also
leads to degradation of water quality in most regions of Asia (Delpla et al., 2009; Park et al., 2010), although this is
also heavily influenced by human activities (Winkel et al., 2011).
Glaciers are important stores of water and any changes have the potential to influence downstream water supply in
the long term (see Section 24.9.2). Glacier mass loss shows a heterogeneous pattern across Asia (Gardner et al.,
2013). Glaciers in the polar section of the Ural Mountains, in the Kodar Mountains of Southeast Siberia, in the
Suntar Khayata and Chersky Ranges of Northeast Siberia, in Georgia and Azerbaijan on the southern flank of the
Greater Caucasus Range, on the Tibetan Plateau (see Box 3-1) and the surrounding areas, and on Puncak Jaya,
Papua, Indonesia lost 9-80% of their total area in different periods within the 1895-2010 time interval (Ananicheva
et al., 2005; Ananicheva et al., 2006; Anisimov et al., 2008; Prentice and Glidden, 2010; Allison, 2011;
Shahgedanova et al., 2012; Yao et al., 2012a; Stokes et al., 2013) due to increased temperature (Casassa et al., 2009;
Shrestha and Aryal, 2011). Changes in the Kamchatka glaciers are driven by both warming and volcanic activity,
with the area of some glaciers decreasing, while others increased because they are covered by ash and clinker
(Anisimov et al., 2008).
24.4.1.3. Projected Impacts
Projected impacts of climate change on future water availability in Asia differ substantially among river basins and
seasons (A1B scenario with 5 GCMs: Immerzeel et al., 2010; A1B with MRI-AGCMS: Nakaegawa et al., 2013) .
There is high confidence that water demand in most Asian countries is increasing because of increases in population,
irrigated agriculture (Lal, 2011) and industry.
Tropical Asia. Future projections (A1B with MRI-AGCMs) suggest a decrease in river runoff in January in the
Chao Phraya River basin in Thailand (Champathong et al., 2013). In a study of the Mahanadi River Basin in India, a
water availability projection (A2, CGCM2) indicated increasing possibility of floods in September but increasing
water scarcity in April (Asokan and Dutta, 2008). In the Ganges, an increase in river runoff could offset the large
increases in water demand due to population growth in a +4C world (ensemble GCMs), due to a projected large
increase in average rainfall, although high uncertainties remains at the seasonal scale (Fung et al., 2011).
Northern and Temperate Asia. Projections (A2 and B2 with the GLASS model) suggest an increase in average
water availability in Russia in the 2070s (Alcamo et al., 2007). In China, a projection (downscaling HadAM3H A2
and B2 scenarios with the PRECIS regional model) suggests that there will be insufficient water for agriculture in
the 2020s and 2040s due to the increases in water demand for non-agricultural uses, although precipitation may
increase in some areas (Xiong et al., 2010). In the late 21st century (MRI-AGCM, A1B), river discharge in northern
Japan is projected to increase in February but decrease in May, due to increased winter precipitation and decreased
spring snowmelt (Sato et al., 2013).
Central and West Asia. Given the already very high level of water stress in many parts of Central Asia, projected
temperature increases and precipitation decreases (SRES scenarios from IPCC AR4 23 models) in the western part
of Kazakhstan, Uzbekistan, and Turkmenistan could exacerbate the problems of water shortage and distribution
(Lioubimtseva and Henebry, 2009). Considering the dependence of Uzbekistan s economy on its irrigated
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agriculture, which consumes more than 90% of the available water resources of the Amu Darya basin, climate
change impacts on river flows would also strongly affect the economy (Schlüter et al., 2010).
24.4.1.4. Vulnerabilities to Key Drivers
It is suggested that freshwater resource will be influenced by changes in rainfall variability, snowmelt or glacier
retreat in the river catchment (Im et al., 2010; Ma et al., 2010; Sato et al., 2012; Yamanaka et al., 2012; Nakaegawa
et al., 2013), and evapotranspiration, which are associated with climate change (Jian et al., 2009). Mismanagement
of water resources has increased tension due to water scarcity in arid areas (Biswas and Seetharam, 2008;
Lioubimtseva and Henebry, 2009; Siegfried et al., 2010; Aarnoudse et al., 2012). Unsustainable consumption of
groundwater for irrigation and other uses is considered to be the main cause of groundwater depletion in the Indian
states of Rajasthan, Punjab and Haryana (Rodell et al., 2009).
24.4.1.5. Adaptation Options
Adaptation of freshwater resources to climate change can be identified as developing adaptive/integrated water
resource management (Sadoff and Muller, 2009; Schlüter et al., 2010) of the trade-offs balancing water availability
against increasing demand, in order to cope with uncertainty and change (Molle and Hoanh, 2009). Examples of the
options include: developing water saving technologies in irrigation (Ngoundo et al., 2007); water infrastructure
development in the Ganges river basin (Bharati et al., 2011); increasing water productivity in the Indus and Ganges
river basins (Cai et al., 2010), Taiwan, China and the Philippines (Barker and Levine, 2012), and Uzbekistan
(Tischbein et al., 2011); changing cropping systems and patterns in West Asia (Thomas, 2008); and water re-use in
China (Yi et al., 2011). During the second half of the 20th century, Asia built many reservoirs and almost tripled its
surface water withdrawals for irrigation. Reservoirs partly mitigate seasonal differences and increase water
availability for irrigation (Biemans et al., 2011). Water management in river basins would benefit from integrated
coordination among countries (Kranz et al., 2010). For example, water management in the Syr Darya river basin
relates to Kyrgyzstan, Tajikistan, Uzbekistan, Turkmenistan, and Kazakhstan (Siegfried et al., 2010), while the
Indus and Ganges-Brahmaputra-Meghna river basins concern Bangladesh, India, Nepal and Pakistan (Uprety and
Salman, 2011).
24.4.2. Terrestrial and Inland Water Systems
24.4.2.1. Sub-Regional Diversity
Boreal forests and grasslands dominate in North Asia, deserts and semi-deserts in Central and West Asia, and alpine
ecosystems on the Tibetan Plateau. Human-dominated landscapes predominate in the other subregions, but the
major natural ecosystems are temperate deciduous and subtropical evergreen forests in East Asia, with boreal forest
in the northeast and grasslands and deserts in the west, while Southeast Asia was largely covered in tropical forests.
South Asia also has tropical forests, with semi-desert in the northwest and alpine ecosystems in the north. Asia
includes several of the world s largest river systems, as well as the world s deepest freshwater lake, Lake Baikal, the
semi-saline Caspian Sea, and the saline Aral Sea.
24.4.2.2. Observed Impacts
Biological changes consistent with climate trends have been reported in the north and at high altitudes, where rising
temperatures have relaxed constraints on plant growth and the distributions of organisms. Few changes have been
reported from tropical lowlands and none linked to climate change with high confidence, although data is
insufficient to distinguish lack of observations from lack of impacts. Impacts on inland water systems have been
difficult to disentangle from natural variability and other human impacts (Bates et al., 2008; Vörösmarty et al.,
2010; Zheng, 2011; see Section 4.3.3.3). For example, the shrinking of the Aral Sea over the last 50 years has
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resulted largely from excessive water extraction from rivers, but was probably exacerbated by decreasing
precipitation and increasing temperature (Lioubimtseva and Henebry, 2009; Kostianoy and Kosarev, 2010).
Phenology and Growth Rates. In humid temperate East Asia, plant observations and satellite measurements of
greenness (Normalized Difference Vegetation Index, NDVI; see 4.3.2.2) show a trend to earlier leafing in spring
since the 1980s, averaging 2 days a decade, although details vary between sites, species and periods (Table 24-SM-6)
(detected with high confidence and attributed to warming with medium confidence.). Earlier spring flowering and
delayed autumn senescence have also been recorded (Table 24-SM-6). Trends in semi-arid temperate regions were
heterogeneous in space and time (Liu et al., 2013a; Yu et al., 2013a, 2013b). Earlier greening has been reported
from boreal forests (Delbart et al., 2008) and from the Hindu-Kush-Himalayan region (Panday and Ghimire, 2012;
Shrestha et al., 2012), but with spatial and temporal heterogeneity. Patterns were also heterogeneous in Central Asia
(Kariyeva et al., 2012). On the Tibetan Plateau, spring growth advanced until the mid-1990s, but the trend
subsequently differs between areas and NDVI datasets (Yu et al., 2010; Yu et al., 2012; Dong et al., 2013; Jin et al.,
2013; Shen et al., 2013; Yu et al., 2013a; Zhang et al., 2013a; Zhang et al., 2013b).
Satellite NDVI for Asia for 1988-2010 shows a general greening trend (i.e. increasing NDVI, a rough proxy for
increasing plant growth), except where water is limiting (Dorigo et al., 2012). Changes at high latitudes (>60oN)
show considerable spatial and temporal variability, despite a consistent warming trend, reflecting water availability
and non-climatic factors (Bi et al., 2013; Jeong et al., 2013). Arctic tundra generally showed increased greening
since 1982, while boreal forests were variable (Goetz et al., 2011; de Jong et al., 2012; Epstein et al., 2012; Xu et al.,
2013). An overall greening trend for 2000-2011 north of the boreal forest correlated with increasing summer warmth
and ice retreat (Dutrieux et al., 2012). In China, trends have varied in space and time, reflecting positive impacts of
warming and negative impacts of increasing drought stress (Peng et al., 2011; Sun et al., 2012; Xu et al., 2012). The
steppe region of northern Kazakhstan showed an overall browning (decreasing NDVI) trend for 1982-2008, linked
to declining precipitation (de Jong et al., 2012). In Central Asia, where NDVI is most sensitive to precipitation
(Gessner et al., 2013), there was a heterogeneous pattern for 1982-2009, with an initial greening trend stalled or
reversed in some areas (Mohammat et al., 2013).
Tree-ring data for 800-1989 for temperate East Asia suggests recent summer temperatures have exceeded those
during past warm periods of similar length, although this difference was not statistically significant (Cook et al.,
2012). Where temperature limits tree growth, growth rates have increased with warming in recent decades (Duan et
al., 2010; Sano et al., 2010; Shishov and Vaganov, 2010; Borgaonkar et al., 2011; Xu et al., 2011; Li et al., 2012;
Chen et al., 2012a, 2012b, 2012c, 2012d; Chen et al., 2013), while where drought limits growth, there have been
increases (Li et al., 2006; Davi et al., 2009; Shao et al., 2010; Yang et al., 2010) or decreases (Li et al., 2007; Davi
et al , 2009; Dulamsuren et al., 2010a, 2011; Kang et al., 2012; Wu et al., 2012; Kharuk et al., 2013; Liu et al.,
2013b) reflecting decreasing or increasing water stress (high confidence in detection, medium confidence in
attribution to climate change). In boreal forest, trends varied between species and locations, despite consistent
warming (Lloyd and Bunn, 2007; Goetz et al., 2011).
Distributions of Species and Biomes. Changes in species distributions consistent with a response to warming have
been widely reported: upwards in elevation (Soja et al., 2007; Bickford et al., 2010; Kharuk et al., 2010a, 2010b,
2010e; Moiseev et al., 2010; Chen et al., 2011; Jump et al., 2012; Telwala et al., 2013; Grigor ev et al., 2013) or
polewards (Tougou et al., 2009; Ogawa-Onishi and Berry, 2013) (high confidence in detection, medium confidence
in attribution to climate change). Changes in the distributions of major vegetation types (biomes) have been reported
from the north and high altitudes, where trees are invading treeless vegetation, and forest understories are being
invaded from adjacent biomes (Soja et al., 2007; Kharuk et al., 2006; Bai et al., 2011; Singh et al., 2012; Wang and
Liu, 2012; Ogawa-Onishi and Berry, 2013). In central Siberia, dark needle conifers (DNC) and birch have invaded
larch-dominated forest over the last three decades (Kharuk et al., 2010c, d; Osawa et al., 2010; Lloyd et al., 2011).
Meanwhile, warming has driven larch stand crown closure and larch invasion into tundra at a rate of 3 10 m/year in
the northern forest-tundra ecotone (Kharuk et al., 2006). Shrub expansion in arctic tundra has also been observed
(Blok et al., 2011; Myers-Smith et al., 2011; see 28.2.3.1.). Soil moisture and light are the main factors governing
the forest-steppe ecotone (Soja et al., 2007; Zeng et al., 2008; Eichler et al., 2011; Kukavskaya et al., 2013) and
Mongolian taiga forests have responded heterogeneously to recent climate changes, but declines in larch growth and
regeneration are more widespread than increases (Dulamsuren et al., 2010a, 2010b).
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Permafrost. Permafrost degradation, including reduced area and increased active layer thickness, has been reported
from parts of Siberia, Central Asia, and the Tibetan Plateau (Romanovsky et al., 2010; Wu and Zhang, 2010; Zhao
et al., 2010; Yang et al., 2013) (high confidence). Most permafrost observatories in Asian Russia show substantial
warming of permafrost during the last 20-30 years (Romanovsky et al., 2008, 2010). Permafrost formed during the
Little Ice Age is thawing at many locations and Late Holocene permafrost has begun to thaw at some undisturbed
locations in northwest Siberia. Permafrost thawing is most noticeable within the discontinuous permafrost zone,
while continuous permafrost is starting to thaw in a few places, so the boundary between continuous and
discontinuous permafrost is moving northwards (Romanovsky et al., 2008, 2010). Thawing permafrost may lead to
increasing emissions of greenhouse gases from decomposition of accumulated organic matter (see Section 4.3.3.4
and 19.6.3.5). In Mongolia, mean annual permafrost temperature at 10-15 m depth increased over the past 10-40
years in the Hovsgol, Hangai and Hentei Mountain regions. Permafrost warming during the past 15 20 years was
greater than during the previous 15-20 years (Sharkhuu et al., 2008; Zhao et al., 2010). In the Kazakh part of the
Tien Shan Mountains, permafrost temperature and active layer thickness have increased since the early 1970s.
Significant permafrost warming also occurred in the eastern Tien Shan Mountains, in the headwaters of the Urumqi
River (Marchenko et al., 2007; Zhao et al., 2010). Monitoring across the Qinghai-Tibet Plateau over recent decades
has also revealed permafrost degradation caused by warming and other impacts. Areas of permafrost are shrinking,
the active layer depth is increasing, the lower altitudinal limit is rising, and the seasonal frost depth is thinning (Li et
al., 2008; Wu and Zhang, 2010; Zhao et al., 2010). In the alpine headwater regions of the Yangtze and Yellow
Rivers, rising temperatures and permafrost degradation have resulted in lower lake levels, drying swamps and
shrinking grasslands (Cheng and Wu, 2007; Wang et al., 2011).
24.4.2.3. Projected Impacts
Phenology and Growth Rates. Trends towards an earlier spring greening and longer growing season are expected to
continue in humid temperate and boreal forest areas, although photoperiod or chilling requirements may reduce
responses to warming in some species (Ge et al., 2013; Hadano et al., 2013; Richardson et al., 2013). Changes in
precipitation will be important for semi-arid and arid ecosystems, as may the direct impacts of atmospheric CO2
concentrations, making responses harder to predict (Liancourt et al., 2012; Poulter et al., 2013). The general
flowering at multi-year intervals in lowland rainforests in Southeast Asia is triggered by irregular droughts (Sakai et
al., 2006), so changes in drought frequency or intensity could have large impacts.
Distributions of Species and Biomes. Climate change is expected to modify the vegetation distribution across the
region (Tao and Zhang, 2010; Wang, 2013), but responses will be slowed by limitations on seed dispersal,
competition from established plants, rates of soil development, and habitat fragmentation (Corlett and Westcott,
2013) (high confidence). Rising CO2 concentrations are expected to favor increased woody vegetation in semi-arid
areas (Higgins and Scheiter, 2012; Donohue et al., 2013; Poulter et al., 2013; Wang, 2013) (medium confidence). In
North Asia, rising temperatures are expected to lead to large changes in the distribution of potential natural
ecosystems (Ni, 2011; Tchebakova et al., 2011; Insarov et al., 2012; Pearson et al., 2013) (high confidence). It is
likely that the boreal forest will expand northward and eastward, and that tundra will decrease, although differences
in models, time periods, and other assumptions have resulted in widely varying projections for the magnitude of this
change (Woodward and Lomas, 2004; Kaplan and New, 2006; Lucht et al., 2006; Golubyatnikov and Denisenko,
2007; Sitch et al., 2008; Korzukhin and Tcelniker, 2010; Tchebakova et al., 2010, 2011; Pearson et al., 2013).
Boreal forest expansion and the continued invasion of the existing larch-dominated forest by dark-needle conifers
could lead to larch reaching the Arctic shore, while the traditional area of larch dominance turns into mixed forest
(Kharuk et al., 2006; Kharuk et al., 2010c). Both the replacement of summer-green larch with evergreen conifers
and expansion of trees and shrubs into tundra decrease albedo, causing regional warming and potentially
accelerating vegetation change (Kharuk et al., 2006; McGuire et al., 2007; Kharuk et al., 2010d; Pearson et al.,
2013). The future direction and rate of change of steppe vegetation are unclear because of uncertain precipitation
trends (Golubyatnikov and Denisenko, 2007; Tchebakova et al., 2010). The role of CO2-fertilization is also
potentially important here (Poulter et al., 2013; see WG1 AR5 Box 6.3).
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In East Asia, subtropical evergreen forests are projected to expand north into the deciduous forest and tropical
forests to expand along China s southern coast (Choi et al., 2011; Wang, 2013), but vegetation change may lag
climate change by decades or centuries (Corlett and Westcott, 2013). On the Tibetan Plateau, projections suggest
that alpine vegetation will be largely replaced by forest and shrubland, with tundra and steppe retreating to the north
(Liang et al., 2012; Wang, 2013). Impacts in Central and West Asia will depend on changes in precipitation. In India,
a dynamic vegetation model (A2 and B2 scenarios) projected changes in more than a third of the forest area by 2100,
mostly from deciduous to evergreen forest in response to increasing rainfall, although fragmentation and other
human pressures are expected to slow these changes (Chaturvedi et al., 2011). By 2100, large areas of tropical and
subtropical lowland Asia are projected to experience combinations of temperature and rainfall outside the current
global range, under a variety of model projections and emission scenarios (Williams et al., 2007; Beaumont et al.,
2010; García-López and Allué, 2013), but the potential impacts of these novel conditions on biodiversity are largely
unknown (Corlett, 2011).
In Southeast Asia, projected climate (A2 and B1 scenarios) and vegetation changes are expected to produce
widespread declines in bat species richness, northward range shifts for many species, and large reductions in the
distributions of most species (Hughes et al., 2012). Projections for various bird species in Asia under a range of
scenarios also suggest major impacts on distributions (Menon et al., 2009; Li et al., 2010; Ko et al., 2012).
Projections for butterflies in Thailand (A2 and B2 scenarios) suggest that species richness within protected areas
will decline c. 30% by 2070-2099 (Klorvuttimontara et al., 2011). Projections for dominant bamboos in the Qinling
Mountains (A2 and B2 scenarios) suggest substantial range reductions by 2100, with potentially adverse
consequences for the giant pandas which eat them (Tuanmu et al., 2012). Projections for snow leopard habitat in the
Himalayas (B1, A1B and A2 scenarios) suggest contraction by up to 30% as forests replace open habitats (Forrest et
al., 2012).
Permafrost. In the Northern Hemisphere, a 20-90% decrease in permafrost area and a 50-300 cm increase in active
layer thickness driven by surface warming is projected for 2100 by different models and scenarios (Schaefer et al.,
2011). It is likely that permafrost degradation in North Asia will spread from the southern and low-altitude margins,
advancing northwards and upwards, but rates of change vary greatly between model projections (Cheng and Wu,
2007; Riseborough et al., 2008; Romanovsky et al., 2008; Anisimov, 2009; Eliseev et al., 2009; Nadyozhina et al.,
2010; Schaefer et al., 2011; Wei et al., 2011). Substantial retreat is also expected on the Qinghai-Tibet Plateau
(Cheng and Wu, 2007). Near-surface permafrost is expected to remain only in Central and Eastern Siberia and parts
of the QTP in the late 21st century.
Inland Waters. Climate change impacts on inland waters will interact with dam construction, pollution, and land-
use changes (Vörösmarty et al., 2010) (see also 24.9.1 and Section 3.3.2). Increases in water temperature will impact
species and temperature-dependent processes (Hamilton, 2010; Dudgeon, 2011; Dudgeon, 2012). Coldwater fish
will be threatened as rising water temperatures make much of their current habitat unsuitable (Yu et al., 2013c).
Climate change is also expected to change flow regimes in running waters and consequently impact habitats and
species that are sensitive to droughts and floods (see Box CC-RF). Habitats that depend on seasonal inundation,
including floodplain grasslands and freshwater swamp forests, will be particularly vulnerable (Maxwell, 2009;
Bezuijen, 2011; Arias et al., 2012). Reduced dry season flows are expected to combine with sea-level rise to
increase saltwater intrusion in deltas (Hamilton, 2010; Dudgeon, 2012), although non-climatic impacts will continue
to dominate in most estuaries (Syvitski et al., 2009). For most Asian lakes, it is difficult to disentangle the impacts
of water pollution, hydro-engineering, and climate change (Battarbee et al., 2012).
24.4.2.4. Vulnerabilities to Key Drivers
Permafrost melting in response to warming is expected to impact ecosystems across large areas (Cheng and Wu,
2007; Tchebakova et al., 2011) (high confidence). The biodiversity of isolated mountains may also be particularly
vulnerable to warming, because many species already have small geographical ranges that will shrink further (La
Sorte and Jetz, 2010; Liu et al., 2010; Chou et al., 2011; Noroozi et al., 2011; Peh et al., 2011; Jump et al., 2012;
Tanaka et al., 2012a; Davydov et al., 2013). Many freshwater habitats are similarly isolated and their restricted-
range species may be equally vulnerable (Dudgeon, 2012). In flatter topography, higher velocities of climate change
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(the speeds that species need to move to maintain constant climate conditions) increase the vulnerabilities of species
that are unable to keep pace, as a result of limited dispersal ability, habitat fragmentation, or other non-climatic
constraints (Corlett and Westcott, 2013). In the tropics, temperature extremes above the present range are a potential
threat to organisms and ecosystems (Corlett, 2011; Jevanandam et al., 2013; Mumby et al., 2013). For much of
interior Asia, increases in drought stress, as a result of declining rainfall and/or rising temperatures, are the key
concern. Because aridity is projected to increase in the northern Mongolian forest belt during the 21st century (Sato
et al., 2007), larch cover will likely be reduced (Dulamsuren et al., 2010a). In the boreal forest region, a longer,
warmer growing season will increase vulnerability to fires, although other human influences may overshadow
climate impacts in accessible areas (Flannigan et al., 2009; Liu et al., 2012; Li et al., 2013; see Section 4.3.3.1.1). If
droughts intensify in lowland Southeast Asia, the synergies between warmth, drought, logging, fragmentation and
fire (Daniau et al., 2012), and tree mortality (Kumagai and Porporato, 2012; Tan et al., 2013), possibly acerbated by
feedbacks between deforestation, smoke aerosols and reduced rainfall (Aragao, 2012; Tosca et al., 2012), could
greatly increase the vulnerability of fragmented forest landscapes (high confidence).
24.4.2.5. Adaptation Options
Suggested strategies for maximizing the adaptive capacity of ecosystems include reducing non-climate impacts,
maximizing landscape connectivity, and protecting refugia where climate change is expected to be less than the
regional mean (Hannah, 2010; Game et al., 2011; Klorvuttimontara et al., 2011; Murthy et al., 2011; Ren et al.,
2011; Shoo et al., 2011; Mandych et al., 2012). Additional options for inland waters include operating dams to
maintain environmental flows for biodiversity, protecting catchments, and preserving river floodplains (Vörösmarty
et al., 2010). Habitat restoration may facilitate species movements across climatic gradients (Klorvuttimontara et al.,
2011; Hughes et al., 2012) and long-distance seed dispersal agents may need protection (McConkey et al., 2012).
Assisted migration of genotypes and species is possible where movements are constrained by poor dispersal, but
risks and benefits need to be considered carefully (Liu et al., 2010; Olden et al., 2010; Tchebakova et al., 2011;
Dudgeon, 2012; Ishizuka and Goto, 2012; Corlett and Westcott, 2013). Ex situ conservation can provide back-up for
populations and species most at risk from climate change (Chen et al., 2009).
24.4.3. Coastal Systems and Low-Lying Areas
24.4.3.1. Sub-Regional Diversity
Asia s coastline includes the global range of shore types. Tropical and subtropical coasts support 45% of the world s
mangrove forest (Giri et al., 2011) and low-lying areas in equatorial Southeast Asia support most of the world s peat
swamp forests, as well as other forested swamp types. Intertidal salt marshes are widespread along temperate and
arctic coasts, while a variety of non-forested wetlands occur inland. Asia supports 40% of the world s coral reef area,
mostly in Southeast Asia, with the world s most diverse reef communities in the coral triangle (Spalding et al.,
2001; Burke et al., 2011). Seagrass beds are widespread and support most of the world s seagrass species (Green
and Short, 2003). Six of the seven species of sea turtle are found in the region and five nest on Asian beaches
(Spotila, 2004). Kelp forests and other seaweed beds are important on temperate coasts (Bolton, 2010; Nagai et al.,
2011). Arctic sea-ice supports a specialized community of mammals and other organisms (see Sections 28.2.3.3. and
28.2.3.4.).
24.4.3.2. Observed Impacts
Most of Asia s non-Arctic coastal ecosystems are under such severe pressure from non-climate impacts that climate
impacts are hard to detect (see Section 5.4.2). Most large deltas in Asia are sinking (as a result of groundwater
withdrawal, floodplain engineering, and trapping of sediments by dams) much faster than global sea-level is rising
(Syvitski et al., 2009). Widespread impacts can be attributed to climate change only for coral reefs, where the
temporal and spatial patterns of bleaching correlate with higher than normal sea surface temperatures (see Section
5.4.2.4 and CC-CR) (very high confidence). Increased water temperatures may also explain declines in large
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seaweed beds in temperate Japan (Nagai et al., 2011; see Section 5.4.2.3). Warming coastal waters have also been
implicated in the northwards expansion of tropical and subtropical macroalgae and toxic phytoplankton (Nagai et al.,
2011), fish (Tian et al., 2012), and tropical corals, including key reef-forming species (Yamano et al., 2011), over
recent decades. The decline of large temperate seaweeds and expansion of tropical species in southwest Japan has
been linked to rising sea surface temperatures (Tanaka et al., 2012b), and these changes have impacted fish
communities (Terazono et al., 2012).
In Arctic Asia, changes in permafrost and the effects of sea-level rise and sea-ice retreat on storm-wave energy have
increased erosion (Are et al., 2008; Razumov, 2010; Handmer et al., 2012). Average erosion rates range from 0.27
m/year (Chukchi Sea) to 0.87 m/year (East Siberian Sea), with a number of segments in the Laptev and East
Siberian Sea experiencing rates greater than 3 m/year (Lantuit et al., 2012).
24.4.3.3. Projected Impacts
Marine biodiversity at temperate latitudes is expected to increase as temperature constraints on warm-water taxa are
relaxed (see Section 6.4.1.1) (high confidence), but biodiversity in tropical regions may fall if, as evidence suggests,
tropical species are already near their thermal maxima (Cheung et al., 2009, 2010; Nguyen et al., 2011) (medium
confidence). Individual fish species are projected to shift their ranges northwards in response to rising sea surface
temperatures (Tseng et al., 2011; Okunishi et al., 2012; Tian et al., 2012). The combined effects of changes in
distribution, abundance and physiology may reduce the body size of marine fishes, particularly in the tropics and
intermediate latitudes (Cheung et al., 2013).
Continuation of current trends in sea-surface temperatures and ocean acidification would result in large declines in
coral-dominated reefs by mid-century (Hoegh-Guldberg, 2011; Burke et al., 2011; see Section 5.4.2.4 and Box CC-
CR) (high confidence). Warming would permit the expansion of coral habitats to the north but acidification is
expected to limit this (Yara et al., 2012). Acidification is also expected to have negative impacts on other calcified
marine organisms (algae, molluscs, larval echinoderms), while impacts on non-calcified species are unclear (Branch
et al., 2013; Kroeker et al., 2013; See CC-OA). On rocky shores, warming and acidification are expected to lead to
range shifts and changes in biodiversity (see Section 5.4.2.2).
Future rates of sea-level rise are expected to exceed those of recent decades (see WGI AR5 Section 13.5.1),
increasing coastal flooding, erosion, and saltwater intrusion into surface and groundwaters. In the absence of other
impacts, coral reefs may grow fast enough to keep up with rising sea-levels (Brown et al., 2011; Villanoy et al.,
2012; see Section 5.4.2.4), but beaches may erode and mangroves, salt marshes, and seagrass beds will decline,
unless they receive sufficient fresh sediment to keep pace or they can move inland (Gilman et al., 2008; Bezuijen,
2011; Kintisch, 2013; see Section 5.3.2.3). Loucks et al. (2010) predict a 96% decline in tiger habitat in
Bangladesh s Sunderbans mangroves with a 28 cm sea-level rise if sedimentation does not increase surface
elevations. Rising winter temperatures are expected to result in poleward expansion of mangrove ecosystems (see
Section 5.4.2.3). Coastal freshwater wetlands may be vulnerable to saltwater intrusion with rising sea-levels, but in
most river deltas local subsidence for non-climatic reasons will be more important (Syvitski et al., 2009).
Current trends in cyclone frequency and intensity are unclear (see 24.3.2 and Box CC-TC), but a combination of
cyclone intensification and sea-level rise could increase coastal flooding (Knutson et al., 2010) and losses of coral
reefs and mangrove forests would exacerbate wave damage (Gedan et al., 2011; Villanoy et al., 2012).
In the Asian Arctic, rates of coastal erosion are expected to increase as a result of interactions between rising sea-
levels and changes in permafrost and the length of the ice-free season (Pavlidis et al., 2007; Lantuit et al., 2012)
(high agreement, medium evidence). The largest changes are expected for coasts composed of loose permafrost
rocks and therefore subject to intensive thermal abrasion. If sea-level rises by 0.5 m over this century, modeling
studies predict that the rate of recession will increase 1.5-2.6-fold for the coasts of the Laptev Sea, East Siberian Sea,
and West Yamal in the Kara Sea, compared to the rate observed in the first years of the 21st century.
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24.4.3.4. Vulnerabilities to Key Drivers
Offshore marine systems are most vulnerable to rising water temperatures and ocean acidification, particularly for
calcifying organisms such as corals. Sea-level rise will be the key issue for many coastal areas, particularly if
combined with changes in cyclone frequency or intensity, or in Arctic Asia, with a lengthening open-water season.
The expected continuing decline in the extent of sea-ice in the arctic may threaten the survival of some ice-
associated organisms (see Section 28.2.2.1), with expanded human activities in previously inaccessible areas an
additional concern (Post et al., 2013).
24.4.3.5. Adaptation Options
The connectivity of marine habitats and dispersal abilities of marine organisms increase the capacity for autonomous
(spontaneous) adaptation in coastal systems (Cheung et al., 2009). Creating marine protected areas where sea
surface temperatures are projected to change least may increase their future resilience (Levy and Ban, 2013). For
coral reefs, potential indicators of future resilience include later projected onset of annual bleaching conditions (van
Hooidonk et al., 2013), past temperature variability, the abundance of heat-tolerant coral species, coral recruitment
rates, connectivity, and macroalgae abundance (McClanahan et al., 2012). Similar strategies may help identify reefs
that are more resilient to acidification (McLeod et al., 2013). Hard coastal defenses, such as sea walls, protect
settlements at the cost of preventing adjustments by mangroves, salt marshes and seagrass beds to rising sea-levels.
Landward buffer zones that provide an opportunity for future inland migration could mitigate this problem (Tobey et
al., 2010). More generally, maintaining or restoring natural shorelines where possible is expected to provide coastal
protection and other benefits (Tobey et al., 2010; Crooks et al., 2011). Projected increases in the navigability of the
Arctic Ocean because of declining sea-ice suggest the need for a revision of environmental regulations in order to
minimize the risk of marine pollution (Smith and Stephenson, 2013).
24.4.4. Food Production Systems and Food Security
It is projected that climate change will affect food security by the middle of the 21st century, with the largest
numbers of food-insecure people located in South Asia (see Chapter 7).
24.4.4.1. Sub-Regional Diversity
AR4 Section 10.4.1.1 pointed out that there will be regional differences within Asia in the impacts of climate change
on food production. Research since then has validated this divergence and new data are available especially for West
and Central Asia (Tables 24-SM-4 and 24-SM-5). In AR4 Section 10.4.1, climate change was projected to lead
mainly to reductions in crop yield. New research shows there will also be gains for specific regions and crops in
given areas. Thus, the current assessment encompasses an enormous variability, depending on the regions and the
crops grown.
24.4.4.2. Observed Impacts
There are very limited data globally for observed impacts of climate change on food production systems (see
Chapter 7) and this is true also for Asia. In Jordan, it was reported that the total production and average yield for
wheat and barley were lowest in 1999 for the period 1996-2006 (Al-Bakri et al., 2010), which could be explained by
the low rainfall during that year, which was 30% of the average (high confidence in detection, low confidence in
attribution). In China, rice yield responses to recent climate change at experimental stations were assessed for the
period 1981 2005 (Zhang et al., 2010). In some places, yields were positively correlated with temperature when
they were also positively related with solar radiation. However, in other places, lower yield with higher temperature
was accompanied by a positive correlation between yield and rainfall (high confidence in detection, high confidence
in attribution). In Japan, where mean air temperature rose by about 1C over the 20th century, effects of recent
warming include phenological changes in many crops, increases in fruit coloring disorders and incidences of chalky
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rice kernels, reductions in yields of wheat, barley, vegetables, flowers, milk and eggs, and alterations in the type of
disease and pest (high confidence in detection, high confidence in attribution) (Sugiura et al., 2012).
24.4.4.3. Projected Impacts
Production. AR4 Section 10.4.1.1 mainly dealt with cereal crops (rice, wheat corn). Since then, impacts of climate
change have been modeled for additional cereal crops and subregions. It is very likely that climate change effects on
crop production in Asia will be variable, negative for specific regions and crops in given areas and positive for other
regions and crops (high agreement, medium evidence). It is also likely that an elevated CO2 concentration in the
atmosphere will be beneficial to most crops (high agreement, medium evidence).
In semi-arid and arid regions of Western Asia, rainfed agriculture is sensitive to climate change both positively and
negatively (Ratnakumar et al., 2011). In the mountainous Swat and Chitral districts of Pakistan (average altitudes
960 and 1500 m above sea level, respectively), there were mixed results as well (Hussain and Mudasser, 2007).
Projected temperature increases of 1.5 and 3°C would lead to wheat yield declines (by 7% and 24% respectively) in
Swat district but to increases (by 14% and 23%) in Chitral district. In India, climate change impacts on sorghum
were analyzed using the InfoCrop-SORGHUM simulation model (Srivastava et al., 2010). A changing climate was
projected to reduce monsoon sorghum grain yield by 2-14% by 2020, with worsening yields by 2050 and 2080. In
the Indo-Gangetic Plains, a large reduction in wheat yields is projected (see below), unless appropriate cultivars and
crop management practices are adopted (Ortiz et al., 2008). A systematic review and meta-analysis of data in 52
original publications projected mean changes in yield by the 2050s across South Asia of 16% for maize and 11% for
sorghum (Knox et al., 2012). No mean change in yield was projected for rice.
In China, modeling studies of the impacts of climate change on crop productivity have had mixed results. Rice is the
most important staple food in Asia. Studies show that climate change will alter productivity in China but not always
negatively. For example, an ensemble-based probabilistic projection shows rice yield in southeastern China would
change on average by 7.5% to 17.5% ( 10.4% to 3.0%), 0.0% to 25.0% ( 26.7% to 2.1%), and 10.0% to 25.0% (
39.2% to 6.4%) during the 2020s, 2050s, and 2080s, respectively, in response to climate change, with (without)
consideration of CO2 fertilization effects, using all 10 combinations of two emission scenarios (A1FI and B1) and
five GCMs (HadCM3, PCM, CGCM2, CSIRO2, and ECHAM4) relative to 1961 1990 levels (Tao and Zhang,
2013a). With rising temperatures, the process of rice development accelerates and reduces the duration for growth.
Wassmann et al. (2009a, 2009b) concluded that, in terms of risks of increasing heat stress, there are parts of Asia
where current temperatures are already approaching critical levels during the susceptible stages of the rice plant.
These include: Pakistan/North India (October), South India (April, August), East India/Bangladesh (March-June),
Myanmar/Thailand/Laos/Cambodia (March-June), Vietnam (April/August), Philippines (April/June), Indonesia
(August) and China (July/August).
There have also been simulation studies for other crops in China. In the Huang-Huai-Hai Plain, China s most
productive wheat growing region, modeling indicated that winter wheat yields would increase on average by 0.2 Mg
ha-1 in 2015-2045 and by 0.8 Mg ha-1 in 2070-2099, due to warmer nighttime temperatures and higher precipitation,
under A2 and B2 scenarios using the HadCM3 model (Thomson et al., 2006). In the North China Plain, an
ensemble-based probabilistic projection projected that maize yield will change by -9.7 to -9.1%, -19.0 to -15.7%,
and -25.5% to -24.7%, during 2020s, 2050s, and 2080s as a percentage of 1961 1990 yields (Tao et al., 2009). In
contrast, winter wheat yields could increase with high probability in future due to climate change (Tao and Zhang,
2013b).
It should be noted that crop physiology simulation models such as those discussed above may overstate the impact
of CO2 fertilization. Free atmosphere carbon exchange (FACE) experiments show that measurable CO2 fertilization
effects are typically less than modeled results (see Section 7.3). Extreme weather events are also expected to
negatively affect agricultural crop production (IPCC, 2012). For example, extreme temperatures could lower yields
of rice (Mohammed and Tarpley, 2009; Tian et al., 2010). With higher precipitation, flooding could also lead to
lower crop production (see SREX Chapter 4).
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Farming Systems and Crop Areas. Since the release of the AR4 (see WGII AR4 Section 10.4.1.2), more
information is available on the impacts of climate change on farming systems and cropping areas in more countries
in Asia and especially in Central Asia. Recent studies validate the likely northward shifts of crop production with
current croplands under threat from the impacts of climate change (medium agreement, medium evidence). Cooler
regions are likely to benefit as warmer temperatures increase arable areas (high agreement, medium evidence).
Central Asia is expected to become warmer in the coming decades and increasingly arid, especially in the western
parts of Turkmenistan, Uzbekistan, and Kazakhstan (Lioubimtseva and Henebry, 2009). Some parts of the region
could be winners (cereal production in northern and eastern Kazakhstan could benefit from the longer growing
season, warmer winters and a slight increase in winter precipitation), while others could be losers (particularly
western Turkmenistan and Uzbekistan, where frequent droughts could negatively affect cotton production, increase
already extremely high water demands for irrigation, and exacerbate the already existing water crisis and human-
induced desertification). In India, the Indo-Gangetic Plains are under threat of a significant reduction in wheat yields
(Ortiz et al., 2008). This area produces 90 million tons of wheat grain annually (about 14-15% of global wheat
production). Climate projections based on a doubling of CO2 using a CCM3 model downscaled to a 30 arc-second
resolution as part of the WorldClim data set showed that there will be a 51% decrease in the most favorable and high
yielding area due to heat stress. About 200 million people (using the current population) in this area whose food
intake relies on crop harvests would experience adverse impacts.
Rice growing areas are also expected to shift with climate change throughout Asia. In Japan, increasing irrigation
water temperature (1.6 2.0°C) could lead to a northward shift of the isochrones of safe transplanting dates for rice
seedlings (Ohta and Kimura, 2007). As a result, rice cultivation period will be prolonged by approximately 25 30
days. This will allow greater flexibility in the cropping season than at present, resulting in a reduction in the
frequency of cool-summer damage in the northern districts. Sea-level rise threatens coastal and deltaic rice
production areas in Asia, such as those in Bangladesh and the Mekong River Delta (Wassmann et al., 2009b). For
example, about 7% of Vietnam s agriculture land may be submerged due to sea-level rise (Dasgupta et al., 2009). In
Myanmar, salt water intrusion due to sea-level rise could also decrease rice yield (Wassmann et al., 2009b).
Fisheries and Aquaculture. Asia dominates both capture fisheries and aquaculture (FAO, 2010). More than half of
the global marine fish catch in 2008 was in the West Pacific and Indian Ocean, and the lower Mekong River basin
supports the largest freshwater capture fishery in the world (Dudgeon, 2011). Fish production is also a vital
component of regional livelihoods, with 85.5% of the world s fishers (28 m) and fish farmers (10 m) in Asia in 2008.
Many more people fish part-time. Fish catches in the Asian Arctic are relatively small, but important for local
cultures and regional food security (Zeller et al., 2011).
Inland fisheries will continue to be vulnerable to a wide range of on-going threats, including overfishing, habitat loss,
water abstraction, drainage of wetlands, pollution, and dam construction, making the impacts of climate change hard
to detect (see also 24.9.1). Most concerns have centered on rising water temperatures and the potential impacts of
climate change on flow regimes, which in turn are expected to affect the reproduction of many fish species (Allison
et al., 2009; Barange and Perry, 2009; Bezuijen, 2011; Dudgeon, 2011; see also Section 24.4.2.3). Sea-level rise is
expected to impact both capture fisheries and aquaculture production in river deltas (De Silva and Soto, 2009). For
marine capture fisheries, Cheung et al. (2009, 2010) used a dynamic bioclimate envelope model to project the
distributions of 1066 species of exploited marine fish and invertebrates for 2005-2055, based on the SRES A1B
scenario and a stable-2000 C02 scenario. This analysis suggests that climate change may lead to a massive
redistribution of fisheries catch potential, with large increases in high-latitude regions, including Asian Russia, and
large declines in the tropics, particularly Indonesia. Other studies have made generally similar predictions, with
climate change impacts on marine productivity expected to be large and negative in the tropics, in part because of
the vulnerability of coral reefs to both warming and ocean acidification (see also Section 24.4.3.3), and large and
positive in arctic and subarctic regions, because of sea-ice retreat and poleward species shifts (Sumaila et al., 2011;
Blanchard et al., 2012; Doney et al., 2012) (high confidence). Predictions of a reduction in the average maximum
body weight of marine fishes by 14-24% by 2050 under a high-emission scenario are an additional threat to fisheries
(Cheung et al., 2013).
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Future Food Supply and Demand. AR4 Section 10.4.1.4 was largely based on global models that included Asia.
There are now a few quantitative studies in Asia and its individual countries. In general, these show that the risk of
hunger, food insecurity and loss of livelihood due to climate change will likely increase in some regions (medium
agreement, low evidence).
Rice is a key staple crop in Asia and 90% or more of the world s rice production is from Asia. An Asia-wide study
revealed that climate change scenarios (using 18 GCMs for A1B, 14 GCMs for A2, and 17 GCMs for B1) would
reduce rice yield over a large portion of the continent (Masutomi et al., 2009). The most vulnerable regions were
western Japan, eastern China, the southern part of the Indochina peninsula, and the northern part of South Asia. In
Russia, climate change may also lead to food production shortfall , which was defined as an event in which the
annual potential (i.e. climate-related) production of the most important crops in an administrative region in a specific
year falls below 50% of its climate-normal (1961 1990) average (Alcamo et al., 2007). The study shows that the
frequency of shortfalls in five or more of the main crop growing regions in the same year is around 2 years/decade
under normal climate but could climb to 5 6 years/decade in the 2070s, depending on the scenario and climate
model (using the GLASS and WaterGAP-2 models and ECHAM and HadCM3 under the A2 and B2 scenarios). The
increasing shortfalls were attributed to severe droughts. The study estimated that the number of people living in
regions that may experience one or more shortfalls each decade may grow to 82 139 million in the 2070s.
Increasing frequency of extreme climate events will pose an increasing threat to the security of Russia s food system.
In contrast, climate change may provide a windfall for wheat farmers in parts of Pakistan. Warming temperatures
would make it possible to grow at least two crops (wheat and maize) a year in mountainous areas (Hussain and
Mudasser, 2007). In the northern mountainous areas, wheat yield was projected to increase by 50% under SRES A2
and by 40% under the B2 scenario, whereas in the sub-mountainous, semi-arid and arid areas, it is likely to decrease,
by the 2080s (Iqbal et al., 2009).
24.4.4.4. Vulnerabilities to Key Drivers
Food production and food security are most vulnerable to rising air temperatures (Wassmann et al., 2009a, 2009b).
Warmer temperatures could depress yields of major crops such as rice. However, warmer temperatures could also
make some areas more favorable for food production (Lioubimtseva and Henebry, 2009). Increasing CO2
concentration in the atmosphere could lead to higher crop yields (Tao and Zhang, 2013a). Sea-level rise will be a
key issue for many coastal areas as rich agricultural lands may be submerged and taken out of production
(Wassmann et al., 2009b).
24.4.4.5. Adaptation Options
Since AR4, there have been additional studies of recommended and potential adaptation strategies and practices in
Asia (Table 24-SM-7) and there is new information for West and Central Asia. There are also many more crop-
specific and country-specific adaptation options available. Farmers have been adapting to climate risks for
generations. Indigenous and local adaptation strategies have been documented for Southeast Asia (Peras et al., 2008;
Lasco et al., 2010; Lasco et al., 2011) and could be used as a basis for future climate change adaptation. Crop
breeding for high temperature condition is a promising option for climate change adaptation in Asia. For example, in
the North China Plain simulation studies show that using high-temperature sensitive varieties, maize yield in the
2050s could increase on average by 1.0 6.0%, 9.9 15.2%, and 4.1 5.6%, by adopting adaptation options of early
planting, xing variety growing duration, and late planting, respectively (Tao and Zhang, 2010). In contrast, no
adaptation will result in yield declines of 13.2 19.1%.
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24.4.5. Human Settlements, Industry, and Infrastructure
24.4.5.1. Sub-Regional Diversity
Around one in every five urban dwellers in Asia lives in large urban agglomerations and almost 50% of these live in
small cities (UN, 2012). North and Central Asia are the most urbanized areas, with over 63% of the population
living in urban areas, with the exception of Kyrgyzstan and Tajikistan (UN-Habitat, 2010; UN ESCAP, 2011). South
and Southwest Asia are the least urbanized subregions, with only a third of their populations living in urban areas.
However, these regions have the highest urban population growth rates within Asia at an average of 2.4% per year
during 2005-2010 (UN ESCAP, 2011). By the middle of this century, Asia s urban population will increase by 1.4
billion and will account for over 50% of the global population (UN, 2012).
24.4.5.2. Observed Impacts
Asia experienced the highest number of weather- and climate-related disasters in the world during the period 2000-
2008 and suffered huge economic losses, accounting for the second highest proportion (27.5%) of the total global
economic loss (IPCC, 2012). Flood mortality risk is heavily concentrated in Asia. Severe floods in Mumbai in 2005
have been attributed to both climatic factors and non-climatic factors. Strengthened capacities to address the
mortality risk associated with major weather-related hazards, such as floods, have resulted in a downward trend in
mortality risk relative to population size, as in East Asia, where it is now a third of its 1980 level (UNISDR, 2011).
24.4.5.3. Projected Impacts
A large proportion of Asia s population lives in low elevation coastal zones that are particularly at risk from climate
change hazards, including sea-level rise, storm surges and typhoons (see Sections 5.3.2.1 and 8.2.2.5, Box CC-TC).
Depending on the region, half to two-thirds of Asia s cities with 1 million or more inhabitants are exposed to one or
multiple hazards, with floods and cyclones the most important (UN, 2012).
Floodplains and Coastal Areas. Three of the world s five most populated cities (Tokyo, Delhi and Shanghai) are
located in areas with high risk of floods (UN, 2012). Flood risk and associated human and material losses are
heavily concentrated in India, Bangladesh, and China. At the same time, the East Asia region in particular is
experiencing increasing water shortages, negatively affecting its socioeconomic, agricultural, and environmental
conditions, which is attributed to lack of rains and high evapotranspiration, as well as over-exploitation of water
resources (IPCC, 2012). Large parts of South, East and Southeast Asia are exposed to a high degree of cumulative
climate-related risk (UN-Habitat, 2011). Asia has more than 90% of the global population exposed to tropical
cyclones (IPCC, 2012); see Box CC-TC). Damage due to storm surge is sensitive to change in the magnitude of
tropical cyclones. By the 2070s, the top Asian cities in terms of population exposure (including all environmental
and socioeconomic factors) to coastal flooding are expected to be Kolkata, Mumbai, Dhaka, Guangzhou, Ho Chi
Minh City, Shanghai, Bangkok, Rangoon, and Hai Phong (Hanson et al., 2011). The top Asian cities in terms of
assets exposed are expected to be Guangdong, Kolkata, Shanghai, Mumbai, Tianjin, Tokyo, Hong Kong, and
Bangkok. Asia includes 15 of the global top 20 cities for projected population exposure and 13 of the top 20 for
asset exposure.
Other Issues in Human Settlements. Asia has a large and rapidly expanding proportion of the global urban
exposure and vulnerability related to climate change hazards (see SREX Section 4.4.3). In line with the rapid urban
growth and sprawl in many parts of Asia, the periurban interface between urban and rural areas deserves particular
attention when considering climate change vulnerability (see also Section 18.4.1). Garschagen et al. (2011) find, for
example, that periurban agriculturalists in the Vietnamese Mekong Delta are facing a multiple burden since they are
often exposed to overlapping risks resulting from (a) socio-economic transformations, such as land title insecurity
and price pressures; (b) local biophysical degradation, as periurban areas serve as sinks for urban wastes; and (c)
climate change impacts, as they do not benefit from the inner-urban disaster risk management measures.
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Nevertheless, the periurban interface is still underemphasized in studies on impacts, vulnerability and adaptation in
Asia.
Groundwater sources, which are affordable means of high-quality water supply in cities of developing countries, are
threatened due to over-withdrawals. Aquifer levels have fallen by 20-50 m in cities such as Bangkok, Manila and
Tianjin and between 10-20 m in many other cities (UNESCO, 2012). The drop in groundwater levels often results in
land subsidence, which can enhance hazard exposure due to coastal inundation and sea-level rise, especially in
settlements near the coast, and deterioration of groundwater quality. Cities susceptible to human-induced subsidence
(mainly, developing country cities in deltaic regions with rapidly growing populations) could see significant
increases in exposure (Nicholls et al., 2008). Settlements on unstable slopes or landslide-prone areas face increased
prospects of rainfall-induced landslides (IPCC, 2012).
Industry and Infrastructure. The impacts of climate change on industry include both direct impacts on industrial
production and indirect impacts on industrial enterprises due to the implementation of mitigation activities (Li,
2008). The impact of climate change on infrastructure deterioration cannot be ignored, but can be addressed by
changes to design procedures, including increases in cover thickness, improved quality of concrete, and coatings and
barriers (Stewart et al., 2012). Climate change and extreme events may have a greater impact on large and medium-
sized construction projects (Kim et al., 2007).
Estimates suggest that by upgrading the drainage system in Mumbai, losses associated with a 1-in-100 year flood
event today could be reduced by as much as 70%, and through extending insurance to 100% penetration, the indirect
effects of flooding could be almost halved, speeding recovery significantly (Ranger et al., 2011). On the east coast
of India, clusters of districts with poor infrastructure and demographic development are also the regions of
maximum vulnerability. Hence, extreme events are expected to be more catastrophic in nature for the people living
in these districts. Moreover, the lower the district is in terms of the infrastructure index and its growth, the more
vulnerable it is to the potential damage from extreme events and hence people living in these regions are prone to be
highly vulnerable (Patnaik and Narayanan, 2009). In 2008, the embankments on the Kosi River (a tributary of the
Ganges) failed, displacing over sixty thousand people in Nepal and three and a half million in India. Transport and
power systems were disrupted across large areas. However, the embankment failure was not caused by an extreme
event but represented a failure of interlinked physical and institutional infrastructure systems in an area
characterized by complex social, political, and environmental relationships (Moench, 2010).
24.4.5.4. Vulnerabilities to Key Drivers
Disruption of basic services such as water supply, sanitation, energy provision, and transportation systems have
implications for local economies and strip populations of their assets and livelihoods , in some cases leading to
mass migration (UN-Habitat, 2010). Such impacts are not expected to be evenly spread among regions and cities,
across sectors of the economy, or among socioeconomic groups. They tend to reinforce existing inequalities and
disrupt the social fabric of cities and exacerbate poverty.
24.4.5.5. Adaptation Options
An ADB and UN report estimates that about two-thirds of the $8 trillion needed for infrastructure investment in
Asia and the Pacific between 2010 and 2020 will be in the form of new infrastructure, which creates tremendous
opportunities to design, finance and manage more sustainable infrastructure (ADB et al., 2012).) Adaptation
measures that offer a no regrets solution are proposed for developing countries, where basic urban infrastructure
is often absent (e.g. appropriate drainage infrastructure), leaving room for actions that both increase immediate well-
being and reduce vulnerability to future climate change (Hallegatte and Corfee-Morlot, 2011). The role of urban
planning and urban planners in adaptation to climate change impacts has been emphasized (Fuchs et al., 2011; IPCC,
2012; Tyler and Moench, 2012). The focus on solely adapting through physical infrastructure in urban areas requires
complementary adapting planning, management, governance and institutional arrangements to be able to deal with
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the uncertainty and the unprecedented challenges implied by climate change (Revi, 2008; Birkmann et al., 2010;
Garschagen and Kraas, 2011).
24.4.6. Human Health, Security, Livelihoods, and Poverty
24.4.6.1. Sub-Regional Diversity
Although rapidly urbanizing, Asia is still predominantly an agrarian society, with 57.28% of its total population
living in rural areas, of which 81.02% are dependent on agriculture for their livelihoods (FAOSTAT, 2011). Rural
poverty is higher than urban poverty, reflecting the heavy dependence on natural resources that are directly
influenced by changes in weather and climate (Haggblade et al., 2010; IFAD, 2010). Rural poverty is expected to
remain more prevalent than urban poverty for decades to come (Ravallion et al., 2007). However, climate change
will also affect urbanizing Asia, where the urban poor will be impacted indirectly, as evident from the food price
rises in the Middle East and other areas in 2007-2008. Certain categories of urban dwellers, such as urban wage
labor households, are particularly vulnerable (Hertel et al., 2010).
Agriculture has been identified as a key driver of economic growth in Asia (World Bank, 2007). Although economic
growth was impressive in recent decades, there are still gaps in development compared to the rest of the world
(World Bank, 2011). Southeast Asia is the third poorest performing region after Sub-Saharan Africa and Southern
Asia in terms of the Human Development Indicators (UN, 2009). Impacts on human security in Asia will primarily
manifest through impacts on water resources, agriculture, coastal areas, resource-dependent livelihoods, and urban
settlements and infrastructure, with implications for human health and well-being. Regional disparities on account of
socioeconomic context and geographical characteristics largely define the differential vulnerabilities and impacts
within countries in Asia (Thomas, 2008; Sivakumar and Stefanski, 2011).
24.4.6.2. Observed Impacts
Floods and Health. Epidemics have been reported after floods and storms (Bagchi, 2007) as a result of decreased
drinking water quality (Harris et al., 2008; Hashizume et al., 2008; Solberg, 2010; Kazama et al., 2012), mosquito
proliferation (Pawar et al., 2008), and exposure to rodent-borne pathogens (Kawaguchi et al., 2008; Zhou et al.,
2011) and the intermediate snail hosts of Schistosoma (Wu et al., 2008). Contaminated urban flood waters have
caused exposure to pathogens and toxic compounds, for example in India and Pakistan (Sohan et al., 2008; Warraich
et al., 2011). Mental disorders and posttraumatic stress syndrome have also been observed in disaster prone areas
(Udomratn, 2008) and, in India, have been linked to age and gender (Telles et al., 2009). See also Chapter 11.4.2.
for flood-attributable deaths.
Heat and Health. The effects of heat on mortality and morbidity have been studied in many countries, with a focus
on the elderly and people with cardiovascular and respiratory disorders (Kan et al., 2007; Guo et al., 2009; Huang et
al., 2010). Associations between high temperatures and mortality have been shown for populations in India and
Thailand (McMichael et al., 2008) and in several cities in East Asia (Kim et al., 2006; Chung et al., 2009). Several
studies have analyzed the health effects of air pollution in combination with increased temperatures (Lee et al.,
2007; Qian et al., 2010; Wong et al., 2010; Yi et al., 2010). Intense heat waves have been shown to affect outdoor
workers in South and East Asia (Nag et al., 2007; Hyatt et al., 2010).
Drought and Health. Dust storms in Southwest, Central and East Asia result in increased hospital admissions and
worsen asthmatic conditions, as well as causing skin and eye irritations (Griffin, 2007; Hashizume et al., 2010; Kan
et al., 2012). Droughts may also lead to wildfires and smoke exposure, with increased morbidity and mortality, as
observed in Southeast Asia (Johnston et al., 2012). Drought can also disrupt food security, increasing malnutrition
(Kumar et al., 2005) and thus susceptibility to infectious diseases.
Water-borne Diseases. Many pathogens and parasites multiply faster at higher temperatures. Temperature increases
have been correlated with increased incidence of diarrheal diseases in East Asia (Huang et al., 2008; Zhang et al.,
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2008; Onozuka et al., 2010). Other studies from South and East Asia have shown an association between diarrheal
outbreaks and a combination of higher temperatures and heavy rainfall (Hashizume et al., 2007; Majra and Gur,
2009; Chou et al., 2010). Increasing coastal water temperatures correlated with outbreaks of systemic Vibrio
vulnificus infection in Israel (Paz et al., 2007) and South Korea (Kim and Jang, 2010). Cholera outbreaks in coastal
populations in South Asia have been associated with increased water temperatures and algal blooms (Huq et al.,
2005). The ENSO cycle and Indian Ocean Dipole have been associated with cholera epidemics in Bangladesh
(Pascual, 2000; Rodó et al., 2002; Hashizume et al., 2011).
Vector-borne Diseases. Increasing temperatures affect vector-borne pathogens during the extrinsic incubation
period and shorten vector life-cycles, facilitating larger vector populations and enhanced disease transmission, whilst
the vector s ability to acquire and maintain a pathogen tails off (Paaijmans et al., 2012). Dengue outbreaks in South
and Southeast Asia are correlated with temperature and rainfall with varying time lags (Su, 2008; Hii et al., 2009;
Hsieh and Chen, 2009; Shang et al., 2010; Sriprom et al., 2010; Hashizume et al., 2012). Outbreaks of vaccine-
preventable Japanese encephalitis have been linked to rainfall in studies from the Himalayan region (Partridge et al.,
2007; Bhattachan et al., 2009), and to rainfall and temperature in South and East Asia (Bi et al., 2007; Murty et al.,
2010). Malaria prevalence is often influenced by non-climate variability factors, but studies from India and Nepal
have found correlations with rainfall (Devi and Jauhari, 2006; Dev and Dash, 2007; Dahal, 2008; Laneri et al., 2010).
Temperature was linked to distribution and seasonality of malaria mosquitoes in Saudi Arabia (Kheir et al., 2010).
The re-emergence of malaria in central China has been attributed to rainfall and increases in temperature close to
water bodies (Zhou et al., 2010). In China, temperature, precipitation, and the virus-carrying index among rodents
have been found to correlate with the prevalence of hemorrhagic fever with renal syndrome (Guan et al., 2009).
Livelihoods and Poverty. An estimated 51% of total income in rural Asia comes from non-farm sources (Haggblade
et al., 2009, 2010), mostly local non-farm business and employment. The contribution of remittances to rural income
has grown steadily (Estudillo and Otsuka, 2010). Significant improvements have been made in poverty eradication
over the past decade (World Bank, 2008), with rapid reductions in poverty in East Asia, followed by South Asia
(IFAD, 2010). A significant part of the reduction has come from population shifts, rapid growth in agriculture, and
urban contributions (Janvry and Sadoulet, 2010). Climate change negatively impacts livelihoods (see Table 24-SM-
4) and these impacts are directly related to natural resources affected by changes in weather and climate. Factors that
have made agriculture less sustainable in the past include input non-responsive yields, soil erosion, natural
calamities, and water and land quality related problems (Dev, 2011). These have predisposed rural livelihoods to
climate change vulnerability. Livelihoods are impacted by droughts (Harshita, 2013; Selvaraju et al., 2006), floods
(Nuorteva et al., 2010; Dun, 2011; Nguyen, 2007; Keskinen et al., 2010) and typhoons (Huigen and Jens, 2006;
Gaillard et al., 2007; Uy et al., 2011). Drought disproportionately impacts small farmers, agricultural laborers, and
small businessmen (Selvaraju et al., 2006), who also have least access to rural safety net mechanisms, including
financial services (IFAD, 2010), despite recent developments in microfinance services in parts of Asia. Past floods
have exposed conditions such as lack of access to alternative livelihoods, difficulty in maintaining existing
livelihoods, and household debts leading to migration in the Mekong region (Dun, 2011). Similar impacts of
repeated floods leading to perpetual vulnerability were found in the Tonle Sap Lake area of Cambodia (Nuorteva et
al., 2010; Keskinen et al., 2010). Typhoon impacts are mainly through damage to the livelihood assets of coastal
populations in the Philippines and the level of ownership of livelihood assets has been a major determinant of
vulnerability (Uy et al., 2011).
24.4.6.3. Projected Impacts
Health Effects. An emerging public health concern in Asia is increasing mortality and morbidity due to heat waves.
An ageing population will increase the number of people at risk, especially those with cardiovascular and respiratory
disorders. Urban heat island effects have increased (Tan et al., 2010), although local adaptation of the built
environment and urban planning will determine the impacts on public health. Heat stress disorders among workers
and consequent productivity losses have also been reported (Lin et al., 2009; Langkulsen et al., 2010). The
relationship between temperature and mortality is often U-shaped (Guo et al., 2009), with increased mortality also
during cold events, particularly in rural environments, even if temperatures do not fall below 0°C (Hashizume et al.,
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2009). However, some studies in developing areas suggest that factors other than climate can be important, so
warming may not decrease cold-related deaths much in these regions (Honda and Ono, 2009).
Climate change will affect the local transmission of many climate-sensitive diseases. Increases in heavy rain and
temperature are projected to increase the risk of diarrheal diseases in, for example, China (Zhang et al., 2008).
However, the impact of climate change on malaria risk will differ between areas, as projected for West and South
Asia (Husain and Chaudhary, 2008; Garg et al., 2009; Majra and Gur, 2009), while a study suggested that the
impact of socioeconomic development will be larger than that of climate change (Béguin et al., 2011).
Climate change is also expected to affect the spatiotemporal distribution of dengue fever in the region, although the
level of evidence differs across geographical locations (Banu et al., 2011). Some studies have developed climate
change-disease prevalence models, for example one for schistosomiasis in China shows an increased northern
distribution of the disease with climate change (Zhou et al., 2008; Kan et al., 2012). Impacts of climate change on
fish production (Qiu et al., 2010) are being studied, along with impacts on chemical pathways in the marine
environment and consequent impacts on food safety (Tirado et al., 2010), including seafood safety (Marques et al.,
2010).
Livelihood and Poverty. Floods, droughts and changes in seasonal rainfall patterns are expected to negatively
impact crop yields, food security and livelihoods in vulnerable areas (Dawe et al., 2008; Kelkar et al., 2008;
Douglas, 2009). Rural poverty in parts of Asia could be exacerbated (Skoufias et al., 2011) due to impacts on the
rice crop and increases in food prices and the cost of living (Hertel et al., 2010; Rosegrant, 2011). The poverty
impacts of climate change will be heterogeneous among countries and social groups (see Table 24-SM-5). In a low
crop productivity scenario, producers in food exporting countries, such as Indonesia, the Philippines and Thailand,
would benefit from global food price rises and reduce poverty, while countries such as Bangladesh would
experience a net increase in poverty of 15% by 2030 (Hertel et al., 2010). These impacts will also differ within food
exporting countries, with disproportionate negative impacts on farm laborers and the urban poor. Skoufias et al.
(2011) project significant negative impacts of a rainfall shortfall on the welfare of rice farmers in Indonesia,
compared to a delay in rainfall onset. These impacts may lead to global mass migration and related conflicts (Laczko
and Aghazarm, 2009; Barnett and Webber, 2010; Warner, 2010; World Bank, 2010). In North Asia, climate-driven
changes in tundra and forest-tundra biomes may influence indigenous peoples who depend on nomadic tundra
pastoralism, fishing and hunting (Kumpula et al., 2011).
24.4.6.4. Vulnerabilities to Key Drivers
Key vulnerabilities vary widely within the region. Climate change can exacerbate current socio-economic and
political disparities and add to the vulnerability of Southeast Asia and Central Asia to security threats that may be
transnational in nature (Jasparro and Taylor, 2008; Lioubimtseva and Henebry, 2009). Apart from detrimental
impacts of extreme events, vulnerability of livelihoods in agrarian communities also arises from geographic settings,
demographic trends, socio-economic factors, access to resources and markets, unsustainable water consumption,
farming practices and lack of adaptive capacity (Mulligan et al., 2011; Acosta-Michlik and Espaldon, 2008; Allison
et al., 2009; Knox et al., 2011; Lioubimtseva and Henebry, 2009; Byg and Salick, 2009; Salick and Ross, 2009;
Salick et al., 2009; Xu et al., 2009; UN, 2009). Urban wage laborers were found to be more vulnerable to cost of
living related poverty impacts of climate change than those who directly depend on agriculture for their livelihoods
(Hertel et al., 2010). In Indonesia, drought-associated fires increase vulnerability of agriculture, forestry and human
settlements, particularly in peatland areas (Murdiyarso and Lebel, 2007). Human health is also a major area of focus
for Asia (Munslow and O'Dempsey, 2010), where the magnitude and type of health effects from climate change
depend on differences in socioeconomic and demographic factors, health systems, the natural and built environment,
land use changes, and migration, in relation to local resilience and adaptive capacity. The role of institutions is also
critical, particularly in influencing vulnerabilities arising from gender (Ahmed and Fajber, 2009), caste and ethnic
differences (Jones and Boyd, 2011), and securing climate-sensitive livelihoods in rural areas (Agrawal and Perrin,
2008).
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24.4.6.5. Adaptation Options
Disaster preparedness on a local community level could include a combination of indigenous coping strategies,
early-warning systems, and adaptive measures (Paul and Routray, 2010). Heat warning systems have been
successful in preventing deaths among risk groups in Shanghai (Tan et al., 2007). New work practices to avoid heat
stress among outdoor workers, in Japan and the UAE have also been successful (Morioka et al., 2006; Joubert et al.,
2011). Early warning models have been developed for haze exposure from wildfires, in for example Thailand (Kim
Oanh and Leelasakultum, 2011), and are being tested in infectious disease prevention and vector control programs,
as for malaria in Bhutan (Wangdi et al., 2010) and Iran (Haghdoost et al., 2008), or are being developed, as for
dengue fever region-wide (Wilder-Smith et al., 2012).
Some adaptation practices provide unexpected livelihood benefits, as with the introduction of traditional flood
mitigation measures in China which could positively impact local livelihoods, leading to reductions in both the
physical and economic vulnerabilities of communities (Xu et al., 2009). A greater role of local communities in
decision making is also proposed (Alauddin and Quiggin, 2008) and in prioritization and adoption of adaptation
options (Prabhakar et al., 2010; Prabhakar and Srinivasan, 2011). Defining adequate community property rights,
reducing income disparity, exploring market-based and off-farm livelihood options, moving from production-based
approaches to productivity and efficiency decision-making based approaches, and promoting integrated decision-
making approaches, have also been suggested (Merrey et al., 2005; Brouwer et al., 2007; Paul et al., 2009; Niino,
2011; Stucki and Smith, 2011).
Climate resilient livelihoods can be fostered through the creation of bundles of capitals (natural, physical, human,
financial and social capital) and poverty eradication (Table 24-SM-8). Greater emphasis on agricultural growth has
been suggested as an effective means of reducing rural poverty (Janvry and Sadoulet, 2010; Rosegrant, 2011).
Bundled approaches are known to facilitate better adaptation than individual adaptation options (Acosta-Michlik and
Espaldon, 2008; Fleischer et al., 2011). Community-based approaches have been suggested to identify adaptation
options that address poverty and livelihoods, as these techniques help capture information at the grassroots (Huq and
Reid, 2007; van Aalst et al., 2008), and help integration of disaster risk reduction, development, and climate change
adaptation (Heltberg et al., 2010), connect local communities and outsiders (van Aalst et al., 2008), address the
location-specific nature of adaptation (Iwasaki et al., 2009; Rosegrant, 2011), help facilitate community learning
processes (Bass and Ramasamy, 2008), and help design location-specific solutions (Ensor and Berger, 2009). Some
groups can become more vulnerable to change after being locked into specialized livelihood patterns, as with fish
farmers in India (Coulthard, 2008).
Livelihood diversification, including livelihood assets and skills, has been suggested as an important adaptation
option for buffering climate change impacts on certain kinds of livelihoods (Selvaraju et al., 2006; Nguyen, 2007;
Agrawal and Perrin, 2008; IFAD, 2011; Keskinen et al., 2010; Uy et al., 2011). The diversification should occur
across assets, including productive assets, consumption strategies and employment opportunities (Agrawal and
Perrin, 2008). Ecosystem-based adaptation has been suggested to secure livelihoods in the face of climate change
(Jones et al., 2012), integrating the use of biodiversity and ecosystem services into an overall strategy to help people
adapt (IUCN, 2009). Among financial means, low-risk liquidity options such as microfinance programs and risk
transfer products can help lift the rural poor from poverty and accumulate assets (Barrett et al., 2007; Jarvis et al.,
2011).
24.4.7 Valuation of Impacts and Adaptation
Economic valuation in Asia generally covers impacts and vulnerabilities of disperse sectors such as food production,
water resources and human health (Aydinalp and Cresser, 2008; Kelkar et al., 2008; Lioubimtseva and Henebry,
2009; Su et al., 2009; Srivastava et al., 2010). Multi-sector evaluation that unpacks the relationships between and
across sectors, particularly in a context of resource scarcity and competition, is very limited. Information is scarce
especially for North, Central and West Asia.
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Generally, annual losses from drought are expected to increase based on various projection under diverse scenarios,
but such losses are expected to be reduced if adaptation measures are implemented (ADB, 2009; Sutton et al., 2013).
It is also stressed that there are great uncertainties associated with the economic aspects of climate change. In China,
the total loss due to drought projected in 2030 is expected to range from $1.1-1.7 billion for regions in northeast
China and about $0.9 billion for regions in north China (CWF et al., 2009), with adaptation measures having the
potential to avert half of the losses. In India, the estimated countrywide agricultural loss in 2030 of over $7 billion
that will severely affect the income of 10% of the population could be reduced by 80% if cost-effective climate
resilience measures are implemented (CWF et al., 2009).
In Indonesia, the Philippines, Thailand and Vietnam, under the A2 scenario, the PAGE2002 integrated assessment
model projects a mean loss of 2.2% of gross domestic product (GDP) by 2100 on an annual basis, if only the market
impact (mainly related to agriculture and coastal zones) is considered (ADB, 2009). This is well above the world s
projected mean GDP loss of 0.6% each year by 2100 due to market impact alone. In addition, the mean cost for the
four countries could reach 5.7% of the GDP if non-market impacts related to health and ecosystems are included and
6.7% of the GDP if catastrophic risks are also taken into account. The cost of adaptation for agriculture and coastal
zones is expected to be about $5 billion/year by 2020 on average. Adaptation that is complemented with global
mitigation measures is expected to be more effective in reducing the impacts of climate change (IPCC, 2007; ADB,
2009; UNFCCC, 2009; MNRE, 2010; Begum et al., 2011).
24.5. Adaptation and Managing Risks
24.5.1. Conservation of Natural Resources
Natural resources are already under severe pressure from land-use change and other impacts in much of Asia.
Deforestation in Southeast Asia has received most attention (Sodhi et al., 2010; Miettinen et al., 2011a), but
ecosystem degradation, with the resulting loss of natural goods and services, is also a major problem in other
ecosystems. Land-use change is also a major source of regional greenhouse gas emissions, particularly in Southeast
Asia (see WGI AR5 Section 6.3.2.2 and Table 6.3). Projected climate change is expected to intensify these pressures
in many areas (see Sections 24.4.2.3 and 24.4.3.3), most clearly for coral reefs, where increases in sea surface
temperature and ocean acidification are a threat to all reefs in the region and the millions of people who depend on
them (see Section 5.4.2.4 and Boxes CC-CR and CC-OA). Adaptation has so far focused on minimizing non-climate
pressures on natural resources and restoring connectivity to allow movements of genes and species between
fragmented populations (see Section 24.4.2.5). Authors have also suggested a need to identify and protect areas that
will be subject to the least damaging climate change ( climate refugia ) and to identify additions to the protected
area network that will allow for expected range shifts, for example by extending protection to higher altitudes or
latitudes. Beyond the intrinsic value of wild species and ecosystems, ecosystem-based approaches to adaptation aim
to use the resilience of natural systems to buffer human systems against climate change, with potential social,
economic and cultural co-benefits for local communities (see Box CC-EA).
24.5.2. Flood Risks and Coastal Inundation
Many coasts in Asia are exposed to threats from floods and coastal inundation (see also 24.4.5.3). Responding to a
large number of climate change impact studies for each Asian country over the past decade (e.g. Karim and Mimura,
2008; Pal and Al-Tabbaa, 2009), various downscaled tools to support, formulate and implement climate change
adaptation policy for local governments are under development. One of the major tools is vulnerability assessment
and policy option identification with Geographical Information Systems (GIS). These tools are expected to be of
assistance in assessing city-specific adaptation options by examining estimated impacts and identified vulnerability
for some coastal cities and areas in Asian countries (e.g. Brouwer et al., 2007; Taylor, 2011; Storch and Downs,
2011). These tools and systems sometimes take the form of integration of top down approaches and bottom-up
(community-based) approaches (see Section 14.5). Whereas top-down approaches give scientific knowledge to local
actors, community-based approaches are built on existing knowledge and expertise to strengthen coping and
adaptive capacity by involving local actors (van Aalst et al., 2008). Community-based approaches may have a
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limitation in that they place greater responsibility on the shoulders of local people without necessarily increasing
their capacity proportionately (Allen, 2006). As the nature of adaptive capacity varies depending on the formulation
of social capital and institutional context in the local community, it is essential for the approaches to be based on an
understanding of local community structures (Adger, 2003).
24.5.3. Economic Growth and Equitable Development
Climate change challenges fundamental elements in social and economic policy goals such as prosperity, growth,
equity and sustainable development (Mearns and Norton, 2010). Economic, social, and environmental equity is an
enduring challenge in many parts of Asia. Generally, the level of wealth (typically GDP) has been used as a measure
of human vulnerability of a country but this approach has serious limitations (Mattoo and Subramanian, 2012;
Dellink et al., 2009). In many cases, social capital, an indicator of equity in income distribution within countries, is a
more important factor in vulnerability and resilience than GDP per capita (Lioubimtseva and Henebry, 2009; Islam
et al., 2006). Furthermore, political and institutional instabilities can undermine the influence of economic
development (Lioubimtseva and Henebry, 2009). Poor and vulnerable countries are at greater risk of inequity and
loss of livelihoods from the impacts of climate extremes as their options for coping with such events are limited.
Many factors contribute to this limitation, including poverty, illiteracy, weak institutions and infrastructures, poor
access to resources, information and technology, poor health care, and low investment and management capabilities.
The overexploitation of land resources including forests, increases in population, desertification and land
degradation pose additional threats (UNDP, 2006). This is particularly true for developing countries in Asia with a
high level of natural resource dependency. Provision of adequate resources based on the burden sharing and the
equity principle will serve to strengthen appropriate adaptation policies and measures in such countries (Su et al.,
2009).
24.5.4. Mainstreaming and Institutional Barriers
Mainstreaming climate change adaptation into sustainable development policies offers a potential opportunity for
good practice to build resilience and reduce vulnerability, depending on effective, equitable and legitimate actions to
overcome barriers and limits to adaptation (ADB, 2005; Lim et al., 2005; Lioubimtseva and Henebry, 2009). The
level of adaptation mainstreaming is most advanced in the context of official development assistance, where donor
agencies and international financial institutions have made significant steps towards taking climate change
adaptation into account in their loan and grant making processes (Gigli and Agrawala, 2007; Klein et al., 2007).
While some practical experiences of adaptation in Asia at the regional, national and local level are emerging, there
can be barriers that impede or limit adaptation. These include challenges related to competing national priorities,
awareness and capacity, financial resources for adaptation implementation, institutional barriers, biophysical limits
to ecosystem adaptation, and social and cultural factors (Lasco et al., 2009; Moser and Ekstrom, 2010; Lasco et al.,
2012). Issues with resource availability might not only result from climate change, but also from weak governance
mechanisms and the breakdown of policy and regulatory structures, especially with common-pool resources (Moser
and Ekstrom, 2010). Furthermore, the impact of climate change depends on the inherent vulnerability of the socio-
ecological systems in a region as much as on the magnitude of the change (Evans, 2010). Recent studies linking
climate-related resource scarcities and conflict call for enhanced regional cooperation (Gautam, 2012).
24.5.5. Role of Higher Education in Adaptation and Risk Management
To enhance the development of young professionals in the field of climate change adaptation, the topic could be
included in higher education, especially in formal education programs. Shaw et al. (2011) mentioned that higher
education in adaptation and disaster risk reduction in the Asia-Pacific region can be done through environment
disaster linkage, focus on hydro-meteorological disasters, and emphasizing synergy issues between adaptation and
risk reduction. Similar issues are also highlighted by other authors (Chhokar, 2010; Niu et al., 2010; Nomura and
Abe, 2010; Ryan et al., 2010). Higher education should be done through lectures and course work, field studies,
internships, and establishing education-research link by exposing the students to field realities. In this regard,
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guiding principles could include: an inclusive curriculum, focus on basic theory, field orientation, multidisciplinary
courses and practical skill enhancement. Bilateral or multilateral practical research programs on adaptation and risk
management by the graduate students and young faculty members would expose them to the real field problems.
24.6. Adaptation and Mitigation Interactions
Integrated mitigation and adaptation responses focus on either land-use changes or technology development and use.
Changes in land use, such as agroforestry, may provide both mitigation and adaptation benefits (Verchot et al.,
2007), or otherwise, depending on how they are implemented. Agroforestry practices provide carbon storage and
may decrease soil erosion, increase resilience against floods, landslides and drought, increase soil organic matter,
reduce the financial impact of crop failure, as well as have biodiversity benefits over other forms of agriculture, as
shown, for example, in Indonesia (Clough et al., 2011). Integrated approaches are often needed when developing
mitigation-adaptation synergies, as seen in waste-to-compost projects in Bangladesh (Ayers and Huq, 2009). Other
adaptation measures that increase biomass and/or soil carbon content, such as ecosystem protection and reforestation,
will also contribute to climate mitigation by carbon sequestration. However, exotic monocultures may fix more
carbon than native mixtures while supporting less biodiversity and contributing less to ecological services, calling
for compromises that favor biodiversity-rich carbon storage (Diaz et al., 2009). The potential for both adaptation and
mitigation through forest restoration is greatest in the tropics (Sasaki et al., 2011). At higher latitudes (>45oN),
reforestation can have a net warming influence by reducing surface albedo (Anderson-Teixeira et al., 2012).
Expansion of biofuel crops on abandoned and marginal agricultural lands could potentially make a large
contribution to mitigation of carbon emissions from fossil fuels, but could also have large negative consequences for
both carbon and biodiversity if it results directly or indirectly in the conversion of carbon-rich ecosystems to
cropland (Fargione et al., 2010; Qin et al., 2011). Mechanisms, such as REDD+, that put an economic price on land-
use emissions, could reduce the risks of such negative consequences (Thomson et al., 2010), but the incentive
structures need to be worked out very carefully (Busch et al., 2012).
Forests and their management are also often emphasized for providing resilient livelihoods and reducing poverty
(Chhatre and Agrawal, 2009; Noordwijk, 2010; Persha et al., 2010; Larson, 2011). Securing rights to resources is
essential for greater livelihood benefits for poor indigenous and traditional people (Macchi et al., 2008) and the need
for REDD+ schemes to respect and promote community forest tenure rights has been emphasized (Angelsen, 2009).
It has been suggested that indigenous people can provide a bridge between biodiversity protection and climate
change adaptation (Salick and Ross, 2009): a point that appears to be missing in the current discourse on ecosystem-
based adaptation. There are arguments against REDD+ supporting poverty reduction due to its inability to promote
productive use of forests, which may keep communities in perpetual poverty (Campbell, 2009), but there is a
contrasting view that REDD+ can work in forests managed for timber production (Putz et al., 2012; Guariguata et
al., 2008), especially through reduced impact logging (Guariguata et al., 2008) and other approaches such as
assuring the legality of forest products, certifying responsible management, and devolving control over forests to
empowered local communities (Putz et al., 2012).
On rivers and coasts, the use of hard defenses (e.g. sea-walls, channelization, bunds, dams) to protect agriculture and
human settlements from flooding may have negative consequences for both natural ecosystems and carbon
sequestration by preventing natural adjustments to changing conditions (see 24.4.3.5). Conversely, setting aside
landward buffer zones along coasts and rivers would be positive for both. The very high carbon sequestration
potential of the organic-rich soils in mangroves (Donato et al., 2011) and peat swamp forests (Page et al., 2011)
provides opportunities for combining adaptation with mitigation through restoration of degraded areas.
Mitigation measures can also result in public health benefits (Bogner et al., 2008; Haines et al., 2009). For example,
sustainable cities with fewer fossil-fuel driven vehicles (mitigation) and more trees and greenery (carbon storage and
adaptation to the urban heat island effect) would have a number of co-benefits, including public health a promising
strategy for triple win interventions (Romero-Lankao et al., 2011). Other examples include efforts to decarbonize
electricity production in India and China that are projected to decrease mortality due to reduced PM5 and PM2.5
particulates (Markandya et al., 2009); policies to increase public transportation, promote walking and cycling, and
reduce private cars that will increase air quality and decrease the health burden, particularly in urban environments
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as projected in India (Woodcock et al., 2009) ; and abandoning the use of biomass fuel or coal for indoor cooking
and heating to improve indoor air quality and respiratory and cardiac health among, in particular, women and
children in India and China (Wilkinson et al., 2009). Conversely, actions to reduce current environmental-public
health issues may often have beneficial mitigation effects, like traffic emissions reduction programs in China (Wu et
al., 2011) and India (Reynolds and Kandikar, 2008).
24.7. Intra-regional and Inter-regional Issues
24.7.1. Trans-boundary Pollution
Many Asian countries and regions face long-distance and trans-boundary air pollution problems. In eastern China,
Japan and the Korean Peninsula, these include dust storms that originate in the arid and semi-arid regions upwind,
with impacts on climate, human health and ecosystems (Huang et al., 2013). The susceptibility of the land surface to
wind erosion is strongly influenced by vegetation cover, which is in turn sensitive to climate change and other
human impacts. In the humid tropics of Southeast Asia, in contrast, the major trans-boundary pollution issue
involves smoke aerosols from burning of biomass and peatlands, mostly during clearance for agriculture (Miettinen
et al., 2011b; Gautam et al., 2013). Apart from the large impact on human health, these aerosols may be having a
significant effect on rainfall in equatorial regions, leading to the possibility of climate-feedbacks, with fires reducing
rainfall and promoting further fires (Tosca et al., 2012). Pollutants of industrial origin are also a huge problem in
many parts of the region, with well-documented impacts on human health (see Section 24.4.6) and the climate (see
WGI AR5 Chapters 7 and 8).
24.7.2. Trade and Economy
The ASEAN Free Trade Agreement (AFTA) and the Indonesia Japan Economic Partnership Agreement (IJEPA)
have positively impacted the Indonesian economy and reduced water pollution, but increased CO2 emissions by
0.46% compared to the business-as-usual situation, mainly due to large emission increases in the transportation
sector (Gumilang et al., 2011). Full liberalization of tariffs and GDP growth concentrated in China and India has led
to transport emissions growing much faster than the value of trade, due to a shift towards distant trading partners
(Cristea et al., 2013). China's high economic growth and flourishing domestic and international trade has resulted in
increased consumption and pollution of water resources (Guan and Hubacek, 2007). Japanese imports from the
ASEAN region are negatively correlated with per capita carbon emissions (Atici, 2012) due to strict regulations in
Japan that prevent import from polluting sectors. Export-led growth is central to the economic progress and well-
being of Southeast Asian countries. Generally, as exports rise, carbon emissions tend to rise. International trading
systems that help address the challenge of climate change need further investigation.
24.7.3. Migration and Population Displacement
Floods and droughts are predominant causes for internal displacement (Internal Displacement Monitoring Center,
2011). In 2010 alone, 38.3 million people were internally displaced; 85% because of hydrological hazards and 77%
in Asia. Floods are increasingly playing a role in migration in the Mekong Delta (Warner, 2010). Often some
migrants return to the vulnerable areas (Piguet, 2008) giving rise to ownership, rights of use, and other issues
(Kolmannskog, 2008). Increasing migration has led to increasing migration-induced remittances contributing to
Asian economies, but has had negligible effect on the poverty rate (Vargas-Silva et al., 2009). In Bangladesh,
migrant workers live and work under poor conditions, such as crowded shelters, inadequate sanitation, conflict and
competition with the local population, and exploitation (Penning-Rowsell et al., 2011). Forced migration can result
from adaptation options such as construction of dams, but the negative outcomes could be allayed by putting proper
safeguards in place (Penning-Rowsell et al., 2011). Managed retreat of coastal communities is a suggested option to
address projected sea-level rise (Alexander et al., 2012). A favorable approach to deal with migration is within a
development framework and through adaptation strategies (Penning-Rowsell et al., 2011; ADB, 2012).
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24.8. Research and Data Gaps
Studies of observed climate changes and their impacts are still inadequate for many areas, particularly in North,
Central and West Asia (Table 24-2). Improved projections for precipitation, and thus water supply, are most
urgently needed. Another priority is developing water management strategies for adaptation to changes in demand
and supply. More research is also needed on the health effects of changes in water quality and quantity.
Understanding of climate change impacts on ecosystems and biodiversity in Asia is currently limited by the poor
quality and low accessibility of biodiversity information (UNEP, 2012). National biodiversity inventories are
incomplete and few sites have the baseline information needed to identify changes. For the tropics, major research
gaps include the temperature dependence of carbon fixation by tropical trees, the thermal tolerances and acclimation
capacities of both plants and animals, and the direct impacts of rising CO2 (Corlett, 2011; Zuidema et al., 2013).
Rising CO2 is also expected to be important in cool-arid ecosystems, where lack of experimental studies currently
limits our ability to make predictions (Poulter et al., 2013). Boreal forest dynamics will be influenced by complex
interactions between rising temperatures and CO2, permafrost thawing, forest fires, and insect outbreaks (Osawa et
al., 2010; Zhang et al., 2011), and understanding this complexity will require enhanced monitoring of biodiversity
and species ranges, improved modeling, and greater knowledge of species biology (Meleshko and Semenov, 2008).
Rice is the most studied crop but there are still significant uncertainties in model accuracy, CO2-fertilization effects,
and regional differences (Masutomi et al., 2009; Zhang et al., 2010; Shuang-He et al., 2011). For other crops, there
is even greater uncertainty. Studies are also needed of the health effects of interactions between heat and air
pollution in urban and rural environments. More generally, research is needed on impacts, vulnerability and
adaptation in urban settlements, especially cities with populations under 500,000, which share half the region s
urban population. Greater understanding is required of the linkages between local livelihoods, ecosystem functions,
and land resources for creating a positive impact on livelihoods in areas with greater dependence on natural
resources (Paul et al., 2009). Increasing regional collaboration in scientific research and policy making has been
suggested for reducing climate change impacts on water, biodiversity and livelihoods in the Himalayan region (Xu
et al., 2009) and could be considered elsewhere. The literature suggests that work must begin now on building
understanding of the impacts of climate change and moving forward with the most cost-effective adaptation
measures (ADB, 2007; Cai et al., 2008; Mathy and Guivarch, 2010; Stage, 2010). For devising mitigation policies,
the key information needed is again the most cost-effective measures (Nguyen, 2007; Cai et al., 2008; Mathy and
Guivarch, 2010).
[INSERT TABLE 24-2 HERE
Table 24-2: The amount of information supporting conclusions regarding observed and projected impacts in Asia.
24.9. Case Studies
24.9.1. Transboundary Adaptation Planning and Management Lower Mekong River Basin
The Lower Mekong River Basin (LMB) covers an area of approximately 606,000 sq. km across the countries of
Thailand, Laos, Cambodia and Vietnam. More than 60 million people are heavily reliant on natural resources, in
particular agriculture and fisheries, for their well-being (MRC, 2009; Dugan et al., 2010; Figure 24-SM-2). Thailand
and Vietnam produced 51% of the world s rice exports in 2008, mostly in the LMB (Mainuddin et al., 2011).
Observations of climate change over the past 30-50 years in the LMB include: an increase in temperature, an
increase in rainfall in the wet season and decreases in the dry season, intensified flood and drought events, and sea-
level rise (ICEM, 2010; IRG, 2010). Agricultural output has been noticeably impacted by intensified floods and
droughts which caused almost 90% of rice production losses in Cambodia during 1996-2001 (Brooks and Adger,
2003; MRC, 2009).Vietnam and Cambodia are two of the countries most vulnerable to climate impacts on fisheries
(Allison et al., 2009; Halls, 2009).
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Existing studies about future climate impacts in the Mekong Basin broadly share a set of common themes (MRC,
2009; Murphy and Sampson, 2013): increased temperature and annual precipitation; increased depth and duration of
flood in the Mekong Delta and Cambodia floodplain; prolonged agricultural drought in the south and the east of the
basin; and sea-level rise and salinity intrusion in the Mekong delta. Hydropower dams along the Mekong River and
its tributaries will also have severe impacts on fish productivity and biodiversity, by blocking critical fish migration
routes, altering the habitat of non-migratory fish species, and reducing nutrient flows downstream (Costanza et al.,
2011; Baran and Guerin, 2012; Ziv et al., 2012). Climate impacts, though less severe than the impact of dams, will
exacerbate these changes (Wyatt and Baird, 2007; Grumbine et al., 2012; Orr et al., 2012; Räsänen et al., 2012; Ziv
et al., 2012).
National climate change adaptation plans have been formulated in all four LMB countries, but transboundary
adaptation planning across the LMB does not exist to date. Effective future transboundary adaptation planning and
management will benefit from: a shared climate projection across the LMB for transboundary adaptation planning;
improved coordination among adaptation stakeholders and sharing of best practices across countries; mainstreaming
climate change adaptation into national and sub-national development plans with proper translation from national
adaptation strategies into local action plans; integration of transboundary policy recommendations into national
climate change plans and policies; integration of adaptation strategies on a landscape scale between ministries and
different levels of government within a country (MRC, 2009; Lian and Bhullar, 2011; Lebel et al., 2012; Kranz et
al., 2010)
A study of the state-of-adaptation practice in the LMB showed that only 11% (45 of 417) of climate-change related
projects in the LMB were on-the-ground adaptation efforts driven by climate risks (Ding, 2012; Neo, 2012; Schaffer
and Ding, 2012). Common features of successful projects include: robust initial gap assessment, engagement of
local stakeholders, and a participatory process throughout (Brown, 2012; Khim, 2012; Mondal, 2012; Panyakul,
2012; Roth and Grunbuhel, 2012). A multi-stakeholder Regional Adaptation Action Network has been proposed
with the intent of scaling up and improving mainstreaming of adaptation through tangible actions following the
theory and successful examples of the Global Action Networks (GANs) (Waddell, 2005; Waddell and Khagram,
2007; WCD, 2000; GAVI, 2011; Schaffer and Ding, 2012) .
24.9.2. Glaciers of Central Asia
In the late 20th century, central Asian glaciers occupied 31,628 km2 (Dolgushin and Osipova). All recent basin-scale
studies document multidecadal area loss (Figure 24-3); where multiple surveys are available, most show accelerating
loss. The rate of glacier area change varies (Table 24-SM-9). Rates between 0.05%/yr and 0.76%/yr have been
reported in the Altai (Surazakov et al., 2007; Shahgedanova et al., 2010; Yao et al., 2012b) and Tien Shan
(Lettenmaier et al., 2009; Sorg et al., 2012), and between 0.13%/yr and 0.30%/yr in the Pamir (Konovalov and
Desinov, 2007; Aizen, 2011a, 2011b, 2011c; Yao et al., 2012b). These ranges reflect varying sub-regional
distributions of glacier size (smaller glaciers shrink faster) and debris cover (which retards shrinkage), but also
varying proportions of ice at high altitudes, where as yet warming has produced little increase in melt (Narama et
al., 2010). Most studies also document mean-annual (e.g. Glazyrin and Tadzhibaeva, 2011, for 1961-1990) and
summertime (e.g. Shahgedanova et al., 2010) warming, with slight cooling in the central and eastern Pamir (Aizen,
2011b). Precipitation increases have been observed more often than decreases (e.g. Braun et al., 2009; Glazyrin and
Tadzhibaeva, 2011).
[INSERT FIGURE 24-3 HERE
Figure 24-3: Losses of glacier area in the Altai-Sayan, Pamir and Tien Shan. Remote sensing data analysis from
1960s (Corona) through 2008 (Landsat, ASTER and Alos Prism).]
Aizen et al. (2007) calculated 21st-century losses of 43% of the volume of Tien Shan glaciers for an 8oC
temperature increase accompanied by a 24% precipitation increase, but probable complete disappearance of glaciers
if precipitation decreased by 16%; a more moderate 2oC increase led to little loss, but only if accompanied by a 24%
precipitation increase. Drawing on CMIP5 simulations, (Radiæ et al., 2013) simulated losses by 2100 of between
25% and 90% of 2006 ice volume (including Tibet but excluding the Altai and Sayan; range of all single-model
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simulations); the 14-GCM model mean losses are 55% for RCP4.5 and 75% for RCP8.5. Similarly, Marzeion et al.
(2012) found 21st-century volume losses of 50% for RCP2.6, about 57% for both RCP4.5 and RCP6.0, and 67% for
RCP8.5.
The glaciers have therefore been a diminishing store of water, and the diminution is projected to continue.
Paradoxically, this implies more meltwater, possibly explaining limited observations of increased runoff (Sorg et al.,
2012), but also an eventual decrease of meltwater yield (see Section 3.4.4). More immediately, it entails a hazard
due to the formation of moraine-dammed glacial lakes (Bolch et al., 2011).
Frequently Asked Questions
FAQ 24.1: What will the projected impact of future climate change be on freshwater resources in Asia?
[to be placed in Section 24.4.1]
Asia is a huge and diverse region, so both climate change and the impact on freshwater resources will vary greatly
depending on location. But throughout the region, adequate water resources are particularly important because of the
massive population and heavy dependence of the agricultural sector on precipitation, river runoff and groundwater.
Overall, there is low confidence in the projections of specifically how climate change will impact future precipitation
on a subregional scale, and thus in projections of how climate change might impact the availability of water
resources. However, water scarcity is expected to be a big challenge in many Asian regions because of increasing
water demand from population growth and consumption per capita with higher standards of living. Shrinkage of
glaciers in central Asia is expected to increase due to climate warming, which will influence downstream river
runoff in these regions. Better water management strategies could help ease water scarcity. Examples include
developing water saving technologies in irrigation, building reservoirs, increasing water productivity, changing
cropping systems and water reuse.
FAQ 24.2: How will climate change affect food production and food security in Asia?
[to be placed in Section 24.4.4]
Climate change impacts on temperature and precipitation will affect food production and food security in various
ways in specific areas throughout this diverse region. Climate change will have a generally negative impact on crop
production Asia, but with diverse possible outcomes [medium confidence]. For example most simulation models
show that higher temperatures will lead to lower rice yields as a result of a shorter growing period. But some studies
indicate that increased atmospheric CO2 that leads to those higher temperatures could enhance photosynthesis and
increase rice yields. This uncertainty on the overall effects of climate change and CO2 fertilization is generally true
for other important food crops such as wheat, sorghum, barley, and maize among others.
Yields of some crops will increase in some areas (e.g. cereal production in north and east Kazakhstan) and
decrease in others (e.g. wheat in the Indo-Gangetic Plain of South Asia). In Russia, climate change may lead to a
food production shortfall, defined as an event in which the annual potential production of the most important crops
falls 50% or more below it's normal average. Sea-level rise is projected to decrease total arable areas and thus food
supply in many parts of Asia. A diverse mix of potential adaptation strategies, such as crop breeding, changing crop
varieties, adjusting planting time, water management, diversification of crops and a host of indigenous practices will
all be applicable within local contexts.
FAQ 24.3: Who is most at risk from climate change in Asia? [to be placed in Section 24.4.6]
People living in low-lying coastal zones and flood plains are probably most at risk from climate change impacts in
Asia. Half of Asia's urban population lives in these areas. Compounding the risk for coastal communities, Asia has
more than 90% of the global population exposed to tropical cyclones. The impact of such storms, even if their
frequency or severity remains the same, is magnified for low lying and coastal zone communities because of rising
sea level [medium confidence]. Vulnerability of many island populations is also increasing due to climate change
impacts. Settlements on unstable slopes or landslide prone-areas, common in some parts of Asia, face increased
likelihood of rainfall-induced landslides.
Asia is predominantly agrarian, with 58% of its population living in rural areas, of which 81% are dependent on
agriculture for their livelihoods. Rural poverty in parts of Asia could be exacerbated due to negative impacts from
climate change on rice production, and a general increase in food prices and the cost of living [high confidence].
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Climate change will have widespread and diverse health impacts. More frequent and intense heatwaves will
increase mortality and morbidity in vulnerable groups in urban areas [high confidence]. The transmission of
infectious disease, such as cholera epidemics in coastal Bangladesh, and schistosomiasis in inland lakes in China,
and diarrheal outbreaks in rural children will be affected due to warmer air and water temperatures and altered rain
patterns and water flows [medium confidence]. Outbreaks of vaccine-preventable Japanese encephalitis in the
Himalayan region and malaria in India and Nepal have been linked to rainfall. Changes in the geographical
distribution of vector-borne diseases, as vector species that carry and transmit diseases migrate to more hospitable
environments, will occur [medium confidence]. These effects will be most noted close to the edges of the current
habitats of these species.
Cross-Chapter Box
Box CC-TC. Building Long-Term Resilience from Tropical Cyclone Disasters
[Yoshiki Saito (Japan), Kathleen McInnes (Australia)]
Tropical cyclones (also referred to as hurricanes and typhoons in some regions or strength) cause powerful winds,
torrential rains, high waves and storm surge, all of which can have major impacts on society and ecosystems.
Bangladesh and India account for 86% of mortality from tropical cyclones (Murray et al., 2012), which is mainly
due to the rarest and most severe storm categories (i.e. Categories 3, 4, and 5 on the Saffir-Simpson scale).
About 90 tropical cyclones occur globally each year (Seneviratne et al., 2012) although interannual variability is
large. Changes in observing techniques particularly after the introduction of satellites in the late 1970s, confounds
the assessment of trends in tropical cyclone frequencies and intensities. Therefore, IPCC (2012) Special Report on
Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation (SREX) concluded
that there is low confidence that any observed long-term (i.e. 40 years or more) increases in tropical cyclone activity
are robust, after accounting for past changes in observing capability (Seneviratne et al., 2012; Chapter 2). There is
also low confidence in the detection and attribution of century scale trends in tropical cyclones. Future changes to
tropical cyclones arising from climate change are likely to vary by region. This is because there is medium
confidence that for certain regions, shorter-term forcing by natural and anthropogenic aerosols has had a measurable
effect on tropical cyclones. Tropical cyclone frequency is likely to decrease or remain unchanged over the 21st
century, while intensity (i.e. maximum wind speed and rainfall rates) is likely to increase (AR5 WG1 Ch 14.6).
Regionally specific projections have lower confidence (see AR5 WG1 Box 14.2).
Longer-term impacts from tropical cyclones include salinisation of coastal soils and water supplies and subsequent
food and water security issues from the associated storm surge and waves (Terry and Chui, 2012). However,
preparation for extreme tropical cyclone events through improved governance and development to reduce their
impacts provides an avenue for building resilience to longer-term changes associated with climate change.
Densely populated Asian deltas are particularly vulnerable to tropical cyclones due to their large population density
in expanding urban areas (Nicholls et al., 2007). Extreme cyclones in Asia since 1970 caused over 0.5 million
fatalities (Murray et al., 2012) e.g., cyclones Bhola in 1970, Gorky in 1991, Thelma in 1998, Gujarat in 1998, Orissa
in 1999, Sidr in 2007, and Nargis in 2008. Tropical cyclone Nargis hit Myanmar on 2 May 2008 and caused over
138,000 fatalities. Several-meter high storm surges widely flooded densely populated coastal areas of the Irrawaddy
Delta and surrounding areas (Revenga et al., 2003; Brakenridge et al., 2013). The flooded areas were captured by a
NASA MODIS image on 5 May 2008 (see Figure TC-1).
[INSERT FIGURE TC-1 HERE
Figure TC-1: The intersection of inland and storm surge flooding. Red shows May 5, 2008 MODIS mapping of the
tropical cyclone Nargis storm surge along the Irrawaddy Delta and to the east, Myanmar. The blue areas to the north
were flooded by the river in prior years. Source: Brakenridge et al., 2013.]
Murray et al. (2012) compared the response to cyclone Sidr in Bangladesh in 2007 and Nargis in Myanmar in 2008
and demonstrated how disaster risk reduction methods could be successfully applied to climate change adaptation.
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Sidr, despite being of similar strength to Nargis, caused far fewer fatalities (3,400 compared to over 138000) and
this was attributed to advancement in preparedness and response in Bangladesh through experience in previous
cyclones such as Bhola and Gorky. The responses included the construction of multistoried cyclone shelters,
improvement of forecasting and warning capacity, establishing a coastal volunteer network, and coastal reforestation
of mangroves. The strategies of disaster risk management for tropical cyclones in coastal areas, that create protective
measures, anticipate and plan for extreme events, increase the resilience of potentially exposed communities. The
integration of activities relating to education, training, and awareness-raising into relevant ongoing processes and
practices is important for the long-term success of disaster risk reduction and management (Murray et al., 2012).
Birkmann and Teichman (2010) caution that while the combination of risk reduction and climate change adaptation
strategies may be desirable, different spatial and temporal scales, norm systems, and knowledge types and sources
between the two goals can confound their effective combination.
Box CC-TC References
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Terry, J., T. F. M. Chui, 2012: Evaluating the fate of freshwater lenses on atoll islands after eustatic sea level rise and cyclone driven inundation:
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Table 24-1: Key risks from climate change and the potential for risk reduction through mitigation and adaptation in
Asia. Key risks are identified based on assessment of the literature and expert judgments, with supporting evaluation
of evidence and agreement in the referenced chapter sections. Each key risk is characterized as very low, low,
medium, high, or very high. Risk levels are presented for the near-term era of committed climate change (here, for
2030-2040), in which projected levels of global mean temperature increase do not diverge substantially across
emissions scenarios. Risk levels are also presented for the longer-term era of climate options (here, for 2080-2100),
for global mean temperature increase of 2°C and 4°C above preindustrial levels. For each timeframe, risk levels are
estimated for the current state of adaptation and for a hypothetical highly adapted state. As the assessment considers
potential impacts on different physical, biological, and human systems, risk levels should not necessarily be used to
evaluate relative risk across key risks. Relevant climate variables are indicated by symbols.
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Table 24-1 (continued)
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Table 24-2: The amount of information supporting conclusions regarding observed and projected impacts in Asia.
/ = Relatively abundant/sufficient information; knowledge gaps need to be addressed but conclusions can be
drawn based on existing information
X = Limited information/no data; critical knowledge gaps, difficult to draw conclusions
NR = Not relevant
Region Topics/Issues North East Asia Southeast South Central West Asia
Asia Asia Asia Asia
O= Observed impacts; P = Projected Impacts O P O P O P O P O P O P
Freshwater Major river runoff / x / / / / / x x x x x
Resources Water supply x x x x x x x x x x x x
Terrestrial and Phenology and growth rates / / / / x x x x x x x x
Inland Water Distributions of species and biomes / / / / x x x / x x x x
Systems Permafrost / / / / / x / / / / / x
Inland waters x x / x x x x x x x x x
Coastal Coral reefs NR NR / / / / / / NR NR / /
Systems and Other coastal ecosystems x x / / x x x x NR NR x x
Low-Lying
Arctic coast erosion / / NR NR NR NR NR NR NR NR NR NR
Areas
Food Rice yield x x / / x / x / x x X /
Production Wheat yield x x x x x x x / x x / /
Systems and Corn yield x x x / x x x x x x x x
Food Security Other crops (e.g. barley, potato) x x / / x x x x x X / /
Vegetables x x / x x x x x x x x x
Fruits x x / x x x x x x x x x
Livestock x x / x x x x x x x x x
Fisheries and aquaculture production x / x / x / x x x x x x
Farming area x / x / x x x / x / x x
Water demand for irrigation x / x / x x x / x x x x
Pest and disease occurrence x x x x x x x / x x x x
Human Floodplains x x / / / / / / x x x x
Settlements, Coastal areas x x / / / / / / NR NR x x
Industry, and Population and assets x x / / / / / / x x x x
Infrastructure Industry and infrastructure x x / / / / / / x x x x
Human Health, Health effects of floods x x x x x x / x x x x x
Security, Health effects of heat x x / x x x x x x x x x
Livelihoods and Health effects of drought x x x x x x x x x x x x
Poverty Water-borne diseases x x x x / x / x x x x x
Vector-borne diseases x x x x / x / x x x x x
Livelihoods and poverty x x / x x x / x x x x x
Economic valuation x x x x / / / / x x x x
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Figure 24-1: The land and territories of 51 countries in Asia.
NOTE: Currently in production and will be brought to specification using the current UN-accepted maps.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 24-2: Observed and projected changes in annual average temperature and precipitation in Asia. (Top panel, left) Observed
temperature trends from 1901-2012 determined by linear regression. [WGI AR5 Figures SPM.1 and 2.21] (Bottom panel, left)
Observed precipitation change from 1951-2010 determined by linear regression. [WGI AR5 Figure SPM.2] For observed
temperature and precipitation, trends have been calculated where sufficient data permits a robust estimate (i.e., only for grid
boxes with greater than 70% complete records and more than 20% data availability in the first and last 10% of the time period).
Other areas are white. Solid colors indicate areas where change is significant at the 10% level. Diagonal lines indicate areas
where change is not significant. (Top and bottom panel, right) CMIP5 multi-model mean projections of annual average
temperature changes and average percent change in annual mean precipitation for 2046-2065 and 2081-2100 under RCP2.6 and
8.5. Solid colors indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline
variability, and >90% of models agree on sign of change. Colors with white dots indicate areas with strong agreement, where
>66% of models show change greater than the baseline variability and >66% of models agree on sign of change. Gray indicates
areas with divergent changes, where >66% of models show change greater than the baseline variability, but <66% agree on sign
of change. Colors with diagonal lines indicate areas with little or no change, less than the baseline variability in >66% of models.
(There may be significant change at shorter timescales such as seasons, months, or days.). Analysis uses model data and methods
building from WGI AR5 Figure SPM.8. See also Annex I of WGI AR5. [Boxes 21-3 and CC-RC]
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Figure 24-3: Losses of glacier area in the Altai-Sayan, Pamir and Tien Shan. Remote sensing data analysis from
1960s (Corona) through 2008 (Landsat, ASTER and Alos Prism).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure TC-1: The intersection of inland and storm surge flooding. Red shows May 5, 2008 MODIS mapping of the
tropical cyclone Nargis storm surge along the Irrawaddy Delta and to the east, Myanmar. The blue areas to the north
were flooded by the river in prior years. Source: Brakenridge et al., 2013.
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Chapter 25. Australasia
Coordinating Lead Authors
Andy Reisinger (New Zealand), Roger Kitching (Australia)
Lead Authors
Francis Chiew (Australia), Lesley Hughes (Australia), Paul Newton (New Zealand), Sandra Schuster (Australia),
Andrew Tait (New Zealand), Penny Whetton (Australia)
Contributing Authors
Jon Barnett (Australia), Susanne Becken (New Zealand), Paula Blackett (New Zealand), Sarah Boulter (Australia),
Andrew Campbell (Australia), Daniel Collins (New Zealand), Jocelyn Davies (Australia), Keith Dear (Australia),
Stephen Dovers (Australia), Kyla Finlay (Australia), Bruce Glavovic (New Zealand), Donna Green (Australia), Don
Gunasekera (Australia), Simon Hales (New Zealand), John Handmer (Australia), Garth Harmsworth (New Zealand),
Alistair Hobday (Australia), Mark Howden (Australia), Graeme Hugo (Australia), David Jones (Australia), Sue
Jackson (Australia), Darren King (New Zealand), Miko Kirschbaum (New Zealand), Jo Luck (Australia), Jan
McDonald (Australia), Kathy McInnes (Australia), Yiheyis Maru (Australia), Johanna Mustelin (Australia), Barbara
Norman (Australia), Grant Pearce (New Zealand), Susan Peoples (New Zealand), Ben Preston (USA), Joseph Reser
(Australia), Penny Reyenga (Australia), Mark Stafford-Smith (Australia), Xiaoming Wang (Australia), Leanne
Webb (Australia)
Review Editors
Blair Fitzharris (New Zealand), David Karoly (Australia)
Contents
Executive Summary
25.1. Introduction and Major Conclusions from Previous Assessments
25.2. Observed and Projected Climate Change
25.3. Socio-Economic Trends Influencing Vulnerability and Adaptive Capacity
25.3.1. Economic, Demographic and Social Trends
25.3.2. Use and Relevance of Socio-Economic Scenarios in Adaptive Capacity/Vulnerability Assessments
25.4. Cross-Sectoral Adaptation: Approaches, Effectiveness, and Constraints
25.4.1. Frameworks, Governance, and Institutional Arrangements
25.4.2. Constraints on Adaptation and Leading Practice Models
25.4.3. Socio-cultural Factors Influencing Impacts of and Adaptation to Climate Change
25.5. Freshwater Resources
25.5.1. Observed Impacts
25.5.2. Projected Impacts
25.5.3. Adaptation
25.6. Natural Ecosystems
25.6.1. Inland Freshwater and Terrestrial Ecosystems
25.6.1.1. Observed Impacts
25.6.1.2. Projected Impacts
25.6.1.3. Adaptation
25.6.2. Coastal and Ocean Ecosystems
25.6.2.1. Observed Impacts
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25.6.2.2. Projected Impacts
25.6.2.3. Adaptation
25.7. Major Industries
25.7.1. Production Forestry
25.7.1.1. Observed and Projected Impacts
25.7.1.2. Adaptation
25.7.2. Agriculture
25.7.2.1. Projected Impacts and Adaptation Livestock Systems
25.7.2.2. Projected Impacts and Adaptation Cropping
25.7.2.3. Integrated Adaptation Perspectives
25.7.3. Mining
25.7.4. Energy Supply, Transmission, and Demand
25.7.5. Tourism
25.7.5.1. Projected Impacts
25.7.5.2. Adaptation
25.8. Human Society
25.8.1. Human Health
25.8.1.1. Observed Impacts
25.8.2.2. Projected Impacts
25.8.3.3. Adaptation
25.8.2. Indigenous Peoples
25.8.2.1. Aboriginal and Torres Strait Islanders
25.8.2.2. New Zealand M ori
25.9. Interactions among Impacts, Adaptation, and Mitigation Responses
25.9.1. Interactions among Local-Level Impacts, Adaptation, and Mitigation Responses
25.9.2. Intra- and Inter-Regional Flow-On Effects between Impacts, Adaptation, and Mitigation
25.10. Synthesis and Regional Key Risks
25.10.1. Economy-wide Impacts and Potential of Mitigation to Reduce Risks
25.10.2. Regional Key Risks as a Function of Mitigation and Adaptation
25.10.3. The Role of Adaptation in Managing Key Risks, and Adaptation limits
25.11. Filling Knowledge Gaps to Improve Management of Climate Risks
References
Chapter Boxes
25-1. Coastal Adaptation Planning and Legal Dimensions
25-2. Adaptation through Water Resources Policy and Management in Australia
25-3. Impacts of a Changing Climate in Natural and Managed Ecosystems
25-4. Biosecurity
25-5. Climate Change Vulnerability and Adaptation in Rural Areas
25-6. Climate Change and Fire
25-7. Insurance as Climate Risk Management Tool
25-8. Changes in Flood Risk and Management Responses
25-9. Opportunities, Constraints, and Challenges to Adaptation in Urban Areas
25-10. Land-based Interactions Among Climate, Energy, Water, and Biodiversity
Frequently Asked Questions
25.1: How can we adapt to climate change if projected future changes remain uncertain?
25.2: What are the key risks from climate change to Australia and New Zealand?
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Executive Summary
The regional climate is changing (very high confidence). The region continues to demonstrate long term trends
toward higher surface air and sea-surface temperatures, more hot extremes and fewer cold extremes, and changed
rainfall patterns. Over the past 50 years, increasing greenhouse gas concentrations have contributed to rising average
temperature in Australia (high confidence) and New Zealand (medium confidence) and decreasing rainfall in south-
western Australia (high confidence). [25.2, Table 25-1]
Warming is projected to continue through the 21st century (virtually certain) along with other changes in
climate. Warming is expected to be associated with rising snow lines (very high confidence), more frequent hot
extremes, less frequent cold extremes (high confidence), and increasing extreme rainfall related to flood risk in
many locations (medium confidence). Annual average rainfall is expected to decrease in south-western Australia
(high confidence) and elsewhere in most of far southern Australia and the north-east South Island and northern and
eastern North Island of New Zealand (medium confidence), and to increase in other parts of New Zealand (medium
confidence). Tropical cyclones are projected to increase in intensity but remain similar or decrease in numbers (low
confidence), and fire weather is projected to increase in most of southern Australia (high confidence) and many parts
of New Zealand (medium confidence). Regional sea level rise will very likely exceed the historical rate (1971-2010),
consistent with global mean trends. [25.2, Table 25-1, Box 25-6; WGI 13.5, 13.6]
Uncertainty in projected rainfall changes remains large for many parts of Australia and New Zealand, which
creates significant challenges for adaptation. For example, projections for average annual runoff in far south-
eastern Australia range from little change to a 40% decline for 2°C global warming above current levels. The dry
end of these scenarios would have severe implications for agriculture, rural livelihoods, ecosystems and urban water
supply, and would increase the need for transformational adaptation (high confidence). [25.2, 25.5.1, 25.6.1, 25.7.2,
Box 25-2, Box 25-5]
Recent extreme climatic events show significant vulnerability of some ecosystems and many human systems
to current climate variability (very high confidence), and the frequency and/or intensity of such events is
projected to increase in many locations (medium to high confidence). For example, high sea surface temperatures
have repeatedly bleached coral reefs in north-eastern Australia (since the late 1970s) and more recently in western
Australia. Recent floods in Australia and New Zealand caused severe damage to infrastructure and settlements and
35 deaths in Queensland alone (2011); the Victorian heat wave (2009) increased heat-related morbidity and was
associated with more than 300 excess deaths, while intense bushfires destroyed over 2,000 buildings and led to 173
deaths; and widespread drought in south-east Australia (1997-2009) and many parts of New Zealand (2007-2009;
2012-13) resulted in substantial economic losses (e.g. regional GDP in the southern Murray Darling Basin was
below forecast by about 5.7% in 2007/08, and New Zealand lost about NZ$3.6b in direct and off-farm output in
2007-09). [Table 25-1, 25.6.2, 25.8.1, Box 25-5, Box 25-6, Box 25-8]
Without adaptation, further changes in climate, atmospheric CO2 and ocean acidity are projected to have
substantial impacts on water resources, coastal ecosystems, infrastructure, health, agriculture and
biodiversity (high confidence). Freshwater resources are projected to decline in far south-west and far south-east
mainland Australia (high confidence) and for rivers originating in the north-east of the South Island and east and
north of the North Island of New Zealand (medium confidence). Rising sea levels and increasing heavy rainfall are
projected to increase erosion and inundation, with consequent damages to many low-lying ecosystems, infrastructure
and housing; increasing heat waves will increase risks to human health; rainfall changes and rising temperatures will
shift agricultural production zones; and many native species will suffer from range contractions and some may face
local or even global extinction. [25.5.1, 25.6.1, 25.6.2, 25.7.2, 25.7.4, Box 25-1, Box 25-5, Box 25-8]
Some sectors in some locations have the potential to benefit from projected changes in climate and increasing
atmospheric CO2 (high confidence). Examples include reduced winter mortality (low confidence), reduced energy
demand for winter heating in New Zealand and southern parts of Australia, and forest growth in cooler regions
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except where soil nutrients or rainfall are limiting. Spring pasture growth in cooler regions would also increase and
be beneficial for animal production if it can be utilized. [25.7.1, 25.7.2, 25.7.4, 25.8.1]
Adaptation is already occurring and adaptation planning is becoming embedded in some planning processes,
albeit mostly at the conceptual rather than implementation level (high confidence). Many solutions for reducing
energy and water consumption in urban areas with co-benefits for climate change adaptation (e.g. greening cities
and recycling water) are already being implemented. Planning for reduced water availability in southern Australia
and for sea-level rise in both countries is becoming adopted widely, although implementation of specific policies
remains piecemeal, subject to political changes, and open to legal challenges. [25.4, Box 25-1, Box 25-2, Box 25-9]
Adaptive capacity is generally high in many human systems, but implementation faces major constraints
especially for transformational responses at local and community levels (high confidence). Efforts to
understand and enhance adaptive capacity and adaptation processes have increased since the AR4, particularly in
Australia. Constraints on implementation arise from: absence of a consistent information base and uncertainty about
projected impacts; limited financial and human resources to assess local risks and to develop and implement
effective policies and rules; limited integration of different levels of governance; lack of binding guidance on
principles and priorities; different attitudes towards the risks associated with climate change, and different values
placed on objects and places at risk. [25.4, 25.10.3, Box 25-1, Table 25-2]
Indigenous peoples in both Australia and New Zealand have higher than average exposure to climate change
due to a heavy reliance on climate-sensitive primary industries and strong social connections to the natural
environment, and face particular constraints to adaptation (medium confidence). Social status and
representation, health, infrastructure and economic issues, and engagement with natural resource industries constrain
adaptation and are only partly offset by intrinsic adaptive capacity (high confidence). Some proposed responses to
climate change may provide economic opportunities, particularly in New Zealand related to forestry. Torres Strait
communities are vulnerable even to small sea level rises (high confidence). [25.3, 25.8.2]
We identify eight regional key risks during the 21st century based on the severity of potential impacts for
different levels of warming, uniqueness of the systems affected, and adaptation options (high confidence).
These risks differ in the degree to which they can be managed via adaptation and mitigation, and some are more
likely to be realized than others, but all warrant attention from a risk-management perspective.
Some potential impacts can be delayed but now appear very difficult to avoid entirely, even with globally
effective mitigation and planned adaptation:
o significant change in community composition and structure of coral reef systems in Australia, driven
by increasing sea-surface temperatures and ocean acidification; the ability of corals to adapt naturally to
rising temperatures and acidification appears limited and insufficient to offset the detrimental effects
[25.6.2, 30.5, Box CC-CR]
o loss of montane ecosystems and some native species in Australia, driven by rising temperatures and
snow lines, increased fire risk and drying trends; fragmentation of landscapes, limited dispersal and
limited rate of evolutionary change constrain adaptation options [25.6.1]
Some impacts have the potential to be severe but can be reduced substantially by globally effective
mitigation combined with adaptation, with the need for transformational adaptation increasing with the rate
and magnitude of climate change:
o increased frequency and intensity of flood damage to settlements and infrastructure in Australia and
New Zealand, driven by increasing extreme rainfall although the amount of change remains uncertain;
in many locations, continued reliance on increased protection alone would become progressively less
feasible [Table 25-1, 25.4.2, Box 25-8, 25.10.3]
o constraints on water resources in southern Australia, driven by rising temperatures and reduced cool-
season rainfall; integrated responses encompassing management of supply, recycling, water
conservation and increased efficiency across all sectors are available and some are being implemented
in areas already facing shortages [25.2, 25.5.2, Box 25-2]
o increased morbidity, mortality and infrastructure damages during heat waves in Australia, resulting
from increased frequency and magnitude of extreme high temperatures; vulnerable populations include
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the elderly and those with existing chronic diseases; population increases and ageing trends constrain
effectiveness of adaptation responses [25.8.1]
o increased damages to ecosystems and settlements, economic losses and risks to human life from
wildfires in most of southern Australia and many parts of New Zealand, driven by rising temperatures
and drying trends; local planning mechanisms, building design, early warning systems and public
education can assist with adaptation and are being implemented in regions that have experienced major
events [25.2, Table 25-1, 25.6.1, 25.7.1, Box 25-6]
For some impacts, severity depends on changes in climate variables that span a particularly large range, even
for a given global temperature change. The most severe changes would present major challenges if realized:
o increasing risks to coastal infrastructure and low-lying ecosystems in Australia and New Zealand
from continuing sea level rise, with widespread damages towards the upper end of projected changes;
managed retreat is a long-term adaptation strategy for human systems but options for some natural
ecosystems are limited due to the rapidity of change and lack of suitable space for landward migration.
Risks from sea level rise continue to increase beyond 2100 even if temperatures are stabilised. [Table
25-1, 25.4.2, Box 25-1, 25.6.1, 25.6.2; WGI 13.5]
o significant reduction in agricultural production in the Murray-Darling Basin and far south-eastern
and south-western Australia if scenarios of severe drying are realised; more efficient water use,
allocation and trading would increase the resilience of systems in the near term but cannot prevent
significant reductions in agricultural production and severe consequences for ecosystems and some rural
communities at the dry end of the projected changes [25.2, 25.5.2, 25.7.2, Box 25-2, Box 25-5]
Significant synergies and trade-offs exist between alternative adaptation responses, and between mitigation
and adaptation responses; interactions occur both within Australasia and between Australasia and the rest of
the world (very high confidence). Increasing efforts to mitigate and adapt to climate change imply an increasing
complexity of interactions, particularly at the intersections among water, energy and biodiversity, but tools to
understand and manage these interactions remain limited. Flow-on effects from climate change impacts and
responses outside Australasia have the potential to outweigh some of the direct impacts within the region,
particularly economic impacts on trade-intensive sectors such as agriculture (medium confidence) and tourism (high
agreement, limited evidence), but they remain among the least explored issues. [25.7.5, 25.9.1, 25.9.2, Box 25-10]
Understanding of future vulnerability of human and mixed human-natural systems to climate change
remains limited due to incomplete consideration of socio-economic dimensions (very high confidence). Future
vulnerability will depend on factors such as wealth and its distribution across society, patterns of ageing, access to
technology and information, labour force participation, societal values, and mechanisms and institutions to resolve
conflicts. These dimensions have received only limited attention and are rarely included in vulnerability
assessments, and frameworks to integrate social, psychological and cultural dimensions of vulnerability with bio-
physical impacts and economic losses are lacking. In addition, conclusions for New Zealand in many sectors, even
for bio-physical impacts, are based on limited studies that often use a narrow set of assumptions, models and data
and hence have not explored the full range of potential outcomes. [25.3, 25.4, 25.11]
25.1. Introduction and Major Conclusions from Previous Assessments
Australasia is defined here as lands, territories, offshore waters and oceanic islands of the exclusive economic zones
of Australia and New Zealand. Both countries are relatively wealthy with export-led economies. Both have
Westminster-style political systems and have a relatively recent history of non-indigenous settlement (Australia in
the late 18th, New Zealand in the early 19th century). Both retain significant indigenous populations.
Principal findings from the IPCC Fourth Assessment Report (AR4) for the region were (Hennessy et al., 2007):
Consistent with global trends, Australia and New Zealand had experienced warming of 0.4 to 0.7°C since 1950
with changed rainfall patterns and sea-level rise of about 70 mm across the region; there had also been a
greater frequency and intensity of droughts and heat waves, reduced seasonal snow cover and glacial retreat.
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Impacts from recent climate changes were evident in increasing stresses on water supply and agriculture, and
changed natural ecosystems; some adaptation had occurred in these sectors but vulnerability to extreme events
such as fire, tropical cyclones, droughts, hail and floods remained high.
The climate of the 21st century would be warmer (virtually certain), with changes in extreme events including
more intense and frequent heat waves, fire, floods, storm surges and droughts but less frequent frost and snow
(high confidence), reduced soil moisture in large parts of the Australian mainland and eastern New Zealand but
more rain in western New Zealand (medium confidence).
Significant advances had occurred in understanding future impacts on water, ecosystems, Indigenous people
and health together with an increased focus on adaptation; potential impacts would be substantial without
further adaptation, particularly for water security, coastal development, biodiversity, and major infrastructure,
but impacts on agriculture and forestry would be variable across the region, including potential benefits in
some areas.
Vulnerability would increase mainly due to an increase in extreme events; human systems were considered to
have a higher adaptive capacity than natural systems.
Hotspots of high vulnerability by 2050 under a medium emissions scenario included:
- significant loss of biodiversity in areas such as alpine regions, the Wet Tropics, the Australian south-west,
Kakadu wetlands, coral reefs and sub-Antarctic islands;
- water security problems in the Murray-Darling basin, south-western Australia and eastern New Zealand;
- potentially large risks to coastal development in south-eastern Queensland and in New Zealand from
Northland to the Bay of Plenty.
25.2. Observed and Projected Climate Change
Australasia exhibits a wide diversity of climates, such as moist tropical monsoonal, arid and moist temperate,
including alpine conditions. Key climatic processes are the Asian-Australian monsoon and the southeast trade winds
over northern Australia, and the subtropical high pressure belt and the mid-latitude storm tracks over southern
Australia and New Zealand. Tropical cyclones also affect northern Australia, and, more rarely, ex-tropical cyclones
affect some parts of New Zealand. Natural climatic variability is very high in the region, especially for rainfall and
over Australia, with the El Nino-Southern Oscillation (ENSO) being the most important driver (McBride and
Nicholls, 1983; Power et al., 1998; Risbey et al., 2009). The southern annular mode, Indian Ocean Dipole and the
Interdecadal Pacific Oscillation are also important regional drivers (Thompson and Wallace, 2000; Salinger et al.,
2001; Cai et al., 2009b). This variability poses particular challenges for detecting and projecting anthropogenic
climate change and its impacts in the region. For example, changes in ENSO in response to anthropogenic climate
change are uncertain (AR5 WGI Ch14) but, given current ENSO impacts, any changes would have the potential to
significantly influence rainfall and temperature extremes, droughts, tropical cyclones, marine conditions and glacial
mass balance (Mullan, 1995; Chinn et al., 2005; Holbrook et al., 2009; Diamond et al., 2012; Min et al., 2013).
Understanding of observed and projected climate change has received much attention since AR4, particularly in
Australia, with a focus on the causes of observed rainfall changes and more systematic analysis of projected changes
from different models and approaches. Climatic extremes have also been a research focus. Table 25-1 presents an
assessment of this body of research for observed trends and projected changes for a range of climatic variables
(including extremes) relevant for regional impacts and adaptation, including examples of the magnitude of projected
change, and attribution, where possible. Most studies are based on CMIP3 models and SRES scenarios, but CMIP5
model results are considered where available (see also AR5 WGI Chap 14 & Atlas; WGII Chapter 21).
The region has exhibited warming to the present (very high confidence) and is virtually certain to continue to do so
(Table 25-1). Observed and CMIP5-modelled past and projected future annual average surface temperatures are
shown in Figures 25-1 and 25-2. For further details see WGI Atlas, AI.68-69. Changes in precipitation have been
observed with very high confidence in some areas over a range of time scales, such as increases in north-western
Australia since the 1950s, the autumn/winter decline since 1970 in south-western Australia and, since the 1990s, in
south-eastern Australia, and over 1950-2004 increases in annual rainfall in the south and west of the South Island
and west of the North Island of New Zealand, and decreases in the north-east of the South Island and east and north
of the North Island. Based on multiple lines of evidence, annual average rainfall is projected to decrease with high
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confidence in south-western Australia. For New Zealand, annual average rainfall is projected to decrease in the
north-eastern South Island and eastern and northern North Island, and increase in other parts of the country (medium
confidence). The direction and magnitude of rainfall change in eastern and northern Australia remains a key
uncertainty (Table 25-1).
This pattern of projected rainfall change is reflected in annual average CMIP5 model results (Figure 25-1), but with
important additional dimensions relating to seasonal changes and spread across models (see also WGI Atlas, AI.70-
71). Examples of the magnitude of projected annual change from 1990 to 2090 (percent model mean change +/-
intermodel standard deviation) under RCP8.5 from CMIP5 are -20+/-13% in south-western Australia, -2+/-21% in the
Murray Darling Basin, and -5+/-22% in southeast Queensland (Irving et al., 2012). Projected changes during winter
and spring are more pronounced and/or consistent across models than the annual changes, e.g. drying in south-
western Australia (-32+/-11%, June to August), the Murray Darling Basin (-16+/-22%, June to August), and southeast
Queensland (-15+/-26%, September to November), whereas there are increases of 15% or more in the west and south
of the South Island of New Zealand (Irving et al., 2012). Downscaled CMIP3 model projections for New Zealand
indicate a stronger drying pattern in the south-east of the South Island and eastern and northern regions of the North
Island in winter and spring (Reisinger et al., 2010) than seen in the raw CMIP5 data; based on similar broader scale
changes this pattern is expected to hold once CMIP5 data are also downscaled (Irving et al., 2012).
Other projected changes of at least high confidence include regional increases in sea surface temperature, the
occurrence of hot days, fire weather in southern Australia, mean and extreme sea level, and ocean acidity (see WGI
6.4.4 for projections); and decreases in cold days and snow extent and depth. Although changes to tropical cyclone
occurrence and that of other severe storms are potentially important for future vulnerability, regional changes to
these phenomena cannot be projected with at least medium confidence as yet (see Table 25-1).
[INSERT FIGURE 25-1 HERE
Figure 25-1: Observed and projected changes in annual average temperature and precipitation. (Top panel, left)
Observed temperature trends from 1901-2012 determined by linear regression [WGI AR5 Figures SPM.1 and 2.21].
(Bottom panel, left) Observed precipitation change from 1951-2010 determined by linear regression [WGI AR5
Figure SPM.2]. For observed temperature and precipitation, trends have been calculated where sufficient data
permits a robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data
availability in the first and last 10% of the time period). Other areas are white. Solid colors indicate areas where
change is significant at the 10% level. Diagonal lines indicate areas where change is not significant. (Top and
bottom panel, right) CMIP5 multi-model mean projections of annual average temperature changes and average
percent change in annual mean precipitation for 2046-2065 and 2081-2100 under RCP2.6 and 8.5. Solid colors
indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline
variability, and >90% of models agree on sign of change. Colors with white dots indicate areas with strong
agreement, where >66% of models show change greater than the baseline variability and >66% of models agree on
sign of change. Gray indicates areas with divergent changes, where >66% of models show change greater than the
baseline variability, but <66% agree on sign of change. Colors with diagonal lines indicate areas with little or no
change, less than the baseline variability in >66% of models. (There may be significant change at shorter timescales
such as seasons, months, or days.). Analysis uses model data and methods building from WGI AR5 Figure SPM.8.
See also Annex I of WGI AR5 [Boxes 21-3 and CC-RC].]
[INSERT FIGURE 25-2 HERE
Figure 25-2: Observed and simulated variations in past and projected future annual average near-surface air
temperature over land areas of Australia (left) and New Zealand (right). Black lines show various estimates from
observational measurements. Shading denotes the 5-95 percentile range of climate model simulations driven with
historical changes in anthropogenic and natural drivers (63 simulations), historical changes in natural drivers
only (34), the RCP2.6 emissions scenario (63), and the RCP8.5 (63). Data are anomalies from the 1986-2005
average of the individual observational data (for the observational time series) or of the corresponding historical all-
forcing simulations. Further details are given in Box 21-3.]
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[INSERT TABLE 25-1 HERE
Table 25-1: Observed and projected changes in key climate variables, and (where assessed) the contribution of
human activities to observed changes. For further relevant information see WGI Chapters 3, 6 (ocean changes,
including acidification), 11, 12 (projections), 13 (sea level) and 14 (regional climate phenomena).]
25.3. Socio-Economic Trends Influencing Vulnerability and Adaptive Capacity
25.3.1. Economic, Demographic and Social Trends
The economies of Australia and New Zealand rely on natural resources, agriculture, minerals, manufacturing and
tourism, but the relative importance of these sectors differs between the two countries. Agriculture and
mineral/energy resources accounted, respectively, for 11% and 55% (Australia) and 56% and 5% (New Zealand) of
the value of total exports in 2010/2011 (ABS, 2012c; SNZ, 2012b). Water abstraction per capita in both countries is
in the top half of the OECD, decreasing since 1990 in Australia but increasing in New Zealand; more than half is
used for irrigation (OECD, 2010, 2013a). Between 1970 and 2011, GDP grew by an average of 3.2% p.a. in
Australia and 2.4% p.a. in New Zealand, with annual GDP per capita growth of 1.8% and 1.2%, respectively (SNZ,
2011; ABS, 2012d). GDP is projected to grow on average by 2.5-3.5% p.a. in Australia and about 1.9% p.a. in New
Zealand to 2050 (Australian Treasury, 2010; Bell et al., 2010) but subject to significant shorter-term fluctuations.
The populations of Australia and New Zealand are projected to grow significantly over at least the next several
decades (very high confidence; ABS, 2008; SNZ, 2012a); Australia s population from 22.3 million in 2011 to 31-43
million by 2056 and 34-62 million by 2101 (ABS, 2008, 2013); New Zealand s population from 4.4 million in 2011
to 5.1-7.1 million by 2061 (SNZ, 2012a). The number of people aged 65 and over is projected to almost double in
the next two decades (ABS, 2008; SNZ, 2012a). More than 85% of the Australasian population lives in urban areas
and their satellite communities, mostly in coastal areas (DCC, 2009; SNZ, 2010b; UN, 2012; see Box 25-9). Urban
concentration and depletion of remote rural areas is expected to continue (Mendham and Curtis, 2010; SNZ, 2010c;
Box 25-5), but some coastal non-urban spaces also face increasing development pressure (Freeman and Cheyne,
2008; Gurran, 2008; Box 25-1). More than 20% of Australasian residents were born overseas (OECD, 2013a).
Poverty rates and income inequality in Australia and New Zealand are in the upper half of OECD countries, and
both measures increased significantly in both countries between the mid-1980s and the late-2000s (OECD, 2013a).
Measurement of poverty and inequality, however, is highly contested, and it remains difficult to anticipate future
changes and their effects on adaptive capacity (Peace, 2001; Scutella et al., 2009; 25.3.2). Indigenous peoples
constitute about 2.5% and 15% of the Australian and New Zealand populations, respectively, but in Australia, their
national share is growing and they constitute a much higher percentage of the population in remote and very remote
regions (ABS, 2009, 2010b; SNZ, 2010a). Indigenous peoples in both countries have lower than average life
expectancy, income and education, implying that changes in socio-economic status and social inclusion could
strongly influence their future adaptive capacity (see 25.8.2).
25.3.2. Use and Relevance of Socio-Economic Scenarios in Adaptive Capacity/Vulnerability Assessments
Demographic, economic and socio-cultural trends influence the vulnerability and adaptive capacity of individuals
and communities (see Chapters 2, 11-13, 16, 20). A limited but growing number of studies in Australasia have
attempted to incorporate such information, e.g. changes in the number of people and percentage of elderly people at
risk (Preston et al., 2008; Baum et al., 2009; Preston and Stafford-Smith, 2009; Roiko et al., 2012), the density of
urban settlements and exposed infrastructure (Preston and Jones, 2008; Preston et al., 2008; Baynes et al., 2012),
population-driven pressures on water demand (Jollands et al., 2007; CSIRO, 2009), and economic and social factors
affecting individual coping, planning and recovery capacity (Dwyer et al., 2004; Khan, 2012; Roiko et al., 2012).
Socio-economic considerations are used increasingly to understand adaptive capacity of communities (Preston et al.,
2008; Smith et al., 2008; Fitzsimons et al., 2010; Soste, 2010; Brunckhorst et al., 2011) and to construct scenarios to
help build regional planning capacity (Energy Futures Forum, 2006; Frame et al., 2007; Pride et al., 2010; Pettit et
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al., 2011; Taylor et al., 2011). Such scenarios, however, are only beginning to be used to quantify vulnerability to
climate change (except e.g. Bohensky et al., 2011; Baynes et al., 2012; Low Choy et al., 2012).
Apart from these emerging efforts, most vulnerability studies from Australasia make no or very limited use of socio-
economic factors, consider only current conditions, and/or rely on postulated correlations between generic socio-
economic indicators and climate change vulnerability. In many cases this limits confidence in conclusions regarding
future vulnerability to climate change and adaptive capacity of human and mixed natural-human systems.
25.4. Cross-Sectoral Adaptation: Approaches, Effectiveness, and Constraints
25.4.1. Frameworks, Governance, and Institutional Arrangements
Adaptation responses depend heavily on institutional and governance arrangements (see Chapters 2, 14, 15, 16, 20).
Responsibility for development and implementation of adaptation policy in Australasia is largely devolved to local
governments and, in Australia, to State governments and Natural Resource Management bodies. Federal/central
government supports adaptation mostly via provision of information, tools, legislation, policy guidance and (in
Australia) support for pilot projects. A standard risk management paradigm has been promoted to embed adaptation
into decision-making practices (AGO, 2006; MfE, 2008b; Standards Australia, 2013), but broader systems and
resilience approaches are used increasingly for natural resource management (Clayton et al., 2011; NRC, 2012).
The Council of Australian Governments agreed a national adaptation policy framework in 2007 (COAG, 2007). This
included establishing the collaborative National Climate Change Adaptation Research Facility (NCCARF) in 2008,
which complemented CSIRO s Climate Adaptation Flagship. The federal government supported a first-pass national
coastal risk assessment (DCC, 2009; DCCEE, 2011), is developing indicators and criteria for assessing adaptation
progress and outcomes (DIICCSRT, 2013), and commissioned targeted reports addressing impacts and management
options for natural and managed landscapes (Campbell, 2008; Steffen et al., 2009; Dunlop et al., 2012), National
and World Heritage areas (ANU, 2009; BMT WBM, 2011), and indigenous and urban communities (Green et al.,
2009; Norman, 2010). Most State and Territory governments have also developed adaptation plans (e.g. DSE, 2013).
In New Zealand, the central government updated and expanded tools to support impact assessments and adaptation
responses consistent with regulatory requirements (MfE, 2008b, c, d, 2010b), and revised key directions for coastal
management (Minister of Conservation, 2010). No cross-sectoral adaptation policy framework or national-level risk
assessments exist, but some departments commissioned high-level impacts and adaptation assessments after the
AR4 (e.g. on agriculture and on biodiversity; Wratt et al., 2008; McGlone and Walker, 2011; Clark et al., 2012).
Public and private sector organisations are potentially important adaptation actors but exhibit large differences in
preparedness, linked to knowledge about climate change, economic opportunities, external connections, size, and
scope for strategic planning (Gardner et al., 2010; Taylor et al., 2012a; Johnston et al., 2013; Kuruppu et al., 2013;
see also Chapters 10, 16). This creates challenges for achieving holistic societal outcomes (see also 25.7-25.9).
Several recent policy initiatives in Australia, while responding to broader socio-economic and environmental
pressures, include goals to reduce vulnerability to climate variability and change. These include establishing the
Murray-Darling Basin Authority to address over-allocation of water resources (Connell and Grafton, 2011; MDBA,
2011), removal of the interest rate subsidy during exceptional droughts (Productivity Commission, 2009), and
management of bush fire and flood risk (VBRC, 2010; QFCI, 2012). These may be seen as examples of
mainstreaming adaptation (Dovers, 2009), but they also demonstrate lag times in policy design and implementation,
windows of opportunity presented by crises (e.g. the Millennium Drought 1997-2009, the Victorian bushfires 2009
and Queensland floods of 2011), and the challenges arising from competing interests in managing finite and
changing water resources (Botterill and Dovers, 2013; Pittock, 2013; Box 25-2).
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25.4.2. Constraints on Adaptation and Emerging Leading Practice Models
A rapidly growing literature since the AR4 confirms, with high confidence, that while the adaptive capacity of
society in Australasia is generally high, there are formidable environmental, economic, informational, social,
attitudinal and political constraints, especially for local governments and small or highly fragmented industries.
Reviews of public- and private-sector adaptation plans and strategies in Australia demonstrate strong efforts in
institutional capacity building, but differences in assessment methods and weaknesses in translating goals into
specific policies (White, 2009; Gardner et al., 2010; Measham et al., 2011; Preston et al., 2011; Kay et al., 2014).
Similarly, local governments in New Zealand to date have focused mostly on impacts and climate-related hazards;
some have developed adaptation plans, but few have committed to specific policies and steps to implementation
(e.g. O'Donnell, 2007; Britton, 2010; Fitzharris, 2010; HRC, 2012; KCDC, 2012; Lawrence et al., 2013b).
Table 25-2 summarises key constraints and corresponding enabling factors for effective institutional adaptation
processes identified in Australia and New Zealand. Scientific uncertainty and resource limitations are reported
consistently as important constraints, particularly for smaller councils. Ultimately more powerful constraints arise,
however, from current governance and legislative arrangements and the lack of consistent tools to deal with dynamic
risks and uncertainty or to evaluate the success of adaptation responses (high agreement, robust evidence; Britton,
2010; Barnett et al., 2013; Lawrence et al., 2013b; Mukheibir et al., 2013; Webb et al., 2013; see also Chapter 16).
[INSERT TABLE 25-2 HERE
Table 25-2: Constraints and enabling factors for institutional adaptation processes in Australasia.]
Some constraints exacerbate others. There is high confidence that the absence of a consistent information base and
binding guidelines that clarify governing principles and liabilities is a challenge particularly for small and resource-
limited local authorities, which need to balance special interest advocacy with longer term community resilience.
This heightens reliance on individual leadership subject to short-term political change and can result in piecemeal
and inconsistent risk assessments and responses between levels of government and locations, and over time (Smith
et al., 2008; Brown et al., 2009; Norman, 2009; Britton, 2010; Rouse and Norton, 2010; Abel et al., 2011;
McDonald, 2011; Rive and Weeks, 2011; Corkhill, 2013; Macintosh et al., 2013). In these situations, planners tend
to rely more on single numbers for climate projections that can be argued in court (Reisinger et al., 2011; Lawrence
et al., 2013b), which increases the risk of maladaptation given the uncertain and dynamic nature of climate risk
(McDonald, 2010; Stafford-Smith et al., 2011; Gorddard et al., 2012; McDonald, 2013; Reisinger et al., 2014).
Vulnerability assessments that take mid- to late-century impacts as their starting point can inhibit actors from
implementing adaptation actions, as distant impacts are easily discounted and difficult to prioritise in competition
with near-term non-climate change pressures (Productivity Commission, 2012). Emerging leading practice models
in Australia (Balston, 2012; HCCREMS, 2012; SGS, 2012) and New Zealand (MfE, 2008a; Britton et al., 2011)
recommend a high-level scan of sectors and locations at risk and emphasise a focus on near-term decisions that
influence current and future vulnerability (which could range from early warning systems to strategic and planning
responses). More detailed assessment can then focus on this more tractable subset of issues, based on explicit and
iterative framing of the adaptation issue (Webb et al., 2013) and taking into account the full lifetime (lead- and
consequence time) of the decision/asset in question (Stafford-Smith et al., 2011).
Participatory processes help balance societal preferences with robust scientific information and ensure ownership by
affected communities but rely on human capital and political commitment (high confidence; Hobson and Niemeyer,
2011; Rouse and Blackett, 2011; Weber et al., 2011; Leitch and Robinson, 2012). Realising widespread and
equitable participation is challenging where policies are complex, debates polarised, legitimacy of institutions
contested and potential transformational changes threaten deeply held values (Gardner et al., 2009a; Gorddard et al.,
2012; Burton and Mustelin, 2013; see also 25.4.3). Regional approaches that engage diverse stakeholders,
government and science providers and support the co-production of knowledge can help overcome some of these
problems but require long-term institutional and financial commitments (e.g. Britton et al., 2011; DSEWPC, 2011;
CSIRO, 2012; IOCI, 2012; Low Choy et al., 2012; Webb and Beh, 2013).
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There is active debate about the extent to which incremental adjustments of existing planning instruments,
institutions and decision-making processes can deal adequately with the dynamic and uncertain nature of climate
change and support transformational responses (Kennedy et al., 2010; Preston et al., 2011; Park et al., 2012; Dovers,
2013; Lawrence et al., 2013b; McDonald, 2013; Stafford-Smith, 2013). Recent studies suggest a greater focus on
flexibility and matching decision-making frameworks to specific problems (Hertzler, 2007; Nelson et al., 2008;
Dobes, 2010; Howden and Stokes, 2010; Randall et al., 2012). Limitations of mainstreamed and autonomous
adaptation and the case for more proactive government intervention are being explored in Australia (Productivity
Commission, 2012; Johnston et al., 2013), but have not yet resulted in new policy frameworks.
_____ START BOX 25-1 HERE _____
Box 25-1. Coastal Adaptation Planning and Legal Dimensions
Sea level rise is a significant risk for Australia and New Zealand (very high confidence) due to intensifying coastal
development and the location of population centres and infrastructure (see 25.3). Under a high emissions scenario
(RCP8.5), global mean sea level would likely rise by 0.53 to 0.97 m by 2100, relative to 1986-2005, whereas with
stringent mitigation (RCP2.6), the likely rise by 2100 would be 0.28 to 0.6 m (medium confidence). Based on current
understanding, only instability of the Antarctic Ice Sheet, if initiated, could lead to a rise substantially above the
likely range; evidence remains insufficient to evaluate its probability, but there is medium confidence that this
additional contribution would not exceed several tenths of a metre during the 21st century (AR5 WGI 13.5). Local
case studies in New Zealand (Fitzharris, 2010; Reisinger et al., 2014) and national reviews in Australia (DCC, 2009;
DCCEE, 2011) demonstrate risks to large numbers of residential and commercial assets as well as key services, with
widespread damages at the upper end of projected ranges (high confidence). In Australia, sea level rise of 1.1 m
would affect over A$226 billion of assets, including up to 274,000 residential and 8,600 commercial buildings
(DCCEE, 2011), with additional intangible costs related to stress, health effects and service disruption (HCCREMS,
2010) and ecosystems (DCC, 2009; BMT WBM, 2011). Under expected future settlement patterns, exposure of the
Australian road and rail network will increase significantly once sea level rises above about 0.5 m (Baynes et al.,
2012). Even if temperatures peak and decline, sea level is projected to continue to rise beyond 2100 for many
centuries, at a rate dependent on future emissions (AR5 WGI 13.5).
Responsibility for adapting to sea level rise in Australasia rests principally with local governments through spatial
planning instruments. Western Australia, South Australia and Victoria have mandatory State planning benchmarks
for 2100, with local governments determining how they should be implemented. Long-term benchmarks in New
South Wales and Queensland have either been suspended or revoked, so local authorities now have broad discretion
to develop their own adaptation plans. The New Zealand Coastal Policy Statement (Minister of Conservation, 2010)
mandates a minimum 100-year planning horizon for assessing hazard risks, discourages hard protection of existing
development and recommends avoidance of new development in vulnerable areas. Non-binding government
guidance recommends a risk based approach, using a base value of 0.5 m sea level rise by the 2090s and considering
the implications of at least 0.8 m and, for longer term planning, an additional 0.1 m per decade (MfE, 2008d).
The incorporation of climate change impacts into local planning has evolved considerably over the past 20 years, but
remains piecemeal and shows a diversity of approaches (Gibbs and Hill, 2012; Kay et al., 2014). Governments have
invested in high-resolution digital elevation models of coastal and flood prone areas in some regions, but many local
governments still lack the resources for hazard mapping and policy design. Political commitment is variable, and
legitimacy of approaches and institutions is often strongly contested (Gorddard et al., 2012), including pressure on
State governments to modify adaptation policies and on local authorities to compensate developers for restrictions
on current or future land uses (LGNZ, 2008; Berry and Vella, 2010; McDonald, 2010; Reisinger et al., 2011).
Incremental adaptation responses can entrench existing rights and expectations about on-going protection and
development, which limit options for more transformational responses such as accommodation and retreat (high
agreement, medium evidence; Gorddard et al., 2012; Barnett et al., 2013; Fletcher et al., 2013; McDonald, 2013).
Strategic regional-scale planning initiatives in rapidly growing regions, like south-east Queensland, allow climate
change adaptation to be addressed in ways not typically achieved by locality- or sector-specific plans, but require
effective coordination across different scales of governance (Serrao-Neumann et al., 2013; Smith et al., 2014).
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Courts in both countries have played an important role in evaluating planning measures. Results of litigation have
varied and, in the absence of clearer legislative guidance, more litigation is expected as rising sea levels affect
existing properties and adaptation responses constrain development on coastal land (MfE, 2008d; Kenderdine, 2010;
Rive and Weeks, 2011; Verschuuren and McDonald, 2012; Corkhill, 2013; Macintosh, 2013).
In addition to raising minimum floor levels and creating coastal set-backs to limit further development in areas at
risk, several councils in Australia and New Zealand have consulted on or attempted to implement managed retreat
policies (ECAN, 2005; BSC, 2010; HDC, 2012; KCDC, 2012). These policies remain largely untested in New
Zealand, but experience in Australia has shown high litigation potential and opposing priorities at different levels of
government, undermining retreat policies (SCCCWEA, 2009; DCCEE, 2010; Abel et al., 2011). Mandatory
disclosure of information about future risks, community engagement and policy stability are critical to support
retreat, but existing-use rights, liability concerns, special interests, community resources, place attachment and
divergent priorities at different levels of government present powerful constraints (high confidence; Hayward,
2008b; Berry and Vella, 2010; McDonald, 2010; Abel et al., 2011; Alexander et al., 2012; Leitch and Robinson,
2012; Macintosh et al., 2013; Reisinger et al., 2014).
_____ END BOX 25-1 HERE _____
25.4.3. Psychological and Socio-cultural Factors Influencing Impacts of and Adaptation to Climate Change
Adapting to climate change relies on individuals accepting and understanding changing risks and opportunities, and
responding to these changes both psychologically and behaviourally (see Chapters 2, 16). The majority of
Australasians accept the reality of climate change and less than 10% fundamentally deny its existence (high
confidence; ShapeNZ, 2009; Leviston et al., 2011; Lewandowsky, 2011; Milfont, 2012; Reser et al., 2012b).
Australians perceive themselves to be at higher risk from climate change than New Zealanders and citizens of many
other countries, which may reflect recent experiences of climatic extremes (Gifford et al., 2009; Agho et al., 2010;
Ashworth et al., 2011; Milfont et al., 2012; Reser et al., 2012c). However, beliefs about climate change and its risks
vary over time, are uneven across society and reflect media coverage and bias, political preferences and gender
(ShapeNZ, 2009; Bacon, 2011; Leviston et al., 2012; Milfont, 2012), which can influence attitudes to adaptation
(Gardner et al., 2010; Gifford, 2011; Reser et al., 2011; Alexander et al., 2012; Raymond and Spoehr, 2013).
Surveys in Australia between 2007 and 2011 show moderate to high levels of climate change concern, distress,
frustration, resolve, psychological adaptation, and carbon-reducing behaviour (high agreement, medium evidence;
Agho et al., 2010; Reser et al., 2012b, c). About two thirds of respondents expected global warming to worsen, with
about half very or extremely concerned that they or their family would be affected directly. Direct experience with
environmental changes or events attributed to climate change, reported by 45% of respondents, was particularly
influential, but the extent to which resulting distress and concern translate into support for planned adaptation has
not been fully assessed (Reser et al., 2012a, b).
Perceived risks and potential losses from climate change depend on values associated by individuals with specific
places, activities and objects. Examples from Australia include the value placed on snow cover in the Snowy
Mountains (Gorman-Murray, 2008, 2010), risks to biodiversity and recreational values in coastal South Australia
(Raymond and Brown, 2011), conflicts between human uses and environmental priorities in national parks (Wyborn,
2009; Roman et al., 2010), and trade-offs between alternative water supplies and relocation in rural areas
(Hurlimann and Dolnicar, 2011). These and additional studies in Australasia confirm that the more individuals
identify with particular places and their natural features, the stronger the perceived potential loss but also the greater
the motivation to address environmental threats (e.g. Rogan et al., 2005; McCleave et al., 2006; Collins and Kearns,
2010; Gosling and Williams, 2010; Raymond et al., 2011; Russell et al., 2013). This indicates that ecosystem-based
climate change adaptation (see Box CC-EA) can provide co-benefits for subjective well-being and mental health,
especially for disadvantaged and indigenous communities (Berry et al., 2010; see also 25.8.2).
At the same time, social and cultural values and norms can constrain adaptation options for communities by limiting
the range of acceptable responses and processes (e.g. place attachment, differing values relating to near- versus long-
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term, private versus public, and economic versus environmental or social costs and benefits, and perceived
legitimacy of institutions). Examples of this are particularly prominent in Australasia in the coastal zone (e.g.
Hayward, 2008a; King et al., 2010; Gorddard et al., 2012; Hofmeester et al., 2012) and acceptance of water
recycling or pricing (e.g. Pearce et al., 2007; Kouvelis et al., 2010; Mankad and Tapsuwan, 2011).
Overall, these studies give high confidence that the experience and threat of climate change and extreme climatic
events are having appreciable psychological impacts, resulting in psychological and subsequent behavioural
adaptations, reflected in high levels of acceptance and realistic concern, motivational resolve, self-reported changes
in thinking, feeling and understanding of climate change and its implications, and behavioural engagement (Reser
and Swim, 2011; Reser et al., 2012a, b, c). However, adequate strategies and systems to monitor trends in
psychological and social impacts, adaptation and vulnerability are lacking, and such perspectives remain poorly
integrated with and dominated by bio-physical and economic characterisations of climate change impacts.
25.5. Freshwater Resources
25.5.1. Observed Impacts
Climate change impacts on water represent a cross-cutting issue affecting people, agriculture, industries and
ecosystems. The challenge of satisfying multiple demands with a limited resource is exacerbated by the high inter-
annual and inter-decadal variability of river flows (Chiew and McMahon, 2002; Peel et al., 2004; Verdon et al.,
2004; McKerchar et al., 2010) particularly in Australia. Declining river flows since the mid-1970s in far south-
western Australia have led to changed water management (see Box 11.2 in Hennessy et al., 2007). The
unprecedented decline in river flows during the 1997-2009 Millennium drought in south-eastern Australia resulted
in low irrigation water allocations, severe water restrictions in urban centres, suspension of water sharing
arrangements and major environmental impacts (Chiew and Prosser, 2011; Leblanc et al., 2012).
25.5.2. Projected Impacts
Figure 25-4 shows estimated changes to mean annual runoff across Australia for a 1°C global average warming
above current levels (Chiew and Prosser, 2011; Teng et al., 2012). The range of estimates arises mainly from
uncertainty in projected precipitation (Table 25-1). Hydrological modelling with CMIP3 future climate projections
indicates that freshwater resources in far south-eastern and far south-west Australia will decline (high confidence; by
0-40% and 20-70%, respectively, for 2°C warming) due to the reduction in winter precipitation (Table 25-1) when
most of the runoff in southern Australia occurs. The percent change in mean annual precipitation in Australia is
generally amplified as a 2 3 times larger percent change in mean annual streamflow (Chiew, 2006; Jones et al.,
2006). This can vary, however, with unprecedented declines in flow in far south-eastern Australia in the 1997 2009
drought (Cai and Cowan, 2008; Potter and Chiew, 2011; Chiew et al., 2013). Higher temperatures and associated
evaporation, tree re-growth following more frequent bushfires (Kuczera, 1987; Cornish and Vertessy, 2001; Marcar
et al., 2006; Lucas et al., 2007), interceptions from farm dams (van Dijk et al., 2006; Lett et al., 2009) and reduced
surface-groundwater connectivity in long dry spells (Petrone et al., 2010; Hughes et al., 2012) can further accentuate
declines. In the longer-term, water availability will also be affected by changes in vegetation and surface-atmosphere
feedbacks in a warmer and higher CO2 environment (Betts et al., 2007; Donohue et al., 2009; McVicar et al., 2010).
[INSERT FIGURE 25-4 HERE
Figure 25-4: Estimated changes in mean annual runoff for 1°C global average warming above current levels. Maps
show changes in annual runoff (percentage change; top row) and runoff depth (millimetres; bottom row), for dry,
median and wet (10th to 90th percentile) range of estimates, based on hydrological modelling using 15 CMIP3 climate
projections (Chiew et al., 2009; CSIRO, 2009; Petheram et al., 2012; Post et al., 2012). Projections for 2°C global
average warming are about twice that shown in the maps (Post et al., 2011). (Figure adapted from Chiew and Prosser,
2011; Teng et al., 2012).]
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In New Zealand, precipitation changes (Table 25-1) are projected to lead to increased runoff in the west and south of
the South Island and reduced runoff in the north-east of the South Island, and the east and north of the North Island
(medium confidence). Annual flows of eastward flowing rivers with headwaters in the Southern Alps (Clutha,
Waimakariri, Rangitata) are projected to increase by 5-10 % (median projection) by 2040 (Bright et al., 2008; Poyck
et al., 2011; Zammit and Woods, 2011) in response to higher alpine precipitation. Most of the increases occur in
winter and spring, as more precipitation falls as rain and snow melts earlier (Hendrikx et al., 2013). In contrast, the
Ashley River, slightly north of this region, is projected to have little change in annual flows, with the increase in
winter flows offset by reduced summer flows (Woods et al., 2008). The retreat of glaciers is expected to have only a
minor impact on river flows in the first half of the century (Chinn, 2001; Anderson et al., 2008).
Climate change will affect groundwater through changes in recharge rates and the relationship between surface
waters and aquifers. Dryland diffuse recharge in most of western, central and southern Australia is projected to
decrease because of the decline in precipitation, with increases in the north and some parts of the east because of
projected increase in extreme rainfall intensity (medium confidence; Crosbie et al., 2010; McCallum et al., 2010;
Crosbie et al., 2012). In New Zealand, a single study projects groundwater recharge in the Canterbury Plains to
decrease by about 10% by 2040 (Bright et al., 2008). Climate change will also degrade water quality, particularly
through increased material washoff following bushfires and floods (Box 25-6, Box 25-8).
25.5.3. Adaptation
The 1997 2009 drought in south-eastern Australia and projected declines in future water resources in southern
Australia are already stimulating adaptation (Box 25-2). In New Zealand, there is little evidence of water resources
adaptation specifically to climate change. Water in New Zealand is not as scarce generally and water policy reform
is driven more by pressure to maintain water quality while expanding agricultural activities, with an increasing focus
on collaborative management (Memon and Skelton, 2007; Memon et al., 2010; Lennox et al., 2011; Weber et al.,
2011) within national guidelines (LWF, 2010; MfE, 2011). Impacts of climate change on water supply, demand and
infrastructure have been considered by several New Zealand local authorities and consultancy reports (Jollands et
al., 2007; Williams et al., 2008; Kouvelis et al., 2010), but no explicit management changes have yet resulted.
_____ START BOX 25-2 HERE _____
Box 25-2. Adaptation through Water Resources Policy and Management in Australia
Widespread drought and projections of a drier future in south-eastern and far south-west Australia (Bates et al.,
2010; CSIRO, 2010; Potter et al., 2010; Chiew et al., 2011) saw extensive policy and management change in both
rural and urban water systems (Hussey and Dovers, 2007; Bates et al., 2008; Melbourne Water, 2010; DSE, 2011;
MDBA, 2011; NWC, 2011; Schofield, 2011). These management changes provide examples of adaptations,
building on previous policy reforms (Botterill and Dovers, 2013). The broad policy framework is set out in the 2004
National Water Initiative and 2007 Commonwealth Water Act. The establishment of the National Water
Commission (2004) and the Murray-Darling Basin Authority (2008) were major institutional reforms. The National
Water Initiative explicitly recognises climate change as a constraint on future water allocations. Official assessments
(NWC, 2009, 2011) and critiques (Connell, 2007; Grafton and Hussey, 2007; Byron, 2011; Crase, 2011; Pittock and
Finlayson, 2011) have discussed progress and shortcomings of the initiative, but assessment of its overall success is
made difficult by other factors such as on-going revisions to allocation plans and time lags to observable impacts.
Rural water reform in south-eastern Australia, focused on the Murray-Darling Basin, is currently being
implemented. The Murray-Darling Basin Plan (MDBA, 2011, 2012) will return 2750 GL/year of consumptive water
(about one fifth of current entitlements) to riverine ecosystems and develop flexible and adaptive water sharing
mechanisms to cope with current and future climates. In 2012, the Australian Government committed more than
A$12 billion nationally to upgrade water infrastructure, improve water use efficiency, and purchase water
entitlements for environmental use. The Basin Plan also includes an environmental watering plan to optimise
environmental outcomes for the Basin. Water markets are a key policy instrument, allowing water use patterns to
adapt to shifting availability and water to move toward higher value uses (NWC, 2010; Kirby et al., 2012). For
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example, the two-thirds reduction in irrigation water use over 2000 2009 in the Basin resulted in only 20%
reduction in gross agricultural returns, mainly because water use shifted to more valuable enterprises (Kirby et al.,
2012). Elsewhere, catchment management authorities and state agencies throughout south-eastern Australia develop
water management strategies to cope with prolonged droughts and climate change (e.g. DSE, 2011). Nevertheless, if
the extreme dry end of future water projections is realised (25.5.2, Figure 25-4), agriculture and ecosystems across
south-eastern and south-western Australia would be threatened even with comprehensive adaptation (see 25.6.1,
25.7.1, 25.7.2; Connor et al., 2009; Kirby et al., 2013).
Climate change and population growth are the two major factors that influence water planning in Australian capital
cities. In Melbourne, for example, planning has centred on securing new supplies that are more resilient to major
climate shocks; increasing use of alternative sources like sewage recycling and stormwater for non-potable water;
programs to reduce demand; water sensitive urban design; and integrated planning that considers climate change
impact on water supply, flood risk and stormwater and wastewater infrastructures (DSE, 2007; Skinner, 2010; DSE,
2011; Rhodes et al., 2012). Melbourne s water augmentation program includes a desalinisation plant with a 150
GL/year capacity (about one third of the current demand), following the lead of Perth where a desalinisation plant
was established in 2006 because of declining inflows since the mid-1970s (Rhodes et al., 2012). Melbourne s water
conservation strategies include water efficiency and rebate programs for business and industry, water smart gardens,
dual flush toilets, grey water systems, rainwater tank rebates, free water-efficient showerheads and voluntary
residential use targets. These conservation measures, together with water use restrictions since the early 2000s, have
reduced Melbourne s total per capita water use by 40% (Fitzgerald, 2009; Rhodes et al., 2012). Similar programs
reduced Brisbane s per capita water use by about 50% (Shearer, 2011), while adoption of water recycling and
rainwater harvesting resulted in up to 60% water savings in some parts of Adelaide (Barton and Argue, 2009).
The success of urban water reforms in the face of drought and climate change can be variously interpreted.
Increasing supply through desalinisation plants and water reuse schemes reduces the risk of future water shortages
and helps cities cope with increasing population. Uptake of household-scale adaptation options has been locally
significant but their long-term sustainability or reversibility in response to changing drivers and societal attitudes
needs further research (Troy, 2008; Brown and Farrelly, 2009; Mankad and Tapsuwan, 2011). Desalinisation plants
can be maladaptive due to their energy demand, and the enhancement of mass supply could create a disincentive for
reducing demand or increasing resilience through diversifying supply (Barnett and O'Neill, 2010; Taptiklis, 2011).
_____ END BOX 25-2 HERE _____
25.6. Natural Ecosystems
25.6.1. Inland Freshwater and Terrestrial Ecosystems
Terrestrial and freshwater ecosystems have suffered high rates of habitat loss and species extinctions since European
settlement in both Australia and New Zealand (Kingsford et al., 2009; Bradshaw et al., 2010; McGlone et al., 2010;
Lundquist et al., 2011; SoE, 2011); many reserves are small and isolated, and some key ecosystems and species
under-represented (Sattler and Taylor, 2008; MfE, 2010a; SoE, 2011). Many freshwater ecosystems are pressured
from over-allocation and pollution, especially in southern and eastern coastal regions in Australia (e.g. Ling, 2010).
Additional stresses include erosion, changes in nutrients and fire regimes, mining, invasive species, grazing and
salinity (Kingsford et al., 2009; McGlone et al., 2010; SoE, 2011). These increase vulnerability to rapid climate
change and provide challenges for both autonomous and managed adaptation (Steffen et al., 2009).
25.6.1.1. Observed Impacts
In Australian terrestrial systems, some recently observed changes in the distribution, genetics and phenology of
individual species, and in the structure and composition of some ecological communities, can be attributed to recent
climatic trends (medium to high confidence; see Box 25-3). Uncertainty remains regarding the role of non-climatic
drivers, including changes in atmospheric CO2, fire management, grazing and land-use. The 1997-2009 drought had
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severe impacts in freshwater systems in the eastern States and the Murray Darling Basin (Pittock and Finlayson,
2011) but in many freshwater systems, direct climate impacts are difficult to detect above the strong signal of over-
allocation, pollution, sedimentation, exotic invasions and natural climate variability (Jenkins et al., 2011). In New
Zealand, few if any impacts on ecosystems have been directly attributed to climate change rather than variability
(Box 25-3; McGlone et al., 2010; McGlone and Walker, 2011). Alpine treelines in New Zealand have remained
roughly stable for several hundred years (high confidence) despite 0.9°C average warming over the past century
(McGlone and Walker, 2011; Harsch et al., 2012).
25.6.1.2. Projected Impacts
Existing environmental stresses will interact with, and in many cases be exacerbated by, shifts in mean climatic
conditions and associated change in the frequency or intensity of extreme events, especially fire, drought and floods
(high confidence; Steffen et al., 2009; Bradstock, 2010; Murphy et al., 2012). Recent drought-related mortality has
been observed for amphibians in south-east Australia (Mac Nally et al., 2009), savannah trees in north-east Australia
(Fensham et al., 2009; Allen et al., 2010), mediterranean-type eucalypt forest in southwest Western Australia
(Matusik et al., 2013), and, eucalypts in sub-alpine regions in Tasmania (Calder and Kirkpatrick, 2008). Mass die-
offs of flying foxes and cockatoos have been observed during heatwaves (Welbergen et al., 2008; Saunders et al.,
2011). These examples provide high confidence that extreme heat and reduced water availability, either singly or in
combination, will be significant drivers of future population losses and will increase the risk of local species
extinctions in many areas (e.g. McKechnie and Wolf, 2010; see also Figure 25-5).
Species distribution modelling (SDM) consistently indicates future range contractions for Australia s native species
even assuming optimistic rates of dispersal, e.g. Western Australian Banksia spp. (Fitzpatrick et al., 2008), koalas
(Adams-Hosking et al., 2011), northern macropods (Ritchie and Bolitho, 2008), native rats (Green et al., 2008b),
greater gliders (Kearney et al., 2010b), quokkas (Gibson et al., 2010), platypus (Klamt et al., 2011), birds (Garnett et
al., 2013; van der Wal et al., 2013), and fish (Bond et al., 2011). In some studies, complete loss of climatically
suitable habitat is projected for some species within a few decades, and therefore increased risk of local and,
perhaps, global extinction (medium confidence). SDM has limitations (e.g. Elith et al., 2010; McGlone and Walker,
2011) but is being improved through integration with physiological (Kearney et al., 2010b) and demographic models
(Keith et al., 2008; Harris et al., 2012), genetic estimates of dispersal capacity (Duckett et al., 2013), and
incorporation into broader risk assessments (e.g. Williams et al., 2008; Crossman et al., 2012).
In Australia, assessments of ecosystem vulnerability have been based on observed changes, coupled with projections
of future climate in relation to known biological thresholds and assumptions about adaptive capacity (e.g. Laurance
et al., 2011; Murphy et al., 2012). There is very high confidence that one of the most vulnerable Australian
ecosystems is the alpine zone due to loss of snow cover, invasions by exotic species, and changed species
interactions (reviewed in Pickering et al., 2008). There is also high confidence in substantial risks to coastal
wetlands such as Kakadu National Park subject to saline intrusion (BMT WBM, 2011); tropical savannas subject to
changed fire regimes (Laurance et al., 2011); inland freshwater and groundwater systems subject to drought, over-
allocation and altered timing of floods (Pittock et al., 2008; Jenkins et al., 2011; Pratchett et al., 2011); peat-forming
wetlands along the east coast subject to drying (Keith et al., 2010); and biodiversity-rich regions such as southwest
Western Australia (Yates et al., 2010a; Yates et al., 2010b) and tropical and sub-tropical rainforests in Queensland
subject to drying and warming (Stork et al., 2007; Shoo et al., 2011; Murphy et al., 2012; Hagger et al., 2013).
The very few studies of climate change impacts on biodiversity in New Zealand suggest that on-going impacts of
invasive species (Box 25-4) and habitat loss will dominate climate change signals in the short- to medium-term
(McGlone et al., 2010), but that climate change has the potential to exacerbate existing stresses (McGlone and
Walker, 2011). There is limited evidence but high agreement that the rich biota of the alpine zone is at risk through
increasing shrubby growth and loss of herbs, especially if combined with increased establishment of invasive
species (McGlone et al., 2010; McGlone and Walker, 2011). Some cold water-adapted freshwater fish and
invertebrates are vulnerable to warming (August and Hicks, 2008; Winterbourn et al., 2008; Hitchings, 2009;
McGlone and Walker, 2011) and increased spring flooding may increase risks for braided-river bird species (MfE,
2008b). For some restricted native species, suitable habitat may increase with warming (e.g. native frogs; Fouquet et
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al., 2010) although limited dispersal ability will limit range expansion. Tuatara populations are at risk as warming
increases the ratio of males to females (Mitchell et al., 2010), although the lineage has persisted during higher
temperatures in the geological past (McGlone and Walker, 2011).
25.6.1.3. Adaptation
High levels of endemism in both countries (Lindenmayer, 2007; Lundquist et al., 2011) are associated with narrow
geographic ranges and associated climatic vulnerability, although there is greater scope for adaptive dispersal to
higher elevations in New Zealand than in Australia. Anticipated rates of climate change, together with fragmentation
of remaining habitat and limited migration options in many regions (Steffen et al., 2009; Morrongiello et al., 2011),
will limit in situ adaptive capacity and distributional shifts to more climatically suitable areas for many species (high
confidence). Significant local and global losses of species, functional diversity, and ecosystem services, and large
scale changes in ecological communities, are anticipated (e.g. Dunlop et al., 2012; Gallagher et al., 2012b; Murphy
et al., 2012).
There is increasing recognition in Australia that rapid climate change has fundamental implications for traditional
conservation objectives (e.g. Steffen et al., 2009; Prober and Dunlop, 2011; Dunlop et al., 2012; Murphy et al.,
2012). Research on impacts and adaptation in terrestrial and freshwater systems has been guided by the National
Adaptation Research Plans (Hughes et al., 2010; Bates et al., 2011) and by research undertaken within the CSIRO
Climate Adaptation Flagship. Climate change adaptation plans developed by many levels of government and Natural
Resource Management (NRM) bodies, supported by substantial Australian government funding, have identified
priorities that include: identification and protection of climatic refugia (Davis et al., 2013; Reside et al., 2013);
restoration of riparian zones to reduce stream temperatures (Davies, 2010; Jenkins et al., 2011); construction of
levees to protect wetlands from saltwater intrusion (Jenkins et al., 2011); reduction of non-climatic threats such as
invasive species to increase ecosystem resilience (Kingsford et al., 2009); ecologically-appropriate fire regimes
(Driscoll et al., 2010); restoration of environmental flows in major rivers (Kingsford and Watson, 2011; Pittock and
Finlayson, 2011); protecting and restoring habitat connectivity in association with expansion of the protected area
network (Dunlop and Brown, 2008; Mackey et al., 2008; Taylor and Philp, 2010; Prowse and Brook, 2011; Maggini
et al., 2013); and, active interventionist strategies such as assisted colonisation to reduce probability of species
extinctions (Burbidge et al., 2011; McIntyre, 2011) or restore ecosystem services (Lunt et al., 2013). Few specific
measures have been implemented and thus their effectiveness cannot yet be assessed. Biodiversity research and
management in New Zealand to date has taken little account of climate change-related pressures and continues to
focus largely on managing pressures from invasive species and predators, freshwater pollution, exotic diseases, and
halting the decline in native vegetation, although a number of specific recommendations have been made to improve
ecosystem resilience to future climate threats (McGlone et al., 2010; McGlone and Walker, 2011).
Climate change responses in other sectors may have beneficial as well as adverse impacts on biodiversity, but few
tools to assess risks from an integrated perspective have been developed (25.9.1, Box 25-10). Assessments of the
impacts of climate change on the provision of ecosystem services (such as pollination and erosion control) via
impacts on terrestrial and freshwater ecosystems are generally lacking. Similarly, the concept of Ecosystem-based
Adaptation, the role of healthy, well-functioning ecosystems in increasing the resilience of human sectors to the
impacts of climate change (see Chapters 4 and 5, and Box CC-EA), is relatively unexplored.
25.6.2. Coastal and Ocean Ecosystems
Australia s 60,000 km coastline spans tropical waters in the north to cool temperate waters off Tasmania and the
sub-Antarctic islands with sovereign rights over ~8.1 million km2, excluding the Australian Antarctic Territory
(Richardson and Poloczanska, 2009). New Zealand has ~18,000 km of coastline, spanning subtropical to sub-
Antarctic waters, and the world's fifth largest Exclusive Economic Zone at 4.2 million km2 (Gordon et al., 2010).
The marine ecosystems of both countries are considered hotspots of global marine biodiversity with many rare,
endemic and commercially important species (Hoegh-Guldberg et al., 2007; Blanchette et al., 2009; Gordon et al.,
2010; Gillanders et al., 2011; Lundquist et al., 2011). The increasing density of coastal populations (see 25.3) and
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stressors such as pollution and sedimentation from settlements and agriculture will intensify non-climate stressors in
coastal areas (high confidence; e.g. Russell et al., 2009). Coastal habitats provide many ecosystem services
including coastal protection (Arkema et al., 2013) and carbon storage, particularly in seagrass, saltmarsh and
mangroves, which could become increasingly important for mitigation (e.g. Irving et al., 2011). Coastal ecosystems
occupy <1% of the land mass but may account for 39% of Australia s average national annual carbon burial
(estimated total: 466 millions tonnes CO2-eq per year; Lawrence et al., 2012).
25.6.2.1. Observed Impacts
There is high confidence that climate change is already affecting the oceans around Australia (Pearce and Feng,
2007; Poloczanska et al., 2007; Lough and Hobday, 2011) and warming the Tasman sea in northern New Zealand
(Sutton et al., 2005; Lundquist et al., 2011); average climate zones have shifted south by more than 200 km along
the northeast and about 100 km along the northwest Australian coasts since 1950 (Lough, 2008). The rate of
warming is even faster in southeast Australia, with a poleward advance of the East Australia Current of ~350 km
over the past 60 years (Ridgway, 2007). Based on elevated rates of ocean warming, southwest and southeast
Australia are recognized as global warming hotspots (Wernberg et al., 2011). It is virtually certain that the increased
storage of carbon by the ocean will increase acidification in the future, continuing the observed trends of the past
decades in Australia as elsewhere (Howard et al., 2012; see also WGI 3.8, 6.44).
Recently observed changes in marine systems around Australia are consistent with warming oceans (high
confidence; Box 25-3). Examples include changes in phytoplankton productivity (Thompson et al., 2009; Johnson et
al., 2011); species abundance of macroalgae (Johnson et al., 2011); growth rates of abalone (Johnson et al., 2011),
southern rock lobster (Pecl et al., 2009; Johnson et al., 2011), coastal fish (Neuheimer et al., 2011) and coral (De'ath
et al., 2009); life cycles of southern rock lobster (Pecl et al., 2009) and seabirds (Cullen et al., 2009; Chambers et
al., 2011); and, distribution of subtidal seaweeds (Johnson et al., 2011; Wernberg et al., 2011; Smale and Wernberg,
2013), plankton (Mcleod et al., 2012), fish (Figueira et al., 2009; Figueira and Booth, 2010; Last et al., 2011; Madin
et al., 2012), sea urchins (Ling et al., 2009) and intertidal invertebrates (Pitt et al., 2010).
Habitat-related impacts are more prevalent in northern Australia (Pratchett et al., 2011), while distribution changes
are reported more often in southern waters (Madin et al., 2012), particularly south-east Australia, where warming
has been greatest. The 2011 marine heat wave in Western Australia caused the first-ever reported bleaching at
Ningaloo reef (Abdo et al., 2012; Feng et al., 2013) resulting in coral mortality (Moore et al., 2012; Depczynski et
al., 2013) and changes in community structure and composition (Smale and Wernberg, 2013; Wernberg et al.,
2013). About 10% of the observed 50% decline in coral cover on the Great Barrier Reef since 1985 has been
attributed to bleaching, the remainder to cyclones and predators (De'ath et al., 2012). Changes in distribution and
abundance of marine species in New Zealand are primarily linked to ENSO-related variability that dominates in
many time series (Clucas, 2011; Lundquist et al., 2011; McGlone and Walker, 2011; Schiel, 2011), although water
temperature is also important (e.g. Beentjes and Renwick, 2001). New Zealand fisheries exported over $1.5 billion
worth of product in 2012 (SNZ, 2013) and variability in ocean circulation and temperature plays an important role in
local fish abundance (e.g. Chiswell and Booth, 2005; Dunn et al., 2009); no climate change impacts have been
reported at this stage (Dunn et al., 2009), although this may be due to insufficient monitoring.
25.6.2.2. Projected Impacts
Even though evidence of climate impacts on coastal habitats is limited to date, confidence is high that negative
impacts will arise with continued climate change (Lovelock et al., 2009; McGlone and Walker, 2011; Traill et al.,
2011; Chapter 6). Some coastal habitats such as mangroves are projected to expand further landward, driven by sea-
level rise and exacerbated by soil subsidence if rainfall declines (medium confidence; Traill et al., 2011), although
this may be at the expense of saltmarsh and constrained in many regions by the built environment (DCC, 2009;
Lovelock et al., 2009; Rogers et al., 2012). Estuarine habitats will be affected by changing rainfall or sediment
discharges, as well as connectivity to the ocean (high confidence; Gillanders et al., 2011). Loss of coastal habitats
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and declines in iconic species will result in substantial impacts on coastal settlements and infrastructure from direct
impacts such as storm surge, and will affect tourism (medium confidence; 25.7.5).
Changes in temperature and rainfall, and sea level rise, are expected to lead to secondary effects, including erosion,
landslips, and flooding, affecting coastal habitats and their dependent species, e.g. loss of habitat for nesting birds
(high confidence; Chambers et al., 2011). Increasing ocean acidification is expected to affect many taxa (medium
confidence; see also Box CC-OA, Chapters 6, 30) including corals (Fabricius et al., 2011), coralline algae (Anthony
et al., 2008), calcareous plankton (Richardson et al., 2009; Thompson et al., 2009; Hallegraeff, 2010), reef fishes
(Munday et al., 2009; Nilsson et al., 2012), bryozoans, and other benthic calcifiers (Fabricius et al., 2011). Deep-sea
scleractinian corals are also expected to decline with ocean acidification (Miller et al., 2011).
The AR4 identified the Great Barrier Reef (GBR) as highly vulnerable to both warming and acidification (Hennessy
et al., 2007). Recent observations of bleaching (GBRMPA, 2009a) and reduced calcification in both the GBR and
other reef systems (Cooper et al., 2008; De'ath et al., 2009; Cooper et al., 2012), along with model and experimental
studies (Hoegh-Guldberg et al., 2007; Anthony et al., 2008; Veron et al., 2009) confirm this vulnerability (see also
Box CC-CR). The combined impacts of warming and acidification associated with atmospheric CO2 concentrations
in excess of 450-500 ppm are projected to be associated with increased frequency and severity of coral bleaching,
disease incidence and mortality, in turn leading to changes in community composition and structure including
increasing dominance by macroalgae (high confidence; Hoegh-Guldberg et al., 2007; Veron et al., 2009). Other
stresses, including rising sea levels, increased cyclone intensity, and nutrient-enriched and freshwater runoff, will
exacerbate these impacts (high confidence; Hoegh-Guldberg et al., 2007; Veron et al., 2009; GBRMPA, 2011).
Thermal thresholds and the ability to recover from bleaching events vary geographically and between species (e.g.
Diaz-Pulido et al., 2009) but evidence of the ability of corals to adapt to rising temperatures and acidification is
limited and appears insufficient to offset the detrimental effects of warming and acidification (robust evidence,
medium agreement; Hoegh-Guldberg, 2012; Howells et al., 2013; Box CC-CR).
Under all SRES scenarios and a range of CMIP3 models, pelagic fishes such as sharks, tuna and billfish are
projected to move further south on the east and west coasts of Australia (high confidence; Hobday, 2010). These
changes depend on sensitivity to water temperature, and may lead to shifts in species-overlap with implications for
by-catch management (Hartog et al., 2011). Poleward movements are also projected for coastal fish species in
Western Australia (Cheung et al., 2012) and a complex suite of impacts are expected for marine mammals
(Schumann et al., 2013). A strengthening East Auckland Current in northern New Zealand is expected to promote
establishment of tropical or sub-tropical species that currently occur as vagrants in warm La Nina years (Willis et
al., 2007). Such shifts suggest potentially substantial changes in production and profit of both wild fisheries
(Norman-Lopez et al., 2011) and aquaculture species such as salmon, mussels and oysters (medium confidence;
Hobday et al., 2008; Hobday and Poloczanska, 2010). Ecosystem models also project changes to habitat and
fisheries production (low confidence; Fulton, 2011; Watson et al., 2012).
25.6.2.3. Adaptation
In Australia, research on marine impacts and adaptation has been guided by the National Adaptation Research Plan
for Marine Biodiversity and Resources (Mapstone et al., 2010), programs within the CSIRO Climate Adaptation
Flagship and the Great Barrier Reef Marine Park Authority (GBRMPA, 2007). Limits to autonomous adaptation are
unknown for almost all species, although limited experiments suggests capacity for response on a scale comparable
to projected warming for some species (e.g. coral reef fish; Miller et al., 2012) and not others (e.g. Antarctic krill;
Kawaguchi et al., 2013). Planned adaptation options include removal of human barriers to landward migration of
species, beach nourishment, management of environmental flows to maintain estuaries (Jenkins et al., 2010), habitat
provision (Hobday and Poloczanska, 2010), assisted colonisation of seagrass and species such as turtles (e.g.
Fuentes et al., 2009) and burrow modification for nesting seabirds (Chambers et al., 2011). For southern species on
the continental shelf, options are more limited because suitable habitat will not be present the next shallow water
to the south is Macquarie Island. There is low confidence about the adequacy of autonomous rates of adaptation by
species, although recent experiments with coral reef fish suggest that some species may adapt to the projected
climate changes (Miller et al., 2012).
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Management actions to increase coral reef resilience include reducing fishing pressure on herbivorous fish,
protecting top predators, managing runoff quality, and minimizing other human disturbances, especially through
marine protected areas (Hughes et al., 2007; Veron et al., 2009; Wooldridge et al., 2012). Such actions will slow,
but not prevent, long-term degradation of reef systems once critical thresholds of ocean temperature and acidity are
exceeded (high confidence), and so novel options, including assisted colonisation and shading critical reefs, have
been proposed but remain untested at scale (Rau et al., 2012). Seasonal forecasting can also prepare managers for
bleaching events (Spillman, 2011).
Adaptation by the fishing industry to shifting distributions of target species is considered possible by most
stakeholders (e.g. southern rock lobster fishery; Pecl et al., 2009). Assisted colonisation to maintain production in
the face of declining recruitment may also be possible for some high value species, and has been trialled for the
southern rock lobster (Green et al., 2010a). Options for aquaculture include disease management, alternative site
selection, and selective breeding (Battaglene et al., 2008), but implementation is only preliminary. Marine protected
area planning is not explicitly considering climate change in either country, but reserve performance will be affected
by projected environment shifts and novel combinations of species, habitats and human pressures (Hobday, 2011).
_____ START BOX 25-3 HERE _____
Box 25-3. Impacts of a Changing Climate in Natural and Managed Ecosystems
Observed changes in species, and in natural and managed ecosystems (25.6.1, 25.6.2, 25.7.2) provide multiple lines
of evidence of the impacts of a changing climate1. Examples of observations published since the AR4 are shown in
Table 25-3.
[INSERT TABLE 25-3 HERE
Table 25-3: Examples of detected changes in species, natural and managed ecosystems, consistent with a climate
change1 signal, published since the AR4. Confidence in detection of change is based on the length of study, and the
type, amount and quality of data in relation to the natural variability in the particular species or system. Confidence
in the role of climate as a major driver of the change is based on the extent to which the detected change is
consistent with that expected under climate change, and to which other confounding or interacting non-climate
factors have been considered and been found insufficient to explain the observed change.]
[FOOTNOTE 1: Consistent with the IPCC definition, a change in climate refers to any statistically detectable signal;
it does not necessarily imply a human cause. See Glossary, Table 25-1 and 25.2.]
_____ END BOX 25-3 HERE _____
25.7. Major Industries
25.7.1. Production Forestry
Australia has about 149 Mha forests, including woodlands. Two Mha are plantations and 9.4 Mha multiple-use
native forests, and forestry contributes around $7 billion annually to GDP (ABARES, 2012). New Zealand s
plantation estate in production forests comprises about 1.7 Mha (90% Pinus radiata), with recent contractions due to
increased profitability of dairying (FOA and MPI, 2012; MfE, 2013).
25.7.1.1. Observed and Projected Impacts
Existing climate variability and other confounding factors have so far prevented the detection of climate change
impacts on forests. Modelled projections are based on ecophysiological responses of forests to CO2, water and
temperatures. In Australia, potential changes in water availability will be most important (very high confidence; e.g.
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reviews by Battaglia et al., 2009; Medlyn et al., 2011b). Modelling future distributions or growth rates indicate that
plantations in south-west Western Australia are most at risk due to declining rainfall, and there is high confidence
that plantation growth will be reduced by temperature increases in hotter regions, especially where species are
grown at the upper range of their temperature tolerances (Medlyn et al., 2011a). Moderate reductions in rainfall and
increased temperature could be offset by fertilisation from increasing CO2 (limited evidence, medium agreement;
Simioni et al., 2009). In cool regions where water is not limiting, higher temperatures could benefit production
(Battaglia et al., 2009). In New Zealand, temperatures are mostly sub-optimal for growth of P. radiata and water
relations are generally less limiting (Kirschbaum and Watt, 2011). Warming is expected to increase P. radiata
growth in the cooler south (very high confidence), whereas in the warmer north, temperature increases can reduce
productivity, but CO2 fertilisation may offset this (medium confidence; Kirschbaum et al., 2012).
Modelling studies are limited by their reliance on key assumptions which are difficult to verify experimentally, e.g.
the degree to which photosynthesis remains stimulated under elevated CO2 (Battaglia et al., 2009). Most studies also
exclude impacts of pests, diseases, weeds, fire and wind damage that may change adversely with climate. Fire, for
instance, poses a significant threat in Australia and is expected to worsen with climate change (see Box 25-6),
especially for the commercial forestry plantations in the southern winter-rainfall regions (Williams et al., 2009;
Clarke et al., 2011). In New Zealand, changes in biotic factors are particularly important as they already affect
plantation productivity. Dothistroma blight, for instance, is a serious pine disease with a temperature optimum that
coincides with New Zealand s warmer, but not warmest, pine-growing regions; under climate change, its severity is,
therefore, expected to reduce in the warm central North Island but increase in the cooler South Island (high
confidence) where it could offset temperature-driven improved plantation growth (Watt et al., 2011a). There is
medium evidence and high agreement of similar future southward shifts in the distribution of existing plantation
weed, insect pest and disease species in Australia (see review in Medlyn et al., 2011b).
25.7.1.2. Adaptation
Depending on the extent of climate changes and plant responses to increasing CO2, the above studies provide limited
evidence but high agreement of potential net increased productivity in many areas, but only where soil nutrients are
not limiting. Adaptation strategies include changes to species or provenance selection towards trees better adapted to
warmer conditions, or adopting different silvicultural options to increase resilience to climatic or biotic stresses,
such as pest challenges (White et al., 2009; Booth et al., 2010; Singh et al., 2010; Wilson and Turton, 2011a). The
greatest barriers to long-term adaptation planning are incomplete knowledge of plant responses to increased CO2 and
uncertainty in regional climate scenarios (medium evidence, high agreement; Medlyn et al., 2011b). The rotation
time of plantation forests of about 30 years or more makes proactive adaptation important but also challenging.
25.7.2. Agriculture
Australia produces 93% of its domestic food requirements and exports 76% of agricultural production (PMSEIC,
2010a). New Zealand agriculture contributes about 56% of total export value and dairy products 27%; 95% of dairy
products are exported (SNZ, 2012b). Agricultural production is sensitive to climate (especially drought; Box 25-5)
but also to many non-climate factors such as management, which thus far has limited both detection and attribution
of climate-related changes (see Chapters 7 and 18; Webb et al., 2012a; Darbyshire et al., 2013). Because the region
is a major exporter providing, for example, over 40% of the world trade in dairy products changes in production
conditions in the region have a major influence on world supply (OECD, 2011). This implies that climate change
impacts could have consequences for food security not just locally but even globally (Qureshi et al., 2013a).
25.7.2.1. Projected Impacts and Adaptation Livestock Systems
Livestock grazing dominates land use by area in the region. At the Australian national level, the net effect of a 3°C
temperature increase (from a 1980-99 baseline) is expected to be a 4% reduction in gross value of the beef, sheep
and wool sector (McKeon et al., 2008). Dairy output is projected to decline in all regions of Australia other than
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Tasmania under a 1°C increase by 2030 (Hanslow et al., 2013). Projected changes in national pasture production for
dairy, sheep and beef pastures in New Zealand range from an average reduction of 4% across climate scenarios for
the 2030s (Wratt et al., 2008) to increases of up to 4% for two scenarios in the 2050s (Baisden et al., 2010) when the
models included CO2 fertilisation and nitrogen feedbacks.
Studies modelling seasonal changes in fodder supply show greater sensitivity in animal production to climate change
and elevated CO2 than models using annual average production, with some impacts expected even under modest
warming (high confidence) in both New Zealand (Lieffering et al., 2012) and Australia (Moore and Ghahramani,
2013). Across 25 sites in southern Australia (an area that produces 85% of sheep and 40% of beef production by
value) modelled profitability declined at most sites by the 2050s because of a shorter growing season due to changes
in both rainfall and temperature (Moore and Ghahramani, 2013). In New Zealand, projected changes in seasonal
pasture growth drove changes in animal production at four sites representing the main areas of sheep production
(Lieffering et al., 2012). In Hawke s Bay, changes in stock number and the timing of grazing were able to maintain
farm income for a period in the face of variable forage supply but not in the longer term. In Southland and Waikato,
projected increases in early spring pasture growth posed management problems in maintaining pasture quality, yet,
if these were met, animal production could be maintained or increased. The temperature-humidity index (THI), an
indicator of potential heat stress for animals, increased from 1960-2008 in the Murray Dairy region of Australia and
further increases and reductions in milk production are projected (Nidumolu et al., 2011). Shading can substantially
reduce, but not avoid, the temperature and humidity effects that produce a high THI (Nidumolu et al., 2011).
Rainfall is a key determinant of inter-annual variability in production and profitability of pastures and rangelands
(Radcliffe and Baars, 1987; Steffen et al., 2011) yet remains the most uncertain change. In northern Australia,
incremental adaptation may be adequate to manage risks of climate change to the grazing industry but an increasing
frequency of droughts and reduced summer rainfall will potentially drive the requirement for transformational
change (Cobon et al., 2009). Rangelands that are currently water-limited are expected to show greater sensitivity to
temperature and rainfall changes than nitrogen-limited ones (Webb et al., 2012b). The water-sparing effect of
elevated CO2 (offsetting reduced water availability from reduced rainfall and increased temperatures) is invoked in
many impact studies but does not always translate into production benefits (Kamman et al., 2005; Newton et al.,
2006; Stokes and Ash, 2007; Wan et al., 2007). The impacts of elevated CO2 on forage production, quality, nutrient
cycling and water availability remains the major uncertainty in modelling system responses (McKeon et al., 2009;
Finger et al., 2010); recent findings of grazing impacts on plant species composition (Newton et al., 2013) and
nitrogen fixation (Watanabe et al., 2013) under elevated CO2 have added to this uncertainty. New Zealand agro-
ecosystems are subject to erosion processes strongly driven by climate; greater certainty in projections of rainfall,
particularly storm frequency, are needed to better understand climate change impacts on erosion and consequent
changes in the ecosystem services provided by soils (Basher et al., 2012).
25.7.2.2. Projected Impacts and Adaptation Cropping
Experiments with elevated CO2 at two sites with different temperatures have shown a wide range in the response of
current wheat cultivars (Fitzgerald et al., 2010). Modelling suggests there is the potential to increase New Zealand
wheat yields under climate change with appropriate choices of cultivars and sowing dates (high confidence; Teixeira
et al., 2012). In Australia, the selection of appropriate cultivars and sowing times are projected to result in increased
wheat yields in high rainfall areas such as southern Victoria under climate change and maintenance of current yields
in some areas expected to be drier (e.g. north-western Victoria; O'Leary et al., 2010). However, if extreme low
rainfall scenarios are realized in areas such as South Australia then changes in cultivars and fertilizer applications
are not expected to maintain current yields by 2080 (Luo et al., 2009). Under the more severe climate scenarios and
without adaptation, Australia could become a net importer of wheat (Howden et al., 2010). One caveat to modelling
studies is that an intercomparison of 27 wheat models found large differences between model outputs for already dry
and hot Australian sites in response to increasing CO2 and temperature (Asseng et al., 2013; Carter, 2013).
Rice production in Australia is largely dependent on irrigation and climate change impacts will strongly depend on
water availability and price (Gaydon et al., 2010). Sugarcane is also strongly water dependent (Carr and Knox,
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2011); yields may increase where rainfall is unchanged or increased, but rising temperatures could drive up
evapotranspiration and increase water use (medium confidence; Park et al., 2010).
Observed trends and modelling for wine-grapes suggest that climate change will lead to earlier budburst, ripening
and harvest for most regions and scenarios (high confidence; Grace et al., 2009; Sadras and Petrie, 2011; Webb et
al., 2012a). Without adaptation, reduced quality is expected in all Australian regions (high confidence; Webb et al.,
2008). Change in cultivar suitability in specific regions is expected (Clothier et al., 2012), with potential for
development of cooler or more elevated sites within some regions (Tait, 2008; Hall and Jones, 2009) and/or
expansion to new regions, with some growers in Australia already relocating (e.g. to Tasmania; Smart, 2010).
Climate change and elevated CO2 impacts on weeds, pests and diseases are highly uncertain (see Box 25.4). Future
performance of currently effective plant resistance mechanisms under elevated CO2 and temperature is particularly
important (Melloy et al., 2010; Chakraborty et al., 2011) as is the future efficacy of widely used biocontrol, i.e. the
introduction or stimulation of natural enemies to control pests (Gerard et al., 2012). Australia is ranked second and
New Zealand fourth in the world in the number of biological control agent introductions (Cock et al., 2010).
25.7.2.3. Integrated Adaptation Perspectives
Future water demand by the sector is critical for planning (Box 25-2). Irrigated agriculture occupies less than 1% of
agricultural land in Australia but accounted for 28% of gross agricultural production value in 2010/11; almost half of
this was produced the Murray Darling Basin, which used 68% of all irrigation water (ABS, 2012b; DAFF, 2012).
Reduced inflow under dry climate scenarios is predicted to reduce substantially the value of agricultural production
in the Basin (high agreement, robust evidence; Garnaut, 2008; Quiggin et al., 2010; Qureshi et al., 2013b), e.g. in
one study by 12-44% to 2030 and 49-72% to 2050 (A1F1; Garnaut, 2008).Water availability also constrains
agricultural expansion: 17 Mha in northern Australia could support cropping but only 1% has appropriate water
availability (Webster et al., 2009). In New Zealand, the irrigated area has risen by 82% since 1999 to over 1 Mha;
76% is on pasture (Rajanayaka et al., 2010). The New Zealand dairy herd doubled between 1980-2009 expanding
from high rainfall zones (>2000 mm annual) into drier, irrigation-dependent areas (600-1000 mm annual); this
dependence will increase with further expansion (Robertson, 2010), which is being supported by the Government s
Irrigation Acceleration Fund.
Many adaptation options such as flexible water allocation, irrigation and seasonal forecasting support managing risk
in the current climate (Howden et al., 2008; Botterill and Dovers, 2013) and adoption is often high (Hogan et al.,
2011a; Kenny, 2011). However, incremental on-farm adaptation has limits (Park et al., 2012) and may hinder
transformational change such as diversification of land use or relocation (see Box 25-5) if it encourages persistence
where climate change may take current systems beyond their response capacity (Marshall, 2010; Park et al., 2012;
Rickards and Howden, 2012). In many cases, transformational change requires a greater level of commitment,
access to more resources, and greater integration across all levels of decision-making that encompass both on- and
off-farm knowledge, processes and values (Marshall, 2010; Rickards and Howden, 2012).
_____ START BOX 25-4 HERE _____
Box 25-4. Biosecurity
Biosecurity is a high priority for Australia and New Zealand given the economic importance of biologically-based
industries and risks to endemic species and iconic ecosystems. The biology and potential risk from invasive and
native pathogenic species will be altered by climate change (high confidence; Roura-Pascual et al., 2011), but
impacts may be positive or negative depending on the particular system.
[INSERT TABLE 25-4 HERE
Table 25-4: Examples of potential consequences of climate change for invasive and pathogenic species relevant to
Australia and New Zealand, with consequence categories based on Hellman et al. (2008).]
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_____ END BOX 25-4 HERE _____
_____ START BOX 25-5 HERE _____
Box 25-5. Climate Change Vulnerability and Adaptation in Rural Areas
Rural communities in Australasia have higher proportions of older and unemployed people than urban populations
(Mulet-Marquis and Fairweather, 2008). Employment and economic prospects depend heavily on the physical
environment and hence are highly exposed to climate (averages, variability and extremes) as well as changing
commodity prices. These interact with other economic, social and environmental pressures, such as changing
government policies (e.g. on drought, carbon pricing; Productivity Commission, 2009; Nelson et al., 2010) and
access to water resources. The vulnerability of rural communities differs within and between countries reflecting
differences in financial security, environmental awareness, policy and social support, strategic skills and capacity for
diversification (Bi and Parton, 2008; Marshall, 2010; Nelson et al., 2010; Hogan et al., 2011b; Kenny, 2011).
Climate change will affect rural industries and communities through impacts on resource availability and
distribution, particularly water. Decreased availability and/or increased demand, or price, in response to climate
change will increase tensions among agricultural, mining, urban and environmental water users (very high
confidence), with implications for governance and participatory adaptation processes to resolve conflicts (see 25.4.2,
25.6.1, 25.7.2, 25.7.3, Box 25-2, Box 25-10). Communities will also be affected through direct impacts on primary
production, extraction activities, critical infrastructure, population health and recreational and culturally significant
sites (see 25.7, 25.8; Kouvelis et al., 2010; Balston et al., 2012).
Altered production and profitability risks and/or land use will translate into complex and interconnected effects on
rural communities, particularly income, employment, service provision, and reduced volunteerism (Stehlik et al.,
2000; Bevin, 2007; Kerr and Zhang, 2009). The prolonged drought in Australia during the early 2000s, for example,
had many interrelated negative social impacts in rural communities, including farm closures, increased poverty,
increased off-farm work and, hence, involuntary separation of families, increased social isolation, rising stress and
associated health impacts, including suicide (especially of male farmers), accelerated rural depopulation and closure
of key services (high agreement, robust evidence; Alston, 2007; Edwards and Gray, 2009; Alston, 2010, 2012;
Hanigan et al., 2012; see also Box CC-GC). Positive social change also occurred, however, including increased
social capital through interaction with community organisations (Edwards and Gray, 2009). While social and
cultural changes have the potential to undermine the adaptive capacity of communities (Smith et al., 2011), robust
ongoing engagement between farmers and the local community can contribute to a strong sense of community and
enhance potential for resilience (McManus et al., 2012; see also 25.4.3).
The economic impact of droughts on rural communities and the entire economy can be substantial. The most recent
drought in Australia (2006/7-2008/9), for example, is estimated to have reduced national GDP by about 0.75%
(RBA, 2006) and regional GDP in the southern Murray Darling Basin was about 5.7% below forecast in 2007/08,
along with the temporary loss of 6000 jobs (Wittwer and Griffith, 2011). Widespread drought in New Zealand
during 2007-2009 reduced direct and off-farm output by about NZ$3.6 billion (Butcher, 2009). The 2012-13 drought
in New Zealand is estimated to have reduced national GDP by 0.3-0.6% and contributed to a significant rise in
global dairy prices, which tempered even greater domestic economic losses (Kamber et al., 2013). Drought
frequency and severity are projected to increase in many parts of the region (Table 25-1).
The decisions of rural enterprise managers have significant consequences for and beyond rural communities
(Pomeroy, 1996; Clark and Tait, 2008). Many current responses are incremental, responding to existing climate
variability (Kenny, 2011). Transformational change has occurred where industries and individuals are relocating part
of their operations in response to recent and/or expectations of future climate or policy change (Kenny, 2011; see
also Box 25-10), e.g. rice (Gaydon et al., 2010), wine-grapes (Park et al., 2012), peanuts (Thorburn et al., 2012) or
changing and diversifying land use in situ (e.g. the recent switch from grazing to cropping in South Australia;
Howden et al., 2010). Such transformational changes are expected to become more frequent and widespread with a
changing climate (high confidence; 25.7.2), with positive or negative implications for the wider communities in
origin and destination regions (Kiem and Austin, 2012).
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Although stakeholders within rural communities differ in their vulnerabilities and adaptive capacities, they are
bound by similar dependence upon critical infrastructure and resources, economic conditions, government policy
direction, and societal expectations (Loechel et al., 2013). Consequently, adaptation to climate change will require
an approach that devolves decision-making to the level where the knowledge for effective adaptations resides, using
open communication, interaction and joint-planning (Nelson et al., 2008; Kiem and Austin, 2013).
_____ END BOX 25-5 HERE _____
_____ START BOX 25-6 HERE _____
Box 25-6. Climate Change and Fire
Fire during hot, dry and windy summers in southern Australia can cause loss of life and substantial property damage
(Cary et al., 2003; Adams and Attiwill, 2011). The Black Saturday bushfires in Victoria in February 2009, for
example, burnt over 3,500 km2, caused 173 deaths, destroyed over 2,000 buildings and caused damages of A$4
billion (Cameron et al., 2009; VBRC, 2010). This fire occurred toward the end of a 13-year drought (CSIRO, 2010)
and after an extended period of consecutive days over 30°C (Tolhurst, 2009).
Climate change is expected to increase the number of days with very high and extreme fire weather (Table 25-1),
with greater changes where fire is weather-constrained (most of southern Australia; many, in particular eastern and
northern, parts of New Zealand) than where it is constrained by fuel load and ignitions (tropical savannas in
Australia). Fire season length will be extended in many already high-risk areas (high confidence) and so reduce
opportunities for controlled burning (Lucas et al., 2007). Higher CO2 may also enhance fuel loads by increasing
vegetation productivity in some regions (Donohue et al., 2009; Williams et al., 2009; Bradstock, 2010; Hovenden
and Williams, 2010; King et al., 2011).
Climate change and fire will have complex impacts on vegetation communities and biodiversity (Williams et al.,
2009). Greatest impacts in Australia are expected in sclerophyll forests of the south-east and south-west (Williams et
al., 2009). Most New Zealand native ecosystems have limited exposure but also limited adaptations to fire (Ogden et
al., 1998; McGlone and Walker, 2011). There is high confidence that increased fire incidence will increase risk in
southern Australia to people, property and infrastructure such as electricity transmission lines (Parsons Brinkerhoff,
2009; O'Neill and Handmer, 2012; Whittaker et al., 2013) and in parts of New Zealand where urban margins expand
into rural areas (Jakes et al., 2010; Jakes and Langer, 2012); exacerbate some respiratory conditions such as asthma
(Johnston et al., 2002; Beggs and Bennett, 2011); and increase economic risks to plantation forestry (Watt et al.,
2008; Pearce et al., 2011). Forest regeneration following wildfires also reduces water yields (Brown et al., 2005;
MDBC, 2007), while reduced vegetation cover increases erosion risk and material washoff to waterways with
implications for water quality (Shakesby et al., 2007; Wilkinson et al., 2009; Smith et al., 2011a).
In Australia, fire management will become increasingly challenging under climate change, potentially exacerbating
conflicting management objectives for biodiversity conservation versus protection of property (high confidence;
O'Neill and Handmer, 2012; Whittaker et al., 2013). Current initiatives centre on planning and regulations, building
design to reduce flammability, fuel management, early warning systems, and fire detection and suppression
(Handmer and Haynes, 2008; Preston et al., 2009; VBRC, 2010; O'Neill and Handmer, 2012). Some Australian
authorities are taking climate change into account when rethinking approaches to managing fire to restore
ecosystems while protecting human life and properties (Preston et al., 2009; Adams and Attiwill, 2011). Improved
understanding of climate drivers of fire risk is assisting fire management agencies, landowners and communities in
New Zealand (Pearce et al., 2008; Pearce et al., 2011), although changes in management to date show little evidence
of being driven by climate change.
_____ END BOX 25-6 HERE _____
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25.7.3. Mining
Australia is the world s largest exporter of coking coal and iron ore and has the world s largest resources of brown
coal, nickel, uranium, lead and zinc (ABS, 2012c). Recent events demonstrated significant vulnerability to climate
extremes: the 2011 floods reduced coal exports by 25-54 million tonnes and led to A$5-9bn revenue lost in that year
(ABARES, 2011; RBA, 2011), and tropical cyclones regularly disrupted mining operations over the past decade
(McBride, 2012; Sharma et al., 2013). Flood impacts were exacerbated by regulatory constraints on mine
discharges, highlighting tensions among industry, social and ecological management objectives (QRC, 2011), and
by flooding affecting road and rail transport to major shipping ports (QRC, 2011; Sharma et al., 2013).
Projected changes in climate extremes imply increasing sector vulnerability without adaptation (high confidence;
Hodgkinson et al., 2010a; Hodgkinson et al., 2010b). Stakeholders have conducted initial climate risk assessments
(Mills, 2009) and perceive the adaptive capacity of the industry to be high (Hodgkinson et al., 2010a; Loechel et al.,
2010; QRC, 2011), but costs and broader benefits are yet to be explored along the value-chain and evaluated for
community support. On-going challenges include competition for energy and water, climate change scepticism,
dealing with contrasting extremes, avoiding maladaptation, and mining-community relations regarding response
options, acceptable mine discharges and post-mining rehabilitation (Loechel et al., 2013; Sharma et al., 2013).
25.7.4. Energy Supply, Demand, and Transmission
Energy demand is projected to grow by 0.5-1.3% per annum in Australasia over the next few decades in the absence
of major new policies (MED, 2011; Syed, 2012). Australia s predominantly thermal power generation is vulnerable
to drought-induced water restrictions, which could require dry-cooling and increased water use efficiency where
rainfall declines (Graham et al., 2008; Smart and Aspinall, 2009). Depending on carbon price and technology costs,
renewable electricity generation in Australia is projected to increase from 10% in 2010/11 to ~33-50% by 2030
(Hayward et al., 2011; Stark et al., 2012; Syed, 2012), but few studies have explored the vulnerability of these new
energy sources to climate change (Bryan et al., 2010; Crook et al., 2011; Odeh et al., 2011).
New Zealand s predominantly hydroelectric power generation is vulnerable to precipitation variability. Increasing
winter precipitation and snow melt, and a shift from snowfall to rainfall will reduce this vulnerability (medium
confidence) as winter/spring inflows to main hydro lakes are projected to increase by 5-10% over the next few
decades (McKerchar and Mullan, 2004; Poyck et al., 2011). Further reductions in seasonal snow and glacial melt as
glaciers diminish, however, would compromise this benefit (Chinn, 2001; Renwick et al., 2009; Srinivasan et al.,
2011). Increasing wind power generation (MED, 2011) would benefit from projected increases in mean westerly
winds but face increased risk of damages and shut-down during extreme winds (Renwick et al., 2009).
Climate warming would reduce annual average peak electricity demands by 1-2% per °C across New Zealand and
2(+/-1)% in New South Wales, but increase by 1.1(+/-1.4)% and 4.6(+/-2.7)% in Queensland and South Australia due to
air conditioning demand (Stroombergen et al., 2006; Jollands et al., 2007; Thatcher, 2007; Nguyen et al., 2010).
Increased summer peak demand, particularly in Australia (see also Figure 25-5), will place additional stress on
networks and can result in black-outs (very high confidence; Jollands et al., 2007; Thatcher, 2007; Howden and
Crimp, 2008; Wang et al., 2010a). During the 2009 Victorian heat wave demand rose by 24% but electrical losses
from transmission lines increased by 53% due to higher peak currents (Nguyen et al., 2010), and successive failures
of the overloaded network temporarily left more than 500,000 people without power (QUT, 2010). Various
adaptation options to limit increasing urban energy demand exist and some are being implemented (see Box 25-9).
There is limited evidence but high agreement that without additional adaptation, distribution networks in most
Australian states will be at high risk of failure by 2031-2070 under non-mitigation scenarios due to increased
bushfire risk and potential strengthening and southward shift of severe cyclones in tropical regions (Maunsell and
CSIRO, 2008; Parsons Brinkerhoff, 2009). Adaptation costs have been estimated at A$2.5 billion to 2015, with
more than half to meet increasing demand for air conditioning and the remainder to increase resilience to climate-
related hazards; underground cabling would reduce bushfire risk but has large investment costs that are not included
above (Parsons Brinkerhoff, 2009). Decentralised ownership of assets constitutes a significant adaptation constraint
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(ATSE, 2008; Parsons Brinkerhoff, 2009). In New Zealand, increasing high winds and temperatures have been
identified qualitatively as the most relevant risks to transmission (Jollands et al., 2007; Renwick et al., 2009).
25.7.5. Tourism
Tourism contributes 2.6-4% of GDP to the economies of Australia and New Zealand (ABS, 2010a; SNZ, 2011). The
net present value of the Great Barrier Reef alone over the next 100 years has been estimated at A$51.4 billion
(Oxford Economics, 2009). Most Australasian tourism is exposed to climate variability and change (see 25.2 for
projected trends), and some destinations are highly sensitive to extreme events (Hopkins et al., 2012). The 2011
floods and Tropical Cyclone Yasi, for example, cost the Queensland tourism industry about A$590 million, mainly
due to cancellations and damage to the Great Barrier Reef (PWC, 2011); and drought in the Murray-Darling Basin
caused an estimated A$70 million loss in 2008 due to reduced visitor days (TRA, 2010).
25.7.5.1. Projected Impacts
Future impacts on tourism have been modelled for several Australian destinations. The Great Barrier Reef is
expected to degrade under all climate change scenarios (25.6.2, 30.5, Box CC-CR), reducing its attractiveness
(Marshall and Johnson, 2007; Bohensky et al., 2011; Wilson and Turton, 2011b). Ski tourism is expected to decline
in the Australian Alps due to snow cover reducing more rapidly than in New Zealand (Pickering et al., 2010;
Hendrikx et al., 2013) and greater perceived attractiveness of New Zealand (Hopkins et al., 2012). Higher
temperature extremes in the Northern Territory are projected with high confidence to increase heat stress and incur
higher costs for air conditioning (Turton et al., 2009). Sea level rise places pressures on shorelines and long-lived
infrastructure but implications for tourist resorts have not been quantified (Buckley, 2008).
Economic modelling suggests that the Australian alpine region would be most negatively affected in relative terms
due to limited alternative activities (Pham et al., 2010), whereas the competitiveness of some destinations (e.g.
Margaret River in Western Australia) could be enhanced by higher temperatures and lower rainfall (Jones et al.,
2010; Pham et al., 2010). An analogue-based study suggests that, in New Zealand, warmer and drier conditions
mostly benefit but wetter conditions and extreme climate events undermine tourism (Wilson and Becken, 2011).
Confidence in outcomes is low, however, due to uncertain future tourist behaviour (Scott et al., 2012; also 25.9.2).
25.7.5.2. Adaptation
Both New Zealand and Australia have formalised adaptation strategies for tourism (Becken and Clapcott, 2011;
Zeppel and Beaumont, 2011). In Australia, institutions at various levels also promote preparation for extreme events
(Tourism Queensland, 2007, 2010; Tourism Victoria, 2010) and strengthening ecosystem resilience to maintain
destination attractiveness (GBRMPA, 2009b). Snow-making is already broadly adopted to increase reliability of
skiing (Bicknell and McManus, 2006; Hennessy et al., 2008b), but its future effectiveness depends on location. In
New Zealand, even though warming will significantly reduce the number of days suitable for snow-making
(Hendrikx and Hreinsson, 2012), sufficient snow could be made in all years until the end of the 21st century to
maintain current minimum operational skiing conditions. Options for resorts in Australia s Snowy Mountains are far
more limited (Hendrikx et al., 2013), where maintaining skiing conditions until at least 2020 would require A$100
million in capital investment into 700 snow guns and 2.5-3.3 GL of water per month (Pickering and Buckley, 2010).
Short investment horizons, high substitutability and a high proportion of human capital compared with built assets
give high confidence that the adaptive capacity of the tourism industry is high overall, except for destinations where
climate change is projected to degrade core natural assets and diversification opportunities are limited (Evans et al.,
2011; Morrison and Pickering, 2011). Strategic adaptation decisions are constrained by uncertainties in regional
climatic changes (Turton et al., 2010), limited concern (Bicknell and McManus, 2006), lack of leadership and
limited coordinated forward planning (Sanders et al., 2008; Turton et al., 2009; Roman et al., 2010; White and
Buultjens, 2012). An integrated assessment of tourism vulnerability in Australasia is not yet possible due to limited
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understanding of future changes in tourism and community preferences (Scott et al., 2012), including the flow-on
effects of changing travel behaviour and tourism preferences in other world regions (see 25.9.2).
25.8.1. Human Health
25.8.1.1. Observed Impacts
Life expectancy in Australasia is high, but shows substantial ethnic and socio-economic inequalities (Anderson et
al., 2006). Mortality increases in hot weather in Australia (high agreement, robust evidence; Bi and Parton, 2008;
Vaneckova et al., 2008) with air pollution exacerbating this association. The last four decades have seen a steady
increase in the ratio of summer to winter mortality in Australia, indicating a health effect from climatic warming
(Bennett et al., 2013). Exceptional heatwave conditions in Australia have been associated with substantial increases
in mortality and hospital admissions in several regional towns and capital cities (high confidence; Khalaj et al.,
2010; Loughnan et al., 2010; Tong et al., 2010a; Tong et al., 2010b). For example, during the heatwave in January
and February 2009 in south-eastern Australia (BoM, 2009), total emergency cases increased by 46% over the three
hottest days. Direct heat-related health problems increased 34-fold, 61% of these being in people aged 75 years or
older, and there were an estimated 374 excess deaths, a 62% increase in all-cause mortality (Victorian Government,
2009a). Mental health admissions increased across all age groups by 7.3% in metropolitan South Australia during
heatwaves (1993-2006; Hansen et al., 2008). Mortality attributed to mental and behavioural disorders increased in
the 65 to 74-year age group and in persons with existing mental health problems (Hansen et al., 2008). Experience
of extreme events also strongly affects psychological well-being (see 25.4.3).
25.8.1.2. Projected Impacts
Projected increases in heatwaves (Figure 25-5) will increase heat-related deaths and hospitalizations, especially
among the elderly, compounded by population growth and ageing (high confidence; Bambrick et al., 2008; Gosling
et al., 2009; Huang et al., 2012). In the southern states of Australia and parts of New Zealand, this may be partly
offset by reduced deaths from cold at least for modest rises in temperature (low confidence; Bambrick et al., 2008;
Kinney, 2012). With strong mitigation, climate change is projected to result in 11% fewer temperature-related
deaths in both 2050 and 2100 in Australia, but 14% and 100% more deaths in 2050 and 2100, respectively, without
mitigation under a hot, dry A1FI scenario (Bambrick et al., 2008; see Chapter 11 for details on temperature-related
health trade-offs). Net results were driven almost entirely by increased mortality in the north, especially Queensland,
consistent with Huang et al. (2012). In a separate study that accounted for increased daily temperature variability, a
threefold increase in heat-related deaths is projected for Sydney by 2100 for the A2 scenario, assuming no
adaptation (Gosling et al., 2009). The number of hot days when physical labor in the sun becomes dangerous is also
projected to increase substantially in Australia by 2070, leading to economic costs from lost productivity, increased
hospitalisations and occasional deaths (medium confidence; Hanna et al., 2011; Maloney and Forbes, 2011).
Water- and food-borne diseases are projected to increase, but the complexity of their relationship to climate and
non-climate drivers means there is low confidence in specific projections. For Australia, 205,000-335,000 new cases
of bacterial gastroenteritis by 2050, and 239,000-870,000 cases by 2100, are projected under a range of emission
scenarios (Bambrick et al., 2008; Harley et al., 2011). Based on their observed positive relationship with
temperature, notifications of salmonellosis notifications are projected to increase 15% for every 1°C increase in
average monthly temperatures (Britton et al., 2010a). Water-borne zoonotic diseases such as cryptosporidiosis and
giardiasis have more complex relationships with climate and are amenable to various adaptations, making future
projections more difficult (Britton et al., 2010b; Lal et al., 2012).
Understanding of the combined effects of climate change and socio-economic development on the distribution of
vector-borne diseases has improved since the AR4. Australasia is projected to remain malaria free under the A1B
emission scenario until at least 2050 (Béguin et al., 2011) and sporadic cases could be treated effectively. The area
climatically suitable for transmission of dengue will expand in Australasia (high confidence; Bambrick et al., 2008;
Aström et al., 2012), but changes in socio-economic factors, especially domestic water-storage may have a more
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important influence on disease incidence than climate (Beebe et al., 2009; Kearney et al., 2009). Impacts of climate
change on Barmah Forest Virus in Queensland depend on complex interactions between rainfall and temperature
changes, together with tidal and socio-economic factors, and thus will vary substantially among different coastal
regions (Naish et al., 2013). The effects of climate change combined with frequent travel within and outside the
region, and recent incursions of exotic mosquito species, could expand the geographic range of other important
arboviruses such as Ross River Virus (medium confidence; Derraik and Slaney, 2007; Derraik et al., 2010).
A growing literature since the AR4 has focused on the psychological impacts of climate change, based on impacts of
recent climate variability and extremes (Doherty and Clayton, 2011; 25.4.3). These studies indicate significant
mental health risks associated with climate-related disasters, in particular persistent and severe drought, floods and
storms; climate impacts may be especially acute in rural communities where climate change places additional
stresses on livelihoods (high confidence; Edwards et al., 2011; see also Box 25-5). Projected population growth and
urbanization could further increase health risks indirectly via climate-related stress on housing, transport and energy
infrastructure and water supplies (low confidence; Howden-Chapman, 2010; see also Box 25-9).
[INSERT FIGURE 25-5 HERE
Figure 25-5: Projected changes in exposure to heat under a high emissions scenario (A1FI). Maps show the average
number of days with peak temperatures >40°C, for ~1990 (based on available meteorological station data for the
period 1975-2004), ~2050 and ~2100. Bar charts show the change in population heat exposure, expressed as person-
days exposed to peak temperatures >40°C, aggregated by State/Territory and including projected population growth
for a default scenario. Future temperatures are based on simulations by the GFDL-CM2 global climate model
(Meehl et al., 2007), re-scaled to the A1FI scenario; simulations based on other climate models could give higher or
lower results. Data from Baynes et al. (2012).]
25.8.1.3. Adaptation
Research since the AR4 has mainly focused on climate change impacts, although some adaptation strategies have
received attention in Australia. These include improving healthcare services, social support for those most at risk,
improving community awareness to reduce adverse exposures, developing early warning and emergency response
plans (Wang and McAllister, 2011), and understanding perceptions of climatic risks to health as they affect adaptive
behaviours (Akompad et al., 2013). In New Zealand, central Government health policies do not identify specific
measures to adapt to climate change (Wilson, 2011). In both countries, policies to reduce risks from extreme events
such as floods and fires will have co-benefits for health (see Box 25-6, Box 25-8).
A review of the southern Australian heatwave of 2009 identified a range of issues including communication failures
with no clear public information or warning strategy, and no clear thresholds for initiating public information
campaigns (Kiem et al., 2010). Emergency services were underprepared and relied on reactive solutions (QUT,
2010). The Victorian government has since developed a heatwave plan to coordinate a state-wide response, maintain
consistent community-wide understanding through a Heat Health alert system, build capacity of councils to support
communities most at risk, support a Heat Health Intelligence surveillance system, and distribute public health
information (Victorian Government, 2009b).
25.8.2. Indigenous Peoples
25.8.2.1. Aboriginal and Torres Strait Islanders
Work since the AR4 includes a national Indigenous adaptation research action plan (Langton et al., 2012), regional
risk studies (Green et al., 2009; DNP, 2010; TSRA, 2010; Nursey-Bray et al., 2013) and scrutiny from an
Indigenous rights perspective (ATSISJC 2009). Socio-economic disadvantage and poor health (SCRGSP 2011)
indicate a disproportionate climate change vulnerability of Indigenous Australians (McMichael et al., 2009)
although there are no detailed assessments. In urban and regional areas, where 75% of the Indigenous population
lives, assessments have not specifically addressed risks to Indigenous people (e.g. Guillaume et al., 2010). In other
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regions, all remote, there is limited empirical evidence of vulnerability (Maru et al., 2012). However, there is high
agreement and medium evidence for significant future impacts from increasing heat stress, extreme events and
increased disease (Campbell et al., 2008; Spickett et al., 2008; Green et al., 2009).
The Indigenous estate comprises more than 25% of the Australian land area (Altman et al., 2007; NNTT, 2013).
There is high agreement but limited evidence that natural resource dependence (e.g. Bird et al., 2005; Gray et al.,
2005a; Kwan et al., 2006; Buultjens et al., 2010) increases Indigenous exposure and sensitivity to climate change
(Green et al., 2009); climate change-induced dislocation, attenuation of cultural attachment to place and loss of
agency will disadvantage Indigenous mental health and community identity (Fritze et al., 2008; Hunter, 2009;
McIntyre-Tamwoy and Buhrich, 2011); and, housing, infrastructure, services and transport, often already inadequate
for Indigenous needs especially in remote Australia (ABS 2010c), will be further stressed (Taylor and Philp, 2010).
Torres Strait island communities and livelihoods are vulnerable to major impacts from even small sea level rises
(high confidence; DCC, 2009; Green et al., 2010b; TSRA 2010).
Little adaptation of Indigenous communities to climate change is apparent to date (but see Burroughs, 2010; GETF
2011; Nursey-Bray et al., 2013; Zander et al., 2013). Plans and policies that are imposed on Indigenous
communities can constrain their adaptive capacity (Ellemor, 2005; Petheram et al., 2010; Veland et al., 2010;
Langton et al., 2012) but participatory development of adaptation strategies is challenged by multiple stressors and
uncertainty about causes of observed changes (Leonard et al., 2013b; Nursey-Bray et al., 2013). Adaptation
planning would benefit from a robust typology (Maru et al., 2011) across the diversity of Indigenous life experience
(McMichael et al., 2009). Indigenous re-engagement with environmental management (e. g. Hunt et al., 2009; Ross
et al., 2009) can promote health (Burgess et al., 2009) and may increase adaptive capacity (Berry et al., 2010;
Davies et al., 2011). There is emerging interest in integrating Indigenous observations of climate change (Green et
al., 2010c; Petheram et al., 2010) and developing inter-cultural communication tools (Woodward et al., 2012;
Leonard et al., 2013b). Extensive land ownership in northern and inland Australia and land management traditions
mean that Indigenous people are well situated to provide greenhouse gas abatement and carbon sequestration
services that may also support their livelihood aspirations (Whitehead et al., 2009; Heckbert et al., 2012).
25.8.2.2. New Zealand M ori
The projected impacts of climate change on M ori society are expected to be highly differentiated, reflecting
complex economic, social, cultural, environmental and political factors (high confidence). Since the AR4, studies
have been either sector-specific (e.g. Insley, 2007; Insley and Meade, 2008; Harmsworth et al., 2010; King et al.,
2012) or more general, inferring risk and vulnerability based on exploratory engagements with varied stakeholders
and existing social, economic, political and ecological conditions (e.g. MfE, 2007b; Te Aho, 2007; King et al., 2010).
The M ori economy depends on climate-sensitive primary industries with vulnerabilities to climate conditions (high
confidence; Packman et al., 2001; NZIER, 2003; Cottrell et al., 2004; TPK, 2007; Tait et al., 2008b; Harmsworth et
al., 2010; King et al., 2010; Nana et al., 2011a). Much of M ori-owned land is steep (>60%) and susceptible to
damage from high intensity rainstorms, while many lowland areas are vulnerable to flooding and sedimentation
(Harmsworth and Raynor, 2005; King et al., 2010). Land in the east and north is also drought prone, and this
increases uncertainties for future agricultural performance, product quality and investment (medium confidence;
Cottrell et al., 2004; Harmsworth et al., 2010; King et al., 2010). The fisheries and aquaculture sector faces
substantial risks (and uncertainties) from changes in ocean temperature and chemistry, potential changes in species
composition, condition and productivity levels (medium confidence; King et al., 2010; see also 25.6.2). At the
community and individual level, M ori regularly utilize the natural environment for hunting and fishing, recreation,
the maintenance of traditional skills and identity, and collection of cultural resources (King and Penny, 2006; King
et al., 2012). Many of these activities are already compromised due to resource-competition, degradation and
modification (Woodward et al., 2001; King et al., 2012). Climate change driven shifts in natural ecosystems will
further challenge the capacities of some M ori to cope and adapt (medium confidence; King et al., 2012).
M ori organizations have sophisticated business structures, governance (e.g. trusts, incorporations) and networks
(e.g. Iwi leadership groups) across the state and private sectors (Harmsworth et al., 2010; Insley, 2010; Nana et al.,
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2011b), critical for managing and adapting to climate change risks (Harmsworth et al., 2010; King et al., 2012).
Future opportunities will depend on partnerships in business, science, research and government (high confidence;
Harmsworth et al., 2010; King et al., 2010) as well as innovative technologies and new land management practices
to better suit future climates and use opportunities from climate policy, especially in forestry (Carswell et al., 2002;
Harmsworth, 2003; Funk and Kerr, 2007; Insley and Meade, 2008; Tait et al., 2008b; Penny and King, 2010).
M ori knowledge of environmental processes and hazards (King et al., 2005; King et al., 2007) as well as strong
social-cultural networks are vital for adaptation and on-going risk management (King et al., 2008); however, choices
and actions continue to be constrained by insufficient resourcing, shortages in social capital, and competing values
(King et al., 2012). Combining traditional ways and knowledge with new and untried policies and strategies will be
key to the long-term sustainability of climate-sensitive M ori communities, groups and activities (high confidence;
Harmsworth et al., 2010; King et al., 2012).
_____ START BOX 25-7 HERE _____
Box 25-7. Insurance as a Climate Risk Management Tool
Insurance helps spread the risk from extreme events across communities and over time and therefore enhances the
resilience of society to disasters (see 10.7). In Australia, insured losses are dominated by meteorological hazards,
including the 2011 Queensland floods and the 1999 Sydney hailstorm (ICA, 2012) with estimated claims of A$3
billion p.a. (IAA, 2011b). In New Zealand, floods and storms are the second most costly natural hazards after
earthquakes (ICNZ, 2013). The number of damaging insured events (up to a certain loss value) has increased
significantly in the Oceania region since 1980 (Schuster, 2013). Normalised insured losses in Australia show no
significant trend from 1967 to 2006 (Crompton and McAneney, 2008; Crompton et al., 2010; Table 10.4), though
this conclusion rests on a simplified accounting of population growth and may also reflect improved building codes
and early warning systems (Nicholls, 2011; IPCC, 2012).
There is high confidence that without adaptive measures, projected increases in extremes (Table 25-1) and
uncertainties in these projections will lead to increased insurance premiums, exclusions and non-coverage in some
locations (IAG, 2011), which will reshape the distribution of vulnerability, e.g., through unaffordability or
unavailability of cover in areas at highest risk (IAA, 2011b, a; NDIR, 2011; Booth and Williams, 2012). Restriction
of cover occurred in some locations following the 2011 flood events in Queensland (Suncorp, 2013).
Insurance can contribute positively to risk reduction by providing incentives to policy holders to reduce their risk
profile (O'Neill and Handmer, 2012), e.g. through resilience ratings given to buildings (TGA, 2009; Edge
Environment, 2011; IAG, 2011). Apart from constituting an autonomous private sector response to extreme events,
insurance can also be framed as a form of social policy to manage climate risks, similar to New Zealand s
government insurance scheme (Glavovic et al., 2010); government measures to reduce or avoid risks also interact
with insurance companies willingness to provide cover (Booth and Williams, 2012). Yet insurance can also act as a
constraint on adaptation, if those living in climate-risk prone localities pay discounted or cross-subsidised premiums
or policies fail to encourage betterment after damaging events by requiring replacement of like for like ,
constituting a missed opportunity for risk reduction (NDIR, 2011; QFCI, 2012; Reisinger et al., 2014; see also 10.7).
The effectiveness of insurance thus depends on the extent to which it is linked to a broader national resilience
approach to disaster mitigation and response (Mortimer et al., 2011).
_____ END BOX 25-7 HERE _____
_____ START BOX 25-8 HERE _____
Box 25-8. Changes in Flood Risk and Management Responses
Flood damages across eastern Australia and both main islands of New Zealand in 2010 and 2011 revealed a
significant adaptation deficit (ICA, 2012; ICNZ, 2013). For example, the Queensland floods in January 2011
resulted in 35 deaths, three quarters of the State including Brisbane declared a disaster zone, and damages to public
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infrastructure of AUD$5-6 billion (Queensland Government, 2011). These floods were associated with a strong
monsoon and the strongest La Nina on record (Cai et al., 2012; CSIRO and BoM, 2012; Evans and Boyer-Souchet,
2012). Flood frequency and severity exhibit strong decadal variability with no significant long-term trend in
Australasia to date (Kiem et al., 2003; Smart and McKerchar, 2010; Ishak et al., 2013).
Flood risk is projected to increase in many regions due to more intense extreme rainfall events driven by a warmer
and wetter atmosphere (medium confidence; Table 25-1). High resolution downscaling (Carey-Smith et al., 2010),
and dynamic catchment hydrological and river hydraulic modelling in New Zealand (Gray et al., 2005b; McMillan
et al., 2010; MfE, 2010b; Ballinger et al., 2011; Duncan and Smart, 2011; McMillan et al., 2012) indicate that the
50-year and 100-year flood peaks for rivers in many parts of the country will increase by 5 10% by 2050 and more
by 2100 (with large variation between models and emissions scenarios), with a corresponding decrease in return
periods for specific flood levels. Studies for Queensland show similar results (DERM et al., 2010). In Australia,
flood risk is expected to increase more in the north (driven by convective rainfall systems) than in the south (where
more intense extreme rainfall may be compensated by drier antecedent moisture conditions), consistent with
confidence in heavy rainfall projections (Table 25-1; Alexander and Arblaster, 2009; Rafter and Abbs, 2009).
Flood risk near river mouths will be exacerbated by storm surge associated with higher sea level and potential
change in wind speeds (McInnes et al., 2005; MfE, 2010b; Wang et al., 2010b). Higher rainfall intensity and peak
flow will also increase erosion and sediment loads in waterways (Prosser et al., 2001; Nearing et al., 2004) and
exacerbate problems from aging stormwater and wastewater infrastructure in cities (Howe et al., 2005; Jollands et
al., 2007; CCC, 2010; WCC, 2010; see also Box 25-9). However, moderate flooding also has benefits through
filling reservoirs, recharging groundwater and replenishing natural environments (Hughes, 2003; Chiew and Prosser,
2011; Oliver and Webster, 2011).
Adaptation to increased flood risk from climate change is starting to happen (Wilby and Keenan, 2012) through
updating guidelines for design flood estimation (MfE, 2010b; Westra, 2012), improving flood risk management
(O'Connell and Hargreaves, 2004; NFRAG, 2008; Queensland Government, 2011), accommodating risk in flood
prone areas (options include raising floor levels, using strong piled foundations, using water-resistant insulation
materials and ensuring weather tightness), and risk reduction and avoidance through spatial planning and managed
relocation (Trotman, 2008; Glavovic et al., 2010; LVRC, 2012; QFCI, 2012). Adaptation options in urban areas also
include ecosystem-based approaches such as retaining floodplains and floodways, restoring wetlands, and
retrofitting existing systems to attenuate flows (Box 25.9; Howe et al., 2005; Skinner, 2010; WCC, 2010).
The recent flooding in eastern Australia and the projected increase in future flood risk have resulted in changes to
reservoir operations to mitigate floods (van den Honert and McAneney, 2011; QFCI, 2012) and insurance practice to
cover flood damages (NDIR, 2011; Phelan, 2011; Box 25-7). However, the magnitude of potential future changes in
flood risk and limits to incremental adaptation responses in urban areas suggest that more transformative approaches
based on altering land-use and avoidance of exposure to future flooding may be needed in some locations, especially
if changes in the upper range of projections are realised (high confidence; Lawrence and Allan, 2009; DERM et al.,
2010; Glavovic et al., 2010; Wilby and Keenan, 2012; Lawrence et al., 2013a).
_____ END BOX 25-8 HERE _____
25.9. Interactions among Impacts, Adaptation, and Mitigation Responses
The AR4 found that individual adaptation responses can entail synergies or trade-offs with other adaptation
responses and with mitigation, but that integrated assessment tools were lacking in Australasia (Hennessy et al.,
2007). Subsequent studies provide detail on such interactions and can inform a balanced portfolio of climate change
responses, but evaluation tools remain limited, especially for local decision-making (Park et al., 2011). A review of
25 specific climate change-associated land-use plans from Australia, for example, found that 14 exhibited potential
for conflict between mitigation and adaptation (Hamin and Gurran, 2009).
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_____ START BOX 25-9 HERE _____
Box 25-9. Opportunities, Constraints, and Challenges to Adaptation in Urban Areas
Considerable opportunities exist for Australasian cities and towns to reduce climate change impacts and, in some
regions, benefit from projected changes such as warmer winters and more secure water supply (Fitzharris, 2010;
Australian Government, 2012). Many tools and practices developed for sustainable resource management or disaster
risk reduction in urban areas are co-beneficial for climate change adaptation, and vice versa, and can be integrated
with mitigation objectives (Hamin and Gurran, 2009). Despite the abundance of potential adaptation options,
however, social, cultural, institutional and economic factors frequently constrain their implementation (high
confidence; see also 25.4.2). The form and longevity of cities and towns, with their concentration of hard and critical
infrastructure such as housing, transport, energy, stormwater and wastewater systems, telecommunications and
public facilities provide additional challenges (see also Chapters 8 and 10, 25.7.4, 25.8.1, Boxes 25-1, 25-2, 25-8).
Transport infrastructure is vulnerable to extreme heat and flooding (QUT, 2010; Taylor and Philp, 2010) but
quantification of future risks remains limited (Gardiner et al., 2009; Balston et al., 2012; Baynes et al., 2012). Table
25-5 summarises some adaptation options, co-benefits and constraints on their adoption in Australasia.
Overall, the implementation of climate change adaptation policy for urban settlements in Australia and New Zealand
has been mixed. The Australian National Urban Policy encourages adaptation, and many urban plans include
significant adaptation policies (e.g. City of Melbourne, 2009; City of Port Phillip, 2010; ACT Government, 2012;
City of Adelaide, 2012). New Zealand also promotes urban adaptation through strategies, plans and guidance
documents (MfE, 2008b; CCC, 2010; WCC, 2010; Auckland Council, 2012; NIWA et al., 2012). Many examples of
incremental urban adaptation exist (Box 25-2, Table 25-5), particularly where these include co-benefits and respond
to other stressors, like prolonged drought in southern Australia and recurrent floods. Experience is much scarcer with
more flexible land-uses, managed relocation and ecosystem-based adaptation that could transform existing settlement
patterns and development trends, and where maintaining flexibility to address long-term climate risks can run against
near-term development pressures (see Boxes 25-1, 25-2, 25-8, CC-EA). Decision-making models that support such
adaptive and transformative changes (25.4.2, Box 25-1) have not yet been implemented widely in urban contexts;
increased coordination among different levels of government may be required to spread costs and balance public and
private, near- and long-term and local and regional benefits (Norman, 2009; Britton, 2010; Norman, 2010; Abel et
al., 2011; Lawrence et al., 2013a; McDonald, 2013; Palutikof et al., 2013; Reisinger et al., 2014).
[INSERT TABLE 25-5 HERE
Table 25-5: Examples of co-beneficial climate change adaptation options for urban areas and barriers to their
adoption. Options in italics are already widely implemented in Australia and New Zealand urban areas.]
_____ END BOX 25-9 HERE _____
25.9.1. Interactions among Local-Level Impacts, Adaptation, and Mitigation Responses
Table 25-6 shows examples of adaptation responses that are either synergistic or entail trade-offs with other impacts
and/or adaptation responses and goals. Adapting proactively to projected climate changes, particularly extremes
such as floods or drought, can increase near-term resilience to climate variability and be a motivation for adopting
adaptation measures (Productivity Commission, 2012). However, exclusive reliance on near-term benefits can
increase trade-offs and result in long-term maladaptation (high confidence). For example, enhancing protection
measures after major flood events, combined with rapid re-building, accumulates fixed assets that can become
increasingly costly to protect as climate change continues, with attendant loss of amenity and environmental values
(Glavovic et al., 2010; Gorddard et al., 2012; McDonald, 2013). Similarly, deferring adoption of increased design
wind speeds in cyclone-prone areas delays near-term investment costs but also reduces the long-term benefit/cost
ratio of the strategy (Stewart and Wang, 2011).
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[INSERT TABLE 25-6 HERE
Table 25-6: Examples of interactions between impacts and adaptation measures in different sectors. In each case,
impacts or responses in one sector have the potential to cause negative impacts or have co-benefits with impacts or
responses in another sector, or with another type of response in the same sector.]
Mitigation actions can contribute to but also counteract local adaptation goals. Energy efficient buildings, for
example, reduce network and health risks during heat waves, but urban densification to reduce transport energy
demand intensifies urban heat islands and, hence, heat-related health risks (25.7.4, 25.8.1). Specific adaptations can
also make achievement of mitigation targets harder or easier. Increased use of air conditioning, for example,
increases energy demand, but energy efficiency and building design can reduce heat exposure as well as energy
demand (25.7.4, Box 25-9). Table 25-7 gives further examples, and Box 25-10 explores the multiple and complex
benefits and trade-offs in changing land-use to simultaneously adapt to and mitigate climate change.
[INSERT TABLE 25-7 HERE
Table 25-7: Examples of interactions between adaptation and mitigation measures (green rows denote synergies
where multiple benefits may be realized, orange rows denote potential tradeoffs and conflicts; grey row gives an
example of complex, mixed interactions). The primary goal may be adaptation or mitigation.]
_____ START BOX 25-10 HERE _____
Box 25-10. Land-based Interactions Among Climate, Energy, Water, and Biodiversity
Climate, water, biodiversity, food and energy production and use are intertwined through complex feedbacks and
trade-offs (see also Box CC-WE). This could make alternative uses of natural resources within rural landscapes
increasingly contested, yet decision support tools to manage competing objectives are limited (PMSEIC, 2010b).
Various policies in Australasia support increased biofuel production and biological carbon sequestration via, for
example, mandatory renewable energy targets and incentives to increase carbon storage. Impacts of increased
biological sequestration activities on biodiversity depend on their implementation. Benefits arise from reduced
erosion, additional habitat, and enhanced ecosystem connectivity, while risks or lost opportunities are associated
with large-scale monocultures especially if replacing more diverse landscapes (Brockerhoff et al., 2008; Giltrap et
al., 2009; Steffen et al., 2009; Todd et al., 2009; Bradshaw et al., 2013).
Photosynthesis transfers water to the atmosphere, so increased sequestration is projected to reduce catchment yields
particularly in southern Australia and affect water quality negatively (CSIRO, 2008; Schrobback et al., 2011;
Bradshaw et al., 2013). Accounting for this water use in water allocations for sequestration activities would increase
their cost and limit the potential of sequestration-driven land-use change (Polglase et al., 2011; Stewart et al., 2011).
Large-scale land-cover changes also affect local and regional climates and soil moisture through changing albedo,
evaporation, plant transpiration and surface roughness (McAlpine et al., 2009; Kirschbaum et al., 2011b), but these
feedbacks have rarely been included in analyses of changing water demands and availability.
Biological carbon sequestration in New Zealand is less water-challenged than in Australia, except where catchments
are projected to become drier and/or are already completely allocated (MfE, 2007a; Rutledge et al., 2011), and
would mostly improve water quality through reduced erosion (Giltrap et al., 2009). Policies to protect water quality
by limiting nitrogen discharge from agriculture have reduced livestock production and greenhouse gas emissions in
the Lake Taupo and Rotorua catchments and supported land-use change towards sequestration (OECD, 2013b).
Trade-offs between biofuel and food production and ecosystem services depend strongly on the type of sequestration
activity and their management relies on the use of consistent principles to evaluate externalities and benefits of
alternative land-uses (PMSEIC, 2010b). First-generation biofuels have been modelled in Australia as directly
competing with agricultural production (Bryan et al., 2010). In contrast, production of woody biofuels in New
Zealand is projected to occur on marginal land, not where the most intense agriculture occurs (Todd et al., 2009).
Falling costs and increasing efficiency of solar energy may limit future biofuel demand, given the limited efficiency
of plants in converting solar energy into usable fuel (e.g. Reijnders and Huijbregts, 2007).
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_____ END BOX 25-10 HERE _____
25.9.2. Intra- and Inter-Regional Flow-On Effects Among Impacts, Adaptation and Mitigation
Recent studies strengthen conclusions from the AR4 (Hennessy et al., 2007) that flow-on effects from climate
change impacts occurring in other world regions can exacerbate or counteract projected impacts in Australasia.
Modelling suggests Australia s terms of trade would deteriorate by about 0.23% in 2050 and 2.95% in 2100 as
climate change impacts without mitigation reduce economic activity and demand for coal, minerals and agricultural
products in other world regions (A1FI scenario; Harman et al., 2008). As a result, Australian Gross National Product
(GNP) is expected to decline more strongly than GDP due to climate change, especially towards the end of the 21st
century (Gunasekera et al., 2008). These conclusions, however, merit only medium confidence, because they rely on
simplified assumptions about global climate change impacts, economic effects and policy responses.
For New Zealand, there is limited evidence but high agreement that higher global food prices driven by adverse
climate change impacts on global agriculture and some international climate policies would increase commodity
prices and hence producer returns. Agriculture and forestry producer returns, for example, are estimated to increase
by 14.6% under the A2 scenario by 2070 (Saunders et al., 2010) and real gross national disposable income by 0.6-
2.3% under a range of non-mitigation scenarios (Stroombergen, 2010) relative to baseline projections in the absence
of global climate change. Some climate policies such as biofuel targets and agricultural mitigation in other regions
would also increase global commodity prices and hence returns to New Zealand farmers (Saunders et al., 2009;
Reisinger et al., 2012). Depending on global implementation, these could more than offset projected average
domestic climate change impacts on agriculture (Tait et al., 2008a). In contrast, higher international agricultural
commodity prices appear insufficient to compensate for the more severe effects of climate change on agriculture in
Australia (see 25.7.2; Gunasekera et al., 2007; Garnaut, 2008).
Climate change could affect international tourism to Australasia through international destination and activity
preferences (Kulendran and Dwyer, 2010; Rosselló-Nadal et al., 2011; Scott et al., 2012), climate policies, and oil
prices (Mayor and Tol, 2007; Becken, 2011; Schiff and Becken, 2011). These potentially significant effects remain
poorly quantified, however, and are not well integrated into local vulnerability studies (Hopkins et al., 2012).
Climate change has the potential to change migration flows within Australasia, particularly due to coastal changes
(e.g. from the Torres Straits islands to mainland Australia), although reliable estimates of such movements do not
yet exist (see 12.4; Green et al., 2010b; McNamara et al., 2011; Hugo, 2012). Migration within countries, and from
New Zealand to Australia, is largely economically driven and sustained by transnational networks, though the
perceived more attractive current climate in Australia is reportedly a factor in migration from New Zealand (Goss
and Lindquist, 2000; Green et al., 2008a; Poot, 2009). The impacts of climate change in the Pacific may contribute
to an increase in the number of people seeking to move to nearby countries (Bedford and Bedford, 2010; Hugo,
2010; McAdam, 2010; Farbotko and Lazrus, 2012; Bedford and Campbell, 2013) and affect political stability and
geopolitical rivalry within the Asia-Pacific region, although there is no clear evidence of this to date and causal
theories are scarce (see 12.4, 12.5; Dupont, 2008; Pearman, 2009). Increasing climate-driven disasters, disease and
border control will stimulate operations other than war for Australasia s armed forces; integration of security into
adaptation and development assistance for Pacific island countries can therefore play a key role in moderating the
influence of climate change on forced migration and conflict (high agreement, robust evidence; Dupont and
Pearman, 2006; Bergin and Townsend, 2007; Dupont, 2008; Sinclair, 2008; Barnett, 2009; Rolfe, 2009).
25.10. Synthesis and Regional Key Risks
25.10.1. Economy-wide Impacts and the Potential of Mitigation to Reduce Risks
Globally effective mitigation could reduce or delay some of the risks associated with climate change and make
adaptation more feasible beyond about 2050, when projected climates begin to diverge substantially between
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mitigation and non-mitigation scenarios (see also 19.7). Literature quantifying these benefits for Australasia has
increased since the AR4 but remains very sparse. Economy-wide net costs for Australia are modelled to be
substantially greater in 2100 under unmitigated climate change (A1FI; GNP loss 7.6%) than under globally effective
mitigation (GNP loss less than 2% for stabilization at 450 or 550 ppm CO2-eq, including costs of mitigation and
residual impacts; Garnaut, 2008). These estimates, however, are highly uncertain and depend strongly on valuation
of non-market impacts, treatment of potentially catastrophic outcomes, and assumptions about adaptation, global
changes and flow-on effects for Australia, and effectiveness and implementation of global mitigation efforts
(Garnaut, 2008). No estimates of climate change costs across the entire economy exist for New Zealand.
The benefits of mitigation in terms of reduced risks have been quantified for some individual sectors in Australia,
e.g. for irrigated agriculture in the Murray-Darling Basin (Quiggin et al., 2008; Quiggin et al., 2010; Valenzuela and
Anderson, 2011; Scealy et al., 2012) and for net health outcomes (Bambrick et al., 2008). Although quantitative
estimates from individual studies are highly assumption-dependent, multiple lines of evidence (see 25.7, 25.8) give
very high confidence that globally effective mitigation would significantly reduce many long-term risks from
climate change to Australia. Benefits differ, however, between States for some issues, e.g. heat and cold mortality
(Bambrick et al., 2008). Few studies consider mitigation benefits explicitly for New Zealand, but scenario-based
studies give high confidence that if global emissions were reduced from a high (A2) to a medium-low (B1)
emissions scenario, this would markedly lower the projected increase in flood risks (Ballinger et al., 2011;
McMillan et al., 2012) and reduce risks to livestock production in the most drought prone regions (Tait et al., 2008a;
Clark et al., 2011). Mitigation would also reduce the projected benefits to production forestry, however, though
amounts depend on the response to CO2 fertilization (Kirschbaum et al., 2011a; 25.7.1).
25.10.2. Regional Key Risks as a Function of Mitigation and Adaptation
The Australia/New Zealand Chapter of the AR4 (Hennessy et al., 2007) concluded with an assessment of aggregated
vulnerability for a range of sectors as a function of global average temperature. Building on recent additional
insights, Table 25-8 shows eight key risks within those sectors that can be identified with high confidence for the
21st century, based on the multiple lines of evidence presented in the preceding sections and selected using the
framework for identifying key risks set out in Chapter 19 (see also Box CC-KR). This combines consideration of
biophysical impacts, their likelihood, timing and persistence, with vulnerability of the affected system, based on
exposure, magnitude of harm, significance of the system and its ability to cope with or adapt to projected
biophysical changes. These key risks differ in the extent to which they can be managed through adaptation and
mitigation and their evolution over time, and some are more likely than others, but all warrant attention from a risk-
management perspective.
[INSERT TABLE 25-8 HERE
Table 25-8: Key regional risks from climate change and the potential for reducing risk through mitigation and
adaptation. Key risks are identified based on assessment of the literature and expert judgments by chapter authors,
with evaluation of evidence and agreement in the supporting chapter sections. Each key risk is characterized on a
scale from very low to very high and presented in three timeframes: the present, near-term (2030-2040), and long-
term (2080-2100). For the near-term era of committed climate change (here, for 2030-2040), projected levels of
global mean temperature increase do not diverge substantially across emissions scenarios. For the longer-term era of
climate options (here, for 2080-2100), risk levels are presented for global mean temperature increase of 2°C and 4°C
above preindustrial levels. For each timeframe, risk levels are estimated for a continuation of current adaptation and
for a hypothetical highly adapted state. Relevant climate variables are indicated by icons. For a given key risk,
change in risk level through time and across magnitudes of climate change is illustrated, but because the assessment
considers potential impacts on different physical, biological, and human systems, risk levels should not necessarily
be used to evaluate relative risk across key risks, sectors, or regions.]
One set of key risks comprises damages to natural ecosystems (significant change in community structure of coral
reefs and loss of some montane ecosystems) that can be moderated by globally effective mitigation but to which
some damage now seems inevitable. For some species and ecosystems, climatically constrained ecological niches,
fragmented habitats and limited adaptive movement collectively present hard limits to adaptation to further climate
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change (high confidence). A second set of key risks (increase in flood risk, water scarcity, heat waves and wild fire)
comprises damages that could be severe but can be reduced substantially by globally effective mitigation combined
with adaptation, with the need for transformational adaptation increasing with the rate and amount of climate
change. A third set of key risks (coastal damages from sea level rise, and loss of agriculture production from severe
drying) comprises potential impacts whose scale remains highly uncertain within the 21st century, even for a given
global temperature change, and where alternative scenarios materially affect levels of concern, adaptation needs and
strategies. Even though scenarios of severe drying (see 25.5.2) or rapid sea level rise approaching 1 m or more by
2100 (see Box 25-2 and WGI 13.5) have low or currently unknown probabilities, the associated impacts would so
severely challenge adaptive capacity, including transformational changes, that they constitute important risks.
A first comparative assessment for Australia of exposure and damages from different hazards up to 2100 indicates
that river flooding will continue to be the most costly source of direct damages to infrastructure, even though the
largest value of assets is exposed to bush fire. Exposure to and damages from coastal inundation are currently
smaller, but would rise most rapidly beyond mid-century if sea level rise exceeds 0.5 m (Baynes et al., 2012).
An emerging risk is the compounding of extreme events, none of which would constitute a key risk in its own right,
but that collectively and cumulatively across space and time could stretch emergency response and recovery
capacity and hamper regional economic development, including through impacts on insurance markets or multiple
concurrent needs for major infrastructure upgrades (NDIR, 2011; Phelan, 2011; Baynes et al., 2012; Booth and
Williams, 2012; Karoly and Boulter, 2013). Efforts are underway to better understand the potential importance of
cumulative impacts and responses, including the challenges arising from impacts and responses across different
levels of government (CSIRO, 2011; Leonard et al., 2013a), but evidence is as yet too limited to identify this as a
key risk consistent with the definitions adopted in this report (see Chapter 19).
Climate change is projected to bring benefits to some sectors and parts of Australasia, at least under limited warming
scenarios associated with globally effective mitigation (high confidence). Examples include an extended growing
season for agriculture and forestry in cooler parts of New Zealand and Tasmania, reduced winter mortality (low
confidence) and reduced winter energy demand in most of New Zealand and southern States of Australia, and
increased winter hydropower potential in New Zealand s South Island (25.7.1, 25.7.2, 25.7.4, 25.8.1).
The literature supporting this assessment of key risks is uneven among sectors and between Australia and New
Zealand; for the latter, conclusions in many sectors are based on limited studies that often use a narrow set of
assumptions, models, and data and which, accordingly, have not explored the full range of potential outcomes.
25.10.3. Challenges to Adaptation in Managing Key Risks, and Limits to Adaptation
Two key and related challenges for regional adaptation are apparent: to identify when and where adaptation may
imply transformational rather than incremental changes; and, where specific interventions are needed to overcome
adaptation constraints, in particular to support transformational responses that require coordination across different
spheres of governance and decision-making (Productivity Commission, 2012; Palutikof et al., 2013). The magnitude
of climate change, especially under scenarios of limited mitigation, and constraints to adaptation suggest that
incremental and autonomous responses will not deliver the full range of available adaptation options nor ensure the
continued function of natural and human systems if some key risks are realized (high confidence; see also 25.4).
Most incremental adaptation measures in natural ecosystems focus on reducing other non-climate stresses but, even
with scaled-up efforts, conserving the current state and composition of the ecosystems most at risk appears
increasingly infeasible (25.6.1, 25.6.2). Maintenance of key ecosystem functions and services requires a radical
reassessment of conservation values and practices related to assisted colonisation and the values placed on
introduced species (Steffen et al., 2009). Divergent views regarding intrinsic and service values of species and
ecosystems imply the need for a proactive discussion to enable effective decision-making and resource allocation.
In human systems, incremental adjustments of current risk management tools, planning approaches and early
warning systems for floods, fire, drought, water resources and coastal hazards can increase resilience to climate
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variability and change especially in the near term (IPCC, 2012; Productivity Commission, 2012; Dovers, 2013). A
purely incremental approach, however, which generally aims to preserve current management objectives,
governance and institutional arrangements, can make later transformational changes increasingly difficult and costly
(high agreement, medium evidence; e.g. Howden et al., 2010; Park et al., 2012; McDonald, 2013; Stafford-Smith,
2013). Examples of transformational changes include: shifting emphasis from protection to accommodation or
avoidance of flood risk, including managed retreat from eroding coasts; the translocation of industries in response to
increasing drought, flood and fire risks or water scarcity; and the associated transformation of the economic and
social base and governance of some rural communities (Boxes 25-1, -2, 5-9; Nelson et al., 2010; Linnenluecke et
al., 2011; Kiem and Austin, 2012; O'Neill and Handmer, 2012; McDonald, 2013; Palutikof et al., 2013).
Consideration of transformational adaptation becomes critical where long life- or lead-times are involved, and where
high up-front costs or multiple interdependent actors create constraints that require coordinated and proactive
interventions (Stafford-Smith et al., 2011; Productivity Commission, 2012; Palutikof et al., 2013). Deferring such
adaptation decisions due to uncertainty about the future will not necessarily minimize costs or ensure adequate
flexibility for future responses, although up-front investment and opportunity costs of adaptation can present
powerful arguments for delayed or staged responses (Stewart and Wang, 2011; Gorddard et al., 2012; Productivity
Commission, 2012; McDonald, 2013). Whether transformational responses are seen as success or failure of
adaptation depends on the extent to which actors accept a change in, or wish to maintain current activities and
management objectives, and the degree to which the values and institutions underpinning the transformation are
shared or contested across stakeholders (Park et al., 2012; Stafford-Smith, 2013). These views will differ not only
between communities and industries but also from person to person depending on their individual value systems,
perceptions of and attitude to risk, and ability to capitalize on opportunities (see also 25.4.3).
25.11. Filling Knowledge Gaps to Improve Management of Climate Risks
The wide range of projected rainfall changes (averages and extremes) and their hydrological amplification are key
uncertainties affecting the scale and urgency of adaptation in agriculture, forestry, water resources, some
ecosystems, and wildfire and flood risks. For ecosystems, agriculture and forestry, these uncertainties are
compounded by limited knowledge of responses of vegetation to elevated CO2, changes in ocean pH, and
interactions with changing climatic conditions. The uncertainties in future impacts are most critical for decisions
with long lifetimes, such as capital infrastructure investment or large-scale changes in land- and water-use.
Uncertainties about the rate of sea level rise, and changes in storm paths and intensity, add to challenges for
infrastructure design. The use of multi-model means and a narrow set of emissions scenarios in many past studies
implies that the full set of climate-related risks and management options remains incompletely explored.
Understanding of ecological and physiological thresholds that, once exceeded, would result in rapid changes in
species, ecosystems and their services, is still very limited. The literature is noticeably sparse in New Zealand and
for arid Australia. These knowledge gaps are compounded by limited information about the effect of global climate
change on patterns of natural climate variability, such as ENSO. Better understanding the effect of evolving natural
climate variability and long-term trends, along with rising CO2 concentrations, on pests, invasive species and native
and managed ecosystems could support more robust ecosystem-based adaptation strategies.
Vulnerability of human and managed systems depends critically on future socio-economic characteristics. Research
into psychological, economic, social and cultural dimensions of vulnerability, adaptive capacity and underpinning
values remains limited and poorly integrated with bio-physical studies. This limits the level of confidence in
conclusions regarding future vulnerabilities and the feasibility and effectiveness of adaptation strategies.
These multiple, persistent and structural uncertainties imply that, in most cases, adaptation requires an iterative risk
management process. While decision-support frameworks are being developed, it remains unclear to what extent
existing governance and institutional arrangements will be able to support more transformational responses,
particularly where competing public and private interests and particularly vulnerable groups are involved. The
enabling or constraining influences on adaptation from interactions among market forces, institutions, governance,
policy and regulatory environments have only recently begun to attract research attention, mostly in Australia.
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Climate change impacts, adaptation and mitigation responses in other world regions will affect Australasia, but our
understanding of this remains very limited. Existing studies suggest that transboundary effects, mediated mostly via
trade but potentially also migration, can be of similar if not larger scale than direct domestic impacts of climate
change for economically important sectors such as agriculture and tourism. However, scenarios used in such studies
tend to be highly simplified. Effective management of risks and opportunities in these sectors would benefit from
better integration of relevant global scenarios of climatic and socio-economic changes into studies of local
vulnerability and adaptation options.
Frequently Asked Questions
FAQ 25-1: How can we adapt to climate change if projected future changes remain uncertain?
[to be inserted at end of Section 25.4.2]
Many existing climate change impact assessments in Australia and New Zealand focus on the distant future (2050 to
2100). When contrasted with more near-term non-climate pressures, the inevitable uncertainty of distant climate
impacts can impede effective adaptation. Emerging best practice in Australasia recognises this challenge and instead
focuses on those decisions that can and will be made in the near future in any case, along with the lifetime of those
decisions, and the risk from climate change during that lifetime. Thus, for example, the choice of next year s annual
crop, even though it is greatly affected by climate, only matters for a year or two and can be adjusted relatively
quickly. Even land-use change among cropping, grazing and forestry industries has demonstrated significant
flexibility in Australasia over the space of a decade. When the adaptation challenge is reframed as implications for
near-term decisions, uncertainty about the distant future becomes less problematic and adaptation responses can be
better integrated into existing decision-making processes and early warning systems.
Some decisions, such as those about long-lived infrastructure and spatial planning and of a public good nature,
must take a long-term view and deal with significant uncertainties and trade-offs between short- and long-term goals
and values. Even then, widely used techniques can help reduce challenges for decision-making including the
precautionary principle , real options , adaptive management , no regrets strategies , or risk hedging . These can
be matched to the type of uncertainty but depend on a regulatory framework and institutions that can support such
approaches, including the capacity of practitioners to implement them robustly.
Adaptation is not a one-off action but will take place along an evolving pathway, in which decisions will be
revisited repeatedly as the future unfolds and more information comes to hand (see Figure 25-3). Although this
creates learning opportunities, successive short-term decisions need to be monitored to avoid unwittingly creating an
adaptation path that is not sustainable as climate change continues, or which would cope only with a limited sub-set
of possible climate futures. This is sometimes referred to as maladaptation. Changing pathways for example,
shifting from on-going coastal protection to gradual retreat from the most exposed areas can be challenging and
may require new types of interactions among governments, industry and communities.
[INSERT FIGURE 25-3 HERE
Adaptation as an iterative risk management process. Individual adaptation decisions comprise well known
aspects of risk assessment and management (top left panel). Each decision occurs within and exerts its own
sphere of influence, determined by the lead- and consequence time of the decision, and the broader regulatory
and societal influences on the decision (top right panel). A sequence of adaptation decisions creates an
adaptation pathway (bottom panel). There is no single correct adaptation pathway, although some decisions,
and sequences of decisions, are more likely to result in long-term maladaptive outcomes than others, but the
judgment of outcomes depends strongly on societal values, expectations and goals.]
FAQ 25-2: What are the key risks from climate change to Australia and New Zealand?
[to be inserted at end of Section 25.10.3]
Our assessment identifies eight key regional risks from climate change. Some impacts, especially on ecosystems, are
by now difficult to avoid entirely. Coral reef systems have a limited ability to adapt naturally to further warming and
an increasingly acidic ocean. Similarly, the habitat for some mountain or high elevation ecosystems and their
associated species is shrinking inexorably with rising temperatures. This implies substantial impacts and some losses
even under scenarios of limited warming. Other risks, however, can be reduced substantially by adaptation,
combined with globally effective mitigation. These include potential flood damages from more extreme rainfall in
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most parts of Australia and New Zealand; constraints on water resources from reducing rainfall in southern
Australia; increased health risks and infrastructure damages from heat waves in Australia; and, increased economic
losses, risks to human life and ecosystem damage from wildfires in southern Australia and many parts of New
Zealand. A third set of risks is particularly challenging to manage robustly because the severity of potential impacts
varies widely across the range of climate projections, even for a given temperature increase. These concern damages
to coastal infrastructure and low-lying ecosystems from continuing sea level rise, where damages would be
widespread if sea level turns out to be at the upper end of current scenarios; and, threats to agricultural production in
both far south-eastern and far south-western Australia, which would affect ecosystems and rural communities
severely at the dry end of projected rainfall changes. Even though some of these key risks are more likely to
materialise than others, and they differ in the extent that they can be managed by adaptation and mitigation, they all
warrant attention from a risk management perspective, given their potential major consequences for the region.
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Table 25-1: Observed and projected changes in key climate variables, and (where assessed) the contribution of human activities to observed changes. For further
relevant information see WGI Chapters 3, 6 (ocean changes, including acidification), 11, 12 (projections), 13 (sea level), and 14 (regional climate phenomena).
(*) medium confidence, (**) high confidence, (***) very high confidence, (****) virtually certain
Climate Observed change Direction of projected change Examples of projected magnitude of change Additional comments
variable (relative to ~1990, unless otherwise stated)
Mean air Aus: Increased by 0.09 +/- 0.03°C per decade Aus and NZ: Increase3-8 (****); greatest over Aus: 0.6-1.5°C (2030 A1B), 1.0-2.5°C (2070 Aus: A significant contribution to observed
temperature since 19111 (***) inland Aus and least in coastal areas and NZ5-8 B1), 2.2-5.0°C (2070 A1FI)3 change attributed to anthropogenic climate
NZ: Increased by 0.09 +/- 0.03°C per decade (***) NZ: 0.3-1.4°C (2040 A1B), 0.7-2.3°C (2090 change10 (**) with some regional variations
since 19092 (***) B1), 1.6-5.1°C (2090 A1FI)5 attributed to atmospheric circulation
variations11,12
CMIP5 RCP4.5, rel. to ~19959:
N Aus: 0.3-1.6°C (2016-2035), 0.7-2.6°C NZ: Observed change partially attributed to
(2046-2065) anthropogenic climate change13 (*)
S Aus & NZ: 0.1-1.0°C (2016-2035), 0.6-
1.7°C (2046-2065)
Sea surface Aus: Increased by about 0.12°C per decade for Aus and NZ: Increase3,7,8 (***) with greater Aus: 0.6-1.0°C (2070 B1) and 1.6-2.0°C
temperature NW&NE Aus and by about 0.2°C per decade increase in the Tasman sea region (*) 3,7 (2070 A1FI) for southern coastal and 1.2-
for SE Aus since 195014,15 (***) 1.5°C (2070 B1) and 2.2-2.5°C (2070 A1FI)
NZ: Increased by about 0.07°C per decade elsewhere3
over 1909-20092 (***) NZ: Similar to projected changes in mean air
temperature for coastal waters5
Air Aus and NZ: Significant trend since 1950: Aus and NZ: Hot days and nights more Aus: Hot days in Melbourne (>35°C max.) Aus: Observed trends partly attributable to
temperature cool extremes have become rarer and hot frequent and cold days and cold nights less increase by 20-40% (2030 A1B), 30-90% anthropogenic climate change (**) as they are
extremes extremes more frequent and intense16-19 frequent during the 21st century3,5,21-24 (**) (2070 B1) and 70-190% (2070 A1FI)3 consistent with mean warming and historical
(**).The Australian heatwave of 2012/13 was NZ: Spring and autumn frost-free land to at simulations18,19,21,25, although other factors
exceptional in heat, duration and spatial extent least triple by 2080s24; up to 60 more hot days may have contributed to high extremes during
20
. (>25°C max.) for northern areas by 20905 droughts26-28
Precipitation Aus: Late autumn/winter decreases in SW Aus Aus: Annual decline in SW Aus (**), Aus: For 2030 A1B, annual changes of-10% Aus: Observed decline in SW is related to
since the 1970s and in SE Aus since the mid elsewhere on most of the southern (*) and NE to +5% (N Aus) and -10% to 0% (S Aus), for atmospheric circulation changes38-40 (***),
1990s, and annual increases in NW Aus since (low confidence) continental edges, with 2070 B1, -15% to +7.5% (N&E Aus) and - other factors41, and partly attributable to
the 1950s29-31 (***) reductions strongest in the winter half 15% to 0% (S Aus), and for 2070 A1FI, -30% anthropogenic climate change40-43 (**). The
NZ: Mean annual rainfall increased over year3,4,9,33-35 (**). Direction of annual change to +20% (N&E Aus) and -30% to +5% (S recent SE rainfall decline is also related to
1950-2004 in the south and west of the South elsewhere is uncertain3,35,36 (Figure 25.1) (**) Aus), with larger changes seasonally3 circulation changes31,44-46 (**), with some
Island and west of the North Island, and NZ: In the South Island, annual increase in the NZ: For 2040 A1B, annual changes of -5% to evidence of an anthropogenic component47
decreased in the north-east of the South Island west and south and decrease in north-east. In +15% (S&W) and -15% to +10% (N&E) and NZ: Observed trends related to increased
and east and north of the North Island32 (***) the North Island, increase in the west and for 2090 A1B, -10% to +25% (S&W) and - westerly winds32. Projected annual trends
decrease in eastern and northern regions 5,34,37 20% to +15% (N&E) based on downscaled dominated by winter and spring trends related
(Figure 25.1) (*) projections with larger changes seasonally5,37 to increased westerlies5
Precipitation Aus: Indices of annual daily extremes (e.g. Aus and NZ: Increase in most regions in the Aus: For 2090 A2, CMIP3 give increases in Aus and NZ: The sign of observed trends
extremes 95th and 99th percentile rainfalls) show mixed intensity of rare daily rainfall extremes (i.e. the intensity of the 20 year daily extreme of mostly reflects trends in mean rainfall (e.g.
or insignificant trends21,48, but significant current 20 year return period events) and in around +200% to -25% depending on region there is a decrease in mean and daily extremes
increase is evident in recent decades for short duration (sub-daily) extremes (*) and an and model52 in SW Aus)21,32,49. Similarly, future increases
shorter duration (sub-daily) events49,50 (**) increase in the intensity of 99 percentile daily NZ: Increases of daily extreme rainfalls of in intensity of extreme daily rainfall are more
NZ: Extreme annual 1-day rainfall decrease in extremes (low confidence)5,8,21,51-56 around 8% per degree C are projected but with likely where mean rainfall is projected to
north and east and increase in west since significant regional variations5,56 increase3,5
193032 (*)
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Drought Aus: Defined using rainfall only, drought Aus and NZ: Drought frequency is projected Aus: Occurrence under 2070 A1B and A2 Aus: Regional warming may have led to an
occurrence over the period 1900-2007 has not to increase in southern Australia 8,54,57,59,60 (*) ranges from a halving to 3 times more increase in hydrological drought (low
changed significantly57 (**) and in many regions of New Zealand58,61 (*) frequent in N. Aus, and 0-5 times more confidence)62,63
NZ: Defined using a soil water balance model, frequent in southern Aus60
there has been no trend in drought occurrence NZ: Time spent in drought in eastern and
since 197258 (*) northern New Zealand is projected to double
or triple by 204061
Winds Aus: Significant decline in storminess over SE Aus: Increases in winds in 20-30°S band, with Aus: Magnitude of simulated mean changes Aus and NZ: Many of past and projected
Aus since 188564 (*), but inconsistent trends in little change to decrease elsewhere, except for may exceed 10% under A1B for 2081-2100 changes in mean wind speed can be related to
wind observations since 197565,66 winter increases over Tasmania. Decrease to relative to 1981-2000 69 changes in atmospheric circulation43,67,68
NZ: Mean westerly flow increased during the little change in extremes (99th percentile) over NZ: Mean westerly flow to increase by NZ: Extreme westerlies and southerlies have
late 20th century (1978 1998), associated with most of Australia except Tasmania in winter69 around 20% in spring and around 70% in slightly increased while extreme easterlies
the positive phase of the IPO67,68 (*) winter, and to decrease by around 20% in have decreased since 196013,71
NZ: Mean westerly winds and extreme winds summer and autumn, by 20905
(based on projected changes in circulation
patterns) are projected to increase, especially
in winter5,70 (*)
Mean sea level Aus: From 1900-2011 the average rate of Aus and NZ: Regional sea level rise will very Aus: Off shore regional sea level rise may Aus and NZ: Satellite estimates of regional
relative sea level rise (SLR) was 1.4+/-0.6 likely exceed the 1971-2000 historical rate, exceed 10% more than global SLR, see AR5 SLR for 1993-2009 are significantly higher
mm/yr72 (***) consistent with global mean trends74. Mean WGI Chap13, Figure 13.2174 than those for 1920-2000, partly reflecting
NZ: The average rate of relative SLR was sea level will continue to rise for at least NZ: Off shore regional sea level rise may be climatic variability72,73,76,77
1.7+/-0.1 mm/yr over 1900-200973 (***) several more centuries74 (***) up to 10% more than global SLR75 NZ: Allowing for glacial isostatic adjustment,
absolute observed SLR is around 2.0mm/yr73,78
Extreme sea Aus and NZ: Extreme sea levels have risen at Aus and NZ: Projected mean SLR will lead to Aus: An increase of mean sea level by 0.1m
level a similar rate to global SLR79 large increases in the frequency of extreme sea increases the frequency of an extreme sea
level events (***), with other changes in storm level event by a factor of between 2 and 10
surges playing a lesser role80-83 over southeastern Australia depending on
location 80-82,
Fire weather Aus: Increased since 1973(**) with 24 out of Aus: Fire weather is expected to increase in Aus: Increase in days with very high and Aus: For the example of Canberra, the
38 sites showing increases in the 90th most of southern Australia due to hotter and extreme fire danger index by 2-30% (2020), 5- projected changes represent the current 17
percentile of the McArthur Forest Fire Danger drier conditions (**), based on explicit model 100% (2050) (using B1 and A2 and two days per year increasing to 18-23 days in 2020
index84 studies carried out for SE Australia 85-88, and climate models, and 1973-2007 base)85 and 20-33 days in 205085
change little or decrease in NE88 (*) NZ: Increase in days with very high and
NZ: Fire danger index is projected to increase extreme fire danger index from around 0 to
in many areas89 (*) 400% (2040) and 0 to 700% (2090) (using
A1B,16 CMIP3 GCMs )89
Tropical Aus: No regional change in the number of Aus: Tropical cyclones are projected to Aus: Modelling study shows a 50% reduction Aus: Regional research on convective storms
cyclones and tropical cyclones (TCs) or in the proportion of increase in intensity and stay similar or in TC occurrence for 2051-2090 relative to is limited but studies have shown a projected
other severe intense TCs over 1981-200790 (*), but decrease in numbers9,94, and occur further 1971-2000, increases in intensity of the decrease in the frequency of cool-season
storms frequency of severe landfalling TCs in NE south94 (low confidence) modelled storms, and occur around 100km tornadoes95, and hail3 in southern Australia,
Aus has declined significantly since the late NZ: Projected increase in the average intensity further south94 and increases in the frequency and intensity of
19th Century91 and east-west distribution of cyclones in the south during winter, but a NZ: Occurrence of conditions conducive to hail in the Sydney region3,96
changed since 1980.92 decrease elsewhere70 (*) convective storm development is projected to
There has been no trend in environments increase by 3 6% by 2070-2100 (A2), relative
suitable for severe thunderstorms93 to 1970-2000, with the largest increases over
the South Island70
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Snow and ice Aus: Late season significant snow depth Aus: Both snow depth and area are projected Aus: Area with at least 30 days cover annually NZ: Atmospheric circulation variations can
decline at three out of four Snowy mountain to decline97 (***) projected to decline 14-54% (2020) and 30- enhance or outweigh multi-decadal trends in
sites over 1957-200297 (**) NZ: Snowline elevations are projected to rise, 93% (2050)97 ice volume over time scales of up to two
NZ: Ice volume declined by 36-61% from the and winter snow volume and days with low NZ: By 2090, peak snow accumulation is decades104,105
mid-late 1800s to the late 1900s98-100, with elevation snow cover are projected to projected to decline by 32-79% at 1000m and
glacier volume reducing by 15% between decrease5,102,103 (***) by 6-51% at 2000 m103
1976 and 2008101 (**)
References: 1 Fawcett et al. (2012); 2 Mullan et al. (2010); 3 CSIRO and BoM (2007); 4 Moise and Hudson (2008); 5 MfE (2008b); 6 AR5-WGI-Atlas-AI68-69; 7 AR5-WGI-Ch11; 8 AR5-WGI-Ch12; 9
AR5-WGI-Ch14; 10 Karoly and Braganza (2005); 11 Hendon et al. (2007); 12 Nicholls et al. (2010); 13 Dean and Stott (2009); 14 Lough (2008); 15 Lough and Hobday (2011); 16 Chambers and Griffiths
(2008); 17 Gallant and Karoly (2010); 18 Nicholls and Collins (2006); 19 Trewin and Vermont (2010); 20 BOM (2013); 21 Alexander and Arblaster (2009); 22 Tryhorn and Risbey (2006); 23 Griffiths et al.
(2005); 24 Tait (2008); 25 Alexander et al. (2007); 26 Deo et al. (2009); 27 McAlpine et al. (2007); 28 Cruz et al. (2010); 29 Hope et al. (2010); 30 Jones et al. (2009); 31 Gallant et al. (2012); 32 Griffiths
(2007); 33 Timbal and Jones (2008); 34 AR5-WGI-Atlas-AI70-71; 35 Irving et al. (2012); 36 Watterson (2012); 37 Reisinger et al. (2010); 38 Bates et al. (2008); 39 Frederiksen and Frederiksen (2007); 40
Hope et al. (2006); 41 Timbal et al. (2006); 42 Cai and Cowan (2006); 43 Frederiksen et al. (2011); 44 Cai et al. (2011); 45 Nicholls (2010); 46 Smith and Timbal (2010); 47 Timbal et al. (2010a); 48 Gallant et
al. (2007); 49 Westra and Sisson (2011); 50 Jakob et al. (2011); 51 Abbs and Rafter (2009); 52 Rafter and Abbs (2009); 53 Kharin et al. (2013); 54 IPCC-SREX-Chapter-3; 55 Westra et al. (2013); 56 Carey-
Smith et al. (2010); 57 Hennessy et al. (2008a); 58 Mullan et al. (2005); 59 Kirono and Kent (2010); 60 Kirono et al. (2011); 61 Clark et al. (2011); 62 Cai and Cowan (2008); 63 Nicholls (2006); 64 Alexander
et al. (2011); 65 McVicar et al. (2008); 66 Troccoli et al. (2012); 67 Parker et al. (2007); 68 Mullan et al. (2001); 69 McInnes et al. (2011a); 70 Mullan et al. (2011); 71 Salinger et al. (2005); 72 Burgette et al.
(2013); 73 Hannah and Bell (2012); 74 AR5-WGI-Ch13; 75 Ackerley et al. (2013); 76 CSIRO and BoM (2012); 77 Meyssignac and Cazenave (2012); 78 Hannah (2004); 79 Menendez and Woodworth
(2010); 80 McInnes et al. (2009); 81 McInnes et al. (2011b); 82 McInnes et al. (2012); 83 Harper et al. (2009); 84 Clarke et al. (2012); 85 Lucas et al. (2007); 86 Hasson et al. (2009); 87 Cai et al. (2009a); 88
Clarke et al. (2011); 89 Pearce et al. (2011); 90 Kuleshov et al. (2010); 91 Callaghan and Power (2011); 92 Hassim and Walsh (2008); 93 Allen and Karoly (2013); 94 Abbs (2012); 95 Timbal et al. (2010b); 96
Leslie et al. (2008); 97 Hennessy et al. (2008b); 98 Hoelzle et al. (2007); 99 Ruddell (1995); 100 Chinn (2001); 101 Chinn et al. (2012); 102 Fitzharris (2004); 103 Hendrikx et al. (2012); 104 Purdie et al.
(2011); 105 Willsman et al. (2010).
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Table 25-2: Constraints and enabling factors for institutional adaptation processes in Australasia.*
Constraint Enabling factors
Uncertainty of projections Improved guidance and tools to manage uncertainty and support adaptive management 1-8
Increased focus on lead and consequence time of decisions and link with current climate
variability and related risks 9-13
Increased communication between practitioners and scientists to identify and provide
decision-relevant data and context 2,3,11,13-17
Availability and cost of data Central provision of relevant core climate and non-climate data, including regional
and models scenarios of projected changes 4,5,7,9,18,19
National first-pass risk assessments 4,5,7,8,18,20-24
Limited financial and human Support for pilot projects 4,8,15,18,24,25
capability and capacity; time Building capacity through institutional commitment and learning 3,5,11,17,23,26-28
lag in developing expertise Central databases on guidance, tools, methodologies, case studies 4,5,7,18,24
Regional partnerships and collaborations, knowledge networks 3,4,8,13,15,17,26,28-30
Unclear problem definition Explicit but iterative framing and scoping of adaptation challenge, to reflect alternative
and goals; unclear standards entry points for stakeholders while meeting expectations of project sponsors to ensure
for risk assessment long-term support 3,11,17,31-34
methodologies and decision Tailoring decision-making frameworks to specific problems 1,2,6,17,35,36
support tools; limited Criteria and tools to monitor and evaluate adaptation success 7,18,37-39
monitoring and evaluation
Unclear or contradictory Clear and coordinated legislative frameworks 5,8,9,15,24,40-45
legislative frameworks and Defined responsibilities for public and private actors, including liabilities from acting and
responsibilities, unclear failure to act 8,9,11,24,41,44,46
liabilities Legally binding guidance on the incorporation of climate change in planning mechanisms
5,7,8,15,38,40
Static planning mechanisms Whole-of-council approach to climate adaptation to break up institutional and professional
and practice; competing silos 15,33,47
mandates and fragmentation Long-term policy commitments and implementation support 5,18,26,33,48
of policies; disciplinary voids Increased policy coherence across sectors, regulations and levels of government
9,26,28,40,42,43,47
or single approaches
Enabling risk-based flexible land-use decisions 4,5,9,49
Strengthening multi-disciplinarity across professional fields 14,29,48
Lack of political leadership; Legally binding guidance and clarification of liabilities and duty of care to reduce
short election cycles; limited dependence on individual leadership 5,7-9,15,24,38,40,46,49
community support, Consistent but audience-specific communication of current and potential future
participation and awareness vulnerability and implications for community values 4,5,7,26,42,43,50
for adaptation Comprehensible communication of and access to response options, and their consistency
with wider development plans 7,26,28,33,39,42,43
Clearly identified entry points for public participation 17,34,38,39,42,48,51-53
* Note: The relevance of each constraint varies among organisations, sectors and location. Some enabling factors are only
beginning to be implemented or have only been suggested in the literature, hence their effectiveness cannot yet be evaluated.
Entries for enabling factors exclude generic mechanisms, such as insurance (see Box 25-7), emergency management and early
warning systems, and funding for pilot studies, capital infrastructure upgrades or retreat schemes.
References: 1 Randall et al. (2012); 2 Verdon-Kidd et al. (2012); 3 Webb et al. (2013); 4 Mukheibir et al. (2013); 5 Lawrence et al. (2013b); 6
Nelson et al. (2008); 7 Britton (2010); 8 Gurran et al. (2008); 9 Productivity Commission (2012); 10 Stafford-Smith et al. (2011); 11 Johnston et al.
(2013); 12 Park et al. (2012); 13 Power et al. (2005); 14 Reisinger et al. (2011); 15 Smith et al. (2008); 16 Stafford-Smith (2013); 17 Yuen et al.
(2012); 18 Webb and Beh (2013); 19 Roiko et al. (2012); 20 DCCEE (2011); 21 DCC (2009); 22 Baynes et al. (2012); 23 Smith et al. (2010); 24
SCCCWEA (2009); 25 DSEWPC (2011); 26 Low Choy et al. (2012); 27 Gardner et al. (2010); 28 Fidelman et al. (2013); 29 Mustelin et al. (2013); 30
Serrao-Neumann et al. (2013); 31 Fünfgeld et al. (2012); 32 Kuruppu et al. (2013); 33 Britton et al. (2011); 34 Alexander et al. (2012); 35 Maru et al.
(2011); 36 Preston et al. (2008); 37 Norman et al. (2012); 38 Rouse and Norton (2010); 39 Preston et al. (2011); 40 Rive and Weeks (2011); 41 Abel et
al. (2011); 42 Norman (2009); 43 Gurran et al. (2006); 44 McDonald (2013); 45 Minister of Conservation (2010); 46 McDonald (2010); 47 Measham
et al. (2011); 48 Rouse and Blackett (2011); 49 McDonald (2011); 50 Hine et al. (2013); 51 Burton and Mustelin (2013); 52 Hobson and Niemeyer
(2011); 53 Gardner et al. (2009a).
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Table 25-3: Examples of detected changes in species, natural and managed ecosystems, consistent with a climate change1 signal, published since the AR4.
Confidence in detection of change is based on the length of study, and the type, amount and quality of data in relation to the natural variability in the particular
species or system. Confidence in the role of climate being a major driver of the change is based on the extent to which the detected change is consistent with that
expected under climate change, and to which other confounding or interacting non-climate factors have been considered and been found insufficient to explain
the observed change.
Type of change and nature Examples Time scale of Confidence in the Potential climate change Confidence in the role
of evidence observations detection of driver(s) 1 of climate vs other
biological change drivers
Morphology Declining body size of southeast Australian ~100 years medium Warming air temperatures medium
passerine birds, equivalent to ~7o latitudinal shift trend significant for 4 ~1.0oC over same period Nutritional cause
Limited evidence (Gardner et al., 2009) out of 8 species, two discounted
(1 study) other species show
same trend but not
statistically
significant
Geographic distribution Southerly range extension of the barrens-forming ~30-50 years high Increased sea surface high
sea urchin Centrostephanus rodgersii from the New (first recorded in temperature (SST), Ocean
High agreement, robust South Wales coast to Tasmania; flow on impacts to Tasmania late warming in SE Australia,
evidence for many marine marine communities including lobster fishery; shift 1970s) increased southerly penetration
species & mobile terrestrial of 160 km per decade over 30 years (Ling, 2008; of the East Australian Current
species Ling et al., 2008; Ling et al., 2009; Banks et al., (EAC), 350 km over 60 years
2010)
Forty-five fish species, representing 27 families distributions high Increased SST SE Australia, medium
(about 30% of the inshore fish families occurring in from late 1880s, increased southerly penetration Changed fishing
the region), exhibited major distributional shifts in 1980s and of EAC practices have potentially
Tasmania (Last et al., 2011) present (1995- contributed to trends
now)
Southward range shift of intertidal species (average ~50 years medium Increased SST in SE Australia medium
minimum distance 116 km) off west coast of Sites resampled (average 0.22oC per decade),
Tasmania; 55% species recorded at more southerly 2007-2008, increased southerly penetration
sites, only 3% species expanded to more northerly compared with of the EAC, 350 km over 60
sites (Pitt et al., 2010) 1950s years
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Life cycles Significant advance in mean emergence date of 1.5 65 years high Increase in local air high
days per decade (1941-2005) in the Common temperatures of 0.16oC per Advance consistent with
Robust evidence, medium Brown butterfly Heteronympha merope in Australia decade (1945-2007) physiologically based
agreement; increasing (Kearney et al., 2010) model of temperature
documentation of advances influence on
in phenology in some development
species (mainly migration Advances in spring phenology of migratory birds, Multiple time high Local climate trends high
and reproduction in birds, and both advances and delays in phenology in other periods from (increasing air temperature, No other potential
emergence in butterflies, seasons at multiple Australian sites: meta-analysis 1960s, all decreased raindays) were more confounding facotrs
flowering in plants) but also of 52 species and 145 datasets (Chambers et al., included 1990s important than broad-scale identified
significant trends towards 2013b) and 2000s drivers such as the Southern
later life cycle events in Oscillation Index. Strongest
some taxa (see meta-analysis associations were with
for Southern Hemisphere decreased raindays.
phenology (Chambers et al., Earlier wine-grape ripening at 9 of 10 sites in Multiple time high Increased length of growing medium
2013a) Australia (Webb et al., 2012) periods up to 64 season, increased average Changed husbandry
years (average 41 temperature and reduced soil techniques, resulting in
years) moisture lower crop yields, may
have contributed to trend
Timing of migration of glass eels, Anguilla spp. 30 years medium Warming water temperatures in low
advanced by several weeks in Waikato River, North (2004-2005 spawning grounds Changes in discharge
island, New Zealand (Jellyman et al., 2009) compared to discounted as
1970s) contributing factor
Marine productivity Otolith ( ear stone ) analyses in long-lived Pacific Birth years high Increasing growth rates in medium
fish indicates significantly increased growth rates ranged 1861- species in top 250m associated Changed fishing pressure
Limited evidence, medium for shallow-water species (<250 m) (3 of 3 1993 (fish 2-128 with warming SST, declining may have contributed to
agreement species), reduced growth rates of deep-water years old) growth rates in species >1000m trend
(>1000 m) species (3 of 3 species); no change associated with long-term
observed in the 2 intermediate-depth species cooling (as indicated by Mg/Ca
(Thresher et al., 2007) ratios and delta18O in deep
water corals)
~50% decline in growth rate and biomass of spring 60 year dataset; high Increased SST and extension medium
phytoplankton bloom in western Tasman Sea decline recorded EAC associated with reduced
(Thompson et al., 2009) over period nutrient availability
1997-2007
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Vegetation change Expansion of monsoon rainforest at expense of ~40 years medium Increases in rainfall and medium
eucalypt savanna and grassland in Northern atmospheric CO2 Changes in fire regimes
Limited agreement & Territory, Australia (Banfai and Bowman, 2007; and land management
evidence; interacting impacts Bowman et al., 2010) practices may have
of changed land practices, contributed to trend
altered fire regimes, Net increase in mire wetland extent (10.2%) and Weather data medium Decline in evapo-transpiration low
increasing atmospheric CO2 corresponding contraction of adjacent eucalypt covers >40 years Resource exploitation,
concentration and climate woodland in seven sub-catchments in south east (depending on fire history and autogenic
trends difficult to Australia (Keith et al., 2010) parameter); mire development
disentangle vegetation discounted
mapping from
1961-1998
Freshwater communities Decline in families of macroinvertebrates that 13 years medium Increasing water temperatures low
favour cooler, faster-flowing habitats in New South (1994-2007) and declining flows Variation in sampling,
Limited evidence Wales streams and increase in families favouring changes in water quality,
(1 study) warmer and more lentic conditions (Chessman, impacts of impoundment
2009) and water extraction may
have contributed to
trends
Disease Emergence and increased incidence of coral 1998 onwards medium Increasing SST high
diseases including white syndrome (since 1998),
Limited evidence, robust and black band disease (since 1993-4) (Bruno et al.,
agreement 2007; Sato et al., 2009; Dalton et al., 2010)
Coral reefs Multiple mass bleaching events since 1979 (see 1979 onwards high Increasing SST high
25.6.2, 30.5)
Robust evidence & high Calcification of Porites on GBR declined 21% 1971-2003; high Increasing SST high
agreement (1971-2003, 4 reefs; Cooper et al., 2008); about 1990-2005 Changes in water quality
11% (1990-2005, 69 reefs; De'ath et al., 2009) discounted
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Table 25-4: Examples of potential consequences of climate change for invasive and pathogenic species relevant to Australia and New Zealand, with consequence
categories based on Hellman et al. (2008).
Consequence Projected change Organism/Ecosystem affected
altered mechanisms Increased risk of introduction of Asiatic Citrus Psyllid, (Diaphorina citri), vector of the disease huanglongbing 1 Australian citrus industry and native citrus and other rutaceaous
of transport and species and endemic psyllid fauna
introduction
altered distribution Nassella neesiana (Chilean needle grass): increased droughts favour establishment 2 Managed pasture in New Zealand
of existing invasive Warming and drying may encourage the spread of existing invasives such as Pheidole megacephala in New Human health and potentially agricultural and natural
& pathogenic species Zealand and provide suitable conditions for other exotic ant species if they invade 3 ecosystems
Reduced climatic suitability for exotic invasive grasses in Australia (11 species including Nassella sp.) 4
Range of the invasive weed Lantana camara (lantana) projected to extend from Northern Australia to Victoria, Australian rangeland
South Australia and Tasmania 5
Projected increases in the range of three recently naturalised sub-tropical plants (Archontophoenix Multiple
cunninghamiana, Psidium guajava, Schefflera actinophylla) 6
Native ecosystems in New Zealand
altered climatic Queensland fruit fly (Bactrocera tryoni) moving southwards 7 Australian horticulture
constraints on Significant association between amphibian declines in upland rainforests of north Queensland and three Native frogs
invasive & consecutive years of warm weather suggests future warming could increase the vulnerability of frogs to
pathogenic species chytridiomycosis caused by the chytrid fungus Batrachochytrium dendrobatadis 8
altered impact of Fusarium pseudograminearum causing crown rot increases under elevated CO2 9 Australian wheat
existing invasive & Increased abundance of the root-feeding nematode Longidorus elongatus under elevated CO2 10 New Zealand pasture
pathogenic species
Increased severity of Swiss needle cast disease caused by Phaeocryptopus gaeumannii 11 Douglas fir plantations in New Zealand, impact more severe in
North Island
altered effectiveness Light brown apple moth, Epiphyas postvittana (Walker) (Lepidoptera:Tortricidae) reduction in natural enemies Australian horticulture
of management due to asynchrony and loss of host species 12
strategies Projected changes in the efficacy of five biological control systems demonstrating a range of potential disruption Pastoral and horticultural systems in New Zealand
mechanisms 13
References: 1 Finlay et al. (2009); 2 Bourdôt et al. (2012); 3 Harris and Barker (2007); 4 Gallagher et al. (2012a); 5 Taylor et al. (2012b); 6 Sheppard (2012); 7 Sutherst et al. (2000); 8 Laurance (2008); 9
Melloy et al. (2010); 10 Yeates and Newton (2009); 11 Watt et al. (2011b); 12 Thomson et al. (2010); 13 Gerard et al. (2012).
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Table 25-5: Examples of co-beneficial climate change adaptation options for urban areas and barriers to their adoption. Options in italics are already widely
implemented in Australia and New Zealand urban areas.
Climate impact Adaptation options Co-benefits Barriers to adoption
Hot days and heatwaves 1-8 Greening cities/roofs; more green spaces; Energy efficiency; reduced risk of Lack of standards; high installation costs;
well-designed energy efficient buildings; blackouts; fewer health impacts; limited understanding of benefits; high
occupant behavioral change; standards for resilient infrastructure and assets; individual discount rate; split of private costs
new and retrofitting of existing resilient community and public benefits
infrastructure and assets; new methods
and material for transport infrastructure to
withstand higher extreme temperature
Decreased water supply and Supply augmentation (water recycling, Water self-sufficiency for current and Potential health impacts of recycled water;
drought rainwater harvesting, increased storage, future demand/population; less lower than expected uptake of demand
desalinisation); demand management; pipe/storage leakage; reduced options and relaxation after crises; trade-offs
[See Box 25-2 for more] infrastructure upgrades; integrated urban environmental impacts from abstraction between supply and demand management;
sensitive design cost and environmental impacts of some
augmentation options
River and local flooding, New standards and improvements to Reduced damages to homes and High implementation cost especially if
coastal erosion and building, water infrastructure (e.g. infrastructure and loss of life; decreased retrospective on existing stock; rezoning/
inundation drainage and sewerage) and transport insurance premiums; habitat protection relocation can affect property prices and are
infrastructure; upgrades of protection highly contested
[See Boxes 25-1 and 25-8 systems; retaining floodplains/floodways;
for more] restoring wetlands; buffers from hazard-
prone areas; raising minimum floor levels;
rezoning/ relocation
Severe storms and tropical New building design to withstand higher Reduced damages to homes and High implementation cost; rezoning/
cyclones 9-12 wind pressures; rezoning/relocation infrastructure and loss of life; decreased relocation can affect property prices and are
insurance premiums highly contested
Corrosion from increased Improved standards for construction Reduced rates of carbonation-induced Effectiveness of coatings varies with age and
atmospheric CO2 levels 13,14 using concrete; application of coatings for corrosion of concrete condition of concrete
existing building stock
References: 1 BRANZ (2007); 2 Coutts et al. (2010); 3 Moon and Han (2011); 4 Stephenson et al. (2010); 5 Williams et al. (2010); 6 CSIRO et al. (2007); 7 Taylor and Philp (2010); 8 QUT (2010); 9
Mason and Haynes (2010); 10 Wang et al. (2010b); 11 Stewart and Wang (2011); 12 Mason et al. (2013); 13 Stewart et al. (2012); 14 Wang et al. (2012).
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Table 25-6: Examples of interactions between impacts and adaptation measures in different sectors. In each case, impacts or responses in one sector have the
potential to conflict (cause negative impacts) or be synergistic (have co-benefits) with impacts or responses in another sector, or with another type of response in
the same sector.
Primary goal Sector(s) Examples of interactions between impacts and adaptation responses
affected
Reduction of bushfire risk Biodiversity, Potential for greater conflict between conservation managers and other park users in Kosciuszko National Park if increasing fire
in natural landscapes tourism incidence causes park closures, either to reduce risk, or to rehabilitate vegetation after fires (Wyborn, 2009), e.g. Objectives of the
Wildfire Management Overlay (WMO) in Victoria conflicts with vegetation conservation (Hughes and Mercer, 2009).
Reduction of risk to energy Biodiversity, Underground cabling would reduce both the susceptibility of transmission networks to fire and ignition sources for wild fires, thus
transmission from energy reducing risks to ecosystems and settlements; constraints include significant investment cost, diverse ownership of assets and lack
bushfires of an overarching national strategy (ATSE, 2008; Parsons Brinkerhoff, 2009; Linnenluecke et al., 2011).
Protection of coastal Biodiversity, Seawalls may provide habitat but these communities have different diversity and structure to those developing on natural substrates
infrastructure tourism (Jackson et al., 2008); groynes potentially alter beach fauna diversity and community structure (Walker et al., 2008); continuing
hard protection against sea level rise results in long-term loss of coastal amenities (Gorddard et al., 2012).
Avoidance of risks from sea Indigenous Relocation can avoid increasing local pressures on communities from sea level rise but raises complex cultural, land rights, legal
level rise via relocation communities and economic issues, e.g. potential relocation of Torres Strait islander communities (Green et al., 2010b; McNamara et al., 2011).
Allocating scarce water Rural areas, Market based instruments such as water trading help allocation of scarce water resources to the highest value uses. The negative
resources via market agriculture, implications of this include potential loss of access to lower value users, which in some areas includes agriculture and drinking
instruments mining water supplies, with potentially significant social, environmental and wider economic consequences (Kiem and Austin, 2012).
Increased water security Biodiversity, Water storage can buffer urban settlements and agricultural systems against high variability in river flows, but altered flow regimes
via augmentation of supply water demand can have significant negative impacts on freshwater ecosystems (Bond et al., 2008; Pittock et al., 2008; Kingsford, 2011).
for urban and agricultural management Discharge from desalination plants (e.g. in Perth and Sydney) can lead to substantial local increases in salinity and temperature,
systems and the accumulation of metals, hydrocarbons and toxic anti-fouling compounds in receiving waters (Roberts et al., 2010);
increasing supply can reduce the effectiveness of demand-side measures (Barnett and O'Neill, 2010; Taptiklis, 2011; Box 25-2).
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Table 25-7: Examples of interactions between adaptation and mitigation measures (green rows denote synergies where multiple benefits may be realized, orange
rows denote potential tradeoffs and conflicts; grey row gives an example of complex, mixed interactions). The primary goal may be adaptation or mitigation.
Primary goal Sector(s) Examples of interactions between adaptation and mitigation responses
affected
Adaptation to Biodiversity, Snowmaking in the Australian Alps would require large additional energy and water resources by 2020 of 2500-3300 ML of water per
decreasing snowfall energy use, month, more than half the average monthly water consumption by Canberra in 2004-05. Increased snowmaking negatively affects
water use vegetation, soils and hydrology of subalpine-alpine areas (Pickering and Buckley, 2010; Morrison and Pickering, 2011; ABS, 2012a).
Air conditioning for Health, Rising temperatures degrade building energy efficiency (Wang et al., 2010a) and increase energy demand and associated CO2 emissions
heat stress energy use if summer cooling needs are met by increased air conditioning (Stroombergen et al., 2006; Thatcher, 2007; Wang et al., 2010a).
Renewable wind Biodiversity Wind-farms can have localised negative effects on bats and birds. However, risk assessment of the potential negative impacts of wind
energy production turbines on threatened bird species in Australia indicated low to negligible impacts on all species modelled (Smales, 2006).
Urban densification Biodiversity, Higher urban density to reduce energy consumption from transport and infrastructure can result in loss of permeable surfaces and tree
water, health cover, intensify flood risks, and exacerbate discomfort and health impacts of hotter summers (Hamin and Gurran, 2009).
Water supply from Energy Meeting increasing urban water demand via desalination plants increases energy demand and CO2 emissions if this demand is met by
desalination demand increased fossil fuel energy generation (Barnett and O'Neill, 2010; Stamatov and Stamatov, 2010).
Secure food Nitrous oxide Net greenhouse gas emissions intensity from dairy systems in southern Australia have been estimated to increase in future in several
production in a and methane locations due to a changing climate and management responses (Cullen and Eckard, 2011; Eckard and Cullen, 2011). A shift towards
warming climate emissions perennial C4 grasses would increase methane emissions from grazing ruminants due to lower feed quality, but studies in south-west
Australia suggest this could be more than offset by increased soil carbon storage (Thomas et al., 2012; Bradshaw et al., 2013).
Housing design to Energy use, Reducing peak energy demand through building design and demand management reduces vulnerability of electricity networks and
reduce peak energy infrastructure, transmission losses during heat waves (Parsons Brinkerhoff, 2009; Nguyen et al., 2010), reduces heat stress during summer and provides
demand health health benefits during winter (Strengers, 2008; Howden-Chapman, 2010; Strengers and Maller, 2011; Ren et al., 2012).
Energy from Biodiversity, New crops such as oil mallees or other eucalypts may provide multiple benefits, especially in marginal areas, displacing fossil fuels or
second-generation rural areas, sequestering carbon, generating income for landholders (essential oils, charcoal, bio-char, biofuels), and providing ecosystem services
biofuels agriculture including reducing erosion (Cocklin and Dibden, 2009; Giltrap et al., 2009; McHenry, 2009).
Reduced emissions Biodiversity, Improved management of savanna fires to reduce the extent of high intensity late season fires could substantially reduce emissions as
from fires livelihoods well as having significant benefits for biodiversity and indigenous employment (Russell-Smith et al., 2009; Bradshaw et al., 2013).
Reduce methane Biodiversity, Feral camels in Australia are projected to double from 1 to 2 million by 2020. Controlling their numbers to reduce methane emissions
emissions from agriculture could have significant biodiversity benefits (NRMMC, 2010; Bradshaw et al., 2013). Economic benefits of reduced grazing competition,
feral camels infrastructure damage and greenhouse gases could outweigh costs of camel reductions (Drucker et al., 2010).
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Table 25-8: Key regional risks from climate change and the potential for reducing risk through mitigation and adaptation. Key risks are identified
based on assessment of the literature and expert judgments by chapter authors, with evaluation of evidence and agreement in the supporting
chapter sections. Each key risk is characterized on a scale from very low to very high and presented in three timeframes: the present, near-term
(2030-2040), and long-term (2080-2100). For the near-term era of committed climate change (here, for 2030-2040), projected levels of global
mean temperature increase do not diverge substantially across emissions scenarios. For the longer-term era of climate options (here, for 2080-
2100), risk levels are presented for global mean temperature increase of 2°C and 4°C above preindustrial levels. For each timeframe, risk levels
are estimated for a continuation of current adaptation and for a hypothetical highly adapted state. Relevant climate variables are indicated by
icons. For a given key risk, change in risk level through time and across magnitudes of climate change is illustrated, but because the assessment
considers potential impacts on different physical, biological, and human systems, risk levels should not necessarily be used to evaluate relative
risk across key risks, sectors, or regions.
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Figure 25-1: Observed and projected changes in annual average temperature and precipitation. (Top panel, left)
Observed temperature trends from 1901-2012 determined by linear regression [WGI AR5 Figures SPM.1 and 2.21].
(Bottom panel, left) Observed precipitation change from 1951-2010 determined by linear regression [WGI AR5
Figure SPM.2]. For observed temperature and precipitation, trends have been calculated where sufficient data
permits a robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data
availability in the first and last 10% of the time period). Other areas are white. Solid colors indicate areas where
change is significant at the 10% level. Diagonal lines indicate areas where change is not significant. (Top and
bottom panel, right) CMIP5 multi-model mean projections of annual average temperature changes and average
percent change in annual mean precipitation for 2046-2065 and 2081-2100 under RCP2.6 and 8.5. Solid colors
indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline
variability, and >90% of models agree on sign of change. Colors with white dots indicate areas with strong
agreement, where >66% of models show change greater than the baseline variability and >66% of models agree on
sign of change. Gray indicates areas with divergent changes, where >66% of models show change greater than the
baseline variability, but <66% agree on sign of change. Colors with diagonal lines indicate areas with little or no
change, less than the baseline variability in >66% of models. (There may be significant change at shorter timescales
such as seasons, months, or days.). Analysis uses model data and methods building from WGI AR5 Figure SPM.8.
See also Annex I of WGI AR5 [Boxes 21-3 and CC-RC].
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Figure 25-2: Observed and simulated variations in past and projected future annual average near-surface air
temperature over land areas of Australia (left) and New Zealand (right). Black lines show various estimates from
observational measurements. Shading denotes the 5-95 percentile range of climate model simulations driven with
historical changes in anthropogenic and natural drivers (63 simulations), historical changes in natural drivers
only (34), the RCP2.6 emissions scenario (63), and the RCP8.5 (63). Data are anomalies from the 1986-2005
average of the individual observational data (for the observational time series) or of the corresponding historical all-
forcing simulations. Further details are given in Box 21-3.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 25-3: Adaptation as an iterative risk management process. Individual adaptation decisions comprise well
known aspects of risk assessment and management (top left panel). Each decision occurs within and exerts its own
sphere of influence, determined by the lead- and consequence time of the decision, and the broader regulatory and
societal influences on the decision (top right panel). A sequence of adaptation decisions creates an adaptation
pathway (bottom panel). There is no single correct adaptation pathway, although some decisions, and sequences of
decisions, are more likely to result in long-term maladaptive outcomes than others, but the judgment of outcomes
depends strongly on societal values, expectations and goals.
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Figure 25-4: Estimated changes in mean annual runoff for 1°C global average warming above current levels. Maps
show changes in annual runoff (percentage change; top row) and runoff depth (millimetres; bottom row), for dry,
median and wet (10th to 90th percentile) range of estimates, based on hydrological modelling using 15 CMIP3
climate projections (Chiew et al., 2009; CSIRO, 2009; Petheram et al., 2012; Post et al., 2012). Projections for 2°C
global average warming are about twice that shown in the maps (Post et al., 2011). (Figure adapted from Chiew and
Prosser, 2011; Teng et al., 2012).
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Figure 25-5: Projected changes in exposure to heat under a high emissions scenario (A1FI). Maps show the average
number of days with peak temperatures >40°C, for ~1990 (based on available meteorological station data for the
period 1975-2004), ~2050 and ~2100. Bar charts show the change in population heat exposure, expressed as person-
days exposed to peak temperatures >40°C, aggregated by State/Territory and including projected population growth
for a default scenario. Future temperatures are based on simulations by the GFDL-CM2 global climate model
(Meehl et al., 2007), re-scaled to the A1FI scenario; simulations based on other climate models could give higher or
lower results. Data from Baynes et al. (2012).
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Chapter 26. North America
Coordinating Lead Authors
Patricia Romero-Lankao (Mexico), Joel B. Smith (USA)
Lead Authors
Debra Davidson (Canada), Noah Diffenbaugh (USA), Patrick Kinney (USA), Paul Kirshen (USA), Paul Kovacs
(Canada), Lourdes Villers Ruiz (Mexico)
Contributing Authors
William Anderegg (USA), Jessie Carr (USA), Anthony Cheng (USA), Thea Dickinson (Canada), Ellen Douglas
(USA), Rob de Loë (Canada), Hallie Eakin (USA), Daniel M. Gnatz (USA), Mary Hayden (USA), Maria Eugenia
Ibarraran Viniegra (Mexico), Elena Jiménez Cisneros (Mexico), Michael D. Meyer (USA), Amrutasri Nori-Sarma
(India), Landy Sánchez Pena (Mexico), Catherine Ngo (USA), Greg Oulahen (Canada), Diana Pape (USA), Ana
Pena del Valle (Mexico), Roger Pulwarty (USA), Ashlinn Quinn (USA), Daniel Runfola (USA), Fabiola S. Sosa-
Rodrigquez (Mexico), Bradley H. Udall (USA), Fiona Warren (Canada), Kate Weinberger (USA), Tom Wilbanks
(USA)
Review Editors
Ana Rosa Moreno (Mexico), Linda Mortsch (Canada)
Volunteer Chapter Scientist
William Anderegg (USA)
Contents
Executive Summary
26.1. Introduction
26.2. Key Trends Influencing Risk, Vulnerability, and Capacities for Adaptation
26.2.1. Demographic and Socioeconomic Trends
26.2.1.1. Current Trends
26.2.1.2. Future Trends
26.2.2. Physical Climate Trends
26.2.2.1. Current Trends
26.2.2.2. Climate Change Projections
26.3. Water Resources and Management
26.3.1. Observed Impacts of Climate Change on Water Resources
26.3.2. Projected Climate Change Impacts and Risks
26.3.2.1. Water Supply
26.3.2.2. Water Quality
26.3.2.3. Flooding
26.3.2.4 Instream Uses
26.3.3. Adaptation
26.4. Ecosystems and Biodiversity
26.4.1. Overview
26.4.2. Tree Mortality and Forest Infestation
26.4.2.1. Observed Impacts
26.4.2.2. Projected Impacts and Risks
26.4.3. Coastal Ecosystems
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26.4.3.1. Observed Climate Impacts and Vulnerabilities
26.4.3.2. Projected Impacts and Risks
26.4.4. Ecosystems Adaptation, and Mitigation
26.5. Agriculture and Food Security
26.5.1. Observed Climate Change Impacts
26.5.2. Projected Climate Change Risks
26.5.3. A Closer Look at Mexico
26.5.4. Adaptation
26.6. Human Health
26.6.1. Observed Impacts, Vulnerabilities and Trends
26.6.1.1. Storm-Related Impacts
26.6.1.2. Temperature Extremes
26.6.1.3. Air Quality
26.6.1.4. Pollen
26.6.1.5. Waterborne Diseases
26.6.1.6. Vectorborne Diseases
26.6.2. Projected Climate Change Impacts
26.6.3. Adaptation Responses
26.7. Key Economic Sectors and Services
26.7.1. Energy
26.7.1.1. Observed Impacts
26.7.1.2. Projected Impacts
26.7.1.3. Adaptation
26.7.2. Transportation
26.7.2.1. Observed Impacts
26.7.2.2. Projected Impacts
26.7.2.3. Adaptation
26.7.3. Mining
26.7.3.1. Observed Impacts
26.7.3.2. Projected Impacts
26.7.3.3. Adaptation
26.7.4. Manufacturing
26.7.4.1. Observed Impacts
26.7.4.2. Projected Impacts
26.7.4.3. Adaptation
26.7.5. Construction and Housing
26.7.5.1. Observed Impacts
26.7.5.2. Projected Impacts
26.7.5.3. Adaptation
26.7.6. Insurance
26.7.6.1. Observed Impacts
26.7.6.2. Projected Impacts
26.7.6.3. Adaptation
26.8. Urban and Rural Settlements
26.8.1. Observed Weather and Climate Impacts
26.8.2. Observed Factors and Processes Associated with Vulnerability
26.8.2.1. Urban Settlements
26.8.2.2. Rural Settlements
26.8.3. Projected Climate Risks on Urban and Rural Settlements
26.8.4. Adaptation
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26.8.4.1. Evidence of Adaptation
26.8.4.2. Opportunities and Constraints
26.9. Federal and Subnational Level Adaptation
26.9.1. Federal Level Adaptation
26.9.2. Subnational Level Adaptation
26.9.3. Barriers to Adaptation
26.9.4. Maladaptation, Trade-Offs, and Co-Benefits
26.10. Key Risks, Uncertainties, Knowledge Gaps, and Research Needs
26.10.1. Key Multi-Sectoral Risks
26.10.2. Uncertainties, Knowledge Gaps, and Research Needs
References
Chapter Boxes
26-1. Adapting in a Transboundary Context: the Mexico-U.S. Border Region
26-2. Wildfires
26-3. Climate Responses in Three North American Cities
Frequently Asked Questions
26.1: What impact is climate having on North America?
26.2: Can adaptation reduce the adverse impacts of climate in North America?
Executive Summary
Overview
North America s climate has changed and some societally-relevant changes have been attributed to
anthropogenic causes (very high confidence) [Figure 26-1]. Recent climate changes and individual extreme
events demonstrate both impacts of climate-related stresses and vulnerabilities of exposed systems (very high
confidence) [Figure 26-2]. Observed climate trends in North America include an increased occurrence of severe hot
weather events over much of the US, decreases in frost days, and increases in heavy precipitation over much of
North America (high confidence). [26.2.2.1] The attribution of observed changes to anthropogenic causes has been
established for some climate and physical systems (e.g., earlier peak flow of snowmelt run-off and declines in the
amount of water stored in spring snowpack in snow-dominated streams and areas of western United States and
Canada (very high confidence) [Figure 26-1]. Evidence of anthropogenic climatic influence on ecosystems,
agriculture, water resources, infrastructure, and urban and rural settlements is less clearly established, though, in
many areas, these sectors exhibit substantial sensitivity to climate variability (high confidence) (26.3.1; 26.3.2;
26.4.2.1; 26.4.2.2; 26.4.3.1; Box 26-3; 26.5.1; 26.7.1.1; 26.7.2; 26.8.1; Figure 26-2).
Many climate stresses that carry risk particularly related to severe heat, heavy precipitation and declining
snowpack will increase in frequency and/or severity in North America in the next decades (very high
confidence). Global warming of approximately 2°C (above the pre-industrial baseline) is very likely to lead to more
frequent extreme heat events and daily precipitation extremes over most areas of North America, more frequent low
snow years, and shifts towards earlier snowmelt runoff over much of the western US and Canada [26.2.2.2].
Together with climate hazards such as higher sea levels and associated storm surges, more intense droughts, and
increased precipitation variability, these changes are projected to lead to increased stresses to water, agriculture,
economic activities and urban and rural settlements (high confidence) [26.3.2.1-26.3.2.4; 26.5.2; 26.7.1.2; 26.8.3].
Global warming of approximately 4°C is very likely to cause larger changes in extreme heat events, daily-scale
precipitation extremes and snow accumulation and runoff, as well as emergence of a locally-novel temperature
regime throughout North America [26.2.2.2]. This higher level of global temperature change is likely to cause
decreases in annual precipitation over much of the southern half of the continent and increases in annual
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precipitation over much of the northern half of the continent [26.2.2.2]. The higher level of warming would present
additional and substantial risks and adaptation challenges across a range of sectors (high confidence). [26.3.3,
26.5.2, 26.6.2, 26.7.2.2, 26.8.3]
We highlight below key findings on impacts, vulnerabilities, projections, and adaptation responses relevant to
specific North American sectors: ecosystems, water, agriculture, human health, urban and rural settlements,
infrastructure and the economy. We then highlight challenges and opportunities for adaptation, and future risks and
adaptive capacity for three key climate-related risks.
Sector-Specific Climate Risks and Adaptation Opportunities
North American ecosystems are under increasing stress from rising temperatures, CO2 concentrations, and
sea-levels, and are particularly vulnerable to climate extremes (very high confidence). Climate stresses occur
alongside other anthropogenic influences on ecosystems, including land-use changes, non-native species, and
pollution, and in many cases will exacerbate these pressures (very high confidence). [26.4.1; 26.4.3]. Evidence since
the Fourth Assessment Report highlights increased ecosystem vulnerability to multiple and interacting climate
stresses in forest ecosystems, through wildfire activity, regional drought, high temperatures, and infestations
(medium confidence) [26.4.2.1; Box 26-2]; and in coastal zones due to increasing temperatures, ocean acidification,
coral reef bleaching, increased sediment load in run-off, sea level rise, storms, and storm surges (high confidence)
[26.4.3.1]. In the near term, conservation and adaptation practices can buffer against climate stresses to some degree
in these ecosystems, both through increasing system resilience, such as forest management to reduce vulnerability to
infestation, and in reducing co-occurring non-climate stresses, such as careful oversight of fishing pressure (medium
confidence) [26.4.4].
Water resources are already stressed in many parts of North America due to non-climate change
anthropogenic forces, and are expected to become further stressed due to climate change (high confidence)
[26.3, 26.3.1]. Decreases in snowpacks are already influencing seasonal streamflows (high confidence) [26.3.1].
While indicative of future conditions, recent floods, droughts, and changes in mean flow conditions cannot yet be
attributed to climate change (medium to high confidence) [26.3.1, 26.3.2]. The 21st century is projected to witness
decreases in water quality and increases in urban drainage flooding throughout most of North America under climate
change as well as a decrease in instream uses such as hydropower in some regions (high confidence) [26.3.2.2,
26.3.2.3, 26.3.2.4]. Additionally, there will be decreases in water supplies for urban areas and irrigation in North
America except in general for southern tropical Mexico, northwest coastal US, and west coastal Canada (high to
medium confidence, 26.3.2.1). Many adaptation options currently available can address water supply deficits;
adaptation responses to flooding and water quality concerns are more limited (medium confidence) [26.3.3].
Effects of temperature and climate variability on yields of major crops have been observed (high confidence)
[25.5.1]. Projected increases in temperature, reductions in precipitation in some regions, and increased
frequency of extreme events would result in net productivity declines in major North American crops by the
end of the 21st Century without adaptation, although the rate of decline varies by model and scenario, and
some regions, particularly in the north, may benefit (very high confidence) [26.5.2]. Given that North America is
a significant source of global food supplies, projected productivity declines here may affect global food security
(medium confidence). At 2°C, adaptation has high potential to off-set projected declines in yields for many crops,
and many strategies offer mitigation co-benefits; but effectiveness of adaptation would be reduced at 4°C (high
confidence). [26.5.3] Adaptation capacity varies widely among producers, and institutional support currently
lacking in some regions greatly enhances adaptive potential (medium confidence) [26.5.4].
Human health impacts from extreme climate events have been observed, although climate change-related
trends and attribution have not been confirmed to-date. Extreme heat events currently result in increases in
mortality and morbidity in North America (very high confidence), with impacts that vary by age, location and
socioeconomic factors (high confidence) [26.6.1.2]. Extreme coastal storm events can cause excess mortality and
morbidity, particularly along the east coast of the United States, and the gulf coast of both Mexico and the United
States (high confidence) [26.6.1.1]. A range of water-, food-, and vector-borne infectious diseases, air pollutants, and
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airborne pollens are influenced by climate variability and change (medium confidence) [26.6.1.3, 26.6.1.4, 26.6.1.5,
26.6.1.6]. Further climate warming in NA will impose stresses on the health sector through more severe extreme
events such as heat waves and coastal storms, as well as more gradual changes in climate and CO2 levels. [26.6.2]
Human health impacts in NA from future climate extremes can be reduced by adaptation measures such as targeted
and sustainable air conditioning, more effective warning and response systems, enhanced pollution controls, urban
planning strategies, and resilient health infrastructure (high confidence) [26.6.3].
Observed impacts on livelihoods, economic activities, infrastructure and access to services in North American
urban and rural settlements have been attributed to sea level rise, changes in temperature and precipitation,
and occurrences of such extreme events as heat waves, droughts and storms (high confidence) [26.8.2.1].
Differences in the severity of climate impacts on human settlements are strongly influenced by context-specific
social and environmental factors and processes that contribute to risk, vulnerability and adaptive capacity such as
hazard magnitude, populations access to assets, built environment features and governance (high confidence)
[26.8.2.1 and 26.8.2.2]. Some of these processes (e.g., the legacy of previous and current stresses) are common to
urban and rural settlements, while others are more pertinent to some types of settlements than others. For example,
human and capital risks are highly concentrated in some highly exposed urban locations, while in rural areas,
geographic isolation and institutional deficits are key sources of vulnerability. Among the most vulnerable are
indigenous peoples due to their complex relationship with their ancestral lands and higher reliance on subsistence
economies, and those urban centers where high concentrations of populations and economic activities in risk-prone
areas combine with several socio-economic and environmental sources of vulnerability (high confidence) [26.8.2.1
and 26.8.2.2]. Although larger urban centers would have higher adaptation capacities, future climate risks from heat
waves, droughts, storms and sea level rise in cities would be enhanced by high population density, inadequate
infrastructures, lack of institutional capacity and degraded natural environments (high agreement, medium evidence)
[26.8.3].
Much of North American infrastructure is currently vulnerable to extreme weather events and, unless
investments are made to strengthen them, would be more vulnerable to climate change (medium confidence).
Water resources and transportation infrastructure are in many cases deteriorating, thus more vulnerable to extremes
than strengthened ones (high confidence). Extreme events have caused significant damage to infrastructure in many
parts of North America; risks to infrastructure are particularly acute in Mexico but are a big concern in all three
countries (high confidence) [26.7].
Most sectors of the North American economy have been affected by and have responded to extreme weather,
including hurricanes, flooding, and intense rainfall (high confidence) (Figure 26-2). Despite a growing
experience with reactive adaptation, there are few examples of proactive adaptation anticipating future climate
change impacts, and these are largely found in sectors with longer-term decision-making, including energy and
public infrastructure. Knowledge about lessons learned and best adaptive practices by industry sector are not well-
documented in the published literature [26.7]. There is an emerging concern that dislocation in one sector of the
economy may have an adverse impact on other sectors due to supply chain interdependency (medium confidence)
[26.7]. Slow onset perils like sea level rise, drought, and permafrost thaw are an emerging concern for some
sectors, with large regional variation in awareness and adaptive capacity (medium confidence).
Adaptation Responses
Adaptation including through technological innovation, institutional strengthening, economic
diversification, and infrastructure design can help to reduce risks in the current climate, and to manage
future risks in the face of climate change (medium confidence) [26.8.4; 26.9.2]. There is increasing attention to
adaptation among planners at all levels of government but particularly at the municipal level, with many
jurisdictions engaging in assessment and planning processes. These efforts have revealed the significant challenges
and sources of resistance facing planners at both the planning and implementation stages, particularly the adequacy
of informational, institutional, financial and human resources, and lack of political will (medium confidence)
[26.8.4.2; 26.9.3]. Specific strategies introduced into policy to date tend to be incremental rather than
transformational. Fiscal constraints are higher for Mexican jurisdictions and sectors than for Canada or the US. The
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literature on sectoral-level adaptation is stronger in the areas of technological and engineering adaptation strategies
than in social, behavioral and institutional strategies. Adaptation actions have the potential to result in synergies or
tradeoffs with mitigation and other development actions and goals (high confidence) [26.8.4.2; 26.9.3].
26.1. Introduction
This chapter assesses literature on observed and projected impacts, vulnerabilities and risks as well as on adaptation
practices and options in three North American countries: Canada, Mexico and United States (US). The North
American Arctic region is assessed in Chapter 28: Polar Regions. North America ranges from the tropics to frozen
tundra, and contains a diversity of topography, ecosystems, economies, governance structures and cultures. As a
result, risk and vulnerability to climate variability and change differ considerably across the continent depending on
geography, scale, hazard, socio-ecological systems, ecosystems, demographic sectors, cultural values and
institutional settings. This chapter seeks to take account of this diversity and complexity as it affects and is projected
to affect vulnerabilities, impacts, risks and adaptation across North America.
No single chapter would be adequate to cover the range and scope of the literature about climate change
vulnerabilities, impacts and adaptations in our three focus countries. (Interested readers are encouraged to review the
following reports: (Instituto Nacional de Ecología y Cambio Climático, 2012a; National Climate Assessment
Development Advisory Committee, 2013). We therefore attempt to take a more integrative and innovative approach.
In addition to describing current and future climatic and socioeconomic trends of relevance to understanding risk
and vulnerability in North America (section 26.2), we contrast climate impacts, vulnerabilities and adaptations
across and within the three countries in the following key sectors: water resources and management (section 26.3);
ecosystems and biodiversity (section 26.4); agriculture and food security (section 26.5); human health (section 26.6);
and key economic sectors and services (section 26.7). We use a comparative and place-based approach to explore
the factors and processes associated with differences and commonalities in vulnerability, risk and adaptation
between urban and rural settlements (section 26.8); and to illustrate and contrast the nuanced challenges and
opportunities adaption entails at the city, the subnational and the national level (sections 26.8.4 and 26.9; Box 26-3).
We highlight two case studies that cut across sectors, systems or national boundaries. The first, on wildfires (Box
26-2), explores some of the connections between climatic, physical and socioeconomic process (e.g., decadal
climatic oscillation, droughts, wildfires land-use, and forest management) and across systems and sectors (e.g., fires
direct and indirect impacts on local economies, livelihoods, built environments and human health). The second takes
a look at one of the world s longest border between a high-income (US) and middle income country (Mexico) and
briefly reflects on the challenges and opportunities of responding to climate change in a transboundary context (Box
26-1). We close with a section (26.10) summarizing key multi-sectoral risks and uncertainties and discussing some
of the knowledge gaps that will need to be filled by future research.
Findings from the Fourth Assessment Report
This section summarizes key findings on North America, as identified in Chapter 13 of the Fourth IPCC assessment
focused on Mexico (Magrin et al., 2007), and Chapter 14 on Canada and the US (Field et al., 2007). It focuses on
observed and projected impacts, vulnerabilities and risks as well as on adaptation practices and options and
highlights areas of agreement and difference between the AR4 s two chapters and our consolidated North American
chapter.
Observed impacts and processes associated with vulnerability. Both chapter 14 [14.2] and our chapter (Figure 26-2)
find that over the past decades, economic damage from severe weather has increased dramatically. Our chapter
confirms that although Canada and the US have considerably more adaptive capacity than Mexico, their
vulnerability depends on the effectiveness and timing of adaptation and the distribution of capacity, which vary
geographically and between sectors. [14.2.6; 14.4; 14.5] [26.2.2; 26.8.2]
Chapters 13 and 14 did not assess impacts, vulnerabilities and risks in urban and rural settlements, but rather
assessed literature on future risks in the following sectors:
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Ecosystems: Both AR4 and our chapter find that ecosystems are under increased stress from increased
temperatures, climate variability and other climate stresses (e.g., sea level rise and storm-surge flooding),
and that these stresses interact with developmental and environmental stresses (e.g., as salt intrusion,
pollution, population growth and the rising value of infrastructure in coastal areas) [13.4.4; 14.2.3; 14.4.3].
Differential capacities for range shifts and constraints from development, habitat fragmentation, invasive
species, and broken ecological connections would alter ecosystem structure, function and services in
terrestrial ecosystems [14.2; 14.4]. Both reports show that dry soils and warm temperatures are associated
with increased wildfire activity and insect outbreaks in Canada and the US [14.2; 14.4; 26.4.2.1].
Water resources: AR4 projects millions in Mexico to be at risk from the lack of adequate water supplies
due to climate change[13.4.3]; our chapter, however, finds that water resources are already stressed by non-
climatic factors, such as population pressure that will be compounded by climate change [26.3.1]. Both
reports find that in the US and Canada rising temperatures would diminish snowpack and increase
evaporation [26.2.2.1], thus affecting seasonal availability of water [14.2.1; 26.3.1]. The reports also agree
that these effects will be amplified by water demand from economic development, agriculture and
population growth, thus imposing further constraints to over-allocated water resources and increasing
competition among agricultural, municipal, industrial and ecological uses [14.4.1; 14.4.6; 26.3.3]. Both
agree water quality will be further stressed [14.4.1; 26.3.2.2; 13.4.3]. There is more information available
now on water adaptation than in AR4 [14.5.1; 26.3.3; 13.5.1.3], and is possible to attribute changes in
extreme precipitation, snowmelt and snowpack to climate change [26.3.1; 14.2.1; 13.2.4]
Agriculture: The AR4 noted that while increases in grain yields in the US and Canada are projected by
most scenarios [14.4.4], in Mexico the picture is mixed for wheat and maize, with different projected
impacts depending on scenario used [13.4.2]. Research since the AR4 has offered more cautious
projections of yield change in North America due to shifts in temperature and precipitation, particularly by
2100; and significant harvest losses due to recent extreme weather events have been observed [26.5.1].
Furthermore, our chapter reports on recent research that underscores the context specific nature of
adaptation capacity and of institutional support and shows that these factors, which greatly enhance
adaptive potential, are currently lacking in some regions [26.5.3].
Health: AR4 focused primarily on a set of future health risks. These include changes in the geographical
distribution and transmission of diseases such as dengue [13.4.5]; increases in respiratory illness, including
exposure to pollen and ozone [14.4] and in mortality from hot temperatures and extreme weather in Canada
and the US. AR4 also projects that climate change impacts on infrastructure and human health in cities of
Canada and the US would be compounded by aging infrastructure, maladapted urban form and building
stock, urban heat islands, air pollution, population growth and an aging population [14.4; 14.5]. Without
increased investments in measures such as early warning and surveillance systems, air conditioning, and
access to health care, hot temperatures and extreme weather in Canada and the US are predicted to result in
increased adverse health impacts [14.4; 14.5]. Our chapter provides a more detailed assessment of these
future risks [26.6], besides assessing a richer literature on observed health impacts [26.6.1].
Adaptation: AR4 found that Mexico has early warning and risk management systems, yet it faces planning
and management barriers. In Canada and the US, a decentralized response framework has resulted in
adaptation that tends to be reactive, unevenly distributed, and focused on coping with rather than
preventing problems [14.5]. Both chapters see mainstreaming climate issues into decision making as key
to successful adaptation [14.5; 13.5]. The current chapter provides a summary of the growing empirical
literature on emerging opportunities and constraints associated with recent institutional adaptation planning
activities since the AR4 [26.3.3; 26.4.3; 236.5.3; 26.6.3; 26.8.4;26.9].
In summary, scholarship on climate change impacts, adaptation and vulnerability has grown considerably since the
AR4 in North America, particularly in Canada and the US. It is possible now not only to detect and attribute to
anthropogenic climate change some impacts such as changes in extreme precipitation, snowmelt and snowpack, but
also to examine trends showing increased insect outbreaks, wildfire events and coastal flooding. These latter trends
have been shown to be sensitive to climate, but, like the local climate patterns that cause them, have not yet been
positively attributed to anthropogenic climate change (see Figure 26-1).
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[INSERT FIGURE 26-1 HERE
Figure 26-1 Detection and attribution of climate change impacts. Comparisons of the adequacy of currently
available data to detect trends and the degree of understanding of causes of those changes in climatic extreme events
in the United States (left; Peterson et al., 2013) and degree of understanding of the climate influence in key impacts
in North America (right). Note that climate influence means that the impact has been documented to be sensitive
to climate, not that it has been attributed to climate change. Filled boxes indicate that formal detection and
attribution to climate change has been performed for the given impact; shaded boxes indicate that a trend has been
detected from background variability in the given impact, but formal attribution to climate change has not occurred
and the trend could be due to other drivers; and open boxes indicate that a trend has not currently been detected. Key
impacts are: 1) earlier peak flow of snowmelt run-off in snow-dominated streams and rivers in western North
America [26.3.1], 2) declines in the amount of water stored in spring snowpack in snow-dominated areas of western
North America [26.3.1], 3) northward and upward shifts in species distributions in multiple taxa of terrestrial
species, although not all taxa and regions [26.4.1], 4) increases in coastal flooding [26.8.1], 5) increases in wildfire
activity, including fire season length and area burned by wildfires in the western United States and boreal Canada
[Box 26-2], 6) storm-related disaster losses in the United States (most of the increase in insurance claims paid has
been attributed to increasing exposure of people and assets in areas of risk) [26.7.6.1, 26.8.1], 7) increases in bark
beetle infestation levels in pine tree species in western North America [26.4.2.1], 8) yield increases due in part to
increasing temperatures in Canada and higher precipitation in the US; yield variances attributed to climate
variability in Ontario and Quebec; yield losses attributed to climate-related extremes across North America [26.5.1],
9) changes in storm-related mortality in the United States [26.6.1.2], 10) changes in heat-related mortality in the
United States [26.6.1.2], 11) increases in tree mortality rates in old-growth forests in the western United States and
western Canada from 1960-2007 [26.4.2.1], 12) changes in flooding in some urban areas due to extreme rainfall
[26.3.1, 26.8.2.1], 13) increase in water supply shortages due to drought [26.8.1, 26.3], and 14) changes in cold-
related heat mortality [26.6.1.2].]
26.2. Key Trends Influencing Risk, Vulnerability, and Capacities for Adaptation
26.2.1. Demographic and Socioeconomic Trends
26.2.1.1. Current Trends
Canada, Mexico and US share commonalities but also differ in key dimensions shaping risk, vulnerability and
adaptation such as population dynamics, economic development, and institutional capacity. During the last years,
the three countries, particularly the US, have suffered economic losses from extreme weather events (Figure 26-2).
Hurricanes, droughts, floods and other climate-related hazards produce risk as they interact with increases in
exposed populations, infrastructure and other assets and with the dynamics of such factors shaping vulnerability as
wealth, population size and structure, and poverty (Figure 26-2 and Figure SPM.1). Population growth has been
slower in Canada and US than in Mexico (Population Division, Department of Economic and Social Affairs, 2011).
Yet population growth in Mexico also decreased from 3.4 percent between 1970-1980 to 1.5 percent yearly during
2000-2010. Populations in the three countries are aging at different rates (Figure 26-2). In 2010, 14.1% of the
population in Canada was 60 years and older, compared to 12.7% in the US, and 6.1% in Mexico (Population
Division, Department of Economic and Social Affairs, 2011). Urban populations have grown faster than rural
populations, resulting in a North America that is highly urbanized (Canada 84.8%, Mexico 82.8% and US 85.8%).
Urban populations are also expanding into peri-urban spaces, producing rapid changes in population and land use
dynamics that can exacerbate risks from such hazards as floods and wildfires (Eakin et al., 2010; Romero-Lankao et
al., 2012a). Mexico has a markedly higher poverty rate (34.8%) than Canada (9.1%) and the US (12.5%) (Figure 26-
2), with weather events and climate affecting poor people s livelihood assets, including crop yields, homes, food
security, and sense of place (Chapter 13, section 26.8.2). Between 1970 and 2012, a 10 percent increase in single
person households who can be vulnerable because of isolation and low income and housing quality (Roorda et al.,
2010), has been detected in the US (Vespa et al., 2013).
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[INSERT FIGURE 26-2 HERE
Figure 26-2: Extreme events illustrating vulnerabilities for Mexico, the United States, and Canada. This figure offers
a graphic illustration of location of extreme events and relevant vulnerability trends. The observed extreme events
have not been attributed to anthropogenic climate change, yet they are climate-sensitive sources of impact
illustrating vulnerability of exposed systems, particularly if projected future increases in the frequency and/or
intensity of such events should materialize. The figure includes:
a) A map (bottom) with population density at 1km resolution highlighting exposure and represented using
2011 Landscan data (Bright et al., 2012).
b) A map (top) with significant weather events taking place during 1993-2012. The map only includes
disasters with overall losses of more than $1 billion US dollars in US, or more than $500 million US dollars
in Mexico and Canada, adjusted to 2012 values (Source: (NatCatSERVICE, 2010). Hence, it does not
include the occurrence of disasters of small and medium impact, and it does not capture the impacts of
disasters on populations livelihoods and wellbeing. Disasters represented by points that are located at the
approximate geographic center of affected regions, frequently span more than one subnational jurisdiction
(e.g., the 2012 drought affected 12 Mexican states, Annex Table).
c) Four panels (right) with trends in socio-demographic indicators used in the literature to measure
vulnerability to hazards (Romero-Lankao et al., 2012): poverty rates, percentage of elderly, GDP per capita
and total population (Sources: Comisión Económica para América Latina y el Caribe; U.S. Census Bureau,
2011; Statistics Canada, 2012).]
While concentrations of growing populations, water, sanitation, transportation and energy infrastructure and
industrial and service sectors in urban areas can be a source of risk, geographic isolation and high dispersion of rural
populations also introduce risk because of long distances to essential services (section 26.8.2). Rural populations are
more vulnerable to climate events due to smaller labor markets, lower income levels and reduced access to public
services. Rural poverty could also be aggravated by changes in agricultural productivity, particularly in Mexico
where 65% of the rural population is poor, agricultural income is seasonal, and most households lack insurance
(Scott, 2007). Food price increases, which may also result from climate events, would contribute to food insecurity
(World Bank, 2011; Lobell et al., 2011).
Migration is a key trend affecting North America, recently with movements between urban centers and from rural
Mexico into Mexico s cities, and in the US. Rates of migration from rural Mexico are positively associated with
natural disaster occurrence and increased poverty trends (Saldana-Zorilla and Sandberg, 2009), and with decreasing
precipitation (Nawrotski et al., 2013). Studies of migration induced by past climate variability and change indicate a
preference for short-range domestic movement, a complex relationship to assets with indications that the poorest are
less able to migrate, and the role of pre-existing immigrant networks in facilitating international migration
(Oppenheimer, 2013).
North America has become more economically integrated following the 1994 North American Free Trade
Agreement. Prior to a 2007-2008 reduction in trade, the three countries registered dynamic growth in industry,
employment and global trade of agricultural and manufactured goods (World Bank, 2009). Notwithstanding North
America s economic dynamism, increased socioeconomic disparities (Autor et al., 2008) have affected such
determinants of vulnerability as differentiated human development and institutional capacity within and across
countries.
_____ START BOX 26-1 HERE _____
Box 26-1. Adapting in a Transboundary Context: the Mexico-U.S. Border Region
Extending over 3111 km (1933 miles; (U.S. Census Bureau, 2011), the border between the United States and
Mexico, which can be defined in different ways (Varady and Ward, 2009), illustrates the challenges and
opportunities of responding to climate change in a transboundary context. Changing regional climate conditions and
socioeconomic processes combined shape differentiated vulnerabilities of exposed populations, infrastructure and
economic activities.
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Since at least 1999, the region has experienced high temperatures and aridity anomalies leading to drought
conditions (Woodhouse et al., 2010; Wilder et al., 2013) affecting large areas on both sides of the border, and
considered the most extreme in over a century of recorded precipitation patterns for the area (Seager and Vecchi,
2010; Cayan et al., 2010; Nielsen-Gammon, 2011). Streamflow in already oversubscribed rivers such as the
Colorado and Rio Grande (Nakaegawa et al., 2013) has decreased. Climatological conditions for the area have been
unprecedented, with sustained high temperatures that may have exceeded any experienced for 1,200 years. While
these changes cannot conclusively be attributed to anthropogenic climate change, they are consistent with climate
change projections (Woodhouse et al., 2010).
The population of the Mexico-US Border is rapidly growing and urbanizing, doubling from just under 7 million in
1983 to over 15 million in 2012 (Peach and Williams, 2007). Since 1994, rapid growth in the area has been fueled
by rapid economic development subsequent to passage of the North American Free Trade Agreement (NAFTA).
Between 1990 and 2001 the number of assembly factories or maquiladoras in Mexico grew from 1700 to nearly
3,800, with 2,700 in the border area. By 2004, it was estimated that more than one million Mexicans were employed
in more than 3,000 maquiladoras located along the border (U.S. Environmental Protection Agency and Secretaría de
Medio Ambiente y Recursos Naturales, 2011; U.S. Environmental Protection Agency, 2012).
Notwithstanding this growth, challenges to adaptive capacity include high rates of poverty in a landscape of uneven
economic development (Wilder et al., 2013). Large sections of the urban population, particularly in Mexico, live in
informal housing lacking the health and safety standards needed to respond to hazards, and with no insurance
(Collins et al., 2011). Any effort to increase regional capacity to respond to climate needs to take existing gaps into
account. Additionally, there is a prevalence of incipient or actual conflict (Mumme, 1999), given by currently or
historically contested allocation of land and water resources (e.g., an over-allocated Colorado river ending in
Mexico above the Sea de Cortes (Getches, 2003). Climate change, therefore, would bring additional significant
consequences for the region s water resources, ecosystems, and rural and urban settlements.
The impacts of regional climatic and non-climatic stresses compound existing urban vulnerabilities that are different
across countries. For instance, besides degrading highly diverse ecosystems (Wilder et al., 2013), residential growth
in flood-prone areas in Ciudad Juarez has not been complemented with the provision of determinants of adaptive
capacity to residents, such as housing, health care and drainage infrastructure. As a result, while differences in mean
hazard scores are not significantly different between Ciudad Juarez (Mexico) and El Paso (US), social vulnerability
and average risk are three times and two times higher in Ciudad Juárez than in El Paso respectively (Collins, 2008).
Projected warming and drying would impose additional burdens on already stressed water resources and ecosystems
and compound existing vulnerabilities for populations, infrastructure and economic activities (Wilder et al., 2013).
The recent drought in the region illustrated the multiple dimensions of climate-related events, including notable
negative impacts on the agricultural sector, water supplies, food security, and risk of wildfire (discussed in Box
26.2) (Wehner et al., 2011; Schwalm et al., 2012; Hoerling et al., 2012).
Adaptation opportunities and constraints are shared across international borders, creating the need for cooperation
among local, national and international actors. Although there are examples of efforts to manage trans-border
environmental issues, such as the US-Mexico International Boundary and Water Commission agreement
(International Boundary and Water Commission, 2012), constraints to effective cooperation and collaboration
include different governance structures (centralized in Mexico, decentralized in the US); institutional fragmentation;
asymmetries in the use and dissemination of information, and language (Wilder et al., 2010; Megdal and Scott,
2011; Wilder et al., 2013).
_____ END BOX 26-1 HERE _____
26.2.1.2. Future Trends
The North American population is projected to continue growing, reaching between 531.8 (B2) and 660.1 (A2)
million by 2050 (International Institute for Applied System Analysis, 2007). The percentage of elderly people (over
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64 years) is also projected to continue to increase, by 23.4%-26.9% in Canada, 12.4%-18.4 % in Mexico, and
17.3%-20.9% in the US by 2050 (B2 and A2 respectively) (International Institute for Applied System Analysis,
2007). The elderly are highly vulnerable to extreme weather events (heat waves in particular, Figure 26-2)
(Martiello and Giacchi, 2010; Diffenbaugh and Scherer, 2011; Romero-Lankao, 2012; White-Newsome et al.,
2012). Numbers of single-person households and female-headed households both of which are vulnerable
because of low income and housing quality are anticipated to increase (Roorda et al., 2010). Institutional capacity
to address the demands posed by increasing numbers of vulnerable populations may also be limited, with resulting
stress on health and the economy.
Three other shifts are projected to influence impacts, vulnerabilities and adaptation to climate change in North
America: urbanization, migration, and socioeconomic disparity. With small differences between countries, both the
concentration of growing populations in some urban areas and the dispersion of rural populations are projected to
continue to define North America by 2050. Assuming no change in climate, between 2005 and 2030 the population
of Mexico-City-Metro-Area will increase by 17.5%, while between 2007 and 2030 available water will diminish by
11.2% (Romero-Lankao, 2010). Conversely, education, a key determinant of adaptive capacity (Chapter 13), is
expected to expand to low-income households, minorities, and women, which could increase the coping capacity of
households and have a positive impact on economic growth (Goujon et al., 2004). However, the continuation of
current patterns of economic disparity and poverty would hinder future adaptive capacity. Inequality in Mexico is
larger (Figure 26-2), having a Gini coefficient (according to which the higher the number the higher economic
disparity) of 0.56, in contrast to 0.317 for Canada and 0.389 for the US (Organisation for Economic Co-operation
and Development, 2010). Mexico is one of five countries in the world that is projected to experience the highest
increases in poverty due to climate-induced extreme events (52% increase in rural households; 95.4% in urban
wage-labor households) (CMIP3, A2) (Ahmed et al., 2009).
Some studies project increased North American migration in response to climate change. Feng, Krueger and
Oppenheimer (2010) estimated the emigration of an additional 1.4 to 6.7 million Mexicans by 2080 based on
projected maize yield declines, range depending on model (B1, UKMO and GDFL). Oppenheimer speculates that
the indirect impacts of migration could be as substantial as the direct effects of climate change in the receiving
area, because the arrival migrants can increase pressure on climate sensitive urban regions (Oppenheimer, 2013,
442).
26.2.2. Physical Climate Trends
Some processes important for climate change in North America are assessed in other Chapters of AR5, including
WGI Chapter 2 (Observations: Atmosphere and Surface), WGI Chapter 4 (Observations: Cryosphere), WGI
Chapter 12 (Long-term Climate Change: Projections, Commitments and Irreversibility), WGI Chapter 14 (Climate
Phenomena and their Relevance for Future Regional Climate Change), WGI Annex I (Atlas of Global and Regional
Climate Projections), and WGII Chapter 21 (Regional Context). In addition, comparisons of emissions,
concentrations, and radiative forcing in the RCPs and SRES scenarios can be found in WGI Annex II (Climate
System Scenario Tables).
26.2.2.1. Current Trends
It is very likely that mean annual temperature has increased over the past century over most of North America (WGI
SPM.1) (Figure 26-3). Observations also show increases in the occurrence of severe hot events over the US over the
late 20th century (Kunkel et al., 2008), a result in agreement with observed late-20th-century increases in extremely
hot seasons over a region encompassing northern Mexico, the US and parts of eastern Canada (Diffenbaugh and
Scherer, 2011). These increases in hot extremes have been accompanied by observed decreases in frost days over
much of North America (Alexander et al., 2006; Brown et al., 2010) WGI 2.6.1, decreases in cold spells over the US
(Kunkel et al., 2008) WGI 2.6.1, and increasing ratio of record high to low daily temperatures over the US (Meehl et
al., 2009). However, warming has been less pronounced and less robust over areas of the central and southeastern
US (e.g., (Alexander et al., 2006; Peterson et al., 2008); WGI 2.6.1; WGI SPM.1) (Figure 26-3). It is possible that
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this pattern of muted temperature change has been influenced by changes in the hydrologic cycle (e.g., (Pan et al.,
2004; Portmann et al., 2009), as well as by decadal-scale variability in the ocean (e.g., (Meehl et al., 2012; Kumar et
al., 2013b).
[INSERT FIGURE 26-3 HERE
Figure 26-3: Observed and projected Changes in annual temperature and precipitation. (Top panel, left) observed
temperature trends from 1901-2012 determined by linear regression [WGI AR5 Figures SPM.1 and 2.21]. (Bottom
panel, left) Observed precipitation change from 1951-2010 determined by linear regression. [WGI AR5 Figure
SPM.2] For observed temperature and precipitation, trends have been calculated where sufficient data permits a
robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data
availability in the first and last 10% of the time period). Other areas are white. Solid colors indicate areas where
change is significant at the 10% level. Diagonal lines indicate areas where change is not significant (Top and bottom
panel, right) CMIP5 multi-model mean projections of annual average temperature changes and average percent
change in annual mean precipitation for 2046-2065 and 2081-2100 under RCP2.6 and 8.5. Solid colors indicate
areas with very strong agreement, where the multi-model mean change is greater than twice the baseline variability,
and >90% of models agree on sign of change. Colors with white dots indicate areas with strong agreement, where
>66% of models show change greater than the baseline variability and >66% of models agree on sign of change.
Gray indicates areas with divergent changes, where >66% of models show change greater than the baseline
variability, but <66% agree on sign of change. Colors with diagonal lines indicate areas with little or no change, less
than the baseline variability in >66% of models. (There may be significant change at shorter timescales such as
seasons, months, or days.). Analysis uses model data and methods building from WGI AR5 Figure SPM.8. See also
Annex I of WGI AR5. [Boxes 21-3 and CC-RC]]
It is very likely that annual precipitation has increased over the past century over areas of the eastern US and Pacific
Northwest (WGI Fig. 2.29) (Figure 26-3). Observations also show increases in heavy precipitation over Mexico, the
US and Canada between the mid-20th century and the early 21st century (Peterson and Baringer, 2009; DeGaetano,
2009; Pryor et al., 2009); WGI 2.6.2. Observational analyses of changes in drought are more equivocal over North
America, with mixed sign of trend in dryness over Mexico, the US and Canada (WGI 2.6.2 and Fig 2.42) (Dai,
2011; Sheffield et al., 2012). There is also evidence for earlier occurrence of peak flow in snow-dominated rivers
globally (Rosenzweig, 2007); WGI 2.6.2). Observed snowpack and snow-dominated runoff have been extensively
studied in the western US and western Canada, with observations showing primarily decreasing trends in the amount
of water stored in spring snowpack from 1960-2002 (with the most prominent exception being the central and
southern Sierra Nevada) (Mote, 2006) and primarily earlier trends in the timing of peak runoff over the 1948-2000
period (Stewart et al., 2006) (WGI 4.5 and Fig. 4.21). Observations also show decreasing mass and length of
glaciers in North America (WGI 4.3 and Fig. 4.9, 4.10, 4.11). Further, in assessing changes in the hydrology of the
western US, it has been concluded that up to 60% of the climate-related trends of river flow, winter air temperature,
and snow pack between 1950 and 1999 are human-induced (Barnett et al., 2008).
Observational limitations prohibit conclusions about trends in severe thunderstorms (WGI 2.6.2) and tropical
cyclones (WGI 2.6.3) over North America. The most robust trends in extratropical cyclones over North America are
determined to be towards more frequent and intense storms over the northern Canadian Arctic and towards less
frequent and weaker storms over the southeastern and southwestern coasts of Canada over the 1953-2002 period
(WGI 2.7.4)(Wang et al., 2006).
WGI concludes that Global mean sea level (GMSL) has risen by 0.19 [0.17 0.21] m over the period 1901 2010
and that it is very likely that the mean rate was 1.7 [1.5 to 1.9] mm yr 1 between 1901 and 2010 and increased to 3.2
[2.8 to 3.6] mm yr 1 between 1993 and 2010 (WGI 3 Executive Summary). In addition, observed changes in
extreme sea level have been caused primarily by increases in mean sea level (WGI 3.7.5). Regional variations in the
observed rate of sea level rise can result from processes related to atmosphere and ocean variability (such as lower
rates along the west coast of the US) or vertical land motion (such as high rates along the US Gulf Coast), but the
persistence of the observed regional patterns is unknown (WGI 3.7.3).
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26.2.2.2. Climate Change Projections
Chapters 11 and 12 of the WGI contribution to the AR5 assess near-term and long-term future climate change,
respectively. Chapter 14 of the WGI contribution assesses processes that are important for regional climate change,
with section 14.8.3 focused on North America. Many of the WGI conclusions are drawn from Annex I of the WGI
contribution to the AR5.
The CMIP5 ensemble projects very likely increases in mean annual temperature over North America, with very
likely increases in temperature over all land areas in the mid- and late-21st-century periods in RCP2.6 and RCP8.5
(Figure 26-3). Ensemble-mean changes in mean annual temperature exceed 2C over most land areas of all three
countries in the mid-21st-century period in RCP8.5 and the late-21st-century period in RCP8.5, and exceed 4C over
most land areas of all three countries in the late-21st-century period in RCP8.5. However, ensemble-mean changes in
mean annual temperature remain within 2C above the late 20th century baseline over most North American land
areas in both the mid- and late-21st-century periods in RCP2.6. The largest changes in mean annual temperature
occur over the high latitudes of the United States and Canada, as well as much of eastern Canada, including greater
than 6C in the late-21st-century period in RCP8.5. The smallest changes in mean annual temperature occur over
areas of southern Mexico, the Pacific Coast of the United States, and the southeastern United States.
The CMIP5 ensemble projects warming in all seasons over North America beginning as early as the 2016-2035
period in RCP2.6, with the greatest warming occurring in winter over the high latitudes (WGI Annex I and Figure
26-3)(Diffenbaugh and Giorgi, 2012). The CMIP5 and CMIP3 ensembles suggest that the response of warm-season
temperatures to elevated radiative forcing is larger as a fraction of the baseline variability than the response of cold-
season temperatures (Diffenbaugh and Scherer, 2011; Kumar et al., 2013b), and the CMIP3 ensemble suggests that
the response of temperature in low-latitude areas of North America is larger as a fraction of the baseline variability
than the response of temperature in high-latitude areas (Diffenbaugh and Scherer, 2011). In addition, CMIP3 and a
high-resolution climate model ensemble suggest that the signal-to-noise ratio of 21st century warming is far greater
over the western US, northern Mexico and the northeastern US than over the central and southeastern US
(Diffenbaugh et al., 2011), a result that is similar to the observed pattern of temperature trend significance in the US
(Figure 26-3).
Most land areas north of 45N exhibit likely or very likely increases in mean annual precipitation in the late-21st-
century period in RCP8.5 (Figure 26-3). The high latitude areas of North America exhibit very likely changes in
mean annual precipitation throughout the illustrative RCP periods, with very likely increases occurring in the mid-
21st-century period in RCP2.6 and becoming generally more widespread at higher levels of forcing. In contrast,
much of Mexico exhibits likely decreases in mean annual precipitation beginning in the mid-21st-century period in
RCP8.5, with the area of likely decreases expanding to cover most of Mexico and parts of the southcentral and
southwestern US in the late-21st-century period in RCP8.5. Likely changes in mean annual precipitation are much
less common at lower levels of forcing. For example, likely changes in mean annual precipitation in the mid- and
late-21st-century periods in RCP2.6 are primarily confined to increases over areas of Canada and Alaska, with no
areas of Mexico and very few areas of the contiguous US exhibiting differences that exceed the baseline variability
in more than 66% of the models.
CMIP5 projects increases in winter precipitation over Canada and Alaska, consistent with projections of a poleward
shift in the dominant cold-season stormtracks (WGI 14.8.3) (Yin, 2005), extratropical cyclones (Trapp et al., 2009)
and areas of moisture convergence (WG1 14.8.3), as well as with projections of a shift towards positive North
Atlantic Oscillation (NAO) trends (Hori et al., 2007) WGI 14.8.3). CMIP5 also projects decreases in winter
precipitation over the southwestern US and much of Mexico associated with the poleward shift in the dominant
stormtracks and the expansion of subtropical arid regions (Seager and Vecchi, 2010); WGI 14.8.3). However, there
are uncertainties in hydroclimatic change in western North America associated with the response of the tropical
Pacific sea surface temperatures (SSTs) to elevated radiative forcing (particularly given the influence of tropical
SSTs on the Pacific North American pattern (PNA) and north Pacific storm tracks) (Cayan et al., 1999; Findell and
Delworth, 2010; Seager and Vecchi, 2010); WGI 14.8.3), and not all CMIP5 models simulate the observed recent
hydrologic trends in the region (Kumar et al., 2013a)
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For seasonal-scale extremes, CMIP5 projects substantial increases in the occurrence of extremely hot seasons over
North America in early, middle and late 21st century periods in RCP8.5 (Diffenbaugh and Giorgi, 2012) (Figure 26-
4). For example, during the 2046-2065 period in RCP8.5, more than 50% of summers exceed the respective late-
20th-century maximum seasonal temperature value over most of the continent. CMIP3 projects similar increases in
extremely hot seasons, including greater than 50% of summers exceeding a mid-20th-century baseline throughout
much of North America by the mid-21st-century in the A2 scenario (Duffy and Tebaldi, 2012), and greater than 70%
of summers exceeding the highest summer temperature observed on record over much of the western US,
southeastern US and southern Mexico by the mid-21st-century in the A2 scenario (Battisti and Naylor, 2009).
CMIP5 also projects substantial decreases in snow accumulation over the US and Canada (Diffenbaugh et al., 2012)
(Figure 26-4), suggesting that the increases in cold-season precipitation over these regions reflect a shift towards
increasing fraction of precipitation falling as rain rather than snow (Diffenbaugh et al., 2012). Over much of the
western US and western Canada, greater than 80% of years exhibit March snow amount that that is less than the
late-20th-century median value beginning in the mid-21st-century period in RCP8.5, with the ensemble-mean change
exceeding 2 standard deviations of the ensemble spread. Likewise, greater than 60% of years exhibit March snow
amount that is less than the late-20th-century minimum value in the late-21st-century period in RCP8.5, with the
ensemble-mean change exceeding 2 standard deviations of the ensemble spread (Diffenbaugh and Giorgi, 2012)
(Figure 26-4). CMIP5 also projects increases in the occurrence of extremely dry summer seasons over much of
Mexico, the US and southern Canada (Figure 26-4). The largest increases occur over southern Mexico, where
greater than 30% of summers in the late-21st-century period in RCP8.5 exhibit seasonal precipitation that is less than
the late 20th century minimum summer precipitation.
[INSERT FIGURE 26-4 HERE
Figure 26-4: Projected changes in extremes in North America. (a) The percentage of years in the 2046 2065 period
of RCP8.5 in which the summer temperature is greater than the respective maximum summer temperature of the
1986 2005 baseline period (Diffenbaugh and Giorgi, 2012). (b) The percentage of years in the 2080-2099 period of
RCP8.5 in which the summer precipitation is less than the respective minimum summer precipitation of the 1986-
2005 baseline period (Diffenbaugh and Giorgi, 2012) (c) The percentage difference in the 20-year return value of
annual precipitation extremes between the 2046-2065 period of RCP4.5 and the 1986-2005 baseline period (from
(Kharin et al., 2013). The hatching indicates areas where the differences are not significant at the 5% level. (d) The
percentage of years in the 2070-2099 period of RCP8.5 in which the March snow water equivalent is less than the
respective minimum March snow water equivalent of the 1976 2005 period (Diffenbaugh et al., 2012). The black
(white) stippling indicate areas where the multimodel mean exceeds 1.0 (2.0) standard deviations of the multi-model
spread. (a-d) The RCPs and time periods are those used in the peer-reviewed studies in which the panels appear. The
2046-2065 period of RCP8.5 and the 2046-2065 period of RCP4.5 exhibit global warming in the range of 2-3C
above the pre-industrial baseline (WGI Fig. 12.40). The 2080-2099 and 2070-2099 periods of RCP8.5 exhibit global
warming in the range of 4-5C above the pre-industrial baseline (WGI Fig. 12.40).]
For daily-scale extremes, almost all areas of North America exhibit very likely increases of at least 5C in the
warmest daily maximum temperature by the late-21st-century period in RCP8.5. Likewise, most areas of Canada
exhibit very likely increases of at least 10C in the coldest daily minimum temperature by the late-21st-century period
in RCP8.5, while most areas of the US exhibit very likely increases of at least 5C and most areas of Mexico exhibit
very likely increases of at least 3C (Sillmann et al., 2013) (WGI Fig. 12.13). In addition, almost all areas of North
America exhibit very likely increases of 5% to 20% in the 20-year return value of extreme precipitation by the mid-
21st-century period in RCP4.5 (Figure 26-4), while most areas of the US and Canada exhibit very likely increases of
at least 5% in the maximum 5-day precipitation by the late-21st-century period in RCP8.5 (Sillmann et al.,
2013)(WGI Fig. 12.13). Further, almost all areas of Mexico exhibit very likely increases in the annual maximum
number of consecutive dry days by the late-21st-century period in RCP8.5 (Sillmann et al., 2013) (WGI Fig. 12.13).
26.3. Water Resources and Management
Water withdrawals are exceeding stressful levels in many regions of North America such as the southwest US,
northern and central Mexico (particularly Mexico City), southern Ontario and the southern Canadian Prairies
(Romero-Lankao, 2010; National Water Commission of Mexico, 2010; Sosa-Rodriguez, 2010; Averyt et al., 2011;
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Environment Canada, 2013a). Water quality is also a concern with 10% to 30% of the surface monitoring sites in
Mexico having polluted water (National Water Commission of Mexico, 2010), and about 44% of assessed stream
miles, and 64% of assessed lake areas in the US not clean enough to support their uses (U.S. Environmental
Protection Agency, 2004). Stations in Canada s 16 most populated drainage basins reported at least fair quality, with
many reporting good or excellent quality (Environment Canada, 2013b). In basins outside of the populated areas
there are some cases of declining water quality where impacts are related to resource extraction, agriculture, and
forestry (Hebben, 2009).
Water management infrastructure in most areas of North America is in need of repair, replacement or expansion
(Section 26.7). Climate change, land use changes and population growth, and demand increases will add to these
stresses(U.S. Global Change Research Program, 2009).
26.3.1. Observed Impacts of Climate Change on Water Resources
Droughts and Floods: As reported in WG1, Chapter 10 and 26.2.2.1, it is not possible to attribute changes in
drought frequency in North America to anthropogenic climate change (Prieto-González et al., 2011; Axelson et al.,
2012; Orlowsky and Senevirantne, 2013) (Figure 26-1). Few discernible trends in flooding have been observed in
the US (Chapter 3). Changes in the magnitude or frequency of flood events have not been attributed to climate
change. Floods are generated by multiple mechanisms (e.g., land use, seasonal changes and urbanization); trend
detection is confounded by flow regulation, teleconnections and long-term persistence (section 26.2.2.1; (Kumar et
al., 2009; Collins, 2009; Smith et al., 2010; Villarini and Smith, 2010; Villarini et al., 2011; Hirsch and Ryberg,
2012; Prokoph et al., 2012; Instituto Nacional de Ecología y Cambio Climático, 2012a; Peterson et al., 2013).
Mean Annual Streamflow: While annual precipitation and runoff increases have been found in the Midwestern and
Northwestern United States, decreases have been observed in southern states (Georgakakos et al., 2013) . Chapter 3,
WG2 notes the correlation between changes in streamflow and observed regional changes in temperature and
precipitation. Kumar et al. (2009) suggest that human activities that have influenced observed trends in streamflow
making attribution of changes to climate difficult in many watersheds. Nonetheless, earlier peak flow of snowmelt
run-off in snow-dominated streams and rivers in western North America has been formally detected and attributed to
anthropogenic climate change (Barnett et al., 2008; Das et al., 2011) (Figure 26-1).
Snow Melt: Warm winters produced earlier runoff and discharge but less snow water equivalent and shortened
snowmelt seasons in many snow-dominated areas of North America (Barnett et al., 2005; Rood et al., 2008; Reba et
al., 2011) (Section 26.2.2., Chapter 3).
26.3.2. Projected Climate Change Impacts and Risks
26.3.2.1. Water Supply
Most of this assessment focuses on surface water as there are few groundwater studies (Tremblay et al., 2011;
Georgakakos et al., 2013). Impacts and risks vary by region and model used.
In arid and semi-arid western US and Canada and in most of Mexico, except the southern tropical area, water
supplies are projected to be further stressed by climate change, resulting in less water availability and increased
drought conditions (Seager et al., 2007; Instituto Mexicano de Tecnología del Agua, 2010; Cayan et al., 2010;
Montero Martinez et al., 2010; MacDonald, 2010; Comisión Nacional del Agua, 2011; Prieto-González et al., 2011;
Bonsal et al., 2012; Sosa-Rodriguez, 2013; Orlowsky and Senevirantne, 2013; Diffenbaugh and Field, 2013).
Compounding factors include salt water intrusion, and increased groundwater and surface water pollution (Leal
Asencio et al., 2008).
In the US southwest and southeast, ecosystems and irrigation are projected to be particularly stressed by decreases in
water availability due to the combination of climate change, growing water demand, and water transfers to urban
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and industrial users (Seager et al., 2009; Georgakakos et al., 2013). In the Colorado River Basin crop irrigation
requirements for pasture grass are projected to increase by 20% by 2040 and by 31% by 2070 (Dwyer et al., 2012).
In the Rio Grande basin, New Mexico, runoff is projected decrease by 8%-30% by 2080 due to climate change.
Water transfers may entail significant transaction costs associated with adjudication and potential litigation; and
might have economic, environmental, social, and cultural impacts that vary by water user (Hurd and Coonrod,
2012). In Mexico, water shortages combined with increased water demands are projected to increase surface and
groundwater over exploitation (Comisión Nacional del Agua, 2011).
Other parts of North American are projected to have different climate risks. The vulnerability of water resources
over the tropical southern region of Mexico is projected to be low for 2050: precipitation decreases from 10%-5% in
the summer and no precipitation changes in the winter. After 2050, greater winter precipitation is projected,
increasing the possibility of damaging hydropower and water storage damns by floods, while precipitation is
projected to decrease by 40%-35% in the summer (Instituto Mexicano de Tecnología del Agua, 2010).
Throughout the 21st century, cities in NW Washington are projected to have drawdown of average seasonal reservoir
storage in the absence of demand reduction because of less snow pack even though annual stream flows increase.
Without accounting for demanding increases, projected reliability of all systems remains above 98% through mid
and late century (Vano et al., 2010a; Comisión Nacional del Agua, 2011). Throughout the eastern US, water supply
systems will be negatively impacted by lost snowpack storage; rising sea levels contributing to increased storm
intensities and salt water intrusion; possibly lower stream-flows; land use and population changes; and other stresses
(Sun et al., 2008; Obeysekera et al., 2011).
In Canada s Pacific Northwest Region, cool season flows are expected to increase, while warm seasons flows would
decrease (Hamlet, 2011). Southern Alberta, where approximately two-thirds of Canadian irrigated land is located, is
projected to experience declines in mean annual stream flow, especially during the summer (Shepherd et al., 2010;
Poirier and de Loë, 2012; Tanzeeba and Gan, 2012). In the Athabasca River Basin in northern Alberta, modeling
results consistently indicate large projected declines in mean annual flows (Kerkhoven and Gan, 2011). In contrast,
modeling results for basins in Manitoba indicate an increase in mean annual runoff (Choi et al., 2009). Some model
results for the Fraser River Basin in British Columbia indicate increases in mean annual runoff by the end of the 21st
Century, while others indicate decreases (Kerkhoven and Gan, 2011). In central Quebec, (Chen et al., 2011b) project
a general increase in discharge during November-April, and a general decrease in summer discharge under most
climate change conditions.
26.3.2.2. Water Quality
Many recent studies project water quality declines due to the combined impacts of climate change and development
(Daley et al., 2009; Tu, 2009; Praskievicz and Chang, 2011; Wilson and Weng, 2011; Tong et al., 2012). Increased
wildfires linked to a warming climate are expected to affect water quality downstream of forested headwater regions
(Emelko et al., 2011). Model simulation of lakes under a range of plausible higher air temperatures (Tahoe, Great
Lakes, Lake Onondaga and shallow polymictic lakes), depending on the system, predict a range of impacts such as
increased phytoplankton, fish and cyanobacteria biomass, lengthened stratification periods with risks of significant
hypolimnetic oxygen deficits in late summer with solubilization of accumulated phosphorous and heavy metals with
accelerated reaction rates, and decreased lake clarity (Dupuis and Hann, 2009; Trumpickas et al., 2009; Sahoo et al.,
2011; Taner et al., 2011). Model simulations have found seasonal climate change impacts on nonpoint source
pollution loads, while others have found no impact (Marshall and Randhir, 2008; Tu, 2009; Taner et al., 2011;
Praskievicz and Chang, 2011).
Changes in physical-chemical-biological parameters and micropollutants are predicted to negatively affect drinking
water treatment and distribution systems (Delpla et al., 2009; Carriere et al., 2010; Emelko et al., 2011). Wastewater
treatment plants would be more vulnerable as increases in rainfall and wet weather lead to higher rates of inflow and
infiltration (New York City Department of Environmental Protection, 2008; King County Department of Natural
Resources and Parks, 2008; Flood and Cahoon, 2011). They would also face reduced hydraulic capacities due to
higher sea levels and increased river and coastal flooding (Flood and Cahoon, 2011), with higher sea levels also
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threatening sewage collection systems (Rosenzweig et al., 2007; King County Department of Natural Resources and
Parks, 2008)
26.3.2.3. Flooding
Projected increases in flooding (Georgakakos et al., 2013) may affect sectors ranging from agriculture and livestock
in southern tropical Mexico (National Water Commission of Mexico, 2010) to urban and water infrastructure in
areas such as Dayton, Ohio, metro Boston and the Californian Bay-Delta region (Committee on Flood Control
Alternatives in the American River Basin et al., 1995; Kirshen et al., 2006; California Department of Water
Resources, 2009; Wu, 2010). Floods could begin earlier, have earlier peaks and longer durations (e.g., southern
Quebec basin). Urbanization can compound the impacts of increased flooding due to climate change, particularly in
the absence of flood management infrastructure that takes climate change into account (Hejazi and Markus, 2009;
Mailhot and Duchesne, 2010; Sosa-Rodriguez, 2010). (Ntelekos et al., 2010) estimate that annual riverine flood
losses in the USA could increase from approximately $2 billion now to $7-$19 billion annually by 2100 depending
upon emission scenario and economic growth rate.
26.3.2.4 Instream Uses
Projections of climate impacts on instream uses vary by region and time-frame. Hydropower generation, affected by
reduced lake levels, is projected to decrease in arid and semi-arid areas of Mexico (Comisión Intersecretarial de
Cambio Climatico, 2009; Sosa-Rodriguez, 2013) and in the Great Lakes (Buttle et al., 2004; Mortsch et al., 2006;
Georgakakos et al., 2013). In the US Pacific Northwest under several emission scenarios. it is projected to increase
in 2040 by approximately 5% in the winter and decrease by approximately 13% in the summer, with annual
reductions of approximately 2.5%. Larger increases and decreases are projected by 2080 (Hamlet et al., 2010). On
the Peribonka River system in Quebec, annual mean hydropower production will similarly decrease in the short-
term increase by as much as 18 % in late 21st century (Minville et al., 2009). Navigation on the Great Lakes,
Mississippi River and other inland waterways may benefit from less ice cover but will be hindered by increased
floods and low river levels during droughts (Georgakakos et al., 2013).
26.3.3. Adaptation
There are a range of structural and non-structural adaptation measures being implemented with many being no-
regret policies. For instance, in preparation for more intense storms, New York City is using green infrastructure to
capture rainwater before it can flood the combined sewer system and is elevating boilers and other equipment above
ground (Bloomberg, 2012). The Mexican cities of Monterrey, Guadalajara, Mexico City and Tlaxcala are reducing
leaks from water systems (Comisión Intersecretarial de Cambio Climatico, 2009; National Water Commission of
Mexico, 2010; Sosa-Rodriguez, 2010; Romero-Lankao, 2010). Regina, SK has increased urban water conservation
efforts (Natural Resources Canada, 2008).
The 540-foot high, 1300-foot long concrete Ross Dam in the state of Washington, US was built on a special
foundation so it could later be raised in height (Simmons, 1974). Dock owners in the Trent-Severn Waterway in the
Great Lakes have moved their docks into deeper water to better manage impacts on shorelines (Coleman et al.,
2013). The South Florida Water Management District is assessing the vulnerability to sea level rise of its aging
coastal flood control system and exploring adaptation strategies, including a strategy known as forward pumping
(Obeysekera et al., 2011). In Cambridge, Ontario, extra capacity culverts are being installed in anticipation of larger
runoff (Scheckenberger et al., 2009).
Water meters have been installed to reduce consumption by different users such as Mexican and Canadian farmers
and in households of several Canadian cities (Instituto Nacional de Ecología y Cambio Climático, 2006; Natural
Resources Canada, 2008). Agreements and regulations are underway such as the 2009 SECURE Water Act which
establishes a federal climate change adaptation program with required studies to assess future water supply risks in
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the western U.S (42 USC § 10363). One such large, multi-year study was recently completed in the US for the
Colorado River (Bureau of Reclamation, 2013) and others are planned. Agreements and regulations are underway,
such as the 2007 Shortage Sharing Agreement for the management of the Colorado River, driven by concerns about
water conservation, planning, better reservoir coordination, and preserving flexibility to respond to climate
change(Bureau of Reclamation, 2007). Quebec Province is requiring dam safety inspections every 10 years to
account for new knowledge on climate change impacts (Centre d'expertise hydrique du Québec, 2003). Expanded
beyond flood and hydropower management to now include climate change, the Columbia River Treaty is a good
example of an international treaty to manage a range of water resources challenges(U.S. Army Corps of Engineers
and Bonneville Power Administration, 2013).
26.4. Ecosystems and Biodiversity
26.4.1. Overview
Recent research has documented gradual changes in physiology, phenology and distributions in North American
ecosystems consistent with warming trends (Dumais and Prévost, 2007). Changes in phenology and species
distributions, particularly in the United States and Canada, have been attributed to rising temperatures, which have
in turn been attributed to anthropogenic climate change via joint attribution (Root et al., 2005; Vose et al., 2012).
Concomitant with 20th century temperature increases, northward and upward shifts in plant, mammal, bird, lizard,
and insect species distributions have been documented extensively in the western United States and eastern Mexico
(Parmesan, 2006; Kelly and Goulden, 2008; Moritz et al., 2009; Tingley et al., 2009; Sinervo et al., 2010). These
distribution shifts consistent with climate-change interact with other environmental changes such as land-use
change, hindering the ability of species to respond (Ponce-Reyes et al., 2013).
A range of techniques have been applied to assess the vulnerability of North American ecosystems and species to
changes in climate (Loarie et al., 2009; Anderson et al., 2009; Glick and Stein, 2011). A global risk analysis based
on dynamic global vegetation models identified boreal forest in Canada as notably vulnerable to ecosystem shift
(Scholze et al., 2006). Since the AR4, the role of extreme events, including droughts, flood, hurricanes, storm
surges, and heat waves, is a more prominent theme in studies of climate change impacts on North American
ecosystems (Chambers et al., 2007; IPCC, 2012).
A number of ecosystems in North America are vulnerable to climate change. For example, species in alpine
ecosystems are at high risk due to limited geographic space into which to expand (Villers-Ruiz and Castaneda-
Aguado, In press). Many forest ecosystems are susceptible to wildfire and large-scale mortality and infestations
events (section 26.4.1). Across the continent, potentially rapid rates of climate change may require location shifts at
velocities well outside the range in historical reconstructions (Sandel et al., 2011; Schloss et al., 2012). Changes in
temperature, precipitation amount, and carbon dioxide concentrations can have different effects across species and
ecological communities (Parmesan, 2006; Matthews et al., 2011), leading to ecosystem disruption and
reorganization (Smith et al., 2011; Dukes et al., 2011), as well as movement or loss.
The following section focuses in more depth on climate vulnerabilities in forests and coastal ecosystems. These
ecosystems span all three North American countries, are illustrative cases of where understanding the opportunities
for conservation and adaptation practices is important, and recent research advances and new evidence of increased
vulnerabilities since AR4 motivate further exploration. Further treatment of grasslands and shrublands can be found
in AR5 WGII 4.3.3.2.2, wetlands and peatlands 4.3.3.3, and tundra, alpine, and permafrost systems in 4.3.3.4.
Additional synthesis of climate change impacts on terrestrial, coastal and ocean ecosystems can be found in Chapter
8 of the US National Climate Assessment (Groffman et al., 2013).
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26.4.2. Tree Mortality and Forest Infestation
26.4.2.1. Observed Impacts
Droughts of unusual severity, extent, and duration have affected large parts of western and southwestern North
America and resulted in regional-scale forest dieback in Canada, US and Mexico. Extensive tree mortality has been
related to drought exacerbated by high summertime temperatures in trembling aspen (Populus tremuloides), pinyon
pine (Pinus edulis) and lodgepole pine (Pinus contorta) since the early 2000s (Breshears et al., 2005; Hogg et al.,
2008; Raffa et al., 2008; Michaelian et al., 2011; Anderegg et al., 2012). In 2011 and 2012 forest dieback in
Northern and central Mexico was associated with extreme temperatures and severe droughts (Comisión Nacional
Forestal, 2012a).Widespread forest-mortality events triggered by extreme climate events can alter ecosystem
structure and function (Phillips et al., 2009; Allen et al., 2010; Anderegg et al., 2013). Similarly, multi-decadal
changes in demographic rates, particularly mortality, indicate climate-mediated changes in forest communities over
longer periods (Hogg and Bernier, 2005; Williamson et al., 2009). Average annual mortality rates increased from
less than 0.5% of trees per year in the 1960s in forests of western Canada and the US to, respectively, 1.5-2.5%
(Peng et al., 2011), and 1.0-1.5% in the 2000s in the US (van Mantgem et al., 2009).
The influences of climate change on ecosystem disturbance, such as insect outbreaks have become increasingly
salient and suggest that these disturbances could have a major influence on North American ecosystems and
economy in a changing climate. In terms of carbon stores these outbreaks have the potential to turn forests into
carbon sources (Kurz et al., 2008a; Kurz et al., 2008b; Hicke et al., 2012). Warm winters in western Canada and US
have increased winter survival of the larvae of bark beetles, helping drive large-scale forest infestations and forest
die-off in western North America since the early 2000s (Bentz et al., 2010). Beginning in 1994, mountain pine
beetle outbreaks have severely affected over 18 million hectares of pine forests in British Columbia, and outbreaks
are expanding northwards (Energy, Mines and Resources: Forest Management Branch, 2012).
26.4.2.2. Projected Impacts and Risks
Projected increases in drought severity in southwestern forests and woodlands in United States and northwestern
Mexico suggest that these ecosystems may be increasingly vulnerable, with impacts including vegetation mortality
(Seager and Vecchi, 2010; Williams et al., 2010; Overpeck and Udall, 2010) and an increase of biological agents
such as beetles, borers, pathogenic fungi, budworms and other pests (Drake et al., 2005). An index of forest drought
stress calibrated from tree rings indicates that projected drought stress by the 2050s in the SRES A2 scenario from
the CMIP3 model ensemble, due primarily to warming-induced rises in vapor pressure deficit, exceeds the most
severe droughts of the past 1,000 years (Park et al., 2013).
Under a scenario with large changes in global temperature (SRES A2) increases in growing-season temperature in
forest soils in southern Quebec are as high as 5.0 C towards the end of the century and decreases of soil water
content reach 20-40% due to elevated evapotranspiration rates (Houle et al., 2012). More frequent droughts in
tropical forests may change forest structure and regional distribution, favoring a higher prevalence of deciduous
species in the forests of Mexico (Drake et al., 2005; Trejo et al., 2011).
Shifts in climate are expected to lead to changes in forest infestation, including shifts of insect and pathogen
distributions into higher latitudes and elevations (Bentz et al., 2010). Predicted climate warming is expected to have
effects on bark beetle population dynamics in the western United States, western Canada, and northern Mexico that
may include increases in developmental rates, generations per year, and changes in habitat suitability (Waring et al.,
2009). As a result, the impacts of bark beetles on forest resources are expected to increase (Waring et al., 2009).
Wildfire, a potentially powerful influence on North American forests in the 21st century, is discussed in Box 26-2.
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26.4.3. Coastal Ecosystems
Highly productive estuaries, coastal marshes and mangrove ecosystems are present along the Gulf coast and the East
and West coasts of North America. These ecosystems are subject to a wide range of non-climate stressors, including
urban and tourist developments and the indirect effects of overfishing (Mortsch et al., 2006; Bhatti et al., 2006;
Lund et al., 2007; Comisión Nacional para el Conocimiento y Uso de la Biodiversidad et al., 2007). Climate change
adds risks from sea-level rise, warming, ocean acidification, extratropical cyclones, altered upwelling, and
hurricanes and other storms.
26.4.3.1. Observed Climate Impacts and Vulnerabilities
Sea level rise, which has not been uniform across the coasts of North America (Crawford et al., 2007; Kemp et al.,
2008; Leonard et al., 2009; Zavala-Hidalgo et al., 2010; Sallenger et al., 2012), is directly related to flooding and
loss of coastal dunes and wetlands, oyster beds, seagrass and mangroves (Feagin et al., 2005; Cooper et al., 2008;
Najjar et al., 2010; Ruggiero et al., 2010; McKee, 2011; Martinez Arroyo et al., 2011).
Increases in sea surface temperature in estuaries alter metabolism, threatening species, especially cold water fish
(Crawford et al., 2007). Historical warm periods have coincided with low salmon abundance and restriction of
fisheries in Alaska (Crozier et al., 2008; U.S. Global Change Research Program, 2009). North Atlantic cetaceans,
and tropical coral reefs in the Gulf of California and the Caribbean have been affected by increases in the incidence
of diseases associated with warm waters and low water quality (Mumby et al., 2011; International Council for the
Exploration of the Sea, 2011).
Increased concentrations of CO2 in the atmosphere due to human emissions are causing ocean acidification
(Executive summary chapters 5 and 6; FAQ 5.1). Along the temperate coasts of North America acidification directly
affects calcareous organisms, including colonial mussel beds, with indirect influences on food webs of benthic
species (Wootton et al., 2008). Increased acidity in conjunction with high temperatures has been identified as a
serious threat to coral reefs and other marine ecosystems in the Bahamas and the Gulf of California(Doney et al.,
2009; Hernández et al., 2010; Mumby et al., 2011).
Tropical storms and hurricanes can have a wide range of effects on coastal ecosystems, potentially altering
hydrology, geomorphology (erosion), biotic structure in reefs and nutrient cycling. Hurricane impacts on the
coastline change dramatically the marine habitat of sea turtles, reducing feeding habitats, such as coral reefs and
areas of seaweed, and nesting places. (Márquez, R. and Jiménez, Ma. del C., 2010; Liceaga-Correa et al., 2011)
26.4.3.2. Projected Impacts and Risks
Projected increases in sea levels, particularly along the coastlines of Florida, Louisiana, North Carolina, and Texas
(Kemp et al., 2008; Leonard et al., 2009; Weiss et al., 2011), will threaten many plants in coastal ecosystems
through increased inundation, erosion, and salinity levels. In settings where landward shifts are not possible, a 1 m
rise in sea level will result in loss of wetlands and mangroves along the Gulf of Mexico of 20% (in Tamaulipas) to
94% (in Veracruz) (Flores Verdugo et al., 2010).
Projected impacts of increased water temperatures include contraction of coldwater fish habitat and expansion of
warm-water fish habitat (Mantua et al., 2010), which can increase the presence of invasive species that threaten
resident populations (Janetos et al., 2008). Depending on scenario, Chinook salmon in the Pacific Northwest may
decline by 20 to 50% by 2040-50 (Battin et al., 2007; Crozier et al., 2008), integrating across restrictions in
productivity and abundance at the southern end of their range and expansions at the northern end (Azumaya et al.,
2007), although habitat restoration and protection particularly at lower elevations may help mitigate declines in
abundance.
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Continuing ocean acidification will decrease coral growth and interactions with temperature increases will lead to
increased risk of coral bleaching, leading to declines in coral ecosystem biodiversity (Veron et al., 2009) (5.4.2.4,
Box CC-OA). Oyster larvae in Chesapeake Bay grew more slowly when reared with CO2 levels between 560 and
480ppm compared to current environmental conditions (Gazeau et al., 2007; Miller et al., 2009; Najjar et al., 2010).
While future trends in thunderstorms and tropical cyclones are uncertain (26.2.2), any changes, particularly an
increase in the frequency of category 4 and 5 storms (Bender et al., 2010; Knutson et al., 2010) could have profound
impacts on mangrove ecosystems, which require 25 years for recovery from storm damage (Kovacs et al., 2004;
Flores Verdugo et al., 2010).
26.4.4. Ecosystems Adaptation, and Mitigation
In North America, a number of adaptation strategies, are being applied in novel and flexible ways to address the
impacts of climate change (Mawdsley et al., 2009; National Oceanic and Atmospheric Administration, 2010;
Gleeson et al., 2011; Poiani et al., 2011). The best of these are based on detailed knowledge of the vulnerabilities
and sensitivities of species and ecosystems, and with a focus on opportunities for building resilience through
effective ecosystem management. Government agencies and nonprofit organizations have established initiatives that
emphasize the value of collaborative dialogue between scientists and practitioners, indigenous communities, and
grass-roots organizations to develop no-regrets and co-benefits adaptation strategies (Ogden and Innes, 2009;
Gleeson et al., 2011; Halofsky et al., 2011; Cross et al., 2012; Instituto Nacional de Ecología y Cambio Climático,
2012b; Cross et al., 2013).
Examples of adaptation measures implemented to respond to climate change impacts on ecosystems are diverse.
They include programs to reduce the incidence of Canadian forest pest infestations (Johnston et al., 2010); breeding
programs for resistance to diseases and insect pests (Yanchuk and Allard, 2009); use of forest programs to reduce
the incidence of forest fires and encourage agroforestry in areas of Mexico (Sosa-Rodriguez, 2013); and selection by
forest or fisheries managers of activities that are more adapted to new climatic conditions (Vasseur and Catto, 2008).
Example programs have addressed commercial fishing, mass tourism (Pratchett et al., 2008), and enforcement
mechanisms for using water regulation technologies to maintain quantity and quality in wetlands around the Great
Lakes and San Francisco, California (Mortsch et al., 2006; Okey et al., 2012). Assisted migration is increasingly
discussed as a potential management option to maintain health and productivity of forests; yet the technique has
logistical and feasibility challenges (Keel, 2007; Hoegh-Guldberg et al., 2008; Winder et al., 2011).
Several lines of evidence indicate that effective adaptation requires changes in approach and becomes much more
difficult if warming exceeds 2°C above preindustrial levels (Comisión Nacional para el Conocimiento y Uso de la
Biodiversidad et al., 2007; Mansourian et al., 2009; U.S. Forest Service, 2010; Glick and Stein, 2011; Barragan et
al., 2011; Instituto Nacional de Ecología y Cambio Climático, 2012b). Even though options for effective adaptation
are increasingly constrained at warming over 2°C, some opportunities will remain. In particular, efforts to maintain
or increase forest carbon stocks can lead to numerous benefits, including not only benefits for atmospheric CO2
(Anderson and Bell, 2009; Anderson et al., 2011). Even where there are opportunities, managers face challenges in
designing management practices that favor carbon stocks, while at the same time maintaining biodiversity,
recognizing the rights of indigenous people, and contributing to local economic development (Food and Agriculture
Organization, 2012).
_____ START BOX 26-2 HERE _____
Box 26-2. Wildfires
Wildfire is a natural process, critical to nutrient cycling, controlling populations of pests and pathogens, biodiversity
and fire-adapted species (Bond and Van Wilgen, 1996). However, since the mid-1980s large wildfire activity in
North America has been marked by increased frequency and duration, and longer wildfire seasons (Westerling et al.,
2006; Williamson et al., 2009). Recent wildfires in western Canada, the US and Mexico relate to long and warm
spring and summer droughts, particularly when they are accompanied by winds (Holden et al., 2007; Comisión
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Nacional Forestal, 2012b). Interacting processes such as land-use changes associated with the expansion of
settlements and activities in peri-urban areas or forested areas, combined with the legacies of historic forest
management that prescribed fire suppression, also substantially increase wildfire risk (Radeloff et al., 2005; Peter et
al., 2006; Theobald and Romme, 2007; Fischlin et al., 2007; Gude et al., 2008; Collins and Bolin, 2009; Hammer et
al., 2009; Brenkert-Smith, 2010).
Drought conditions are strongly associated with wildfire occurrence, as dead fuels such as needles and dried stems
promote the incidence of firebrands and spot fires (Keeley and Zedler, 2009; Liu et al., 2012). Drought trends vary
across regions (Groisman et al., 2007; Girardin et al., 2012): the western US has experienced drier conditions since
the 1970s (Peterson et al., 2013); drought periods in Alberta and Idaho have coincided with large burned areas
(Pierce and Meyer, 2008; Kulshreshtha, 2011); heterogeneous patterns of drought severity and a reduction of
wildfire risk have been detected for the circumboreal region (Girardin et al., 2009). Decadal climatic oscillations
also contribute to differences in drought, and thus in wildfire occurrences. The areas burned in the continent boreal
forest and in northwest and central Mexico correlate to the dynamics of seasonal land/ocean temperature variability
(Macias Fauria and Johnson, 2006; Skinner et al., 2006; Villers-Ruíz and Hernández-Lozano, 2007; Macias Fauria
and Johnson, 2008; Girardin and Sauchyn, 2008); which is shifting toward hotter temperatures and longer droughts.
Such human practices as slash-and-burn agriculture can have negative impacts on Mexican forests (Bond and
Keeley, 2005; Comisión Nacional de Áreas Naturales Protegidas and The Nature Conservancy, Programa México,
2009).
Drought index projections and climate change regional models show increases in wildfire risk during the summer
and fall on the southeast Pacific coast, Northern Plains and the Rocky Mountains (Liu et al., 2012). In places like
Sierra Nevada, mixed conifer forests, which have a natural cycle of small, non-crown fires, are projected to have
massive crown-fires (Bond and Keeley, 2005) (Table 26-1).
While healthy forests (Davis, 2004) and many fire-maintained systems that burn at lower intensities can provide
carbon sequestration and thus mitigation co-benefits (e.g., Longleaf pine savanna, Sierra mixed-conifer)(Fried et al.,
2008; North et al., 2012), forests affected by pests and fires are less effective carbon sinks, and wildfires themselves
are a source of emissions.
Wildfires pose a direct threat to human lives, property and health. Over the last 30 years, 155 people were killed in
wildfires across North America, including 103 in the United States, 50 in Mexico and 2 in Canada (Centre for
Research on the Epidemeology of Disasters, 2012). Direct effects include injury and respiratory effects from smoke
inhalation, with firefighters at increased risk (Naeher et al., 2007; Reisen and Brown, 2009; Reisen et al., 2011).
Wildfire activity causes impacts on human health (section 26.6).
Minimizing adverse effects of wildfires involves short-term and long-term strategies such as planned manipulation
of vegetation composition and stand structure (Girardin et al., 2012; Terrier et al., 2013), suppression of fires where
required, fuel treatments, use of fire-safe materials in construction, community planning, and reduction of arson. Not
all negative consequences of fire can be avoided, though a mixture of techniques can be used to minimize adverse
effects (Girardin et al., 2012). Prescribed fire may be an important tool for managing fire risk in Canada and the US
(Hurteau and North, 2010; Wiedinmyer and Hurteau, 2010; Hurteau et al., 2011). Managers in the US have
encouraged reduction of flammable vegetation around structures with different levels of success (Stewart et al.,
2006). However, such efforts depend largely on land-use planning, the socio-economic capacity of communities at
risk, the extent of resource dependence, community composition, and the risk perceptions, attitudes and beliefs of
decision-makers, private property owners, and affected populations (McFarlane, 2006; Repetto, 2008; Collins and
Bolin, 2009; Martin et al., 2009; Trainor et al., 2009; Brenkert-Smith, 2010). Indigenous peoples are at higher risk
from wildfire and may have unique requirements for adaptation strategies (Carroll et al., 2010; Christianson et al.,
2012a; Christianson et al., 2012b).
Effective forest management requires stakeholder involvement and investment. The provision of adequate
information on smoke, prescribed fire, pest management, and forest thinning is crucial, as is building trust between
stakeholders and land managers (Dombeck et al., 2004; Flint et al., 2008; Chang et al., 2009). Institutional shifts
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from reliance on historical records toward incorporation of climate forecasting in forest management is also crucial
to effective adaptation (McKenzie et al., 2004; Millar et al., 2007; Kolden and Brown, 2010).
_____ END BOX 26-2 HERE _____
26.5. Agriculture and Food Security
Projected declines in global agricultural productivity (Chapter 7) have implications for food security among North
Americans. Because North America is a major exporter (Food and Agriculture Organization, 2009; Schlenker and
Roberts, 2009), shifts in agricultural productivity here may have implications for global food security. Canada and
the US are relatively food secure, although households living in poverty are vulnerable. 17.6% of Mexicans are food
insecure (Monterroso et al., 2012). Indigenous peoples are highly vulnerable due to high reliance on subsistence
(Chapter 12). While this section focuses on agricultural production, food security is related to multiple factors (See
Chapter 7).
26.5.1. Observed Climate Change Impacts
Historic yield increases are attributed in part to increasing temperatures in Canada and higher precipitation in the US
(Pearson et al., 2008; Sakurai et al., 2011; Nadler and Bullock, 2011); (high agreement, medium evidence), although
multiple non-climatic factors affect historic production rates. In many North American regions optimum
temperatures have been reached for dominant crops, thus continued regional warming would diminish rather than
enhance yields (Jones et al., 2005) (high confidence). Regional yield variances over time have been attributed to
climate variability e.g., Ontario (Cabas et al., 2010) and Quebec (Almaraz et al., 2008). Since 1999 a marked
increase in crop losses attributed to climate-related events such as drought, extreme heat and storms has been
observed across North America (Hatfield et al., 2013), with significant negative economic effects (Swanson et al.,
2007; Chen and McCarl, 2009; Costello et al., 2009) (high confidence). In Mexico, agriculture accounted for 80% of
weather-related financial losses since 1990 (Saldana-Zorrilla, 2008) (Figure 26-2).
26.5.2. Projected Climate Change Risks
Studies project productivity gains in northern regions and where water is not projected to be a limiting factor, across
models, time frames and scenarios (Hatfield et al., 2008; Pearson et al., 2008; Wheaton et al., 2010; Stöckle et al.,
2010); (high confidence). Overall yields of major crops in North America are projected to decline modestly by mid-
century and more steeply by 2100 among studies that do not consider adaptation (very high confidence). Certain
regions and crops may experience gains in the absence of extreme events, and projected yields vary by climate
model (Paudel and Hatch, 2012; Liu et al., 2013).
Among studies projecting yield declines, two factors stand out: exceedance of temperature thresholds, and water
availability. Yields of several important North American agriculture sectors including grains, forage, livestock and
dairy decline significantly above temperature thresholds (Wolfe et al., 2008; Schlenker and Roberts, 2009; Craine
et al., 2010) Temperature increases affect product quality as well e.g., coffee (Lin, 2007), wine grapes (Hayhoe et
al., 2004; Jones et al., 2005), wheat (Porter and Semenov, 2005), fruits and nuts (Lobell et al., 2006), and cattle
forage (Craine et al., 2010).
Projected temperature increases would reduce corn, soy and cotton yields by 2020, with declines ranging from 30-
82% by 2099 depending on crop and scenario (steepest decline for corn, A1) (Schlenker and Roberts, 2009). Studies
also project increasing inter-annual yield variability over time (Sakurai et al., 2011; Urban et al., 2012). Several
studies focus on California, one of North America s most productive agricultural regions. Modest and variable yield
changes among several California crops are projected to 2026, with yield declines from 9-29% by 2097 (A2,
DAYCENT model). (Lobell and Field, 2011; Lee et al., 2011) found little negative effect for California perennials
by 2050 due to projected climate change, assuming irrigation access (GCM ensemble, A2 and B1). (Hannah et al.,
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2013), however, project large declines in land suitability for California viticulture by 2050 (with increases further
north) with RCPs 4.5 and 8.5 (GCM ensemble); declines greater under RCP 8.5. Heat-induced livestock stress,
combined with reduced forage quality, would reduce milk production and weight gain in cattle (Wolfe et al., 2008;
Hernandez et al., 2011).
Precipitation increases off-set but do not entirely compensate for temperature-related declines in productivity
(Kucharik and Serbin, 2008). In regions projected to experience increasing temperatures combined with declining
precipitation, declines in yield and quality are more acute (Craine et al., 2010; Monterroso Rivas et al., 2011).
Projected change in climate will reduce soil moisture and water availability in the US Western/Southwest, the
Western Prairies in Canada, and central and northern Mexico (Pearson et al., 2008; U.S. Global Change Research
Program, 2009; Cai et al., 2009; Esqueda et al., 2010; Vano et al., 2010b; Kulshreshtha, 2011) (very high
confidence). CMIP5 models indicate soil moisture decreases across the continent in Spring and Summer under
RCP8.5, with high agreement (Dirmeyer et al., 2013). Based on a combined exposure/consumptive water use model,
the US Great Plains is identified as one of four global future vulnerability hotspots for water availability from the
2030s and beyond, where anticipated water withdrawals would exceed 40% of freshwater resources (Liu et al.,
2013). In western US and Canada, projected earlier Spring snowmelt and reduced snowpack would affect
productivity negatively regardless of precipitation, as water availability in Summer and Fall are reduced (Schlenker
et al., 2007; Forbes et al., 2011; Kienzle et al., 2012).
Projected increases in extreme heat, drought and storms affect productivity negatively (Chen and McCarl, 2009;
Kulshreshtha, 2011). The northeastern and southeastern US have been identified as vulnerability hotspots for corn
and wheat production respectively by 2045 with vulnerability worsening thereafter, using a combined drought
exposure and adaptive capacity assessment, with only slight differences between A1B and B2 scenarios (Fraser et
al., 2013). Central North America is identified as among the globe s regions of highest risk of heat stress by 2070
(NIES GCM; A1B) (Teixeira et al., 2013).
26.5.3. A Closer Look at Mexico
Much of Mexico s landbase is already marginal for two of the country s major crops: corn and beef (Buechler,
2009). Severe desertification in Mexico due to non-climate drivers further compromises productivity (Huber-
Sannwald et al., 2006). Land classified suitable for rain-fed corn is projected to decrease from 6.2% currently to
between 3% and 4.3% by 2050 (UKHadley B2, ECHAM5/MPI A2) (Monterroso Rivas et al., 2011). The
distribution of most races of corn is expected to be reduced and some eliminated by 2030 (A2, three climate models)
(Ureta et al., 2012). Precipitation declines of 0-30% are projected over Mexico by 2040; with the most acute
declines in Northwestern Mexico, the primary region of irrigated grain farming (declines steeper in A2 than A1B
(18 model ensemble).
Although projected increases in precipitation may contribute to increase in rangeland productivity in some regions
(Monterroso Rivas et al., 2011), a study in Veracruz indicates that the effects of projected maximum summer
temperatures on livestock heat stress are expected to reach the Danger level (at which losses can occur) by 2020
and continue to rise (A2, B2, three GCMs) (Hernandez et al., 2011). Coffee, an economically important crop
supporting 500,000 primarily indigenous households (González Martínez, 2006), is projected to decline 34% by
2020 in Veracruz if historic temperature and precipitation trends continue (Gay et al., 2006); see also (Schroth et al.,
2009), on declines in Chiapas).
Many of Mexico s agricultural communities are also considered highly vulnerable, due to high sensitivity and/or
low adaptive capacity (Monterroso et al., 2012). The agriculture sector here consists primarily of small farmers
(Claridades Agropecuarias 2006), who face high livelihood risks due to limited access to credit and insurance (Eakin
and Tucker, 2006; Wehbe et al., 2008; Saldana-Zorilla and Sandberg, 2009; Walthall et al., 2012).
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26.5.4. Adaptation
The North American agricultural industry has the adaptive capacity to off-set projected yield declines and capitalize
on opportunities under 2° warming. (Butler and Huybers, 2012) project a reduction in US corn yield loss from 14%
to 6% with 2° warming, with spatial shifts in varietal selection (not accounting for variability in temperature and
precipitation). Incremental strategies, such as planting varieties better suited to future climate conditions and
changing planting dates, have been observed across the continent (Bootsma et al., 2005; Conde et al., 2006; Eakin
and Appendini, 2008; Coles and Scott, 2009; Nadler and Bullock, 2011; Paudel and Hatch, 2012; Campos et al.,
2013). In some sectors we are seeing multi-organizational investments in adaptation. International coffee retailers
and non-governmental organizations, for example, are engaged in enhancing coffee farmers adaptive capacity
(Schroth et al., 2009; Soto-Pinto and Anzueto, 2010). Other strategies specifically recommended for Mexico include
soil remediation; improved use of climate information; rainwater capture and drip irrigation (Sosa-Rodriguez, 2013).
New crop varieties better suited to future climates, including GMOs, are under development in the US (e.g. (Chen et
al., 2012), although potential risks have been noted (Quist and Chapela, 2001). Current trends in agricultural
practices in commercial regions such as the Midwestern US, however, amplify productivity risks posed by climate
change (Hatfield et al., 2013). Incremental strategies will have reduced effectiveness under a 2099/4°C warming
scenario, which would require more systemic adaptation, including production and livelihood diversification
(Howden et al., 2007; Mehta et al., 2013; Smith and Gregory, 2013; Asseng et al., 2013).
Some adaptive strategies impose financial costs and risks onto producers (Craine et al., 2010;Wolfe et al., 2008),
which may be beyond the means of smallholders (Mercer et al., 2012) or economically precluded for low-value
crops. Technological improvements improve yields under normal conditions but do not protect harvests from
extremes (U.S. Global Change Research Program, 2009;Wittrock et al., 2011). Others may have maladaptive effects
(e.g. increased groundwater and energy consumption). Crop-specific weather index insurance, for example (widely
implemented in Mexico to support small farmers), may impose disincentives to invest in diversification and
irrigation (Fuchs and Wolff, 2010).
Many strategies have co-benefits, however, in fact investments in agricultural adaptation represent a cost-effective
mitigation strategy (Lobell et al., 2013). Low- and no-till practices reduce soil erosion and runoff, protect crops from
extreme precipitation (Zhang and Nearing, 2005), retain soil moisture, reduce biogenic and geogenic greenhouse gas
emissions (Nelson et al., 2009; Suddick et al., 2010), and build soil organic carbon (Aguilera et al., 2013). Planting
legumes and weed management on pastures enhance both forage productivity and soil carbon sequestration (Follett
and Reed, 2010). Shade perennials increase soil moisture retention (Lin, 2010) and contribute to local cooling
(Georgescu et al., 2011). Crop diversification mediates the impacts of climate and market shocks (Eakin and
Appendini, 2008) and enhances management flexibility (Chhetri et al., 2010).
Barriers and Enablers
Market forces and technical feasibility alone are insufficient to foster sectoral-level adaptation (Kulshreshtha, 2011).
Institutional support is key, found to be inadequate in many contexts (Bryant et al., 2008; Klerkx and Leeuwis,
2009; Jacques et al., 2010; Tarnoczi and Berkes, 2010; Brooks and Loevinsohn, 2011; Alam et al., 2012; Anderson
and McLachlan, 2012)(high confidence). Even many suggested adaptation strategies with anticipated economic
benefits are often not adopted by farmers, suggesting the need for more attention to culture and behavior (Moran et
al., 2013). Attitudinal studies among US farmers indicate limited acknowledgement of anthropogenic climate
change, associated with lower levels of support for adaptation (Arbuckle Jr et al., 2013; Gramig et al., 2013) (high
agreement, medium evidence).
Other key enablers are access to and quality of information (Tarnoczi and Berkes, 2010; Tarnoczi, 2011; Baumgart-
Getz et al., 2012; Tambo and Abdoulaye, 2012), particularly regarding optimum crop management, production
inputs and optimum crop-specific geographic information. Social networks are important for information
dissemination and farmer support (Chiffoleau, 2009; Wittrock et al., 2011; Baumgart-Getz et al., 2012). Networks
among producers may be especially important to the level of awareness and concern farmers hold about climate
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change(Frank et al., 2010; Sánchez-Cortés and Chavero, 2011), while also enabling extensive farmer-to-farmer
exchange of adaptation strategies (Eakin et al., 2009).
26.6. Human Health
Large national assessments of climate and health have been carried out in the US and Canada (Belanger et al.,
2008a) (see references in 26.1). These have highlighted the potential for changes in impacts of extreme storm and
heat events, air pollution, pollen, and infectious diseases, drawing from a growing NA research base analyzing
observed and projected relationships among weather, vulnerability and health. The causal pathways leading from
climate to health are complex, and can be modified by factors including economic status, pre-existing illness, age,
other health risk factors, access to health care, built and natural environments, adaptation actions and others. Human
health is an important dimension of adaptation planning at the local level, much of which has so-far focused on
warning and response systems to extreme heat events (New York State Climate Action Council, 2012).
26.6.1. Observed Impacts, Vulnerabilities and Trends
26.6.1.1. Storm-Related Impacts
The magnitude of health impacts of extreme storms depends on interactions between exposure and characteristics of
the affected communities (Keim, 2008). Coastal and low-lying infrastructure and populations can be vulnerable due
to flood-related interruptions in communications, healthcare access, and mobility. Health impacts can arise through
direct pathways of traumatic death and injury (e.g., drowning, impacts of blowing and falling objects; contact with
power wires) as well as more indirect, longer-term pathways related to damage to health and transportation
infrastructure, contamination of water and soil, vector-borne diseases, respiratory diseases and mental health
(Gamble et al., 2008). Infectious disease impacts from flooding include creation of breeding sites for vectors (Ivers
and Ryan, 2006) and bacterial transmission through contaminated water and food sources causing gastrointestinal
disease. Chemical toxins can be mobilized from industrial or contaminated sites (Euripidou and Murray, 2004).
Elevated indoor mold levels associated with flooding of buildings and standing water are identified as risk factors
for cough, wheeze and childhood asthma (Bornehag et al., 2001; Jaakkola et al., 2005). Mental health impacts can
arise due to the stress of evacuation, property damage, economic loss, and household disruption (Weisler et al.,
2006; Gamble et al., 2008; Berry et al., 2010; Bethel et al., 2011). Since 1970, there has been no clear trend in US
hurricane deaths, once the singular Katrina event is set aside (Blake et al., 2007).
26.6.1.2. Temperature Extremes
Studies throughout North America have shown that high temperatures can increase the mortality and/or morbidity
(e.g., Medina-Ramon and Schwartz, 2007; Kovats and Hajat, 2008; O'Neill and Ebi, 2009; Anderson and Bell, 2009;
Knowlton et al., 2009; Deschenes et al., 2009; Kenny et al., 2010; Hajat and Kosatsky, 2010; Cueva-Luna et al.,
2011; Hurtado-Díaz et al., 2011; Romero-Lankao et al., 2012b). Extremely cold temperatures have also been
associated with increased mortality (Medina-Ramon and Schwartz, 2007), an effect separate from the seasonal
phenomenon of excess winter mortality, which does not appear to be directly related to cold temperatures (Kinney,
2012). To date, trends over time in cold-related deaths have not been investigated. Most available NA evidence
derives from the US and Canada, though one study reported significant heat- and cold-related mortality impacts in
Mexico City (McMichael et al., 2008). US EPA has tracked the death rate in the US from 1979 to 2009 for which
death certificates list the underlying cause of death as heat related (U.S. Environmental Protection Agency, Office of
Atmospheric Programs, 2012). No clear trend upwards or downwards is yet apparent in this indicator. Note that this
case definition is thought to significantly underestimate the total impacts of heat on mortality.
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26.6.1.3. Air Quality
Ozone and particulate matter (e.g., PM2.5 and PM10) have been associated with adverse health effects in many
locations in North America (Romero-Lankao et al., 2013b). Emissions, transport, dilution, chemical transformation,
and eventual deposition of air pollutants all can be influenced by meteorological variables such as temperature,
humidity, wind speed and direction, and mixing height (Kinney, 2008). Although air pollution emission trends will
play a dominant role in future pollution levels, climate change may make it harder to achieve some air quality goals
(Jacob and Winner, 2009). Forest fire is a source of particle emissions in NA, and can lead to increased cardiac and
respiratory-disease incidence, as well as direct mortality (Rittmaster et al., 2006; Ebi et al., 2008). The indoor
environment also can affect health in many ways, e.g., via penetration of outdoor pollution, emissions or pollutants
indoors, moisture-related problems, and transmission of respiratory infections. Indoor moisture leads to mold
growth, a problem that is exacerbated in colder regions such as northern NA in the winter (Potera, 2011). Climate
variability and change will affect indoor air quality, but with direction and magnitude that remains largely unknown
(Institute of Medicine, 2011).
26.6.1.4. Pollen
Exposure to pollen has been associated with a range of allergic outcomes, including exacerbations of allergic rhinitis
(Cakmak et al., 2002; Villeneuve et al., 2006) and asthma (Delfino, 2002). Temperature and precipitation in the
months prior to the pollen season affect production of many types of tree and grass pollen (Reiss and Kostic, 1976;
Minero et al., 1998; Lo and Levetin, 2007; U.S. Environmental Protection Agency, 2008). Ragweed pollen
production is responsive to temperatures and to CO2 concentrations (Ziska and Caulfield, 2000; Wayne et al., 2002;
Ziska et al., 2003; Singer et al., 2005). Because pollen production and release can be affected by temperature,
precipitation, and CO2 concentrations, pollen exposure and allergic disease morbidity could change in response to
climate change. However, to date, the timing of the pollen season is the only evidence for observed climate-related
impacts. Many studies have indicated that pollen seasons are beginning earlier (Emberlin et al., 2002; Rasmussen,
2002; Clot, 2003; Teranishi et al., 2006; Frei and Gassner, 2008; Levetin and Van, 2008; Ariano et al., 2010).
Ragweed season length has increased at some monitoring stations in the United States (Ziska et al., 2011). Research
on trends in NA has been hampered by the lack of long-term, consistently collected pollen records (U.S.
Environmental Protection Agency, 2008).
26.6.1.5. Waterborne Diseases
Waterborne infections are an important source of morbidity and mortality in North America. Commonly reported
infectious agents in US and Canadian outbreaks include legionella bacterium, the cryptosporidium parasite,
campylobacter, and giardia (Belanger et al., 2008b; Center for Disease Control and Prevention, 2011). Cholera
remains an important agent in Mexico (Greer et al., 2008). Risk of waterborne illness is greater among the poor,
infants, elderly, pregnant women, and immune-compromised individuals (Rose et al., 2001; Gamble et al., 2008) .
In Mexico City, declining water quality has led to ineffective disinfection of drinking water supplies (Mazari-Hiriart
et al., 2005; Sosa-Rodriguez, 2010).
Changes in temperature and hydrological cycles can influence the risk of waterborne diseases (Curriero et al., 2001;
Greer et al., 2008; Harper et al., 2011). Severe storms have been shown to play a role in water-borne disease risks in
Canada (Thomas et al., 2006). Floods enhance the potential for runoff to carry sediment and pollutants to water
supplies (Karl et al., 2008). Disparities in access to treated water were identified as a key determinant of under age-5
morbidity due to water borne illnesses in the central State of Mexico (Jimenez-Moleon and Gomez-Albores, 2011).
26.6.1.6. Vectorborne Diseases
The extent to which climate change has altered, and will alter, the geographic distribution of vectors of infectious
disease remains uncertain because of the inherent complexity of the ecological system. Spatial and temporal
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distribution of disease vectors depend not only on climate factors, but also on land use/change, socio-economic and
socio-cultural factors, prioritization of vector control, access to health care and human behavioral responses to
perception of disease risk, among other factors (Lafferty, 2009; Wilson, 2009). Although temperature drives
important biological processes in these organisms, climate variability on a daily, seasonal or interannual scale may
result in organism adaptation and shifts, though not necessarily expansion, in geographic range (Lafferty, 2009;
Tabachnick, 2010; McGregor, 2011). Range shifts may alter the incidence of disease depending on host
receptiveness and immunity, as well as the ability of the pathogen to evolve so that strains are more effectively and
efficiently acquired (Reiter, 2008; Beebe et al., 2009; Rosenthal, 2009; Russell, 2009; Epstein, 2010).
North Americans are currently at risk from a number of vector-borne diseases, including Lyme disease (Ogden et
al., 2008; Diuk-Wasser et al., 2010), dengue fever (Jury, 2008; Ramos et al., 2008; Johansson et al., 2009; Kolivras,
2010; Degallier et al., 2010; Lambrechts et al., 2011; Riojas-Rodriguez et al., 2011; Lozano-Fuentes et al., 2012),
West Nile virus (Morin and Comrie, 2010; Gong et al., 2011) and Rocky Mountain spotted fever, to name a few.
Risk is increasing from invasive vector-borne pathogens, such as chikungunya (Ruiz-Moreno et al., 2012) and Rift
Valley fever viruses (Greer et al., 2008). There is also potential risk from invasive vector-borne pathogens, such as
chikungunya (Ruiz-Moreno et al., 2012) and Rift Valley fever viruses (Greer et al., 2008). Mexico is listed as high
risk for dengue fever by the WHO. There has been an increasing number of cases of Lyme disease in Canada and
Lyme disease vectors are spreading along climate-determined trajectories (Leighton et al., 2012; Koffi et al., 2012).
26.6.2. Projected Climate Change Impacts
Projecting future consequences of climate warming for heat-related mortality and morbidity is challenging, due in
large part to uncertainties in the nature and pace of adaptations that populations and societal infrastructure will
undergo in response to long-term climate change (Kinney et al., 2008). Additional uncertainties arise from changes
over time in population demographics, economic well-being, and underlying disease risk, as well as in the model-
based predictions of future climate and our understanding of the exposure-response relationship for heat-related
mortality. However, climate warming will lead to continuing health stresses related to extreme high temperatures,
particularly for the northern parts of North America. The health implications of warming winters remain uncertain
(Kinney, 2012).
Several recent studies have projected future health impacts due to air pollution in a changing climate (Knowlton et
al., 2004; Bell et al., 2007; Tagaris et al., 2009; Tagaris et al., 2010; Chang et al., 2010). There is a large literature
examining future climate influences on outdoor air quality in North America, particularly for ozone (Murazaki and
Hess, 2006; Steiner et al., 2006; Tao et al., 2007; Kunkel et al., 2007; Holloway et al., 2008; Lin et al., 2008; Nolte
et al., 2008; Wu et al., 2008; Avise et al., 2009; Chen et al., 2009; Liao et al., 2009; Racherla and Adams, 2009; Lin
et al., 2010; Tai et al., 2010). This work suggests with medium confidence that ozone concentrations could increase
under future climate change scenarios if emissions of precursors were held constant (Jacob and Winner, 2009).
However, analyses show that future increases can be offset through measures taken to limit emission of pollutants
(Kelly et al., 2012). The literature for PM2.5 is more limited than that for ozone, and shows a more complex pattern
of climate sensitivities, with no clear net influence of warming temperatures (Liao et al., 2007; Tagaris et al., 2008;
Avise et al., 2009; Pye et al., 2009; Mahmud et al., 2010). On the other hand, PM2.5 plays a crucial role. Regarding
outdoor pollen, warming will lead to further changes in the seasonal timing of pollen release (high confidence).
Another driver of future pollen could be changing spatial patterns of vegetation as a result of climate change.
Regarding clean water supplies, extreme precipitation can overwhelm combined sewer systems and lead to overflow
events that can threaten human health (Patz et al., 2008). Conditional on a future increase in such events, we can
anticipate increasing risks related to water-borne diseases.
Whether future warmer winters in the United States and Canada will promote transmission of diseases like dengue
and malaria is uncertain, in part, because of access to amenities such as screening and air-conditioning that provide
barriers to human-vector contact. Socio-economic factors also play important roles in determining risks. Better
longitudinal datasets and empirical models are needed to address research gaps on climate-sensitive infectious
diseases, as well as to provide a better mechanism for weighting the roles of external drivers such as climate change
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on a macro/micro scale, human-environmental changes on a regional to local scale, and extrinsic factors in the
transmission of vector-borne infectious diseases (Wilson, 2009; McGregor, 2011).
26.6.3. Adaptation Responses
Early warning and response systems can be developed to build resilience to events like heat waves, storms and
floods (Ebi, 2011) and protect susceptible populations, which include infants, children, the elderly, individuals with
pre-existing diseases, and those living in socially and/or economically disadvantaged conditions (Pinkerton et al.,
2012). Adaptation planning at all scales to build resilience for health systems in the face of a changing climate is a
growing priority (Kinney et al., 2011).
Adaptation to heat events can occur via physiologic mechanisms, indoor climate control, urban-scale cooling
initiatives, and with implementation of warning and response systems (Romero-Lankao et al., 2012b). Additional
research is needed on the extent to which warning systems prevent deaths (Harlan and Ruddell, 2011). Efforts to
reduce GHG emissions could provide health co-benefits, including reductions in heat-related and respiratory
illnesses (Health Chapter, US National Climate Assessment).
26.7. Key Economic Sectors and Services
There is mounting evidence that many economic sectors across North America have experienced climate impacts
and are adapting to the risk of loss and damage from weather perils. This section covers the literature for the energy,
transportation, mining, manufacturing, construction and housing, and insurance sectors in North America. Recent
studies find a range of adaptive practices and adaptation responses to experience with extreme events, and only an
emerging consideration of proactive adaptation in anticipation of future global warming.
26.7.1. Energy
26.7.1.1. Observed Impacts
Energy demand for cooling has increased as building stock and air conditioning penetration have increased
(Wilbanks et al., 2012). Extreme weather currently poses risk to the energy system (Wilbanks et al., 2012). For
example, Hurriacne Sandy results in a loss of power to 8.5 million customers in the Northeast US (National Oceanic
and Atmospheric Administration, 2013). Energy consumption is a major user of water resources in North America,
with 49% of the water withdrawals in the US for thermoelectric power (Kenny et al., 2009).
26.7.1.2. Projected Impacts
Demand for summer cooling is projected to increase and demand for winter heating is projected to decrease. Total
energy demand in North America is projected to increase in coming decades because of non-climate factors
(Galindo, 2009; National Energy Board, 2011; Energy Information Administration, 2013). Climate change is
projected to have varying geographic impacts. In Canada, a net decrease in residential annual energy demand is
projected by 2050 and by 2100 (Isaac and Van Vuuren, 2009; Schaeffer et al., 2012). It is difficult to project
changes in net energy demand in the US because of uncertainties in such factors as climate change, and change in
technology, population, and energy prices. Peak demand for electricity is projected to increase more than the
average demand for electricity, with capacity expansion needed in many areas (Wilbanks et al., 2012). Given the
projected increases in energy demand in the southern United States from climate change (Auffhammer and
Aroonruengsawat, 2011; Auffhammer and Aroonruengsawat, 2012) it is reasonable to conclude that Mexico will
have a net increase in demand.
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Major water resource related concerns include effects of increased cooling and other demands for water and water-
scarcity in the west; effects of extreme weather events, sea-level rise, hurricanes, and seasonal droughts in the
southeast; and effects of increased cooling demands in the northern regions (Wilbanks et al., 2008; Wilbanks et al.,
2012; McDonald, 2012; U.S. Department of Energy, 2013) .
The magnitude of projected impacts on hydropower potential will vary significantly between regions and within
drainage basins(Desrochers et al., 2009; Shrestha et al., 2012; Kienzle et al., 2012). Annual mean hydropower
production in the Peribonka River in Quebec is estimated to increase by approximately 10% by mid-century and
20% late in the century under the A2 scenario (Minville et al., 2009).
Higher temperatures and increased climate variability can have adverse impacts on renewable energy production
such as wind and solar (U.S. Department of Energy, 2013). Changing cloud cover affects solar energy resources,
changes in winds affect wind power potentials, and temperature change and water availability can affect biomass
production (Wilbanks et al., 2008; U.S. Department of Energy, 2013) .
26.7.1.3. Adaptation
Many adaptations are underway to reduce vulnerability of the energy sector to extreme climate events such as heat,
drought, and flooding (U.S. Department of Energy, 2013). Adaptation includes many approaches such as increased
supply and demand efficiency (e.g., through more use of insulation), more use of urban vegetation and reflective
surfaces, improved electric grid, reduced reliance on above ground distribution systems, and distributed power
(Wilbanks et al., 2012). Important barriers to adaptation include uncertainty about future climate change, inadequate
information on costs of adaptation, lack of climate resilient energy technologies, and limited price signals (U.S.
Department of Energy, 2013). Strategies resulting in energy demand reduction would reduce greenhouse gas
emissions and reduce the vulnerability of the sector to climate change.
26.7.2. Transportation
26.7.2.1. Observed Impacts
Much of the transportation infrastructure across North America is aging, or inadequate (Mexico) which may make it
more vulnerable to damage from extreme events and climate change. Approximately 11% of all US bridges are
structurally deficient, 20% of airport runways are in fair or poor condition, and more than half of all locks are more
than 50 years old (US Department of Transportation 2013). More than US$2 trillion is needed to bring infrastructure
in the US up to good condition (American Society of Civil Engineers, 2009) p. 6. Canadian infrastructure had an
investment deficit of C$125 billion in the 1980s and 1990s (Mirza and Haider, 2003).
Some transportation systems have been harmed (Figure 26-2). For example, in 2008, Hurricane Ike caused $2.4
billion in damages to ports and waterways in Texas. (McDonald, 2012). The superflood in Tennessee and
Kentucky in 2010 caused $2.3 billion in damage (National Oceanic and Atmospheric Administration, 2013).
Hurricane Sandy resulted in flooding of portions of New York City s subway system, overtopping of runways at La
Guardia airport, and caused $400 million in damage to the New Jersey transit system (National Oceanic and
Atmospheric Administration, 2013).
26.7.2.2. Projected Impacts
Scholarship on projected climate impacts on transportation infrastructure focuses mostly on US and Canada.
Increases in high temperatures, intense precipitation, drought, sea level, and storm surge could affect transportation
across the United States. The greatest risks would be to coastal transportation infrastructure, but there could be
benefits to marine and lake transportation in high latitudes from less ice cover (Transportation Research Board,
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2008). A 1-meter sea level rise combined with a 7-meter storm surge could inundate over half of the highways,
arterials, and rail lines in the US Gulf coast (Savonis et al., 2008). Declining water levels in the Great Lakes would
increase shipping costs by restricting vessel drafts and reducing vessel cargo volume (Millerd, 2011). In southern
Canada by the 2050s, cracking of roads from freeze and thaw would decrease under the B2 and A2 scenarios,
structures would freeze later and thaw earlier, while higher extreme temperatures could increase rutting (Mills et al.,
2009) and related maintenance and rehabilitation costs (Canadian Council of Professional Engineers, 2008).
A 1 to 1.5°C increase in global mean temperature would increase the costs of keeping paved and unpaved roads in
the United States in service by, respectively, US$2 to US$3 billion per year by 2050 (Chinowsky et al., 2013). Tens
of thousands to more than 100,000 bridges in the US could be vulnerable to increasing peak river flows in the mid-
and late-21st Century under the A1B and A2 scenarios. Strengthening vulnerable bridges to be less vulnerable to
climate change is estimated to cost approximately US$100 to $250 billion (Wright et al., 2012).
26.7.2.3. Adaptation
Adaptation steps are being taken in North America, particularly to protect transportation infrastructure from sea
level rise and storm surge in coastal regions. Almost all of the major river and bay bridges destroyed by Hurricane
Katrina surge waters were rebuilt at higher elevations, and the design of the connections between the bridge decks
and piers were strengthened (Grenzeback and Luckmann, 2006).
Adaptation actions include protecting coastal transportation from sea level rise and more intense coastal storms or
possibly relocating infrastructure. Many Midwestern states are examining channel protection and drainage designs,
while transportation agencies in Canada and the United States have been preparing to manage the aftermath of
extreme weather events (Meyer et al., 2013). In addition, new materials may be needed so pavement and rail lines
can better withstand more extreme temperatures.
26.7.3. Mining
26.7.3.1. Observed Impacts
Climatic sensitivities of mining activities, including exploration, extraction, processing, operations, transportation
and site remediation, have been noted in the limited literature (Chiotti and Lavender, 2008; Furgal and Prowse,
2008; Meza-Figueroa et al., 2009; Ford et al., 2010a; Locke et al., 2011; Gómez-álvarez et al., 2011; Kirchner et al.,
2011; Pearce et al., 2011; Stratos Inc, 2011). Drought-like conditions have affected the mining sector by limiting
water supply for operations (Pearce et al., 2011), enhancing dust emissions from quarries (Pearce et al., 2011) and
increasing concentrations of heavy metals in sediments (Gómez-álvarez et al., 2011). Heavy precipitation events
have caused untreated mining wastewater to be flushed into river systems (Pearce et al., 2011). High loads of
contamination (from metals, sulfate and acid) at three mine sites in the United States were measured during
rainstorm events following dry periods (Nordstrom, 2009).
26.7.3.2. Projected Impacts
Climate change is perceived by Canadian mine practitioners as an emerging risk, and in some cases, a potential
opportunity (Ford et al., 2010a; Ford et al., 2011; Pearce et al., 2011; National Round Table on the Environment and
the Economy, 2012), with potential impacts on transportation (Ford et al., 2011) and limited water availability
(Acclimatise, 2009) from projected drier conditions (Sun et al., 2008; Seager and Vecchi, 2010) being identified as
key issues.
An increase in heavy precipitation events projected for much of North America (Warren and Egginton, 2008;
Nordstrom, 2009) would adversely affect the mining sector. A study on acid rock damage drainage in Canada
concluded that an increase in heavy precipitation events presented a risk of both environmental impacts and
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economic costs (Stratos Inc, 2011) Damage to mining infrastructure from extreme events, for active and post-
operation mines, is also a concern (Pearce et al., 2011). Climate change impacts that affect the bottom-line of
mining companies (through direct impacts or associated costs of adaptation), would have consequences for
employment, for both the mining sectors and local support industries (Backus et al., 2013).
26.7.3.3. Adaptation
Despite increasing awareness, there are presently few documented examples of proactive adaptation planning within
the mining sector (Acclimatise, 2009; Ford et al., 2010a; Ford et al., 2011). However, adjustments to management
practices to deal with short-term water shortages, including reducing water intake, increasing recycling and
establishing infrastructure to move water from tailing ponds, pits and quarries, have worked successfully in the past
(Chiotti and Lavender, 2008). Integrating climate change considerations at the mine planning and design phase
increases the opportunity for effective and cost-efficient adaptation (Stratos Inc, 2011).
26.7.4. Manufacturing
26.7.4.1. Observed Impacts
There is little literature focused on climate change and manufacturing, although one study suggested that
manufacturing is among the most sensitive sectors to weather in the US (Lazo et al., 2011). Weather affects the
supply of raw material, production process, transportation of goods, and demand for certain products. In 2011,
automobile manufacturers in North America experienced production losses associated with shortages of components
due to flooding in Thailand (Newswire, 2011). In 2013, reduced cattle-supply and higher feed prices associated with
drought in Texas led to a decision to close a beef processing plant (Beef Today Editors, 2013). Drought also caused
delays for barge shipping on the Mississippi River in 2012 (Reuters, 2012). Major storms, like Hurricanes Sandy,
Katrina and Andrew, significantly disrupted manufacturing activities, including plant shutdowns due to direct
damages and/or loss of electricity and supply disruptions due to unavailability of parts, and difficulties delivering
products due to compromised transportation networks (Baade et al., 2007; Dolfman et al., 2007).
26.7.4.2. Projected Impacts
The drier conditions (Sun et al., 2008; Seager and Vecchi, 2010; Wehner et al., 2011) would present challenges,
especially for manufacturers located in regions already experiencing water stress. This could lead to increased
conflicts over water between sectors and regions, and affect the ability of regions to attract new facilities or retain
existing operations. A study of the effect of changes in precipitation (A1B scenario) on 70 industries in the US
between 2010 and 2050 found potentially significant losses in production and employment due to declines in water
availability and the interconnectedness of different industries (Backus et al., 2013).
Another potential concern for manufacturing relates to impacts of heat on worker safety and productivity. Several
studies suggest that higher temperatures and humidity would lead to decreased productivity and increased
occupational health risks (e.g., Kjellstrom et al., 2009; Hanna et al., 2011; Kjellstrom and Crowe, 2011).
26.7.4.3. Adaptation
Some companies are beginning to recognize the risks climate change presents to their manufacturing operations, and
consider strategies to build resilience (National Round Table on the Environment and the Economy, 2012). Coca
Cola has a water stewardship strategy focusing on improving water use efficiency at its manufacturing plants, while
Rio Tinto Alcan is assessing climate change risks for their operations and infrastructure, which include vulnerability
of transport systems, increased maintenance costs, and disruptions due to extreme events (National Round Table on
the Environment and the Economy, 2012). Air conditioning is a viable and effective adaptation option to address
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some of the impacts of warming, though it does incur greater demands for electricity and additional costs (Scott et
al., 2008a). Sourcing raw materials from different regions and relocating manufacturing plants are other adaptation
strategies that can be used to increase resiliency and reduce vulnerability.
26.7.5. Construction and Housing
26.7.5.1. Observed Impacts
The risk of damage from climate change is important for construction industries, though little research has
systematically explored the topic (Morton et al., 2011). Private data from insurance companies report a significant
increase in severe weather damage to buildings and other insured infrastructure over several decades (Munich Re,
2012).
26.7.5.2. Projected Impacts
Most studies project a significant further increase in damage to homes, buildings and infrastructure (Bjarndadottir et
al., 2011; IPCC, 2012). Affordable adaptation in design and construction practices could reduce much of the risk of
climate damage for new buildings and infrastructure, involving reform in Building Codes and other standards
(Feltmate and Thistlethwaite, 2012). However, adaptation best practices in design and construction are often
prohibitively expensive to apply to existing buildings and infrastructure, so much of the projected increase in climate
damage risk involves existing buildings and infrastructure.
26.7.5.3. Adaptation
Engineering and construction knowledge exists to design and construct new buildings to accommodate the risk of
damage from historic extremes and anticipated changes in severe weather (Kelly, 2010; Ministry of Municipal
Affairs and Housing, 2011; Insurance Institute for Business and Home Safety, 2012). Older buildings may be retrofit
to increase resilience, but these changes are often more expensive to introduce into an existing structure than if they
were included during initial construction.
The housing and construction industries have made advances toward climate change mitigation by incorporating
energy efficiency in building design (Heap, 2007). Less progress has been made in addressing the risk of damage
from extreme weather events (Kenter, 2010). In some markets, like the Gulf Coast of the United States, change is
under way in the design and construction of new homes in reaction to recent hurricanes (Levina et al., 2007;
Kunreuther and Michel-Kerjan, 2009; Insurance Institute for Business and Home Safety, 2011), but in most markets
across North America there has been little change in building practices. The cost of adaptation measures combined
with limited long-term liability for future buildings has influenced some builders to take a wait-and-see attitude
(Morton et al., 2011). Exploratory work is under way to consider implementation of building codes that would focus
on historic weather experience and also introduce expected future weather risks (Auld et al., 2010; Ontario Ministry
of Environment, 2011).
26.7.6. Insurance
26.7.6.1. Observed Impacts
Property insurance and reinsurance companies across North America experienced a significant increase in severe
weather damage claims paid over the past three or four decades(Cutter and Emrich, 2005; Munich Re, 2011; Bresch
and Spiegel, 2011). Most of the increase in insurance claims paid has been attributed to increasing exposure of
people and assets in areas of risk (Pielke Jr et al., 2008; Barthel and Neumayer, 2012). A role for climate change has
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not been excluded, but the increase to date in damage claims is largely due to growth in wealth and population
(IPCC, 2012).
Severe weather and climate risks have emerged over the past decade as the leading cost for property insurers across
North America, resulting in significant change in industry practices. The price of insurance increased in regions
where the risk of loss and damage has increased. Discounts have been introduced where investments in adaptation
have reduced the risk of future weather losses (Mills, 2012). Further detailed discussion on the insurance sector and
climate change can be found in section 10.7.
26.7.6.2. Projected Impacts
Without adaptation, there is an expectation that severe weather insurance damage claims would increase
significantly over the next several decades across North America (World Bank, 2010). The risk of damage is
expected to rise due to continuing growth in wealth, the population living at risk, and climate change. There is also
an expectation that some weather perils in North America will increase in severity, including Atlantic hurricanes and
the area burned by wildfire (Karl et al., 2008; Balshi et al., 2009), and other perils in frequency, including intense
rainfall events (IPCC, 2012).
26.7.6.3. Adaptation
The insurance industry is one of the most studied sectors in North America in terms of climate impacts and
adaptation. Most adaptation in the insurance industry has been in response to an increase in severe weather damage,
with little evidence of proactive adaptation in anticipation of future climate change (Mills and Lecomte, 2006; Mills,
2007; Mills, 2009; Kunreuther and Michel-Kerjan, 2009; Leurig, 2011; Autorite des Marches Financiers, 2011;
Gallagher, 2012). In addition to pricing decisions based on an actuarial analysis of historic loss experience, many
insurance companies in the United States and Canada now use climate model information to help determine the
prices they charge and discounts they offer. Most insurance companies have established specialized claims handling
procedures for responding to catastrophic events (Kovacs, 2005; Mills, 2009).
A recent study of more than 2,000 major catastrophes since 1960 found that insurance is a critical adaptive tool
available to help society minimize the adverse economic consequences of natural disasters (von Peter et al., 2012).
Government insurance programs for coverage of flood in the United States have been affected by recent hurricanes
and previously subsidized premiums have been changed to more accurately reflect risk (Federal Emergency
Management Agency, 2013). In the United States and Canada, homeowners make extensive use of insurance to
manage a broad range of risks, and those with insurance recover quickly following most extreme weather events.
However the majority of public infrastructure is not insured and it frequently takes more than a decade before
government services fully recover. In contrast, Mexico has a well-developed program for financing the rebuilding of
public infrastructure following a disaster (FONDEN) but insurance markets are only beginning to emerge for
homeowners and businesses. In 2012, per capita spending on property and casualty insurance was US$2,239.20 in
the United States, US$2,040.40 in Canada, and US$113.00 in Mexico (Seiler et al., 2013).
Insurance companies are also working to influence the behavior of their policyholders to reduce the risk of damage
from climate extremes (Kovacs, 2005; Anderson et al., 2006; Mills, 2009). For example, the industry supports the
work of the Insurance Institute for Business and Home Safety in the United States, and the Institute for Catastrophic
Loss Reduction in Canada, in working to champion change in the building code and communicate to property
owners, governments and other stakeholders best practices for reducing the risk of damage from hurricanes,
tornadoes, winter storms, wildfire, flood and other extremes.
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26.8. Urban and Rural Settlements
Recently a growing body of literature and national assessments have focused on climate-related impacts,
vulnerabilities and risks in North American settlements (e.g., US-NCA chapters 11 and 14 and AR5 chapters 8 and
9).
26.8.1. Observed Weather and Climate Impacts
Observed impacts on lives, livelihoods, economic activities, infrastructure and access to services in North American
human settlements have been attributed to sea level rise (26.2.2.1), changes in temperature and precipitation, and
occurrences of extreme events like heat waves, droughts and storms (Figure 26-2).
Only a handful of these impacts have been attributed to anthropogenic climate change, such as shifts in Pacific
Northwest marine ecosystems, which have restricted fisheries and thus affected fishing communities (U.S. Global
Change Research Program, 2009). As well, (MacKendrick and Parkins, 2005; Parkins and MacKendrick, 2007;
Parkins, 2008; Holmes, 2010) identified 30 communities and 25,000 families in British Columbia negatively
affected by the mountain pine beetle outbreak (See 26.4.1.1).
While droughts are among the more notable extreme events affecting North American urban and rural settlements
recently, with severe occurrences in the Canadian Prairies causing economic and employment losses (2001-2),
changes in drought frequency in North America have not been attributed to anthropogenic climate change (Figure
26-1). The 2010-2012 drought across much of the US and Northern Mexico was considered the most severe in a
century (MacDonald, 2010). It affected 80% of agricultural land in the US, with 2,000 counties designated disaster
zones by September (U.S. Department of Agriculture, Economic Research Service, 2012). Impacts include the loss
of 3.2 million tons of maize in Mexico, placing 2.5 million at risk of food insecurity (Direccion General de
Comunicacion Social, 2012). Among the most severely affected were indigenous peoples, such as the Rarámuri of
Chihuahua (ibid.). Closely associated with droughts, the impacts of recent wildfires have been significant (See Box
26.2), and have intensified inequalities in vulnerability between amenity migrants and low-income residents in peri-
urban areas of California and Colorado (Collins and Bolin, 2009).
Other extreme-events include heat-waves, resulting in excess urban mortality (O'Neill and Ebi, 2009; Romero-
Lankao et al., 2012b); and affecting infrastructure and built environments e.g., road pavement in Chicago buckled
under temperatures over 100oF (CBS Chicago, 2012); in Colorado two wildfires burned over 600 homes (National
Climate Data Center, 2013).
Extreme storms and extreme precipitation have also impacted several North American regions (Figures 26-1 and 26-
2). Flood frequency has increased in some cities, a trend sometimes associated with more intense precipitation (e.g.,
Mexico City and Charlotte NC, US) (Villarini et al., 2009; Magana, 2010), while in others this trend is associated
with a transition from flood events dominated by snowmelt to those caused by warm-season thunderstorms (e.g.,
Québec, Canada and Milwaukee, US) (Ouellet et al., 2012; Yang et al., 2013). As illustrated by Sandy (Neria and
Shultz, 2012; Powell et al., 2012), storms impact human health and healthcare access (section 26.6.1.1), and impacts
on infrastructure and the built environment have been costly. Heavy precipitation, storm surges, flash-floods and
wind, including flooding on the US East Coast, and Midwest (2011), hurricanes and floods in the city of Villa
Hermosa (Comisión Económica para América Latina y el Caribe, ) and other urban areas in southern Mexico (2004-
5), have compromised homes and businesses (Comfort, 2006; Kirshen et al., 2008; Jonkman et al., 2009; Romero-
Lankao, 2010). Hurricane Wilma alone caused $1.8 billion in damage, among the biggest insurance losses in Latin
American history (Galindo et al., 2009).
The impacts of interacting hazards compound vulnerabilities (26.8.2). Coastal settlements are at risk from the
combined occurrence of coastal erosion, health effects, infrastructure and economic damage from storm surges.
Earlier thaw (Friesinger and Bernatchez, 2010), SLR, and coastal flooding have been detected along the mid-
Atlantic, Gulf of Mexico, and St. Lawrence (Kirshen et al., 2008; Zavala-Hidalgo et al., 2010; Friesinger and
Bernatchez, 2010; Rosenzweig et al., 2011; Tebaldi et al., 2012).
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Climate impacts on the ecosystem-function and -services (e.g., water supplies, biodiversity or flood protection)
provided to human settlements are another concern. While acknowledged in some places (e.g., Mexico City Climate
Action Plan), they have received relatively less scholarship attention (Hunt and Watkiss, 2011).
26.8.2. Observed Factors and Processes Associated with Vulnerability
Differences in the severity of climate impacts on human settlements are strongly influenced by context-specific
vulnerability factors and processes (Table 26.1, Cutter et al., 2013), some of which are common to many
settlements, while others are more pertinent to some types of settlements than others. Human settlements
simultaneously face a multilevel array of non-climate-related hazards (e.g., economic, industrial, technological) that
contribute to climate change vulnerability (McGranahan et al., 2007; Satterthwaite et al., 2007; Romero-Lankao and
Dodman, 2011). In the following we highlight key sources of vulnerability for urban and rural systems.
26.8.2.1. Urban Settlements
Hazard risks in urban settlements are enhanced by the concentration of populations, economic activities, cultural
amenities and built environments particularly when they are in highly-exposed locations such as coastal and arid
areas. Cities of concern include those in the Canadian prairies and US-Mexico border region; and major urban areas
including Boston, New York, Chicago, Washington DC, Los Angeles, Villa Hermosa, Mexico City and Hermosillo
(Comisión Económica para América Latina y el Caribe; Bin et al., 2007; Collins, 2008; Kirshen et al., 2008; Collins
and Bolin, 2009; Gallivan et al., 2009; Rosenzweig et al., 2010; Hayhoe et al., 2010; Romero-Lankao, 2010;
Wittrock et al., 2011).
Risks may also be heightened by multiple interacting hazards. Slow-onset events such as urban heat-islands, for
instance, interact with poor air-quality in large North American cities to exacerbate climate impacts on human health
(Romero-Lankao et al., 2013a). As illustrated by recent weather events (Figure 26.2), however, hazard interactions
can also follow individual, high-magnitude extreme events of short duration, with cascading effects across
interconnected energy, transportation, water and health infrastructures and services to contribute to and compound
urban vulnerability (Gasper et al., 2011). Wildfire vulnerability in the southwest has been compounded by peri-
urban growth (Collins and Bolin, 2009; Brenkert-Smith, 2010). Under current financial constraints in many cities,
climate-related economic losses can reduce resources available to address social issues, thus threatening institutional
capacity and urban livelihoods (Kundzewicz et al., 2008).
The urbanization process and urban built-environments of North America can amplify climate impacts as they
change land-use and land-surface physical characteristics (e.g., surface albedo, Chen et al., 2011a). A 34% increase
in US urban land development (Alig et al., 2004) between 1982 and 1997 had implications for water supplies and
extreme event impacts. Effects on water are of special concern (section 26.3), as urbanization can enhance or reduce
precipitation, depending on climate regime, geographical location and regional patterns of land, energy and water
use (Cuo et al., 2009). Urbanization also has significant impacts on flood climatology through atmospheric
processes tied to the Urban Heat Island (UHI), the Urban Canopy Layer (UCL), and the aerosol composition of
airsheds (Ntelekos et al., 2010). The UHI can also increase health risks differentially, due to socio-spatial
inequalities across and within North American cities (Harlan et al., 2008; Miao et al., 2011).
Urbanization imposes path dependencies that can amplify or attenuate vulnerability (Romero-Lankao and Qin,
2011). For example, the overexploitation of Mexico City s aquifer by 19.1 - 22.2 m3/s has reduced groundwater
levels and caused subsidence, undermining building foundations and infrastructure and increasing residents
vulnerability to earthquakes and heavy rains (Romero-Lankao, 2010).
Elements of the built-environment such as housing stock, urban form, the condition of water and power
infrastructures, and changes in urban and ecological services also affect vulnerability. Large, impermeable surfaces
and buildings disrupt drainage channels and accelerate run-off (Walsh et al., 2005). Damage from floods can be
much more catastrophic if drainage or waste collection systems are inadequate to accommodate peak flows
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(Richardson, 2010; Sosa-Rodriguez, 2010). While many Canadian and US cities are in need of infrastructure
adaptation upgrades (Doyle et al., 2008; Conrad, 2010), Mexican cites are faced with existing infrastructure deficits
(Niven et al., 2010; Hardoy and Romero Lankao, 2011), and high levels of socio-spatial segregation (Smolka and
Larangeira, 2008) (section 26.7).
Recent weather hazards (Figure 26-2) illustrate that economic activities and highly-valued physical capital of cities
(real estate, interconnected infrastructure systems) are very sensitive to climate-related disruptions that can result in
high impacts; activities in some urban areas are particularly exposed to key resource constraints (e.g., water in the
US-Mexico Border; oil industry in Canada, US and Mexico; Levy et al., 2010; Conrad, 2010); others are dependent
upon climate-sensitive sectors (e.g., tourism) (Lal et al., 2011). Disruptions to production, services and livelihoods,
and changes in the costs of raw materials also impact the economic performance of cities (Hunt and Watkiss, 2011).
Cities are relatively better endowed than rural populations with individual and neighborhood assets such as income,
education, quality of housing and access to infrastructure and services that offer protection from climate hazards.
However, intra-urban socio-spatial differences in access to these assets shape response capacities (Harlan and
Ruddell, 2011; Romero-Lankao et al., 2013a). All this means that class and socio-spatial segregation are key
determinants not only of vulnerability but also of inequalities in risk generation and distribution within cities.
Economic elites are better positioned to access the best land and enjoy the rewards of environmental amenities such
as clean air, safe drinking water, open space, and tree shade (Morello-Frosch et al., 2002; Harlan et al., 2006; Harlan
et al., 2008; Ruddell et al., 2011). Although wealthy sectors are moving into risk prone coastal and forested areas
(Collins, 2008), and certain hazards (air pollution) affect both rich and poor alike (Romero-Lankao et al., 2013a),
climate risks tend to be disproportionally borne by the poor or otherwise marginalized populations (Cutter et al.,
2008; Collins and Bolin, 2009; Romero-Lankao, 2010; Wittrock et al., 2011). In some cities, marginalized
populations are moving to peri-urban areas with inadequate services, a portfolio of precarious livelihood
mechanisms, and inappropriate risk-management institutions (Collins and Bolin, 2009; Eakin et al., 2010;
Monkkonen, 2011; Romero-Lankao et al., 2012a).
Although cities have comparatively higher access than rural municipalities to determinants of institutional capacity
such as human resources and revenue pools, their governance arrangements are often hampered by jurisdictional
conflicts, asymmetries in information and communication access, fiscal constraints on public services including
emergency personnel, and top-down decision making. These governance issues exacerbate urban vulnerabilities and
constrain urban adaptation planning (Carmin et al., 2012; Romero-Lankao et al., 2013a).
26.8.2.2. Rural Settlements
The legacy of previous and current stresses contributing to rapid population growth or loss in North American rural
communities, reduced employment, or degradation of local knowledge systems, can increase vulnerability
(Brklacich et al., 2008; Coles and Scott, 2009; McLeman, 2010). North American rural communities have a higher
proportion of lower income and unemployed populations and higher poverty than cities (Whitener and Parker, 2007;
Lal et al., 2011; Skoufias et al., 2011). 55% of Mexico s rural residents live in poverty, and the livelihood of 72% of
these is in farming (Saldana-Zorrilla, 2008). US and Canadian rural communities have older populations (McLeman,
2010) and lower education levels (Lal et al., 2011). Indigenous communities have lower education levels, and high
levels of poverty, but are younger than average populations (Downing and Cuerrier, 2011). The legacy of their
colonial history, furthermore, has stripped Indigenous communities of land and many sources of social and human
capital (Brklacich et al., 2008; Hardess et al., 2011). Conversely, rural and indigenous community members possess
valuable local and experiential knowledge regarding regional ecosystem services (Nakashima et al., 2011).
Rural economies have limited economic diversity and relatively high dependence on climate-sensitive sectors
(Johnston et al., 2008; Natural Resources Canada, 2008; Molnar, 2010); they are sensitive to climate-induced
reductions in resource supply and productivity, in addition to direct exposure to climate hazards (Daw et al., 2009).
Single-sector economic dependence contributes significantly to vulnerability (Cutter et al., 2003). Engagement in
export markets presents opportunity but also exposure to economic volatility (Eakin, 2006; Saldana-Zorilla and
Sandberg, 2009), and economic downturns take attention away from climate change adaptation. Farming and fishing
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provide both economic and food security, the impacts of climate thus posing a double threat to livelihood (Badjek et
al., 2010), particularly among women (Bee et al., 2013). Inter-related factors affecting vulnerability in forestry and
fishing communities include over-harvesting, and the cumulative environmental effects of multiple land use
activities (Brklacich et al., 2008). Many tourism-based communities are dominated by seasonal economies and low-
wage, service-based employment (Tufts, 2010), and small businesses that lack resources for emergency planning
(Hystad and Keller, 2006; Hystad and Keller, 2008). Non-renewable resource industries are sensitive to power,
water, and transportation disruptions associated with hazards.
Geographic isolation can be a key source of vulnerability for rural communities in North America, imposing long
commutes to essential services like hospitals, and non-redundant transportation corridors that can be compromised
during extreme events (Chouinard et al., 2008). Many Indigenous communities are isolated, raising the costs and
limiting the diversity of imported food, fuel and other supplies, rendering the ability to engage in subsistence
harvesting especially critical for both cultural and livelihood wellbeing (Andrachuk and Pearce, 2010; Hardess et al.,
2011). Many indigenous peoples also maintain strong cultural attachment to ancestral lands, and thus are especially
sensitive to declines in the ability of that land to sustain their livelihoods and cultural wellbeing (Downing and
Cuerrier, 2011).
Rural physical infrastructure is often inadequate to meet service needs or is in poor condition (McLeman and
Gilbert, 2008; Krishnamurthy et al., 2011), especially for Indigenous communities (section 26.9, Brklacich et al.,
2008; Hardess et al., 2011; Lal et al., 2011). A lack of redundant power and communication services can
compromise hazard response capacity.
26.8.3. Projected Climate Risks on Urban and Rural Settlements
Urbanization, migration, economic disparity, and institutional capacity will influence future impacts and adaptation
to climate change in North American human settlements (section 26.2.1). Water related concerns are assessed in
26.3.2.1 and 26.3.2.3). We describe below a variety of future climate risks identified in the literature, many of which
focus on cities (Chapters 8 and 9) and, with the exception of larger centers such as New York and Boston, are
qualitative in nature (Hunt and Watkiss, 2011). This is due in part to the difficulty in downscaling the prediction of
trends in key trends in climate parameters to an appropriate scale
Model-based sea-level-rise (SLR) projections of future risks to cities are characterized by large uncertainties due to
global factors (e.g., the dynamics of polar ice-sheets WGI) and regional factors (e.g., regional shifts in ocean
circulation, high of the adjacent ocean and local land high) (Blake et al., 2011) (WGI chapter 3). The latter will
determine differential SLR impacts on regional land development of coastal settlements (U.S. Government
Accountability Office, 2007; Yin et al., 2009; Sobel et al., 2010; Conrad, 2010; Millerd, 2011), making some areas
particularly vulnerable to inundation (Cooper and Sehlke, 2012). SLR can also exacerbate vulnerability to extreme
events such as hurricanes (Frazier et al., 2010).
Temperature increases would lead to additional health hazards. Baseline warmer temperatures in cities are expected
to be further elevated by extreme heat events whose intensity and frequency is projected to increase during the 21st
century (section 26.2.2), particularly in northern mid-latitude cities (Jacob and Winner, 2009). Participation in some
outdoor activities would also increase as a result of projected increases in warm days (Scott and McBoyle, 2007).
Projected snowfall declines in Canada and the Northeast US would reduce length of winter sport seasons and thus
affect the economic wellbeing of some communities (McBoyle et al., 2007; Scott et al., 2008b).
Any increase in frequency of extreme events, such as intense precipitation, flooding and prolonged dry periods
would affect particularly the populations, economic activities, infrastructures and services on coasts, flood-prone
deltas and arid regions (Nicholls et al., 2008; Kirshen et al., 2008; Richardson, 2010; Weiss et al., 2011). For
example, by the end of this century, New York City is projected to experience nearly twice as many extreme
precipitation days compared to today (A2, mean ensemble of 17 models). (Ntelekos et al., 2010; Cayan et al., 2010)
project an increase in the number and duration of droughts in the southwest US, with most droughts expected to last
over five years by 2050 (GDFL CM2.1 and CNRM CM3, A2 and B1). Assuming no adaptation, total loses from
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river flooding in metropolitan Boston are estimated to exceed $57 billion by 2100, of which $26 billion is attributed
to climate change (Nicholls et al., 2008; Kirshen et al., 2008; Richardson, 2010; Weiss et al., 2011).
Future climate risks on lives and livelihoods have been relatively less studied. A handful of studies focused on
forestry are notable, indicating potentially substantial shifts in livelihood options without adaptation. Sohngen and
Sedjo (Sohngen and Sedjo, 2005) estimate losses from climate change in the Canadian/US timber sector of $1.4
$2.1 billion per year over the next century. Anticipated future supply reductions in British Columbia as a
consequence of the pine beetle outbreak vary from 10 to 62% (Patriquin et al., 2007). Substantial declines in suitable
habitat for valued tree species in Mexico have been projected (Gómez-Mendoza and Arriaga, 2007; Gomez-Diaz et
al., 2011).
Scholars are starting to project future risks from interacting hazards. For instance, by 2070 with a 0.5m rise in sea-
level and under scenarios of socioeconomic growth, storm surges and subsidence, populations at risk in New York,
Miami and New Orleans might increase three-fold, while asset exposure will increase more than 10-fold (Hanson et
al., 2011).
Essential infrastructure and services are key concerns (sections 26.3 and 26.7). Increased occurrence of drought
affecting water availability is projected for southwestern US/Northern Mexico, the southern Canadian Prairies and
central Mexico, combined with projected increases in water demand due to rapid population growth and agriculture
(Schindler and Donahue, 2006; MacDonald, 2010; Lal et al., 2011). Using A1B and A2 scenarios, (Escolero-
Fuentes et al., 2009) projected that by 2050, Mexico City and its watersheds will experience a more intense
hydrological cycle and a reduction of between 10 to 17% in per capita available water. Sea-level rise is predicted to
threaten water and electricity infrastructure with inundation and increasing salinity (Sharp, 2010).
26.8.4. Adaptation
26.8.4.1. Evidence of Adaptation
26.8.4.1.1. What are populations doing? Autonomous Adaptation
As illustrated by recent extreme events (Figure 26-2), individuals and households in North America have not only be
affected by extremes, but have also been responding to climate impacts mostly through incremental actions, for
example by purchasing additional insurance, or reinforcing homes to withstand extreme weather (Simmons and
Sutter, 2007; Romero-Lankao et al., 2012a). Some individuals respond by diversifying livelihoods (Newland et al.,
2008; Rose and Shaw, 2008) or migrating (See 26.1.1) (Black et al., 2011).
The propensity to respond to climate and weather hazards is strongly influenced not only by access to household
assets, but also by community and governmental support. The emergency response to Sandy illustrates this.
Although New York and New Jersey witnessed vivid scenes of medical humanitarianism, because of inadequate
communication and coordination among agencies, public health support did not always reach those most in need
(Abramson and Redlener, 2013).
The perceived risks of climate change among individuals are equally important. Strong attachment to place and
occupation may motivate willingness to support incremental adaptation, enhance coping capacity and foster adaptive
learning (Collins and Bolin, 2009; Romero-Lankao, 2010; Aguilar and Santos, 2011; Wittrock et al., 2011). They
have also been found to serve as barriers to transformational adaptation (Marshall et al., 2012). Residents of the US
stand out in international research as holding lower levels of perceived risk of climate change (AXA Ipsos, 2012),
which may limit involvement in household-level adaptation, or support for public investments in adaptation.
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26.8.4.1.2. What are governments doing? Planned Adaptation
Leadership in adaptation is far more evident locally than at other tiers of government in North America (Richardson,
2010; Vasseur, 2011; Vrolijks et al., 2011; Henstra, 2012; Carmin et al., 2012). Few municipalities have moved into
the implementation stage, however; most programs are in the process of problem diagnosis and planning (Perkins et
al., 2007; Moser and Satterthwaite, 2008; Romero-Lankao and Dodman, 2011). Systematic assessments of
vulnerability are rare, particularly in relation to population groups (Vrolijks et al., 2011). Surveys of municipal
leaders showed adaptation is rarely incorporated into planning, due to lack of resources, information and expertise
(Horton and Richardson, 2011), and the prevalence of other issues considered higher priority, suggesting the need
for subnational and federal-level facilitation in the form of resources and enabling regulations .
Climate change policies have been motivated by concerns for local economic or energy security and the desire to
play leadership roles (Rosenzweig et al., 2010; Anguelovski and Carmin, 2011; Romero-Lankao et al., 2013a).
Some policies constitute integrated strategies (New York) (Perkins et al., 2007; Rosenzweig et al., 2010), and
coordinated participation of multiple municipalities (Vancouver) (Richardson, 2010). Sector-specific climate risk
management plans have also emerged (e.g., water conservation in Phoenix, US and Regina, Canada; wildfire
protection in Kamloops, Canada and Boulder, US). Municipalities affected by the mountain pine beetle have taken
many steps toward adaptation (Parkins, 2008), and coastal communities in eastern Canada are investing in saltwater
marsh restoration to adapt to rising sea levels (Marlin et al., 2007). Green roofs, forest thinning and urban
agriculture have all been expanding (Chicago, New York, Kamloops, Mexico City), as have flood protection (New
Orleans, Chicago), private and governmental insurance policies (section 26.10, Browne and Hoyt, 2000; Ntelekos et
al., 2010), safe saving schemes (common in Mexico), air pollution controls (Mexico City), and hazard warning
systems (Collins and Bolin, 2009; Coffee et al., 2010; Romero-Lankao, 2010; Aguilar and Santos, 2011).
26.8.4.2. Opportunities and Constraints
Adaptation in human settlements is influenced by local access to resources, political will and the capacity for
institutional-level attention and multilevel/multisectoral coordination (Burch, 2010; Romero-Lankao et al., 2013a)
(discussed further in the next section).
26.8.4.2.1. Adaptation is path-dependent
Adaptation options are constrained by past settlement patterns and decisions. The evolution of cities as economic
hubs, for example, affects vulnerability and resilience (Leichenko, 2011). Urban expansion into mountain,
agricultural, protected and otherwise risk-prone areas (Boruff et al., 2005; McGranahan et al., 2007; Collins and
Bolin, 2009; Conrad, 2010) invariably alters regional environments. Development histories foreclose some
resilience pathways. Previous water development, for example, can result in irreversible overexploitation and
degradation of water resources.
26.8.4.2.2. Institutional capacity
At all levels of governance, adaptation in North America is affected by numerous determinants of institutional
capacity. Three have emerged in the literature as particularly significant challenges for urban and rural settlements:
Economic Resources: Rural communities face limited revenues combined with higher costs of supplying
services (Williamson et al., 2008; Posey, 2009). Small municipal revenue pools translate into fiscal
constraints necessary to support public services, including emergency personnel and health care (Lal et al.,
2011). Although large cities tend to have greater fiscal capacity, most do not receive financial support for
adaptation (Carmin et al., 2012), yet face the risk of higher economic losses.
Information and social capital: Differences in access and use of information, and capacity for learning and
innovation, affect adaptive capacity (Romero-Lankao et al., 2013a). Levels of knowledge and prioritization
can be low among municipal planners. Information access can be limited, even among environmental
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planners (Picketts et al., 2012). The relationship between trust and participation in support networks (social
capital) and adaptive capacity is generally positive, however strong social bonds may support narratives
that under-estimate climate risk (Wolf et al., 2010; Romero-Lankao et al., 2012b).
Participation: Considering the overlap among impacts and sources of vulnerability in North American
human settlements, long-term effectiveness of local adaptation hinges upon inclusion of all stakeholders.
Stakeholder involvement lengthens planning time frames, may elicit conflicts, and power relationships can
constrain access (Few et al., 2007; Colten et al., 2008). However, effective stakeholder engagement has
tremendously enhanced adaptation planning, eliciting key sources of information regarding social values,
securing legitimacy (Aguilar and Santos, 2011), and fostering adaptive capacity of involved stakeholders.
_____ START BOX 26-3 HERE _____
Box 26-3. Climate Responses in Three North American Cities
With populations of 20.5, 14 and 2.3 million people, respectively, the metropolitan areas of Mexico City, New York,
and Vancouver are facing multiple risks that climate change is projected to aggravate. These risks range from sea
level rise, coastal flooding and storm surges in New York and Vancouver to heat waves, heavy rains and associated
flooding, air pollution, and heat-island effects in all the three cities (Rosenzweig and Solecki, 2010; Leon and Neri,
2010; City of Vancouver, 2012). Many of these risks result not only from long-term global and regional processes of
environmental change, but also from local changes in land and water uses and in atmospheric emissions induced by
urbanization (Romero-Lankao, 2010; Leon and Neri, 2010; Kinney et al., 2011; Solecki, 2012).
The three cities have been frontrunners in the climate arena. In Mexico City, the Program of Climate Action 2008
2012 (PAC) and the 2011 Law for Mitigation and Adaptation to Climate Change are parts of a larger 15-year Green
Agenda, with most of designated funds committed to reducing 7 million tonnes of CO2 equivalent by 2012
(Romero-Lankao et al., 2013). New York City s and Vancouver s Plans are similarly mitigation-centred. As of 2007
New-York s long-term sustainability plan included adaptation (Solecki, 2012; Ray et al., 2013), while Vancouver
launched its municipal adaptation plan in July 2012. The shifts in focus from mitigation to adaptation have followed
as it has become increasingly clear that even if mitigation efforts are wholly successful, some adverse impacts due to
climate change are unavoidable.
Urban leaders in all three cities have emerged as global leaders in sustainability. Mayor Bloomberg of New York;
Mayor Ebrard of Mexico City, and David Cadman of Vancouver have, respectively, led the C40, World Mayors
Council on Climate Change and International Council for Local Environmental Initiatives (ICLEI). Scientists,
private sector actors and nongovernmental organizations have been of no lesser importance. To take advantage of a
broad-based interaction between various climate change actors, Mexico City has set up a Virtual Climate Change
Centre to serve as a repository of knowledge, models and data on climate change impacts, vulnerability and risks
(Romero-Lankao et al., 2013). Information sharing by climate change actors has also taken place in New York,
where scientists, and insurance and risk management experts have served on the Panel on Climate Change to advise
the city on the science of climate change impacts and protection levels specific to the city s critical infrastructure
(Solecki, 2012): 564).
The climate plans of the three cities are far reaching, including mitigation and adaptation strategies related to their
sustainability goals. The three cities emphasize different priorities in their climate action plans. Mexico City seeks to
reduce water and transportation emissions through such actions as improvements in infrastructure and changes in the
share of public transport. Vancouver has prioritized the separation of sanitary and stormwater systems, yet this
adaptation is not expected to be complete until 2050 (City of Vancouver, 2012). It will also take New York much
time, money and energy to expand adaptation strategies beyond the protection of water systems to include all
essential city infrastructure (Ray et al., 2013). Overall, few proposed actions will result in immediate effects, and
instead call for additional planning, highlighting the significant effort necessary for comprehensive responses.
Overall, adaptation planning in the three cities faces many challenges. In all three regions, multi-jurisdictional
governance structures with differing approaches to climate change challenge the ability for coordinated responses
(Solecki, 2012; Romero-Lankao et al., 2013). Conflicts in priorities and objectives between various actors and
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sectors are also prevalent (Burch, 2010). For instance, authorities in Mexico City concerned with avoiding growth
into risk-prone and conservation areas (Aguilar and Santos, 2011) compete for regulatory space within a policy
agenda that is already coping with a wide range of economic and developmental imperatives (Romero-Lankao et al.,
2013).
Climate responses require new types of localized scientific information, such as vulnerability analyses and flood risk
assessments, which are not always available (Romero-Lankao et al., 2012a; Ray et al., 2013). Little is known, for
instance, about how to predict and respond to common and differential levels of risk experienced by different human
settlements. Comprehensive planning is still limited as well. For example, although scholarship exists on disparities
in household- and population-level vulnerability and adaptive capacity (Villeneuve and Burnett, 2003; Cutter et al.,
2003; Douglas et al., 2012; Romero-Lankao et al., 2013b), equity concerns have received relatively less attention by
either of the three cities. Even when local needs are identified, such as the need to protect higher risk homeless and
low-income populations (Vancouver), they are often not addressed in action plans.
_____ END BOX 26-3 HERE _____
26.9. Federal and Subnational Level Adaptation
Along with many local governments (section 26.8.4), federal, and subnational tiers of government across North
America are developing climate change adaptation plans. These initiatives, which began at the subnational levels
(e.g., (Nunavut Department of Sustainable Development, 2003), appear to be preliminary and relatively little has
been done to implement specific measures.
26.9.1. Federal Level Adaptation
All three national governments are addressing adaptation to some extent, with a national strategy and a policy
framework (Mexico), a federal policy framework (Canada), and the United States having delegated all federal
agencies to develop adaptation plans.
In 2005, the Mexican government created the Inter-Secretarial Commission to Climate Change (CICC Comisión
Inter-Secretarial de Cambio Climático) to coordinate national public policy on climate change (Comisión Inter-
Secretarial de Cambio Climático, 2005; Sosa-Rodriguez, 2013).The government s initiatives are being delivered
through the National Strategy for Climate Change 2007-2012 (Intersecretarial Commission on Climate Change,
2007) and, the Special Programme on Climate Change 2009-2012, which identify priorities in research, cross-
sectoral action such as developing early warning systems, and capacity development to support mitigation and
adaptation actions (Comisión Inter-Secretarial de Cambio Climático, 2009). The Policy Framework for Medium
Term Adaptation (Consejo Intersecretarial de Cambio Climático, 2010) aims at framing a single national public
policy approach on adaptation with a time-horizon up to 2030. The General Law of Climate Change requires state
governments to implement mitigation and adaptation actions (Diario Oficial de la Federación, 2012).
Canada is creating a Federal Adaptation Policy Framework intended to mainstream climate risks and impacts into
programs and activities to help frame government priorities (Environment Canada, 2011a). In 2007, the federal
Government made a four-year adaptation commitment to develop six Regional Adaptation Collaboratives (RAC) in
provinces across Canada, ranging in size and scope, from flood protection and drought planning, to extreme
weather risk management; and assessing the vulnerability of Nunavut s mining sector to climate change (Natural
Resources Canada, 2011). In 2011, the federal government renewed financial support for several adaptation
programs and provided new funding to create a Climate Adaptation and Resilience Program for Aboriginals and
Northerners, and Enhancing Competitiveness in a Changing Climate program (Environment Canada, 2011b).
Canada recently launched an Adaptation Platform to advance adaptation priorities across the country (Natural
Resources Canada, 2013).
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The US government embarked in 2009 on a government-wide effort to have all federal agencies address adaptation;
to apply understanding of climate change to agency missions and operations; to develop, prioritize, and implement
actions; and to evaluate adaptations and learn from experience. (The White House, 2009; Bierbaum et al., 2012). A
2013 plan issued by the President enhanced the US government effort supporting adaptation (Executive Office of
the President, 2013). The US Government provides technical and information support for adaptation by non-federal
actors, but does not provide direct financial support for adaptation (Parris et al., 2010).
Some federal agencies took steps to address climate change adaptation prior to this broader interagency effort. In
2010, the US Department of Interior created Climate Science Centers to integrate climate change information and
management strategies in eight regions and 21 Landscape Conservation Cooperatives (Secretary of the Interior,
2010), while the US Environmental Protection Agency s Office of Water developed a climate change strategy (U.S.
Environmental Protection Agency, National Water Program, 2011).
26.9.2. Subnational Level Adaptation
A number of states and provinces in all three countries have developed adaptation plans. For example, in Canada,
Quebec's 2013-2020 adaptation strategy outlines 17 objections covering a number of managed sectors and
ecosystems (Government of Quebec, 2012). British Columbia is modernizing its Water Act to alter water allocation
during drought to reduce agricultural crop and livestock loss and community conflict, while protecting aquatic
ecosystems (British Columbia Ministry of the Environment, 2010).
In the US California was the first state to publish an adaptation plan calling for a 20% reduction in per capita water
use by 2020 (California Natural Resources Agency, 2009). Maryland first developed a plan on coastal resources and
then broadened it to cover human health, agriculture, ecosystems, water resources, and infrastructure (Maryland
Commission on Climate Change Adaptation and Response Working Group, 2008; Maryland Department of the
Environment on behalf of the Maryland Commission on Climate Change, 2010). The State of Washington is
addressing environment, infrastructure, and communities; human health and security; ecosystems, species, and
habitat; and natural resources (Washington State Built Environment: Infrastructure & Communities Topic Advisory
Group, 2011; Washington State Human Health and Security Topic Advisory Group, 2011; Washington State
Species, Habitats and Ecosystems Topic Advisory Group, 2011; Washington State Natural Resources Working
Lands and Waters Topic Advisory Group, 2011).
Of the three national governments, only Mexico requires that states develop adaptation plans. In Mexico, seven of
31 states, Veracruz, Mexico City, Nuevo León, Guanajuato, Puebla, Tabasco, and Chiapas, have all developed their
State Programmes for Climate Change Action (Programas Estatales de Acción ante el Cambio Climático - PEACC),
while Baja California Sur, Hidalgo, and Campeche are in the final stage and 17 states are still in the planning and
developing stage (Instituto de Ecología del Estado de Guanajuato, 2011). The proposed adaptation actions focus
mainly on: 1) reducing physical and social vulnerability of key sectors and populations; 2) conservation and
sustainable management of ecosystems, biodiversity, and ecosystem services; 3) developing risk management
strategies; 4) strengthening water management; 5) protecting human health, and; 6) improving current urban
development strategies, focusing on settlements and services, transport and land use planning.
26.9.3. Barriers to Adaptation
Chapter 16 provides a more in-depth discussion on adaptation barriers and limits. Adaptation plans tend to exist as
distinct documents and are often not integrated into other planning activities (Preston et al., 2011). Most adaptation
activities have only involved planning for climate change rather than specific actions, and few measures have been
implemented (Preston et al., 2011; Bierbaum et al., 2012).
Even though Canada and the US are relatively well-endowed in their capacity to adapt, there are significant
constraints on adaptation, with financing being a significant constraint in all three countries (Carmin et al., 2012).
Barriers include legal constraints (e.g., (Jantarasami et al., 2010) lack of coordination across different jurisdictions
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(Smith et al., 2009; National Research Council, 2010; Instituto Nacional de Ecología y Cambio Climático, 2012b),
leadership (Smith et al., 2009; Moser and Ekstrom, 2010), and divergent perceptions about climate change
(Bierbaum et al., 2012; Moser, 2013). Although obtaining accurate scientific data was ranked less important by
municipalities(Carmin et al., 2012), an important constraint is lack of access to scientific information and capacity to
manage and use it (Moser and Ekstrom, 2010; Instituto Nacional de Ecología y Cambio Climático, 2012b).
Adaptation activities in developed countries such as the US tend to address hazards and propose adaptations that
tend to protect current activities rather than facilitate long term change. In addition, the adaptation plans generally do
not attempt to increase adaptive capacity (Eakin and Patt, 2011). However, making changes to institutions needed to
enable or promote adaptations can be costly (Marshall, 2013).
Although multilevel and multisectoral coordination is a key component of effective adaptation, it is constrained by
factors such as mismatch between climate and development goals, political rivalry, and lack of national support to
regional and local efforts (Brklacich et al., 2008; Sander-Regier et al., 2009; Brown, 2009; Sydneysmith et al.,
2010), (Craft and Howlett, 2013; Romero-Lankao et al., 2013a). Traditionally, environmental or engineering
agencies are responsible for climate issues (e.g., Mexico City, Edmonton and London, Canada), but have neither the
decision making power nor the resources to address all dimensions involved. Adaptation planning requires long-
term investments by government, business, grassroots organizations and individuals (e.g., Romero-Lankao, 2007;
Croci et al., 2010; Sarah, 2010; Richardson, 2010).
26.9.4. Maladaptation, Trade-Offs, and Co-Benefits
Adaptation strategies may introduce trade-offs or maladaptive effects for policy goals in mitigation, industrial
development, energy security, and health (Hamin and Gurran, 2009; Laukkonen et al., 2009). Snow-making
equipment, for example, mediates snowpack reductions, but has high water and energy requirements (Scott et al.,
2007). Irrigation and air conditioning have immediate adaptive benefits for North American settlements, but are
energy-consumptive. Sea walls protect coastal properties, yet negatively affect coastal processes and ecosystems
(Richardson, 2010).
Conventional sectoral approaches to risk management and adaptation planning undertaken at different temporal and
spatial scales have exacerbated vulnerability in some cases e.g., peri-urban areas in Mexico (Eakin et al., 2010;
Romero-Lankao, 2012). Approaches that delegate response planning to residents in the absence of effective
knowledge exchange have resulted in maladaptive effects (Friesinger and Bernatchez, 2010).
Other strategies offer synergies and co-benefits. Policies addressing air pollution (Harlan and Ruddell, 2011) or
housing for the poor, particularly in Mexico (Colten et al., 2008), can often be adapted at low or no cost to fulfill
adaptation and sustainability goals (Badjek et al., 2010). Efforts to temper declines in production or competitiveness
in rural communities could involve mitigation innovations, including carbon sequestration forest plantations
(Holmes, 2010). Painting roofs white reduces the effects of heat and lowers energy demand for cooling (Akbari et
al., 2009).
Adaptation planning can be greatly enhanced by incorporating regionally or locally-specific vulnerability
information (Clark et al., 1998; Barsugli et al., 2012; Romsdahl et al., 2013). Methods for mapping vulnerability
have been improved and effectively utilized (Romero-Lankao et al., 2013b). Similarly, strategies supporting cultural
preservation and subsistence livelihood needs among Indigenous peoples would enhance adaptation (Ford et al.,
2010b), as would integrating traditional culture with other forms of knowledge, technologies, education and
economic development (Hardess et al., 2011).
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26.10. Key Risks, Uncertainties, Knowledge Gaps, and Research Needs
26.10.1. Key Multi-Sectoral Risks
We close this chapter with our assessment of key current and future regional risks from climate change with an
evaluation of the potential for risk reduction through adaptation (Table 26-1). Two of the three examples, wildfires
and urban floods, illustrate that multiple climate drivers can result in multiple impacts (e.g., loss of ecosystems
integrity, property damage and health impacts due to wildfires and urban floods). The three risks evaluated in Table
26-1 also show that relative risks depend on the context specific articulation and dynamics of such factors as
The magnitude and rate of change of relevant climatic and non-climatic drivers and hazards. For instance,
the risk of urban floods depends not only on global climatic conditions (current versus future global mean
temperatures of 2 C and 4 C), but also on urbanization, a regional source of hazard risk that can enhance or
reduce precipitation, as it affects the hydrologic cycle and, hence, has impacts on flood climatology
(section 26.8.2.1);
The internal properties and dynamics of the system being stressed. For example, some ecosystems are more
fire-adapted than others. Some populations are more vulnerable to heat-stress because of age, pre-existing
medical conditions, working conditions and lifestyles (e.g., outdoor workers, athletes);
Adaptation potentials and limits. For example, while residential air conditioning (A/C) can effectively
reduce health risk, availability and usage of A/C is often limited among the most vulnerable individuals.
Furthermore, A/C is sensitive to power failures and its use has mitigation implications.
[INSERT TABLE 26-1 HERE
Table 26-1: Key risks from climate change and the potential for risk reduction through adaptation. Key risks are
identified based on assessment of the literature and expert judgments made by authors of this chapter, with
supporting evaluation of evidence and agreement in the referenced chapter sections. Each key risk is characterized
as very low, low, medium, high, or very high. Risk levels are presented for the near-term era of committed climate
change (here, for 2030-2040), in which projected levels of global mean temperature increase do not diverge
substantially across emissions scenarios. Risk levels are also presented for the longer-term era of climate options
(here, for 2080-2100), for global mean temperature increase of 2°C and 4°C above preindustrial levels. For each
timeframe, risk levels are estimated for the current state of adaptation and for a hypothetical highly adapted state. As
the assessment considers potential impacts on different physical, biological, and human systems, risk levels should
not necessarily be used to evaluate relative risk across key risks. Relevant climate variables are indicated by
symbols.]
The judgments about risk conveyed by the Table 26-1 are based on assessment of the literature and expert judgment
by chapter authors living under current socio-economic conditions. Therefore, risk levels are estimated for each
timeframe, assuming a continuation of current adaptation potentials and constraints. Yet over the course of the 21st
century, socioeconomic and physical conditions can change considerably for many sectors, systems and places. The
dynamics of wealth generation and distribution, technological innovations, institutions, even culture, can
substantially affect North American levels of risk tolerance within the social and ecological systems considered in
the Table (see also Box TS.8).
26.10.2. Uncertainties, Knowledge Gaps, and Research Needs
The literature on climate impacts, adaptation and vulnerability in North America has grown considerably, as has the
diversity of sectors and topics covered (e.g., urban and rural settlements, food security, and adaptation at local, state
and national levels). However, limitations in the topical and geographical scope of this literature are still a challenge
(e.g., more studies have focused on insurance than on economic sectors such as industries, construction and
transportation ). It is also challenging to summarize results across many studies and identify trends in the literature
when there are differences in methodology, theoretical frameworks and causation narratives (e.g., between outcome
and contextual approaches) making it hard to compare apples to oranges (Romero-Lankao et al., 2012b) While the
US and Canada have produced large volumes of literature, Mexico lags well behind. It was, therefore, difficult to
devote equal space to observed and projected impacts, vulnerabilities and adaptations in Mexico in comparison with
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its Northern neighbors. With its large land area, population and important, albeit understudied, climate change risks
and vulnerabilities, more climate change research focusing on Mexico is direly needed.
The literature on North America tends to be dominated by sector level analyses. Yet, climate change interacts with
other physical and social processes to create differential risks and impact levels. These differences are mediated by
context-specific physical and social factors shaping the vulnerability of exposed systems and sectors. Furthermore,
while studies often focus on isolated sectorial effects, impacts happen in communities, socio-ecologic systems and
regions, and shocks and dislocations in one sector or region often affect other sectors and regions due to social and
physical interdependencies. This point is illustrated by our border region and wildfire boxes and the human
settlements section, which discuss place-based impacts, vulnerabilities and adaptations. Unfortunately, literature
using placed-based or integrated approaches to these complexities is limited. Indeed although in early drafts the
authors of this chapter attempted to put more emphasis on place-based analysis and comparisons, the literature was
inadequate to support such an effort. The IPCC includes chapters on continents and large regions to make it possible
to assess how multiple climate change impacts can affect these large areas. However, this macro view gives
insufficient detail on context specific local impacts and risks, missing the on-the-ground reality that the effects of
climate change are and will be experienced at much smaller scales, and those smaller scales are often where
meaningful mitigation and adaptation actions can be generated. In order to give local actors relevant information on
which to base these local actions, more research is needed to better understand the local and regional effects of
climate change across sectors.
Frequently Asked Questions
FAQ 26.1: What impact is climate having on North America? [to remain at the end of the chapter]
Recent climate changes and extreme events demonstrate clear impacts of climate-related stresses in North America
(high confidence). There has been increased occurrence of severe hot weather events over much of the US and
increases in heavy precipitation over much of North America (high confidence). Such events as droughts in northern
Mexico and south-central US, floods in Canada, and hurricanes such as Sandy, demonstrate exposure and
vulnerability to extreme climate (high confidence). Many urban and rural settlements, agricultural production, water
supplies, and human health have been observed to be vulnerable to these and other extreme weather events (Figure
26-2). Forest ecosystems have been stressed through wildfire activity, regional drought, high temperatures, and
infestations, while aquatic ecosystems are being affected by higher temperatures and sea level rise.
Many decision makers, particularly in the United States and Canada, have the financial, human and institutional
capacity to invest in resilience, yet a trend of rising losses from extremes has been evident across the continent
(Figure 26-2), largely due to socio-economic factors, including a growing population, equity issues and increased
property value in areas of high exposure. In addition, climate change is very likely to lead to more frequent extreme
heat events and daily precipitation extremes over most areas of North America, more frequent low snow years, and
shifts towards earlier snowmelt runoff over much of the western US and Canada (high confidence). These changes
combined with higher sea levels and associated storm surges, more intense droughts, and increased precipitation
variability are projected to lead to increased stresses to water, agriculture, economic activities and urban and rural
settlements (high confidence).
FAQ 26.2: Can adaptation reduce the adverse impacts of climate in North America?
[to remain at the end of the chapter]
Adaptation including land use planning, investments in infrastructure, emergency management, health programs,
and water conservation has significant capacity to reduce risks from current climate and climate change (Figure
26-3). There is increasing attention to adaptation among planners at all levels of government but particularly at the
municipal level, with many jurisdictions engaging in assessment and planning processes. Yet, there are few
documented examples of implementation of proactive adaptation and these are largely found in sectors with longer-
term decision-making, including energy and public infrastructure (high confidence). Adaptation efforts have
revealed the significant challenges and sources of resistance facing planners at both the planning and
implementation stages, particularly the adequacy of informational, institutional, financial and human resources, and
lack of political will (medium confidence).
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While there is high capacity to adapt to climate change across much of North America, there are regional and
sectoral disparities in economic resources, governance capacity, and access to and ability to utilize information on
climate change which limit adaptive capacity in many regions and among many populations such as the poor and
indigenous communities. For example, there is limited capacity for many species to adapt to climate change, even
with human intervention. At lower levels of temperature rise, adaptation has high potential to off-set projected
declines in yields for many crops, but this effectiveness is expected to be much lower at higher temperatures. The
risk that climate stresses will cause profound impacts on ecosystems and society including the possibility of
species extinction or severe adverse socio-economic shocks highlights limits to adaptation.
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Ziska, L., K. Knowlton, C. Rogers, D. Dalan, N. Tierney, M. Ann, W. Filley, J. Shropshire, L.B. Ford, C. Hedberg,
P. Fleetwood, K.T. Hovanky, and T. Kavanaugh, 2011: Recent warming by latitude associated with increased
length of ragweed pollen season in central North America. Proceedings of the National Academies of Science,
108(10), 4248-4251.
Ziska, L.H. and F.A. Caulfield, 2000: Rising CO2 and pollen production of common ragweed (Ambrosia
artemisiifolia), a known allergy-inducing species: implications for public health. Australian Journal of Plant
Physiology, 27, 893-898.
Ziska, L.H., D.E. Gebhard, D.a. Frenz, S. Faulkner, B.D. Singer, and J.G. Straka, 2003: Cities as harbingers of
climate change: Common ragweed, urbanization, and public health. Journal of Allergy and Clinical
Immunology, 111(2), 290-295.
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Table 26-1: Key risks from climate change and the potential for risk reduction through adaptation. Key risks are
identified based on assessment of the literature and expert judgments made by authors of this chapter, with
supporting evaluation of evidence and agreement in the referenced chapter sections. Each key risk is characterized
as very low, low, medium, high, or very high. Risk levels are presented for the near-term era of committed climate
change (here, for 2030-2040), in which projected levels of global mean temperature increase do not diverge
substantially across emissions scenarios. Risk levels are also presented for the longer-term era of climate options
(here, for 2080-2100), for global mean temperature increase of 2°C and 4°C above preindustrial levels. For each
timeframe, risk levels are estimated for the current state of adaptation and for a hypothetical highly adapted state. As
the assessment considers potential impacts on different physical, biological, and human systems, risk levels should
not necessarily be used to evaluate relative risk across key risks. Relevant climate variables are indicated by
symbols.
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Figure 26-1 Detection and attribution of climate change impacts. Comparisons of the adequacy of currently
available data to detect trends and the degree of understanding of causes of those changes in climatic extreme events
in the United States (left; Peterson et al., 2013) and degree of understanding of the climate influence in key impacts
in North America (right). Note that climate influence means that the impact has been documented to be sensitive
to climate, not that it has been attributed to climate change. Filled boxes indicate that formal detection and
attribution to climate change has been performed for the given impact; shaded boxes indicate that a trend has been
detected from background variability in the given impact, but formal attribution to climate change has not occurred
and the trend could be due to other drivers; and open boxes indicate that a trend has not currently been detected. Key
impacts are: 1) earlier peak flow of snowmelt run-off in snow-dominated streams and rivers in western North
America [26.3.1], 2) declines in the amount of water stored in spring snowpack in snow-dominated areas of western
North America [26.3.1], 3) northward and upward shifts in species distributions in multiple taxa of terrestrial
species, although not all taxa and regions [26.4.1], 4) increases in coastal flooding [26.8.1], 5) increases in wildfire
activity, including fire season length and area burned by wildfires in the western United States and boreal Canada
[Box 26-2], 6) storm-related disaster losses in the United States (most of the increase in insurance claims paid has
been attributed to increasing exposure of people and assets in areas of risk) [26.7.6.1, 26.8.1], 7) increases in bark
beetle infestation levels in pine tree species in western North America [26.4.2.1], 8) yield increases due in part to
increasing temperatures in Canada and higher precipitation in the US; yield variances attributed to climate
variability in Ontario and Quebec; yield losses attributed to climate-related extremes across North America [26.5.1],
9) changes in storm-related mortality in the United States [26.6.1.2], 10) changes in heat-related mortality in the
United States [26.6.1.2], 11) increases in tree mortality rates in old-growth forests in the western United States and
western Canada from 1960-2007 [26.4.2.1], 12) changes in flooding in some urban areas due to extreme rainfall
[26.3.1, 26.8.2.1], 13) increase in water supply shortages due to drought [26.8.1, 26.3], and 14) changes in cold-
related heat mortality [26.6.1.2]. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 26-2: Extreme events illustrating vulnerabilities for Mexico, the United States, and Canada. This figure offers
a graphic illustration of location of extreme events and relevant vulnerability trends. The observed extreme events
have not been attributed to anthropogenic climate change, yet they are climate-sensitive sources of impact
illustrating vulnerability of exposed systems, particularly if projected future increases in the frequency and/or
intensity of such events should materialize. The figure includes:
a) A map (bottom) with population density at 1km resolution highlighting exposure and represented using
2011 Landscan data (Bright et al., 2012).
b) A map (top) with significant weather events taking place during 1993-2012. The map only includes
disasters with overall losses of more than $1 billion US dollars in US, or more than $500 million US dollars
in Mexico and Canada, adjusted to 2012 values (Source: (NatCatSERVICE, 2010). Hence, it does not
include the occurrence of disasters of small and medium impact, and it does not capture the impacts of
disasters on populations livelihoods and wellbeing. Disasters represented by points that are located at the
approximate geographic center of affected regions, frequently span more than one subnational jurisdiction
(e.g., the 2012 drought affected 12 Mexican states, Annex Table).
c) Four panels (right) with trends in socio-demographic indicators used in the literature to measure
vulnerability to hazards (Romero-Lankao et al., 2012): poverty rates, percentage of elderly, GDP per capita
and total population (Sources: Comisión Económica para América Latina y el Caribe; U.S. Census Bureau,
2011; Statistics Canada, 2012).
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Figure 26-3: Observed and projected Changes in annual temperature and precipitation. (Top panel, left) observed
temperature trends from 1901-2012 determined by linear regression [WGI AR5 Figures SPM.1 and 2.21]. (Bottom
panel, left) Observed precipitation change from 1951-2010 determined by linear regression. [WGI AR5 Figure
SPM.2] For observed temperature and precipitation, trends have been calculated where sufficient data permits a
robust estimate (i.e., only for grid boxes with greater than 70% complete records and more than 20% data
availability in the first and last 10% of the time period). Other areas are white. Solid colors indicate areas where
change is significant at the 10% level. Diagonal lines indicate areas where change is not significant (Top and
bottom panel, right) CMIP5 multi-model mean projections of annual average temperature changes and average
percent change in annual mean precipitation for 2046-2065 and 2081-2100 under RCP2.6 and 8.5. Solid colors
indicate areas with very strong agreement, where the multi-model mean change is greater than twice the baseline
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variability, and >90% of models agree on sign of change. Colors with white dots indicate areas with strong
agreement, where >66% of models show change greater than the baseline variability and >66% of models agree on
sign of change. Gray indicates areas with divergent changes, where >66% of models show change greater than the
baseline variability, but <66% agree on sign of change. Colors with diagonal lines indicate areas with little or no
change, less than the baseline variability in >66% of models. (There may be significant change at shorter timescales
such as seasons, months, or days.). Analysis uses model data and methods building from WGI AR5 Figure SPM.8.
See also Annex I of WGI AR5. [Boxes 21-3 and CC-RC]
Figure 26-4: Projected changes in extremes in North America. (a) The percentage of years in the 2046 2065 period
of RCP8.5 in which the summer temperature is greater than the respective maximum summer temperature of the
1986 2005 baseline period (Diffenbaugh and Giorgi, 2012). (b) The percentage of years in the 2080-2099 period of
RCP8.5 in which the summer precipitation is less than the respective minimum summer precipitation of the 1986-
2005 baseline period (Diffenbaugh and Giorgi, 2012) (c) The percentage difference in the 20-year return value of
annual precipitation extremes between the 2046-2065 period of RCP4.5 and the 1986-2005 baseline period (from
(Kharin et al., 2013). The hatching indicates areas where the differences are not significant at the 5% level. (d) The
percentage of years in the 2070-2099 period of RCP8.5 in which the March snow water equivalent is less than the
respective minimum March snow water equivalent of the 1976 2005 period (Diffenbaugh et al., 2012). The black
(white) stippling indicate areas where the multimodel mean exceeds 1.0 (2.0) standard deviations of the multi-model
spread. (a-d) The RCPs and time periods are those used in the peer-reviewed studies in which the panels appear. The
2046-2065 period of RCP8.5 and the 2046-2065 period of RCP4.5 exhibit global warming in the range of 2-3C
above the pre-industrial baseline (WGI Fig. 12.40). The 2080-2099 and 2070-2099 periods of RCP8.5 exhibit global
warming in the range of 4-5C above the pre-industrial baseline (WGI Fig. 12.40).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Chapter 27. Central and South America
Coordinating Lead Authors
Graciela Magrin (Argentina), José Marengo (Brazil)
Lead Authors
Jean-Phillipe Boulanger (France), Marcos S. Buckeridge (Brazil), Edwin Castellanos (Guatemala), Germán Poveda
(Colombia), Fabio R. Scarano (Brazil), Sebastián Vicuna (Chile)
Contributing Authors
Eric Alfaro (Costa Rica), Fabien Anthelme (France), Jonathan Barton (UK), Nina Becker (Germany), Arnaud
Bertrand (France), Ulisses Confalonieri (Brazil), Carlos Demiguel (Spain), Bernard Francou (France), Rene
Garreaud (Chile), Inigo Losada (Spain), Melanie McField (USA), Carlos Nobre (Brazil), Patricia Romero Lankao
(Mexico), Paulo Saldiva (Brazil), Jose Luis Samaniego (Mexico), Amanda Pereira de Souza (Brazil), María
Travasso (Argentina), Ernesto Viglizzo (Argentina), Alicia Villamizar (Venezuela)
Review Editors
Leonidas Osvaldo Girardin (Argentina), Jean Pierre Ometto (Brazil)
Volunteer Chapter Scientist
Nina Becker (Germany)
Contents
Executive Summary
27.1. Introduction
27.1.1. The Central and South America Region
27.1.2. Summary of the AR4 and SREX Findings
27.1.2.1. AR4 Findings
27.1.2.2. SREX Findings
27.2. Major Recent Changes and Projections in the Region
27.2.1. Climatic Stressors
27.2.1.1. Climate Trends, Long-term Changes in Variability, and Extremes
27.2.1.2. Climate Projections
27.2.2. Non-Climatic Stressors
27.2.2.1. Trends and Projections in Land Use and Land Use Change
27.2.2.2. Trends and Projections in Socioeconomic Conditions
27.3. Impacts, Vulnerabilities and Adaptation Practices
27.3.1. Freshwater Resources
27.3.1.1. Observed and Projected Impacts and Vulnerabilities
27.3.1.2. Adaptation Practices
27.3.2. Terrestrial and Inland Water Systems
27.3.2.1. Observed and Projected Impacts and Vulnerabilities
27.3.2.2. Adaptation Practices
27.3.3. Coastal Systems and Low-Lying Areas
27.3.3.1. Observed and Projected Impacts and Vulnerabilities
27.3.3.2. Adaptation Practices
27.3.4. Food Production Systems and Food Security
27.3.4.1. Observed and Projected Impacts and Vulnerabilities
27.3.4.2. Adaptation Practices
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27.3.5. Human Settlements, Industry, and Infrastructure
27.3.5.1. Observed and Projected Impacts and Vulnerabilities
27.3.5.2. Adaptation Practices
27.3.6. Renewable Energy
27.3.6.1. Observed and Projected Impacts and Vulnerabilities
27.3.6.2. Adaptation Practices
27.3.7. Human Health
27.3.7.1. Observed and Projected Impacts and Vulnerability
27.3.7.2. Adaptation Strategies and Practices
27.4. Adaptation Opportunities, Constraints, and Limits
27.4.1. Adaptation Needs and Gaps
27.4.2. Practical Experiences of Autonomous and Planned Adaptation, including Lessons Learned
27.4.3. Observed and Expected Barriers to Adaptation
27.5. Interactions between Adaptation and Mitigation
27.6. Case Studies
27.6.1. Hydropower
27.6.2. Payment for Ecosystem Services
27.7. Data and Research Gaps
27.8. Conclusions
References
Chapter Boxes
27-1. Extreme Events, Climate Change Perceptions, and Adaptive Capacity in Central America
27-2. Vulnerability of South American Megacities to Climate Change: The Case of the Metropolitan Region of
Sao Paulo (MRSP)
Frequently Asked Questions
27.1: What is the impact of glacier retreat on natural and human systems in the tropical Andes?
27.2: Can PES be used as an effective way for helping local communities to adapt to climate change?
27.3: Are there emerging and re emerging human diseases as a consequence of climate variability and change in
the region?
Executive Summary
Significant trends in precipitation and temperature have been observed in Central America (CA) and South
America (SA) (high confidence). Besides, changes in climate variability and in extreme events have severely
affected the region (medium confidence). Increasing trends in annual rainfall in Southeastern South America
(SESA; 0.6 mm/day/50years during 1950-2008) contrast with decreasing trends in CA and Central-Southern Chile
(-1mm/day /50 years during 1950-2008). Warming has been detected throughout CA and SA (near to 0.7-1°C/40
years since the mid-1970 s), except for a cooling off the Chilean coast of about -1 C°/40 years. Increases in
temperature extremes have been identified in CA and most of tropical and subtropical SA (medium confidence),
while more frequent extreme rainfall in SESA has favoured the occurrence of landslides and flash floods (medium
confidence). (27.2.1.1, Table 27-1, Box 27-1)
Climate projections suggest increases in temperature, and increases or decreases in precipitation for CA and
SA by 2100 (medium confidence). Post-AR4 climate projections, derived from dynamic downscaling forced by
CMIP3 models for various SRES scenarios, and to different global climate models from the CMIP5 for various
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RCPs (4.5 and 8.5), warming varies from +1.6°C to +4.0°C in CA, and +1.7°C to +6.7°C in SA (medium
confidence). Rainfall changes for CA range between -22% to +7% by 2100, while in SA rainfall varies
geographically, most notably showing a reduction of -22% in Northeast Brazil, and an increase of +25% in SESA
(low confidence). By 2100 projections show an increase in dry spells in tropical SA east of the Andes, and in warm
days and nights in most of SA (medium confidence) (27.2.1.2, Table 27-2).
Changes in stream flow and water availability have been observed and projected to continue in the future in
CA and SA, affecting already vulnerable regions (high confidence). The Andean cryosphere is retreating
affecting the seasonal distribution of streamflows (Table 27-3) (high confidence). Increasing runoffs in the La Plata
River basin and decreasing ones in the Central Andes (Chile, Argentina) and in CA in the second half of the 20th
century were associated with changes in precipitation (high confidence). Risk of water supply shortages will
increase owing to precipitation reductions and evapotranspiration increases in semi-arid regions (high confidence)
(Table 27-4), thus affecting water supply for cities (high confidence) (27.3.1.1; 27.3.5), hydropower generation (high
confidence) (27.3.6; 27.6.1) and agriculture (27.3.1.1). Current practices to reduce the mismatch between water
supply and demand could be used to reduce future vulnerability (medium confidence). Ongoing constitutional and
legal reforms towards more efficient and effective water resources management and coordination constitute another
adaptation strategy (medium confidence) (27.3.1.2).
Land use change contributes significantly to environmental degradation exacerbating the negative impacts of
climate change (high confidence). Deforestation and land degradation are mainly attributed to increased extensive
and intensive agriculture. The agricultural expansion, in some regions associated with increases in precipitation, has
affected fragile ecosystems, such as the edges of the Amazon forest and the tropical Andes. Even though
deforestation rates in the Amazon have decreased substantially since 2004 to a value of 4,656 km2/yr in 2012, other
regions like the Cerrado still present high levels of deforestation with average rates as high as 14,179 km2/yr for the
period 2002-2008 (27.2.2.1).
Conversion of natural ecosystems is the main cause of biodiversity and ecosystem loss in the region, and is a
driver of anthropogenic CC (high confidence). CC is expected to increase the rates of species extinction
(medium confidence). For instance, vertebrate species turnover until 2100 will be as high as 90% in specific areas
of CA and the Andes Mountains. In Brazil, distribution of some groups of birds and plants will be dislocated
southwards, where there are less natural habitats remaining. However, CA and SA have still large extensions of
natural vegetation cover for which the Amazon is the main example (27.3.2.1). Ecosystem-based adaptation
practices are increasingly common across the region, such as the effective management and establishment of
protected areas, conservation agreements and community management of natural areas (27.3.2.2).
Socioeconomic conditions have improved since AR4; however there is still a high and persistent level of
poverty in most countries resulting in high vulnerability and increasing risk to climate variability and change
(high confidence). Poverty levels in most countries remain high (45% for CA and 30% for SA for year 2010) in
spite of the sustained economic growth observed in the last decade. Human Development Index varies greatly
between countries, from Chile and Argentina with the highest values, and Guatemala and Nicaragua with the lowest
values in 2007. The economic inequality translates into inequality in access to water, sanitation and adequate
housing, particularly for the most vulnerable groups translating into low adaptive capacities to climate change
(27.2.2.2).
Sea-level rise (SLR) and human activities on coastal and marine ecosystems pose threats to fish stocks, corals,
mangroves, recreation and tourism, and control of diseases (high confidence). SLR varied from 2 to 7 mm/yr
between 1950 and 2008. Frequent coral bleaching events associated to ocean warming and acidification occur in the
Mesoamerican Coral Reef. In CA and SA, the main drivers of mangrove loss are deforestation and land conversion
to agriculture and shrimp ponds (27.3.3.1). Brazilian fisheries co-management (a participatory multi-stakeholder
process) is an example of adaptation since it favours a balance between conservation of marine biodiversity, the
improvement of livelihoods, and the cultural survival of traditional populations (27.3.3.2).
Changes in agricultural productivity with consequences for food security associated to CC are expected to
exhibit large spatial variability (medium confidence). In SESA, where projections indicate more rainfall, average
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productivity could be sustained or increased until the mid-century (SRES: A2, B2) (Table 27-5) (medium
confidence). In CA, northeast of Brazil and parts of the Andean region increases in temperature and decreases in
rainfall could decrease the productivity in the short-term (by 2030), threatening the food security of the poorest
population (Table 27-5) (medium confidence). Considering that SA will be a key food producing region in the
future, one of the challenges will be to increase the food and bioenergy quality and production while maintaining
environmental sustainability under CC (27.3.4.1). Some adaptation measures include crop, risk, and water use
management along with genetic improvement (27.3.4.2) (high confidence).
Renewable energy (RE) based on biomass has a potential impact on land use change and deforestation and
could be affected by CC (medium confidence). Sugarcane and soy are likely to respond positively to CO2 and
temperature changes, even with a decrease in water availability, with an increase in productivity and production
(high confidence). The expansion of sugarcane, soy and oil palm may have some effect on land use, leading to
deforestation in parts of Amazon, CA among other regions, and loss of employment in some countries (medium
confidence) (27.3.6.1). Advances in second-generation bioethanol from sugarcane and other feedstocks will be
important as a measure of mitigation (27.3.6.2).
Changes in weather and climatic patterns are negatively affecting human health in CA and SA, either by
increasing morbidity, mortality, and disabilities (high confidence), and through the emergence of diseases in
previously non-endemic areas (high confidence). With very high confidence climate-related drivers are associated
with respiratory and cardiovascular diseases, vector- and water-borne diseases (malaria, dengue, yellow fever,
leishmaniasis, cholera, and other diarrheal diseases), Hantaviruses and Rotaviruses, chronic kidney diseases, and
psychological trauma. Air pollution is associated with pregnancy-related outcomes and diabetes, among others
(27.3.7.1). Vulnerabilities vary with geography, age, gender, race, ethnicity, and socio-economic status, and are
rising in large cities (27.3.7.2) (very high confidence). Climate change will exacerbate current and future risks to
health, given the region s population growth rates and vulnerabilities in existing health, water, sanitation and waste
collection systems, nutrition, pollution and food production in poor regions (medium confidence).
In many CA and SA countries, a first step toward adaptation to future climate changes is to reduce the
vulnerability to present climate. Long-term planning and the related human and financial resource needs may be
seen as conflicting with present social deficit in the welfare of the CA and SA population. Various examples
demonstrate possible synergies between development, adaptation and mitigation planning, which can help local
communities and governments to allocate efficiently available resources in the design of strategies to reduce
vulnerability. However, the generalization of such actions at continental scale requires that both the CA and SA
citizens and governments are faced with the challenge of building a new governance model, where imperative
development needs, vulnerability reduction and adaptation strategies to climate stresses will be truly intertwined.
(27.3.4, 27.4.1, 27.4.2, 27.4.3, 27.4.4, 27.5).
27.1. Introduction
27.1.1. The Central and South America Region
The CA and SA region harbors unique ecosystems, the highest biodiversity in the planet and has a variety of eco-
climatic gradients. Unfortunately, this natural wealth is threatened by advancing agricultural frontiers resulting from
a rapidly growing agricultural and cattle production (Grau and Aide, 2008). The region experienced a steady
economic growth in the last decade, accelerated urbanization and important demographic changes; poverty and
inequality are decreasing continuously, but at a low pace (ECLAC, 2011c). Adaptive capacity is improving in part
thanks to poverty alleviation and development initiatives (McGray et al., 2007).
The region has multiple stressors on natural and human systems derived in part from significant land use changes
and exacerbated by climate variability/climate change. Climate variability at various time scales has been affecting
social and natural systems, and extremes in particular have affected large regions. During 2000-2010, almost 630
weather and climate extreme events occurred in CA and SA, leaving near to 16,000 fatalities and 46.6 million
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people affected; and generating economical losses amounting to US$ 208 million (CRED, 2011). Land is facing
increasing pressure from competing uses like cattle ranching, food production and bioenergy.
The region is regarded as playing a key role in future world economy because countries like Brazil, Chile, Colombia
and Panama, among others, are rapidly developing and becoming economically important in the world scenario. The
region is bound to be exposed to the pressure related to increasing land use and industrialization. Therefore, it is
expected to have to deal with increasing emission potentials. Thus, science-based decision-making is thought to be
an important tool to control innovation and development of the countries in the region.
Two other important contrasting features characterize the region: having the biggest tropical forest of the planet on
the one side, and possessing the largest potential for agricultural expansion and development during the next decades
on the other. This is the case because the large countries of SA, especially, would have a major role in food and
bioenergy production in the future, as long as policies towards adaptation to global climate change (GCC) will be
strategically designed. The region is already one of the top producers and user of bioenergy and this experience will
serve as an example to other developing regions as well as developed regions.
27.1.2. Summary of the AR4 and SREX Findings
27.1.2.1. AR4 Findings
According to AR4-Chapter 13 (Latin America), during the last decades of the 20th century, unusual extreme weather
events have been severely affecting the LA region contributing greatly to the strengthening of the vulnerability of
human systems to natural disasters. In addition, increases in precipitation were observed in SESA, northwest Peru
and Ecuador; while decreases were registered in southern Chile, southwest Argentina, southern Peru and western
CA since 1960. Mean warming was near to 0.1C/decade. The rate of SLR has accelerated over the last 20 years
reaching 2-3mm/year. The glacier-retreat trend has intensified, reaching critical conditions in the Andean countries.
Rates of deforestation have been continuously increasing mainly due to agricultural expansion, and land degradation
has been intensified for the entire region.
Mean warming for LA at the end of 21st century could reach 1C to 4C (SRES B2) or 2C to 6C (SRES A2)
(medium confidence) (AR4-Ch13-pp583). Rainfall anomalies (positive or negative) will be larger for the tropical
part of LA. The frequency and intensity of weather and climate extremes is likely to increase (medium confidence).
Future impacts include: Significant species extinctions, mainly in tropical LA (high confidence). Replacement of
tropical forest by savannas, and semi-arid vegetation by arid vegetation (medium confidence). Increases in the
number of people experiencing water stress (medium confidence). Probable reductions in rice yields and possible
increases of soy yield in SESA (AR4-Ch13-pp583); and increases in crop pests and diseases (medium confidence)
(AR4-Ch13-pp607). Some coastal areas affected by sea level rise, weather and climatic variability and extremes
(high confidence) (AR4-Ch13-pp584).
Some countries have made efforts to adapt to climate change and variability, for example through the conservation
of key ecosystems (e.g. biological corridors in Mesoamerica, Amazonia, and Atlantic forest; compensation for
ecosystem services in Costa Rica,), the use of early warning systems and climate forecast (e.g. fisheries in eastern
Pacific, subsistence agriculture in NE Brazil), and the implementation of disease surveillance systems (e.g.
Colombia) (AR4-Ch13-pp 591). However, several constraints like the lack of basic information, observation and
monitoring systems; the lack of capacity-building and appropriate political, institutional and technological
frameworks; low income; and settlements in vulnerable areas, outweigh the effectiveness of these efforts.
27.1.2.2. SREX Findings
As reported on Chapter 3.4 of the IPCC Special Report on Managing the Risks of Extreme Events and Disasters to
Advance Climate Change Adaptation (SREX) (IPCC, 2012b), a changing climate leads to changes in the frequency,
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intensity, spatial extent or duration of weather and climate extremes, and can result in unprecedented extremes.
Levels of confidence in historical changes depend on the availability of high quality and homogeneous data, and
relevant model projections. This has been a major problem in CA and SA, where a lack of long-term homogeneous
and continuous climate and hydrological records, and of complete studies on trends have not allowed for an
identification of trends in extremes, particularly in CA. Recent observational studies and projections from global and
regional models suggest changes in extremes. With medium confidence, increases in warm days and decreases in
cold days, as well as increases on warm nights and decreases in cold nights have been identified in CA, Northern
SA, NEB, SESA and west coast of SA. In CA, there is low confidence that any observed long-term increase in
tropical cyclone activity is robust, after accounting for past changes in observing capabilities. In other regions, such
as the Amazon region, insufficient evidence, inconsistencies among studies and detected trends result in low
confidence of observed rainfall trends. While it is likely that there has been an anthropogenic influence on extreme
temperature in the region, there is low confidence in attribution of changes in tropical cyclone activity to
anthropogenic influences.
Projections for the end of the 21st century for differing emissions scenarios (SRES A2 and A1B) show that for all
CA and SA, models project substantial warming in temperature extremes. It is likely that increases in the frequency
and magnitude of warm daily temperature extremes and decreases in cold extremes will occur in the 21st century on
the global scale. With medium confidence, it is very likely that the length, frequency and/or intensity of heat waves
will experience a large increase over most of SA, with weaker tendency towards increasing in SESA. With low
confidence, the models also project an increase of the proportion of total rainfall from heavy falls for SESA and the
West coast of SA; while for Amazonia and the rest of SA and CA there are not consistent signals of change. In some
regions, there is low confidence in projections of changes in fluvial floods. Confidence is low due to limited
evidence and because the causes of regional changes are complex. There is medium confidence that droughts will
intensify along the 21st century in some seasons and areas due to reduced precipitation and/or increased
evapotranspiration in Amazonia and NEB.
The character and severity of the impacts from climate extremes depend not only on the extremes themselves but
also on exposure and vulnerability. These are influenced by a wide range of factors, including anthropogenic climate
change, natural climate variability, and socioeconomic development.
27.2. Major Recent Changes and Projections in the Region
27.2.1. Climatic Stressors
27.2.1.1. Climate Trends, Long-term Changes in Variability, and Extremes
In CA and SA, decadal variability and changes in extremes have been affecting large sectors of the population,
especially those more vulnerable and exposed to climate hazards. Observed changes in some regions have been
attributed to natural climate variability, while in other regions they have been attributed to land use change, e.g.
increase urbanization, meaning that land use change is a result of anthropogenic drivers. Table 27-1 summarizes
observed trends in the region s climate.
[INSERT TABLE 27-1 HERE
Table 27-1: Regional observed changes in temperature, precipitation and climate extremes in various sectors of CA
and SA. Additional information on changes in observed extremes can be found in the IPCC SREX (Seneviratne et
al., 2012) and Chapter 2 IPCC WGI AR5 [2.4, 2.5, 2.6]]
Since around 1950, in CA and the North American Monsoon System (NAMS), rainfall has been starting
increasingly later and has become more irregular in space and time, while rainfall has been increasing and the
intensity of rainfall has been increasing during the onset season (see references in Table 27-1). Arias et al. (2012)
relate those changes to decadal rainfall variations in NAMS.
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The West coast of SA experienced a prominent but localized coastal cooling of about 1 C during the past 30-50
years extending from central Peru down to central Chile. This occurs in connection with an increased upwelling of
coastal waters favored by the more intense trade winds (Falvey and Garreaud, 2009; Gutiérrez et al., 2011a;
Gutiérrez et al., 2011b; Kosaka and Xie, 2013; Narayan et al., 2010; Schulz et al., 2012). In the extremely arid
northern coast of Chile, rainfall, temperature and cloudiness show strong interannual and decadal variability, and
since the mid-70s, the minimum daily temperature, cloudiness and precipitation have decreased. In central Chile, a
negative precipitation trend was observed over the period 1935-1976, and an increase after 1976, while further
south, the negative trend in rainfall that prevailed since the 1950s has intensified by the end of the 20th century
(Quintana and Aceituno, 2012).
To the east of the Andes, northeast Brazil (NEB) exhibits large interannual rainfall variability, with a slight decrease
since the 1970s (Marengo et al. 2013a). Droughts in this region (e.g. 1983, 1987, 1998) have been associated with El
Nino and/or a warmer Tropical North Atlantic Ocean. However, not all El Nino years result on drought in NEB, as
the drought 2012-2013 occurred during La Nina (Marengo et al., 2013a).
In the La Plata Basin in SESA, various studies have documented interannual and decadal scale circulation changes
that have led to decreases in the frequency of cold nights in austral summer, as well as to increases in warm nights
and minimum temperatures during the last 40 years. Simultaneously, a reduction in the number of dry months in the
warm season is found since the mid-1970s, while heavy rain frequency is increasing in SESA (references in Table
27-1). In SESA, increases in precipitation are responsible for changes in soil moisture (Collini et al., 2008; Saulo et
al., 2010), and although feedback mechanisms are present at all scales, the effect on atmospheric circulation is
detected at large scales. Moreover, land use change studies in the Brazilian southern Amazonia for the last decades
showed that the impact on the hydrological response is time lagged at larger scales (Rodriguez et al., 2010)
In the central Andes, in the Mantaro Valley (Peru), precipitation shows a strong negative trend, while warming is
also detected (SENAMHI, 2007). In the southern Andes of Peru air temperatures have increased during 1964-2006,
but no clear signal on precipitation changes has been detected (Marengo et al., 2009a). In the northern Andes
(Colombia, Ecuador), changes in temperature and rainfall in 1961-90 have been identified by Villacís (2008). In the
Patagonia region, Masiokas et al. (2008) have identified an increase of temperature together with precipitation
reductions during 1912-2002. Vuille et al. (2008a) found that climate in the tropical Andes has changed significantly
over the past 50 60 years. Temperature in the Andes has increased by approximately 0.1 °C/ decade, with only two
of the last 20 years being below the 1961 90 average. Precipitation has slightly increased in the second half of the
20th century in the inner tropics and decreased in the outer tropics. The general pattern of moistening in the inner
tropics and drying in the subtropical Andes is dynamically consistent with observed changes in the large-scale
circulation, suggesting a strengthening of the tropical atmospheric circulation. Moreover, a positive significant trend
in mean temperature of 0.09 oC per decade during 1965-2007 has been detected over the Peruvian Andes by Lavado
et al. (2012).
For the Amazon basin, Marengo (2004) and Satyamurty et al. (2010) concluded that no systematic unidirectional
long-term trends towards drier or wetter conditions in both the northern and southern Amazon have been identified
since the 1920s. Rainfall fluctuations are more characterized by inter-annual scales linked to ENSO or decadal
variability. Analyzing a narrower time period, Espinoza et al. (2009a; 2009b) found that mean rainfall in the
Amazon basin for 1964 2003 has decreased, with stronger amplitude after 1982, especially in the Peruvian western
Amazonia (Lavado et al., 2012), consistent with reductions in convection and cloudiness in the same region (Arias
et al., 2011). Recent studies by Donat et al. (2013) suggest that heavy rains are increasing in frequency in
Amazonia. Regarding seasonal extremes in the Amazon region, two major droughts and three floods have affected
the region from 2005 to 2012, although these events have been related to natural climate variability rather than to
deforestation (Espinoza et al., 2013; Espinoza et al., 2011; Espinoza et al., 2012; Lewis et al., 2011; Marengo et al.,
2008; Marengo et al., 2012; Marengo et al., 2013a; Satyamurty et al., 2013). On the impacts of land use changes on
changes in the climate and hydrology of Amazonia, Zhang et al. (2009) suggest that biomass-burning aerosols can
work against the seasonal monsoon circulation transition, thus re-inforce the dry season rainfall pattern for Southern
Amazonia, while Wang et al. (2011) suggests the importance of deforestation and vegetation dynamics on decadal
variability of rainfall in the region. Costa and Pires (2010) have suggested a possible decrease in precipitation due to
soybean expansion in Amazonia, mainly as a consequence of its very high albedo. In the SAMS region, positive
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trends in rainfall extremes have been identified in the last 30 years with a pattern of increasing frequency and
intensity of heavy rainfall events, and earlier onsets and late demise of the rainy season (see Table 27-1).
______START BOX 27-1 HERE______
Box 27-1. Extreme Events, Climate Change Perceptions, and Adaptive Capacity in Central America
Central America has traditionally been characterized as a region with high exposure to geo-climatic hazards derived
from its location and topography and with high vulnerability of its human settlements (ECLAC, 2010c). It has also
been identified as the most responsive tropical region to climate change (Giorgi, 2006). Evidence for this has been
accumulating particularly in the last 30 years with a steady increase in extreme events including storms, floods and
droughts. In the period 2000-2009, 39 hurricanes occurred in the Caribbean basin compared to 15 and 9 in the
decade of 1980 and 1990 respectively (UNEP-ECLAC, 2010). The impacts of these events on the population and the
economy of the region have been tremendous: the economic loss derived from 11 recent hydrometereological events
evaluated added to US $13,642 millions and the number of people impacted peaked with hurricane Mitch in 1998
with over 600,000 persons affected (ECLAC, 2010c). A high percentage of the population in CA live on or near
highly unstable steep terrain with sandy, volcanic soils prone to mudslides which are the main cause of casualties
and destruction (Restrepo and Alvarez, 2006).
The increased climatic variability in the past decade certainly changed the perception of people in the region with
respect to climate change. In a survey to small farmers in 2003, Tucker et al. (2010) found that only 25% of
respondents included climate events as a major concern. A subsequent survey in 2007 (Eakin et al., 2013) found that
over 50% of respondents cited drought conditions and torrential rains as their greatest concern. Interestingly, there
was no consensus on the direction in climate change pattern: the majority or households in Honduras reported an
increase in the frequency of droughts but in Costa Rica and Guatemala a decrease or no trend at all was reported; a
similar discrepancy in answers was reported with the issue of increase rainfall. But there was general agreement in
all countries that rainfall patterns were more variable resulting in higher difficulty in recognizing the start of the
rainy season.
The high levels of risk to disasters in CA are the result of high exposure to hazards and the high vulnerability of the
population and its livelihoods derived from elevated levels of poverty and social exclusion (Programa Estado de la
Nación-Región, 2011). Disaster management in the region has focused on improving early warning systems and
emergency response for specific extreme events (Saldana-Zorrilla, 2008) but little attention has been paid to
strengthening existing social capital in the form of local organizations and cooperatives. These associations can be
central in increasing adaptive capacity through increased access to financial instruments and strategic information on
global markets and climate (Eakin et al., 2011). There is a need to increase the communication of the knowledge
from local communities involved in processes of autonomous adaptation to policy makers responsible for
strengthening the adaptive capacities in CA (Castellanos et al., 2013).
______END BOX 27-1 HERE______
27.2.1.2. Climate Projections
Since the AR4, substantial additional regional analysis has been carried out using the CMIP3 model ensemble. In
addition, projections from CMIP5 models and new experiences using regional models (downscaling) have allowed
for a better description of future changes in climate and extremes in CA and SA. Using CMIP3 and CMIP5 models,
Giorgi (2006), Diffenbaugh et al. (2008), Xu et al. (2009), Diffenbaugh and Giorgi (2012) and Jones and Carvalho
(2013) have identified areas of CA/western North America and the Amazon as persistent regional climate change
hotspots throughout the 21st century of the RCP8.5 and RCP4.5. Table 27-2 summarizes projected climatic changes
derived from global and regional models for the region, indicating the projected change, models, emission scenarios,
time spans and references.
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[INSERT TABLE 27-2 HERE
Table 27-2: Regional projected changes in temperature, precipitation, and climate extremes in different sectors of
CA and SA. Various studies used A2 and B2 scenarios from CMIP3 and various RCPs scenarios for CMIP5, and
different time slices from 2010 to 2100. In order to make results comparable, the CMIP3 and CMIP5 at the time
slice ending in 2100 are included. Additional information on changes in projected extremes can be found in the
IPCC SREX (see IPCC, 2012b), and Chapters 9 and 14 from IPCC WG1AR5 [9.5, 9.6 and 14.2, 14.7]]
In CA and Northern Venezuela, projections from CMIP3 models and from downscaling experiments suggest
precipitation reductions and warming together with an increase in evaporation, and reductions in soil moisture for
most of the land during all seasons by the end of the 21th century (see references in Table 27-2). However, the spread
of projections is high for future precipitation.
Analyses from global and regional models in tropical and subtropical SA show common patterns of projected
climate in some sectors of the continent. Projections from CMIP3 regional and high resolution global models show
by the end of the 21st century for the A2 emission scenario, a consistent pattern of increase of precipitation in SESA,
Northwest of Peru and Ecuador and western Amazonia, while decreases are projected for northern SA, Eastern
Amazonia, central eastern Brazil, NEB, the Altiplano and southern Chile (Table 27-2). For some regions,
projections show mixed results in rainfall projections for the Amazonia and the SAMS region suggesting high
uncertainties on the projections (Table 27-2).
As for extremes, CMIP3 models and downscaling experiments show increases in dry spells are projected for Eastern
Amazonia and NEB, while rainfall extremes are projected to increase in SESA, in western Amazonia, Northwest
Peru and Ecuador, while over southern Amazonia, northeastern Brazil and eastern Amazonia, the maximum number
of consecutive dry days tends to augment, suggesting a longer dry season. Increases in warm nights throughout SA
are also projected by the end of the 21st century (see references in Table 27-2). Shiogama et al. (2011) suggest that
although the CMIP3 ensemble mean assessment suggested wetting across most of SA, the observational constraints
indicate a higher probability of drying in the eastern Amazon basin.
The CMIP5 models project an even larger expansion of the monsoon regions in NAMS in future scenarios (Jones
and Carvalho, 2013; Kitoh et al., 2013). A comparison from eight models from CMIP3 and CMIP5 identifies some
improvements in the new generation models. For example, CMIP5 inter-model variability of temperature in summer
was lower over northeastern Argentina, Paraguay and northern Brazil, in the last decades of the 21st century, as
compared to CMIP3. Although no major differences were observed in both precipitation datasets, CMIP5 inter-
model variability was lower over northern and eastern Brazil in summer by 2100 (Blázquez and Nunez, 2013; Jones
and Carvalho, 2013).
The projections from the CMIP5 models at regional level for CA and SA (using the same regions from the IPCC
SREX) are shown in Figure 27-1, and update some of these previous projections based on SRES A2 and B2
emission scenarios from CMIP3. Figure 27-1 shows that in relation to the baseline period 1986-2005, for CA and
northern South America-Amazonia, temperatures are projected to increase approximately by 0.6 °C and 2 °C for the
RCP2.6 scenario, and by 3.6 C and 5.2 °C for the RCP8.5 scenario. For the rest of South America, increases by
about 0.6 °C to 2 °C are projected for the RCP4.5 and by about 2.2 °C to 7 °C for the RCP8.5 scenario. The
observed records show increases of temperature from 1900 to 1986 by about 1 °C. For precipitation, while for CA
and northern South America-Amazonia precipitation is projected to vary between +10% to -25% (with large spread
among models). For NEB, there is a spread among models between +30 to -30% making hard to identify any
projected rainfall change. This spread is much lower in the western coast of South America and SESA, where the
spread is between +20 and -10% (IPCC WG2 Chapter 21, Box 21-3).
Figure 27-2 shows that by late-21st century, the CMIP5 derived projections for RCP8.5 projected: CA: mean annual
warming of 2.5C and rainfall reduction of 10%, and reduction in summertime precipitation. SA: mean warming of
4C, with rainfall reduction up to 15% in tropical SA east of the Andes, and an increase of about 15-20% in SESA,
and in other regions of the continent. Changes in mid-21st century are small. Both Figures 27-1 and 27-2 show that
there is some degree of uncertainty on climate change projections for regions, particularly for rainfall in CA and
tropical SA.
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[INSERT FIGURE 27-1 HERE
Figure 27-1: Observed and simulated variations in past and projected future annual average temperature over the
Central and South American regions defined in IPCC (2012a). Black lines show various estimates from
observational measurements. Shading denotes the 5-95 percentile range of climate model simulations driven with
"historical" changes in anthropogenic and natural drivers (63 simulations), historical changes in "natural" drivers
only (34), the "RCP2.6" emissions scenario (63), and the "RCP8.5" (63). Data are anomalies from the 1986-2006
average of the individual observational data (for the observational time series) or of the corresponding historical all-
forcing simulations. Further details are given in Box 21-3.]
[INSERT FIGURE 27-2 HERE
Figure 27-2: Projected changes in annual average temperature and precipitation. CMIP5 multi-model mean
projections of annual average temperature changes (left panel) and average percent change in annual mean
precipitation (right panel) for 2046-2065 and 2081-2100 under RCP2.6 and 8.5. Solid colors indicate areas with very
strong agreement, where the multi-model mean change is greater than twice the baseline variability, and>90% of
models agree on sign of change. Colors with white dots indicate areas with strong agreement, where>66% of models
show change greater than the baseline variability and>66% of models agree on sign of change. Gray indicates areas
with divergent changes, where>66% of models show change greater than the baseline variability, but<66% agree on
sign of change. Colors with diagonal lines indicate areas with little or no change, less than the baseline variability
in>66% of models. (There may be significant change at shorter timescales such as seasons, months, or days.).
Analysis uses model data and methods building from WGI AR5 Figure SPM.8. See also Annex I of WGI AR5.
[Boxes 21-3 and CC-RC]]
27.2.2. Non-Climatic Stressors
27.2.2.1. Trends and Projections in Land Use and Land Use Change
Land use change is a key driver of environmental degradation for the region that exacerbates the negative impacts
from climate change (Lopez-Rodriguez and Blanco-Libreros, 2008; Sampaio et al., 2007). The high levels of
deforestation observed in most of the countries in the region have been widely discussed in the literature as a
deliberate development strategy based on the expansion of agriculture to satisfy the growing world demand for food,
energy and minerals (Benhin, 2006; Grau and Aide, 2008; Mueller et al., 2008). Land is facing increasing pressure
from competing uses, among them cattle ranching, food and bioenergy production. The enhanced competition for
land increases the risk of land use changes, which may lead to negative environmental and socio-economic impacts.
Agricultural expansion has relied in many cases on government subsidies, which have often resulted in lower land
productivity and more land speculation (Bulte et al., 2007; Roebeling and Hendrix, 2010). Some of the most
affected areas due to the expansion of the agricultural frontier are fragile ecosystems such as the edges of the
Amazon forest in Brazil, Colombia, Ecuador and Peru, and the tropical Andes including the Paramo, where
activities such as deforestation, agriculture, cattle ranching and gold mining are causing severe environmental
degradation (ECLAC, 2010d), and the reduction of environmental services provided by these ecosystems.
Deforestation rates for the region remain high in spite of a reducing trend in the last decade (Fearnside, 2008;
Ramankutty et al., 2007). Brazil is by far the country with the highest area of forest loss in the world according to
the latest FAO statistics (2010): 21,940 km per year equivalent to 39% of the world deforestation for the period
2005-2010. Bolivia, Venezuela and Argentina follow in deforested area (Figure 27-3) with 5.5%, 5.2% and 4.3% of
the total world deforestation respectively. The countries of CA and SA lost a total of 38,300 km of forest per year in
that period (69% of the total world deforestation) (FAO, 2010). These numbers are limited by the fact that many
countries do not have comparable information through time, particularly for recent years. Aide et al. (2013)
completed a wall-to-wall analysis for the region for the period 2001-2010 analyzing not only deforestation but also
reforestation and reported very different results than FAO (2010) for some countries where reforestation seems to be
higher than deforestation, particularly in Honduras, El Salvador, Panama, Colombia and Venezuela. For Colombia
and Venezuela, these results are contradictory with country analyses that align better with the FAO data (Armenteras
et al., 2013; Rodríguez et al., 2010).
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[INSERT FIGURE 27-3 HERE
Figure 27-3: Area deforested per year for selected countries in CA and SA (2005-2010). Notice three countries listed
with a positive change in forest cover (based on data from FAO, 2010).]
Deforestation in the Amazon forest has received much international attention in the last decades, both because of its
high rates, and its rich biodiversity. Brazilian Legal Amazon is now one of the best-monitored ecosystems in terms
of deforestation since 1988 (INPE, 2011). Deforestation for this region peaked in 2004 and has steadily declined
since then to a lowest value of 4,656 Km2/yr for the year 2012 (see Figure 27-4). Such reduction results from a
series of integrated policies to control illegal deforestation particularly enforcing protected areas, which now shelter
54% of the remaining forests of the Brazilian Amazon (Soares-Filho et al., 2010). Deforestation in Brazil is now
highest in the Cerrado (drier ecosystem south of Amazon) with an average value of 14,179 Km2/yr for the period
2002-2008 (FAO, 2009).
[INSERT FIGURE 27-4 HERE
Figure 27-4: Deforestation rates in the Brazilian Amazonia (km/year) based on measurements by the PRODES
project (INPE, 2011).]
The area of forest loss in CA is considerably less than in SA, owing to smaller country sizes (Carr et al., 2009), but
when relative deforestation rates are considered, Honduras and Nicaragua show the highest values for CA and SA
(FAO, 2010). At the same time, CA includes some countries where forest cover shows a small recovery trend in the
last years: Costa Rica, El Salvador, Panama and possibly Honduras where data is conflicting in the literature (Aide
et al., 2013; FAO, 2010). This forest transition is the result of: (1) economies less dependent on agriculture, and
more on industry and services (Wright and Samaniego, 2008); (2) processes of international migration with the
associated remittances (Hecht and Saatchi, 2007), and (3) a stronger emphasis on the recognition of environmental
services of forest ecosystems (Kaimowitz, 2008). The same positive trend is observed in some SA countries (Figure
27-3). However, a substantial amount of forest is gained through (single-crop) plantations, most noticeably in Chile
(Aguayo et al., 2009), which has a much lower ecological value than the depleted natural forests (Echeverría et al.,
2006; Izquierdo et al., 2008).
Land degradation, is also an important process compromising extensive areas of CA and SA very rapidly. According
to data from the Global Land Degradation Assessment and Improvement (GLADA) project of the Global
Environmental Facility (GEF), additional degraded areas reached 16.4% of the entire territory of Paraguay, 15.3% of
Peru and 14.2% of Ecuador for the period 1982-2002. In CA, Guatemala shows the highest proportion of degraded
land, currently at 58.9% of the country s territory, followed by Honduras (38.4%) and Costa Rica (29.5%); only El
Salvador shows a reversal of the land degradation process, probably due to eased land exploitation following
intensive international migratory processes (ECLAC, 2010d).
Deforestation and land degradation are mainly attributed to increased extensive and intensive agriculture. Two
activities have traditionally dominated the agricultural expansion: soy production (only in SA) and beef; but more
recently, biomass for biofuel production has become as important (Nepstad and Stickler, 2008) with some regions
also affected by oil and mining extractions. Deforestation by small farmers, mainly coming from families who
migrate in search for land is relatively low: extensive cattle production is the predominant land use in deforested
areas of tropical and subtropical Latin America (Wassenaar et al., 2007). Cattle is the only land use variable
correlated with deforestation in Colombia (Armenteras et al., 2013) and in the Brazilian Amazon the peak of
deforestation in 2004 (Figure 27-4) was primarily the result of increased cattle ranching (Nepstad et al., 2006).
Mechanized farming, agro-industrial production and cattle ranching are the major land use change drivers in eastern
Bolivia but subsistence agriculture by indigenous colonists is also important (Killeen et al., 2008).
In recent years, soybean croplands have expanded continuously in SA, becoming increasingly more important in the
agricultural production of the region. Soybean-planted area in Amazonian states (mainly Mato Grosso) in Brazil
expanded 12.1% per year during the 1990s, and 16.8% per year from 2000 to 2005 (Costa et al., 2007). This
landscape-scale conversion from forest to soy and other large-scale agriculture can alter substantially the water
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balance for large areas of the region resulting in important feedbacks to the local climate (Hayhoe et al., 2011;
Loarie et al., 2011) (see section 27.3.4.1).
Soybean and beef production have also impacted other ecosystems next to the Amazon, such as the Cerrado (Brazil)
and the Chaco dry forests (Bolivia, Paraguay, Argentina and Brazil). Gasparri et al. (2008) estimated carbon
emissions from deforestation in Northern Argentina, and concluded that deforestation in the Chaco forest has
accelerated in the past decade from agricultural expansion and is now the most important source of carbon emission
for that region. In northwest Argentina (Tucumán and Salta provinces) 14,000 km 2 of dry forest were cleared from
1972 to 2007 as a result of technological improvements and increasing rainfall (Gasparri and Grau, 2009).
Deforestation continued during the 1980s and 1990s resulting in cropland area covering up to 63% of the region by
2005 (Viglizzo et al., 2011). In central Argentina (northern Córdoba province), cultivated lands have increased from
3% to 30% (between 1969 and 1999); and the forest cover has decreased from 52.5% to 8.2%. This change has also
been attributed to the synergistic effect of climatic, socioeconomic, and technological factors (Zak et al., 2008).
Losses in the Atlantic forest are estimated in 29% of the original area in 1960, and in 28% of the Yunga forest area,
mainly due to cattle ranching migration from the Pampas and Espinal (Viglizzo et al., 2011).
Oil palm is a significant biofuel crop also linked to recent deforestation in tropical CA and SA. Its magnitude is still
small compared with deforestation related to soybean and cattle ranching, but it is considerable for specific countries
and expected to increase due to increasing demands for biofuels (Fitzherbert et al., 2008). The main producers of
palm oil in the region are Colombia and Ecuador, followed by Costa Rica, Honduras, Guatemala and Brazil; this
country has the largest potential for expansion, as nearly half of the Amazonia is suitable for oil palm cultivation
(Butler and Laurance, 2009). Oil palm production is also growing in the Amazonian region of Peru, where 72% of
new plantations have expanded into forested areas representing 1.3% of the total deforestation for that country for
the years 2000-2010 (Gutiérrez-Vélez et al., 2011).
However, forests are not the only important ecosystems threatened in the region. An assessment of threatened
ecosystems in SA by Jarvis et al. (2010) concluded that grasslands, savannas and shrublands are more threatened
than forests, mainly from excessively frequent fires (>1/year) and grazing pressure. An estimation of burned land in
Latin America by Chuvieco et al. (2008) also concluded that herbaceous areas presented the highest occurrence of
fires. In the Río de la Plata region (Central-East Argentina, southern Brazil, and Uruguay), grasslands decreased
from 67.4% to 61.4% between 1985 and 2004. This reduction was associated with an increase in annual crops,
mainly soybean, sunflower, wheat, and maize (Baldi and Paruelo, 2008).
Even with technological changes that might result in agricultural intensification, the expansion of pastures and
croplands is expected to continue in the coming years (Kaimowitz and Angelsen, 2008; Wassenaar et al., 2007),
particularly from an increasing global demand for food and biofuels (Gregg and Smith, 2010) with the consequent
increase in commodity prices. This agricultural expansion will be mainly in Latin America and Sub-Saharan Africa
as these regions hold two-thirds of the global land with potential to expand cultivation (Nepstad and Stickler, 2008).
It is important to consider the policy and legal needs to keep this process of large-scale change under control as
much as possible; Takasaki (2007) showed that policies to eliminate land price distortions and promote
technological transfers to poor colonists could reduce deforestation. It is also important to consider the role of
indigenous groups; there is a growing acknowledgment that recognizing the land ownership and authority of
indigenous groups can help central governments to better manage many of the natural areas remaining in the region
(Larson, 2010; Oltremari and Jackson, 2006). The impact of indigenous groups on land use change can vary:
Oliveira et al. (2007) found that only 9% of the deforestation in the Peruvian Amazon between 1999 and 2005
happened in indigenous territories but Killeen et al. (2008) found that Andean indigenous colonists in Bolivia were
responsible for the largest land cover changes in the period 2001-2004. Indigenous groups are important
stakeholders in many territories in the region and their well-being should be considered when designing responses to
pressures on the land by a globalized economy (Gray et al., 2008; Killeen et al., 2008).
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27.2.2.2. Trends and Projections in Socioeconomic Conditions
Development in the region has traditionally displayed four characteristics: low growth rates, high volatility,
structural heterogeneity and a very unequal income distribution (Bárcena, 2010; ECLAC, 2008). This combination
of factors has generated high and persistent poverty levels (45% for CA and 30% for SA for year 2010), with the
rate of poverty being generally higher in rural than urban areas (ECLAC, 2009b). SA has based its economic growth
in natural resource exploitation (mining, energy, agricultural), which involves direct and intensive use of land and
water, and in energy-intensive and, in many cases, highly polluting natural-resource-based manufactures. In turn,
CA has exploited its proximity to the North American market and its relatively low labor costs (ECLAC, 2010e).
The region shows a marked structural heterogeneity, where modern production structures coexist with large
segments of the population with low productivity and income levels (ECLAC, 2010g). The GDP per capita in SA is
twice that of CA; in addition, in the latter poverty is 50% higher (see Figure 27-5).
[INSERT FIGURE 27-5 HERE
Figure 27-5: Evolution of GDP per capita and poverty (income below US$ 2 per day) from 1990-2010: CA and SA
(US-Dollars per inhabitant at 2005 prices and percentages) (ECLAC on the basis of CEPALSTAT (2012) and
ECLAC (2011c)).]
The 2008 financial crisis reached CA and SA through exports and credits, remittances and worsening expectations
by consumers and producers (Bárcena, 2010; Kacef and López-Monti, 2010). This resulted in the sudden stop of six
consecutive years of robust growth and improving social indicators (ECLAC, 2010e), which contributed to higher
poverty in 2009 after six years where poverty had declined by 11%. Poverty rates fell from 44% to 33% of the total
population from 2003 to 2008 (Figure 27-5), leaving 150 million people in this situation while extreme poverty
diminished from 19.4% to 12.9% (which represents slightly more than 70 million people) (ECLAC, 2009b).
In the second half of 2009 industrial production and exports began to recover and yielded a stronger economic
performance (GDP growth of 6.4% in SA and 3.9% in CA in 2010) (ECLAC, 2012). SA benefited the most because
of the larger size of their domestic markets and the greater diversification of export markets. Conversely, slower
growth was observed in CA with more open economies and a less diversified portfolio of trading partners and a
greater emphasis on manufacturing trade (ECLAC, 2010g).
The region is expected to continue to grow in the short term, albeit at a pace that is closer to potential GDP growth,
helped by internal demand as the middle class becomes stronger and as credit becomes more available. In SA, this
could be boosted by external demand from the Asian economies as they continue to grow at a rapid pace. The
macroeconomic challenge is to act counter cyclically creating conditions for productive development that is not
based solely on commodity exports (ECLAC, 2010f).
In spite of its economic growth, CA and SA still display high and persistent inequality: most countries have Gini
coefficients between 0.5 and 0.6, whereas the equivalent figures in a group of 24 developed countries vary between
under 0.25 and around 0.4. The average per capita income of richest 10% of households is approximately 17 times
that of the poorest 40% of households (ECLAC, 2010g). Nevertheless, during the first decade of the century, prior to
the financial crisis, the region has shown a slight but clear trend towards a more equitable distribution of income and
a stronger middle class population resulting in a higher demand for goods (ECLAC, 2010g; ECLAC, 2011b; UN,
2010). Latin American countries also reported gains in terms of human development, although these gains have
slowed down slightly over recent years. In comparative terms, the performance of countries as measured by the
Human Development Index (HDI) varied greatly in 2007 (from Chile with 0.878 and Argentina with 0.866 to
Guatemala with 0.704 and Nicaragua with 0.699) although those with lower levels of HDI showed notably higher
improvements than countries with the highest HDI (UNDP, 2010).
Associated with inequality are disparities in access to water, sanitation and adequate housing for the most vulnerable
groups - for example indigenous peoples, Afro-descendants, children and women living in poverty- and in their
exposure to the effects of climate change. The strong heterogeneity of subnational territorial entities in the region
takes the form of high spatial concentration and persistent disparities in the territorial distribution of wealth
(ECLAC, 2010g; ECLAC, 2011b; UN, 2010).
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The region faces significant challenges in terms of environmental sustainability and adaptability to a changing
climate (UN, 2010), resulting from the specific characteristics of its population and economy already discussed and
aggravated with a significant deficit in infrastructure development. The countries in CA and SA have made progress
in incorporating environmental protection into decision-making processes, and particularly in terms of
environmental institutions and legislation, but there are still difficulties to effectively incorporate environmental
issues into relevant public policies (UN, 2010). While climate change imposes new challenges, it also provides an
opportunity to shift development and economic growth patterns towards a more environmentally friendly course.
27.3. Impacts, Vulnerabilities, and Adaptation Practices
27.3.1. Freshwater Resources
CA and SA are regions with a high average but unevenly distributed water resources availability (Magrin et al.,
2007a). The main user of water is agriculture followed by the region s 580 million inhabitants (including the
Caribbean), of which 86% had access to water supply by 2006 (ECLAC, 2010b). According to the International
Energy Agency (IEA), the region meets 60% of its electricity demand through hydropower generation, which
contrast with the 20% average contribution of other regions (see Table 27-6 and case study 27.6.1).
27.3.1.1. Observed and Projected Impacts and Vulnerabilities
In CA and SA there are many evidences of changing hydrologic related conditions. The most robust trend for major
rivers is found in the sub-basins of the La Plata River basin (high confidence based on high agreement, robust
evidence). This basin, second only to the Amazon in size, shows a positive trend in streamflow in the second half of
the 20th century at different sites (Amsler and Drago, 2009; Conway and Mahé, 2009; Dai et al., 2009; Dai, 2011;
Doyle and Barros, 2011; Krepper et al., 2008; Krepper and Zucarelli, 2010a; Pasquini and Depetris, 2007; Saurral et
al., 2008). An increase in precipitation and a reduction in evapotranspiration from land use changes have been
associated with the trend in streamflows (Doyle and Barros, 2011; Saurral et al., 2008), with the former being more
important in the southern sub-basins and the latter in the northern ones (Doyle and Barros, 2011) (see section
27.2.1). Increasing trends in streamflows have also been found in the Patos Lagoon in southern Brazil (Marques,
2012) and Laguna Mar Chiquita (a closed lake), and in the Santa Fe Province, both in Argentina, with ecological
and erosive consequences (Bucher and Curto, 2012; Pasquini et al., 2006; Rodrigues Capítulo et al., 2010; Troin et
al., 2010; Venencio and García, 2011).
There is no clear long-term trend for the Amazon River. Espinoza et al. (2009a; 2011) showed that the 1974-2004
apparent stability in mean discharge at the main stem of the Amazon in Obidos is explained by opposing regional
features of Andean rivers (e.g. increasing trends during the high-water period in Peruvian and Colombian Amazons
and decreasing trend during the low-water period in Peruvian and Bolivian Amazons (Lavado et al., 2012). In recent
years extremely low levels were experienced during the droughts of 2005 and 2010, while record high levels were
detected during the 2009 and 2012 floods (see section 27.2.1). Major Colombian rivers draining to the Caribbean
Sea (Magdalena and Cauca) exhibit decreasing trends along their main channels (Carmona and Poveda, 2011), while
significant trends are absent for all other major large rivers in the Brazilian North East, and northern SA (Dai et al.,
2009). Dai (2011) showed a drying trend in CA rivers.
A rapid retreat and melting of the tropical Andes glaciers of Venezuela, Colombia, Ecuador, Peru and Bolivia has
been further reported following the IPCC AR4, through use of diverse techniques (high confidence based on high
agreement and robust evidence). Rabatel et al. (2013) provides a synthesis of these studies (specific papers are
presented in Table 27-3a). Tropical glaciers retreat has accelerated in the second half of the 20th century (area loss
between 20-50%), especially since the late 1970s in association with increasing temperature in the same period
(Bradley et al., 2009). In early stages of glacier retreat associated streamflow tends to increase due to an acceleration
of glacier melt, but after a peak in streamflow as the glacierized water reservoir gradually empties, runoff tends to
decrease, as evidenced in the Cordillera Blanca of Peru (Baraer et al., 2012; Chevallier et al., 2011), where seven
out of nine river basins have probably crossed a critical threshold, exhibiting a decreasing dry-season discharge
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(Baraer et al., 2012). Likewise, glaciers and icefields in the extra tropical Andes located in Central-South Chile and
Argentina face significant reductions (see review in Masiokas et al. (2009) and details in Table 27-3b), with their
effect being compounded by changes in snowpack extent, thus magnifying changes in hydrograph seasonality by
reducing flows in dry seasons and increasing them in wet seasons (Pizarro et al., 2013; Vicuna et al., 2013). Central-
South Chile and Argentina also face significant reductions in precipitation as shown in section 27.2.1, contributing
to runoff reductions in the last decades of the 20th century (Rubio-Álvarez and McPhee, 2010; Seoane and López,
2007; Urrutia et al., 2011; Vicuna et al., 2013), corroborated with long-term trends found through dendrochronology
(Lara et al., 2007; Urrutia et al., 2011). Trends in precipitation and runoff are less evident in the Central-North
region in Chile (Fiebig-Wittmaack et al., 2012; Souvignet et al., 2012).
[INSERT TABLE 27-3 HERE
Table 27-3: Observed trends related to Andean cryosphere: a) Andean tropical glacier trends, and b) extra tropical
Andean cryosphere (glaciers, snowpack, runoff effects) trends.]
As presented in Table 27-4, the assessment of future climate scenarios implications in hydrologic related conditions
shows a large range of uncertainty across the spectrum of climate models (mostly using CMIP3 simulations with the
exception of Demaria et al. (2013) and scenarios considered. Nohara et al. (2006) studied climate change impacts on
24 of the main rivers in the world considering a large number of GCMs, and found no robust change for the Paraná
(La Plata Basin) and Amazon Rivers. Nevertheless, in both cases the average change showed a positive value
consistent at least with observations for the La Plata Basin. In a more recent work Nakaegawa et al. (2013a) showed
statistically significant increase for both basins in a study that replicated that of Nohara et al. (2006) but with
different hydrologic model. Focusing in extreme flows Guimberteau et al. (2013) show that by the middle of the
century no change is found in high flow on the main stem of the Amazon River but there is a systematic reduction in
low-flow streamflow. In contrast the northwestern part of the Amazon River shows a consistent increase in high
flow and inundated area (Guimberteau et al., 2013; Langerwisch et al., 2013). On top of such climatic uncertainty,
future streamflows and water availability projections are confounded by the potential effects of land use changes
(Coe et al., 2009; Georgescu et al., 2013; Moore et al., 2007).
[INSERT TABLE 27-4 HERE
Table 27-4: Synthesis of projected climate change impacts on hydrologic related variables in CA and SA basins and
major glaciers.]
The CA region shows a consistent future runoff reduction. Maurer et al. (2009)studied climate change projections
for the Lempa River basin, one of the largest basins in CA, covering portions of Guatemala, Honduras and El
Salvador. They showed that future climate projections (increase in evaporation and reduction in precipitation) imply
a reduction of 20% in inflows to major reservoirs in this system (see Table 27-4). Imbach et al. (2012) found similar
results using a modeling approach that also considered potential changes in vegetation. These effects could have
large hydropower generation implications as discussed in the case study (see section 27.6.1).
The evolution of tropical Andes glaciers associated future climate scenarios has been studied using trend (e.g.
Poveda and Pineda (2009), regression (e.g. Juen et al. (2007) and Chevallier et al. (2011) and explicit modeling (e.g.
Condom et al., 2012) analysis. These studies indicate that glaciers will continue their retreat (Vuille et al., 2008a)
and even disappear as glacier Equilibrium Line Altitudes rises, with larger hydrological effects during the dry season
(Gascoin et al., 2011; Kaser et al., 2010). This is expected to happen during the next 20-50 years (Chevallier et al.,
2011; Juen et al., 2007) (see Table 27-4). After that period water availability during the dry months is expected to
diminish. A projection by Baraer et al. (2012) for the Santa River in the Peruvian Andes finds that once the glaciers
are completely melt, annual discharge would decrease by 2% 30%, depending on the watershed. Glacier retreat can
exacerbate current water resources related vulnerability (Bradley et al., 2006; Casassa et al., 2007; Mulligan et al.,
2010; Vuille et al., 2008b), diminishing the mountains water regulation capacity, making it more expensive and less
reliable the supply of water for diverse purposes, as well as for ecosystems integrity (Buytaert et al., 2011). Impacts
on economic activities associated with conceptual scenarios of glacier melt reduction have been monetized (Vergara
et al., 2007), representing about US$100 million in the case of water supply for Quito, and between US$212 million
to US$ 1.5 billion in the case of the Peruvian electricity sector due to losses of hydropower generation (see case
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study 27.6.1). Andean communities will face an important increase in their vulnerability, as documented by Mark et
al. (2010), Pérez et al. (2010) and Buytaert and De Bievre (2012).
In Central Chile, Vicuna et al. (2011) project changes in the seasonality of streamflows of the upper snowmelt-
driven watersheds of the Limarí River, associated with temperature increases and reductions in water availability
owing to a reduction (increase) in precipitation (evapotranspiration). Similar conclusions are derived across the
Andes on the Limay River in Argentina by Seoane and López (2007). Under these conditions, semiarid highly
populated basins (e.g. Santiago, Chile) and with extensive agriculture irrigation and hydropower demands are
expected to increase their current vulnerability (ECLAC, 2009a; Fiebig-Wittmaack et al., 2012; Souvignet et al.,
2010; Vicuna et al., 2012) (high confidence) (see Table 27-4). Projected changes in the cryosphere conditions of the
Andes could affect the occurrence of extreme events, such as extreme low and high flows (Demaria et al., 2013).
Glacial-lake outburst floods occurring in the icefields of Patagonia (Dussaillant et al., 2010; Marín et al., 2013),
volcanic collapse and debris flow associated with accelerated glacial melting in the tropical Andes (Carey, 2005;
Carey et al., 2012b; Fraser, 2012), and with volcanoes in southern Chile and Argentina (Tormey, 2010), as well as
scenarios of water quality pollution by exposure to contaminants owing to glaciers retreat (Fortner et al., 2011).
Another semiarid region that has been studied thoroughly is the NEBian (Hastenrath, 2012). De Mello et al. (2008),
Gondim et al. (2008), Souza et al. (2010) and Montenegro and Ragab (2010) have shown that future climate change
scenarios would decrease water availability for agriculture irrigation owing to reductions in precipitation and
increases in evapotranspiration (medium confidence). Krol and Bronstert (2007) and Krol et al. (2006) presented an
integrated modeling study that linked projected impacts on water availability for agriculture with economic impacts
that could potentially drive full-scale migrations in the Brazilian northeast region.
27.3.1.2. Adaptation Practices
At an institutional level, a series of policies have been developed to reduce vulnerability to climate variability as
faced today in different regions and settings. In 1997, Brazil instituted the National Water Resources Policy and
created the National Water Resources Management System under the shared responsibility between the States and
the Federal government. Key to this new regulation has been the promotion of decentralization and social
participation through the creation of National Council of Water Resources and their counterparts in the states, the
States Water Resources Councils. The challenges and opportunities dealing with water resources management in
Brazil in the face of climate variability and climate change have been well studied (Abers, 2007; Engle et al., 2011;
Kumler and Lemos, 2008; Lorz et al., 2012; Medema et al., 2008). Other countries in the region are following
similar approaches. In the last years, there have been constitutional and legal reforms towards more efficient and
effective water resources management and coordination among relevant actors in Honduras, Nicaragua, Ecuador,
Peru, Uruguay, Bolivia and Mexico; although in many cases, these innovations have not been completely
implemented (Hantke Domas, 2011). Institutional and governance improvements are required to assure an effective
implementation of these adaptation measures (e.g. Halsnaes and Verhagen, 2007; Engle and Lemos, 2010; Lemos et
al., 2010; Zagonari, 2010; Pittock, 2011; and Kirchhoff et al. 2013).
With regards to region specific freshwater resources issues it is important to consider adaptation to reduce
vulnerabilities in the communities along the tropical Andes and the semi-arid basins in Chile-Argentina, North East
Brazil, and the northern CA basins. Different issues have been addressed in the assessment of adaptation strategies
for tropical Andean communities such as the role of governance and institutions (Lynch, 2012; Young and Lipton,
2006), technology (Carey et al., 2012a), and the dynamics of multiple stressors (Bury et al., 2013; McDowell and
Hess, 2012). Semiarid regions are characterized by pronounced climatic variability and often by water scarcity and
related social stress (Krol and Bronstert, 2007; Scott et al., 2012; 2013). Adaptation tools to face the threats of
climate change for the most vulnerable communities in the Chilean semi-arid region are discussed by Young et al.
(2010) and Debels et al. (2009). In CA, Benegas et al. (2009), Manuel-Navarrete et al. (2007) and Aguilar et al.
(2009) provide different frameworks to understand vulnerability and adaptation strategies to climate change and
variability in urban and rural contexts, although no specific adaptation strategies are suggested. The particular
experience in NEB provides other examples of adaptation strategies to manage actual climate variability. Broad et
al. (2007) and Sankarasubramanian et al. (2009) studied the potential benefits of streamflow forecast as a way to
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reduce the impacts of climate change and climate variability on water distribution under stress conditions. An
historical review and analysis of drought management in this region are provided by Campos and Carvalho (2008).
Souza Filho and Brown (2009) studied different water distribution policy scenarios finding that the best option
depended on the degree of water scarcity. The study by Nelson and Finan (2009) provides a critical perspective of
drought-related policies, arguing that they constitute an example of maladaptation as they do not try to solve the
causes of vulnerability and instead undermine resilience. Tompkins et al. (2008) are also critical of risk reduction
practices in this region because they have fallen short of addressing the fundamental causes of vulnerability needed
for efficient longer-term drought management. Other types of adaptation options that stem from studies on arid and
semiarid regions are related to: a) increase in water supply from groundwater pumping (Burte et al., 2011; Döll,
2009; Kundzewicz and Döll, 2009; Nadal et al., 2013; Zagonari, 2010); fog interception practices (Holder, 2006;
Klemm et al., 2012), and reservoirs and irrigation infrastructure (Fry et al., 2010; Vicuna et al., 2010; 2012); b)
improvements in water demand management associated with increased irrigation efficiency and practices (Bell et
al., 2011; Geerts et al., 2010; Jara-Rojas et al., 2012; Montenegro and Ragab, 2010; Van Oel et al., 2010), and
changes towards less water intensive crops (Montenegro and Ragab, 2010).
Finally flood management practices also provide a suite of options to deal with actual and future vulnerabilities
related to hydrologic extremes, such as the management of ENSO-related events in Peru via participatory (Warner
and Oré, 2006) or risk reduction approaches (Khalil et al., 2007), the role of land use management (Bathurst et al.,
2010; 2011; Coe et al., 2011), and flood hazard assessment (Mosquera-Machado and Ahmad, 2006) (medium
confidence).
27.3.2. Terrestrial and Inland Water Systems
27.3.2.1. Observed and Projected Impacts and Vulnerabilities
CA and SA house the largest biological diversity and several of the world s megadiverse countries (Guevara and
Laborde, 2008; Mittermeier et al., 1997). However, land use change has led to the existence of six biodiversity
hotspots, i.e. places with a great species diversity that show high habitat loss and also high levels of species
endemism: Mesoamerica, Chocó-Darien-Western Ecuador, Tropical Andes, Central Chile, Brazilian Atlantic forest,
and Brazilian Cerrado (Mittermeier et al., 2005). Thus, conversion of natural ecosystems is the main proximate
cause of biodiversity and ecosystem loss in the region (Ayoo, 2008). Tropical deforestation is the second largest
driver of anthropogenic climate change on the planet, adding up to 17%-20% of total greenhouse gas emissions
during the 1990s (Gullison et al., 2007; Strassburg et al., 2010). In parallel, the region has still large extensions of
wilderness areas for which the Amazon is the most outstanding example. Nevertheless, some of these areas are
precisely the new frontier of economic expansion. For instance, between 1996 and 2005 Brazil deforested about
19,500 km2 per year, which represented 2% to 5% of global annual CO2 emissions (Nepstad et al., 2009). Between
2005 and 2009, deforestation in the Brazilian Amazon dropped by 36%, which is partly related to the network of
protected areas that now covers around 45.6% of the biome in Brazil (Soares-Filho et al., 2010). Using LandSHIFT
modeling framework for land use change and the IMPACT projections of crop/livestock production, Lapola et al.
(2011) projected that zero deforestation in the Brazilian Amazon forest by 2020 (and of the Cerrado by 2025) would
require either a reduction of 26% 40% in livestock production until 2050 or a doubling of average livestock density
from 0.74 to 1.46 head per hectare. Thus, climate change may imply reduction of yields and entail further
deforestation.
Local deforestation rates or rising greenhouse gases globally drive changes in the regional SA that during this
century might lead the Amazon rainforest into crossing a critical threshold at which a relatively small perturbation
can qualitatively alter the state or development of a system (Cox et al., 2000; Lenton et al., 2008; Nobre and Borma,
2009; Salazar et al., 2007; Sampaio et al., 2007). Various models are projecting a risk of reduced rainfall and higher
temperatures and water stress, that may lead to an abrupt and irreversible replacement of Amazon forests by
savanna-like vegetation, under a high emission scenario (A2), from 2050-2060 to 2100 (Betts et al., 2004; 2008;
Cox et al., 2004; Malhi et al., 2008; Malhi et al., 2009; Marengo et al., 2011c; Nobre and Borma, 2009; Salazar et
al., 2007; Sampaio et al., 2007; Sitch et al., 2008). The possible savannization or die-back of the Amazon region
would potentially have large-scale impacts on climate, biodiversity and people in the region. The possibility of this
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die-back scenario occurring, however, is still an open issue and the uncertainties are still very high (Rammig et al.,
2010; Shiogama et al., 2011).
Plant species are rapidly declining in CA, SA, Central and West Africa, and Southeast Asia (Bradshaw et al., 2009).
Risk estimates of plant species extinction in the Amazon, which do not take into account possible climate change
impacts, range from 5%-9% by 2050 with a habitat reduction of 12%-24% (Feeley and Silman, 2009) to 33% by
2030 (Hubbell et al., 2008). The highest percentage of rapidly declining amphibian species occurs in CA and SA.
Brazil is among the countries with most threatened bird and mammal species (Bradshaw et al., 2009).
A similar scenario is found in inland water systems. Among the components of aquatic biodiversity, fish are the
best-known organisms (Abell et al., 2008) with Brazil accounting for the richest icthyofauna of the planet (Nogueira
et al., 2010). For instance, the 540 Brazilian small microbasins host 819 fish species with restrict distribution.
However, 29% of these microbasins have historically lost more than 70% of their natural vegetation cover and only
26% show a significant overlap with protected areas or indigenous reserves. Moreover, 40% of the microbasins
overlap with hydrodams (see 27.6.1 and Chapter 3) or have few protected areas and high rates of habitat loss
(Nogueira et al., 2010).
The faster and more severe the rate of climate change, the more severe the biological consequences such as species
decline (Brook et al., 2008). Vertebrate fauna in North and South America is projected to suffer species losses until
2100 of at least 10%, as forecasted in over 80% of the climate projections based on low emissions scenario (Lawler
et al., 2009). Vertebrate species turnover until 2100 will be as high as 90% in specific areas of CA and the Andes
Mountains for emission scenarios varying from low B1 to mid-high A2 (Lawler et al., 2009). Elevational specialists,
i.e. a small proportion of species with small geographic ranges restricted to high mountains, are most frequent in the
Americas (e.g. Andes and Sierra Madre) and might be particularly vulnerable to global warming because of their
small geographic ranges and high energetic and area requirements, particularly birds and mammals (Laurance et al.,
2011). In Brazil, projections for Atlantic forest birds (Anciaes and Peterson, 2006), endemic bird species (Marini et
al., 2009), and plant species (by 2055, scenarios HHGSDX50 and HHGGAX50; Siqueira and Peterson, 2003) of the
Cerrado indicate that distribution will dislocate towards the South and Southeast, precisely where fragmentation and
habitat loss are worse. Global climate change is also predicted to increase negative impacts worldwide, including
SA, on freshwater fisheries due to alterations in physiology and life histories of fish (Ficke et al., 2007).
In addition to climate change impacts at individual species level, biotic interactions will be affected. Modifications
in phenology, structure of ecological networks, predator-preys interactions and non-trophic interactions among
organisms have been forecasted (Brooker et al., 2008; Walther, 2010). The outcome of non-trophic interactions
among plants is expected to shift along with variation in climatic parameters, with more facilitative interactions in
more stressful environments, and more competitive interactions in more benign environments (Anthelme et al.,
2012; Brooker et al., 2008). These effects are expected to have a strong influence of community and ecosystem (re-)
organization given the key engineering role played by plants on the functioning of ecosystems (Callaway, 2007).
High Andean ecosystems, especially those within the tropics, are expected to face exceptionally strong warming
effects during the 21th century because of their uncommonly high altitude (Bradley et al., 2006). At the same time
they provide a series of crucial ecosystem services for millions people (Buytaert et al., 2011). For these reasons
shifts in biotic interactions are expected to have negative consequences on biodiversity and ecosystem services in
this region.
Although in the region biodiversity conservation is largely confined to protected areas, with the magnitude of
climatic changes projected for the century, it is expected that many species and vegetational types will lose
representativeness inside such protected areas (Heller and Zavaleta, 2009).
27.3.2.2. Adaptation Practices
The sub-set of practices that are multi-sectoral, multi-scale, and based on the premise that ecosystem services reduce
the vulnerability of society to climate change are known as Ecosystem-based Adaptation (EbA) (Vignola et al.,
2009; see also Glossary and CC-EA). Schemes such as the payment for environmental services (PES) and
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community management fit the concept of EbA that begins to spread in CA and SA (Vignola et al., 2009). The
principle behind these schemes is the valuation of ecosystem services that should reflect both the economic and
cultural benefits derived from the human-ecosystem interaction and the capacity of ecosystems to secure the flow of
these benefits in the future (Abson and Termansen, 2011).
Since PES schemes have developed more commonly in CA and SA than in other parts of the world (Balvanera et
al., 2012), this topic will be covered as a case study (see 27.6.2).
Ecological restoration, conservation in protected areas, and community management can all be important tools for
adaptation. A meta-analysis of 89 studies by Benayas et al. (2009) (with timescale of restoration varying from <5 to
300 years), including many in SA, showed that ecological restoration enhances the provision of biodiversity and
environmental services by 44% and 25%, respectively, as compared to degraded systems (Benayas et al., 2009).
Moreover, ecological restoration increases the potential for carbon sequestration and promotes community
organization, economic activities and livelihoods in rural areas (Chazdon, 2008), as seen in examples of the
Brazilian Atlantic Forest (Calmon et al., 2011; Rodrigues et al., 2011). In that sense, Locatelli et al. (2011) revised
several ecosystem conservation and restoration initiatives in CA and SA that simultaneously help mitigate and adapt
to climate change. Chazdon et al. (2009) also highlight the potential of restoration efforts to build ecological
corridors (see Harvey et al., 2008, for example in Central America).
The effective management of natural protected areas and the creation of new protected areas within national
protected area systems and community management of natural areas are also efficient tools to adapt to climate
change and to reconcile biodiversity conservation with socio-economic development (e.g., Bolivian Andes -
Hoffmann et al., 2011; Panama Oestreicher et al. 2009 ). Porter-Bolland et al. (2012) compared protected areas
with areas under community management in different parts of the tropical world, including CA and SA, and found
that protected areas have higher deforestation rates than areas with community management. Similarly, Nelson and
Chomitz (2011) found for the region that (i) protected areas of restricted use reduced fire substantially, but multi-use
protected areas are even more effective; and that (ii) in indigenous reserves the incidence of forest fire was reduced
by 16% as compared to non-protected areas. This contrasts with the findings of Miteva et al. (2012) that found
protected areas more efficient in constraining deforestation than other schemes. Other good examples of adaptive
community management in the continent include community forest concessions (e.g., Guatemala; Radachowsky et
al., 2012), multiple-use management of forests (Guariguata et al., 2012 ; see also examples in Brazil Klimas et al.,
2012, Soriano et al., 2012, and Bolívia Cronkleton et al., 2012); and local communities where research and
monitoring protocols are in place to pay the communities for collecting primary scientific data (Luzar et al., 2011).
27.3.3. Coastal Systems and Low-Lying Areas
27.3.3.1. Observed and Projected Impacts and Vulnerabilities
Climate change is altering coastal and marine ecosystems (Hoegh-Guldberg and Bruno, 2010). Coral reefs (Chapter
5, Cross-Chapter Box CC-CR), seagrass beds, mangroves, rocky reefs and shelves, and seamounts have few to no
areas left in the world that remain unaffected by human influence (Halpern et al., 2008). Anthropogenic drivers
associated with climate change decreased ocean productivity, altered food web dynamics, reduced abundance of
habitat-forming species, shifting species distributions, and greater incidence of disease (Hoegh-Guldberg and Bruno,
2010). Coastal and marine impacts and vulnerability are often associated with collateral effects of climate change
such as sea-level rise, ocean warming and ocean acidification (Cross-Chapter Box CC-OA). Overfishing, habitat
pollution and destruction, and the invasion of species also negatively impact biodiversity and the delivery of
ecosystem services (Guarderas et al., 2008; Halpern et al., 2008). Such negative impacts lead to losses that pose
significant challenges and costs for societies, particularly in developing countries (Hoegh-Guldberg and Bruno,
2010). For instance, the Ocean Health Index (Halpern et al., 2012) that measures how healthy the coupling of the
human-ocean system is for every coastal country (including parameters related to climate change), indicates that CA
countries rank amongst the lowest values. For SA, Suriname stands out with one of the highest scores.
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Coastal states of Latin America and the Caribbean have a human population of more than 610 million, 3/4 of who
live within 200 km of the coast (Guarderas et al., 2008). For instance, studying seven countries in the region (El
Salvador, Nicaragua, Costa Rica, Panama, Colombia, Venezuela, Ecuador), Lacambra and Zahedi (2011) found that
more than 30% of the population lives in coastal areas directly exposed to climatic events. Large coastal populations
are related to the significant transformation marine ecosystems have been undergoing in the region. Fish stocks,
places for recreation and tourism, and controls of pests and pathogens are all under pressure (Guarderas et al., 2008;
Mora, 2008). Moreover, SLR varied from 2 to 7 mm/yr between 1950 and 2008 in CA and SA. The Western
equatorial border, influenced by the ENSO phenomenon, shows a lower variation (of about 1 mm yr-1) and a range
of variation under El Nino events of the same order of magnitude that the sustained past changes (Losada et al.,
2013). The distribution of population is a crucial factor for inundation impact, with coastal areas being non-
homogeneously impacted. A scenario of 1m SLR would affect some coastal populations in Brazil and the Caribbean
islands (see Figure 27-6). (ECLAC, 2011a)
[INSERT FIGURE 27-6 HERE
Figure 27-6: Current and predicted coastal impacts and coastal dynamics in response to climate change.]
Coastal impacts - based on trends observed and projections, the figure shows how potential impacts may be
distributed in the region. Three cases: a) flooding: since flooding probability increases with increasing sea-level, one
may expect a higher probability of flooding in locations showing >40% of change over the last 60 years in the 100-
years total sea-level (excluding hurricanes). The figure also identifies urban areas where the highest increase in
flooding level has been obtained.; b) beach erosion: it increases with potential sediment transport, thus locations
where changes in potential sediment transport have increased over a certain threshold have a higher probability to be
eroded; c) sea-ports and reliability of coastal structures: the figure shows locations where, in the case of having a
protection structure in place, there is a reduction in the reliability of the structures due to the increase in the design
wave height estimates (ECLAC, 2011a).
Coastal dynamics - information based on historical time series that have been obtained by a combination
of data reanalysis, available instrumental information and satellite information. Advanced statistical techniques have
been used for obtaining trends including uncertainties (Izaguirre et al., 2013; Losada et al., 2013).]
The greatest flooding levels (hurricanes not considered) in the region are found in Rio de La Plata area, which
combine a 5 mm yr-1 change in storm surge with SLR changes in extreme flooding levels (ECLAC, 2011a; Losada
et al., 2013). Extreme flooding events may become more frequent since return periods are decreasing, and urban
coastal areas in the eastern coast will be particularly affected, while at the same time beach erosion is expected to
increase in southern Brazil and in scattered areas at the Pacific coast. (ECLAC, 2011a)
The majority of literature concerning climate change impacts for coastal and marine ecosystems considers coral
reefs (see also Chapter 5, Cross-Chapter Box CC-CR), mangroves and fisheries. Coral reefs are particularly
sensitive to climate-induced changes in the physical environment (Baker et al., 2008) to an extent that 1/3 of the
more than 700 species of reef-building corals worldwide are already threatened with extinction (Carpenter et al.,
2008). Coral bleaching and mortality are often associated with ocean warming and acidification (Baker et al., 2008).
If extreme sea surface temperatures are to continue, the projections of scenarios SRES (A1FI, 3C sensitivity, and
A1B with 2C and 4.5C sensitivity) indicate that it is possible that the Mesoamerican coral reef will collapse by
mid-century (between 2050 and 2070), causing major economic losses (WB, 2009). Extreme high sea surface
temperatures have been increasingly documented in the western Caribbean near the coast of CA and have resulted in
frequent bleaching events (1993, 1998, 2005, and again in 2010) of the Mesoamerican coral reef, located along the
coasts of Belize, Honduras and Guatemala (Eakin et al., 2010). Reef but also mangrove ecosystems are estimated to
contribute greatly to goods and services in economic terms. In Belize, for example, this amount is approximately
US$395-US$559 million annually, primarily through marine-based tourism, fisheries and coastal protection (Cooper
et al., 2008). In the Eastern Tropical Pacific, seascape trace abundance of cement and elevated nutrients in upwelled
waters are factors that help explain high bioerosion rates of local coral reefs (Manzello et al., 2008). In the
southwestern Atlantic coast, eastern Brazilian reefs might suffer a massive coral cover decline in the next 50 years
Francini-Filho et al. (2008). This estimate is based on coral disease prevalence and progression rate, along with
growth rate of Mussismilia braziliensis - a major reef-building coral species that is endemic in Brazil. These authors
also pointed out that coral diseases intensified between 2005 and 2007 based on qualitative observations since the
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1980s and regular monitoring since 2001. They have also predicted that - the studied coral species will be nearly
extinct in less than a century if the current rate of mortality due to disease is not reversed.
Mangroves are largely affected by anthropogenic activities whether or not they are climate driven. All mangrove
forests, along with important ecosystem goods and services, could be lost in the next 100 years if the present rate of
loss continues (1-2% a year) (Duke et al., 2007). Moreover, estimates are that climate change may lead to a
maximum global loss of 10 15% of mangrove forest by 2100 (Alongi, 2008). In CA and SA, some of the main
drivers of loss are deforestation and land conversion, agriculture and shrimp ponds (Polidoro et al., 2010). The
Atlantic and Pacific coasts of CA are some of the most endangered in the planet with regards to mangroves, since
approximately 40% of the present mangroves species are threatened with extinction (Polidoro et al., 2010).
Approximately 75% of the mangrove extension of the planet is concentrated in 15 countries, among which Brazil is
included (Giri et al., 2011). The rate of survival of original mangroves lies between 12.8% and 47.6% in the Tumaco
Bay (Colombia), resulting in ecosystem collapse, fisheries reduction and impacts on livelihoods (Lampis, 2010).
Gratiot et al. (2008) project for the current decade an increase of mean high water levels of 6 cm followed by 90m
shoreline retreat implying flooding of thousands of hectares of mangrove forest along the coast of French Guiana.
Peru and Colombia are two of the eight most vulnerable countries to climate change impacts on fisheries, due to the
combined effect of observed and projected warming, to species and productivity shifts in upwelling systems, to the
relative importance of fisheries to national economies and diets, and limited societal capacity to adapt to potential
impacts and opportunities (Allison et al., 2009). Fisheries production systems are already pressured by overfishing,
habitat loss, pollution, invasive species, water abstraction and damming (Allison et al., 2009). In Brazil, a decadal
rate of 0.16 trophic level decline (as measured by the Marine Trophic Index, which refers to the mean trophic level
of the catch) has been detected through most of the northeastern coast, between 1978 and 2000, which is one of the
highest rates documented in the world (Freire and Pauly, 2010).
Despite the focus in the literature on corals, mangroves and fisheries, there is evidence that other benthic marine
invertebrates that provide key services to reef systems, such as nutrient cycling, water quality regulation, and
herbivory, are also threatened by climate change (Przeslawski et al., 2008). The same applies for seagrasses for
which a worldwide decline has accelerated from a median of 0.9% yr-1 before 1940 to 7% yr-1 since 1990, which is
comparable to rates reported for mangroves, coral reefs, tropical rainforests and place seagrass meadows among the
most threatened ecosystems on earth (Waycott et al., 2009).
A major challenge of particular relevance at local and global scales will be to understand how these physical
changes will impact the biological environment of the ocean (e.g., Gutiérrez et al., 2011b), as the Humboldt Current
system -flowing along the west coast of SA- is the most productive upwelling system of the world in terms of fish
productivity.
27.3.3.2. Adaptation Practices
Designing marine protected areas (MPAs) that are resilient to climate change is a key adaptation strategy in coastal
and marine environments (McLeod et al., 2009). By 2007, Latin America and the Caribbean (which includes CA
and SA countries) had over 700 MPAs established covering around 1.5% of the coastal and shelf waters, most of
which allow varying levels of extractive activities (Guarderas et al., 2008). This protected area cover, however, is
insufficient to preserve important habitats or connectivity among populations at large biogeographic scales
(Guarderas et al., 2008).
Nevertheless, examples of adaptation in CA and SA are predominantly related to MPAs. In Brazil, a protected area
type known as Marine Extractive Reserves currently benefits 60,000 small-scale fishermen along the coast (Moura
et al., 2009). Examples of fisheries co-management, a form of a participatory process involving local fishermen
communities, government, academia and NGOs, are reported to favor a balance between conservation of marine
fisheries, coral reefs and mangroves on the one hand (Francini-Filho and Moura, 2008), and the improvement of
livelihoods, as well as the cultural survival of traditional populations on the other (Hastings, 2011; Moura et al.,
2009).
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Significant financial and human resources are expended annually in the marine reserves to support reef management
efforts. These actions, including the creation of marine reserves to protect from overfishing, improvement of
watershed management, and protection or replanting of coastal mangroves, are proven tools to improve ecosystem
functioning. In Mesoamerican reefs, (Carilli et al., 2009) found out that such actions may also actually increase the
thermal tolerance of corals to bleaching stress and thus the associated likelihood of surviving future warming.
In relation to mangroves, in addition to marine protected areas that include mangroves and functionally linked
ecosystems, Gilman et al. (2008) list a number of other relevant adaptation practices: coastal planning to facilitate
mangrove migration with sea-level rise, management of activities within the catchment that affect long-term trends
in the mangrove sediment elevation, better management of non-climate stressors, and the rehabilitation of degraded
areas. However, such types of practices are not frequent in the region.
On the other hand, the implementation of adaptation strategies to sea level rise or to address coastal erosion is more
commonly seen in many countries in the region (Lacambra and Zahedi, 2011). For instance, redirecting new
settlements to better-protected locations and to promote investments in appropriate infrastructure shall be required in
the low elevation coastal zones (LECZ) of the region, particularly in lower income countries with limited resources,
which are especially vulnerable. The same applies to countries with high shares of land (e.g., Brazil ranking 7th
worldwide of the total land area in the LECZ) and/or population (e.g., Guyana and Suriname rank 2nd and 5th by the
share of population in the LECZ, having respectively 76% and 55% of their populations in such areas (McGranahan
et al., 2007). Adaptation will demand effective and enforceable regulations and economic incentives, all of which
require political will as well as financial and human capital (McGranahan et al., 2007). Adaptive practices
addressing river flooding are also being made available as in the study of Casco et al. (2011) for the low Parana
river, in Argentina; (see also Chapters 5 and 6 for coastal and marine adaptation).
27.3.4. Food Production Systems and Food Security
27.3.4.1. Observed and Projected Impacts and Vulnerabilities
Increases in the global demand for food and biofuels promoted a sharp increase in agricultural production in SA and
CA mainly associated with the expansion of planted areas (see Chapter 7), and this trend is predicted to continue in
the future (see 27.2.2.1). Ecosystems are being and will be affected in isolation and synergistically by climate
variability/change and land use changes, which are comparable drivers of environmental change (see 27.2.2.1;
27.3.2.1). By the end of 21th century (13 GCMs, under SRES A1B and B1) SA could lose between 1% and 21% of
its arable land due to CC and population growth (Zhang and Cai, 2011).
Optimal land management could combine efficient agricultural and biofuels production with ecosystem preservation
under CC, however current practices are leading to a deterioration of ecosystems throughout the continent (see
section 27.3.2). In southern Brazilian Amazonia water yields (mean daily discharge (mm.day-1)) were near four
times higher in soy than forested watersheds, and showed greater seasonal variability (Hayhoe et al., 2011). In the
Argentinean Pampas current land use changes disrupt water and biogeochemical cycles and may result in soil
salinization, altered C and N storage, surface runoff and stream acidification (Berthrong et al., 2009; Farley et al.,
2009; Nosetto et al., 2008). In central Argentina flood extension was associated with the dynamics of groundwater
level that has been influenced by precipitation and land use change (Viglizzo et al., 2009).
Observed impacts: The SESA region has shown significant increases in precipitation and wetter soil conditions
during the 20th century (Giorgi, 2002) (see Table 27-1) that benefited summer crops and pastures productivity, and
contributed to the expansion of agricultural areas (Barros, 2010; Hoyos et al., 2012). Wetter conditions observed
during 1970-2000 (in relation to 1930-1960) led to increases in maize and soybean yields (9% to 58%) in Argentina,
Uruguay and Southern Brazil (Magrin et al., 2007b). Even if rainfall projections estimate increases of about 25% in
SESA for 2100, agricultural systems could be threatened if climate reverts to a drier situation due to interdecadal
variability. This could put at risk the viability of continuous agriculture in marginal regions of the Argentina s
Pampas (Podestá et al., 2009). During the 30 s and 40 s, dry and windy conditions together with deforestation,
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overgrazing, overcropping, and non-suitable tillage produced severe dust storms, cattle mortality, crop failure, and
rural migration (Viglizzo and Frank, 2006).
At the global scale (see Chapter 7), warming since 1981 has reduced wheat, maize and barley productivity, although
the impacts were small compared with the technological yield gains over the same period (Lobell and Field, 2007).
In central Argentina, simulated potential wheat yield without considering technological improvements- has been
decreasing at increasing rates since 1930 (1930-2000: -28 kg/ha/year; 1970-2000: -53 kg/ha/year) in response to
increases in minimum temperature during October-November (1930-2000: +0.4°C/decade; 1970-2000:
+0.6°C/decade) (Magrin et al., 2009). The observed changes in the growing season temperature and precipitation
between 1980 and 2008 have slowed the positive yield trends due to improved genetics in Brazilian wheat, maize
and soy, as well as Paraguayan soy. In contrast, rice in Brazil and soybean in Argentina have benefited from
precipitation and temperature trends (2011). In Argentina, increases in soybean yield may be associated with
weather types that favour the entry of cold air from the south reducing thermal stress during flowering and pod set,
and weather types that increase the probability of dry days at harvest (Bettolli et al., 2009).
Projected impacts: The assessment of future climate scenarios implications in food production and food security
(see Table 27-5) shows a large range of uncertainty across the spectrum of climate models and scenarios. One of the
uncertainties is related to the effect of CO2 on plant physiology. Many crops (such as soybean, common bean, maize
and sugarcane) can probably respond with an increasing productivity as a result of higher growth rates and better
water use efficiency. However, food quality could decrease due to higher sugar contents in grain and fruits, and
decreases in the protein content in cereals and legumes (DaMatta et al., 2010). Uncertainties associated with climate
and crop models, as well as with the uncertainty in human behavior, potentially lead to large error bars on any long-
term prediction of food output. However, the trends presented here represent the current available information.
In SESA, some crops could be benefited until mid-21st-century if CO2 effects are considered (see Table 27-5),
although interannual and decadal climate variability could provoke important damages. In Uruguay and Argentina
productivity could increase or remain almost stable until the 2030s-2050s depending on the SRES scenario
(ECLAC, 2010c). Warmer and wetter conditions may benefit crops towards the southern and western zone of the
Pampas (ECLAC, 2010c; Magrin et al., 2007c). In South Brazil, irrigated rice yield (Walter et al., 2010) and bean
productivity (Costa et al., 2009) is expected to increase. If technological improvement is considered, the
productivity of common bean and maize could increase between 40% and 90% (Costa et al., 2009). Sugarcane
production could benefit as warming could allow the expansion of planted areas towards the south, where low
temperatures are a limiting factor (Pinto et al., 2008). Increases in crop productivity could reach 6% in Sao Paulo
state towards 2040 (Marin et al., 2009). In Paraguay the yields of soybean, maize and wheat could have slight
variations (-1.4% to +3.5%) until 2020 (ECLAC, 2010a).
In Chile and western Argentina, yields could be reduced by water limitation. In central Chile (30S to 42S)
temperature increases, reduction in chilling hours and water shortages may reduce productivity of winter crops,
fruits, vines and radiata pine. Conversely, rising temperatures, more moderate frosts and more abundant water will
very likely benefit all species towards the South (ECLAC, 2010a; Meza and Silva, 2009). In northern Patagonia
(Argentina) fruit and vegetable growing could be negatively affected because of a reduction in rainfall and in
average flows in the Neuquén River basin. In the north of the Mendoza basin (Argentina) increases in water
demand, due to population growth, may compromise the availability of subterranean water for irrigation, pushing up
irrigation costs and forcing many producers out of farming towards 2030. Also, water quality could be reduced by
the worsening of existing salinization processes (ECLAC, 2010a).
In CA, NEB and parts of the Andean region (Table 27-5) CC could affect crop yields, local economies and food
security. It is very likely that growing season temperatures in parts of tropical SA, east of the Andes and CA exceed
the extreme seasonal temperatures documented from 1900 to 2006 at the end of this century (23 GCMs), affecting
regional agricultural productivity and human welfare (Battisti and Naylor, 2009). For NEB, declining crop yields in
subsistence crops such as beans, corn and cassava are projected (Lobell et al., 2008; Margulis et al., 2010). In
addition, increases in temperature could reduce the areas currently favorable to cowpea bean (Silva et al., 2010). The
highest warming foreseen for 2100 (5.8 °C, under SRES A2 scenario) could make the coffee crop unfeasible in
Minas Gerais and Sao Paulo (SE Brazil) if no adaptation action is accomplished. Thus, the coffee crop may have to
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be transferred to southern regions where temperatures are lower and the frost risk will be reduced (Camargo, 2010).
With +3C, Arabica coffee is expected to expand in the extreme south of Brazil, the Uruguayan border and North of
Argentina (Zullo et al., 2011). Brazilian potato production could be restricted to a few months in currently warm
areas, which today allow potato production all around the year (Lopes et al., 2011). Large losses of suitable
environments for the Pequi tree (Caryocar brasiliense; an economically important Cerrado fruit tree) are projected
by 2050, mainly affecting the poorest communities in Central Brazil (Nabout et al., 2011). In the Amazon region
soybean yields would be reduced by 44% in the worst scenario (HadCM3 and no CO2 fertilization) by 2050 (Lapola
et al. (2011). By 2050, according to 17GCMs under SRES A2 scenario, 80% of crops will be impacted in more than
60% of current areas of cultivation in Colombia, with severe impacts in perennial and exportable crops (Ramirez-
Villegas et al., 2012).
Teixeira et al. (2013) identified hot spots for heat stress towards 2071-2100 under the A1B scenario and suggest that
rice in South East Brazil, maize in CA and SA, and soybean in Central Brazil will be the crops and zones most
affected by increases in temperature.
In CA, changes projected in climate could severely affect the poorest population and specially their food security
increasing the current rate of chronic malnutrition. Currently, Guatemala is the most food insecure country by
percentage of the population (30.4%) and the problem has been increasing in recent years (FAO, 2012). The impact
of climate variability and change is a great challenge in the region. As an example, the recent rust problem on the
coffee sector of 2012/2013 has affected near to 600.000 ha (55% of the total area) (ICO, 2013) and will reduce
employment by 30% to 40% for the harvest 2013/2014 (FEWS NET, 2013). At least 1.4 million people in
Guatemala, El Salvador, Honduras and Nicaragua depend on the coffee sector, which is very susceptible to climate
variations. In Panamá, the large interannual climate variability will continue to be the dominant in uence on
seasonal maize yield into the coming decades (Ruane et al., 2013). In the future, warming conditions combined with
more variable rainfall are expected to reduce maize, bean and rice productivity (ECLAC, 2010c); rice and wheat
yields could decrease up to 10% by 2030 (Lobell et al., 2008 (medium confidence) (see Table 26-7). In CA, near to
90% of agricultural production destined to internal consumption is composed by maize (70%), bean (25%) and rice
(6%) (ECLAC, 2011d).
[INSERT TABLE 27-5 HERE
Table 27-5: Impacts on agriculture.]
CC may also alter the current scenario of plant diseases and their management, having effects on productivity (Ghini
et al., 2011). In Argentina, years with severe infection of late cycle diseases in soybean could increase; severe
outbreaks of the Mal de Rio Cuarto virus in maize (natural vectors: Delphacodes kuscheli and Delphacodes
hayward) could be more frequent; and wheat head fusariosis will increase slightly in the south of the Pampas region
by the end of the century (ECLAC, 2010a). In Brazil favorable areas for soybean and coffee rusts will move toward
the south, particularly for the hottest scenario of 2080 (Alves et al., 2011). Potato late blight (Phytophtora infestans)
severity is expected to increase in Perú (Giraldo et al., 2010).
The choice of livestock species could change in the future. For example, by 2060, under a hot and dry scenario, beef
and dairy cattle, pigs and chickens production choice could decrease between 0.9 and 3.2%, while sheep election
could increase by 7% mainly in the Andean countries (Seo et al., 2010). Future climate could strongly affect milk
production and feed intake in dairy cattle in Brazil, where substantial modifications in areas suitable for livestock,
mainly in the Pernambuco region, are expected (Silva et al., 2009). Warming and drying conditions in Nicaragua
could reduce milk production, mainly among farmers that are already seriously affected under average dry season
conditions (Lentes et al., 2010).
CC impact on regional welfare will depend not only on changes in yield, but also in international trade. According to
Hertel et al. (2010), by 2030, global cereal price could change between increases of 32% (low-productivity scenario)
or decreases of 16% (optimistic yield scenario). A rise in prices could benefit net exporting countries like Brazil,
where gains from terms of trade shifts could outweigh the losses due to CC. Despite experiencing significant
negative yield shocks, some countries tend to gain from higher commodity prices. However, most poor household
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are food purchasers and rising commodity prices tend to have a negative effect on poverty (von Braun, 2007).
According to Chapter 7, increases in prices during 2007-2009 led to rising poverty in Nicaragua.
27.3.4.2. Adaptation Practices
Genetic advances and suitable soil and technological management may induce an increase in some crops yield
despite unfavorable future climate conditions. In Argentina, genetic techniques, specific scientific knowledge and
land-use planning are viewed as promising sources of adaptation (Urcola et al., 2010). Adjustments in sowing dates
and fertilization rates could reduce negative impacts or increase yields in maize and wheat crops in Argentina and
Chile (Magrin et al., 2009; Meza and Silva, 2009; Travasso et al., 2009b). Furthermore, in central Chile and
southern Pampas in Argentina warmer climates could allow performing two crops per season increasing productivity
per unit land (Meza et al., 2008; Monzon et al., 2007). In Brazil, adaptation strategies for coffee crops include:
planting at high densities, vegetated soil, accurate irrigation and breeding programs, and shading management
system (arborization) (Camargo, 2010). Shading is also used in Costa Rica and Colombia. In South Brazil, a good
option for irrigated rice could be to plant early cultivars (Walter et al., 2010).
Water management is other option for a needed better preparedness regarding water scarcity (see section 27.3.1). In
Chile, the adoption of water conservation practices depends on social capital, farm size and land use; and the
adoption of technologies that require investment depend on the accesses to credit and irrigation water subsidies
(Jara-Rojas et al., 2012). Deficit irrigation could be an effective measure for water savings in dry areas such as the
Bolivian Altiplano (quinoa), central Brazil (tomatoes) and northern Argentina (cotton) (Geerts and Raes, 2009). In
rainfed crops adaptive strategies might need to look at the harvest, storage, temporal transfer and efficient use of
rainfall water. In addition, some agronomic practices like: fallowing, crop sequences, groundwater management, no-
till operations, cover crops and fertilization could improve the adaptation to water scarcity (Quiroga and Gaggioli,
2011).
One approach to adapting to future CC is by assisting people to cope with current climate variability (Baethgen,
2010), for which the use of climatic forecasts in agricultural planning presents a measure. Increased access and
improvement of climate forecast information enhances the ability of the farmers in the Brazilian Amazon to cope
with El Nino impacts (Moran et al., 2006). The Southern Oscillation Index for maize, and the South Atlantic Sea
Surface Temperature for soybean and sunflower were the best indicators of annual crop yield variability in
Argentina (Travasso et al., 2009a). Another possibility to cope with extreme events consists in transferring weather-
related risks by using different types of rural insurance (Baethgen, 2010). Index insurance is one mechanism that has
been recently introduced to overcome obstacles to traditional agricultural and disaster insurance markets (see
chapter 15). For the support of such parametric agricultural insurance, a Central American climate database was
recently established (SICA, 2013).
Local and indigenous knowledge have the potential to bring solutions even in the face of rapidly changing climatic
conditions (Altieri and Koohafkan, 2008; Folke et al., 2002); although, migration, climate change, and market
integration are reducing indigenous capacity for dealing with weather and climate risk (Pérez et al., 2010; Valdivia
et al., 2010). Crop diversification is used in the Peruvian Andes to suppress pest outbreaks and dampen pathogen
transmission (Lin, 2011). In Honduras, Nicaragua and Guatemala traditional practices have proven more resilient to
erosion and runoff and have helped retain more topsoil and moisture (Holt-Gimenez, 2002). In El Salvador, if local
sustainability efforts continue the future climate vulnerability index could only slightly increase by 2015 (Aguilar et
al., 2009). Studies with indigenous farmers in highland Bolivia and Peru indicate that constraints on access to key
resources must be addressed for reducing vulnerability over time (McDowell and Hess, 2012; Sietz et al., 2012). In
Guatemala and Honduras adaptive response between coffees farmers is mainly related to land availability, while
participation in organized groups and access to information contribute to adaptive decision-making (Tucker et al.,
2010). Otherwise, adaptation may include an orientation towards non-farming activities to sustain their livelihoods
and be able to meet their food requirements (Sietz, 2011). In NEB increasing vulnerability related to degradation of
natural resources (due to over use of soil and water) encouraged farmers toward off-farm activities, however they
could not improve their well-being (Sietz et al., 2006; Sietz et al., 2011). Migration is other strategy in ecosystems
and regions at high risk of climate hazards (see Section 27.3.1.1). During 1970-2000 LAC has had the great rate of
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net migration per population in the dryland zones (Sherbinin et al., 2012). In CA near to 25% of the surveyed
households reported some type of migration during the coffee crisis (Tucker et al., 2010). Some migrations, e.g.
Guatemala 1960s-1990s; El Salvador 1950s-1980s; NEB 1960s-present, have provoked conflict in receiving areas
(Reuveny, 2007).
Shifting in agricultural zoning has been an autonomous adaptation observed in SA. In Argentina e.g., increases in
precipitation promoted the expansion of the agricultural frontier to the West and North of the traditional agricultural
area, resulting in environmental damage that could be aggravated in the future (Barros, 2008; República Argentina,
2007). Adjustment of production practices, like farmers in the semi-arid zones of mountain regions of Bolivia have
begun as they noticed strong changes in the climate since the 1980s, including upward migration of crops, selection
of more resistant varieties and water capturing, presents a further adaptation measure (PNCC, 2007).
Organic systems could enhance adaptive capacity due to the application of traditional skills and farmers knowledge,
soil fertility-building techniques and a high degree of diversity (ITC, 2007). As mentioned previously, crop
diversity, local knowledge, soil conservation, and economic diversity are all documented strategies for managing
risk in CA and SA. A controversial, but important issue in relation to adaptation is the use of genetically modified
plants to produce food, with biotech crops being a strategy to cope with the needed food productivity increase
considering global population trend (see Chapter 7). Brazil and Argentina are the 2nd and 3rd fastest growing biotech
crop producers in the world after the US (Marshall, 2012). However, this option is problematic for the small farms
(Mercer et al., 2012), which are least favorable towards GMO (??) (Soleri et al., 2008). According to Eakin and
Wehbe (2009) some practices could be an adaptive option for specific farm enterprises, but may have maladaptive
implications at regional scales, and over time, become maladaptive for individual enterprises.
27.3.5. Human Settlements, Industry, and Infrastructure
According to the World Bank database (WB, 2012) CA and SA are the geographic regions with the second highest
urban population (79%), behind North America (82%) and well above the world average (50%). Therefore this
section focuses on assessing the literature on climate change impacts and vulnerability of urban human settlements.
The information provided should be complemented with other sections of the chapter (see 27.2.2.2; 27.3.1; 27.3.3;
and 27.3.7)
27.3.5.1. Observed and Projected Impacts and Vulnerabilities
Urban human settlements suffer from many of the vulnerabilities and impacts already presented in several sections
of this chapter. The provision of critical resources and services as already discussed in the chapter water, health and
energy and of adequate infrastructure and housing remain determinants of urban vulnerability that are enhanced by
climate change (Roberts, 2009; Romero-Lankao et al., 2013b; Romero-Lankao et al., 2012b; Smolka and
Larangeira, 2008; Winchester, 2008).
Water resource management for example (see section 27.3.1), is a major concern for many cities that need to
provide both drinking water and sanitation (Henríquez Ruiz, 2009). More than 20% of the population in the region
are concentrated in the largest city in each country (WB, 2012), hence water availability for human consumption in
the region s megacities (e.g. Sao Paulo, Santiago, Lima, Buenos Aires) is of great concern. In this context, reduction
in glacier and snowmelt related runoff in the Andes poses important adaptation challenges for many cities, e.g. the
metropolitan areas of Lima, La Paz/El Alto and Santiago de Chile (Bradley et al., 2006; Hegglin and Huggel, 2008;
Melo et al., 2010). Flooding is also a preoccupation in several cities. In Sao Paulo for example, according to
Marengo et al. (2009b; 2013b) the number of days with rainfall above 50 mm were almost zero during the 1950s
and now they occur between 2 to 5 times per year (2000-2010). The increase in precipitation is one of the expected
risks affecting the city of Sao Paulo as presented in Box 27-2. Increases in flood events during 1980-2000 have been
observed also in the Buenos Aires province and Metropolitan Area (Andrade and Scarpati, 2007; Barros et al., 2008;
Hegglin and Huggel, 2008; Nabel et al., 2008). There are also the combined effects of climate change impacts,
human settlements features and other stresses, such as more intense pollution events (Moreno, 2006; Nobre et al.,
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2011; Nobre, 2011; Romero-Lankao et al., 2013b) and more intense hydrological cycles from urban heat-island
effects. In terms of these combined effects, peri-urban areas and irregular settlements pose particular challenges to
urban governance and risk management given their scale, lack of infrastructure and socio-economic fragility
(Romero-Lankao et al., 2012).
______START BOX 27-2 HERE______
Box 27-2. Vulnerability of South American Megacities to Climate Change: The Case of the Metropolitan
Region of Sao Paulo (MRSP)
Research in the Metropolitan Region of Sao Paulo (MRSP), between 2009 and 2011, represents a comprehensive
and interdisciplinary project on the impacts of climate variability and change, and vulnerability of Brazilian
megacities. Studies derived from this project (Marengo et al., 2013b; Nobre et al., 2011) identify the impacts of
climate extremes on the occurrence of natural disasters and human health. These impacts are linked to a projected
increase of 38% in the extension of the urban area of the MRSP by 2030, accompanied by a projected increase in
rainfall extremes. These may induce an intensification of urban flash floods and landslides, affecting large populated
areas already vulnerable to climate extremes and variability. The urbanization process in the MRSP has been
affecting the local climate, and the intensification of the heat island effect to a certain degree may be responsible for
the 2°C warming detected in the city during the last 50 years (Nobre et al., 2011). This warming has been further
accompanied by an increase in heavy precipitation as well as more frequent warm nights (Marengo et al., 2013b;
Silva Dias et al., 2012). By 2100, climate projections based on data from 1933-2010 show an expected warming
between 2-3°C in the MRSP, together with a possible doubling of the number of days with heavy precipitation in
comparison to the present (Marengo et al., 2013b; Silva Dias et al., 2012).
With the projected changes in climate and in the extension of the MRSP (Marengo et al., 2013b) more than 20% of
the total area of the city could be potentially affected by natural disasters. More frequent floods may increase the
risk of leptospirosis, which, together with increasing air pollution and worsening environmental conditions that
trigger the risk of respiratory diseases, would leave the population of the MRSP more vulnerable. Potential
adaptation measures include a set of strategies that need to be developed by the MRSP and its institutions to face
these environmental changes. These include improved building controls to avoid construction in risk areas,
investment in public transportation, protection of the urban basins and the creation of forest corridors in the
collecting basins and slope regions. The lessons learned suggest that the knowledge on the observed and projected
environmental changes, as well as on the vulnerability of populations living in risk areas is of great importance for
defining adaptation policies that in turn constitute a first step towards building resilient cities that in turn improve
urban quality of life in Brazil.
______END BOX 27-2 HERE______
Changes in prevailing urban climates have led to changing patterns of disease vectors, and water-borne disease
issues linked to water availability and subsequent quality (see section 27.3.7). The influence of climate change on
particulate matter and other local contaminants is another concern (Moreno, 2006; Romero-Lankao et al., 2013b). It
is important to highlight the relationship between water and health, given the problems of water stress and intense
precipitation events affecting many urban centers. Both relate to changing disease risks, as well as wider problems
of event-related mortalities and morbidity, and infrastructure and property damage. These risks are compounded for
low-income groups in settlements with little or no service provision, e.g. waste collection, piped drinking water,
sanitation, (ECLAC, 2008). Existing cases of flooding, air pollution and heat waves reveal that not only low-income
groups are at risk, but also that wealthier sectors are not spared. Factors such as high-density settlement (Barros et
al., 2008) and the characteristics of some hazards explain this e.g., poor and wealthy alike are at risk from air
pollution and temperature in Santiago de Chile and Bogota (2013b; Romero-Lankao et al., 2012a).
There are also other climate change risks in terms of economic activity location and impacts on urban manufacturing
and service workers, e.g. thermal stress (Hsiang, 2010), and the forms of urban expansion or sprawl into areas where
ecosystem services may be compromised and risks enhanced, e.g. floodplains. Both processes are also related to
rising motorization rates that facilitate suburban development and new regional agglomerations that bring pressure
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to bear on land uses that favor infiltration, surface cooling and biodiversity; the number of light vehicles in Latin
America and the Caribbean is expected to double between 2000 and 2030, and be three times the 2000 figure by
2050 (ECLAC, 2009c).
While urban populations face diverse social, political, economic and environmental risks in daily life, climate
change adds a new dimension to these risk settings (Pielke Jr et al., 2003; Roberts, 2009; Romero-Lankao and Qin,
2011). Since urban development remains fragile in many cases, with weak planning responses, climate change can
compound existing challenges. The probabilities and magnitudes of these events in each urban center will differ
significantly according to socioeconomic, institutional and physical contexts.
27.3.5.2. Adaptation Practices
The direct and indirect effects of climate change as flooding, heat islands and food insecurity present cities with a
set of challenges and opportunities for mainstreaming flood management, warning systems and other adaptation
responses with sustainability goals (Bradley et al., 2006; Hardoy and Pandiella, 2009; Hegglin and Huggel, 2008;
Romero-Lankao, 2010; Romero-Lankao et al., 2013a; Romero-Lankao, 2012).
Urban populations, economic activities and authorities have a long experience of responding to climate-related
hazards, particularly through disaster risk management (e.g., Tucuman and San Martin, Argentina (Plaza and
Pasculi, 2007; Sayago et al., 2010)), and land use and economic development planning to a limited extent (Barton,
2009). Climate policies can build on these. Local administrations participate in the ICLEI, C40, IDB Emerging and
Sustainable Cities Initiative (ESCI) (IDB, 2013), and other networks, demonstrating their engagement in the
generation of more climate-resilient cities. In smaller settlements, there is less capacity for adequate responses (e.g.,
climate change and vulnerability information (Hardoy and Romero-Lankao, 2011). Policies, plans and programs are
required to reduce social vulnerability, and identify and reduce potential economic effects of climate on the local
economy. Rio de Janeiro, for example, with its coastline property and high dependence on tourists (and their
perceptions of risk), cannot ignore these climate-related hazards (Gasper et al., 2011).
Poverty and vulnerability, as interlinked elements of the adaptation challenge in CA and SA, remain pivotal to
understanding how urban climate policies can be streamlined with broader development issues and not solely the
capacity to respond to climate change (Hardoy and Pandiella, 2009; Hardoy and Romero-Lankao, 2011; Winchester
and Szalachman, 2009). These broader links include addressing the determinants of vulnerability (e.g., access to
education, health and infrastructure, and to emergency response systems (Romero-Lankao, 2007a; Romero-Lankao
and Qin, 2011). Among these response options, a focus on social assets has been highlighted by Rubin and Rossing
(2012), rather than a, purely, physical asset focus.
Much urbanisation involves in-migrating or already resident, low-income groups and their location in risk-prone
zones (Costa Fereira et al., 2011). The need to consider land use arrangements, particularly urban growth on risk-
prone zones, as part of climate change adaptation highlights the role of green areas that mitigate the heat island
effect and reduce risks from landslides and flooding (Krellenberg et al., 2013; Rodríguez Laredo, 2011).
In the case of governance frameworks, there is clear evidence that incorporation of climate change considerations
into wider city planning is still a challenge, as are more inter-sectoral and participative processes that have been
linked to more effective policies (Barton, 2009; Barton, 2013; De Oliveira, 2009; Romero-Lankao et al., 2013a).
Several metropolitan adaptation plans have been generated over the last five years, e.g. Bogotá, Buenos Aires,
Esmeraldas, Quito, and Sao Paulo, although for the most part they have been restricted to the largest
conglomerations and are often included as an addition to mitigation plans (Carmin et al., 2009; Luque et al., 2013;
Romero-Lankao, 2007b; Romero-Lankao et al., 2013a; Romero-Lankao et al., 2012b).
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27.3.6. Renewable Energy
27.3.6.1. Observed and Projected Impacts and Vulnerabilities
Table 27-6 shows the relevance of RE in the Latin America energy matrix as compared to the world for 2009
according to the International Energy Agency statistics (IEA, 2012). Hydropower is the most representative source
of RE and therefore analyzed separately from this section and all other RE sources (see case study in section
27.6.1.). Geothermal energy will be not discussed as it is assumed that there is no impact of climate change on the
effectiveness of this energy type (Arvizu et al., 2011).
[INSERT TABLE 27-6 HERE
Table 27-6: Comparison of consumption of different energetics in Latin America and the world (in thousand tons of
oil equivalent (ktoe) on a net calorific value basis).]
Hydro, wind energy and biofuel production might be sensitive to climate change in Brazil (Lucena et al., 2009).
With the vital role that RE plays in mitigating the effects of GCC, being by far the most important sources of non-
hydro RE in SA and CA, this sensitivity demands the implementation of RE projects that will increase knowledge
on the crops providing bioenergy.
For historical reasons, CA and SA developed sugarcane as bioenergy feedstock. Brazil accounts for the most
intensive RE production as bioethanol, which is used by the majority of the cars in the country (Goldemberg, 2008)
whereas biodiesel comprises 5% of all diesel nationwide. With the continent s long latitudinal length, the expected
impacts of climate change on plants will be complex due to a wide variety of climate conditions, so that different
crops would have to be used in different regions. In Brazil, most of the biodiesel comes from soybeans, but there are
promising new sources such as palm oil (Lucena et al., 2009). The development of palm oil as well as soybean are
important factors that induce land use change, with a potential to influence stability of forests and biodiversity in
certain key regions in SA, such as the Amazon (section 27.2.2.1).
Biofuels can help CA and SA to decrease emissions from energy production and use. However, RE might imply
potential problems such as those related to positive net emissions of greenhouse gases, threats to biodiversity, an
increase in food prices and competition for water resources (section 27.2.3), some of which can be reverted or
attenuated (Koh and Ghazoul, 2008). For example, the sugarcane agro-industry in Brazil combusts bagasse to
produce electricity, providing power for the bioethanol industry and increasing sustainability. The excess heat
energy is then used to generate bioelectricity, thus allowing the biorefinery to be self-sufficient in energy utilization
(Amorim et al., 2011; Dias et al., 2012). In 2005/2006 the production of bioelectricity was estimated to be 9.2 kWh
per ton of sugarcane (Macedo et al., 2008), approximately 2% of Brazil s total energy generation production.
Most bioenergy feedstocks at present in production in CA and SA are grasses. In the case of sugarcane, the
responses to the elevation of CO2 concentration up to 720 ppmv have been shown to be positive in terms of biomass
production and principally regarding water use efficiency (De Souza et al., 2008).
The production of energy from renewable sources such as hydro- and wind power is greatly dependent on climatic
conditions and therefore may be impacted in the future by GCC. Lucena et al. (2010a) suggests an increasing energy
vulnerability of the poorest regions of Brazil to GCC together with a possible negative influence on biofuels
production and electricity generation, mainly biodiesel and hydropower respectively. (JAM: we use CC and GCC, I
believe that we use should use CC only).
Expansion of biofuel crops in Brazil might cause both direct and indirect land use changes (e.g., biofuel crops
replacing rangelands, which previously replaced forests) with the direct land use changes, according to simulation
performed by Lapola et al. (2010) of the effects for 2020. The same study shows that sugarcane ethanol and
biodiesel derived from soybean each contribute with about one half of the indirect deforestation projected for 2020
(121.970 km2) (Lapola et al., 2010). Thus, indirect land use changes, especially those causing the rangeland frontier
to move further into the Amazonian forests, might potentially offset carbon savings from biofuels production.
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The increase in global ethanol demand is leading to the development of new hydrolytic processes capable of
converting cellulose into ethanol (Dos Santos et al., 2011). The expected increase in the hydrolysis technologies is
very likely to balance the requirement of land for biomass crops. Thus, the development of these technologies has a
strong potential to diminish social (e.g. negative health effects due the burning process, poor labor conditions) and
environmental impacts (e.g. loss of biodiversity, water and land uses) whereas at it can improve the economic
potential of sugarcane. One adaptation measure will be to increase the productivity of bioenergy crops due to
planting in high productivity environments with highly developed technologies, in order to use less land. As one of
the main centers of biotech agriculture application in the world (Gruskin, 2012), the region has a great potential to
achieve this goal.
As the effects previously reported on crops growing in SESA might prevail (see 27.3.4.1), i.e. that an increase in
productivity may happen due to increasing precipitation, future uncertainty will have to be dealt with by preparing
adapted varieties of soybean in order to maintain food and biodiesel production, mainly in Argentina as it is one of
the main producers of biodiesel from soybean in the world (Chum et al., 2011).
Other renewable energy sources such as wind power generation may also be vulnerable, raising the need for
further research. According to Lucena et al. (2009; 2010b) the projections of changes in wind power in Brazil, may
favor the use of this kind of energy in the future.
27.3.6.2. Adaptation Practices
RE will become increasingly more important over time as this is closely related with the emissions of GHG
(Fischedick et al., 2011). Thus, RE could have an important role as adaptation means to provide sustainable energy
for development in the region (see also 27.6.1). However, the production of RE requires large available areas for
agriculture, which is the case of Argentina, Bolivia, Brazil, Chile, Colombia, Peru and Venezuela, that together
represent 90% of the total area of CA and SA. However, for small countries it might not be possible to use
bioenergy. Instead, they could benefit in the future from other types of RE, such as geothermal, eolic, photovoltaic
etc., depending on policies and investment in different technologies. This is important because economic
development is thought to be strongly correlated with an increase in energy use (Smil, 2000), which is itself
associated with an increase in emissions (Sathaye et al., 2011).
Latin America is second to Africa in terms of technical potential for bioenergy production from rain-fed
lignocellulosic feedstocks on unprotected grassland and woodlands (Chum et al., 2011). Among the most important
adaptation measures regarding RE are: (1) management of land use change (LUC); and (2) development of policies
for financing and management of science and technology for all types of RE in the region.
If carefully managed, biofuel crops can be used as a means to regenerate biodiversity as proposed by Buckeridge et
al. (2012) highlighting that the technology for tropical forest regeneration has become available and that forests
could share land with biofuel crops (such as sugarcane) taking advantage of forests mitigating potential. A possible
adaptation measure could be to expand the use of reforestation technology to other countries in CA and SA.
One of the main adaptation issues is related to the food vs. fuel issues (Valentine et al., 2012). This is important
because an increase in bioenergy feedstocks might threaten primary food production in a scenario expected to feed
future populations with an increase of 70% in production (Gruskin, 2012; Valentine et al., 2012). This is particularly
important in the region as it has one of the highest percentages of arable land available for food production in the
world (Nellemann et al., 2009). As CA and SA develop new strategies to produce more RE there might be a
pressure for more acreage to produce bioenergy. Because climate change will affect bioenergy and food crops at the
same time, their effects, as well as the adaptation measures related to agriculture will be similar. The main risks
identified by Arvizu et al. (2011) are: (1) business as usual; (2) un-reconciled growth, and (3) environment and food
vs. fuel. Thus, the most important adaptation measures will be the ones related to the control of economic growth,
environmental management and agriculture production. The choice for lignocellulosic feedstocks (e.g. sugarcane
second generation technologies) will be an important mitigation/adaptation measure because these feedstocks do not
compete with food (Arvizu et al., 2011). In the case of sugarcane, for instance, an increase of ca. 40% in the
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production of bioethanol is expected as a result of the implantation of second generation technologies coupled with
the first generation ones already existent in Brazil (De Souza et al., 2013; Dias et al., 2012).
Biodiesel production has the lowest costs in Latin America (Chum et al., 2011) due to the high production of
soybean in Brazil and Argentina. The use of biodiesel to complement oil-derived diesel is a productive choice for
adaptation measures regarding this bioenergy source. Also, the cost of ethanol, mainly derived from sugarcane, is
the lowest in CA, SA and Latin America (Chum et al., 2011) and as an adaptation measure, such costs, as well as the
one of biodiesel, should be lowered even more by improving technologies related to agricultural and industrial
production of both. Indeed, it has been reported that in LA the use of agricultural budgets by governments for
investment in public goods induces faster growth, decreasing poverty and environmental degradation (López and
Galinato, 2007). The pressure of soy expansion due to biodiesel demand can lead to land use change and
consequently to economic teleconnections, as suggested by Nepstad et al. (2006). These teleconnections may link
Amazon deforestation derived from soy expansion to the economic growth in some developing countries due to
changes in the demand of soy. These effects may possibly mean a decrease in jobs related to small to big farms in
agriculture in Argentina (Tomei and Upham, 2009) on the one hand, and deforestation in the Amazon due to the
advance of soybean cropping in the region on the other (Nepstad and Stickler, 2008).
27.3.7. Human Health
27.3.7.1. Observed and Projected Impacts and Vulnerability
Changes in weather extremes and climatic patterns are affecting human health (high confidence), by increasing
morbidity, mortality, and disabilities, and through the emergence of diseases in previously non-endemic regions
(high confidence) (Rodríguez-Morales, 2011; Winchester and Szalachman, 2009). Heat waves and cold spells have
increased urban mortality rates (Bell et al., 2008; Hajat et al., 2010; Hardoy and Pandiella, 2009; McMichael et al.,
2006; Muggeo and Hajat, 2009). Outbreaks of vector- and water-borne diseases were triggered in CA by hurricane
Mitch in 1998 (Costello et al., 2009; Rodríguez-Morales et al., 2010), while the 2010-2012 Colombian floods
caused hundreds of deaths and thousands of displaced people (Hoyos et al., 2013).
The number of cases of malaria have increased in Colombia during the last five decades alongside air temperatures
(Arevalo-Herrera et al., 2012; Poveda et al., 2011), but also in urban and rural Amazonian regions undergoing large
environmental changes (Cabral et al., 2010; Da Silva-Nunes et al., 2012; Gil et al., 2007; Tada et al., 2007). Malaria
transmission has reached 2,300 m in the Bolivian Andes, and vectors are found at higher altitudes from Venezuela to
Bolivia (Benítez and Rodríguez-Morales, 2004; Lardeux et al., 2007; Pinault and Hunter, 2011). Although the
incidence of malaria has decreased in Argentina, its vector density has increased in the northwest along with climate
variables (Dantur Juri et al., 2011; Dantur Juri et al., 2010). El Nino drives malaria outbreaks in Colombia (Mantilla
et al., 2009; Poveda et al., 2011) amidst other factors (Osorio et al., 2007; Restrepo-Pineda et al., 2008; Rodríguez-
Morales et al., 2006). Linkages between ENSO and malaria are also reported in Ecuador and Peru (Anyamba et al.,
2006; Kelly-Hope and Thomson, 2010), French Guiana (Hanf et al., 2011), Amazonia (Olson et al., 2009), and
Venezuela (Moreno et al., 2007).
Unlike malaria, dengue fever (DF) and its hemorrhagic variant (DHF) are mostly urban diseases whose vector is
affected by climate conditions. Their incidence have risen in tropical America in the last 25 years, causing annual
economic losses of US$ 2.1+[1 to 4] billion (Shepard et al., 2011; Tapia-Conyer et al., 2009; Torres and Castro,
2007). Environmental and climatic variability affect their incidence in CA (Fuller et al., 2009; Mena et al., 2011;
Rodríguez-Morales et al., 2010), in Colombia (Arboleda et al., 2009), and in French Guiana alongside malaria
(Carme et al., 2009; Gharbi et al., 2011). In Venezuela, DF increases during La Nina (Herrera-Martinez and
Rodríguez-Morales, 2010; Rodríguez-Morales and Herrera-Martinez, 2009). Weather and climate variability are
also associated with DF in southern SA (Costa et al., 2010; De Carvalho-Leandro et al., 2010; Degallier et al., 2010;
Honório et al., 2009; Lowe et al., 2011), involving also demographic and geographic factors in Argentina (Carbajo
et al., 2012). In Rio de Janeiro a 1°C increase in monthly minimum temperature led to a 45% increase of DF in the
next month, and 10 mm increase in rainfall to a 6% increase (Gomes et al., 2012). Despite large vaccination
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campaigns, the risk of Yellow Fever (YF) outbreaks has increased mostly in tropical America s densely populated
poor urban settings (Gardner and Ryman, 2010), alongside climate conditions (Jentes et al., 2011).
Schistosomiasis (SCH) is endemic in rural areas of Suriname, Venezuela, the Andean highlands, and rural and
peripheral urbanized regions of Brazil (Barbosa et al., 2010; Igreja, 2011; Kelly-Hope and Thomson, 2010). It is
highly likely that SCH will increase in a warmer climate (Lopes et al., 2010; Mangal et al., 2008; Mas-Coma et al.,
2009). Vegetation indices are associated with human fascioliasis in the Andes (Fuentes, 2004).
Hantaviruses (HV) have been recently reported throughout the region (Jonsson et al., 2010; MacNeil et al., 2011),
and El Nino and climate change augment their prevalence (Dearing and Dizney, 2010). Variation in HV reservoirs
in Patagonia is strongly dependent on climate and environmental conditions (Andreo et al., 2012; Carbajo et al.,
2009). In Venezuela, Rotavirus (RV) is more frequent and more severe in cities with minimal seasonality (Kane et
al., 2004). The peak of RV in Guatemala occurs in the dry season, causing 60% of total diarrhoea cases (Cortes et
al., 2012).
In spite of its rapid decline, climate-sensitive Chagas disease is still a major public health issue (Abad-Franch et al.,
2009; Araújo et al., 2009; Gottdenker et al., 2011; Moncayo and Silveira, 2009; Tourre et al., 2008). Climate also
affects the most prevalent mycosis (Barrozo et al., 2009), and ENSO is associated with outbreaks of bartonellosis in
Peru (Payne and Fitchett, 2010).
The high incidence of cutaneous leishmaniasis (CL) in Bolivia is exacerbated during La Nina (García et al., 2009;
Gomez et al., 2006). CL is affected in Costa Rica by temperature, forest cover and ENSO (Chaves et al., 2008), and
in Colombia by land cover, altitude, climatic variables, and El Nino (Cárdenas et al., 2006; 2007; 2008; Valderrama-
Ardila et al., 2010), and decreases during La Nina in Venezuela (Cabaniel et al., 2005). CL in Suriname peaks
during the March dry season (35%) (Van der Meide et al., 2008), and in French Guiana is intensified after the
October-December dry season (Rotureau et al., 2007). Incidence of visceral leishmaniasis (VL) has increased in
Brazil (highest in LA) in association with El Nino and deforestation (Cascio et al., 2011; Ready, 2008; Sortino-
Rachou et al., 2011), as in Argentina, Paraguay, and Uruguay (Bern et al., 2008; Dupnik et al., 2011; Fernández et
al., 2012; Salomón et al., 2011). VL transmission in Venezuela is associated with rainfall seasonality (Feliciangeli et
al., 2006; Rodríguez-Morales et al., 2007). Besides, the incidence of skin cancer in Chile has increased in recent
years, concomitantly with climate and geographic variables (Salinas et al., 2006).
Onchocerciasis (river blindness) vector exhibits seasonal biting rates (Botto et al., 2005; Rodríguez-Pérez et al.,
2011), and leptospirosis is prevalent in CA s warm-humid tropical regions (Valverde et al., 2008). Other climate-
driven infectious diseases are ascariasis and gram-positive cocci in Venezuela (Benítez et al., 2004; Rodríguez-
Morales et al., 2010), and Carrion s disease in Peru (Huarcaya et al., 2004).
Sea water temperature affects the abundance of cholera s bacteria (Hofstra, 2011; Jutla et al., 2010; Koelle, 2009;
Marcheggiani et al., 2010), which explains the outbreaks during El Nino in Peru, Ecuador, Colombia and Venezuela
(Cerda Lorca et al., 2008; Gavilán and Martínez-Urtaza, 2011; Holmner et al., 2010; Martínez-Urtaza et al., 2008;
Murugaiah, 2011; Salazar-Lindo et al., 2008).
The worsening of air quality and higher temperatures in urban settings are increasing chronic respiratory and
cardiovascular diseases, and morbidity from asthma and rhinitis (Grass and Cane, 2008; Gurjar et al., 2010; Jasinski
et al., 2011; Martins and Andrade, 2008; Rodriguez et al., 2011), but also artherosclerosis, pregnancy-related
outcomes, cancer, cognitive deficit, otitis, and diabetes (Olmo et al., 2011). Dehydration from heatwaves increases
hospitalizations for chronic kidney diseases (Kjellstrom et al., 2010), affecting construction, sugarcane and cotton
workers in CA (Crowe et al., 2009; 2010; Kjellstrom and Crowe, 2011; Peraza et al., 2012).
Extreme weather/climate events affect mental health in Brazil (depression, psychological distress, anxiety, mania
and bipolar disorder), in particular in drought-prone areas of NEB (Coelho et al., 2004; Volpe et al., 2010). Extreme
weather, meager crop yields, and low GDP are also associated with increased violence (McMichael et al., 2006).
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Multiple factors increase the region s vulnerability to climate change: precarious health systems, malnutrition,
inadequate water and sanitation services, poor waste collection and treatment systems, air, soil and water pollution,
lack of social participation, and inadequate governance (Luber and Prudent, 2009; Rodríguez-Morales, 2011;
Sverdlik, 2011). Human health vulnerabilities in the region depend on geography, age (Graham et al., 2011;
Martiello and Giacchi, 2010; Perera, 2008; Astrom et al., 2011), gender (Oliveira et al., 2011), race, ethnicity, and
socio-economic status (Diez Roux et al., 2007; Martiello and Giacchi, 2010). Neglected Tropical Diseases in LA
cause 1.5-5.0 million disability-adjusted life years (DALYs) (Hotez et al., 2008). Vulnerability of mega-cities (see
27.3.5) is aggravated by access to clean water, rapid spread of diseases (Borsdorf and Coy, 2009), and migration
from rural areas forced by disasters (Borsdorf and Coy, 2009; Campbell-Lendrum and Corvalán, 2007; Hardoy and
Pandiella, 2009). Human health vulnerabilities have been assessed in Brazil through composite indicators involving
downscaled climate scenarios, epidemiological variables and socio-economic projections (Barbieri and Confalonieri,
2011; Confalonieri et al., 2009; ; FIOCRUZ, 2011). The Andes and CA are among the regions of highest predicted
losses [1% to 27%] in labor productivity from future climate scenarios (Kjellstrom et al., 2009).
27.3.7.2. Adaptation Strategies and Practices
Adaptation efforts in the region ((Blashki et al., 2007; Costello et al., 2011) are hampered by lack of political
commitment, gaps in scientific knowledge, and institutional weaknesses (Keim, 2008; Lesnikowski et al., 2011;
Olmo et al., 2011) (see 27.4.3). Research priorities and current strategies must be reviewed (Halsnaes and Verhagen,
2007; Karanja et al., 2011; Romero and Boelaert, 2010), and preventive/responsive systems must be put in place
(Bell, 2011) to foster adaptive capacity (Campbell-Lendrum and Bertollini, 2010; Huang et al., 2011). Colombia
established a pilot adaptation program to cope with changes in malaria transmission and exposure (Poveda et al.,
2011). The city of Sao Paulo has implemented local pollution control measures, with the co-benefit of reducing
GHG emissions (De Oliveira, 2009; Nath and Behera, 2011).
Human wellbeing indices must be explicitly stated as adaptation policies in LA (e.g., Millennium Development
Goals) (Franco-Paredes et al., 2007; Halsnaes and Verhagen, 2007; Mitra and Rodriguez-Fernandez, 2010). South-
south cooperation and multidisciplinary research are required to design relevant adaptation and mitigation strategies
(Team and Manderson, 2011; Tirado et al., 2010).
27.4. Adaptation Opportunities, Constraints, and Limits
27.4.1. Adaptation Needs and Gaps
During the last years, the study of adaptation to climate change has progressively switched from an impact-focused
approach (mainly climate-driven) to include a vulnerability-focused vision (Boulanger et al., 2011). As a
consequence, the development and implementation of systemic adaptation strategies, involving institutional, social,
ecosystem, environmental, financial and capacity components (see Chapter 14), to cope with present climate
extreme events is a key step toward climate change adaptation, especially in SA and CA countries. While different
frameworks and definitions of vulnerability exist, a general tendency aims at studying vulnerability to climate
change especially in SA and CA focusing on the following aspects: urban vulnerability (e.g. Hardoy and Pandiella,
2009; Heinrichs and Krellenberg, 2011), rural community (McSweeney and Coomes, 2011; Ravera et al., 2011),
rural farmer vulnerability (Oft, 2010), and sectoral vulnerability (see 27.3). The approach used can be holistic or
systemic (Carey et al., 2012b; Ison, 2010), where climate drivers are actually few with respect to all other drivers
related to human and environment interactions including physical, economic, political and social context, as well as
local characteristics such as occupations, resource uses, accessibility to water, etc. (Manuel-Navarrete et al., 2007;
Young et al., 2010).
In developing and emergent countries, there exists a general consensus that the adaptive capacity is low,
strengthened by the fact that poverty is key determinant of vulnerability in Latin America (to climate related natural
hazards, see Rubin and Rossing, 2012) and thus a limit to resilience (Pettengell, 2010) leading to a low human
development trap (UNDP, 2007). However, Magnan (2009) suggests that this analysis is biased by a relative
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immaturity of the science of adaptation to explain what are the processes and the determinants of adaptive capacity .
Increasing research efforts on the study of adaptation is therefore of great importance to improve our understanding
of the actual societal, economical, community and individual drivers defining the adaptive capacity. Especially, a
major focus on traditions and their transmission (Young and Lipton, 2006) may actually indicate potential adaption
potentials in remote and economically poor regions of SA and CA. Such a potential does not dismiss the fact that the
nature of future challenges may actually not be compared to past climate variability (e.g. glacier retreat in the
Andes).
Coping with new situations may require new approaches such as a multilevel risk governance (Corfee-Morlot et al.,
2011; Young and Lipton, 2006) associated with decentralization in decision-making and responsibility. While the
multilevel risk governance and the local participatory approach are interesting frameworks for strengthening
adaptation capacity, perception of local and national needs is diverging, challenging the implementation of
adaptation strategies in CA/SA (Salzmann et al., 2009). At present, despite an important improvement during the
last years, there still exists a certain lack of awareness of environmental changes and mainly their implications for
livelihoods and businesses (Young et al., 2010). Moreover, considering the limited financial resources of some
states in CA and SA, long-term planning and the related human and financial resource needs may be seen as
conflicting with present social deficit in the welfare of the population. This situation weakens the importance of
adaptation planning to climate change in the political agenda (Carey et al., 2012b), and requires therefore
international involvement as one facilitating factor in natural hazard management and climate change adaptation, in
accordance with the respect to sovereignty, the international conventions including the United Nations Framework
Convention on Climate Change. In addition, as pointed out by McGray et al. (2007), development, adaptation and
mitigation issues are not separate issues. Especially, development and adaptation strategies should be tackled
together in developing countries such as SA and CA, focusing on strategies to reduce vulnerability. The poor level
of adaptation of present-day climate in SA and CA countries is characterized by the fact that responses to disasters
are mainly reactive rather than preventive. Some early warning systems are being implemented, but the capacity of
responding to a warning is often limited, particularly among poor populations. Finally, actions combining public
communication (and education), public decision-maker capacity-building and a synergetic development-adaptation
funding will be key to sustain the adaptation process that CA and SA require to face future climate change
challenges.
27.4.2. Practical Experiences of Autonomous and Planned Adaptation, including Lessons Learned
Adaptation processes in many cases have been initiated a few years ago, and there is still a lack of literature to
evaluate their efficiency in reducing vulnerability and building resilience of the society against climate change.
However, experiences of effective adaptation and maladaptation are slowly being documented (see also 27.4.3),
some lessons have already been learned from these first experiences (see section 27.3), and tools, such as the Index
of Usefulness of Practices for Adaptation (IUPA) to evaluate adaptation practices have been developed for the
region (Debels et al., 2009). Evidenced by these practical experiences, there is a wide range of options to foster
adaptation and thus adaptive capacity in CA and SA. In CA and SA, many societal issues are strongly connected to
development goals and are often considered priority in comparison to adaptation efforts to climate change. However,
according to the 135 case studies analyzed by McGray et al. (2007), 21 of which were in CA and SA, the synergy
between development and adaptation actions allows to ensuring a sustainable result of the development projects.
Vulnerability and disaster risk reduction may not always lead to long-term adaptive capacity (Nelson and Finan,
2009; Tompkins et al., 2008), except when structural reforms based on good governance (Tompkins et al., 2008)
and negotiations (Souza Filho and Brown, 2009) are implemented. While multi-level governance can help to create
resilience and reduce vulnerability (Corfee-Morlot et al., 2011; Roncoli, 2006; Young and Lipton, 2006), capacity-
building (Eakin and Lemos, 2006), good governance and enforcement (Lemos et al., 2010; Pittock, 2011) are key
components.
Autonomous adaptation experience are mainly realized at local levels (individual or communitarian) with examples
found for instance for rural communities in Honduras (McSweeney and Coomes, 2011), indigenous communities in
Bolivia (Valdivia et al., 2010) and coffee agroforestry systems in Brazil (De Souza et al., 2012). However, such
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adaptation processes do not always respond specifically to climate forcing. For instance, the agricultural sector
adapts rapidly to economic stressors, while, despite a clear perception of climate risks, it may last longer before
responding to climate changes (Tucker et al., 2010). In certain regions or communities, such as Anchioreta in Brazil
(Bonatti et al., 2012), adaptation is part of a permanent process and is actually tackled through a clear objective of
vulnerability reduction, maintaining and diversifying a large set of natural varieties of corn allowing the farmers to
diversify their planting. Another kind of autonomous adaptation is the southward displacement of agriculture
activities (e.g. wine, coffee) though the purchase of lands, which will become favorable for such agriculture
activities in a warmer climate. In Argentina, the increase of precipitation observed during the last 30 years
contributed to a westward displacement of the annual crop frontier. However, local adaptation to climate and non-
climate drivers may undermine long-term resilience of socio-ecological systems when local, short-term strategies
designed to deal with specific threats or challenges do not integrate a more holistic and long-term vision of the
system at threat (Adger et al., 2011). Thus, policy should identify the sources of and conditions for local resilience
and strengthen their capacities to adapt and learn (Adger et al., 2011; Borsdorf and Coy, 2009; Eakin et al., 2011),
as well as to integrate new adapted tools (Oft, 2010). This sets the question of convergence between the local-
scale/short-term and broad scale/long-term visions in terms of perceptions of risks, needs to adapt and appropriate
policies to be implemented (Eakin and Wehbe, 2009; Salzmann et al., 2009). Even if funding for adaptation is
available, the overarching problem is the lack of capacity and/or willingness to address the risks, especially those
threatening lower income groups (Satterthwaite, 2011a). Adaptation to climate change cannot eliminate the extreme
weather risks, and thus efforts should focus on disaster preparedness and post-disaster response (Sverdlik, 2011).
Migration is the last resort for rural communities facing water stress problems in CA and SA (Acosta-Michlik et al.,
2008).
In natural hazard management contributing to climate change adaptation, specific cases such as the one in Lake 513
in Peru (Carey et al., 2012b) clearly allowed to identify facilitating factors for a successful adaptation process
(technical capacity, disaster events with visible hazards, institutional support, committed individuals, and
international involvement) as well as impediments divergent risk perceptions, imposed government policies,
institutional instability, knowledge disparities, and invisible hazards). In certain cases, forward-looking learning
(anticipatory process), as a contrast to learning by shock (reactive process), has been found as a key element for
adaptation and resilience (Tschakert and Dietrich, 2010) and should be promoted as a tool for capacity-building at
all levels (stakeholders, local and national governments). Its combination with role-playing game and agent-based
models (Rebaudo et al., 2011) can strengthen and accelerate the learning process.
Planned adaptation policies promoted by governments have been strengthened by the participation in international
networks, where experience and knowledge can be exchanged. As an example, the C40 Cities- Climate Leadership
Group or ICLEI include Bogota (Colombia), Buenos Aires (Argentina), Caracas (Venezuela), Curitiba, Rio de
Janeiro and Sao Paulo (Brazil), Lime (Peru) and Santiago de Chile (Chile). Most of these cities have come up with
related action and strategy plans (e.g. Action Plan Buenos Aires 2030, Plan of Caracas 2020 or the Metropolitan
Strategy to CCA of Lima) (C40 Cities, 2011).
At a regional policy level, an example of intergovernmental initiatives in SA and CA is the Ibero-American
Programme on Adaptation to Climate Change (PIACC), developed by the Ibero-American Network of Climate
Change Offices (RIOCC) (Keller et al., 2011b). For CA specifically, the Central American Commission for
Environment and Development (CCAD) brings together the environmental ministries of the Central American
Integration System (Sistema de la Integración Centroamericana (SICA)) that released its climate change strategy in
2010 (CCAD-SICA, 2010; Keller et al., 2011a).
These initiatives demonstrate that there has been a growing awareness of CA and SA governments on the need to
integrate climate change and future climate risks in their policies. Up to date, in total 18 regional Non-Annex
countries, including Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador,
Guatemala, Honduras, Nicaragua, Guyana, Panama, Paraguay, Peru, Suriname, Uruguay and Venezuela, have
already published their first and/or second National Communication to the UNFCCC (see UNFCCC, 2012) allowing
to measure the country s emissions and to assess its present and future vulnerability.
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27.4.3. Observed and Expected Barriers to Adaptation
Adaptation is a dynamic process, which to be efficient requires a permanent evolution and even transformation of
the vulnerable system. Such transformation process can be affected by several constraints, including constraints
affecting the context of adaptation as well as the implementation of policies and measures (see 16.3.2).
Major constraints related to the capacity and resources needed to support the implementation of adaptation policies
and processes comprehend access to (Lemos et al., 2010) and exchange of knowledge (e.g. adaptive capacity can be
enhanced by linking indigenous knowledge and scientific knowledge; Valdivia, 2010), the access to and quality of
natural resources (López-Marrero, 2010), the access to financial resources, especially for poor households (Hickey
and Weis, 2012; Rubin and Rossing, 2012; Satterthwaite, 2011b) as well as for institutions (Pereira et al., 2009),
technological resources (López-Marrero, 2010), technical assistance (Eakin et al., 2011; Guariguata, 2009) , as well
as the fostering of public-private technology transfer (La Rovere et al., 2009a; Ramirez-Villegas et al., 2012) and
promotion of technical skills (Hickey and Weis, 2012), and social asset-based formation at the local level (Rubin
and Rossing, 2012).
In terms of framing adaptation, as constraint to affect the adaptation context, it is usually considered that a major
barrier to adaptation is the perception of risks and many studies focused on such an issue (Bonatti et al., 2012). Also,
new studies (Adger et al., 2009) identified social limits to possible adaptation to climate change in relation with
issues of values and ethics, risk, knowledge and culture, even though such limits can evolve in time. Indeed, while
being a necessary condition, perception may not be the main driver for initiating an adaptation process. As pointed
out by Tucker et al. (2010) with a specific focus on CA, exogenous factors (economic, land tenure, cost, etc.) may
actually strongly constrain the decision-making process involved in possible adaptation process. In that sense,
efficient governance and management are key components in the use of climate and non-climate information in the
decision-making and adaptation process. As a consequence, it is difficult to describe adaptation without defining at
which level it is thought. Indeed, while a lot of efforts are invested in national and regional policy initiatives, most of
the final adaptation efforts will be local. National and international (transborder) governance is key to build adaptive
capacity (Engle and Lemos, 2010) and therefore to strengthen (or weaken) local adaptation through efficient policies
and delivery of resources. At a smaller scale (Agrawal, 2008), local institutions can strongly contribute to
vulnerability reduction and adaptation. However, at all levels, the efficiency in national and local adaptation
activities strongly depend on the capacity-building and information transmission to decision-makers (Eakin and
Lemos, 2006).
27.5 Interactions between Adaptation and Mitigation
Synergies between adaptation and mitigation strategies on the local level can be reached due to self-organization of
communities in cooperatives (see The SouthSouthNorth Capacity Building Module on Poverty Reduction (see
SSN, 2006), which manages recycling or renewable energy production, leading to an increase in energy availability,
thus production capacity and therefore new financial resources. Moreover, Venema and Cisse (2004) support also
the development of decentralized renewable energy solutions for the growth of renewable energy in CA and SA (see
also section 27.3.6) next to large infrastructure project (see their case studies for Argentina and Brazil).
In spite of their smaller size (individual or communitarian), these solutions offer adaptation and mitigation benefits.
On one hand, fossil-based energy consumption is reduced, while energy availability is increased. On the other hand,
reduction of energy precariousness is key in any development strategy. Thus, it allows local community and
individuals to growing socially and economically; and therefore to reducing its vulnerability avoiding the poverty
trap (UNDP, 2007), and to initiating an adaptation process based on non-fossil fuel energy sources. Such initiatives
also depend on local and organizational leaderships (UN-Habitat, 2011).
Such integrated strategies of income generation as adaptation measures as well as production of renewable energy
are also identified for vulnerable, small farmers diversifying their crops towards crops for vegetable oil and biodiesel
production in Brazil. Barriers identified concern capacity building and logistical requirements making policy tools,
credit mechanism, and organization into cooperatives, and fostered research necessary (La Rovere et al., 2009b).
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Other promising interactions of mitigation and adaptation are identified e.g. for the management of Brazilian
tropical natural and planted forest (Guariguata, 2009).
At national and regional scales, CA and SA countries will require the allocation of human and financial resources to
adapt to climate change. While resources are limited, too large an economic dependence of these countries to fossil
fuels will reduce their adaptive capacity. The reduction in energy consumption and the integration of renewable
energies in their energetic matrix is therefore a key issue for all these countries in order to sustain their development
and growth and therefore increase their adaptive capacity (see also section 27.3.6).
Reforestation and avoided deforestation are important practices that contribute to both mitigation and adaptation
efforts in the region as in other parts of the world. Maintaining forest cover can provide a suite of environmental
services including local climate regulation, water regulation, reduced soil erosion, all of which can reduce the
vulnerability of communities to variable climate (see section 27.3.2.2 on Ecosystem-based adaptation) (Vignola et
al., 2009).
27.6. Case Studies
27.6.1. Hydropower
Hydropower is the main source of renewable energy in CA and SA (see section 27.3.6). The region is only second to
Asia in terms of hydropower energy generation in the world, displaying a 20% share of total annual generation and
an average regional capacity factor of over 50% (Table 5-1 SRREN; IPCC, 2011). As a result, the region has by far
the largest proportion of electricity generated through hydropower facilities (Table 27-6 in section 27.3.6.1). The
hydropower proportion of total electricity production is over 40% in the region, and in some cases is near or close to
80%, as in the case of Brazil, Colombia and Costa Rica (IEA, 2012). Although there is debate, especially in tropical
environments, about GHG emissions from hydropower reservoirs (Fearnside and Pueyo, 2012), this form of
electricity generation is often seen as a major contributor to mitigating GHG emissions worldwide (see IPCC
SRREN [5]; Kumar et al., 2011). But on the other hand, hydropower is a climate-related sector, thus making it prone
to the potential effects of changing climate conditions (see section 27.3.1.1). In this regards the CA and SA region
constitute a unique example to study these relations between climate change mitigation and adaptation in relation to
hydropower generation.
Diverse studies have analyzed the potential impacts of climate change on hydropower generation (see details in
Table 27-4 in section 27.3.1.1). Maurer et al. (2009) studied future conditions for the Lempa River (El Salvador,
Honduras and Guatemala) showing a potential reduction in hydropower capacity of 33% to 53% by 2070-2099
(Maurer et al., 2009). A similar loss is expected for the Sinu-Caribe basin in Colombia were, despite a general
projection of increased precipitation, losses due to evaporation enhancement reduces inflows to hydroelectric
systems, thus reducing electricity generation up to 35% (Ospina-Norena et al., 2009a). Further studies (Ospina-
Norena et al., 2011a; 2011b) have estimated vulnerability indices for the hydropower sector in the same basin, and
identified reservoir operation strategies to reduce this vulnerability. Overall reductions in hydropower generation
capacity are also expected in Chile for the main hydropower generation river basins: Maule, Laja and Biobio
(ECLAC, 2009a; McPhee et al., 2010; Stehr et al., 2010), and also in the Argentinean Limay River basin (Seoane
and López, 2007). Ecuador, on the other hand, faces an increase in generation capacity associated with an increment
in precipitation on its largest hydroelectric generation Paute River basin (Buytaert et al., 2010). Brazil, although
being the country with the largest installed hydroelectric capacity in the region, still has unused generation capacity
in sub-basins of the Amazon River (Soito and Freitas, 2011). However, future climate conditions plus environmental
concerns pose an important challenge for the expansion of the system (Andrade et al., 2012; Finer and Jenkins,
2012; Freitas and Soito, 2009). According to Lucena et al. (2009), hydropower systems in southern Brazil (most
significantly the Parana River system) could face a slight increase in energy production under an A2 scenario.
However, the rest of the country s hydropower system, and especially those located in NEB, could face a reduction
in power generation, thus reducing the reliability of the whole system (Lucena et al., 2009).
An obvious implication of the mentioned impacts is the need to replace the energy lost through alternative (see
27.3.6.2) or traditional sources. Adaptation measures have been studied for Brazil (Lucena et al., 2010a), with
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results implying an increase in natural gas and sugarcane bagasse electricity generation in the order of 300 TWh,
increase in operation costs in the order of 7 billion USD annually and 50 billion USD in terms of investment costs
by 2035. In Chile, the study by ECLAC (2009a) assumed that the loss in hydropower generation, in the order of 18
TWh for the 2011-2040 period (a little over 10% of actual total hydropower generation capacity) would be
compensated by the least operating cost source available, coal-fired power plant, implying an increase of 2 MTCO2e
of total GHG emissions (emissions for the electricity sector in Chile totaled 25 MTCO2e in 2009). Ospina-Norena
(2011a; 2011b) studied some adaptation options, such as changes in water use efficiency or demand growth that
could mitigate the expected impacts on hydropower systems in the Colombian Sinú-Caribe River basin. Changes in
seasonality and total availability could also increase complexities in the management of multiple-use dedicated
basins in Peru (Condom et al., 2012; Juen et al., 2007), Chile (ECLAC, 2009a), and Argentina (Seoane and López,
2007), that could affect the relationship between different water users within a basin. It is worth noting that those
regions which are projected to face an increase in streamflow and associated generation capacity, such as Ecuador or
Costa Rica, also share difficulties in managing deforestation, erosion and sedimentation which limits the useful life
of reservoirs (see section 27.3.1.1). In these cases it is important to consider these effects in future infrastructure
operation (Ferreira and Teegavarapu, 2012) and planning, and also to enhance the on-going process of recognizing
the value of the relation between ecosystem services and hydropower system operations (Leguía et al., 2008) (see
more on PES in section 27.3.2.2 and 27.6.2).
27.6.2. Payment for Ecosystem Services
Payment for ecosystem services (PES) is commonly described as a set of transparent schemes for securing a well-
defined ecosystem service (or a land use capable to secure that service) through conditional payments or
compensations to voluntary providers (Engel et al., 2008; Tacconi, 2012). Van Noordwijk et al. (2012) provides a
broader definition to PES by arguing that it encompasses three complementary approaches, (i) the one above, i.e.,
commodification of pre-defined ecosystem services so that prices can be negotiated between buyers and sellers; plus
(ii) compensation for opportunities forgone voluntarily or by command and control decisions; and (iii) coinvestment
in environmental stewardships. Therefore, the terms ´conservation agreements´, ´conservation incentives´ and
´community conservation´ are often used as synonyms or as something different or broader than PES (Cranford and
Mourato, 2011; Milne and Niesten, 2009). For simplicity, we refer to PES in its broadest sense (sensu Van
Noordwijk et al., 2012).
Services subjected to such types of agreements often include regulation of freshwater flows, carbon storage,
provision of habitat for biodiversity, and scenic beauty (De Koning et al., 2011; Montagnini and Finney, 2011).
Since the ecosystems that provide the services are mostly privately owned, policies often aim at supporting
landowners to maintain the provision of services over time (Kemkes et al., 2010). Irrespective of the debate of as to
whether payments or compensations should be designed to focus on actions or results (Gibbons et al., 2011),
experiences in Colombia, Costa Rica and Nicaragua show that PES can finance conservation, ecosystem restoration,
and better land use practices (Montagnini and Finney, 2011; see also Table 27-5). However, based on examples
from Ecuador and Guatemala, Southgate et al. (2010) argue that uniformity of payment for beneficiaries can be
inefficient if recipients accept less compensation in return for conservation measures, or if recipients that promote
greater environmental gains receive only the prevailing payment. Other setbacks to PES schemes might include
cases where there is a perception of commoditization of nature and its intangible values (e.g. Bolivia, Cuba, Ecuador
and Venezuela); other cases where mechanisms are inefficient to reduce poverty; slowness to build trust between
buyers and sellers, as well as gender and land tenure issues that might arise (Asquith et al., 2008; Balvanera et al.,
2012; Peterson et al., 2010; Van Noordwijk et al., 2012).
Table 27-7 lists selected examples of PES schemes in Latin America, but a more complete and detailed list is given
in Balvanera et al. (2012).
[INSERT TABLE 27-7 HERE
Table 27-7: Cases of government-funded PES schemes in CA and SA.]
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The PES concept (or ´fishing agreements´) also applies to coastal and marine areas, although only a few cases have
been reported. Begossi (2011) argues that this is due to three factors: origin (the mechanism was originally designed
for forests), monitoring (marine resources such as fish are more difficult to monitor than terrestrial resources) and
definition of resource boundaries in offshore water. One example of a compensation mechanism in the region is the
so-called defeso, in Brazil. It consists of a period (reproductive season) when fishing is forbidden by the government
and fishermen receive a financial compensation. It applies to shrimp, lobster and both marine and freshwater
fisheries (Begossi et al., 2011).
27.7. Data and Research Gaps
The scarcity and difficult availability of high resolution high quality and continuous climate, oceanic and
hydrological data, together with the availability of only very few complete regional studies, pose challenges for the
region to address changes in climate variability and the identification of trends in extremes, in particular for CA.
This situation hampers studies on frequency and variability of extremes, as well as impacts and vulnerability
analyses of the present and future climates, and the development of vulnerability assessments and adaptation actions.
Related to observed impacts in most sectors, there is an imbalance in information availability among countries.
While more studies have been performed for Brazil, Southern SA and SESA region, much less are available for CA
and for some regions of tropical SA. An additional problem is poor dissemination of results in peer-reviewed
publications since most information is available only as grey literature. There is a need for studies focused on
current impacts and vulnerabilities across sectors throughout CA and SA, with emphasis on extremes to improve
risk management assessments.
The complex interactions between climate and non-climate drivers difficult the assessment of impacts and
projections, as is the case for water availability and streamflows owing to current and potential deforestation;
overfishing and pollution regarding the impacts on fisheries, or impacts on hydroenergy production. The lack of
interdisciplinary integrated studies limits our understanding of the complex interactions between natural and socio-
economic systems. In addition, accelerating deforestation and land use changes, as well as changes in economic
conditions, impose a continuous need for updated and available data sets that feed basic and applied studies.
To address the global challenge of food security and food quality, both important issues in CA and SA, investment
in scientific agricultural knowledge need to be reinforced, mainly with regard to the integration of agriculture with
organic production; and the integration of food and bioenergy production. It is necessary to consider ethical aspects
when the competition for food and bioenergy production is analyzed to identify which activity is most important at a
given location and time and whether bioenergy production would affect food security for a particular population.
Sea level rise and coastal erosion are also relevant issues; the lack of comparable measurements of sea level rise in
CA and SA difficult the present and future integrated assessment of the impacts of SLR in the region. Of local and
global importance will be improving our understanding of the physical oceanic processes, in particular of the
Humboldt Current system flowing along the west coast of SA, being this one of the most fish productive system
worldwide.
More information and research about the impacts of climate variability and change on human health is needed. One
problem is the difficulty in accessing health data that are not always archived and ready to be used in integrated
studies. Another need refers to building the necessary critical mass of transdisciplinary scientists to tackle the
climate change-human health problems in the region. The prevailing gaps in scientific knowledge hamper the
implementation of adaptation strategies, thus demanding a review of of research priorities towards better disease
control. With the aim of further studying the health impacts of climate change and identifying resilience, mitigation
and adaptation strategies, South-South cooperation and multidisciplinary research are considered to be relevant
priorities.
In spite of the uncertainty that stems from global and regional climatic projections, the region needs to act in
preparation of a possible increase in climate variability and in extremes. It is necessary to undertake research
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activities leading to public policies to assist societies in coping with current climate variability, as for example, risk
assessment and risk management. Other important aspect since the AR4 is the improvement of climate modeling and
the generation of high-resolution climate scenarios, that in countries in CA and SA resulted in the first integrated
regional studies on impacts and vulnerability assessments of climate change focusing on sectors such as agriculture,
energy and human health.
Research on adaptation and the scientific understanding of the various processes and determinants of adaptive
capacity is also mandatory for the region, with particular emphasis on increasing adaptation capacity involving the
traditional knowledge of ancestral cultures and how this knowledge is transmitted. Linking indigenous knowledge
with scientific knowledge is important. Although some adaptation processes have been initiated in the recent years,
there is no literature assessing their efficiency so far.
The research agenda needs to address vulnerability and foster adaptation in the region; encompassing an inclusion of
the regions researchers and focusing also on governance structures and action-oriented research that addresses
resource distribution inequities.
Regional and international partnerships, and research networks and programs have allowed linking those programs
with local strategies for adaptation and mitigation, also providing opportunities to address research gaps and
exchange among researchers. Examples are the European Union funded projects CLARIS LPB in SESA and
AMAZALERT in Amazonia. Other important initiatives come from the IAI, WHO, GEF, IDB, ECLAC (CEPAL),
La Red and BirdLife International, among others. The same holds for local international networks such as ICLEI or
C40, of which CA and SA cities form part. The weADAPT initiative is a good example on how CA and SA
practitioners, researchers and policy makers can have access to credible, high quality information and to share
experiences and lessons learnt in other regions of the world.
27.8. Conclusions
CA and SA harbor unique ecosystems and maximum biodiversity, with a variety of eco-climatic gradients rapidly
changing from development initiatives. Agricultural and beef production as well as bioenergy crops are on the raise
mostly by expanding agricultural frontiers. Poverty and inequality are decreasing, but at a low pace. Socioeconomic
development shows a high level of heterogeneity and a very unequal income distribution, resulting in high
vulnerability to climatic conditions. There is still a high and persistent level of poverty in most countries (45% for
CA and 30% for SA for year 2010) in spite of the sustained economic growth observed in the last decade.
The IPCC AR4 and SREX reports contain ample evidence of increase in extreme climate events in CA and SA.
During 2000-2010, 630 weather and climate extreme events lead to 16,000 fatalities, and 46.6 million people
affected, with estimated losses of US$ 208 million. During 2000-2009, 39 hurricanes occurred in the CA-Caribbean
basin compared to 15 and 9 in the decade of 1980 and 1990, respectively. In SESA, more frequent and intense
rainfall extremes have favored an increase in the occurrence of flash floods and landslides. In Amazonia extreme
droughts were reported in 2005 and 2010 and record floods were observed in 2009 and 2012. In 2012-2013 an
extreme drought affected NEB.
While warming occurred in most of CA and SA, cooling was detected off the coast of Southern Peru and Chile.
There is growing evidence that Andean glaciers (both tropical and extratropical) are retreating in response to
warming trends. Increases in precipitation were registered in SESA, CA and the NAMS regions, while decreases
were observed in Southern Chile, and a slight decrease in NEB after the middle 1970s. In CA it has been observed a
gradual delay of the beginning of the rainfall season. SLR varied from 2 to 7 mm/yr between 1950 and 2008 in CA
and SA, which is a reason for concern since a large proportion of the population of the region lives by the coast.
Land use and land cover change are key drivers of regional environmental change in SA and CA. Natural
ecosystems are affected by climate variability/change and land use change. Deforestation, land degradation, and
biodiversity loss are mainly attributed to increased extensive agriculture for traditional export activities and
bioenergy crops. Agricultural expansion has affected fragile ecosystems, causing severe environmental degradation
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and reducing the environmental services provided by these ecosystems. Deforestation has intensified the process of
land degradation, increasing the vulnerability of communities exposed floods, landslides and droughts. Plant species
are rapidly declining in CA and SA, with a high percentage of rapidly declining amphibian species. However, the
region has still large extensions of natural vegetation cover, with the Amazon being the main example. Ecosystem-
based Adaptation practices, such as the establishment of protected areas and their effective management,
conservation agreements, community management of natural areas, and payment for ecosystem services are
increasingly more common across the region.
Figure 27-7 summarizes of some of the main observed trends in global environmental change drivers across
different representative regions of CA and SA. Changes in climate and non-climate drivers have to be compounded
with other socioeconomic related trends; such as the rapid urbanization process experienced the region
Some observed impacts on human and natural systems can be directly or indirectly attributed to human influences,
and can be summarized as (Figure 27-8):
Changes in river flow variability in the Amazon River during the last two decades, and robust positive
trends in streamflow in sub-basins of the La Plata River basin, and increased dryness for most of the river
basins in west coast of South America during the last 50 years.
Reduction in tropical glaciers and icefields in tropical and extra tropical Andes over the second half of the
20th century that can be attributed to an increase in temperature
Coastal erosion, bleaching of coral reefs in the coast of CA, and reduction in fisheries stock.
Increase in agricultural yield in SESA, and shifting in agricultural zoning: significant expansion of
agricultural areas, mainly in climatically marginal regions.
Increase in frequency and extension of dengue fever, yellow fever and malaria.
However, for some impacts the number of concluding studies is still insufficient, leading to low levels of confidence
for attribution to human influences.
[INSERT FIGURE 27-7 HERE
Figure 27-7: Summary of observed changes in climate and other environmental factors in representative regions of
CA and SA. The boundaries of the regions in the map are conceptual (neither geographic nor political precision).
Information and references to changes provided are presented in different sections of the chapter.]
[INSERT FIGURE 27-8 HERE
Figure 27-8: Observed impacts of climate variations and attribution of causes in CA and SA.]
By the end of the century, the CMIP5 derived projections for RCP8.5 projected: CA: mean annual warming of 2.5C
(range: 1.5°C to 5.0 °C), mean rainfall reduction of 10% (range: -25% to +10%), and reduction in summertime
precipitation; SA: mean warming of 4C (range: 2.0°C to 5.0 °C), with rainfall reduction up to 15% in tropical SA
east of the Andes, and an increase of about 15-20% in SESA, and in other regions of the continent. Increases in
warm days and nights are very likely to occur in most of SA. SESA: Increases in heavy precipitation. Northeastern
South America: increases in dry spell. However, there is some degree of uncertainty on climate change projections
for regions, particularly for rainfall in CA and tropical SA.
Current vulnerability in terms of water supply in the semi-arid zones and the tropical Andes is expected to increase
even further due to climate change. This would be exacerbated by the expected glacier retreat, precipitation
reduction and increase evapotranspiration demands as expected in the semi-arid regions of CA and SA. These
scenarios would affect water supply for large cities, small communities, hydropower generation and food
production. There is a need for re-assessing current practices to reduce the mismatch between water supply and
demand in order to reduce future vulnerability, and to implement constitutional and legal reforms towards more
efficient and effective water resources management.
SLR due to climate change and human activities on coastal and marine ecosystems pose threats to fish stocks,
corals, mangroves, recreation and tourism, and diseases control in CA and SA. Coral reefs, mangroves, fisheries,
and other benthic marine invertebrates that provide key ecosystem services, such as nutrient cycling, water quality
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regulation, and herbivory, are also threatened by climate change. It is possible that the Mesoamerican coral reef will
collapse by mid-century (between 2050 and 2070), causing major economic and environmental losses. In the
southwestern Atlantic coast, eastern Brazilian reefs might suffer a massive coral cover decline in the next 50
years. In the Rio de La Plata area extreme-flooding events may become more frequent since return periods are
decreasing, and urban coastal areas in the eastern coast will be particularly affected. Beach erosion is expected to
increase in southern Brazil and in scattered areas at the Pacific coast.
Urban populations in CA and SA face diverse social, political, economic and environmental risks in daily life, and
climate change will add a new dimension to these risks. Since urban development remains fragile in many cases,
with weak planning responses, climate change can compound existing challenges, e.g. water supply in cities from
glacier, snowmelt and paramos related runoff in the Andes (Lima, La Paz/El Alto, Santiago de Chile, Bogota),
flooding in several cities like Sao Paulo and Buenos, and health related challenges in many cities of the region.
Climate change will affect individual species and biotic interactions. Vertebrate fauna will suffer major species
losses especially in high altitude areas; elevational specialists might be particularly vulnerable because of their small
geographic ranges and high energetic requirements; freshwater fisheries can suffer alterations in physiology and life
histories. In addition, modifications in phenology, structure of ecological networks, predator-preys interactions and
non-trophic interactions among organisms will affect biotic interactions. Shifts in biotic interactions are expected to
have negative consequences on biodiversity and ecosystem services in High Andean ecosystems. Although in the
region biodiversity conservation is largely confined to protected areas it is expected that many species and
vegetational types will lose representativeness inside such protected areas.
Changes in food production and food security are expected to have a great spatial variability, with a wide range of
uncertainty mainly related to climate and crop models. In SESA average productivity could be sustained or
increased until the mid-century, although interannual and decadal climate variability are likely to impose important
damages. In other regions like NEB, CA, and some Andean countries agricultural productivity could decrease in the
short-term, threatening the food security of the poorest population. The expansion of pastures and croplands is
expected to continue in the coming years, particularly from an increasing global demand for food and biofuels. The
great challenge for CA and SA will be to increase the food and bioenergy production and at the same time sustain
the environmental quality in a scenario of climate change.
Renewable energy provides a great potential for adaptation and mitigation. Hydropower is currently the main source
of RE in CA and SA, followed by biofuels. SESA is one of the main sources of production of the feedstocks for
biofuels production, mainly with sugarcane and soybean, and future climate conditions may lead to an increase in
productivity and production. Advances in second generation biofuels will be important as a measure of adaptation,
as they have the potential to increase biofuels productivity. In spite of the large amount of arable land available, the
expansion of biofuels might have some direct and indirect land use change effects, producing teleconnections that
could lead to deforestation of native tropical forests and loss of employment in some countries. This might also
affect food security.
Changes in weather and climatic patterns are negatively affecting human health in CA and SA, either by increasing
morbidity, mortality, and disabilities, and through the emergence of diseases in previously non-endemic regions.
Multiple factors increase the region s vulnerability to climate change: precarious health systems, malnutrition,
inadequate water and sanitation services, population growth, poor waste collection and treatment systems, air, soil
and water pollution, food in poor regions, lack of social participation, and inadequate governance. Vulnerabilities
vary with geography, age, gender, race, ethnicity, and socio-economic status, and are rising in large cities. Climate
change and variability may exacerbate current and future risks to health.
Climate change will bring modifications to environmental conditions in space and time, and the frequency and
intensity of weather and climate processes. In many CA and SA countries, a first step toward adaptation to climate
change is to reduce the vulnerability to present climate, taking into account future potential impacts, particularly of
weather and climate extremes. Long-term planning and the related human and financial resource needs may be seen
as conflicting with present social deficit in the welfare of the CA and SA population. Such conditions weaken the
importance of adaptation planning to climate change on the political agenda. Currently, there are few experiences on
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synergies between development, adaptation and mitigation planning, which can help local communities and
governments to allocate available resources in the design of strategies to reduce vulnerability and to develop
adaptation measures. Facing a new climate system and, in particular, the exacerbation of extreme events, will call
for new ways to manage human and natural systems for achieving sustainable development.
[INSERT TABLE 27-8 HERE
Table 27-8: Key risks from climate change and the potential for risk reduction through mitigation and adaptation.]
Frequently Asked Questions
FAQ 27.1: What is the impact of glacier retreat on natural and human systems in the tropical Andes?
[to be inserted at end of Section 27.3.1.2]
The retreat of glaciers in the tropical Andes mountains, with some fluctuations, started after the Little Ice Age (16th
to 19th centuries), but the rate of retreat (area reduction between 20-50%) has accelerated since the late 1970s. the
changes in runoff from glacial retreat into the basins fed by such runoff vary depending on the size and phase of
glacier retreat. In an early phase, runoff tends to increase due to accelerated melting, but after a peak, as the
glacierized water reservoir gradually empties, runoff tends to decrease. This reduction in runoff is more evident
during dry months when glacier melt is the major contribution to runoff (high confidence).
A reduction in runoff could endanger high Andean wetlands (bofedales) and intensify conflicts between
different water users among the highly vulnerable populations in high elevation Andean tropical basins. Glacier
retreat has also been associated with disasters such as glacial lake outburst floods that are a continuous threat in the
region. Glacier retreat could also impact activities in high mountainous ecosystems such as alpine tourism,
mountaineering and adventure tourism (high confidence).
FAQ 27.2: Can payment for ecosystem services (PES) be used as an effective way to help local communities
adapt to climate change? [to be inserted in Section 27.3.3.2]
Ecosystems provide a wide range of basic services, like providing breathable air, drinkable water, and moderating
flood risk (very high confidence). Assigning values to these services and designing conservation agreements based
on these (broadly known as PES), can be an effective way to help local communities adapt to climate change. It can
simultaneously help protect natural areas, and improve livelihoods and human well-being (medium confidence).
However, during design and planning, a number of factors need to be taken into consideration at the local level in
order to avoid potentially negative results. Problems can arise if a) the plan sets poor definitions about whether the
program should focus just on actions to be taken or the end result of those actions, b) many perceive the initiative as
commoditization of nature and its intangible values, c) the action is inefficient to reduce poverty, d) difficulties
emerge in building trust between various stakeholders involved in agreements, and e) there are eventual gender or
land tenure issues.
FAQ 27.3: Are there emerging and re emerging human diseases as a consequence of climate variability and
change in the region? [to be inserted in Section 27.3.7.2]
Human health impacts have been exacerbated by variations and changes in climate extremes. Climate-related
diseases have appeared in previously non-endemic regions (e.g. malaria in the Andes, dengue in CA and Southern
SA) (high confidence). Climate variability and air pollution have also contributed to increase the incidence of
respiratory and cardiovascular, vector- and water-borne and chronic kidney diseases, Hantaviruses and rotaviruses,
pregnancy-related outcomes, and psychological trauma (very high confidence). Health vulnerabilities vary with
geography, age, gender, ethnicity, and socio-economic status, and are rising in large cities. Without adaptation
measures (e.g. extending basic public health services), climate change will exacerbate future health risks, owing to
population growth rates and existing vulnerabilities in health, water, sanitation and waste collection systems,
nutrition, pollution, and food production in poor regions (medium confidence).
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Table 27-1: Regional observed changes in temperature, precipitation and climate extremes in various sectors of CA and SA. Additional information on changes
in observed extremes can be found in the IPCC SREX (Seneviratne et al., 2012) and Chapter 2 IPCC WGI AR5 [2.4, 2.5, 2.6]
Region Period Observed changes
CA and Northern SA/Variable
Precipitation in the NAMS (Englehart and Douglas, 2006) 1943-02 +0.94 mm/day/58 years
Rainfall onset in NAMS (Grantz et al., 2007) 1948-04 -10 to -20 days/57 years
Summertime Precipitation in NAMS (Anderson et al., 2010) 1931-00 +17.6 mm century 1
Rainfall extremes (P95) in NAMS (Cavazos et al., 2008) 1961-98 +1.3% decade 1
Cold days and nights in CA and Northern SA (Donat et al., 2013) 1951-10 Cold days: -1 day decade 1 , Cold nights: -2 day decade 1
Warm days and nigths in Northern SA ((Donat et al., 2013) 1951-10 Warm days: +2 to +4 day decade 1 , warm nights: +1 to +3 day
decade 1
Heavy precipitation (R10) in Northern SA ((Donat et al., 2013) 1951-10 +1 to +2 day decade 1 ,
Consecutive dry days (CDD) in Northern SA (Donat et al., 2013) 1951-10 -2 day decade 1 ,
West Coast of SA
SST and air temperatures off coast of Peru and Chile (15S-35S) (Falvey and Garreaud, 2009; 1960-10 -0.25 oC /decade, -0.7 oC /11 years for 2002-2012
Gutiérrez et al., 2011a; Gutiérrez et al., 2011b; Kosaka and Xie, 2013)
Temperature, precipitation, cloud cover, and number of rainy days since the middle 1970´s off 1920-09 -1 oC /40 years, -1.6 mm/40 years, -2 octas/40 years, and -0.3
coast of Chile (18S-30S) (Schulz et al., 2012) days/40 years
Wet days until 1970, increase after that, reduction in the precipitation rate in southern Chile 1900-07 -0.34% until 1970 and +0.37 after that, -0.12 %
(37S-43S) (Quintana and Aceituno, 2012)
Cold days and nights in all South American coast (Donat et al., 2013) 1951-10 Cold days: -1 days decade-1; cold nights: -2 days decade-1
Warm nights in all South American coast, warm days in the northern coast of South America, 1951-10 Warm night: -1 days decade-1; warm days: +3 days decade-1 ;
warm days off the coast of Chile (Donat et al., 2013) warm days: -1 days decade-1
Warm nights in the coast of Chile (Dufek et al., 2008) 1961-90 +5 to +9%/31 years
Dryness as estimated by the Palmer Drought Severity Index (PDSI) for most of the west coast 1950-08 -2 to -4 / 50 years
of SA (Chile, Ecuador, Northern Chile) (Dai, 2011)
Heavy precipitation (R95) in northern and central Chile (Dufek et al., 2008) 1961-90 -45 to -105 mm/31 years
Temperature and precipitation in southern Chile (Vicuna et al., 2013) 1976-08 -45 to -105 mm/31 years
SESA
Mean annual air temperature in southern Brazil (Sansigolo and Kayano, 2010) 1913-06 +0.5 to +0.6 oC /decade, -31.4 - -47.6 mm/decade
Frequency of cold days and nights, warm days in Argentina and Uruguay (Rusticucci and 1935-02 -1.2%/decade, -1%/decade/, +0.2%/decade
Renom, 2008)
Highest annual maximum temperature, lowest annual minimum air temperature in Argentina 1956-03 +0.8 C/47 years, +0.6C/47 years
and Uruguay (Rusticucci and Tencer, 2008)
Warm nights in Argentina and Uruguay and southern Brazil (Rusticucci, 2012) 1960-09 10-20%/41 years
Warm nights in most of the region (Dufek et al., 2008) 1961-90 +7 to +9%/31 years
Cold nights in most of the region (Dufek et al., 2008) 1961-90 -5 to -9%/31 years
Cold days and nights in most of the region (Donat et al., 2013) 1951-10 warm nights: +3 days decade-1; warm days: +4 days decade-1
Warm days and nights in most of the region (Donat et al., 2013) 1951-10 Cold nights: -3 days decade-1; cold days: -3 days decade-1
Consecutive dry days (CDD) in the La Plata Basin countries (Argentina, Bolivia and Paraguay) 1961-90 +15 to +21 days/31 years, -21 to -27 days/31 years
and decrease of CDD in SA South of 30 S (Dufek et al., 2008)
Number of dry months during the warm season October-March in the Pampas region between 1904-00 From 2-3 months in 1904-1920 to 1-2 months from 1980-2000
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25S-40S (Barrucand et al., 2007)
Moister conditions as estimated by the Palmer Drought Severity Index (PDSI) in most of 1950-08 0 to 4/50 years
SESA (Dai, 2011)
Rainfall trends in the Parana River Basin (Dai et al., 2009) 1948-08 +1.5 mm/day/50 years
Number of days with precipitation above 10 mm (R10) in most of the region (Donat et al., 1951-10 +2 days/decade-1
2013)
Heavy precipitation (R95) in most of the region (Donat et al., 2013) 1951-10 +1% decade-1 and -4 days decade --1
Heavy precipitation (R95) in most of the region (Dufek et al., 2008) 1961-90 +45 to +135 mm/31 years
Heavy precipitation (R95) in the state of Sao Paulo (Dufek and Ambrizzi, 2008) 1950-99 +50 to +75 mm/40 years
Consecutive dry days (CDD) in the state of Sao Paulo (Dufek and Ambrizzi, 2008) 1950-90 -25 to-50 days/40 years
Lightning activity significantly with change in temperature in the state of Sao (Pinto and Pinto, 1951-06 +40% per 1oC for daily and monthly
2008; Pinto et al., 2013) time scales and approximately 30% per 1 oC for decadal
timescale
Number of days with rainfall above 20 mm in the city of Sao Paulo (Marengo et al., 2013; 2005-11 +5 to +8 days/11 years
Silva Dias et al., 2012)
Excess rainfall events duration after 1950 (Krepper and Zucarelli, 2010) 1901-03 + 21 months/53 years
Dry events and events of extreme dryness from 1972 to 1996 (Vargas et al., 2011) 1972-96 -29 days/24 years
Number of dry days in Argentina (Rivera et al., 2013) 1960-05 -2 to -4 days/decade
Extreme daily rainfall in La Plata Basin (Penalba and Robledo, 2010) 1950-00 +33 to +60% increase in Spring, Summer and Autumn, -10 to -
25% decrease in winter
Frequency of heavy rainfall in Argentina, Southern Brazil and Uruguay (Re and Barros, 2009) 1959-02 +50 to +150 mm/43 years
Annual precipitation in the La Plata Basin (Doyle et al., 2012)(Doyle and Barros, 2011) 1960-05 +5 mm/year
(Doyle and Barros, 2011)
Andes
Mean maximum temperature along the Andes, and increase in the number of frost days 1921-10 +0.10 to +0.12 oC /decade in 1921-2010, and +0.23-0.24 oC
(Marengo et al., 2011b) /decade during 1976-2010; 8 days/decade during 196-2002
Air temperature and changes in precipitation Northern Andes (Colombia, Ecuador) (Villacís, 1961-90 +0.1 C to +0.22 oC /decade, -4 to +4 %/decade years
2008)
Temperature and precipitation in northern and central Andes of Peru (SENAMHI, 2005; 2007; 1963-06 +0.2 to +0.45 oC /decade, -20 to -30%/40 years
2009a; 2009c; 2009d)
Temperature and precipitation in the southern Andes of Peru (Marengo et al., 2011b; 1964-06 +0.2 to 0.6 oC /decade, -11 to +2 mm/decade
SENAMHI, 2007; 2009a; 2009b; 2009c; 2009d)
Air temperature and rainfall over Argentinean and Chilean Andes and Patagonia (Falvey and 1950-90 +0.2 to 0.45 oC /decade, -10 to -12%/decade
Garreaud, 2009; Masiokas et al., 2008)
Number of days with rainfall above 10 mm (R10) (Donat et al., 2013) 1950-10 -3 days decade-1
Dryness in the Andes between 35.65 S-39.9 S using the PDSI (Christie et al., 2011) 1950-03 -7 PDSI/53 years
Rainfall decrease in the Mantaro Valley, central Andes of Peru (SENAMHI, 2009c) 1970-05 -44 mm/decade
Air temperature in Colombian Andes (Poveda and Pineda, 2009) 1959-07 +1 oC /20 years
Amazon region
Decadal variability of rainfall in northern and southern Amazonia (Marengo et al., 2009; 1920-08 -3 STD/30 years in northern Amazonia and +4 STD/30 years in
Satyamurty et al., 2010) southern Amazonia since the middle 1970 s
Rainfall in all the region (Espinoza et al., 2009a; 2009b) 1975-03 -0.32 %/28 years
Onset of the rainy season in southern Amazonia (Butt et al., 2011; Marengo et al., 2011b) 1950-10 -1 month since 1976 to 2010
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Precipitation in the SAMS core region (Wang et al., 2012) 1979-08 + 2 mm/day decade 1
Onset becomes steadily earlier from 1948 to early 1970s, demise dates have remained later, 1948-08 SAMS from 170 days (1948 1972) to 195 days (1972 1982).
and SAMS duration was longer after 1972 (Carvalho et al., 2011)
Spatially varying trends of heavy precipitation (R95), increase in many areas and insufficient 1961-90 +100 mm/31 years in western and extreme eastern Amazonia,
evidence in others (Marengo et al., 2009)
Spatially varying trends in dry spells in (CDD), increase in many areas and decrease in others 1961-90 +15 mm/31 years in western Amazonia, -20 mm/ in southern
(Marengo et al., 2009; Marengo et al., 2010) Amazonia
Rainfall in most of Amazonia and in western Amazonia (Dai et al., 2009; Dai, 2011) 1948-08 +1 mm/day/50 years, -1.5 mm/day/50 years
Dryness as estimated by the Palmer Drought Severity Index PDI in southern Amazonia and 1950-08 -2 to -4/50 years, +2 to +4 /50 years
moister conditions in western Amazonia (Dai, 2011)
Seasonal mean convection and cloudiness (Arias et al., 2011) 1984-07 +30 W/m2/23 years, -8 %/23 years
Onset of rainy season in southern Amazonia due to land use change (Butt et al., 2011) 1970-10 -0.6 days/30 years
Precipitation in the region (Gloor et al., 2013) 1990-10 -20 mm/21 years
Northeast Brazil
Rainfall trends interior Northeast Brazil and in northern Northeast Brazil (Dai et al., 2009; Dai, 1948-08 -0.3 mm/day/50 years, +1.5 mm/day/50 years
2011)
Heavy precipitation (R95) in some areas, and in southern Northeast Brazil (Silva and Azevedo, 1970-06 -2 mm/24 years to + 6 mm/24 years,
2008)
Consecutive dry days CDD in most of southern Northeast Brazil (Silva and Azevedo, 2008) 1970-06 -0.99 days/24 years
Total annual precipitation in northern Northeast Brazil (Santos and Brito, 2007) 1970-06 +1 to +4 mm/year/24 years
Spatially varying trends in heavy precipitation (R95) in northern Northeast Brazil (Santos and 1970-06 -0.1 to +5 mm/yeas/24 years
Brito, 2007)
Spatially varying trends in heavy precipitation (R95) and consecutive dry days (CDD) in 1935-06 -0.4 to +2.5 mm/year/69 years, -1.5 to +1.5 days/year/69 years,
northern Northeast Brazil (Santos et al., 2009)
Dryness in Southern Northeast Brazil as estimated by the PDSI, and northern Northeast Brazil 1950-08 -2 to -4/50 years, 0 to +1/50 years
(Dai, 2011)
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Table 27-2: Regional projected changes in temperature, precipitation, and climate extremes in different sectors of CA and SA. Various studies used A2 and B2
scenarios from CMIP3 and various RCPs scenarios for CMIP5, and different time slices from 2010 to 2100. In order to make results comparable, the CMIP3 and
CMIP5 at the time slice ending in 2100 are included. Additional information on changes in projected extremes can be found in the IPCC SREX (see IPCC,
2012), and Chapters 9 and 14 from IPCC WG1AR5 [9.5, 9.6 and 14.2, 14.7]
Region Models and scenarios Projected changes
CA and Northern SA
Leaf Area Index (LAI), evapotranspiration by 2070-2099 in CA (Imbach et al., 2012) 23 CMIP3 models, A2 Evapotranspiration: +20%; LAI:-20%+0.94
mm/day/58 years
Air temperature by 2075 and 2100 in CA (Aguilar et al., 2009) 9 CMIP3 models, A2 +2.2 C by 2075; +3.3 C by 2100
Rainfall in CA and Venezuela, air temperature in the region (Hall et al., 2013; Kitoh et al., 2011) 20 km MRI-AGCM3.1S Rainfall decrease/increase of about -
model, A1B 10%/+10%, by 2079. Temperature increases
of about +2.5-+3.5 oC by 2079
Precipitation and evaporation in most of the region. Soil moisture in most land areas in all 20 km MRI-AGCM3.1S Precipitation decrease of about -5 mm/day,
seasons (Nakaegawa et al., 2013b) model, A1B evaporation increase of about +3 to +5
mm/day; soil moisture to decrease by -5
mm/day.
Rainfall in Nicaragua, Honduras, Northern Colombia and Northern Venezuela, rainfall in Costa PRECIS forced by the Rainfall: -25 to -50%, and +25 to +50%.
Rica and Panama. Temperature in all region by 2071-2100 (Campbell et al., 2011) HadAM3, A2 Temperature: +3 to +6 C
Precipitation and temperature in northern SA, decrease in interior Venezuela, temperature Eta forced with HadCM3, Increases by +30 to 50%, and reductions
increases by 2071-2100 (Marengo et al., 2011a) A1B between -10 to -20%; temperature: +4 to +5 C
Precipitation and temperature by 2100 in CA (Karmalkar et al., 2011) PRECIS forced with Precipitation: -24 to -48%; temperature: +4 to
HadAM3, A2 +5 C
Warm nights, consecutive dry days and heavy precipitation in Venezuela, by 2100 (Marengo et PRECIS forced with Increase by +12 to +18%, +15 to +25 days and
al., 2009; 2010) HadAM3, A2 reduction of 75 to 105 days
Air temperature and precipitation in CA by 2100 (Giorgi and Diffenbaugh, 2008) 23 CMIP3 models, A1B Increase by +3 to +5 oC; reduction by -10 to -
30%
Consecutive dry days and in heavy precipitation by 2099 (Kamiguchi et al., 2006) 20 km MRI-AGCM3.1S Increase by +5 days, and between +2 to +8 %
model, A1B
Rainfall over Panama by 2099 (Fábrega et al., 2013) 20 km MRI-AGCM3.1S Increase by +5 %
model, A1B
West Coast of SA
Precipitation, runoff and temperature at the Limari river basin in semi-arid Chile by 2100 PRECIS forced with Precipitation: -15 % to -25%; runoff: -6 to -
(Vicuna et al., 2011) HadAM3, A2 27%; temperature: + 3 to +4 oC
Ai temperature and surface winds in west coast of SA (Chile) by 2100 (Garreaud and Falvey, 15 CMIP3 models, Temperature: +1 oC; coastal winds: +1.5 m/sec
2009) PRECIS forced with
HadAM3, A2
Precipitation in the bands 5N-10S, and 25S-30S, and 10S-25S and 30S-50S; temperature Eta model forced with Increases of 30-40%; increases of 3 to 5 oC
increase between by 2100 (Marengo et al., 2011a) HadCM3, A1B
Warm nights, consecutive dry days, and heavy precipitation in 5N-5S by 2100 (Marengo et al., PRECIS forced with Increase of +3 to +18%, reduction of -5 to 8
2009; 2010) HadAM3, A2 days, increase by +75 to +105 days
Air temperature, increase of precipitation between 0 and 10S, and between 20 and 40S by 2100 23 CMIP3 models, A1B Increase of +2 to +3 oC; increase by 10%,
(Giorgi and Diffenbaugh, 2008) reduction by -10 to -30%
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Consecutive dry days between 5 N and 10 S and south of 30S, heavy precipitation between 5S- 20 km MRI-AGCM3.1S Increase by 10 days and between +2 to +10%
20S and south of 20S by 2099 (Kamiguchi et al., 2006) model, A1B
Precipitation between 15 and 35 S; and south of 40S; temperature by 2100 (Nunez et al., 2009) MM5 forced with Precipitation: -2 mm/day; +2 mm/day;
HadAM3, A2 temperature: +2.5 oC
Precipitation in Panama and Venezuela, heavy precipitation in Panama and in Venezuela, RCA forced with Precipitation:-1 to -3 mm/day,
consecutive dry days over Panama and Colombia by 2099 (Sörensson et al., 2010) ECHAM5-MPI OM
model, A1B
SESA
Precipitation and runoff, an in air temperature by 2100 (Marengo et al., 2011a) Eta forced with HadCM3, Precipitation: + 20 to +30%; Runoff: +10 to
A1B +20%; air temperature: +2.5 to +3.5 oC
Precipitation and temperature in the La Plata basin by 2050 (Cabré et al., 2010) MM5 forced with Precipitation: +0.5 to 1.5 mm/day;
HadAM3, A2 temperature: +1.5 oC to 2.5 oC.
Warm nights, consecutive dry days and heavy precipitation by 2100 (Menendez and Carril, 7 CMIP3 models, A1B Warm nights: +10 to +30%; Consecutive dry
2010) days: +1 to +5 days; Heavy precipitation: +3
to +9 %.
Precipitation during summer and spring, and in fall and winter by 2100 (Seth et al., 2010) 9 CMIP3 models, A2 Precipitation: + 0.4 to +0.6 mm/day, -0.02 to -
0.04 mm/day
Warm nights, consecutive dry days and heavy precipitation by 2100 (Marengo et al., 2009; PRECIS forced with Increase of +6 to +12%, +5 to +20 days, +75
2010) HadAM3, A2 to +105 days
Air temperature and rainfall by 2100 (Giorgi and Diffenbaugh, 2008) 23 CMIP3 models, A1B Increase by +2 to +4 oC, increase by +20 to
+30 %
Consecutive dry days and in heavy precipitation by 2099 (Kamiguchi et al., 2006) 20 km MRI-AGCM3.1S Increase by +5 to +10% and by +2 to +8 %
model, A1B
Precipitation in north central Argentina, decrease in southern Brazil, increase of air temperature MM5 forced with Increase of +0.5 to +1 mm/day, reduction of -
by 2100 (Nunez et al., 2009) HadAM3, A2 0.5 mm/day, increase of +3 to +4.5 oC
Drought frequency, intensity and duration in South America South of 20 S for 2011-40 relative 15 CMIP5 models, RCP Frequency increase between 10-20%, increase
to 1979-2008 (Penalba and Rivera, 2013) 4.5 and 8.5 in severity between 5-15% and reduction in
duration between 10 and 30%
Precipitation, heavy precipitation, reduction of consecutive dry days in the eastern part of the RCA forced with the Increase of +2 mm/day, of +5 to +15 mm,
region, increase in the western part of the region by 2099 (Sörensson et al., 2010) ECHAM5 mode, A1B reduction of -10 days and increase of +5 days
Precipitation in SESA by 2100 (Sörensson et al., 2010) 9 CMIP3 models, A1B Increase between +0.3 to +0.5 mm/day
Andes
Precipitation and temperature, increase by 2100 in the Altiplano (Minvielle and Garreaud, 2011) 11 CMP3 models, A2 Precipitation: -10 to -30 %; temperature:>3 oC
Precipitation at 5N-5S, and 30S-45 S, at 5-25 S; temperature by 2100 (Marengo et al., 2011a) Eta forced with HadCM3, Increase between +10 and +30%, decrease by
A1B -20 to -30%, increase of +3.5 to +4.5 oC
Warm nights, heavy precipitation and consecutive dry days south of 15 S by 2100 (Marengo et PRECIS forced with Increase by +3 to +18%, reduction by -10 to -
al., 2009) HadAM3, A2 20 days, and -75 to -105 days
Air temperature, rainfall between 0-10S and reduction between 10-40 S (Giorgi and 23 CMIP3 models, A1B Increase by +3 to +4 oC, increase by 10% and
Diffenbaugh, 2008) reduction by -10%
Consecutive dry days and increase of heavy precipitation by 2099 (Kamiguchi et al., 2006) 20 km MRI-AGCM3.1S Reduction by -5 days, increase by +2 to +4 %
model, A1B south of 20S
Precipitation, heavy precipitation, and consecutive dry days by 2070-99 (Sörensson et al., 2010) RCA forced with Increases of +1 to +3 mm/day, +5 mm and of
ECHAM5, A1B +5 to +10 days
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Summer precipitation and surface air temperature in the Altiplano region by 2099 (Minvielle and 9 CMIP3 models, A2 Reduction in precipitation between -10% and -
Garreaud, 2011) 30%, an temperature increase of +3 oC
Temperature and rainfall in lowland Bolivia inn 2070-2999 (Seiler et al., 2013) 5 CMIP3 models (A1B) Increase from 2.5 oC to 5 oC, reduction of 9%
ad 5 CMIP5 models annual precipitation
(RCP4.5, 8.5)
Precipitation in the dry season, temperature and evapotranspiration 2079-98 (Guimberteau et al., CMIP3 models, A1B -1.1 mm; +2 oC; +7%
2013)
Amazon region
Rainfall in central and eastern Amazonia, and in western Amazonia, air temperature in all region Eta forced with HadCM3, Precipitation: -20 to -30%, +20 to +30%;
by 2100 (Marengo et al., 2011a) A1B temperature: +5 to +7 oC
Intensity of the South Atlantic Convergence Zone and in rainfall in the South American 10 CMIP3 models, A1B Precipitation: -100 to -200 mm/20 years
monsoon region, 2081-2100 (Bombardi and Carvalho, 2009)
Precipitation in western Amazonia during summer and in winter in Amazonia by 2100 (Mendes 5 CMIP3 models, A2 and +1.6% in summer and -1.5% in winter
and Marengo, 2010) ANN
Number of South American Low Level Jet east of the Andes events (SALLJ), and in the PRECIS forced by +50 events of SALLJ during summer,
moisture transport from Amazonia to the La Plata basin by 2090 (Soares and Marengo, 2009) HadAM3, A2 increase in moisture transport by 50%
Precipitation in the South American monsoon during summer and spring, and during fall and 9 CMIP3 models, A2 Increase of +0.15 to +0.4 mm/, reductions of -
winter by 2100 (Seth et al., 2010) 0.10 to -0.26 mm/day
Warm nights, consecutive dry days in eastern Amazonia, heavy precipitation in western PRECIS forced with Increase of +12 to +15%, by 25-30 days in
Amazonia and in eastern Amazonia by 2100 (Marengo et al., 2009) HadAM3, A2 eastern Amazonia, increase in western
Amazonia by 75-105 days and reduction by -
15 to 75 days in eastern Amazonia
Increase in air temperature, rainfall increase in western Amazonia and decrease in eastern CMIP3 models, A1B Increase of +4 to +6 oC, increase of +10% and
Amazonia by 2100 (Giorgi and Diffenbaugh, 2008) decrease between -10 to -30%
Reduction of consecutive dry days and increase in heavy precipitation by 2099 (Kamiguchi et 20 km MRI-AGCM3.1S Reduction of -5 to -10 days, increase by +2 to
al., 2006) model, A1B +8 %
Onset and late demise of the rainy season in SAMS by 2040-2050 relative to 1951-80 (Jones and 10 CMIP5 models, Onset 14 days earlier than present, demise 17
Carvalho, 2013) RCP8.5 days later than present
Precipitation in SAMS during the monsoon wet season in 2071-2100 relative to 1951-80 (Jones 10 CMIP5 models, Increase of 300 mm during the wet season
and Carvalho, 2013) RCP8.5
Precipitation in western Amazonia, heavy precipitation in northern Amazonia and in southern RCA forced with the Increase of +1 to +3 mm/day, reduction of -1
Amazonia, consecutive dry days in western Amazonia and increase by 2099 (Sörensson et al., ECHAM5 model, A1B to -3 mm, increase of +5 to +10 mm, decrease
2010) of -5 to -10 days, increase by +20 to +30 days
Northeast Brazil
Rainfall and temperature in the entire region by 2100 (Marengo et al., 2011a) Eta forced with HadCM3, Precipitation: -20 to -20%; temperature: +3 to
A1B +4 oC
Warm nights, consecutive dry days, heavy precipitation by 2100 (Marengo et al., 2009) PRECIS forced with Increase by +18 to +24%, by +25 to +30 days
HadAM3, A2 and -15 to 75 days
Air temperature and precipitation by 2100 (Giorgi and Diffenbaugh, 2008) 23 CMIP3 models, A1B Increase of +2 to +4 oC, reduction -10 to -30%
Consecutive dry days and heavy precipitation by 2099 (Kamiguchi et al., 2006) 20 km MRI-AGCM3.1S Reduction of -5 to -10% and increase of +2 to
model, A1B +6 %
Precipitation, heavy precipitation and consecutive dry days by 2099 (Sörensson et al., 2010) RCA forced with Increase of +1 to +2 mm/day, increase by +5
ECHAM5 model, A1B to +10 mm, and increase by +10 to +30 days
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Table 27-3: Observed trends related to Andean cryosphere.
a) Andean tropical glacier trends
Country Significant changes recorded References
Documented massifs (latitude) (Variable code numbera) Value of trend (period of observed trend)
Venezuela (1) +300/+500m between Little Ice Age (LIA) maximum and today. Polissar et al. (2006); Morris et al. (2006)
Cordillera de Merida (10°N) (5) Accelerated melting since 1972. Risk of disappearing completely as ELA close to the
highest peak (Pico Bolívar, 4979 m)
Colombia (3) LIA maximum between AD 1600 and 1850 Ruiz et al. (2008); Ceballos et al. (2006);
Parque Los Nevados (4°50N) (3) Many small/low elevation (<5000 m a.s.l.) glaciers have disappeared Poveda and Pineda (2009); IDEAM
Sierra Nevada del Cocuy (6°30N) (3) -60/-84% (1850-2000); -50% (last 50 yrs); -10/-50% (past 15yrs); Retreat 3.0km/yr (2012); Rabatel et al. (2013)
Sierra Nevada de Santa Marta (since 2000)
(10°40N)
Ecuador (1) +300 between the middle 18th (maximum LIA) and the last decades of the 20th C; Francou et al. (2007); Vuille et al. (2008);
Antisana (0°28S) ~+200m (20th C) Jomelli et al. (2009); Cáceres (2010);
Chimborazo and Carihuayrazo (3) ~-45% (1976-2006). Glaciers below 5300m in process of extinction Rabatel et al. (2013)
(1°S)
Peru (1) ~+100m (between LIA maximum and beginning 20th C); +150m (20th C) Raup et al. (2007); Jomelli et al. (2009);
Cordillera Blanca (9°S) (3) -12/-17% (18th C); -17/-20% (19th C); -20/-35% (1960s-2000s); -26% (1962 and 2000) UGHR (2010); Bury et al. (2011); Mark et
(4) -8 m decade 1 since 1970 (Yanamarey glacier) al. (2010); Baraer et al. (2012); Rabatel et
(8) +1.6% (+/- 1.1) (glaciers with >20 percent glacier area); (7) Seven out of nine watersheds al. (2013)
decreasing dry-season discharge
Coropuna volcano (15°33S) (3) -26% (1962-2000) Racoviteanu et al. (2007)
Cordillera Vilcanota (13°55S) (3) 10 times faster in 1991-2005 compared to 1963-2005 Thompson et al. (2006; 2011)
(2-4) about -30% of area and about -45% of volume since 1985 Salzmann et al. (2013)
Bolivia (1) +300 m (between LIA maximum and late 20th C); +180/+200m (20th C) Rabatel et al. (2006; 2008); Francou et al.
Cordillera Real and Cordillera (2007); Vuille et al. (2008); Jomelli et al.
(3) -48% (1976-2006) in the Cordillera Real; Chacaltaya vanished in 2010.
Quimza Cruz (16°S) (2011); Soruco et al. (2009); Gilbert et al.
(5) Zongo glacier have lost a mean of 0.4m w.e./yr in the 1991-2011 period; glaciers in the (2010); Rabatel et al. (2013)
Cordillera Real lost -43% of their volume from 1963 to 2006 (maximum from 1976 to 2006).
(2) +1.1+/-0.2°C over the 20th century at ~6340 m a.s.l.
Sur Lipez, Caquella, 21°30S (7) Evidence of recent degradation of Caquella rock glacier (South Bolivian Altiplano) Francou et al. (1999)
b) Extra tropical Andean cryosphere (glaciers, snowpack, runoff effects) trends.
Region Significant changes recorded and reference References
Documented massifs/latitude (Variable code numbera) Value of trend (period of observed trend)
Chile, Argentina and Bolivia and (6) No significant trend. Foster et al. (2009)
Argentinean Patagonia
Dessert Andes (17°S-31°S)
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Huasco basin glaciers (29°S) (5) 0.84 m w.e./yr (2003/2004 2007/2008) Nicholson et al. (2009); Rabatel et al.
(2011); Gascoin et al. (2011)
Central Andes (31°S-36°S)
Piloto/Las Cuevas (32°S) (5) - 10.50 m w.e. (last 24 yrs) Leiva et al. (2007)
Aconcagua basin glaciers (33°S) (3) -20% (last 48 years); (2) -14% (1955-2006). Nicholson et al. (2009); Bown et al.
(8) Significant decrease in Aconcagua basin streamflow (2008); Pellicciotti et al. (2007)
Central Andes glaciers (33 36 °S) (3) -3% (since 1955); (4) 50/ 9my 1 (during 20th C); (5) -0.76/-0.56 m/y (during 20th C) Le Quesne et al. (2009)
ELA across central Andes (1) +122 +/- 8 m (winter) and 200 +/- 6 m (summer) (1975 and 2001) Carrasco et al. (2005)
Snowpack (30 °S -37°S) (6) Positive, though nonsignificant, linear trend (1951 2005); (8) Mendoza river Masiokas et al. (2006)
streamflow. Possible link to rising temperatures and snowpack/glacier effects. Not Vich et al. (2007)
conclusive; (8) Increase in high and low flows possibly associated with increase in Vicuna et al. (2013)
temperature and effects on snowpack
Morenas coloradas rock glacier (7) Significant change in active layer possibly associated with warming processes. Trombotto and Borzotta (2009)
(32-33°S)
Cryosphere in the Andes of (5) Expansion of thermokarst depressions Bodin et al. (2010)
Santiago (33.5°S.)
Basins (28-47 °S) (8) Not significant increase in February runoff possible increase of glacier melt (1950 Casassa et al. (2009)
2007).
Basins (30-40 °S) (8) Significant negative timing trend (CT date shifting towards earlier in the year) for 23 Cortés et al. (2011)
out of the 40 analyzed series.
Patagonian Andes (36°S-55°S)
Streamflow from basins (28-47 °S) (8) Not significant increase in February run-off trends that might suggest an increase of Casassa et al. (2009)
glacier melt in the Andes (1950 2007).
NW Patagonia (38°-45°S) (4) Recession of 6 glaciers based on areal photograph analysis. Masiokas et al. (2008)
Casa Pangue glacier (41°S) (5) 2.3+/-0.6 m/y (1961-1998); (4) 3.6+/-0.6 m/y (1981-1998) Bown and Rivera (2007)
Manso
Glacier
(41°S) (8) Reduction in discharge associated with reduction in melt and precipitation Pasquini et al. (2013)
North Patagonian Icefield (NPI) (8) Glacial lake outburst flood (GLOF) possible response to retreat of Calafate glacier (20th Harrison et al. (2006)
C)
Southern Patagonia Icefield (SPI) (4) Larger retreating rates observed on the west side coinciding with lower elevations of the Barcaza et al. (2009)
ELAs
NPI, SPI, Cordillera Darwin (4) 5.7-12.2 km (1945-2005) Lopez et al. (2010)
Icefield
Gran Campo Nevado (GCN) (53 (5) Slow retreat from Late LIA. Acceleration started 60 years ago Strelin and Iturraspe (2007)
°S) (5) -2.8% of glacier length per decade (1942-2002); (3) -2.4% per decade (1942-2002) Schneider et al. (2007)
Cordón Martial glaciers (54 °S) (5) -1.6 m/year or -27.9 +/- 11 km3/year (2002-2006) Chen et al. (2007)
Proglacial lakes (40-50°S) (8) Summertime negative trend on lakes indicating that melt water is decreasing Pasquini et al. (2008)
Notes: a Variable coding: (1) Increase in Equilibrium line altitude (ELA); (2) Atmospheric warming revealed by englacial temperature measured at high elevation; (3) Area
reduction; (4) Frontal retreat; (5) Volume (water equivalent) reduction; (6) Snow cover; (7) Rock glaciers; (8) Runoff change;
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Table 27-4: Synthesis of projected climate change impacts on hydrologic related variables in CA and SA basins and major glaciers.
Notes: a Variable coding: (1) Runoff/Discharge; (2) Demand; (3) Recharge; (4) Glaciar change; (5) Unmet demand/water availability
Region Hydrologic Variable Projected Changea Period GCM (GHG Scenarios) References
Basins studied
La Plata Basin and SESA
Paraná (1): + 4.9% (not robust) 2081-2100 CMIP3 (A1B) Nohara et al. (2006)
(1): +10 to +20% 2100 Eta-HadCM3 (A1B) Marengo et al. (2011a)
(1) + 18.4% (significant) 2075-2100 CMIP3 models (A1B) Nakaegawa et al. (2013a)
Grande (Parana) (1) +20/-20% Different periods 7 CMIP3 models Todd et al. (2011) ; Gosling et al. (2011);
Nóbrega et al. (2011)
Itaipu (Parana) (1) 2010 2040: Left bank: 5/ 15%; Right 2010 2040 and CCCMA-CGCM2 (A2) Rivarola et al. (2011)
bank: +30%; (1) 2070-2100: 0 to 30% 2070-2100
Concordia (1): -40% 2070-2100 HadRM3P Perazzoli et al. (2013)
Carcaraná (2) Increase 2010-2030 HadCM3 (A2) Venencio and García (2011)
(3) Slight reduction
Amazon Basin
Peruvian Amazon (1) Some basins increased, some reduced Three time slices BCM2, CSMK3 and MIHR (A1B, Lavado et al. (2011)
B1)
Alto Beni-Bolivia (1) Increase and reduction. 2070-2100 CMIP3 models (A1B) Fry et al. (2012)
(3) Always reduction
(5) Increase in water stress
Ecuador - (1) Some scenarios increase some reduction 2070-2100 CMIP3 models (A1B) Buytaert et al. (2011)
Tomebamba/Paute
Amazon at Obidos (1) + 5.4% (not robust) 2081-2100 CMIP3 models (A1B) Nohara et al. (2006)
(1) +6% 2000-2100 ECBilt-CLIO-VECODE (A2) Aerts et al. (2006)
(1) + 3.7% (significant) 2075-2100 CMIP3 models (A1B) Nakaegawa et al. (2013a)
(1) No change in high flow. Reduction in low 2046 65/2079 98 8 AR4 GCMs (B1, A1B and A2) Guimberteau et al. (2013)
flow
Amazon -Orinoco (1) -20% 2050s HadCM3 (A2) Palmer et al.(2008)
Brazil (1) Consistent decrease 2050s HadCM3 (A1b) and CMIP3 Arnel and Gosling (2013)
Tropical Andes
Colombian (4) Disappearance by 2020s linear extrapolation Poveda and Pineda (2009)
glaciers
Cordillera Blanca (1) Increase for next 20-50 years, reduction 2005-2020 Temperature output only (B2) Chevallier et al. (2011)
glaciarized basins afterwards
(4) 2050: area -38/-60%. Increased seasonality 2050 (climatology) Not specified (A1, A2, B1, B2) Juen et al. (2007)
(4) 2080: area -49/-75%. Increased seasonality
(4) Increased seasonality 2030 16 CMIP3 (A1B, B1) Condom et al. (2012)
Basins providing (5) Inner tropics: Only small change. Increase 2010-2039 and 2040- 19 CMIP3 models (A1B, A2) Buytaert and De Bievre (2012)
water to cities of in precipitation an increase in ET. 2069
Bogota, Quito, (5) Outer tropics: Severe reductions. Decrease
Lima and La Paz in precipitation and increase in ET
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Central Andes
Limari (1) -20/-40%. 2070-2100 HadCM3 (A2, B2) Vicuna et al. (2011)
(1) -20% 2010-2040 15 CMIP3 (A1B, B2, B1) Vicuna et al. (2012)
(1) -30/-40%; (1) Change in seasonality 2070-2100
Maipo (1) -30% Three 30-year periods HadCM3 (A2, B2) Melo et al. (2010); ECLAC (2009); Meza
(5) Unmet demand up to 50% 2070-2090 et al. (2012)
Mataquito (1) Reduction in average and low flows. Three 30-year periods CMIP3 (A2, B1) and CMIP5 Demaria et al. (2013)
Increase in high flows (RCP4.5 and 8.5)
Maule, Laja (1) -30% Three 30-year periods HadCM3 (A2, B2) McPhee et al. (2010); ECLAC (2009)
Bio Bio (1) (-81%/+ 7%) 2070-2100 8 GCMs (6 SRES) Stehr et al. (2010)
Limay (1) -10/-20%. 2080s (climatology) HadCM2 (NS) Seoane and López (2007)
North East Brazil (NEB)
Brazilian Federal(1) No significant change up to 2025. (1) 2000-2100 HadCM2, ECHAM4 (NS) Krol et al. (2006); Krol and Bronstert
States of Ceara´ After 2025: strong reduction with ECHAM4; (2007)
and Piau ´ slight increase with HadCM2.
Paracatu (Sao (1) A2: +31/+131%; 2000-2100 HadCM3 (A2, B2) De Mello et al. (2008)
Francisco) (1) B2: no significant change
Jaguaribe (2) Demand: +33/+44% 2040 HadCM3 (A2, B2) Gondim et al. (2012; 2008)(2008)
(2) Irrigation water needs: +8/+9% 2025-2055 HadCM3 (B2)
Parnaiba (1) -80% 2050s HadCM3 (A2) Palmer et al. (2008)
Mimoso catchment (1) Dry scenario: -25/-75%; 2010 2039, 2040 CSMK3 and HadCM3 (A2, B1) Montenegro and Ragab (2010)
(2) Wet scenario: +40/+140%; (3) Similar 2069, and 2070 2099
Tapacurá River (1) B1: -4.89%, -14.28%, -20.58% Three 30-year CSMK3 and MPEH5 (A2, B1) Montenegro and Ragab (2012)
(1) A2: +25.25%, 39.48% and 21.95% periods
Bengue catchment (1) -15% reservoir yield Sensitivity scenario in 2100 selected from TAR and AR4 Krol et al. (2011)
GCMs with good skill. + 15% PET, -10% Precip
Aquifer in NEB (3) Reduction 2040-2070 HadCM3, ECHAM4 (A2,B2) Hirata and Conicelli (2012)
North SA
Essequibo Guyana (1) -50% 2050s HadCM3 (A2) Palmer et al. (2008)
Magdalena (1) Not significant changes in near future. End 2015 2035 and CMIP3 multi-model ensemble (A1B) Nakaegawa and Vergara (2010)
(Colombia) of 21st changes in seasonality. 2075 2099
Sinu (Colombia) (1) -2/-35% 2010-2039 CCSRNIES, CSIROMK2B, CGCM2, Ospina-Norena et al.(2009a; 2009b)
HadCM3 (A2)
CA
Lempa (1) B1: -13%; (1) A2: -24% 2070-2100 CMIP3 (A2, B1) Maurer et al. (2009)
Grande de (1) -70% 2050s HadCM3 (A2) Palmer et al. (2008)
Matagalpa
Mesoamerica (1) Decrease across the region 2070-2100 CMIP3 (A2, A1B, B1) Imbach et al. (2012)
(1) Consistent decrease 2050s HadCM3 and CMIP3 (A1b) Arnel and Gosling (2013)
(1) Consistent reduction in Northern CA 2050 2099 30 GMCs (A1b) Hidalgo et al. (2013)
Panama (1) The
Pacific
basins:
+35/40%
2075 2099 MRI- AGCM3.1
(A1B) Fábrega et al. (2013)
(1) Bocas del Toro: -50%
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Table 27-5: Impacts on agriculture.
Country/ Activity Time slice SRES CO2 Changes Source
Region
Uruguay Annual 2030/2050/2070/2100 A2 +185/-194/-284/-508 ECLAC
(SESA) crops 2030/2050 B2 +92/+169 (2010)
2030/2050/2070/2100 A2 +174/-80/-160/-287 (*1)
Livestock 2030/2050 B2 +136/+182
2030/2050/2070/2100 A2 +15/+39/+52/+19
Forestry 2030/2050/2070 B2 +6/+13/+18
(Paraguay Cassava 2020/2050/2080 A2 +16/+22/+22 ECLAC
(SESA) Wheat 2020/2050/2080 A2 +4/-9/-13 (2010)
B2 -1/+1/-5
Maize 2020/2050/2080 A2 +3/+3/+8
B2 +3/+1/+6 A2
Soybean 2020/2050/2080 A2 0/-10/-15
B2 0/-15/-2
Bean 2020/2050/2080 A2 -1/+10/+16
Argentina Maize 2080 A2/B2 N -24/-15 ECLAC
(SESA) A2/B2 Y +1/0 (2010)
Soybean 2080 A2/B2 N -25/-14
A2/B2 Y +14/+19
Wheat 2080 A2/B2 N -16/-11
A2/B2 Y +3/+3
Soybean 2020/2050/2080 A2 Y +24/+42/+48 Travasso et
B2 Y +14/+30/+33 al. (2008)
Maize 2020/2050/2080 A2 Y +8/+11/+16
B2 Y +5/+5/+9
Brazil Rice 2CO2/0C Y +60 Walter et al.
(SESA) 2CO2/+5C Y +30 (2010)
Bean 2050-2080 A2 N Up to -30% Costa et al.
2020-2050-2080 A2+CO2 Y Up to: +30/+30/+45 (2009)
2020-2050-2080 A2+CO2+T Y Up to: +45/+75/+90 (*2)
Maize 2050-2080 A2 N Up to -30%
2050-2080 A2+CO2 Y Near to -15%
2020-2050-2080 A2+CO2+T Y Up to: +40/+60/+90
Arabica 0 to +1C +1.5% Zullo et al.
coffee +1 to +2C +15.9% (2011)
+2 to +3C +28.6% (*3)
+3 to +4C -12.9%
Brazil Sugarcane 2040 Pessimistic +6% Marin et al.
Sao Pablo 2040 Optimistic +2% (2009)
Brazil Cassava 2030 N 0 to -10 Lobell et al.
Northeast Maize 2030 N 0 to -10 (2008)
Rice 2030 N -1 to -10
Wheat 2030 N -1 to -14
Maize -20 to -30 Margulis et
Bean -20 to -30 al. (2010)
Rice -20 to -30
Cowpea +1.5C -26% Silva et al.
bean +3.0C -44% (2010)
+5.0C -63% (*3)
Central America Maize A2 0/0/-10/-30 ECLAC
(CA) Bean 2030/2050/2070/2100 A2 -4/-19/-29/-87 (2010)
Rice A2 +3/-3/-14/-63
Rice 2020-2040 N 0 to -10 Lobell et al.
Wheat 2020-2040 N -1 to -9 (2008)
Panamá Maize 2020-2050-2080 A2 Y -0.5/+2.4/+4.5 Ruane et al.
2020-2050-2080 B1 Y -0.1/-0.8/+1.5 (2011)
Andean Region Wheat N -14 to +2 Lobell et al.
2020-2040
Barley N -1 to -8 (2008)
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Potato N 0 to -5
Maize N 0 to -14
Colombia All main 2050 17GCM-A2 80% of crops Ramirez et
crops impacted in more al. (2012)
than 60% of current
cultivated areas
Chile Maize 2050 A1FI Y -5% to -10% Meza and
34.6S/38.5S Wheat 2050 A1FI Y -10% to -20% Silva (2009)
Changes are expressed as differences in relative yield (%), except for (*1) and (*3)
N: Without considering CO2 biological effects; Y: Considering CO2 biological effects
(*1) Gross Value of Production (millions of American dollars)
(*2) Huge spatial variability, the values are approximated
(*3) Changes in the percentage of areas with low climate risk
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Table 27-6: Comparison of consumption of different energetics in Latin America and the world (in thousand tonnes of oil equivalent (ktoe) on a net calorific
value basis).
LATAM World
Energy resource TFC (via TFC (via
TFC (non TFC (non
electricity Total TFC electricity TFC
electricity) electricity)
generation) generation)
Coal and Peat 9,008 3% 1,398 2% 10,406 3% 831,897 12% 581,248 40% 1,413,145 17%
Fossil
Oil 189,313 55% 8,685 13% 197,998 48% 3,462,133 52% 73,552 5% 3,535,685 44%
Natural Gas 59,44 17% 9,423 14% 68,863 17% 1,265,862 19% 307,956 21% 1,573,818 19%
Nuclear Nuclear 0 0% 1,449 2% 1,449 0% 0 0% 193,075 13% 193,075 2%
Biofuels and
82,997 24% 2,179 3% 85,176 21% 1,080,039 16% 20,63 1% 1,100,669 14%
waste
Hydro 0 0% 45,92 66% 45,92 11% 0 0% 238,313 17% 238,313 3%
Renewable
Geothermal,
solar, wind,
408 0% 364 1% 772 0% 18,265 0% 26,592 2% 44,857 1%
other
renewable
TOTAL 341,166 100% 69,418 100% 410,584 100% 6,658,196 100% 1,441,366 100% 8,099,562 100%
* TFC: Total final consumption
Source: IEA, 2012
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Table 27-7: Cases of government-funded PES schemes in CA and SA.
Countries Level Start Name Benefits References
Brazil Sub-national 2007 Bolsa Floresta By 2008, 2700 traditional and indigenous Viana (2008)
(Amazonas families already benefitted: financial
state) compensation and health assistance in
exchange for zero deforestation in primary
forests.
Costa Rica National 1997 FONAFIFO PES is a strong incentive for reforestation Montagnini and
fund and, for agroforestry ecosystems alone, Finney (2011)
over 7,000 contracts have been set since
2003, and nearly 2 million trees were
planted.
Ecuador National 2008 Socio-Bosque By 2010, the program already included De Koning et al.
more than half a million hectares of natural (2011)
ecosystems protected and has over 60,000
beneficiaries.
Guatemala National 1997 Programa de By 2009, the program included 4,174 Instituto Nacional
Incentivos beneficiaries who planted 94,151 hectares de Estadística
Forestales, of forest. In addition, 155,790 hectares of (2011)
PINFOR natural forest were under protection with
monetary incentives.
Table 27-8: Key risks from climate change and the potential for risk reduction through mitigation and adaptation.
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Figure 27-1: Observed and simulated variations in past and projected future annual average temperature over the
Central and South American regions defined in IPCC (2012). Black lines show various estimates from
observational measurements. Shading denotes the 5-95 percentile range of climate model simulations driven with
"historical" changes in anthropogenic and natural drivers (63 simulations), historical changes in "natural" drivers
only (34), the "RCP2.6" emissions scenario (63), and the "RCP8.5" (63). Data are anomalies from the 1986-2006
average of the individual observational data (for the observational time series) or of the corresponding historical all-
forcing simulations. Further details are given in Box 21-3.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 27-2: Projected changes in annual average temperature and precipitation. CMIP5 multi-model mean
projections of annual average temperature changes (left panel ) and average percent change in annual mean
precipitation (right panel) for 2046-2065 and 2081-2100 under RCP2.6 and 8.5. Solid colors indicate areas with very
strong agreement, where the multi-model mean change is greater than twice the baseline variability, and>90% of
models agree on sign of change. Colors with white dots indicate areas with strong agreement, where>66% of models
show change greater than the baseline variability and>66% of models agree on sign of change. Gray indicates areas
with divergent changes, where>66% of models show change greater than the baseline variability, but<66% agree on
sign of change. Colors with diagonal lines indicate areas with little or no change, less than the baseline variability
in>66% of models. (There may be significant change at shorter timescales such as seasons, months, or days.).
Analysis uses model data and methods building from WGI AR5 Figure SPM.8. See also Annex I of WGI AR5.
[Boxes 21-3 and CC-RC]
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Figure 27-3: Area deforested per year for selected countries in CA and SA (2005-2010). Notice three countries listed
with a positive change in forest cover (based on data from FAO, 2010).
Figure 27-4: Deforestation rates in the Brazilian Amazonia (km/year) based on measurements by the PRODES
project (INPE, 2011).
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Figure 27-5: Evolution of GDP per capita and poverty (income below US$ 2 per day) from 1990-2010: CA and SA
(US-Dollars per inhabitant at 2005 prices and percentages) (ECLAC on the basis of CEPALSTAT (2012) and
ECLAC (2011b)).
Figure 27-6: Current and predicted coastal impacts and coastal dynamics in response to climate change.
Coastal impacts - based on trends observed and projections, the figure shows how potential impacts may be
distributed in the region. Three cases: a) flooding: since flooding probability increases with increasing sea-level, one
may expect a higher probability of flooding in locations showing >40% of change over the last 60 years in the 100-
years total sea-level (excluding hurricanes). The figure also identifies urban areas where the highest increase in
flooding level has been obtained.; b) beach erosion: it increases with potential sediment transport, thus locations
where changes in potential sediment transport have increased over a certain threshold have a higher probability to be
eroded; c) sea-ports and reliability of coastal structures: the figure shows locations where, in the case of having a
protection structure in place, there is a reduction in the reliability of the structures due to the increase in the design
wave height estimates (ECLAC, 2011a).
Coastal dynamics - information based on historical time series that have been obtained by a combination
of data reanalysis, available instrumental information and satellite information. Advanced statistical techniques have
been used for obtaining trends including uncertainties (Izaguirre et al., 2013; Losada et al., 2013).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 27-7: Summary of observed changes in climate and other environmental factors in representative regions of
CA and SA. The boundaries of the regions in the map are conceptual (neither geographic nor political precision).
Information and references to changes provided are presented in different sections of the chapter.
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Figure 27-8: Observed impacts of climate variations and attribution of causes in CA and SA.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Chapter 28. Polar Regions
Coordinating Lead Authors
Joan Nymand Larsen (Iceland), Oleg A. Anisimov (Russian Federation)
Lead Authors
Andrew Constable (Australia), Anne Hollowed (USA), Nancy Maynard (USA), Pal Prestrud (Norway), Terry
Prowse (Canada), John Stone (Canada)
Contributing Authors
Terry Callaghan (UK), Mark Carey (USA), Peter Convey (UK), Andrew Derocher (Canada), Peter T. Fretwell
(UK), Bruce C. Forbes (Finland), Solveig Glomsrd (Norway), Dominic Hodgson (UK), Eileen Hofmann (USA),
Grete K. Hovelsrud (Norway), Gita L Ljubicic (Canada), Harald Loeng (Norway), Eugene Murphy (UK), Steve
Nicol (Australia), Anders Oskal (Norway), James D. Reist (Canada), Phil Trathan (UK), Barbara Weinecke
(Australia), Fred Wrona (Canada)
Review Editors
Maria Ananicheva (Russian Federation), F. Stuart Chapin III (USA)
Volunteer Chapter Scientist
Vasiliy Kokorev (Russian Federation)
Contents
Executive Summary
28.1. Introduction
28.2. Observed Changes and Vulnerability under Multiple Stressors
28.2.1. Hydrology and Freshwater Ecosystems
28.2.1.1. Arctic
28.2.1.2. Antarctic
28.2.2. Oceanography and Marine Ecosystems
28.2.2.1. Arctic
28.2.2.2. Antarctica
28.2.3. Terrestrial Ecosystems
28.2.3.1. Arctic
28.2.3.2. Antarctica
28.2.4. Health and Well-Being of Arctic Residents
28.2.4.1. Direct Impacts of a Changing Climate on the Health of Arctic Residents
28.2.4.2. Indirect Impacts of Climate Change on the Health of Arctic Residents
28.2.5. Indigenous Peoples and Traditional Knowledge
28.2.6. Economic Sectors
28.2.6.1. Arctic
28.2.6.2. Antarctica and the Southern Ocean
28.3. Key Projected Impacts and Vulnerabilities
28.3.1. Hydrology and Freshwater Ecosystems
28.3.1.1. Arctic
28.3.1.2. Antarctica
28.3.2. Oceanography and Marine Ecosystems
28.3.2.1. Ocean Acidification in the Arctic and Antarctic
28.3.2.2. Arctic
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28.3.2.3. Antarctica and the Southern Ocean
28.3.3. Terrestrial Environment and Related Ecosystems
28.3.3.1. Arctic
28.3.3.2. Antarctica
28.3.4. Economic Sectors
28.3.4.1. Fisheries
28.3.4.2. Forestry and Farming
28.3.4.3. Infrastructure, Transportation, and Terrestrial Resources
28.4. Human Adaptation
28.5. Research and Data Gaps
References
Frequently Asked Questions
28.1: What will be the net socio-economic impacts of change in the polar regions?
28.2: Why are changes in sea ice so important to the polar regions?
Executive Summary
Additional and stronger scientific evidence has accumulated since the AR4 that reinforces key findings made in the
Fourth Assessment Report (AR4).
The impacts of climate change, and the adaptations to it, exhibit strong spatial heterogeneity in the Polar
Regions because of the high diversity of social systems, bio-physical regions and associated drivers of change
(high confidence). [28.2.2] For example, the tree line has moved northward and upward in many, but not all, Arctic
areas (high confidence) and significant increases in tall shrubs and grasses have been observed in many places (very
high confidence). [28.2.3.2]
Some marine species will shift their ranges in response to changing ocean and sea ice conditions in the Polar
Regions (medium confidence). The response rate and the spatial extent of the shifts will differ by species based on
their vulnerability to change and their life history. [28.2.2; 28.3.2] Loss of sea ice in summer and increased ocean
temperature is expected to enhance secondary pelagic production in some but not all regions of the Arctic Ocean
with associated changes in the energy pathways within the marine ecosystem (medium confidence). These changes
are expected to alter the species composition and carrying capacity with associated impacts on marine fish and
shellfish populations (medium confidence). [28.2.2.1] Also, changes in sea ice and the physical environment to the
west of the Antarctic Peninsula are altering phytoplankton stocks and productivity, and krill (high confidence).
[28.2.2.2]
Climate change is impacting terrestrial and freshwater ecosystems in some areas of Antarctica and the
Arctic. This is due to ecological effects resulting from reductions in the duration and extent of ice and snow cover
and enhanced permafrost thaw (very high confidence), and through changes in the precipitation-evaporation balance
(medium confidence). [28.2]
The primary concern for polar bears over the foreseeable future is the recent and projected loss of annual ice
over continental shelves, decreased ice duration, and decreased ice thickness (high confidence). Of the two
subpopulations where data are adequate for assessing abundance effects, it is very likely that the recorded population
declines are caused by reductions in sea ice extent. [28.2.2.1.2; 28.3.2.2.2]
Rising temperatures, leading to the further thawing of permafrost and changing precipitation patterns have
the potential to affect infrastructure and related services in the Arctic (high confidence). Particular concerns
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are associated with the damage of residential buildings due to thawing permafrost, including, Arctic cities; small,
rural settlements; and storage facilities for hazardous materials. [28.2.4; 28.2.4.2; 28.2.5]
In addition, there is new scientific evidence that has emerged since the AR4.
The physical, biological and socio-economic impacts of climate change in the Arctic have to be seen in the
context of often interconnected factors that include not only environmental changes caused by drivers other
than climate change but also demography, culture and economic development. Climate change has
compounded some of the existing vulnerabilities caused by these other factors (high confidence). [28.2.4; 28.2.5;
28.4] For example, food security for many indigenous and rural residents in the Arctic is being impacted by climate
change and in combination with globalization and resource development projected to increase significantly in the
future (high confidence). [28.2.4]
The rapid rate at which climate is changing in the Polar Regions will impact natural and social systems (high
confidence) and may exceed the rate at which some of their components can successfully adapt (low to medium
confidence). [28.2.4; 28.4] The decline of Arctic sea-ice in summer is occurring at a rate that exceeds most model
projections (high confidence) and evidence of similarly rapid rates of change is emerging in some regions of
Antarctica. [IPCC WGI Chapter 14] In the future, trends in Polar Regions of populations of marine mammals, fish
and birds will be a complex response to multiple stressors and indirect effects (high confidence). [28.3.2] Already,
accelerated rates of change in permafrost thaw, loss of coastal sea ice, sea level rise and increased weather intensity
are forcing relocation of some indigenous communities in Alaska (high confidence). [28.2.4.2; 28.2.5; 28.3.4]
Shifts in the timing and magnitude of seasonal biomass production could disrupt matched phenologies in the
food webs, leading to decreased survival of dependent species (medium confidence). If the timing of primary and
secondary production is no longer matched to the timing of spawning or egg release, survival could be impacted
with cascading implications to higher trophic levels. This impact would be exacerbated if shifts in timing occur
rapidly (medium confidence). [28.2.2; 28.3.2] Climate change will increase the vulnerability of terrestrial
ecosystems to invasions by non-indigenous species, the majority likely to arrive through direct human assistance
(high confidence).
Ocean acidification has the potential to inhibit embryo development and shell formation of some zooplankton
and krill in the Polar Regions with potentially far-reaching consequences to food webs in these regions
(medium confidence). Embryos of Antarctic krill have been shown to be vulnerable to increased concentrations of
CO2 in the water (high confidence). As well, there is increasing evidence that pelagic mollusks (pteropods) are
vulnerable to ocean acidification (medium confidence). [28.2.2; 28.3.2]
There is increased evidence that climate change will have large effects on Arctic communities, especially
where narrowly based economies leave a smaller range of adaptive choices. [28.2.6.1; 28.4] Some commercial
activities will become more profitable while others will face decline. Increased economic opportunities are expected
with increased navigability in the Arctic Ocean and the expansion of some land- and freshwater-based transportation
networks. [28.2.6.1.3; 28.3.4.3] The informal, subsistence-based economy will be impacted (high confidence). There
is high confidence that changing sea-ice conditions will result in more difficult access for hunting marine mammals.
[28.2.6.1.6] Although Arctic residents have a history of adapting to change, the complex inter-linkages between
societal, economic, and political factors and climatic stresses represent unprecedented challenges for northern
communities, particularly as the rate of change will be faster than the social systems can adapt (high confidence).
[28.4; 28.4.1; 28.2.5]
Impacts on the health and well-being of Arctic residents from climate change are significant and projected to
increase especially for many indigenous peoples (high confidence). [28.2.4] These are expected to vary among
the diverse settlements, which range from small, remote predominantly indigenous communities to large cities and
industrial settlements (high confidence), especially those located in highly vulnerable locations along ocean and
river shorelines. [28.2.4]
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28.1. Introduction
Several recent climate impact assessments on Polar Regions have been undertaken, including the synthesis report on
Snow, Water, Ice and Permafrost in the Arctic (AMAP, 2011), the State of the Arctic Coast 2010 (2011) reports, the
Antarctic Climate and the Environment (Turner et al., 2009, 2013), Arctic Resilience Interim Report 2013 (2013),
and the findings of the International Polar Year (IPY) (Krupnick et al., 2011). These reports draw a consistent
pattern of climate-driven environmental, societal and economic changes in the Polar Regions in recent decades. In
this chapter, we use the scientific literature, including these reports, to consolidate the assessment of the impacts of
climate change on Polar Regions from 2007, advance new scientific evidence of impacts and identify key gaps in
knowledge on current and future impacts.
Previous IPCC reports define the Arctic as the area within the Arctic Circle (66oN), and the Antarctic as the
continent with surrounding Southern Ocean south of the polar front, which is generally close to 58S (IPCC, 2007).
For the purpose of this report we use the conventional IPCC definitions as a basis, while incorporating a degree of
flexibility when describing the Polar Regions in relation to particular subjects.
[INSERT FIGURE 28-1 HERE
Figure 28-1: Location maps of the north and south polar regions. Credit: P. Fretwell, British Antarctic Survey.]
Changes in the physical and chemical environments of the Polar Regions are detailed in IPCC WG-1 report. The
Arctic has been warming since the 1980s at approximately twice the global rate demonstrating the strongest
temperature changes (~ 1 °C per decade) in winter and spring, and smallest in autumn. Sea ice declined at an
average rate of 13% per decade; the Arctic Ocean is projected to become nearly ice-free in summer within this
century. The duration of snow cover extent and snow depth are decreasing in North-America while increasing in
Eurasia. Since late 1970s permafrost temperatures have increased between 0.5 to 2 °C. In the Southern Hemisphere,
the strongest rates of atmospheric warming are occurring in the western Antarctic Peninsula (WAP, between 0.2 and
0.3 °C per decade) and the islands of the Scotia Arc, where there have also been increases in oceanic temperatures
and large regional decreases in winter sea ice extent and duration. Warming, although less than WAP, has also
occurred in the continental margins near to Bellingshausen Sea, Prydz Bay and the Ross Sea, with areas of cooling
in between. Land regions have experienced glacial recession and changes in the ice and permafrost habitats in the
coastal margins. The Southern Ocean continues to warm, with increased freshening at the surface due to
precipitation leading to increased stratification. In both Polar Regions, surface waters will become seasonally
corrosive to aragonite within decades, with some regions being affected sooner than others (Box CC-OA, WGI AR5
Chapter 6). Observations and models indicate that the carbon cycle of the Arctic and Southern Oceans will be
impacted by climate change and increased CO2.
28.2. Observed Changes and Vulnerability under Multiple Stressors
28.2.1. Hydrology and Freshwater Ecosystems
28.2.1.1. Arctic
Arctic rivers and lakes continue to show pronounced changes to their hydrology and ecology. Previously noted
increases in Eurasian Arctic river flow (1936-1999; Peterson et al., 2002) could not, for a similar period (1951-
2000), be attributed with certainty to precipitation changes (Milliman et al., 2008) but has been, including more
recent extreme increases (2007), to enhanced poleward atmospheric moisture transport (Zhang et al., 2013). By
contrast, decreased flow in high-latitude Canadian rivers (1964-2000; average -10%) does match that for
precipitation (Déry and Wood, 2005). Recent data (1977-2007) for 19 circumpolar rivers also indicates an area-
weighted average increase of +9.8% ( 17.1 to 47.0%; Overeem and Syvitski, 2010) accompanied by shifts in flow
timing, with May snowmelt increasing (avg. 66%) but flow in the subsequent month of peak discharge decreasing
(~7%). Across the Russian Arctic, dates of spring maximum discharge have also become earlier, particularly in the
most recent [1960-2001] period analyzed (average -5d; range for 4 regions +0.2 to -7.1 d), but no consistent trend
exists for magnitude (average -1%; range +21 to -24%; Shiklomanov et al., 2007). Earlier timing was most
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pronounced in eastern, colder continental climates where increases in air temperature have been identified as the
dominant control (Tan et al., 2011).
Increases have also occurred in winter low flows for many Eurasian and North American rivers (primarily late 20th
century; Smith et al., 2007; St. Jacques and Sauchyn, 2009; Walvoord and Striegl, 2007; Ye et al., 2009), the key
exceptions being decreases in eastern North America and unchanged flow in small basins of eastern Eurasia
(Rennermalm et al., 2010). Most of such studies suggest permafrost thaw (WGI, Ch.4) has increased winter flow,
while others suggest increases in net winter precipitation minus evapotranspiration (Landerer et al., 2010; Rawlins et
al., 2009a, b). Insufficient precipitation stations preclude deciphering the relative importance of these factors (WGI,
Ch. 2.5.1).
The surface-water temperatures of large water bodies has warmed (1985-2009; Schneider and Hook, 2010),
particularly for mid- and high latitudes of the northern hemisphere with spatial patterns generally matching those for
air temperature. Where water bodies warmed more rapidly than air temperature, decreasing ice cover was suggested
as enhancing radiative warming. Paleolimnological evidence indicates that highest primary productivity was
associated with warm, ice-free summer conditions and the lowest with periods of perennial ice (Melles et al., 2007).
Increasing water temperatures affect planktonic and benthic biomass and lead to changes in species composition
(Christoffersen et al. 2008; Heino et al. 2009, Jansson et al. 2010). Reduced ice cover with higher air temperatures
and evaporation are responsible for the late 20th c early 21st c desiccation of some Arctic ponds (Smol and Douglas,
2007).
Changes have occurred in the size and number of permafrost lakes over the last half-century (Hinkel et al., 2007;
Marsh et al., 2009), but their patterns and rates of change are not consistent because of differing thawing states,
variations in warming and effects of human activities (Hinket et al. 2007; Prowse and Brown, 2010a). Thawing
permafrost affects the biogeochemistry of water entering lakes and rivers (Frey and McClelland, 2009; Kokelj et al.,
2009) and their ecological structure and function (Lantz and Kokelj, 2008; Mesquita et al., 2010; Thompson et al.,
2008), such as enhancing eutrophication by a shift from pelagic to benthic-dominated production (Thompson et al.,
2012).
The aquatic ecosystem health and biodiversity of northern deltas is dependent on combined changes in the elevation
of spring river ice-jam floods and sea level (Lesack and marsh, 2007, 2010). Diminishing ice shelves (last half-
century) have also caused a decline in the number of freshwater epishelf lakes that develop behind them (Veillette et
al., 2008; Vincent et al., 2009). While such biophysical dependencies have been established, temporal trends in such
river-delta and epishelf lake impacts and their linkages to changing climate remain to be quantified precisely.
An interplay of freshwater-marine conditions also affect the timing, growth, run size and distribution of several
Arctic freshwater and anadromous fish. Key examples include: the timing of marine exit of Yukon River Chinook
salmon (Oncorhynchus tshawytscha; 1961-2009) varied with air and sea temperatures and sea ice cover (Mundy and
Evenson, 2011); the growth of young-of-year Arctic cisco (Coregonus autumnalis; 1978 2004) varied in response
to lagged sea-ice concentration and Mackenzie River discharge, also indicating that decreased sea-ice concentration
and increased river discharge enhanced marine primary production leading to more favorable foraging conditions
(Von Biela et al. (2011); and factors that influence the water level and freshening of rivers, as well as the strength,
duration and directions of prevailing coastal winds, affect survival of anadromous fishes during coastal migration
and their subsequent run size (Fechhelm et al., 2007).
28.2.1.2. Antarctic
Biota of Antarctic freshwater systems (lakes, ponds, short streams and seasonally wetted areas) are dominated by
benthic microbial communities of cyanobacteria and green algae in a simple food web. Mosses occur in some
continental lakes with higher plants absent. Planktonic ecosystems are typically depauperate and include small
algae, bacteria and colourless flagellates, with few metazoans and no fish (Quesada and Velázquez, 2012). Recent
compilations of single-year datasets have reinforced previous conclusions on the changing freshwater habitats in
Antarctica (Verleyen et al., 2012). In regions where the climate has warmed the physical impacts on aquatic
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ecosystems include loss of ice and perennial snow cover, increasing periods of seasonal open water, increased water
column temperatures and changes in water column stratification. In some areas a negative water balance has occured
due to increased temperature and changes in wind strength driving enhanced evaporation and sublimation and
leading to increased salinity in lakes in recent decades (Hodgson et al., 2006a). In other areas, especially glacial
forelands, increased temperatures have led to greater volumes of seasonal meltwater in streams and lakes together
with increased nutrient fluxes (high confidence). In both cases the balance between precipitation and evaporation
can have detectable effects on lake ecosystems (medium confidence) through changes in water body volume and
lake chemistry (Lyons et al., 2006; Quesada et al., 2006). Non-dilute lakes with a low lake depth to surface area
ratio are most susceptible to inter-annual and inter-decadal variability in the water balance, as measured by changes
in specific conductance (high confidence) (Verleyen et al., 2012). Warming in the northwestern Antarctic Peninsula
region has resulted in permafrost degradation in the last c. 50 yr impacting surface geomorphology and hydrology
(Bockheim et al., 2013) with the potential to increase soil biomass.
28.2.2. Oceanography and Marine Ecosystems
28.2.2.1. Arctic
28.2.2.1.1. Marine plankton, fish, and other invertebrates
Working Group I documents the expected physical and chemical changes that will occur in Arctic marine
ecosystems (WGI AR5 Chapters 4, 6, and 11). In addition to climate change, naturally occurring interannual,
decadal, and multi-decadal variations in climate will influence the Arctic Ocean and its neighboring high latitude
seas (WGII Chapter 5). In recent years (2007-2012) ocean conditions in the Bering Sea have been cold (Stabeno et
al., 2012a), while the Barents Sea has been warm (Lind and Ingvaldsen, 2012).
In this section we build on previous reviews of observed species responses to climate (Wassman et al. 2011) to
summarize the current evidence of the impact of physical and chemical changes in marine systems on the
phenology, spatial distribution and production of Arctic marine species. For each type of response, the implications
for phytoplankton, zooplankton, fish and shellfish are discussed. The implications of these changes on marine
ecosystem structure and function will be the result of the synergistic effects of all three types of biological
responses.
Phenological response
The timing of spring phytoplankton blooms is a function of seasonal light, hydrographic conditions, and the timing
of sea ice breakup (Wassman, 2011). In addition to the open water phytoplankton bloom, potentially large ice algal
blooms can form under the sea ice (Arrigo, 2012). During the period 1997-2009, a trend towards earlier
phytoplankton blooms was detected in approximately 11% of the area of the Arctic Ocean (Kahru et al., 2011). This
advanced timing of annual phytoplankton blooms coincided with decreased sea ice concentration in early summer.
Brown and Arrigo (2013) studied the timing and intensity of spring blooms in the Bering Sea from 1997-2010 and
found that in northern regions, sea ice consistently retreated in late spring and was associated with ice-edge blooms,
whereas, in the southern regions the timing of sea ice retreat varied, with ice-edge blooms associated with late ice
retreat, and open water blooms associated with early ice retreat. Given the short time series and limited studies, there
is medium confidence that climate variability and change has altered the timing and the duration of phytoplankton
production.
The life cycles of calanoid copepods in the Arctic Ocean and Barents Sea are timed to utilize ice algal and
phytoplankton blooms (Falk-Petersen et al. 2009; Sreide et al., 2010; Darnis et al. 2012). Based on a synthesis of
existing data, Hunt et al. (2011) hypothesized that in the southeastern Bering Sea, ocean conditions and the timing of
sea ice retreat influences the species composition of dominant zooplankton, with lipid rich copepods being more
abundant in cold years.
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There is ample evidence that the timing of spawning and hatching of some fish and shellfish is aligned to match
larval emergence with seasonal increases availability of prey (Gjosaeter et al., 2009; Vikeb et al., 2010; Bouchard
and Fortier, 2011; Drinkwater et al., 2011). These regional phenological adjustments to local conditions occurred
over many generations (Ormseth and Norcross, 2009; Geffen et al., 2011; Kristiansen et al., 2011). There is medium
to high confidence that climate induced disruptions in this synchrony can result in increased larval or juvenile
mortality or changes in the condition factor of fish and shellfish species in the Arctic marine ecosystems.
Observed spatial shifts
Spatial heterogeneity in primary production has been observed (Lee et al., 2010; Grebmeier, 2012). Simulation
modeling studies show that spatial differences in the abundance of four species of copepod can be explained by
regional differences in the duration of the growing season and temperature (Ji et al., 2012). Retrospective studies
based on surveys from 1952-2005 in the Barents Sea revealed that changes in the species composition, abundance
and distribution of euphausiids were related to climate-related changes in oceanographic conditions (Zhukova et al.,
2009).
Retrospective analysis of observed shifts in the spatial distribution of fish and shellfish species along latitudinal and
depth gradients showed observed spatial shifts were consistent with expected responses of species to climate change
(Simpson et al., 2011; Poloczanska et al. 2013; Box CC-MB, Chapter 30). Retrospective studies from the Bering
Sea, Barents Sea, and the northeast Atlantic Ocean and Icelandic waters, showed that fish shift their spatial
distribution in response to climate variability (i.e. interannual, decadal or multi-decadal changes in ocean
temperature; Mueter and Litzow, 2008; Sundby and Nakken, 2008; Hátún et al., 2009; Valdimarsson et al., 2012;
Kotwicki and Lauth, 2013). There are limits to the movement potential of some species. Vulnerability assessments
indicate that the movement of some sub-arctic fish and shellfish species into the Arctic Ocean may be impeded by
the presence of water temperatures on the shelves that fall below their thermal tolerances (Hunt et al., 2013;
Hollowed et al., 2013). Coupled bio-physical models have reproduced the observed spatial dynamics of some the
species in the Bering and Barents Seas, and are being used to explain the role of climate variability and change on
the distribution and abundance of some species (Huse and Ellingsen, 2008; Parada et al., 2010). In summary, there is
medium to high confidence based on observations and modeling that some fish and shellfish have shifted their
distribution in response to climate impacts on the spatial distribution and volume of suitable habitat.
Observed variations in production
Seasonal patterns in light, sea ice cover, freshwater input, stratification and nutrient exchange act in concert to
produce temporal cycles of ice algal and phytoplankton production in Arctic marine ecosystems (Wassmann, 2011;
Perrette et al., 2011; Tremblay et al., 2012). Satellite observations and model estimates for the period 1988 2007
showed that phytoplankton productivity increased in the Arctic Ocean in response to a downward trend in the extent
of summer sea ice (Zhang et al., 2010). Satellite data provided evidence of a 20% increase in annual net primary
production in the Arctic Ocean between 1998 and 2009 in response to extended ice free periods (Arrigo and van
Dijken, 2011). Regional trends in primary production will differ in response to the amount of open water area in
summer (Arrigo and van Dijken, 2011). Other studies showed gross primary production increased with increasing
air temperature in the Arctic Basin and Eurasian shelves (Slagstad et al., 2011). A recent 5 year study (2004-2008)
in the Canadian Basin showed that smaller phytoplankton densities were higher than larger phytoplankton densities
in years when sea surface temperatures were warmer, the water column was more stratified, and nutrients were more
depleted during the Arctic summer (Li et al., 2009; Morán et al., 2010). Additional observations will help to resolve
observed differences between in-situ and satellite derived estimates of primary production (Matrai et al., 2013). In
conclusion, based on recent observations and modeling, there is medium to high confidence that primary production
has increased in the Arctic Ocean in response to changes in climate and its impact on the duration and areal extent of
ice free periods in summer.
Regional differences in zooplankton production have been observed. During a period of ocean warming (1984-
2010), Dalpadado et al. (2012) observed an increase in the biomass of lipid rich euphausiids in the Barents Sea and
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relatively stable levels of biomass and production of Calanus finmarchicus. In the Bering Sea, observations over the
most recent decade in the southeast Bering Sea showed C. marshallae were more abundant in cold years than warm
years (Coyle et al., 2011).
There is strong evidence that climate variability impacts the year-class strength of Arctic marine fish and shellfish
through its influence on: predation risk; the quality, quantity and availability of prey; and reproductive success
(Mueter et al., 2007; Bakun 2010; Drinkwater et al., 2010). Regional differences in the species responses to climate
change will be a function of the exposure of the species to changing environmental conditions, the sensitivity of the
species to these changes (Beaugrand and Kirby, 2010) and the abilities of species to adapt to changing conditions
(Pörtner and Peck, 2010; Donelson et al., 2011). There is high confidence that shifts in ocean conditions have
impacted the abundance of fish and shellfish in Arctic regions. Observed trends in the abundance of commercial fish
and shellfish may also be influenced by historical patterns of exploitation (Vert-pre et al., 2013).
28.2.2.1.2. Marine mammals, polar bear, and sea birds
Studies on responses of Arctic and subarctic marine mammals to climate change are limited and vary according to
insight into their habitat requirements and trophic relationships (Laidre et al., 2008). Many Arctic and subarctic
marine mammals are highly specialized, have long-life spans, and are poorly adapted to rapid environmental change
(Moore and Huntington, 2008), and changes may be delayed until significant sea ice loss has occurred (Freitas et al.,
2008; Laidre et al., 2008).
Climate change effects on Arctic and subarctic marine mammal species will vary by life history, distribution, and
habitat specificity (high confidence). Climate change will improve conditions for a few species, have minor negative
effects for others, and some will suffer major negative effects (Ragen et al., 2008; Laidre et al., 2008). Climate
change resilience will vary and some ice-obligate species should survive in regions with sufficient ice and some may
adapt to ice-free conditions (Moore and Huntington, 2008). Less ice-dependent species may be more adaptable but
an increase in seasonally migrant species could increase competition (Moore and Huntington, 2008).
Climate change vulnerability was associated with feeding specialization, ice dependence, and ice reliance for prey
access and predator avoidance (Laidre et al., 2008). There is medium agreement on which species life histories are
most vulnerable. Hooded seals (Cystophora cristata) and narwhal (Monodon monoceros) were identified as most at
risk and ringed seals (Pusa hispida) and bearded seals (Erignathus barbatus) as least sensitive (Laidre et al., 2008).
Kovacs et al. (2010) shared concern for hooded seals and narwhal but had concerns for ringed seals and bearded
seals. Narwhal may have limited ability to respond to habitat alteration (Williams et al. 2011). Species that spend
only part of the year in the Arctic (e.g., gray whale (Eschrichtius robustus), killer whale (Orcinus orca)) may benefit
from reduced ice (Moore, 2008; Laidre et al., 2008; Higdon and Ferguson, 2009; Matthews et al., 2011; Ferguson et
al., 2012). Killer whale expansion into the Arctic could cause a trophic cascade (Higdon and Ferguson, 2009)
although there is limited evidence at this time.
There is limited evidence although medium agreement that generalists and pelagic feeding species may benefit from
increased marine productivity from reduced ice while benthic feeding species near continental shelf habitats may do
poorly (Bluhm and Gradinger, 2008). There is limited evidence but high agreement that dietary or habitat specialists
will do poorly with reduced ice. Reduction of summer/autumn ice was the primary extrinsic factor affecting Pacific
walrus (Odobenus rosmarus) with predictions of distribution changes, reduced calf recruitment, and longer-term
predictions of high extinction probability (Cooper et al., 2006; MacCracken, 2012). Summer ice retreat may make
migration to such habitats energetically unprofitable for ringed seals (Freitas et al., 2008). Ice loss threatens Baltic
ringed seals (Kovacs and Lydersen, 2008). In Hudson Bay, earlier spring break-up and changes in snow cover over
lairs have reduced ringed seal recruitment (Ferguson et al., 2005). Changes in snowfall over the 21st century were
projected to reduce ringed seal habitat for lairs by 70% (Hezel et al., 2012). Similarly, harp seal (Pagophilus
groenlandicus) breeding habitat was affected by changing ice conditions that could reduce pup survival (Bajzak et
al., 2011). While there is limited evidence, there are concerns that climate change may cause indirect effects on
Arctic marine mammals health (e.g., pathogen transmission, food web changes, toxic chemical exposure, shipping,
and development) (Burek et al., 2008).
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Empirical studies provide direct insight into the mechanisms of climate change impact on polar bears (Ursus
maritimus) but modelling allows predictive capacity (Hunter et al., 2010; Amstrup et al., 2010; Durner et al., 2011;
Castro de la Guardia et al., 2013).
Polar bears are highly specialized and use annual ice over the continental shelves as their preferred habitat (Durner
et al., 2009; Miller et al., 2012). The recent and projected loss of annual ice over continental shelves, decreased ice
duration, decreased ice thickness, and habitat fragmentation is causing reduced food intake, increased energy
expenditure, and increased fasting in polar bears (high confidence) (Stirling and Parkinson, 2006; Regehr et al.,
2007; Durner et al., 2009; Amstrup et al., 2010; Hunter et al., 2010; Derocher et al., 2011; Rode et al., 2012;
Sahanatien and Derocher, 2012; Castro de la Guardia et al., 2013).
Subpopulation response varies geographically. Only 2 of the 19 subpopulations, Western Hudson Bay (Regehr et al.,
2007) and the Southern Beaufort Sea (Regehr et al., 2010; Rode et al., 2010a) have data series adequate for clear
identification of abundance effects related to climate change. Many other subpopulations show characteristics
associated with decline but some remain stable. Declining ice is causing lower body condition, reduced individual
growth rates, lower fasting endurance, lower reproductive rates, and lower survival (high confidence) (Regehr et al.,
2007; Regehr et al., 2010; Rode et al., 2010a; Molnar et al., 2011; Rode et al., 2012). Condition is a precursor to
demographic change (very high confidence) (Regehr et al., 2010; Hunter et al., 2010; Rode et al., 2010a; Robinson
et al., 2011). The decline in the subpopulation in Western Hudson Bay by 21% between 1987 and 2004 was related
to climate change (medium confidence) (Regehr et al., 2007).
Replacement of multiyear ice by annual ice could increase polar bear habitat (low confidence) (Derocher et al.,
2004). Increasing the distance to multiyear ice and terrestrial refugia at maximal melt may result in drowning, cub
mortality, and increased energetic costs (Monnett and Gleason, 2006; Durner et al., 2011; Pagano et al., 2012).
There is robust evidence of changes in sea ice conditions changing polar bear distribution including den areas (high
confidence) (Fischbach et al., 2007; Schliebe et al., 2008; Gleason and Rode, 2009; Towns et al., 2010; Derocher et
al., 2011). The number of human-bear interactions are projected to increase with warming (high confidence)
(Stirling and Parkinson, 2006; Towns et al., 2009).
Use of terrestrial resources by polar bears was suggested as adaptive (Dyck et al., 2007; Dyck and Romberg, 2007;
Armstrong et al., 2008; Dyck et al., 2008; Dyck and Kebreab, 2009; Rockwell and Gormezano, 2009; Smith et al.,
2010). Polar bears cannot adapt to terrestrial foods (Stirling et al., 2008b; Amstrup et al., 2009; Slater et al., 2010;
Rode et al., 2010b), and will most likely not be able to adapt to climate change and reduced sea ice extent (very high
confidence). Changing ice conditions are linked to cannibalism (Amstrup et al., 2006), altered feeding (Cherry et al.,
2009), unusual hunting behaviour (Stirling et al., 2008a), and diet change (Iverson et al., 2006; Thiemann et al.,
2008) (medium confidence).
Upwelling or subsurface convergence areas found in frontal zones and eddies, and the marginal ice zone, are
associated with high marine productivity important to Arctic seabirds (e.g. (Irons et al., 2008). Long-term or
permanent shifts in convergence areas and the marginal ice-edge zone induced by climate change may cause
mismatch between the timing of breeding and the peak in food availability and, thus, potentially have strong
negative impacts on seabird populations (Gaston et al., 2005; Moline et al., 2008; Gaston et al., 2009; Gremillet and
Boulinier, 2009) (medium confidence).
The contrasting results from the relatively few studies of impacts of climate change on Arctic seabirds, demonstrate
that future impacts will be highly variable between species and between populations of the same species (medium
confidence). Retreating sea ice and increasing SSTs have favored some species and disadvantaged others (Gaston et
al. 2005; Byrd et al. 2008; Irons et al. 2008; Karnovsky et al. 2010; Fredriksen et al. 2013). Some species of sea
birds respond to a wide range of sea surface temperatures via plasticity of their foraging behaviour, allowing them to
maintain their fitness levels (Grémillet et al. 2012). Phenological changes and changes in productivity of some
breeding colonies have been observed (Byrd et al. 2008; Gaston and Woo 2008; Moe et al. 2009). Negative trends
in population size, observed over the last few decades for several species of widespread Arctic seabirds, may be
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related to over-harvesting and pollution as well as climate change effects (Gaston, 2011). For those species whose
distribution is limited by sea ice and cold water, polar warming could be beneficial (Mehlum 2012).
A major ecosystem shift in the Northern Bering Sea starting in the mid 1990s caused by increased temperatures and
reduced sea ice cover had a negative impact on benthic prey for diving birds and these populations have declined in
the area (Grebmeier et al., 2006). More recently, the Bering Sea has turned colder again.
28.2.2.2. Antarctica
Productivity and food web dynamics in the Southern Ocean are dominated by the extreme seasonal fluctuations of
irradiance and the dynamics of sea ice, along with temperature, carbonate chemistry and vertical mixing (Massom
and Stammerjohn, 2010; Boyd et al., 2012; Murphy et al., 2012a). Moreover, there is large-scale regional variability
in habitats (Grant et al., 2006) and their responses to climate change (WGI). Antarctic krill, Euphausia superba
(hereafter, krill ) is the dominant consumer, eating diatoms, and, in turn, is the main prey of fish, squid, marine
mammals and seabirds. Krill is dominant from the Bellingshausen Sea east through to the Weddell Sea and the
Atlantic sector of the Southern Ocean (Rogers et al., 2012). In the East Indian and southwest Pacific sectors of the
Southern Ocean, the krill-dominated system lies to the south of the Southern Boundary of the Antarctic Circumpolar
Current (Nicol et al., 2000a,b) while to the north copepods and myctophid fish are most important (Rogers et al.,
2012). Further west, where the Weddell Sea exerts an influence, krill are found as far north as the Subantarctic
Circumpolar Current Front (Jarvis et al., 2010). Where sea ice dominates for most of the year, ice-obligate species
(e.g. Euphausia crystallorophias and Peluragramma antarcticum) are most important (Smith et al., 2007).
Few studies were available in AR4 to document and validate the changes in these systems resulting from climate
change. Those studies reported increasing abundance of benthic sponges and their predators, declining populations
of krill, Adélie and emperor penguins, and Weddell seals, and a possible increase in salps, noting some regional
differences in these trends. The importance of climate processes in generating these changes could not be
distinguished from the indirect consequences of the recovery of whale and seal populations from past over-
exploitation (Trathan and Reid, 2009; Murphy et al., 2012a,b).
28.2.2.2.1. Marine plankton, krill, fish, and other invertebrates
Distributions of phytoplankton and zooplankton have moved south with the frontal systems (Hinz et al., 2012;
Mackey et al., 2012), including range expansion into the Southern Ocean from the north by the coccolithophorid,
Emiliania huxleyi (Cubillos et al., 2007), and the red-tide dinoflagellate Noctiluca scintillans (McLeod et al., 2012)
(medium confidence). There is insufficient evidence to determine whether other range shifts are occurring.
Collapsing ice shelves are altering the dynamics of benthic assemblages by exposing areas previously covered by ice
shelves, allowing increased primary production and establishment of new assemblages (e.g. collapse of the Larson
A/B ice shelves) (Peck et al., 2009; Gutt et al., 2011) (medium confidence). More icebergs are grounding, causing
changes in local oceanography and declining productivity that consequently affects productivity of benthic
assemblages (Thrush and Cummings 2011) (low confidence). Iceberg scour on shallow banks is also increasing,
disrupting resident benthic assemblages (Barnes and Souster 2011; Gutt et al., 2011) (medium confidence).
Primary production is changing regionally in response to changes in sea ice, glacial melt and oceanographic features
(Arrigo et al., 2008; Boyd et al., 2012) (medium confidence). Off the west Antarctic Peninsula, phytoplankton stocks
and productivity have decreased north of 63°S, but increased south of 63°S (Montes-Hugo et al., 2009) (WGII
Chapter 6) (high confidence). This study (based on time-series of satellite-derived and measured chlorophyll
concentrations) also indicated a change from diatom-dominated assemblages to ones dominated by smaller
phytoplankton (Montes-Hugo et al., 2009). The reduced productivity in the north may be tempered by increased
inputs of iron through changes to ocean processes in the region (Dinniman et al., 2012) (low confidence).
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Since the 1980s, Antarctic krill densities have declined in the Scotia Sea (Atkinson et al., 2004), in parallel with
regional declines in the extent and duration of winter sea ice (Flores et al., 2012). Uncertainty remains over changes
in the krill population because this decline was observed using net samples and is not reflected in acoustic
abundance time series (Nicol and Brierley 2010); the observed changes in krill density may have been partly a result
of changes in distribution (Murphy et al. 2007). Nevertheless, given its dependence on sea ice (Nicol et al., 2008)
the krill population may already have changed and will be subject to further alterations (high confidence).
The response of krill populations is probably a complex response to multiple stressors. Decreases in recruitment of
post-larval krill across the Scotia Sea have been linked to declines in sea-ice extent in the Antarctic Peninsula region
(Wiedenmann et al., 2009) (medium confidence) but these declines may have been offset by increased growth
arising from increased water temperature in that area (Wiedenmann et al., 2008). However, near South Georgia krill
productivity may have declined as a result of the increased metabolic costs of increasing temperatures (Hill et al.,
2013) (low confidence). The combined effects of changing sea ice, temperature and food have not been investigated.
28.2.2.2.2. Marine mammals and sea birds
In general, many Southern Ocean seals and seabirds exhibit strong relationships to a variety of climate indices, and
many of these relationships are negative to warmer conditions (Trathan et al., 2007; Barbraud et al., 2012; Forcada
et al., 2012) (low confidence). Regional variations in climate change impacts on habitats and food will result in a
mix of direct and indirect effects on these species. For example, Adélie penguin colonies are declining in recent
decades throughout the Antarctic Peninsula while the reduction in chinstrap penguins is more regional (Lynch et al.,
2012) and related to reductions in krill availability (Lima and Estay, 2013). In contrast gentoo penguins are
increasing in that region and expanding south (Lynch et al., 2012) (high confidence). This may be explained by the
reduced sea ice habitats and krill availability in the north resulting in a southward shift of krill predators, particularly
those dependent on sea ice (Forcada et al., 2012) and the replacement of these predators in the north by species that
do not depend on sea ice, such as gentoo penguins and elephant seals (Costa et al., 2010; Trivelpiece et al., 2011;
Ducklow et al., 2012; Murphy et al., 2013) (low confidence). A contrasting situation is in the Ross Sea where Adélie
penguin populations have increased (Smith et al., 2012). The mechanisms driving these changes are currently under
review and may be more than simply sea ice (Lynch et al., 2012; Melbourne-Thomas et al., 2013). For example, too
much or too little sea ice may have negative effects on the demography of Adélie and emperor penguins (see
Barbraud et al., 2012 for review). Also, increased snow precipitation which accumulates in breeding colonies can
decrease survival of chicks of Adélie penguins when accompanied by reduced food supply (Chapman et al., 2011).
Changes elsewhere are less well known. Some emperor penguin colonies have decreased in recent decades
(Barbraud et al., 2008; Jenouvrier et al., 2009) (low confidence) and one breeding site has been recorded as having
been vacated (Trathan et al., 2011). However, there is insufficient evidence to make a global assessment of their
current trend. In the subantarctic of the Indian sector, reductions in seal and seabird populations may indicate a
region-wide shift to a system with lower productivity (Weimerskirch et al., 2003; Jenouvrier et al., 2005a, b) (low
confidence) but commercial fishing activities may also play a role.
Where frontal systems are shifting south, productive foraging areas also move to higher latitudes. In the Indian
sector, this is thought to be causing declines in king penguin colonies on subantarctic islands (Péron et al., 2010)
(low confidence), while the shift in wind patterns may be causing changes to the demography of albatross
(Weimerskirch et al., 2012) (low confidence).
As identified in the last assessment, some species populations may suffer as a result of fisheries while others are
recoverying from past over-exploitation, either of which may confound interpretation of the response of these
species and their food webs to climate change. The recovery of Antarctic fur seals on some subantarctic islands has
been well documented and their populations may now be competing with krill-eating macaroni penguins (Trathan et
al., 2012). More recently, there has been confirmation that populations of some Antarctic whales are recovering,
such as humpbacks (Nicol et al., 2008; Zerbini et al., 2010), suggesting that food is currently not limiting. In
contrast, a number of albatross and petrel populations are declining as a result of incidental mortality in longline
fisheries in southern and temperate waters where these birds forage (Croxall et al., 2012).
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28.2.3. Terrestrial Ecosystems
28.2.3.1. Arctic
Arctic terrestrial ecosystems have undergone dramatic changes throughout the late Pleistocene and Holocene (last
130 000 years) mainly driven by natural climate change. Significant altitudinal and latitudinal advances and retreats
in tree line have been common, animal species have gone extinct, and animal populations have fluctuated
significantly throughout this period e.g. (Lorenzen et al., 2011; Salonen et al., 2011; Mamet and Kershaw, 2012).
28.2.3.1.1. Phenology
Phenological responses attributable to warming are apparent in most Arctic terrestrial ecosystems (medium
confidence). They vary from earlier onset and later end of season in western Arctic Russia (Zeng et al., 2013), to
little overall trend in plant phenology in the Swedish sub Arctic (Callaghan et al., 2010), to dramatic earlier onset of
phenophases in Greenland (Hye et al., 2007; Post et al., 2009a; Callaghan et al., 2011a) (Figure 28-2).
[INSERT FIGURE 28-2 HERE
Figure 28-2: Temporal change in onset of flowering (plants), median date of emergence (arthropods) and clutch
initiation dates (birds) in high-Arctic Greenland. Red dots are statistically significant, blue dots are not. Source:
Hye et al., 2007.]
28.2.3.1.2. Vegetation
The latest assessment of changes in NDVI (Normalized Difference Vegetation Index, a proxy for plant productivity)
from satellite observations between 1982 and 2012 shows that about a third of the Pan-Arctic has substantially
greened, <4% browned and > 57% did not change significantly (Xu et al., 2013) (Figure 28-3). The greatest
increases reported in recent years were in the North American high Arctic, along the Beaufort Sea and the east
European Arctic (Zhang et al., 2008; Pouliot et al., 2009; Bhatt et al., 2010; Forbes et al., 2010; Walker et al., 2011;
Epstein et al., 2012; Macias-Fauria et al., 2012; Xu et al., 2013).
[INSERT FIGURE 28-3 HERE
Figure 28-3: Significant changes (p< 0.01) in photosynthetically active period NDVI between 1982 and 2012.
Source: Xu et al., 2013.]
The positive trends in NDVI are associated with increases in the summer warmth index (sum of the monthly-mean
temperatures above freezing expressed as oC per month) that have increased on average by 5oC per month for the
Arctic as a whole (Xu et al., 2013). However, the even greater 10 to 12oC per month increase for the land adjacent to
the Chukchi and Bering Seas (Figure 28-3) was associated with decreases in NDVI. On the Yamal Peninsula in
Russia the pattern of NDVI is partly due to surface disturbance, such as landslide activity (Walker et al., 2009).
Small rodent cycles reduce NDVI in sub Arctic Sweden, by decreasing biomass and changing plant species
composition (Olofsson et al., 2012). The changing NDVI signal should therefore generally be interpreted with care.
In common with treeline trees and herbs, the abundance and biomass of deciduous shrubs and graminoids (grasses
and grass-like plants) have increased substantially in certain parts of the Arctic tundra in recent years, but remained
stable or decreased in others (very high confidence). Attribution for the increases and decreases in deciduous shrubs
and graminoids is heterogeneous with drivers varying among different regions (very likely), including Arctic
warming, differences in herbivory, industrial development, legacies from past land use, and changes in moisture
(Post and Pedersen 2008; Olofsson et al., 2009; Forbes et al., 2009 and 2010; Kitti et al., 2009; Kumpula et al.,
2011 and 2012; Myers-Smith et al., 2011; Elmendorf et al., 2012b;Callaghan et al., 2011b and 2013: Gamon et al.,
2013).
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Shrubs have generally expanded their ranges and/or growth over the last 20 years (Danby and Hik, 2007; Hudson
and Henry, 2009; Forbes et al., 2010; Hallinger et al., 2010; Rundqvist et al., 2011; Hedenas et al., 2011; Hill and
Henry, 2011; Myers-Smith et al., 2011a,b; Callaghan et al., 2011b; Elmendorf et al., 2012a,b; Macias-Fauria et al.,
2012), and have varied from dramatic, i.e. 200% area increase in study plots (Rundqvist et al., 2011) in sub arctic
Sweden to early invasion of a fell field community on west Greenland by low shrubs (Callaghan et al., 2011a).
A synthesis (61 sites: (Elmendorf et al., 2012a) of experimental warming studies of up to 20 years duration in tundra
sites worldwide, showed, overall, increased growth of deciduous shrubs and graminoids, decreased cover of mosses
and lichens, and decreased species diversity and evenness. Elmendorf et al., (2012a) point out that the groups that
increased most in abundance under simulated warming were graminoids in cold regions and primarily shrubs in
warm regions of the tundra. However, strong heterogeneity in responses to the experimental warming suggested that
other factors could moderate the effects of climate warming significantly like herbivory, differences in soil nutrients
and pH, precipitation, winter temperatures and snow cover, and species composition and density.
Snow bed habitats have decreased in sub arctic Sweden (Björk and Molau, 2007; Hedenas et al., 2011). In other
plant communities, changes have been less dramatic, ranging from small increases in species richness in the south
west Yukon of the Canadian sub Arctic (Danby et al., 2011), through subtle changes in plant community
composition in west and southeast Greenland (Daniëls and De Molenaar, 2011; Callaghan et al., 2011a) to 70 year
stability of a plant community on Svalbard (Prach et al., 2010).
The responses to Arctic warming of lichen and bryophyte (mosses) diversity have been heterogenous, varying from
consistent negative effects to significant increases in recent years (Hudson and Henry, 2009; Tmmervik et al.,
2009; Tmmervik et al., 2012). Forbes and Kumpula (2009) recorded long-term and widespread lichen degradation
in northern Finland attributed more to trampling of dry lichens by reindeer in summer than winter consumption as
forage.
Palaeorecords of vegetation change indicate that the northern tree line should extend upwards and northwards during
current climate warming (Callaghan et al., 2005) because tree line is related to summer warmth (e.g. Harsch et al.,
2009). Although the tree line has moved northwards and upwards in many Arctic areas, it has not shown a general
circumpolar expansion in recent decades (high confidence).
Model projections that suggest a displacement of between 11 and 50% of tundra by forest by 2100 (see references in
Callaghan et al., 2005) and shifts upslope by 2 to 6 m per year (Moen et al., 2004) and northwards by 7.4 20 km per
year (Kaplan and New, 2006) might be overestimating rate of tree line advance by a factor of up to 2000 (Van
Bogaert et al., 2011). The fastest upslope shifts of tree lines recorded during 20th century warming are 1 to 2 m per
year (Shiyatov et al., 2007; Kullman and Öberg, 2009) whereas the fastest so-far recorded northward-migrating tree
line replaces tundra by taiga at a rate of 3 10 m per year (Kharuk et al., 2006). In some areas, the location of the tree
line has not changed or has changed very slowly (Payette, 2007; MacDonald et al., 2008a). A global study by
Harsch et al. (2009) showed that only 52% of 166 global tree line sites studied had advanced over the past 100
years. In many cases the tree line has even retreated (Cherosov et al., 2010). At the small scale, the tree line has
shown increase, decrease and stability in neighboring locations (Van Bogaert et al., 2011; Lloyd et al., 2011).
Evidence for densification of the forest at the sub Arctic tree line is robust and consistent within Fennoscandia
(Tmmervik et al., 2009; Rundqvist et al., 2011; Hedenas et al., 2011) and Canada (Danby and Hik, 2007).
Dendroecological studies indicate enhanced conifer recruitment during the twentieth century in the northern Siberian
taiga (Briffa et al., 2008). Some of the changes are dramatic, such as an increase in area of mountain birch in study
plots in northern Sweden by 600% between 1977/8 and 2009/10 (Rundqvist et al., 2011) and a doubling of tree
biomass in Finnmarksvidda in northern Norway since 1957 (Tmmervik et al., 2009). However, model projections
of displacement of deciduous forest by evergreen forest (Wolf et al., 2008; Wramneby et al., 2010) have not so far
been validated.
Where the mountain birch tree line has increased in elevation and shrub (e.g. willow, dwarf birch) abundance has
increased, the response can be an interaction between climate warming, herbivory pressure and earlier land use
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(Olofsson et al., 2009; Hofgaard et al., 2010; Van Bogaert et al., 2011). In Fennoscandia and Greenland, heavy
grazing by large herbivores may significantly check deciduous low erect shrub (e.g. dwarf shrub and willow) growth
(Post et al., 2008; Kitti et al., 2009; Olofsson et al., 2009).
Less moisture from snow and more rain now favors broadleaf trees over conifers and mosses in some areas (Juday,
2009) while moisture deficits are reducing the growth of some northern forests (Goetz et al., 2005; Verbyla, 2008;
Yarie, 2008) and making them more susceptible to insect pest outbreaks (see references in (Callaghan et al., 2011c).
Death of trees through drought stress or insect pest activity will increase the probability of fire that will have
positive feedbacks (increase warming) on the climate (Mack et al., 2011).
28.2.3.1.3. Changes in animal populations
The documented collapse or dampening of population cycles of voles and lemmings over the last 20-30 years in
parts of Fennoscandia and Greenland (Schmidt et al., 2012), can be attributed with high confidence to climate
change (Ims et al., 2007; Gilg et al., 2009; Ims et al., 2011; Kausrud et al., 2009). A shortening of the snow season
and more thaw and/or rain events during the winter will influence on the subnivean space which provide thermal
insulation, access to food, and protection from predators (Berg et al., 2008; Johansson et al., 2011; Kausrud et al.,
2009). However, the causes of the changes in the lemming and vole cycles are still being debated as factors other
than climate change may also be of importance (Brommer et al., 2010; Krebs, 2011).
Climate-mediated range expansion both in altitude and latitude of insect pests, and increased survival due to higher
winter temperatures, has been documented for bark beetles in North America (Robertson et al., 2009) and for
geometrid moths in Fennoscandia (Jepsen et al., 2008; Callaghan et al., 2010; Jepsen et al., 2011), causing more
extensive forest damage than before. Outbreaks of insect pests like geometrid moths can even reduce the strengths
of CO2 sinks in some areas (Heliasz et al., 2011).
The decline in wild reindeer and caribou (both Rangifer tarandus) populations in some regions of about 30 percent
over the last 10-15 years has been linked both to climate warming and anthropogenic landscape changes (Post et al.,
2009a; Vors and Boyce 2009; Russell and Gunn 2010). Even though most of the Arctic has warmed, the decline in
the populations has not been uniform. Some of the North American large, wild herds have for example declined by
75-90 percent, while other wild herds and semi-domestic herds in Fennoscandia and Russia have been stable or even
increased (Gunn et al., 2009; Vors and Boyce, 2009; Joly et al., 2011; Forbes et al., 2009; Forbes 2010; Kumpula et
al., 2012).
The expected and partially observed increased primary productivity of Arctic tundra may potentially increase the
supply of food for Arctic ungulates. However, the overall quality of forage may decline during warming, for
example if the nitrogen content of key fodder species for ungulates were to drop during warming (Turunen et al.,
2009; Heggberget et al., 2010), while lichen biomass, an important winter fodder for reindeer, is decreasing over
parts of the Arctic region. Herbivory also changes the vegetation itself in concert with the warming, further
complicating the prediction of vegetation changes and their impacts on ungulate populations (van Der Wal et al.,
2007; Turunen et al., 2009).
More frequent rain-on-snow icing events and thicker snow-packs caused by warmer winters and increased
precipitation may restrict access to vegetation and may have profound negative influences on the population
dynamics of Arctic ungulates (Berg et al., 2008; Forchhammer et al., 2008; Miller and Barry, 2009; Stien et al.,
2010; Hansen et al., 2011; Stien et al., 2012). Such events have caused heavy mortality in some semi-domestic
reindeer herds and musk oxen in recent years (Grenfell and Putkonen, 2008; Forbes, 2009; Bartsch et al., 2010), and
have also been shown to synchronize the dynamics of a resident vertebrate community (small mammals, reindeer
and Arctic fox) in Svalbard (Hansen et al., 2013). In contrast, Tyler et al. (2008) and Tyler (2010) suggested that
generally warmer winters enhance the abundance of reindeer populations.
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It has been suggested that warming-induced trophic mismatches between forage availability and quality and timing
of calving have a role in the decline of circumpolar reindeer and caribou populations (Post and Forchhammer, 2008;
Post et al., 2009a,b), although such trophic mismatch has been disputed (Griffith et al., 2010).
Adjustment via phenotypic plasticity instead of adaptation by natural selection is expected to dominate vertebrate
responses to rapid Arctic climate change, and many such adjustments have already been documented (Gilg et al.,
2012).
28.2.3.1.4. Long-term trends and event-driven changes
Long-term climate change impacts on vegetation and animal populations are accelerated when tipping points are
triggered by events such as extreme weather, fire, insect pest and disease outbreaks. The impacts of winter thaw
events on ecosystems are now well-documented e.g. (Bokhorst et al., 2011) but studies of the severe impacts of
tundra fires on vegetation and biospheric feedbacks are recent (Mack et al., 2011). Results from experimental winter
thaws were validated by a natural event in northern Norway and Sweden in 2007 that reduced NDVI by almost 30%
over at least 1400 km2 (Bokhorst et al., 2009). Studies on relationships between climate change and plant disease are
rare but Olofsson et al., (2011) showed that increased snow accumulation led to a higher incidence of fungal growth
on sub Arctic vegetation.
28.2.3.2. Antarctica
Antarctic terrestrial ecosystems occur in 15 biologically-distinct areas (Terauds et al., 2012) with those in the
maritime and sub-Antarctic islands experiencing the warmest temperatures, reduced extreme seasonality and
greatest biodiversity (Convey, 2006). In the cooler conditions on the continent, species must be capable of exploiting
the short periods where temperature and moisture availability are above physiological and biochemical thresholds.
In many areas there is no visible vegetation, with life being limited, at the extreme, to endolithic (within rock)
communities of algae, cyanobacteria, fungi, bacteria and lichens (Convey, 2006).
Few robust studies are available of biological responses to observed climatic changes in natural Antarctic terrestrial
ecosystems. The rapid population expansion and local-scale colonisation by two native flowering plants
(Deschampsia antarctica and Colobanthus quitensis) in maritime Antarctica (Parnikoza et al., 2009) remains the
only published repeat long-term monitoring study of any terrestrial vegetation or location in Antarctica. Radiocarbon
dating of moss peat deposits has shown that growth rates and microbial productivity have risen rapidly on the
Antarctic Peninsula since the 1960s, consistent with temperature changes, and are unprecedented in the last 150
years (Royles et al., 2013). In east Antarctica moss growth rates over the last 50 yrs have been linked to changes in
wind speed and temperature and their influence on water availability (Clarke et al., 2012). A contributing factor is
that air temperatures have increased past the critical temperature at which successful sexual reproduction (seed set)
can now take place, changing the dominant mode of reproduction, and increasing the potential distance for dispersal
(Convey, 2011) (low confidence). Similar changes in the local distribution and development of typical cryptogamic
vegetation of this region have been reported (Convey, 2011), including the rapid colonisation of ice free ground
made available through glacial retreat and reduction in extent of previously permanent snow cover (Olech and
Chwedorzewska, 2011). As these vegetation changes create new habitat, there are concurrent changes in the local
distribution and abundance of the invertebrate fauna that then colonise them (low confidence).
28.2.4. Health and Well-Being of Arctic Residents
The warming Arctic and major changes in the cryosphere are significantly impacting the health and well-being of
Arctic residents and projected to increase especially, for many indigenous peoples. While impacts are expected to
vary among the diverse settlements that range from small, remote predominantly indigenous to large cities and
industrial settlements, this section focuses more on health impacts of climate change on indigenous, isolated, and
rural populations because they are especially vulnerable to climate change due to a strong dependence on the
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environment for food, culture and way of life; their political and economic marginalization; existing social, health,
poverty disparities; as well as their frequent close proximity to exposed locations along ocean, lake or river
shorelines (Ford and Furgal, 2009; Galloway-McLean, 2010; Larsen et al., 2010; Cochran et al., 2013).
28.2.4.1. Direct Impacts of a Changing Climate on the Health of Arctic Residents
Direct impacts of climate changes on the health of Arctic residents include extreme weather events, rapidly changing
weather conditions, and increasingly unsafe hunting conditions (physical/mental injuries, death, disease),
temperature-related stress (limits of human survival in thermal environment, cold injuries, cold-related diseases),
and UV-B radiation (immunosuppression, skin cancer, non-Hodgkin s lymphoma, cataracts) (Revich, 2008; AMAP,
2009 ; IPCC, 2012). Intense precipitation events and rapid snowmelt are expected to impact the magnitude and
frequency of slumping and active layer detachment resulting in rock falls, debris flow, and avalanches (Kokelj et al.,
2009; Ford et al., 2010). Other impacts from weather, extreme events, and natural disasters are the possibility of
increasingly unpredictable, long duration and/or rapid onset of extreme weather events, storms, inundation by large
storm surges, which, in turn, may create risks to safe travel or subsistence activities, loss of access to critical
supplies and services to rural or isolated communities (e.g. food, fuel, telecommunications), and risk of being
trapped outside one s own community (Laidre et al., 2008; Parkinson, 2009; Brubaker et al., 2011b). Changing river
and sea ice conditions affect the safety of travel for indigenous populations especially, and inhibit access to critical
hunting, herding and fishing areas (Andrachuk and Pearce, 2010; Derksen et al., 2012; Huntington and Watson,
2012).
Cold exposure has been shown to increase the frequency of certain injuries (e.g. hypothermia, frostbite) or accidents,
and diseases (respiratory, circulatory, cardiovascular, musculoskeletal skin) (Revich and Shaposhmikov, 2010).
Studies in Northern Russia have indicated an association between low temperatures and social stress and cases of
cardiomyopathy (Revich and Shaposhnikov, 2010). It is expected that winter warming in the Arctic will reduce
winter mortality rates, primarily through a reduction in respiratory and cardiovascular deaths (Shaposhnikov et al.,
2010). Researchers project that a reduction in cold-related injuries may occur, assuming that the standard for
protection against the cold is not reduced (including individual behavior-related factors) (Nayha, 2005). Conversely,
studies are showing respiratory and cardio stress associated with extreme warm summer days and that rising
temperatures are accompanied by increased air pollution and mortality, especially in Russian cities with large
pollution sources (Revich, 2008; Revich and Shaposhnikov, 2012).
28.2.4.2. Indirect Impacts of Climate Change on the Health of Arctic Residents
Indirect effects of climate change on the health of Arctic residents include a complex set of impacts such as changes
in animal and plant populations (species responses, infectious diseases), changes in the physical environment (ice
and snow, permafrost), diet (food yields, availability of country food), the built environment (sanitation
infrastructure, water supply system, waste systems, building structures), drinking water access, contaminants (local,
long-range transported), and coastal issues (harmful algal blooms, erosion) (Maynard and Conway, 2007; Brubaker
et al., 2011a; Parkinson and Evengard, 2009; Chapter 11).
In addition to the climate change impacts and processes, are the complicated impacts from contaminants such as
POPs (persistent organic pollutants), radioactivity, and heavy metals (e.g., mercury) which create additional and/or
synergistic impacts on the overall health and well-being of all Arctic communities (Armitage et al., 2011;
UNEP/AMAP, 2011; Teran et al., 2012). Ambient temperature variability and temperature gradients directly affect
the volatilization, remobilization, and transport pathways of mercury and POPs in the atmosphere, ocean currents,
sea ice and rivers. Transport pathways, intercompartmental distribution, bioaccumulation and transformation of
environmental contaminants such as persistent organic pollutants (POPs), mercury (Hg) and radionuclides in the
Arctic may consequently be affected by climate change (AMAP 2011;UNEP/AMAP 2011; Ma et al., 2011; Teng et
al., 2012) (high confidence). Ma et al. (2011) and Hung et al. (2010) demonstrated that POPs are already being
remobilized into the air from sinks in the Arctic region as a result of decreasing sea ice and increasing temperatures.
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Contaminants and human health in the Arctic are tightly linked to the climate and Arctic ecosystems by factors such
as contaminant cycling and climate (increased transport to and from the Arctic), and the related increased risks of
transmission to residents through subsistence life ways (Maynard, 2006; AMAP, 2010; Armitage et al., 2011;
UNEP/AMAP 2011; Teran et al., 2012). The consumption of traditional foods by indigenous peoples places these
populations at the top of the Arctic food chain and through biomagnification, therefore, they may receive some of
the highest exposures in the world to certain contaminants (Armitage et al., 2011; UNEP/AMAP, 2011).
Contaminants such as POPs are known for their adverse neurological and medical effects on humans, particularly,
the developing fetus, children, women of reproductive age and the elderly, thus it is important to include
contaminants as a significant part of any climate impact assessment (UNEP/AMAP, 2011).
Radioactivity in the Arctic is also a concern because there are many potential and existing radionuclide sources in
some parts of the Arctic and contamination can remain for long periods of time in soils and some vegetation,
creating potentially high exposures for people (AMAP, 2010). Climate changes can mobilize radionuclides
throughout the Arctic environment, and also potentially impact infrastructure associated with nuclear activities by
changes in permafrost, precipitation, erosion, and extreme weather events (AMAP, 2010).
Warming temperatures are enabling increased overwintering survival and distribution of new insects that sting and
bite as well as many bird, animal, and insect species that can serve as disease vectors and, in turn, causing an
increase in human exposure to new and emerging infectious diseases (Parkinson et al., 2008; Epstein and Ferber,
2011). Examples of new and emerging diseases are tick-borne encephalitis (brain infection) in Russia and Canada
(Ogden et al., 2010; Tokarevich et al., 2011) and Sweden (Lindgren and Gustafson, 2001), Giardia spp. and
Cryptosporidium spp. infection of ringed seals (Phoca hispida) and bowhead whales (Balaena mysticetus) in the
Arctic Ocean (Hughes-Hanks et al., 2005). It is also expected that temperature increases will increase the incidence
of zoonotic diseases as relocations of animal populations occur (Revich et al., 2012; Hueffler et al., 2013).
Harmful algal blooms (HABs), whose biotoxins can be a serious health hazard to humans or animals (paralysis,
death), are increasing globally and expected to increase in the Arctic, and HABS are influenced directly by climate
change related factors such as temperature, winds, currents, nutrients and runoff (Portier et al., 2010; Epstein and
Ferber, 2011; Walsh et al., 2011; Chapters 6; 11). Increasing ocean temperatures have caused an outbreak of a
cholera-like disease, Vibrio parahaemolyticusin, in Alaskan oysters (McLaughlin et al., 2005). In addition, warmer
temperatures raise the possibility of anthrax exposure in Siberia from permafrost thawing of historic cattle burial
grounds (Revich and Podolnaya, 2011).
The impacts of climate change on food security and basic nutrition are critical to human health because subsistence
foods from the local environment provide Arctic residents, especially, indigenous peoples, with unique cultural and
economic benefits necessary to well-being and contribute a significant proportion of daily requirements of nutrition,
vitamins and essential elements to the diet (Ford and Berrang-Ford, 2009; Ford, 2009). However, climate change is
already an important threat due to the decrease in predictability of weather patterns, low water levels and streams,
timing of snow, ice extent and stability, impacting the opportunities for successful hunting, gathering, fishing and
access to food sources and increasing the probability of accidents (Ford and Furgal, 2009; Ford et al., 2010).
Populations of marine and land mammals, fish and water fowl are also being reduced or displaced, thus, reducing
the traditional food supply (Gearheard et al., 2006; West and Hovelsrud, 2010; Lynn et al., 2013).
Traditional food preservation methods such as drying of fish and meat, fermentation, and ice cellar storage are being
compromised by warming temperatures, thus further reducing food available to the community (Brubaker et al.,
2011b). For example, food contamination caused by thawing of permafrost ice cellars is occurring and
increasingly wet conditions make it harder to dry food for storage (Hovelsrud et al., 2011). Indigenous people
increasingly have to abandon their semi-nomadic lifestyles, limiting their overall flexibility to access traditional
foods from more distant locations (www.arctichealthyukon.ca). These reductions in the availability of traditional
foods plus general globalization pressures are forcing indigenous communities to increasingly depend upon
expensive, non-traditional and often less healthy western foods, increasing the rates of modern diseases associated
with processed food and its packaging, such as cardiovascular diseases, diabetes, dental cavities, and obesity
(Armitage et al., 2011; Berrang-Ford et al., 2011; Brubaker et al., 2011b).
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Climate change is beginning to threaten community and public health infrastructure, often in communities with no
central water supply and treatment sources. This is especially serious in low-lying coastal Arctic communities (e.g.,
Shishmaref, Alaska, USA; Tuktoyaktuk, Northwest Territories, Canada) through increased river and coastal
flooding and erosion, increased drought and thawing of permafrost, resulting in loss of reservoirs, damage to landfill
sites, or sewage contamination (GAO, 2009; Bronen, 2011). Salt-water intrusion and bacterial contamination may
also be threatening community water supplies (Parkinson et al., 2008; Virginia and Yalowitz, 2012). Quantities of
water available for drinking, basic hygiene and cooking are becoming limited due to damaged infrastructure,
drought, and changes in hydrology (Virginia and Yalowitz, 2012). Disease incidence caused by contact with human
waste may increase when flooding and damaged infrastructure spreads sewage in villages with no municipal water
supply. This can result in higher rates of hospitalization for pneumonia, influenza, skin infections, and respiratory
viral infections (Parkinson and Evengard, 2009; Virginia and Yalowitz, 2012). Compounding these impacts in rural
areas as well as cities are respiratory and other illnesses caused by airborne pollutants (e.g., contaminants, microbes,
dust, mold, pollen, smoke) (Revich, 2008; Rylander and Schilling, 2011; Revich and Shaposhnikov, 2012).
It is now well-documented that the many climate-related impacts on Arctic communities are causing significant
psychological and mental distress and anxiety among residents (Portier et al., 2010; Coyle and Susteren, 2012; AR5
Chapter 11; Levintova, 2010). For example, changes in the physical environment (e.g., through thawing permafrost
and erosion) which may lead to forced or voluntary relocation of residents out of their villages or loss of traditional
subsistence species are causing mental health impacts among indigenous and other vulnerable, isolated populations
(Curtis et al., 2005; Albrecht et al., 2007; Coyle and Susteren, 2012; Maldonado et al, 2013). Special concern has
been expressed by many communities about the unusually high and increasing numbers of suicides in the Arctic
especially among indigenous youth, and efforts are under way to try to develop a thorough assessment as well as
establish effective intervention efforts (Albrecht et al., 2007; Portier et al., 2010; USARC, 2010).
28.2.5. Indigenous Peoples and Traditional Knowledge
Indigenous populations in the Arctic the original Native inhabitants of the region are considered especially
vulnerable to climate change, due to their close relationship with the environment and its natural resources for
physical, social, and cultural well-being (Nuttall et al., 2005; Parkinson, 2009; Cochran et al., 2013). Arctic
indigenous peoples are estimated to number between 400,000 and 1.3 million (Bogoyavlensky and Siggner, 2004;
Galloway-McLean, 2010). According to the 2010 census data, there are 68,3 thousand indigenous people living in
the Russian Arctic. These Arctic residents depend heavily on the region s terrestrial, marine and freshwater
renewable resources, including fish, mammals, birds, and plants; however, the ability of indigenous peoples to
maintain traditional livelihoods such as hunting, harvesting, and herding is increasingly being threatened by the
unprecedented rate of climate change (Nakashima et al., 2012; Cochran et al., 2013). In habitats across the Arctic,
climate changes are affecting these livelihoods through decreased sea ice thickness and extent, less predictable
weather, severe storms, sea level rise, changing seasonal melt/freeze-up of rivers and lakes, changes in snow type
and timing, increasing shrub growth, permafrost thaw, and storm-related erosion which, in turn, are causing such
severe loss of land in some regions that a number of Alaskan coastal villages are having to relocate entire
communities (Oskal, 2008; Mahoney et al., 2009; Forbes and Stammler, 2009; Bartsch et al., 2010; Weatherhead et
al., 2010; Brubaker et al., 2011b; Bronen, 2011; Bongo et al., 2012; Eira et al., 2012; McNeeley, 2012; Huntington
and Watson, 2012; Maldonado et al., 2013). In addressing these climate impacts, indigenous communities must at
the same time consider multiple other stressors such as resource development (oil and gas, mining), pollution,
changes in land use policies, changing forms of governance, and the prevalence in many indigenous communities of
poverty, marginalization, and resulting health disparities (Abryutina, 2009; Reinert et al., 2009; Magga et al., 2011;
Nakashima et al., 2012 Vuojala-Magga et al., 2011).
Traditional knowledge is the historical knowledge of indigenous peoples accumulated over many generations and it
is increasingly emerging as an important knowledge base for more comprehensively addressing the impacts of
environmental and other changes as well as development of appropriate adaptation strategies for indigenous
communities (IPCC AR4; IPCC AR5, chp 15; Oskal, 2008; Reinert et al., 2008; Wildcat, 2009; Nakashima et al.,
2012; Magga et al., 2011; Vuojala-Magga et al., 2011; Vogesser et al, 2013) For example, Saami reindeer herders
have specialized knowledge of dynamic snow conditions, which mediate access to forage on autumn, winter and
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spring reindeer rangelands (Roturier and Roue, 2009; Eira et al., 2012; Vikhamar-Schuler et al., 2013) and
traditional governance systems for relating to natural environments (Sara, 2013). Increasingly, traditional knowledge
is being combined with western scientific knowledge to develop more sustainable adaptation strategies for all
communities in the changing climate.
For example, at Clyde River, Nunavut, Canada, Inuit experts and scientists both note that wind speed has increased
in recent years and that wind direction changes more often over shorter periods (within a day) than it did during the
past few decades (Gearheard et al., 2010; Overland et al., 2012). In Norway, Sámi reindeer herders and scientists are
both observing direct and indirect impacts to reindeer husbandry such as changes in snow and ice cover, forage
availability and timing of river freeze-thaw patterns from increasing temperatures. (Eira et al.,2012). On the Yamal
Peninsula in western Siberia, detailed Nenets observations and recollections of iced-over autumn and winter pastures
due to rain-on-snow events have proven suitable for calibrating the satellite-based microwave sensor SeaWinds
(Bartsch et al., 2010) and NASA s AMSR-E sensor (Bongo et al., 2012).
28.2.6. Economic Sectors
28.2.6.1. Arctic
28.2.6.1.1. Agriculture and forestry
Climate change presents benefits and costs for forestry and agriculture (Aaheim et al., 2009; Hovelsrud et al., 2011).
In Iceland for example tree limits are found at higher altitudes than before, and productivity of many plants has
increased (Björnsson et al., 2011). Grain production in Iceland, has increased in the last two decades, and work on
soil conservation and forestry has benefited from warming (Sigurdsson et al., 2007; Björnsson et al., 2011), but also
the number of new insect pests on trees and shrubs has increased in the past 20 years. A strong relationship between
rate of new insect pest colonisation and outbrake intensity in forests exists with changes in annual temperature
during the past century (Halldórsson et al., 2013). Climate change impacts on species change and fire frequency
have potential impact on commercial forest harvesting activity. Vulnerability of forestry to changes that affect road
conditions and thus accessibility during thawing periods has been found in Sweden (Keskitalo, 2008). A case study
on Greenland found challenges for plant diseases in potatoes and grass fields, with pathogens and pests present in
agricultural cropping systems, e.g. black scurf (Rhizoctonia) and common scab (Streptomyces scabies) (Neergaard
et al., 2009).
28.2.6.1.2. Open and freshwater fisheries
Current commercial fisheries are sharply divided between regions of high-yield and value commercial fisheries in
the southern Bering Sea, Baffin Bay, the east and west Greenland Seas, the Iceland Shelf Sea, the deep
Norwegian/Greenland Sea, and the Barents Seas and subsistence fisheries in the coastal regions of the Arctic Ocean.
The relative absence of commercial fishing activity in the Arctic Ocean results from a combination of fisheries
policy, the abundance of the resource, the lack of infrastructure for capturing and processing fish, and the difficulties
in accessing fishing grounds especially during winter. In most regions, fisheries management strategies have been
developed to build sustainable fisheries and rebuild overfished stocks (Froese and Proelß, 2010; Livingston et al.,
2011). Recently observed changes in the spatial distribution and abundance of mackerel (Scomber scombrus) has
challenged existing international agreements for shared resources in the North Atlantic (Arnason, 2012; Astthorsson
et al., 2012). Although loss of sea ice in summer is allowing greater access to fisheries resources in the Arctic
Ocean, some nations have prohibited commercial fishing within their EEZ until there is sufficient understanding of
stock status to ensure that proposed fisheries would be managed sustainably (Wilson and Ormseth, 2009; Stram and
Evans, 2009).
Several Arctic coastal sea-run fishes are targeted for subsistence and commercial use in the Arctic. Commercial
transactions from fishing are typically for local markets, however, the socioeconomic and cultural importance of
these fishes to Indigenous Peoples far outweighs their monetary value. Reist et al. (2006) and Fechhelm et al. (2007)
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found that climate related factors that influenced the water level and freshening of rivers were related to run size of
arctic cisco (Coregonus autumnalis). Similarly, a recent study based on Chinook salmon (Oncorhynchus
tshawytscha) run timing for the period (1961-2009) showed that success in the fishery was dependent on the timing
of the marine exit, which was tightly coupled to environmental conditions that were linked to climate (Mundy and
Evenson, 2011).
28.2.6.1.3. Marine transportation
Observations and climate models indicate that in the period between 1979-1988 and 1998-2007 the number of days
with ice free conditions (less than 15% ice concentration) increased by 22 days along the Northern Sea Route (NSR)
in the Russian Arctic, and by 19 days in the Northwest Passage (NWP) in the Canadian Arctic, while the average
duration of the navigation season in the period 1980-1999 was 45 and 35 days, respectively (Mokhow and Khon,
2008). Increased shipping associated with the opening of the NSR will lead to increased resource extraction on land
and in the sea, and with two-way commodity flows between the Atlantic and Pacific. The future status of marine,
terrestrial and freshwater biota may be negatively affected due to substantial coastal infrastructure to facilitate
offshore developments (Meschtyb et al., 2010). Also, the frequency of marine transportation along the NSR is at its
highest during the most productive and vulnerable season for fish and marine mammals, which is the late
spring/summer, when these resources can be found throughout the NSR area (Ostreng, 2006).
28.2.6.1.4. Infrastructure
Much of the physical infrastructure in the Arctic rely on and are adapted to local sea-ice conditions, permafrost, and
snow (Huntington et al., 2007; Sundby and Nakken, 2008; West and Hovelsrud, 2010; Forbes, 2011; Sherman et al.,
2009). Damage from ice action and flooding to installations such as bridges, pipelines, drilling platforms, and
hydropower poses major economic costs and risks, which are more closely linked to the design of the structure than
with thawing permafrost. Current engineering practices are designed to help minimize the impacts (Prowse et al.,
2009). Much of the infrastructure has been built with weather conditions in mind, but remains vulnerable and
inadequate to respond to environmental emergencies, natural disasters, and non-environmental accidents (NRTEE,
2009). Northern safety, security, and environmental integrity are much dependent upon transportation infrastructure.
Ice as a provisioning system provides a transportation corridor and a platform for a range of activities and access to
food sources in the Arctic (Eicken et al., 2009).
In Northern Canada climate warming presents an additional challenge for northern development and infrastructure
design. While the impacts of climate change become increasingly significant over the longer time scales, in the short
term of greater significance will be the impacts associated with ground disturbance and construction (Smith and
Risebrough, 2010).
Climate change impacts have increased the demand for improved communication infrastructure and related services
and community infrastructure for the safety and confidence in drinking water (NRTEE, 2009). The access, treatment
and distribution of drinking water is generally dependent upon a stable platform of permafrost for pond or lake
retention. Several communities have reported the need for more frequent water-quality testing both municipal
systems and untreated water sources to ensure its suitability for drinking (Furgal, 2008).
28.2.6.1.5. Resource exploration
The Arctic has large reserves of minerals (Lindholt, 2006; Peters et al., 2011; Harsem et al., 2011) and potentially
large reserves of undiscovered sources of raw minerals, and oil and gas. Predicted new access to offshore energy
resources is hypothesized to be a significant share of the global supply of oil and gas (Gautier et al., 2009; Berkman
et al., 2010).The socio-economic impacts of oil and gas exploration activity may be positive or negative (Duhaime
et al., 2004; Huntington et al., 2007; Forbes, 2008; Kumpula et al., 2011; Forbes et al., 2009; Harsem et al., 2011).
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Climatic warming is accelerating access to northern lands for development (Forbes et al., 2009). Yamal in Western
Siberia has approximately 90 % of Russia s gas reserves, but at the same time represents the largest area of reindeer
herding in the world (Jernsletten and Klokov, 2002; Stammler, 2005; Forbes and Kumpula, 2009). Development
activities to obtain these resources would shrink the grazing lands, and have been characterized as one of the major
human activities in the Arctic contributing to loss of available room for adaptation for reindeer husbandry (Oskal,
2008; Forbes et al., 2009; Nuttall et al., 2005). Sharp increases in future oil and gas and other resource development
in the Russian North and other Arctic regions is anticipated - along with associated infrastructure, pollution, and
other by products of development which will reduce the availability of pasturelands for reindeer and indigenous
communities (Derome and Lukina, 2011; Degteva and Nellermann, 2013).
28.2.6.1.6. Informal, subsistence-based economy
Hunting, gathering, herding, and fishing for subsistence, as well as commercial fishing, all play an important role in
the mixed cash-subsistence economies (Nuttall et al., 2005; Poppel and Kruse, 2009; Larsen and Huskey, 2010;
Crate et al., 2010). In the early 1990s initially in western Canada, and later elsewhere - indigenous communities
started reporting climate change impacts (Berkes and Armitage, 2010). According to some herders, whalers and
walrus hunters non-predictable conditions resulting from more frequent occurrence of unusual weather events are
the main effect of recent warming (Forbes and Stammler, 2009; Ignatowski and Rosales, 2013; Forbes et al., 2009).
The Inuit and Saami have expressed strong concern about the effects of climate warming on their livelihoods
(Forbes and Stammler, 2009; Magga et al., 2011). For the Inuit, the issues revolve around sea ice conditions, such as
later freeze-up in autumn, earlier melt-out and faster sea ice retreat in spring, and thinner, less predictable ice in
general (Krupnik and Jolly, 2002; Cochran et al., 2013). Diminished sea ice translates into more difficult access for
hunting marine mammals, and greater risk for the long-term viability of subsistence species such as polar bear
populations (Laidre et al., 2008). Most Inuit communities depend to some extent on marine mammals for nutritional
and cultural reasons, and many benefit economically from polar bear and narwhal hunting. A reduction in these
resources represents a potentially significant economic loss (Hovelsrud et al., 2008). Among Fennoscandian Saami,
the economic viability of reindeer herding is threatened by competition with other land users coupled with strict
agricultural norms (Forbes, 2006; Magga et al., 2011). Reindeer herders are concerned that more extreme weather
may exacerbate this situation (Oskal, 2008).
Climate change is affecting reindeer herding communities through greater variability in snow melt/freeze, ice,
weather, winds, temperatures and precipitation, which, in turn are affecting snow quality and quantity the most
critical environmental variables for reindeer sustainability (Magga et al., 2011; Eira et al., 2012). Increasing
temperature variations in wintertime, with temperatures rising above freezing with rain, followed by refreezing
( rain-on-snow conditions), are becoming more frequent, forming ice layers in the snow which then block the
animals access to their forage and subsequent starvation (Maynard et al., 2011; Eira et al., 2012; Bongo et al.,
2012).
28.2.6.2. Antarctica and the Southern Ocean
Economic activities in the Antarctic have been limited to fishing and tourism (IPCC WG2, 2007). Ship-based
tourism is a significant industry in Antarctica but does not involve permanent shore-based infrastructure. Over
recent decades, the number of tourists landing in Antarctica has risen from 7322 in 1996/1997 to 32,637 in
2007/2008 (IAATO, 2012). Visits generally coincide with the times when wildlife are breeding and are often
restricted because of the presence of fast ice, sea ice or icebergs. They are expected to continue to increase, with an
increasing chance of terrestrial alien species being introduced from tourism and other vectors as ice-free areas
increase from climate change (Chown et al., 2012). Scientific activity by a number of nations is also taking place
and has the potential to impact upon local ecologies. Mineral resource activity is prohibited south of 60°S under the
Protocol on Environmental Protection to the Antarctic Treaty.
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Fisheries in Antarctica, primarily through fisheries for Antarctic krill, could amount to approximately 6% of existing
global marine capture fisheries (Nicol et al., 2011). The pattern of the krill fishery has been affected by changes in
the sea ice extent around the Antarctic Peninsula where the fishery has been taking advantage of the ice-free
conditions and taking more of its catch during winter in that region (high confidence) (Kawaguchi et al., 2009).
Ecosystem-based management of krill fisheries by the Commission for the Conservation of Antarctic Marine Living
Resources (CCAMLR) is yet to include procedures to account for climate change impacts, although the need to do
so has been identified (Trathan and Agnew, 2010; Constable, 2011).
28.3. Key Projected Impacts and Vulnerabilities
28.3.1. Hydrology and Freshwater Ecosystems
28.3.1.1. Arctic
Accompanying projected increases in high-latitude river flow (WGI Ch.12.4.5.4. and WGII Ch. 3.4.5.) are earlier
spring runoff (Dankers and Middelkoop, 2008; Hay and McCabe, 2010; Pohl et al., 2007), greater spring snowmelt
(Adam et al., 2009) and increases in spring sediment fluxes (Lewis and Lamoureux, 2010). Enhanced permafrost
thaw (WGI Ch. 12.4.6.2) will continue to affect the dynamics of thermokarst lakes and related ecological effects
(Section 28.2.1.1). Thawing permafrost and changes in the hydrological regime of the Arctic rivers, particularly
those traversing regions affected by industrial developments, will increase the contaminant flow (Nikanorov et
al., 2007). Loss of glacier ice masses will alter runoff hydrographs, sediment loads, water chemistry, thermal
regimes, and related channel stability, habitat and biodiversity (Milner et al., 2009; Moore et al. 2009).
Although snow, freshwater ice and permafrost affect the morphology of arctic alluvial channels, their future
combined effects remain unclear (McNamara and Kane, 2009). For small permafrost streams, however, longer
projected periods of flowing water will modify nutrient and organic matter processing (Greenwald et al., 2008;
Zarnetske et al., 2008) but long-term negative impacts of increased sediment load on biological productivity could
outweigh any positive effects from increased nutrient loading (Bowden et al., 2008).
Changes to river-ice flooding are also projected to occur due to changes in i) hydraulic gradients for near-coastal
locations because of sea-level rise, ii) streamwise air-temperature gradients, and iii) the timing and magnitude of
spring snowmelt (Prowse et al. 2011). Synergistic/antagonistic effects among these factors, however, require
detailed site-specific analyses for accurate projections of future conditions (Beltaos and Prowse, 2009). Reduced
(increased) ice-jam flooding will have positive (negative) benefits for river-side northern communities/infrastructure
but could also alter delta-riparian (Lesack and Marsh, 2010) and coastal-marine (Emmerton et al., 2008) ecosystems.
The quality of river water entering the marine environment will also be affected by the reduction or loss of stamukhi
lakes that process river inputs (Dumas et al., 2006; Galand et al., 2008).
Future changes to lake-ice regimes will include: delayed freeze-up, advanced break-up, thinner ice and changes in
cover composition (especially white ice in areas of enhanced winter precipitation), increased water temperature, and
earlier and longer-lasting summer stratification (Dibike et al. 2011), all of which will affect a range of aquatic
processes, including secondary productivity (Prowse and Brown, 2010b; Borgstrm and Museth, 2005; Prowse et
al., 2007). Patterns of species richness and diversity are also projected to change with alterations to ice duration -
increased open-water periods favouring the development of new trophic levels, colonization of new aquatic species
assemblages (Vincent et al., 2009), greater atmosphere-water gas exchange and a decrease in winter kill of resident
fish with cascading effects on lower trophic levels (Balayla et al., 2010). The loss of ice, however, can also decrease
key habitat availability and quality (Vincent et al., 2008).
Geochemical responses of Arctic lakes will also be altered. As observed for thermokarst lakes, the loss of ice cover
and associated warming can greatly increase methane production (Laurion et al., 2010; Metje and Frenzel, 2007).
Because temperature sensitivity has a stronger control over methane production than oxidation (Duc et al., 2010),
elevated water temperatures will enhance methanogenesis, causing increased methane release from sediments. The
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net balance of these two processes operating under a broad range of future changing environmental factors,
however, remains to be quantified (Laurion et al., 2010; Walter et al., 2007a, b; 2008).
As well as methane, increased water temperatures are projected to lead to reduced organic carbon (OC) burial.
Projections, based on a range of six climate warming scenarios (Solomon et al., 2007), indicates that there will be a
4-27% decrease (0.9-6.4 TgC yr-1) in OC burial across lakes of the northern boreal zone by the end of the 21st
Century as compared to rates for the approximately last half-century (Gudasz et al., 2010). Although these estimates
assume that future organic carbon delivery will be similar to present-day conditions, even with enhanced supply
from thawing permafrost, higher water temperatures will increase organic carbon mineralization and thereby lower
burial efficiency. The amount of burial also depends on lake depth and mixing regimes. For non-thermally stratified
shallow lakes, there will be a greater opportunity for water-sediment mixing and hence, greater carbon recycling
back into the water column. By contrast, for lakes that become increasingly thermally stratified, carbon sinking
below the thermocline will tend not to return to the surface until an increasing later fall turnover, thereby decreasing
the probability of sediment-stored carbon being returned to the water column (Flanagan et al., 2006).
Changes in ice cover, thermal regimes and stratification patterns will also affect the fate of contaminants in northern
lakes. Higher water temperatures can enhance the methylation of mercury and modify food-web and energy
pathways, such as through enhanced algal scavenging (a major foodweb entry pathway for mercury) resulting in
increased mercury bio-availability to higher trophic levels (Carrie et al., 2010; Outridge et al., 2007).
28.3.1.2. Antarctica
This assessment reinforces conclusions of AR4. Increased temperatures will impact aquatic ecosystems in
Antarctica (high confidence) but the exact nature of these impacts will vary regionally. The most vulnerable
freshwater systems are in the northern Antarctic Peninsula and maritime Antarctic islands, where a small increase in
temperature can have widespread ecosystem impacts because the average temperature is within a few degrees of the
melting point (Quesada and Velázquez 2012) (high confidence). Potential impacts are expected to range from
immediate catastrophic impacts such as loss of bounding ice masses causing drainage of freshwater and epishelf
lakes (Smith et al., 2006; Hodgson, 2011), to more gradual impacts on changes in the amount and duration of
catchment ice and snow cover, accelerated glacier melting, declining volumes of precipitation falling as snow,
permafrost, active layer and hydrological changes, such as water retention times (e.g. Vieira et al., 2010, Quesada
and Velázquez 2012, Bockheim et al., 2013) (medium confidence).
Changes in the thickness and duration of seasonal ice cover, longer melt seasons and larger volumes of water
flowing into the lakes are expected in the future (Lyons et al., 2006) (medium confidence) but the ecological effects
will vary between lakes, depending on their depth to surface area ratio, with insufficient evidence to fully assess
future changes in these systems. Longer ice free seasons may cause physical conditions to be more favorable for
primary production (Hodgson and Smol 2008) but very high irradiances experienced during summer in some
systems can substantially inhibit algal blooms under ice free conditions (Tanabe et al., 2007), which would favor the
growth of benthic cyanobacteria species (Hodgson et al., 2005). In other lakes, increases in meltwater supply may
increase suspended solids and reduce light penetration and may offset the increases in the underwater light regime
predicted as a result of extended ice free periods (Quesada et al., 2006).
Under a warming climate an increase in microbial biomass is likely because of the increased water supply from
glacial melt and warmer temperatures, and could result in further development of soils and elevated nutrient and
dissolved organic carbon delivery to lakes (Velázquez et al., 2013). This organic supply will promote growth and
reproduction in the benthos and plankton and imbalances in population dynamics (Quesada and Velázquez, 2013).
Nutrient enrichment of some freshwater habitats in the vicinity of fur seal colonies will increase because of
expanding fur seal populations (Quayle et al. 2013) (high confidence).
Away from glacial forelands, increasing aridity will occur in the long-term in some areas of the continent (Hodgson
et al., 2006b) and on subantarctic islands (Smith Jr et al., 2012) (medium confidence). Closed basin lakes can dry up
completely causing local extinctions or retreat into cryptic or resistant life-cycle stages, as experienced in Arctic
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lakes (Smol and Douglas, 2007b). Other effects include dessication of moss banks due to increased evaporation and
sublimation rates (Wasley et al., 2006) (medium confidence). Studies have also shown that warming of once cold
freshwater habitats in Antarctica will allow the sub- and maritime Antarctic species to re-invade and establish self-
maintaining populations on the Antarctic continent, particularly where human vectors are involved (Barnes et al.,
2006, Hodgson et al., 2006b) (medium confidence). For other organisms with lower dispersal capabilities there is
increasing evidence of endemism, particularly in microbial groups (Vyverman et al., 2010), with a possibility that
surface Antarctic lakes contain endemic species that are relicts of Gondwana (cf. Convey and Stevens 2007) and that
would become extinct should they be lost from these lakes as a result of climate change..
28.3.2. Oceanography and Marine Ecosystems
28.3.2.1. Ocean Acidification in the Arctic and Antarctic
Ocean acidificaiton on polar marine food webs can have considerable implications (medium certainty). For example,
if some regions in the Arctic become understaturated with respect to aragonite (the primary structural component of
the shells of some marine calcifiers such as mollusks and urchins) the growth and survial of these organisms will be
impacted (WG I, Chapter 6 Figure 6.28; Chierici and Fransson, 2009; Fabry et al., 2009; Yamamoto-Kawai et al.,
2009). In laboratory experiments, Arctic pteropods (Limacina helicina, a small planktonic mollusk) held under
conditions consistent with projected ocean warming and acidification in the Arctic Ocean in early spring were able
to extend their shells in corosive waters but dissolution marks were observed (Comeau et al., 2010; Comeau et al.,
2012). Additional studies are needed to scale up regional impacts to assess the population level impact of ocean
acidification on Limacina helicina and other vulnerable species (Orr et al., 2009). At the current time there is
insufficient data to fully assess the ecosystem consequences of acidification on pteropods because it is unclear
whether other species, with a similar nutritive value, will replace pteropods.
In the Southern Ocean, foraminifera have thinner shells than in the Holocene and there is evidence for shell
thickness to be related to atmospheric CO2, supporting the hypothesis that ocean acidification will affect this
abundant protozoan in this region (Moy et al., 2009). Similarly, shells are thinner from sediment traps in aragonite
under-saturated water (below the aragonite saturation horizon - ASH) compared to those captured above the ASH in
Subantarctic waters, but there is no time series of data related to change in the ASH (Roberts et al., 2011). Shell
dissolution has been observed in surface waters in the Atlantic sector as a result of both upwelling and atmosperhic
changes in CO2 (Bednarsek et al., 2012) (medium confidence). Other impacts of acidification on Southern Ocean
organisms are currently uncertain, but short term negative impacts need to be considered together with an
organism s capacity to adapt in the longer term (Watson et al., 2012).
Only a few studies have been conducted on commercially exploited polar species on ocean acidification. Antarctic
krill embryonic development (Kawaguchi et al., 2011) and post-larval krill metabolic physiology (Saba et al., 2012)
may be impeded by elevated CO2 concentrations, which may negatively impact the reproductive success of krill
more generally under emission scenarios used in CMIP5 (Kawaguchi et al., 2013) (medium confidence). Long et al.
(2013) examined the effects of acidification on red king crab (Paralithodes camtschaticus) and found animals
exposed to reduced pH exhibited increased hatch duration, decreased egg yolk, increased larval size, and decreased
larval survival. In contrast, Hurst et al., (2012) conducted laboratory experiments at levels of elevated CO2 predicted
to be present in the Gulf of Alaska and Bering Sea in the next century and found that juvenile walleye pollock
exhibited a general resiliency of growth energetics to the direct effects of CO2 changes.
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28.3.2.2. Arctic
28.3.2.2.1. Marine plankton, fish, and other invertebrates
Phenological response
Projected changes in the timing, spatial distribution and intensity of spring blooms may result in mis-matches with
the timing of the emergence of Arctic grazers (Sreide et al., 2010). Based on past experience, some species will
adapt to local conditions by shifting key life cycle events (hatch-date, maturity schedule and reproductive timing) or
diet to accommodate differences in the regional timing and availability of prey and environmental conditions
(Ormseth and Norcross, 2007; Sundby and Nakken, 2008; Vikeb et al., 2010, Darnis et al.,2012). For example, loss
of sea ice cover in spring is expected to change fish behavior in ice bound areas (Mundy and Evenson, 2011). It is
uncertain whether endemic animals will be able to alter key phenologies fast enough to keep pace with the projected
rates of change in the Arctic Ocean.
Projected spatial shifts
Simulation studies revealed that a 2 week longer growing season and a 2 degree C increase in temperature would not
be sufficient to allow expatriate species (Calanus finmarchicus or C. marshallae) to invade the Arctic Ocean (Ji et
al., 2012). Ellingsen et al., (2008) projected future zooplankton distribution and abundance in the Barents Sea for
the period 1995-2059 using a regional climate model which was forced with climate model output based on the
IPCC-SRES B2 scenario. They projected that by 2059, Atlantic origin zooplankton will increase and Arctic origin
zooplankton will decrease in the Barents Sea.
The literature is mixed with respect to the potential for future movement of fish and shellfish into the Arctic Ocean.
Modeling studies project that marine fish stocks potentially will shift their distributions into the Arctic Ocean
resulting in an increase in biodiversity in the region (Cheung et al., 2009; Cheung et al., 2011; Box CC-MB).
However, other studies show the persistence of cold sea water temperatures on the shelf regions of the Arctic Ocean
and Northern Bering Sea will restrict or retard the movement of several sub-arctic fish and shellfish species into the
Arctic Ocean (Sigler et al., 2011; Stabeno et al., 2012b; Hunt et al., 2013). In waters off the coasts of Europe there is
a potential for increased fish production because of the combined effects of intrusion of Atlantic water over the
relatively broader shelf regions and advective corridors for larval drift and range expansion of spawners. Huse and
Ellingsen (2008) forced a spatially explicit coupled bio-physical model for the Barents Sea with future climate
scenarios to project the implications of climate change on the spawning distribution of capelin (Mallotus villosus).
Projections show that the spawning distribution of capelin will shift to the east and new spawning grounds will be
colonized. A key factor governing this expansion will be the availability of pelagic prey. In the Bering Sea, there is
evidence that planktivorous species like walleye pollock (Theragra chalcogramma) in the eastern Bering Sea will
shift their distribution in response to shifts in ocean temperature (Kotwicki and Lauth, 2013). In summary, the
spatial distribution of fish and shellfish in the Barents and Bering Seas will shift in response to climate change (high
confidence)
Projected impacts on production
In the deep basins of the Arctic Ocean the number of ice free days in summer are expected to result longer
productive seasons (Slagstad et al., 2011, high confidence). Ellingsen et al. (2008) projected that annual primary
production would increase by 2059 in the Barents Sea. Tremblay et al. (2012) hypothesized that longer ice free
periods in summer in the Arctic Ocean could provide for more opportunities for episodic nutrient pulses that would
enhance secondary production through the growing season. However, in the Arctic Ocean, these changes in primary
production may be offset later in the year by increased zooplankton grazing (Olli et al., 2007) or nutrient depletion
due to stronger stratification and shifts in the mixed layer depth (Tremblay et al., 2012; Wassmann, 2011).
Therefore, there is medium confidence that annual phytoplankton production will increase the central Arctic Ocean.
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In the few cases where future abundance of fish has been projected using climate change scenarios, species
exhibited different trends related to their vulnerability. Forward extrapolation of observed responses suggests that
increased summer sea surface temperatures in the Bering Sea and Barents Sea will cause a decrease in the
abundance of energy rich copepods and euphausiids (Coyle et al., 2011; Slagstad et al., 2011). This change in prey
quality is expected to lower survival of walleye pollock in the eastern Bering Sea by 2050 (Mueter et al., 2011).
Climate enhanced stock projection models showed time trends in cross-shelf transport of juvenile northern rock sole
(Lepidopsetta polyxystra) to nursery areas will not be substantially altered by climate change (Wilderbuer et al.,
2012).
28.3.2.2.2. Marine mammals, polar bear, and seabirds
The effects of the projected reduction is sea ice extent in this century (Wang and Overland, 2011) on Arctic marine
mammals and sea birds will vary spatially and temporally (Laidre et al., 2008). Many ice-associated marine
mammals and sea birds will be affected by ice loss with altered species distributions, migration patterns, behaviour,
interspecific interactions, demography, population changes, and vulnerability to extinction but there is limited
evidence of changes for most species (high confidence).
The polar bear population of the Southern Beaufort Sea is projected to decline by 99% by 2100 with a probability
estimated at 0.80-0.94 under A1B (Hunter et al., 2010). The Northern Beaufort Sea population is stable although
decline is predicted with warming (Stirling et al., 2011). Projected extirpation of approximately two-thirds of the
world s polar bears was predicted for mid-century under A1B (Amstrup et al., 2008). Aspects of this study were
criticized (Armstrong et al., 2008) but refuted (Amstrup et al., 2009). The two-thirds decline is consistent with other
studies and has robust evidence with medium agreement. Projected extinction of polar bears is unlikely. There is
very high confidence of subpopulation extirpation.
Several factors other than climate influence sea bird population dynamics (Regular et al., 2010), and projections of
changes with a continued Arctic warming are therefore highly uncertain. Pattern of change will be non-uniform and
highly complex (ACIA, 2005). At present, the resolution of AOGCMs are not detailed enough to project spatial
changes in mesoscale oceanographic features like frontal zones and eddies of importance to Arctic sea birds.
It is likely that the high Arctic seabird species partly or completely dependent on the sympagic ecosystem or the cold
Arctic waters close to the ice-edge will be negatively impacted if the projected changes in these physical parameters
occur (medium confidence). A general increase in SSTs, retreat of the ice cover, and earlier break up of fast ice may
improve the environmental conditions and food abundance for sea bird species that have their range in the southern
part of the Arctic or south of the Arctic (medium confidence). A poleward expansion of the range of these species is
expected during a continued warming (medium confidence).
Several factors other than climate influence sea bird population dynamics (Regular et al., 2010), and projections of
changes with a continued Arctic warming are therefore highly uncertain. Pattern of change will be non-uniform and
highly complex (ACIA 2005). At present, the resolution of AOGCMs are not detailed enough to project spatial
changes in mesoscale oceanographic features like frontal zones and eddies of importance to Arctic sea birds.
28.3.2.3. Antarctica and the Southern Ocean
Continued rising temperatures in the Southern Ocean will result in increased metabolic costs in many ectothermic
pelagic species, southward movement of temperate species and contraction of the range of polar species (medium
confidence). Southward movement of ocean fronts and associated biota that are prey of subantarctic island-based
predators, will result in energetic inefficiencies for some of those predators (Péron et al., 2012; Weimerskirch et al.,
2012) (low confidence).
For Antarctic krill, insufficient evidence is available to predict what will happen to the circumpolar productivity of
krill because of regional variability of the effects of climate change on the different factors (positive and negative)
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that affect krill, directly and indirectly. For example, increased metabolic and growth rates from warming may be
countered by a reduced food supply and the effects of ocean acidification (28.2.2.2, 28.3.2.1). Also, areas that are
already warm may result in slower growth with further warming, such as could happen in the northern Scotia Arc
(Wiedenmann et al., 2008; Hill et al., 2013). Models of recruitment and population dynamics indicate that the
biomass of krill will decline if surface warming continues, but preliminary projections incorporating a range of
factors are uncertain (low confidence) (Murphy et al., 2007, 2012b). Physiological and behavioural responses might
also ameliorate impacts. For example, krill are now known to exploit the full depth of the ocean, which could
provide escapes from further warming (Schmidt et al., 2011) as well as refuge from air-breathing predators.
The strong dependence of species in more southern regions (e.g. southern west Antarctic Peninsula, WAP, and Ross
Sea region) on sea-ice means that changes in sea-ice distribution will cause spatial shifts in the structure of ice-
obligate food webs (Murphy et al., 2012b) (low confidence). Projections show that the loss of summer sea ice from
the west Antarctic Peninsula is expected to result in ice-dependent seals declining and being replaced by other seal
species that are not dependent on sea ice (Siniff et al., 2008, Costa et al., 2010) (low confidence). There is
insufficient evidence to determine whether there will be a mismatch in phenologies of different species as a result of
changes in the winter sea ice season (timing and winter extent), such as might occur if the timing of sea ice melt was
not at a time of optimal growing conditions for phytoplankton (Trathan and Agnew, 2010).
Reductions in krill abundance in the marine food webs around the South Atlantic islands may result in a shift in their
structure towards a more fish-centred ecosystem as observed in the Indian Sector (Trathan, et al., 2007; Shreeve et
al., 2009; Waluda et al., 2010; Trathan et al., 2012; Murphy et al., 2012a; Murphy et al., 2012b) (low confidence).
Also, salps have been postulated to be competitors with krill for phytoplankton around the Antarctic Peninsula when
oceanic conditions displace shelf and near-shelf waters during times of low sea ice (Ducklow et al., 2012). In the
absence of krill, longer food chains have lower trophic efficiency (Murphy et al., 2012; Muprhy et al., 2013) and the
long-term implications of this for higher trophic levels are unknown.
Coastal environments will be impacted by the dynamics of fast ice, ice shelves and glacier tongues. These factors
will positively affect local primary production and food web dynamics (Peck et al., 2009) but negatively affect
benthic communities (Barnes and Souster, 2011) (low confidence). Projections of the response of emperor penguins
and Southern Ocean seabirds based on AR4 model outputs for sea ice and temperature in east Antarctica indicate
that general declines in these populations are to be expected if sea ice habitats decline in the future (Barbraud et al.,
2011; Jenouvrier et al., 2012) (low confidence). However, these responses are also expected to be regionally specific
because of the regional differences in expectations of change in the ice habitats (high confidence). Additional studies
at other sites are needed to improve confidence levels of predictions.
28.3.3. Terrestrial Environment and Related Ecosystems
28.3.3.1. Arctic
The boreal forest is generally projected by models to move northward under a warming climate, that will displace
between 11% and 50% of the tundra within 100 years (Callaghan et al., 2005; Wolf et al., 2008; Tchebakova et al.,
2009; Wramneby et al., 2010 in a pattern similar to that which occurred during the early Holocene climatic warming
(high confidence). Pearson et al. (2013) projected that at least half of vegetated Arctic areas will shift to a different
physiognomic class, and woody cover will increase by as much as 52%, in line with what has been occurring in
northwest Eurasia (Macias-Fauria et al. 2012).
Dynamic vegetation models applied to Europe and the Barents Region project a general increase in net annual
primary production by climate warming and CO2 fertilization (Wolf et al., 2008; Wramneby et al., 2010; Anisimov
et al., 2011). Boreal needle-leaved evergreen coniferous forest replaces tundra and expands into the mountain areas
of Fennoscandia, but this advance may be delayed or prevented in regions already occupied by clonal deciduous
shrubs whose in situ growth has increased significantly in recent decades (Macias-Fauria et al. 2012).
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In contrast to these expected results, shrubs, currently expanding in area in many Arctic locations, were modelled to
decrease in extent over the next 100 years after an initial increase (Wolf et al., 2008). Also counter-intuitively,
tundra areas increased in the projections. This was a result of changes at the highest latitudes that opened land for
colonisation at a rate exceeding displacement of tundra by shrubs in the south.
Several studies have calculated the magnitude of the effects of vegetation change in the Arctic on negative
feedbacks of CO2 sequestration and increased evapo-transpiration and the positive feedback of decreased albedo
(Swann et al.,2010; Wramneby et al., 2010; Wolf et al., 2010; Pearson et al., 2013). It is likely that vegetation
changes will result in an overall positive feedback on the climate.
Recent changes and results of climate change simulation experiments in the field have shown that there are
considerable uncertainties in the projected rates of change e.g. (Van Bogaert et al. 2010). Furthermore, the models
do not yet include vertebrate and invertebrate herbivory, extreme events such as tundra fire and extreme winter
warming damage or changes in land use that either reduce the rate of vegetation change or open up niches for rapid
change. Projections suggest increases in the ranges of the autumn and winter moths that have outbreaks in
populations resulting in the defoliation of birch forest (Jepsen et al., 2008 and 2011), and a general increase in the
background (non-outbreak) invertebrate herbivores (Wolf et al., 2008).
Animal terrestrial biodiversity is generally projected to increase in the Arctic during warming by immigration of
new species from the south, vegetation changes, and indirectly by introduction of invasive species caused by
increased human activities and increased survival of such species (high confidence) (Post et al., 2009; Gilg et al.,
2012; CAFF 2013). Many native Arctic species will most likely be increasingly threatened during this century.
28.3.3.2. Antarctica
Projected effects of climate change on Antarctic terrestrial species are limited to knowledge of their
ecophysiological tolerances to changes in air temperature, wind speed, precipitation (rain and snowfall), permafrost
thaw and exposure of new habitat through glacial/ice retreat. The climate is expected to become more tolerable to a
number of species, leading to increases in biomass and extent of existing ecological communities.
The frequency with which new potential colonising plant and animal species arrive in Antarctica (particularly the
Antarctic Peninsula region) from lower latitudes, and the subsequent probability of their successful establishment
will increase with regional climate warming and associated environmental changes (Chown et al., 2012) (high
confidence). Human-assisted transfers of biota may be more important by two orders of magnitude than natural
introductions (Frenot et al., 2005) as the transfer is faster and avoids extreme environments such as altitude or
oceans (Barnes et al., 2006). The potential for anthropogenic introduction of non-indigenous species to Antarctic
terrestrial areas, which could have devastating consequences to the local biodiversity, will increase (Convey et al.,
2009; Convey, 2011, Hughes and Convey, 2010; Braun et al., 2012) (high confidence). At present, established non-
indigenous species in the sub- and maritime Antarctic are very restricted in their distributions (Frenot et al., 2005).
Climate change could result in a greater rate of spread of invasive species through colonisation of areas exposed by
glacial retreat, as has occurred at South Georgia (Cook et al., 2010) and in the maritime Antarctic (Olech and
Chwedorzewska 2011). Biosecurity measures may be needed to help control dispersal of established non-indigenous
species to new locations, particularly given the expected increase in human activities in terrestrial areas (Hughes and
Convey, 2010; Convey et al., 2011). An important gap in understanding is the degree to which climate change may
facilitate some established but localised alien species to become invasive and widespread (Frenot et al., 2005;
Convey 2010; Hughes and Convey 2010; Cowan et al., 2011), which has been shown for the sub-Antarctic (Chown
et al., 2012).
Overall, the likely impacts of existing and new non-indigenous species on the native terrestrial ecosystems of
Antarctica and the sub-Antarctic islands, along with the continued increased presence of Antarctic fur seals are
likely to have far greater importance over the timescale under consideration than are those attributable to climate
change itself (Turner et al., 2009; Convey and Lebouvier, 2009; Convey, 2010).
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28.3.4. Economic Sectors
Projections of economic costs of climate change impacts for different economic sectors in the Arctic are limited, but
current assessments suggest that there will be both benefits and costs (Forbes, 2011; AMAP, 2011). Non-Arctic
actors are likely to receive most of the benefits from increased shipping and commercial development of renewable
and non-renewable resources, while indigenous peoples and local Arctic communities will have a harder time
maintaining their way of life (Hovelsrud et al., 2011).
Contributing to the complexity of measuring the future economic effects of climate change is the uncertainty in
future predictions and the rapid speed of change, which are linked with the uncertainty of the technological and
ecological effects of such change (NorAcia, 2010). Communities within the same eco-zone may experience different
effects from identical climate-related events because of marked local variations in site, situation, culture and
economy (Clark et al., 2008).
Economic cost estimates have been made for the case of the Alaskan economy, for example, which suggest that a
heavy reliance on climate-sensitive businesses such as tourism, forestry, and fisheries, renders the economy
vulnerable to climate change, and that Alaska Native peoples, reliant on the biodiversity of the Alaskan ecosystem,
are being affected disproportionately (Epstein and Ferber, 2011). Some Alaskan villages such as Shishmaref,
Kivalina, and Newtok have already lost critical infrastructure and services and are becoming unlivable due to storm
damage and coastal erosion but the high costs and limitations of government mechanisms are significant barriers to
the actual relocation of these communities (Bronen, 2011; Cochran et al., 2013; Maldonado et al., 2013).
28.3.4.1. Fisheries
Climate change will impact the spatial distribution and catch of some open ocean fisheries in the Barents and Bering
Seas (high certainty). The future of commercial fisheries in Arctic Ocean is uncertain. There is strong evidence and
considerable data showing links between climate driven shifts in ocean conditions and shifts in the spatial
distribution and abundance of commercial species in the Bering and Barents Seas (Section 28.3.2.2.1). In limited
cases, coupled bio-physical models or climate enhanced stock projection models have been used to predict future
commercial yield or shifts in fishing locations. However, these predictions are uncertain (Huse and Ellingsen, 2008;
Ianelli et al., 2011; Wilderbuer et al. 2012). Cheung et al. (2011) used projections from an earth system model to
estimate shifts in bio-climatic windows that included climate change effects on biogeochemistry (oxygen and
acidity) and primary production to project future catch potential of 120 demersal fish and invertebrates. Results from
their model suggested that the catch potential will increase in the Barents and Greenland Seas and regions > 70o
north latitude (Cheung et al., 2011). In contrast, vulnerability analysis suggests that only a few species are expected
to be abundant enough to support viable fisheries in the Arctic Ocean (Hollowed et al., 2013). Potential fisheries for
snow crab on shelf areas of the Arctic Ocean may be limited by the associated impacts of ocean acidification. If
fisheries develop in the Arctic Ocean, adoption of sustainable strategies for management will be a high priority
(Molenaar, 2009). The moratorium on fishing in the U.S. portion of the Chukchi and Beaufort Seas would prevent
fishing until sufficient data become available to manage the stock sustainably (Wilson and Ormseth, 2009).
Predicting of how harvesters will respond to changing economic, institutional and environmental conditions under
climate change is difficult. Current techniques, track fishers choices based on revenues and costs associated with
targeting a species in a given time and area with a particular gear given projected changes in the abundance and
spatial distribution of target species (Haynie and Pfeiffer, 2012). However, estimates of future revenues and costs
will depend, in part, on future demand for fish, global fish markets and trends in aquaculture practices (Merino et
al., 2012; Rice and Garcia, 2011).
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28.3.4.2. Forestry and Farming
Climate change is likely to have positive impacts for agriculture, including extended growing season (Grnlund,
2009; Falloon and Betts, 2009; Tholstrup and Rasmussen, 2009), although variations across regions are expected
(Hovelsrud et al., 2011), and the importance of impacts to the Arctic economy will likely remain minor (Eskeland
and Flottorp, 2006). Potential positive effects of climatic warming for forestry include decreased risk of snow
damage. Kilpelainen et al. (2010) estimate a 50% decrease in snow damage in Finland towards the end of the
century. A warmer climate is likely to impact access conditions and plant diseases for forestry and farming.
Grnlund (2009) found in the case of Northern Norway where about half of the arable land area is covered by
forest and 40% by marshland that the potential harnessing of arable land for farming will be at the cost of forestry
production, or dried-up marshlands, which may contribute to more greenhouse emissions. Larger field areas may
contribute to land erosion through rainfall and predicted unstable winters, and may increase conditions for plant
diseases and fungal infections (Grnlund, 2009). If the winter season continues to shorten due to climate change (Xu
et al., 2013), accessibility to logging sites will be negatively affected. Accessibility is higher when frozen ground
makes transportation possible in sensitive locations or areas that lack road. If weather changes occur when logging
has taken place, sanding of roads may be necessary which carries significant economic costs. Impact on carrying
capacity of ground or road accessibility will thus affect forestry economically. Challenges may include limited
storage space for wood (Keskitalo, 2008).
28.3.4.3. Infrastructure, Transportation, and Terrestrial Resources
Rising temperatures and changing precipitation patterns have the potential to affect all infrastructure types and
related services, as much of the infrastructure in the North is dependent upon the cryosphere to, for example, provide
stable surfaces for buildings and pipelines, contain waste, stabilize shorelines and provide access to remote
communities in the winter (Furgal and Prowse, 2008; Huntington et al., 2007; Sherman et al., 2009; Sundby and
Nakken, 2008; West and Hovelsrud, 2010; Forbes, 2011). In the long term marine and freshwater transportation will
need to shift its reliance from ice routes to open-water or land-based transportation systems. Of appropriate
community adaptations to the predicted changes relocation is one option to deal with persistent flooding and bank
erosion (Furgal, 2008; NRTEE, 2009). Changing sea-ice (multiyear) conditions are suspected i.e. to have a
regulating impact on marine shipping and coastal infrastructure through possible hazards on them (Eicken et. al.,
2009).
By adapting transportation models to integrate monthly climate model (CCSM3) predictions of air temperature,
combined with datasets on land cover, topography, hydrography, built infrastructure, and locations of human
settlements, estimates have been made of changes to inland accessibility for northern landscapes northward of 40N
by mid-21st Century (Stephenson et al., 2011). Milder air temperatures and/or increased snowfall reduce the
possibilities for constructing inland winter-road networks, including ice roads, with the major seasonal reductions in
road potential (based on a 2000 kg vehicle) being in the winter shoulder-season months of November and April. The
average decline (compared to a baseline of 2000-2014) for eight circumpolar countries was projected to be -14%,
varying from -11 to -82%. In absolute terms, Canada and Russia (both at -13%) account for the majority of
declining winter-road potential with ~1x106 km2 being lost (See Table 28.1). The winter road season has decreased
since the 1970s on the Alaska´s North Slope, from as much as 200 to 100 days in some areas (Hinzman, et al.,
2005).
Climate change is expected to lead to a nearly ice free Arctic Ocean in late summer and increased navigability of
Arctic marine waters. New possibilities for shipping routes and extended use of existing routes may result from
increased melting of sea ice (Paxian et al., 2010; Corbett et al., 2010; Khon et al., 2010; Peters et al., 2011;
Stephenson et al., 2011).
Projections made by Stephenson et al. (2011) suggest that all five Arctic littoral states will gain increased maritime
access to their current exclusive economic zones, especially Greenland (+28% relative to baseline), Canada (+19%),
Russia (+16%) and United States (+15%). In contrast, Iceland, Norway, Sweden, and Finland display little or no
increase in maritime accessibility,(Table 28.1) (Stephenson et al., 2011).
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GCMs generally underestimate the duration of the ice-free period in the Arctic Ocean and simulate slower changes
than those observed in the past decades (Stroeve et al., 2007). Mokhow and Khon (2008) used a sub-set of climate
models that better than other GCMs reproduce the observed sea ice dynamics to project the duration of the
navigation season along the NSR and through the NWP under the moderate SRES-A1B emission scenario.
According to their results, by the end of the 21st century the NSR may be open for navigation 4.5+/-1.3 months per
year, while the NWP may be open 2-4 months per year (Figure 28-4). The models did not predict any significant
changes of the ice conditions in the NWP until the early 2030s.
[INSERT FIGURE 28-4 HERE
Figure 28-4: Projected duration of the navigation period (days) over the Northwest Passage and Northern Sea Route.
Source: Mokhow and Khon, 2008.]
[INSERT TABLE 28-1 HERE
Table 28-1: Annually averaged changes in inland and maritime transportation accessibility by mid-century (2045
2059) versus baseline (2000 2014). Source: Stephenson et al., 2011.]
An increase in the length of the summer shipping season, with sea-ice duration expected to be 10 days shorter by
2020 and 20-30 days shorter by 2080, is likely to be the most obvious impact of changing climate on Arctic marine
transportation (Prowse et al., 2009). Reduction in sea ice and increased marine traffic could offer opportunities for
economic diversification in new service sectors supporting marine shipping. Loss of sea ice may open up waterways
and opportunities for increased cruise traffic (e.g. Glomsrd and Aslaksen, 2009), and add to an already rapid
increase in cruise tourism (Stewart et al., 2010; Stewart et al., 2007; Howell et al., 2007). Climate change has
increased the prevalence of cruise tourism throughout Greenland, Norway, Alaska and Canada because of
decreasing sea ice extent.
Projected declines in sea-ice covers leading to development of integrated land and marine transportation networks
in Northern Canada may stimulate further mine exploration and development (Prowse et al., 2009). These
possibilities however also come with challenges including their predicted contribution to the largest change in
contaminant movement into or within the Arctic, as well as their significant negative impacts on the traditional ways
of life of northern residents (Furgal and Prowse, 2008). Added shipping and economic activity will increase the
amount of black carbon and reinforce warming trends in the region (Lack and Corbett, 2012), leading to additional
economic activity.
Longer shipping season and improved access to ports may lead to increased petroleum activities, although possible
increased wave activity and coastal erosion may increase costs related to infrastructure and technology. Peters et al.
(2011) find by using a bottom-up shipping model and a detailed global energy market model to construct emission
inventories of Arctic shipping and petroleum activities in 2030 and 2050 and based on estimated sea-ice extent
that there will be rapid growth in transit shipping; oil and gas production will be moving into locations requiring
more ship transport; and this will lead to rapid growth in emissions from oil and gas transport by ship.
The Arctic contains vast resources of oil, which is hard to replace as transportation fuel, and vast resources of gas, a
more climate benign fuel than coal. Petroleum resources are unevenly distributed among Arctic regions and states.
Arctic resources will play a growing role in the world economy, but increased accessibility is expected to create
challenges for extraction, transport, engineering, search-and-rescue needs and responses to accidents (Hovelsrud et
al., 2011), and climatic change presents the oil and gas industry with challenges in terms of planning and predictions
(Harsem et al., 2011). Increased emissions due to rapid growth in Arctic Ocean transportation of oil and gas are
projected (Peters et al., 2011). Due to high costs and difficult access conditions the impact on future oil and gas
production in the Arctic remains unclear (Peters et al., 2011; Lindholdt and Glomsrd, 2012).
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28.4. Human Adaptation
There is general agreement that both indigenous and non-indigenous people in the Arctic have a history of adapting
to natural variability in the climate and natural resource base, as well as recent socio-economic, cultural and
technological changes (Forbes and Stammler, 2009; Wenzel, 2009; West and Hovelsrud, 2010; Ford and Pearce,
2010; Bolton et al., 2011Cochran et al., 2013). Climate change exacerbates the existing stresses faced by Arctic
communities (Rybraten and Hovelsrud, 2010; Crate and Nuttall, 2009) and is only one of many important factors
influencing adaptation (Berrang-Ford et al., 2011). Climate adaptation needs to be seen in the context of these inter-
connected and mutually reinforcing factors (Tyler et al., 2007; Hovelsrud and Smit, 2010). The challenges faced
today by communities in the Arctic are complex and interlinked and are testing their traditional adaptive capacity.
Climatic and other large-scale changes have potentially large effects on Arctic communities, in particular where
simple economies leave a narrower range of adaptive choices (Anisimov and Vaughan, 2007; Ford and Furgal,
2009; Andrachuk and Pearce, 2010; Ford et al., 2010; Forbes, 2011; Berkes et al., 2003). There is considerable
evidence that changing weather patterns, declining sea-ice and river as well as lake ice, thawing permafrost, and
plant and animal species abundance and composition have consequences for communities in the Arctic (see 28.2.4,
28.2.5.2 and 28.3.4). Sea-ice is particularly important for coastal communities which rely upon it for transportation
between communities and hunting areas (Krupnik et al., 2010). Changes in the duration and condition of sea ice and
the consequent changes to country food availability significantly impact the wellbeing of communities (Furgal and
Seguin, 2006; Ford and Berrang-Ford, 2009; Ford et al., 2010), outdoor tourism (Dawson et al., 2010) and hunting
and fishing (Wiig et al., 2008; Brander, 2010).
Adaptation to climate change is taking place at the local and regional levels where impacts are often felt most
acutely and the resources most readily available (Oskal, 2008; Hovelsrud and Smit, 2010). Current experiences and
projections of future conditions often lead to technological adaptation responses such as flood and water
management and snow avalanche protection (West and Hovelsrud, 2010; Hovelsrud and Smit, 2010) rather than
policy responses (Hedensted Lund et al., 2012; Rudberg et al., 2012). Climate variability and extreme events are
found to be salient drivers of adaptation (Berrang-Ford et al., 2011; Dannevig et al., 2012; Amundsen et al., 2010).
The lack of local scale climate projections, combined with uncertainties in future economic, social and technological
developments often act as barriers to adaptation. These barriers, together with other societal determinants such as
ethics, cultures, and attitudes towards risk may cause inaction (Adger et al., 2009; West and Hovelsrud, 2010).
Resolving divergent values across and within different communities poses a challenge for governance regimes. A
determining factor in building adaptive capacity is the flexibility of enabling institutions to develop robust options
(Keskitalo et al., 2009; Hovelsrud and Smit 2010; Forbes et al., 2009; Ford and Goldhar, 2012; Whyte, 2013). In the
North American and Scandinavian context, adaptive co-management responses have been developed through land
claims settlements and/or multi-scale institutional cooperation to foster social learning (Berkes, 2009; Armitage et
al., 2008).
Indigenous Peoples
While Arctic indigenous peoples with traditional lifestyles are facing unprecedented impacts to their ways of life
from climate change and resource development (oil and gas, mining, forestry, hydropower, tourism, etc.), they are
already implementing creative ways of adapting (Cruikshank, 2001; Forbes et al., 2006; Krupnik and Ray, 2007;
Salick and Ross, 2009; Green and Raygorodetsky, 2010; Cullen-Unsworth et al., 2011; Alexander et al., 2011;
Bongo et al., 2012). Examples of indigenous adaptation strategies have included changing resource bases, shifting
land use and/or settlement areas, combining technologies with traditional knowledge, changing timing and location
of hunting, gathering, herding, and fishing areas, and improving communications and education (Galloway McLean,
2010; Bongo et al., 2012). Protection of grazing land will be the most important adaptive strategy for reindeer
herders under climate change (Forbes et al., 2009; Magga et al., 2011; Kumpula et al., 2012; Degteva and
Nellemann, 2013).
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The adaptive capacity of Arctic indigenous peoples is largely due to an extensive traditional knowledge and cultural
repertoire, and flexible social networks (see Chapter 12, section 12.3) (Williams and Hardison, 2013). The dynamic
nature of traditional knowledge is valuable for adapting to current conditions (Kitti et al.,. 2006; Tyler et al., 2007;
Eira et al., 2012). The sharing of knowledge ensures rapid responses to crises (Ford et. al., 2007). In addition,
cultural values such as sharing, patience, persistence, calmness, respect for elders and the environment are
important. Some studies suggest that traditional knowledge may not always be sufficient to meet the rapid changes
in climate (see also Chapter 12) and it may be perceived to be less reliable because the changing conditions are
beyond the current knowledge range (Ingram et al., 2002; Ford et al., 2006; Valdivia et al., 2010; Hovelsrud et al.,
2010).
Over the last half-century, the adaptive capacity in some indigenous communities has been challenged by the
transition from semi-nomadic hunting groups to permanent settlements (Ford et al., 2010). Forced or voluntary
migration as an adaptation response can have deep cultural impacts (Shearer, 2011,2012; Maldonado et al.,
2013).The establishment of permanent communities, particularly those associated with new industrial development,
can also lead to increasing employment opportunities and income diversification for indigenous peoples. The
intergenerational transfers of knowledge and skills through school curricula, land camps, and involvement in
community-based monitoring programmes may strengthen adaptive capacity (Bolton et al., 2011; Hovelsrud and
Smit, 2010; Ford et. al., 2007; Forbes 2007).
Renewable resource harvesting remains a significant component of Arctic livelihoods and with climate change
hunting and fishing has become a riskier undertaking and many communities are already adapting (Gearheard et al.,
2011; Laidler et al., 2011). Adaptation includes taking more supplies when hunting; constructing permanent shelters
on land as refuges from storms; improved communications infrastructure; greater use of global positioning systems
(GPS) for navigation; synthetic aperture radar (SAR) to provide estimates of sea-ice conditions (Laidler et al., 2011)
and the use of larger or faster vehicles (Ford et al., 2010). Avoiding dangerous terrain can result in longer and time-
consuming journeys which can be inconvenient to those with wage-earning employment (Ford et al., 2007).
Reindeer herders have developed a wide range of adaptation strategies in response to changing pasture conditions.
These include: moving herds to better pastures (Bartsch et al., 2010); providing supplemental feeding (Helle and
Jaakkola, 2008; Forbes and Kumpula, 2009); retaining a few castrated reindeer males to break through heavy ice-
crust (Oskal, 2008; Reinert et al., 2008); ensuring an optimal herd size (Forbes et al., 2009; Tyler et al., 2007); and
creating multicultural initiatives combining traditional knowledge with scientific (Vuojala-Magga et al., 2011;
Bongo et al., 2012). Coastal fishers have adapted to changing climate by targeting different species and diversifying
income sources (Hovelsrud et al., 2010).
In some Arctic countries indigenous peoples have successfully negotiated land claims rights and have become key
players in addressing climate change (Abele et al., 2009). In some instances this has given rise to tensions over
land/water use between traditional livelihoods and new opportunities (e.g. tourism and natural resource
development) (Forbes et al., 2006; Hovelsrud and Smit, 2010). Some territorial governments in Northern Canada
have promoted adaptation by providing hunter support programs (Ford et al., 2006, 2010).
The health of many indigenous people is being affected by the interaction of changes in the climate with ongoing
changes in human, economic and biophysical systems (Donaldson et al., 2010). The distribution of traditional foods
between communities and the use of community freezers in the Canadian Arctic has improved food security, an
important factor for health (Ford et al., 2010). While wage employment may enhance the possibilities for adaptive
capacity, greater involvement in full time jobs can threaten social and cultural cohesion and mental well-being by
disrupting the traditional cycle of land-based practices (Berner et al., 2005; Furgal, 2008).
28.5. Research and Data Gaps
There remains a poor knowledge of coupling among, and thresholds within, bio-geophysical and socio-economic
processes to fully assess the effects of a changing climate, and to separate them from those due to other
environmental stressors:
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Existing integrative models are either lacking or insufficiently validated to project and to assess the
cascading effects on, and feedbacks from the systems in the Polar Regions, in particular socio-economic
systems.
There is a need to enhance or establish a coordinated network of long-term representative sites for
monitoring and assessment of climate change detection and attribution studies in Polar Regions. Regional
differences and confounding variables will need to be considered in designing field and modelling studies.
Standardised methods and approaches of biophysical and socio-economic analysis along with coordinated
sampling in more regions will be necessary.
There are more specific research gaps, including:
Many mechanisms of how climate change and ocean acidification may be affecting polar ecosystems have
been proposed but few studies of physiological tolerances of species, long term field studies of ecosystem
effects and ecosystem modelling studies are available to be able to attribute with high confidence current
and future change in these ecosystems to climate change.
More comprehensive studies including long-term monitoring on the increasing impacts from climate
changes on Arctic communities (urban and rural) and their health, well-being, traditional livelihoods and
life ways are needed. There is a need to assess more fully vulnerabilities and to develop response capacities
at the local and regional level.
Frequently Asked Questions
FAQ 28.1: What will be the net socio-economic impacts of change in the polar regions?
[to remain at the end of the chapter]
Climate change will have costs and benefits for Polar Regions. Climate change, exacerbated by other large-scale
changes, can have potentially large effects on Arctic communities, where relatively simple economies leave a
narrower range of adaptive choices.
In the Arctic, positive impacts include new possibilities for economic diversification, marine shipping,
agricultural production, forestry, and tourism. The Northern Sea Route is predicted to have up to 125 days per year
suitable for navigation by 2050, while the heating energy demand in the populated Arctic areas is predicted to
decline by 15%. In addition, there could be greater accessibility to offshore mineral and energy resources although
challenges related to environmental impacts and traditional livelihoods are possible.
Changing sea ice condition and permafrost thawing may cause damage to bridges, pipelines, drilling platforms,
hydropower and other infrastructure. This poses major economic costs and human risks, although these impacts are
closely linked to the design of the structure. Furthermore, warmer winter temperatures will shorten the accessibility
of ice roads that are critical for communications between settlements and economic development and have
implications for increased costs.. Statistically, a long-term mean increase of 2 to 3°C in autumn and spring air
temperature produces an approximate 10 to 15 day delay in freeze-up and advance in break-up, respectively.
Particular concerns are associated with projected increase in the frequency and severity of ice-jam floods on
Siberian rivers. They may have potentially catastrophic consequences for the villages and cities located in the river
plain, as exemplified by the 2001 Lena River flood, which demolished most of the buildings in the city of Lensk.
Changing sea ice conditions will impact indigenous livelihoods, and changes in resources, including marine
mammals, could represent a significant economic loss for many local communities. Food security and health and
well-being are expected to be impacted negatively.
In the Antarctic, tourism is expected to increase, and risks exist of accidental pollution from maritime accidents,
along with an increasing likelihood of the introduction of alien species to terrestrial environments. Fishing for
Antarctic krill near to the Antarctic continent is expected to become more common during winter months in areas
where there is less winter sea ice.
FAQ 28.2: Why are changes in sea ice so important to the polar regions? [to remain at the end of the chapter]
Sea ice is a dominant feature of Polar Oceans. Shifts in the distribution and extent of sea ice during the growing
season impacts the duration, magnitude and species composition of primary and secondary production in the Polar
Regions. With less sea ice many marine ecosystems will experience more light, which can accelerate the growth of
phytoplankton, and shift the balance between the primary production by ice algae and water-borne phytoplankton,
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with implications for Arctic food webs. In contrast, sea ice is also an important habitat for juvenile Antarctic krill,
providing food and protection from predators. Krill is a basic food source for many species in polar marine
ecosystems.
Changes in sea ice will have other impacts, beyond these bottom-up consequences for marine foodwebs.
Mammals and birds utilize sea ice as haul-outs during foraging trips (seals, walrus, and polar bears in the Arctic and
seals and penguins in the Antarctic). Some seals (e.g. Bearded seals in the Arctic and crabeater and leopard seals in
the Antarctic) give birth and nurse pups in pack ice. Shifts in the spatial distribution and extent of sea ice will alter
the spatial overlap of predators and their prey. According to model projections, within 50-70 years loss of hunting
habitats may lead to elimination of polar bears from seasonally ice-covered areas, where two thirds of their world
population currently live. The vulnerability of marine species to changes in sea ice will depend on the exposure to
change, which will vary by location, as well as the sensitivity of the species to changing environmental conditions
and the adaptive capacity of each species. More open waters and longer ice-free period in the northern seas enhance
the effect of wave action and coastal erosion with implications for coastal communities and infrastructure.
While the overall sea ice extent in the Southern Ocean has not changed markedly in recent decades, there have
been increases in oceanic temperatures and large regional decreases in winter sea ice extent and duration in the
western Antarctic Peninsula region of West Antarctica and the islands of the Scotia Arc.
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Table 28-1: Annually averaged changes in inland and maritime transportation accessibility by mid-century (2045
2059) versus baseline (2000 2014).
Change in winter road- Change in maritime-accessible
accessible land area (km2) ocean area (km2) (Type A
(2,000 kg GVWRvehicle) vessel) current EEZ
Canada -13% 19%
Finland -41% 0%
Greenland -11% 28%
Iceland -82% <1%
Norway -51% 2%
Russia -13% 16%
Sweden -46% 0%
USA (Alaska) -29% 5%
High seas n/a 406%
Total -14% 23%
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Figure 28-1: Location maps of the north and south polar regions.
Credit: P. Fretwell, British Antarctic Survey.
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Figure 28-2: Temporal change in onset of flowering (plants), median date of emergence (arthropods) and clutch
initiation dates (birds) in high-Arctic Greenland. Red dots are statistically significant, blue dots are not. Source:
Hye et al., 2007. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 28-3: Significant changes (p< 0.01) in photosynthetically active period NDVI between 1982 and 2012.
Source: Xu et al., 2013. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 28-4: Projected duration of the navigation period (days) over the Northwest Passage and Northern Sea Route.
Source: Mokhow and Khon, 2008. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Chapter 29. Small Islands
Coordinating Lead Authors
Leonard Nurse (Barbados), Roger McLean (Australia)
Lead Authors
John Agard (Trinidad and Tobago), Lino Pascal Briguglio (Malta), Virginie Duvat (France), Netatua Pelesikoti
(Samoa), Emma Tompkins (UK), Arthur Webb (Fiji)
Contributing Authors
John Campbell (New Zealand), Dave Chadee (Trinidad and Tobago), Shobha Maharaj (Trinidad and Tobago),
Veronique Morin (Canada), Geert Jan van Oldenborgh (Netherlands), Rolph Payet (Seychelles), Daniel Scott
(Canada)
Review Editors
Thomas Spencer (UK), Kazuya Yasuhara (Japan)
Volunteer Chapter Scientist
Veronique Morin (Canada)
Contents
Executive Summary
29.1. Introduction
29.2. Major Conclusions from Previous Assessments
29.3. Observed Impacts of Climate Change, including Detection and Attribution
29.3.1. Observed Impacts on Island Coasts and Marine Biophysical Systems
29.3.1.1. Sea-Level Rise, Inundation, and Shoreline Change on Small Islands
29.3.1.2. Coastal Ecosystem Change on Small Islands: Coral Reefs and Coastal Wetlands
29.3.2. Observed Impacts on Terrestrial Systems: Island Biodiversity and Water Resources
29.3.3. Observed Impacts on Human Systems in Small Islands
29.3.3.1. Observed Impacts on Island Settlements and Tourism
29.3.3.2. Observed Impacts on Human Health
29.3.3.3. Observed Impacts of Climate Change on Relocation and Migration
29.3.3.4. Observed Impacts on Island Economies
29.3.4. Detection and Attribution of Observed Impacts of Climate Change on Small Islands
29.4. Projected Integrated Climate Change Impacts
29.4.1. Non-Formal Scenario-Based Projected Impacts
29.4.2. Projected Impacts for Islands based on Scenario Projections
29.4.3. RCP Projections and Implications for Small Islands
29.5. Inter- and Intra-Regional Trans-Boundary Impacts on Small Islands
29.5.1. Large Ocean Waves from Distant Sources
29.5.2. Trans-Continental Dust Clouds and their Impact
29.5.3. Movement and Impact of Introduced and Invasive Species across Boundaries
29.5.4. Spread of Aquatic Pathogens within Island Regions
29.5.5. Trans-Boundary Movements and Human Health
29.6. Adaptation and Management of Risks
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29.6.1. Addressing Current Vulnerabilities on Small Islands
29.6.2. Practical Experiences of Adaptation on Small Islands
29.6.2.1. Building Adaptive Capacity with Traditional Knowledge, Technologies, and Skills on
Small Islands
29.6.2.2. Addressing Risks on Small Islands
29.6.2.3. Working Collectively to Address Climate Impacts on Small Islands
29.6.2.4. Addressing Long-Term Climate Impacts and Migration on Small Islands
29.6.3. Barriers and Limits to Adaptation in Small Island Settings
29.6.4. Mainstreaming and Integrating Climate Change into Development Plans and Policies
29.7. Adaptation and Mitigation Interactions
29.7.1. Assumptions/Uncertainties Associated with Adaptation and Mitigation Responses
29.7.2. Potential Synergies and Conflicts
29.8. Facilitating Adaptation and Avoiding Maladaptation
29.9. Research and Data Gaps
References
Frequently Asked Questions
29.1: Why is it difficult to detect and attribute changes on small islands to climate change?
29.2: Why is the cost of adaptation to climate change so high in small islands?
29.3: Is it appropriate to transfer adaptation and mitigation strategies between and within small island countries
and regions?
Executive Summary
Current and future climate-related drivers of risk for small islands during the 21st century include sea-level
rise, tropical and extra-tropical cyclones, increasing air and sea surface temperatures, and changing rainfall
patterns (high confidence, robust evidence, high agreement) [WGI 14, Table 29-1]. Current impacts associated
with these changes confirm findings reported on small islands from the AR4 and previous IPCC assessments. The
future risks associated with these drivers include loss of adaptive capacity [29.6.2.1, 29.6.2.3] and ecosystem
services critical to lives and livelihoods in small islands [29.3.1, 29.3.2, 29.3.3].
Sea-level rise poses one of the most widely recognized climate change threats to low-lying coastal areas on
islands and atolls (high confidence, robust evidence and high agreement) [29.3.1]. It is virtually certain that
global mean sea-level rise rates are accelerating [WGI 13.2.2.1]. Projected increases to the year 2100 (RCP 4.5:
0.35m to 0.70m, WGI 13.5.1, Table 29-1) superimposed on extreme sea-level events (e.g. swell waves, storm
surges, ENSO) present severe sea-flood and erosion risks for low-lying coastal areas and atoll islands (high
confidence). Likewise, there is high confidence that wave over-wash of sea water will degrade fresh ground water
resources [29.3.2] and that sea surface temperature rise will result in increased coral bleaching and reef degradation
[29.3.1.2]. Given the dependence of island communities on coral reef ecosystems for a range of services including
coastal protection, subsistence fisheries and tourism, there is high confidence that coral reef ecosystem degradation
will negatively impact island communities and livelihoods.
Given the inherent physical characteristics of small islands, the AR5 reconfirms the high level of vulnerability
of small islands to multiple stressors, both climate and non-climate (high confidence, robust evidence, high
agreement). However, the distinction between observed and projected impacts of climate change is often not clear
in the literature on small islands (high agreement) [29.3]. There is evidence that this challenge can be partly
overcome through improvements in baseline monitoring of island systems and downscaling of climate-model
projections, which would heighten confidence in assessing recent and projected impacts [WGI 9.6, 29.3, 29.4, 29.9].
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Small islands do not have uniform climate change risk profiles (high confidence). Rather their high diversity in
both physical and human attributes and their response to climate-related drivers means that climate change impacts,
vulnerability and adaptation will be variable from one island region to another and between countries in the same
region [Fig 29.1; Table 29.3]. In the past, this diversity in potential response has not always been adequately
integrated in adaptation planning.
There is increasing recognition of the risks to small islands from climate-related processes originating well
beyond the borders of an individual nation or island. Such trans-boundary processes already have a negative
impact on small islands (high confidence, robust evidence, medium agreement). These include: airborne dust from
the Sahara and Asia, distant-source ocean swells from mid- high latitudes, invasive plant and animal species and the
spread of aquatic pathogens. For island communities the risks associated with existing and future invasive species
and human health challenges are projected to increase in a changing climate [29.5.4].
Adaptation to climate change generates larger benefit to small islands when delivered in conjunction with
other development activities, such as disaster risk reduction and community based approaches to
development (medium confidence) [29.6.4]. Addressing the critical social, economic and environmental issues of
the day, raising awareness and communicating future risks to local communities [29.6.3] will likely increase human
and environmental resilience to the longer-term impacts of climate change [29.6.1, 29.6.2.3, Figure 29-5].
Adaptation and mitigation on small islands are not always trade-offs, but can be regarded as complementary
components in the response to climate change (medium confidence). Examples of adaptation-mitigation inter-
linkages in small islands include energy supply and use, tourism infrastructure and activities, and functions and
services associated with coastal wetlands. The alignment of these sectors for potential emission reductions together
with adaptation, offer co-benefits and opportunities in some small islands [29.7.2, 29.8]. Lessons learned from
adaptation and mitigation experiences in one island may offer some guidance to other small island states, though
there is low confidence in the success of wholesale transfer of adaptation and mitigation options when the local
lenses through which they are viewed differ from one island state to the next, given the diverse cultural, socio-
economic, ecological and political values [29.6.2, 29.8].
The ability of small islands to undertake adaptation and mitigation programs, and their effectiveness, can be
substantially strengthened through appropriate assistance from the international community (medium
confidence). However, caution is needed to ensure such assistance is not driving the climate change agenda in small
islands, as there is a risk that critical challenges confronting island governments and communities may not be
addressed . Opportunities for effective adaptation can be found by, for example, empowering communities and
optimizing the benefits of local practices that have proven to be efficacious through time, and working
synergistically to progress development agendas [29.8, 29.6.2.3, 29.6.3].
29.1. Introduction
It has long been recognized that greenhouse gas emissions from small islands are negligible in relation to global
emissions, but that the threats of climate change and sea-level rise to small islands are very real. Indeed, it has been
suggested that the very existence of some atoll nations is threatened by rising sea levels associated with global
warming. Whilst such scenarios are not applicable to all small island nations, there is no doubt that on the whole the
impacts of climate change on small islands will have serious negative effects especially on socio-economic
conditions and bio-physical resources although impacts may be reduced through effective adaptation measures.
The small islands considered in this chapter are principally sovereign states and territories located within the tropics
of the southern and western Pacific Ocean, central and western Indian Ocean, the Caribbean Sea, and the eastern
Atlantic off the coast of west Africa, as well as in the more temperate Mediterranean Sea.
Although these small islands nations are by no means homogenous politically, socially, or culturally, or in terms of
physical size and character or economic development, there has been a tendency to generalise about the potential
impacts on small islands and their adaptive capacity. In this chapter we attempt to strike a balance between
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identifying the differences between small islands as well as recognising that small islands tend to share a number of
common characteristics that have distinguished them as a particular group in international affairs. Also in this
chapter we reiterate some of the frequently voiced and key concerns relating to climate change impacts,
vulnerability and adaptation whilst emphasising a number of additional themes that have emerged in the literature on
small islands since the IPCC Fourth Assessment. These include the relationship between climate change policy,
activities and development issues; externally generated trans-boundary impacts; and the implications of risk in
relation to adaptation and the adaptive capacity of small island nations.
29.2. Major Conclusions from Previous Assessments
Small islands were not given a separate chapter in the IPCC First Assessment (FAR) in1990 though they were
discussed in the chapter on World Oceans and Coastal Zones (Tsyban et al., 1990). Two points were highlighted.
First, that a 30-50 cm sea-level rise projected by 2050 would threaten low islands, and that a 1 m rise by 2100
would render some island countries uninhabitable (Tegart et al., 1990). Second, the costs of protection works to
combat sea-level rise would be extremely high for small island nations. Indeed, as a percentage of GDP the
Maldives, Kiribati, Tuvalu, Tokelau, Anguilla, Turks and Caicos, Marshall Islands and Seychelles were ranked
among the ten nations with the highest protection costs in relation to GDP (Tsyban et al., 1990). Over twenty years
later these two points continue to be emphasized. For instance, although small islands represent only a fraction of
total global damage projected to occur due to a sea-level rise of 1.0 m by 2100 (SRES A1 scenario) the actual
damage costs for the small island states is enormous in relation to the size of their economies with several small
island nations being included in the group of ten countries with the highest relative impact projected for 2100
(Anthoff et al., 2010).
The Second Assessment (SAR) in 1995 confirmed the vulnerable state of small islands, now included in a specific
chapter titled Coastal Zones and Small Islands (Bijlsma et al., 1996). However, importantly the SAR recognized
that both vulnerability and impacts would be highly variable between small islands and that impacts were likely to
be greatest where local environments are already under stress as a result of human activities (Bijlsma et al., 1996).
The report also summarized results from the application of a common methodology for vulnerability and adaptation
analysis that gave new insights into the socio-economic implications of sea-level rise for small islands including:
negative impacts on virtually all sectors including tourism, freshwater resources, fisheries and agriculture, human
settlements, financial services and human health; protection is likely to be very costly; and, adaptation would
involve a series of tradeoffs. It also noted that major constraints to adaptation on small islands included: lack of
technology and human resource capacity, serious financial limitations, lack of cultural and social acceptability and
uncertain political and legal frameworks. Integrated coastal and island management was seen as a way of
overcoming some of these constraints.
The Third Assessment (TAR) in 2001 included a specific chapter on Small Island States . In confirming previously
identified concerns of small island states two factors were highlighted, the first relating to sustainability noting that
with limited resources and low adaptive capacity, these islands face the considerable challenge of meeting the
social and economic needs of their populations in a manner that is sustainable (Nurse et al., 2001). And the second,
that there were other issues faced by small island states concluding that for most small islands the reality of climate
change is just one of many serious challenges with which they are confronted (Nurse et al., 2001). Both of these
themes are raised again and assessed in the light of recent findings in the present chapter.
Until the Fourth Assessment (AR4) in 2007, sea-level rise had dominated vulnerability and impact studies of small
island states. Whilst a broader range of climate change drivers and geographical spread of islands was included in
the Small Islands chapter, Mimura et al. (2007) prefaced their assessment by noting that the number of
independent scientific studies on climate change and small islands since the TAR had been quite limited and in
their view the volume of literature in refereed international journals relating to small islands and climate change
since publication of the TAR is rather less than that between the SAR in 1995 and TAR in 2001 (Mimura et al.,
2007).
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Since AR4 the literature on small islands and climate change has increased substantially. A number of features
distinguish the literature we review here from that included in earlier assessments. First, the literature appears more
sophisticated and does not shirk from dealing with the complexity of small island vulnerability, impacts and
adaptation or the differences between islands and island states. Second, and related to the first, the literature is less
one-dimensional, and deals with climate change in a multidimensional manner as just one of several stressors on
small island nations. Third, the literature also critiques some aspects of climate change policy, notably in relation to
critical present-day development and security needs of small islands [29.3.3.1] as well as the possibility that some
proposed adaptation measures may prove to be maladaptive [29.8]. Fourth, many initiatives have been identified in
recent times that will reduce vulnerability and enhance resilience of small islands to on-going global change
including improving risk knowledge and island resource management while also strengthening socio-economic
systems and livelihoods (Hay, 2013).
29.3. Observed Impacts of Climate Change, including Detection and Attribution
The distinction between observed impacts of climate change and projected impacts is often unclear in the small
islands literature and discussions. Publications frequently deal with both aspects of impacts interchangeably, and use
observed impacts from, for instance an extreme event, as an analogy to what may happen in the future due to climate
change (e.g. Lo-Yat et al., 2011). The key climate and ocean drivers of change that impact small islands include
variations in air and ocean temperatures, ocean chemistry, rainfall, wind strength and direction, sea-levels and wave
climate and particularly the extremes such as tropical cyclones, drought and distant storm swell events. All have
varying impacts, dependent on the magnitude, frequency, temporal and spatial extent of the event, as well as on the
bio-physical nature of the island (Figure 29.1) and its social, economic and political setting.
[INSERT FIGURE 29-1 HERE
Figure 29-1: Representative tropical island typologies. From top-left: a young, active volcanic island (with
altitudinal zonation) and limited living perimeter reefs (purple zone at outer reef edge), through to an atoll (centre
bottom) and raised limestone island (bottom right) dominated by ancient reef deposits (brown + white fleck). Atolls
have limited, low-lying land areas but well developed reef/lagoon systems. Islands composed of continental rocks
are not included in this figure, but see Table 29-3.]
29.3.1. Observed Impacts on Island Coasts and Marine Biophysical Systems
29.3.1.1. Sea-Level Rise, Inundation, and Shoreline Change on Small Islands
Sea-level rise poses one of the most widely recognized climate change threats to low-lying coastal areas (Church
and White 2011; Cazenave and Llovel, 2010; Nicholls and Cazenave, 2010). This is particularly important in small
islands where the majority of human communities and infrastructure is located in coastal zones with limited on-
island relocation opportunities especially on atoll islands (Woodroffe, 2008) (Figure 29-1). Over much of the 20th
Century, global mean sea level rose at a rate between 1.3 to 1.7 mm yr 1 and since 1993, at a rate between 2.8 to 3.6
mm yr 1 (WGI, Table 13.1) and acceleration is detected in longer records since 1870 (Merrifield et al., 2009; Church
and White, 2011; WGI, 13.2.2.1). Rates of sea-level rise are however not uniform across the globe and large
regional differences have been detected including in the Indian Ocean and tropical Pacific, where in some parts rates
have been significantly higher than the global average (Meyssignac et al., 2012) (refer also to 5.3.2.2). In the
tropical western Pacific where a large number of small island communities exist, rates up to four times the global
average (approximately 12 mm yr -1) have been reported between 1993 and 2009. These are generally thought to
describe short-term variations associated with natural cyclic climate phenomena such as ENSO (El Nino-Southern
Oscillation) which has a strong modulating effect on sea level variability with lower/higher-than-average sea level
during El Nino/La Nina events of the order of +/-20-30 cm (Becker et al., 2012; Cazenave and Remy, 2011). Large
interannual variability in sea level has also been demonstrated from the Indian Ocean (e.g. Chagos Archipelago,
Dunne et al., 2012) whilst Palanisamy et al. (2012) found that over the last 60 years the mean rate of sea-level rise in
the Caribbean region was similar to the global average ~ 1.8mm yr-1.
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There are few long-term sea-level records available for individual small island locations. Reported sea flooding and
inundation is often associated with transient phenomena, such as storm waves and surges, deep ocean swell and
predicted astronomical tidal cycles (Vassie et al., 2004; Zahibo et al., 2007; Komar and Allan, 2008; Haigh et al.,
2011). For example, high spring tide floods at Fongafale Island, Funafuti Atoll, Tuvalu, have been well publicized
and areas of the central portion of Fongafale are already below high spring tide level. However, rates of relative sea-
level rise at Funafuti between 1950 2009 have been approximately three times higher than the global average
(Becker et al., 2012) and saline flooding of internal low-lying areas occurs regularly, and is expected to become
more frequent and extensive over time (Yamano et al., 2007).
Documented cases of coastal inundation and erosion often cite additional circumstances such as vertical subsidence,
engineering works, development activities or beach mining as the causal process. Four examples can be cited. First,
on the Torres Islands, Vanuatu communities have been displaced due to increasing inundation of low-lying
settlement areas due to a combination of tectonic subsidence and sea-level rise (Ballu et al., 2011). Second, on
Anjouan Island, Comores in the Indian Ocean, Sinane et al. (2010) found beach aggregate mining was a major
contributing factor influencing rapid beach erosion. Third, the intrinsic exposure of rapidly expanding settlements
and agriculture in the low-lying flood prone Rewa Delta, Fiji is shown by Lata and Nunn (2012) to place
populations in increasingly severe conditions of vulnerability to flooding and marine inundation. Fourth, Hoeke et
al. (2013) describe a 2008 widespread inundation event that displaced some 63,000 people in Papua New Guinea
and Solomon Islands alone. That event was primarily caused by remotely generated swell waves, and the severity of
flooding was greatly increased by anomalously high regional sea levels linked with ENSO and on-going sea-level
rise. Such examples serve to highlight that extreme events superimposed on a rising sea-level baseline are the main
drivers that threaten the habitability of low-lying islands as sea levels continue to rise.
Since the AR4 a number of empirical studies have documented historical changes in island shorelines. Historical
shoreline position change over 20 60 years on 27 central Pacific atoll islands showed that total land area remained
relatively stable in 43 per cent of islands, whilst another 43 per cent had increased in area, and the rest showed a net
reduction in land area (Webb and Kench, 2010). Dynamic responses were also found in a four year study of 17
relatively pristine islands on two other central Pacific atolls in Kiribati by Rankey (2011) who concluded that sea-
level rise was not likely to be the main influencing factor in these shoreline changes. Similarly in French Polynesia
Yates et al. (2013) showed mixed shoreline change patterns over the last 40 50 years with examples of both
erosion and accretion in the 47 atoll islands assessed. Sea-level rise did not appear to be the primary control on
shoreline processes on these islands. On uninhabited Raine Island on the Great Barrier Reef, Dawson and Smithers
(2010) also found that shoreline processes were dynamic but that island area and volume increased 6 per cent and 4
per cent respectively between 1967 and 2007. Overall, these studies of observed shoreline change on reef islands
conclude that for rates of change experienced over recent decades normal seasonal erosion and accretion processes
appear to predominate over any long-term morphological trend or signal at this time. Ford s (2013) investigation of
Wotje Atoll, Marshall Islands also found shoreline variability between 1945 and 2010 but that overall accretion had
been more prevalent than erosion up until 2004. From 2004 to the present 17 out of 18 islands became net erosive,
potentially corresponding to the high sea levels in the region over the last 10 years. On the high tropical islands of
Kauai and Maui, Hawaii, Romine and Fletcher (2013) found shoreline change was highly variable over the last
century but that recently chronic erosion predominated with over 70% of beaches now being erosive. Finally, it is
important to note the majority of these studies warn that: (1) past changes cannot be simply extrapolated to
determine future shoreline responses; and (2) rising sea level will incrementally increase the rate and extent of
erosion in the future.
In many locations changing patterns of human settlement and direct impacts on shoreline processes present
immediate erosion challenges in populated islands and coastal zones (Yamano et al., 2007; Storey and Hunter, 2010;
Novelo-Casanova and Suarez, 2010) and mask attribution to sea-level rise. A study of Majuro atoll (Marshall
Islands) found that erosion was widespread but attribution to sea-level rise was obscured by pervasive anthropogenic
impacts to the coastal system (Ford, 2012) (see also 5.4.4). Similarly a study of three islands in the Rosario
Archipelago (Colombia) reported shoreline retreat over a 50-55 year period and found Grande, Rosario, and Tesoro
Islands had lost 6.7, 8.2 and 48.7 per cent of their land area respectively. Erosion was largely attributed to poor
management on densely settled Grande Island, whilst sea-level rise and persistent northeast winds enhanced erosion
on uninhabited Rosario and Tesoro (Restrepo et al., 2012). Likewise, Cambers (2009) reported average beach
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erosion rates of 0.5 m yr-1 in eight Caribbean islands from 1985-2000. Whilst the study could not quantify the extent
of attribution it noted that greater erosion rates were positively correlated with the number of hurricane events.
Alternately, Etienne and Terry (2012) found a Category 4 tropical cyclone that passed within 30 km of Taveuni
Island (Fiji) nourished shorelines with fresh coralline sediments despite localized storm damage. Whilst these
studies contribute to improved understanding of island shoreline processes and change since AR4, the warning of
increased vulnerability of small island shores and low-lying areas to inundation and erosion in response to sea-level
rise and other potential climate change stressors is not diminished.
29.3.1.2. Coastal Ecosystem Change on Small Islands: Coral Reefs and Coastal Wetlands
Coral reefs are an important resource in small tropical islands and wellbeing of many island communities is linked
to their on-going function and productivity. Reefs play a significant role in supplying sediment to island shores and
in dissipating wave energy thus reducing the potential foreshore erosion. They also provide habitat for a host of
marine species upon which many island communities are dependent for subsistence foods as well as underpinning
beach and reef-based tourism and economic activity (Bell et al., 2011; Perch-Nielsen, 2010). The documented
sensitivity of coral reef ecosystems to climate change is summarised elsewhere (see Chap 5, Box CC-CR).
Increased coral bleaching and reduced reef calcification rates due to thermal stress and increasing CO2 concentration
are expected to affect the functioning and viability of living reef systems (Hoegh-Guldberg et al., 2007; Eakin et al.,
2009). Some studies already implicate thermal stress in reduced coral calcification rates (Tanzil et al., 2009) and
regional declines in calcification of corals that form reef framework (De'ath et al., 2009; Cantin et al., 2010).
Unprecedented bleaching events have been recorded in the remote Phoenix Islands (Kiribati) with nearly 100 per
cent coral mortality in the lagoon and 62 per cent mortality on the outer leeward slopes of the otherwise pristine
reefs of Kanton Atoll during 2002 / 2003 (Alling et al., 2007). Similar patterns of mortality were observed in four
other atolls in the Phoenix group and temperature-induced coral bleaching was also recorded in isolated Palmyra
Atoll during the 2009 ENSO event (Williams et al., 2010). In 2005 extensive bleaching was recorded at 22 sites
around Rodrigues Island in the western Indian Ocean with up to 75 per cent of the dominant species affected in
some areas (Hardman et al., 2007). Studies of the severe 1998 El Nino bleaching event in the tropical Indian Ocean
showed reefs in the Maldives, Seychelles and Chagos Islands were among the most impacted (Cinner et al., 2012;
Tkachenko, 2012). In 2005 a reef survey around Barbados following a Caribbean regional bleaching event revealed
the most severe bleaching ever recorded with approximately 70 per cent of corals impacted (Oxenford et al., 2008).
Globally, the incidence and implications of temperature-related coral bleaching in small islands is well documented
and combined with the effects of increasing ocean acidification these stressors could threaten the function and
persistence of island coral reef ecosystems (see Chap 5, Box CC-OA).
Island coral reefs have limited defences against thermal stress and acidification. However studies such as Cinner et
al. (2012) and Tkachenko (2012) highlight that whilst recovery from bleaching is variable, some reefs show greater
resilience than others. There is also some evidence to show that coral reef resilience is enhanced in the absence of
other environmental stresses such as declining water quality. In Belize chronologies of growth rates in massive
corals (Montastraea faveolata) over the past 75 150 years suggest that the bleaching event in 1998 was
unprecedented and its severity appeared to stem from reduced thermal tolerance related to human coastal
development (Carilli et al., 2010). Likewise a study over a 40 year period (1960s 2008) in the Grand Recif of
Tulear, Madagascar concluded that severe degradation of the reef was mostly ascribed to direct anthropogenic
disturbance, despite an average 1 0C increase in temperature over this period (Harris et al., 2010). Coral recovery
following the 2004 bleaching event in the central Pacific atolls of Tarawa and Abaiang (Kiribati) was also noted to
be improved in the absence of direct human impacts (Donner et al., 2010) and isolation of bleached reefs was shown
by Gilmour et al. (2013) to be less inhibiting to reef recovery than direct human disturbance.
The loss of coral reef habitat has detrimental implications for coastal fisheries (Pratchett et al., 2009) in small
islands where reef-based subsistence and tourism activities are often critical to the wellbeing and economies of
islands (Bell et al., 2011). In Kimbe Bay, Papua New Guinea 65 per cent of coastal fish are dependent on living
reefs at some stage in their life cycle and that following degradation of the reef fish abundance declined (Jones et al.,
2004). Even where coral reef recovery has followed bleaching, reef associated species composition may not recover
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to its original state (Pratchett et al., 2009; Donner et al., 2010). Sea Surface Temperature (SST) anomaly events can
be associated with a lag in the larval supply of coral reef fishes, as reported by Lo-Yat et al. (2011) between 1996
and 2000 at Rangiroa Atoll, French Polynesia. Higher temperatures have also been implicated in negatively
affecting the spawning of adult reef species (Munday et al., 2009; Donelson et al., 2010).
Like coral reefs, mangroves and sea grass environments provide a range of ecosystem goods and services (Polidoro
et al., 2010; Waycott et al., 2009) and both habitats play a significant role in the wellbeing of small island
communities. Mangroves in particular serve a host of commercial and subsistence uses as well as providing natural
coastal protection from erosion and storm events (Ellison, 2009; Krauss et al., 2010; Waycott et al., 2011).
Sea-level rise is reported as the most significant climate change threat to the survival of mangroves (Waycott et al.,
2011). Loss of the seaward edge of mangroves at Hungry Bay, Bermuda has been reported by Ellison (1993) who
attributes this process to sea-level rise and the inability of mangroves to tolerate increased water depth at the
seaward margin. Elsewhere in the Caribbean and tropical Pacific observations vary in regards to the potential for
sedimentation rates in mangroves forests to keep pace with sea-level rise (McKee et al., 2007; Krauss et al., 2003).
In Kosrae and Pohnpei Islands (Federated States of Micronesia), Krauss et al. (2010) found significant variability in
mangrove average soil elevation changes due to deposition from an accretion deficit of 4.95 mm y-1 to an accretion
surplus of 3.28 mm y-1 relative to the estimated rate of sea-level rise. Such surpluses are generally reported from
high islands where additional sediments can be delivered from terrestrial runoff. However, Rankey (2011) described
natural seaward migration (up to 40m) of some mangrove areas between 1969 and 2009 in atolls in Kiribati
suggesting sediment accretion can also occur in sediment rich reefal areas and in the absence of terrigenous inputs.
The response of seagrass to climate change is also complex, regionally variable and manifest in quite different ways.
A study of seven species of sea grasses from tropical Green Island, Australia highlighted the variability in response
to heat and light stress (Campbell et al., 2006). Light reduction may be a limiting factor to sea grass growth due to
increased water depth and sedimentation (Ralph et al., 2007). Ogston and Field (2010) observed that a 20 cm rise in
sea-level may double suspended sediment loads and turbidity in shallow waters on fringing reefs of Molokai,
Hawaiian Islands, with negative implications to photosynthetic species such as seagrass. Otherwise, temperature
stress is most commonly reported as the main expected climate change impact on seagrass (e.g. Campbell et al.,
2006; Waycott et al., 2011). Literature on seagrass diebacks in small islands is scarce but research in the Balearic
Islands (Western Mediterranean) has shown that over a six-year study, seagrass shoot mortality and recruitment
rates were negatively influenced by higher temperature (Marbá and Duarte, 2010). (See also Chapter 5.4.2.3 for
further discussion of impacts on mangrove and sea grass communities).
29.3.2. Observed Impacts on Terrestrial Systems: Island Biodiversity and Water Resources
Climate change impacts on terrestrial biodiversity on islands, frequently interacting with several other drivers
(Blackburn et al., 2004; Didham et al., 2005), fall into three general categories namely: (a) ecosystem and species
horizontal shifts and range decline; (b) altitudinal species range shifts and decline mainly due to temperature
increase on high islands; and (c) exotic and pest species range increase and invasions mainly due to temperature
increase in high latitude islands. Due to the limited area and isolated nature of most islands, these effects are
generally magnified compared to continental areas and may cause species loss especially in tropical islands with
high numbers of endemic species. For example, in two low-lying islands in the Bahamas, Greaver and Sternberg
(2010) found that during periods of reduced rainfall the shallow freshwater lens subsides and contracts landward and
ocean water infiltrates further inland negatively impacting on coastal strand vegetation. Sea-level rise has also been
observed to threaten the long-term persistence of freshwater-dependent ecosystems within low-lying islands in the
Florida Keys (Goodman et al., 2012). On Sugarloaf Key, Ross et al. (2009) found pine forest area declined from 88
to 30 h from 1935 to 1991 due to increasing salinisation and rising ground water, with vegetation transitioning to
more saline tolerant species such as mangroves.
Whilst there are many studies that report observations associated with temperature increases in mid- and high-
latitude islands, such as the Falkland Islands and Marion Islands in the south Atlantic and south Indian ocean
respectively (Bokhorst et al., 2007, 2008; Le Roux et al., 2005) and Svalbard in the Arctic (Webb et al., 1998) there
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are few equivalent studies in tropical small islands. A recent study of the tropical Mauritius kestrel indicate changing
rainfall conditions in Mauritius over the last 50 years have resulted in this species having reduced reproductive
success due to a mismatch between the timing of breeding and peak food abundance (Senapathi et al., 2011).
Increasing global temperatures may also lead to altitudinal species range shifts and contractions within high islands
with an upward creep of the tree line and associated fauna (Benning et al., 2002; Krushelnycky et al., 2013). A study
in the Hawaiian Islands which assessed data from 21 stations over 85 years showed a rapid rise in surface
temperatures over the last 30 years with stronger warming in mountain areas (CCSP, 2008). Comparative vegetation
distribution and composition studies in sub-Antarctic Marion Island, found an altitudinal shift of 3.4 m yr -1 for plant
species (Parolo and Rossi, 2008). Comparable effects also occur in the tropics such as in Hawaii Volcano National
Park where comparison of sample plots over a 40 year period from 1966-67 to 2008 show fire-adapted grasses
expanded upward along a warming tropical elevation gradient (Angelo and Daehler, 2013). Reduction in the
numbers and sizes of endemic populations caused by such habitat constriction and changes in species composition in
mountain systems may result in the demise and possibly extinction of endemic species (Chen et al., 2009; Pauli et
al., 2007; Sekercioglu et al., 2008; Krushelnycky et al., 2013). Altitudinal temperature change has also been
reported to influence the distribution for disease vectors such as mosquitoes potentially threatening biota
unaccustomed to such vectors (Freed et al., 2005; Atkinson and LaPointe, 2009).
Freshwater supply in small island environments has always presented challenges and has been an issue raised in all
previous IPCC reports. On high volcanic and granitic islands small and steep river catchments respond rapidly to
rainfall events and watersheds generally have restricted storage capacity. On porous limestone and low atoll islands
surface runoff is minimal and water rapidly passes through the substrate into the groundwater lens. Rainwater
harvesting is also an important contribution to freshwater access and alternatives like desalination have had mixed
success in small island settings due to operational costs (White and Falkland, 2010).
Rapidly growing demand, land use change, urbanisation and tourism are already placing significant strain on the
limited freshwater reserves in small island environments (Cashman et al., 2010; Emmanuel and Spence, 2009;
White and Falkland, 2010). In the Caribbean, where there is considerable variation in the types of freshwater
supplies utilised, concern over the status of freshwater availability has been expressed for at least the past 30 years
(Cashman et al., 2010). There have also been economic and management failures in the water sector not only in the
Caribbean (Mycoo, 2007) but also in small islands in the Indian (Payet and Agricole, 2006) and Pacific oceans
(Moglia et al., 2008a, 2008b; White et al., 2007).
These issues also occur on a background of decreasing rainfall and increasing temperature. Rainfall records
averaged over the Caribbean region for 100 years (1900-2000) show a consistent 0.18 mm yr -1 reduction in rainfall,
a trend that is projected to continue (Jury and Winter, 2010). In contrast, analysis of rainfall data over the past 100
years from the Seychelles has shown substantial variability related to ENSO. Nevertheless an increase in average
rainfall from 1959 to 1997 and an increase in temperature of ~ 0.25 o C per decade have occurred (Payet and
Agricole, 2006). Long-term reduction in streamflow (median reduction of 22 23%) has been detected in the
Hawaiian Islands over the period 1913 2008, resulting in reduced freshwater availability for both human use and
ecological processes (Bassiouni and Oki 2013). Detection of long-term statistical change in precipitation is an
important prerequisite towards a better understanding the impacts of climate change in small island hydrology and
water resources.
There is a paucity of empirical evidence linking saline (sea-water) intrusion into fresh groundwater reserves due
simply to incremental sea-level rise at this time (e.g. Rozell and Wong, 2010). However this dynamic must be the
subject of improved research given the importance of groundwater aquifers in small island environments. White and
Falkland s (2010) review of existing small island studies indicates that a sea-level increase of up to 1 m would have
negligible salinity impacts on atoll island groundwater lenses so long as there is adequate vertical accommodation
space, island shores remain intact, rainfall patterns do not change and direct human impacts are managed. However,
wave overtopping and washover can be expected to become more frequent with sea-level rise and this has been
shown to impact freshwater lenses dramatically. On Pukapuka Atoll, Cook Islands storm surge over-wash occurred
in 2005. This caused the freshwater lenses to become immediately brackish and took 11 months to recover to
conductivity levels appropriate for human use (Terry and Falkland, 2010). The ability of the freshwater lens to float
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upwards within the substrate of an island in step with incremental sea-level rise also means that in low-lying and
central areas of many atoll islands the lens may pond at the surface. This phenomenon already occurs in central
areas of Fongafale Island, Tuvalu, and during extreme high king tides large areas of the inner part of the island
become inundated with brackish waters (Yamano et al., 2007; Locke, 2009).
29.3.3. Observed Impacts on Human Systems in Small Islands
29.3.3.1. Observed Impacts on Island Settlements and Tourism
Whilst traditional settlements on high islands in the Pacific were often located inland, the move to coastal locations
was encouraged by colonial and religious authorities and more recently through the development of tourism (Barnett
and Campbell, 2010). Now the majority of settlement, infrastructure and development are located on lowlands along
the coastal fringe of small islands. In the case of atoll islands, all development and settlement is essentially coastal.
It follows that populations, infrastructure, agricultural areas and fresh groundwater supplies are all vulnerable to
extreme tides, wave and surge events and sea-level rise (Walsh et al., 2012). Population drift from outer islands or
from inland, together with rapid population growth in main centres and lack of accommodation space drives
growing populations into ever more vulnerable locations (Connell, 2012). Additionally, without adequate resources
and planning, engineering solutions such as shoreline reclamation also place communities and infrastructure in
positions of increased risk (Duvat, 2013; Yamano et al., 2007).
Many of the environmental issues raised by the media relating to Tuvalu, the Marshall Islands and Maldives are
primarily relevant to the major population centre and its surrounds, which are Funafuti, Majuro and Male
respectively. As an example Storey and Hunter (2010) indicate the Kiribati problem does not refer to the whole of
Kiribati but rather to the southern part of Tarawa atoll where pre-existing issues of severe overcrowding,
proliferation of informal housing and unplanned settlement, inadequate water supply, poor sanitation and solid waste
disposal, pollution and conflict over land ownership are of concern. They argue that these problems require
immediate resolution if the vulnerability of the South Tarawa community to the real and alarming threat of climate
change is to be managed effectively (Storey and Hunter, 2010).
On Majuro atoll, rapid urban development and the abandonment of traditional settlement patterns has resulted in
movement from less vulnerable to more vulnerable locations on the island (Spennemann, 1996). Likewise,
geophysical studies of Fongafale Island, the capital of Tuvalu, show that engineering works during World War II,
and rapid development and population growth since independence, has led to the settlement of inappropriate
shoreline and swampland areas, leaving communities in heightened conditions of vulnerability (e.g. Yamano et al.,
2007). Ascribing direct climate change impacts in such disturbed environments is problematic due to the existing
multiple lines of stress on the island s biophysical and social systems. However, it is clear that such pre-existing
conditions of vulnerability add to the threat of climate change in such locations. Increased risk can also result from
lack of awareness, particularly in communities in rural areas and outer islands ( periphery ) of archipelagic countries
such as Cook Islands, Fiji, Kiribati and Vanuatu, whose climate change knowledge often contrasts sharply with that
of communities in the major centres ( core ). In the core, communities tend to be better informed and have higher
levels of awareness about the complex issues associated with climate change than in the periphery (Nunn et. al.,
2013).
The issue of coastal squeeze remains a concern for many small islands as there is a constant struggle to manage the
requirements for physical development against the need to maintain ecological balance (Fish et al., 2008; Gero et
al., 2011; Mycoo, 2011). Martinique in the Caribbean exemplifies the point, where physical infrastructure prevents
the beach and wetlands from retreating landward as a spontaneous adaptation response to increased rates of coastal
erosion (Schleupner, 2008). Moreover, intensive coastal development in the limited coastal zone combined with
population growth and tourism has placed great stress on the coast of some islands and has resulted in dense
aggregations of infrastructure and people in potentially vulnerable locations.
Tourism is an important weather and climate-sensitive sector on many small islands and has been assessed on
several occasions, including in previous IPCC assessments. There is currently no evidence that observed climatic
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changes in small island destinations or source markets have permanently altered patterns of demand for tourism to
small islands, and the complex mix of factors that actually determines destination choices under a changing climate
still need to be fully evaluated (Scott et al., 2012a). However, there are cases reported that clearly show severe
weather-related events in a destination country (e.g. heavy, persistent rainfall in Martinique: Hubner and Gössling,
2012; Hurricanes in Anguilla: Forster et al., 2012) can significantly influence visitors perception of the desirability
of the location as a vacation choice.
Climate can also impact directly on environmental resources that are major tourism attractions in small islands.
Widespread resource degradation challenges such as beach erosion and coral bleaching have been found to
negatively impact the perception of destination attractiveness in various locations, for example in Martinique
(Schleupner, 2008), Barbados and Bonaire (Uyarra et al., 2005). Similarly dive tourists are well aware of coral
bleaching, particularly the experienced diver segment (Gössling et al., 2012a; Klint et al., 2012). Therefore more
acute impacts are felt by tourism operators and resorts that cater to these markets. Houston (2002) and Buzinde et al.
(2010) also indicate that beach erosion may similarly affect accommodation prices in some destinations.
Consequently, some countries have begun to invest in a variety of resource restoration initiatives including artificial
beach nourishment, coral and mangrove restoration and the establishment of marine parks and protected areas
(McClanahan et al., 2008; Mycoo and Chadwick, 2012). There is no analysis of how widespread such investments
are or their capability to cope effectively with future climate change. The tourism industry and investors are also
beginning to consider the climate risk of tourism operations (Scott et al., 2012b) including those associated with the
availability of freshwater. Freshwater is limited on many small islands, and changes in its availability or quality
during drought events linked to climate change have adverse impacts on tourism operations (UNWTO 2012).
Tourism is a seasonally significant water user in many island destinations and in times of drought, concerns over
limited supply for residents and other economic activities become heightened (Gössling et al., 2012b). The
increasing use of desalination plants is one adaptation to reduce the risk of water scarcity in tourism operations.
29.3.3.2. Observed Impacts on Human Health
Globally, the effects of climate change on human health will be both direct and indirect, and are expected to
exacerbate existing health risks, especially in the most vulnerable communities where the burden of disease is
already high (refer to Chapter 11.3, 11.5 and 11.6.1, this volume). Many small island states currently suffer from
climate-sensitive health problems, including morbidity and mortality from extreme weather events, certain vector-
and food- and water-borne diseases (Lozano, 2006; Barnett and Campbell, 2010; Cashman et al.,2010; Pulwarty et
al., 2010; McMichael and Lindgren, 2011). Extreme weather and climate events such as tropical cyclones, storm
surges, flooding, and drought can have both short- and long-term effects on human health, including drowning,
injuries, increased disease transmission, and health problems associated with deterioration of water quality and
quantity. Most small island nations are in tropical areas with weather conducive to the transmission of diseases such
as malaria, dengue, filariasis and schistosomiasis.
The linkages between human health, climate variability and seasonal weather have been demonstrated in several
recent studies. The Caribbean has been identified as a highly endemic zone for leptospirosis with Trinidad and
Tobago, Barbados and Jamaica representing the highest annual incidence (12, 10 and 7.8 cases per 100,000
population) in the world with only the Seychelles being higher (43.2 per 100,000 population) (Pappas et al., 2008).
Studies conducted in Guadeloupe demonstrated a link between El Nino occurrence and leptospirosis incidence with
rates increasing to 13 per 100,000 population in El Nino years as opposed to 4.5 cases per 100,000 inhabitants in La
Nina and neutral years (Herrmann-Storck et al., 2008). In addition, epidemiological studies conducted in Trinidad
reviewed the incidence of leptospirosis during the period 1996-2007 and showed seasonal patterns in the occurrence
of confirmed leptospirosis cases, with significantly (P<0.001) more cases occurring in the wet season, May to
November (193 cases), than during the dry season, December to May (66 cases) (Mohan et al., 2009). Recently
changes in the epidemiology of leptospirosis have been detected especially in tropical islands with the main factors
being climatic and anthropogenic ones (Pappas et al., 2008). These factors may be enhanced with increases in
ambient temperature and changes in precipitation, vegetation and water availability as a consequence of climate
change (Russell, 2009).
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In Pacific islands the incidence of diseases such as malaria and dengue fever has been increasing, especially
endemic dengue in Samoa, Tonga and Kiribati (Russell, 2009). While studies conducted so far in the Pacific have
only established a direct link between malaria, dengue and climate variability, these and other health risks including
from cholera, are projected to increase as a consequence of climate change (Russell, 2009; refer also to Chapter
11.2.4 and 11.2.5 this volume, for detailed discussion on the link between climate change and projected increases in
the outbreak of dengue and cholera). Dengue incidence is also a major health concern in other small island
countries, including Trinidad and Tobago, Singapore, Cape Verde, Comoros and Mauritius (Chadee 2009; Koh et
al., 2008; Van Kleef et al., 2010; Teles, 2011). In the specific cases of Trinidad and Tobago and Singapore the
outbreaks have been significantly correlated with rainfall and temperature, respectively (Chadee et al., 2007; Koh et
al., 2008).
Previous IPCC assessments have consistently shown that human health on islands can be seriously compromised by
lack of access to adequate, safe, freshwater and adequate nutrition (Nurse et al., 2001; Mimura et al., 2007). Lovell
(2011) notes that in the Pacific many of the anticipated health effects of climate change are expected to be indirect,
connected to the increased stress and declining well-being that comes with property damage, loss of economic
livelihood and threatened communities. There is also a growing concern in island communities in the Caribbean and
Pacific and Indian oceans, that fresh water scarcity, more intense droughts and storms could lead to a deterioration
in standards of sanitation and hygiene (Cashman et al., 2010; McMichael and Lindgren, 2011). In such
circumstances, increased exposure to a range of health risks including communicable (transmissible) diseases would
be a distinct possibility.
Ciguatera fish poisoning (CFP) occurs in tropical regions and is the most common non-bacterial food-borne illness
associated with consumption of fish. Distribution and abundance of the organisms that produce these toxins, chiefly
dinoflagellates of the genus Gambierdiscus, are reported to correlate positively with water temperature.
Consequently, there is growing concern that increasing temperatures associated with climate change could increase
the incidence of CFP in the island regions of the Caribbean (Morrison et al., 2008; Tester et al., 2010), Pacific
(Rongo and van Woesik, 2011; Chan et al., 2011), the Mediterranean (Aligizaki and Nikolaidis, 2008; refer also to
section 29.5.5), and the Canary Islands in the Atlantic (Pérez-Arellano et al., 2005). A recent Caribbean study
sought to characterise the relationship between sea surface temperatures (SSTs) and CFP incidence and to determine
the effects of temperature on the growth rate of organisms responsible for CFP. Results from this work show that in
the Lesser Antilles high rates occur in areas that experience the warmest water temperatures and which show the
least temperature variability (Tester et al., 2010). There are also high rates in the Pacific in Tokelau, Tuvalu,
Kiribati, Cook Islands and Vanuatu (Chan et al., 2011).
The influence of climatic factors on malaria vector density and parasite development is well established (Béguin et
al., 2011; Chaves and Koenraadt, 2010). Previous studies have assessed the potential influence of climate change on
malaria, using deterministic or statistical models (Hay et al., 2009; Martens et al., 1999; Parham and Michael, 2010;
Pascual et al., 2006). While the present incidence of malaria on small islands is not reported to be high, favourable
environmental and social circumstances for the spread of the disease are present in some island regions and are
expected to be enhanced under projected changes in climate in Papua New Guinea, Guyana, Suriname and French
Guyana (Michon et al., 2007; Rawlins et al., 2008; Figueroa, 2008). In the Caribbean, the occurrence of
autochthonous malaria in non-endemic island countries in the last ten years suggests that all of the essential malaria
transmission conditions now exist. Rawlins et al. (2008) call for enhanced surveillance, recognizing the possible
impact of climate change on the spread of the anopheles mosquito vector and malaria transmission.
29.3.3.3. Observed Impacts of Climate Change on Relocation and Migration
Evidence of human migration as a response to climate change is scarce for small islands. While there is general
agreement that migration is usually driven by multiple factors (Black et al., 2011), several authors highlight the lack
of empirical studies of the effect of climate-related factors, such as sea-level rise, on island migration (Liller and
Van den Broeck, 2011; Mortreux and Barnett, 2009). Furthermore, there is no evidence of any government policy
that allows for climate refugees from islands to be accepted into another country (Bedford and Bedford, 2010).
This finding contrasts with the early desk-based estimates of migration under climate change such as the work of
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Myers (2002). These early studies have been criticised as they fail to acknowledge the reality of climate impacts on
islands, the capacity of islands and islanders to adapt, or the actual drivers of migration (Barnett and O'Neill, 2012).
Studies of island migration commonly reveal the complexity of a decision to migrate and rarely identify a single
cause. For example, when looking at historical process of migration within the Mediterranean it appears that rising
levels of income, coupled with a decreased dependence on subsistence agriculture has left the Mediterranean less
vulnerable to all environmental stressors, resulting in a reduced need for mobility to cope with environmental or
climatic change (de Haas, 2011). Studies from the Pacific have also shown that culture, lifestyle and a connection to
place are more significant drivers of migration than climate (Barnett and Webber, 2010). For example, a Pacific
Access Category of migration has been agreed between New Zealand and Tuvalu that permits 75 Tuvaluans to
migrate to New Zealand every year (Kravchenko, 2008). Instead of enabling climate driven migration, this
agreement is designed to facilitate economic and social migration as part of the Pacific island lifestyle (Shen and
Gemenne, 2011). To date there is no unequivocal evidence that reveals migration from islands is being driven by
anthropogenic climate change.
There is however some evidence that environmental change has played a role in Pacific Island migration in the past
(Nunn, 2007). In the Pacific environmental change has been shown to affect land use and land rights, which in turn
have become drivers of migration (Bedford and Bedford, 2010). In a survey of 86 case studies of community
relocations in Pacific islands, Campbell et al. (2005) found that environmental variability and natural hazards
accounted for 37 communities relocating. In the Pacific, where land rights are a source of conflict, climate change
could increase levels of stress associated with land rights and impact on migration (Campbell, 2010; Weir and
Virani, 2011). While there is not yet a climate fingerprint on migration and resettlement patterns in all small islands,
it is clear that there is the potential for human movement as a response to climate change. To better understand the
impact of climate change on migration there is an urgent need for robust methods to identify and measure the effects
of the drivers of migration on migration and resettlement.
29.3.3.4. Observed Impacts on Island Economies
The economic and environmental vulnerabilities of small islands states are well documented (Briguglio et al., 2009,
Bishop, 2012). Such vulnerabilities, which render the states at risk of being harmed by economic and environmental
conditions, stem from intrinsic features of these vulnerable states, and are not usually governance induced. However,
governance does remain one of the challenges for island countries in the Pacific in the pursuit of sustainable
development through economic growth (Prasad, 2008). Economic vulnerability is often the result of a high degree of
exposure to economic conditions often outside the control of small island states, exacerbated by dependence on a
narrow range of exports and a high degree of dependence on strategic imports, such as food and fuel (Briguglio et
al., 2009). This leads to economic volatility, a condition that is harmful for the economy of the islands (Guillaumont,
2010).
There are other economic downsides associated with small size and insularity. Small size leads to high overhead
cost per capita, particularly in infrastructural outlays. This is of major relevance to climate change adaptation that
often requires upgrades and redesign of island infrastructure. Insularity leads to high cost of transport per unit,
associated with purchases of raw materials and industrial supplies in small quantities, and sales of local produced
products to distant markets. These disadvantages are associated with the inability of small islands to reap the
benefits of economies of scale resulting in a high cost of doing business in small islands (Winters and Martins,
2004).
High costs are also associated with the small size of island states when impacted by extreme events such as
hurricanes and droughts. On small islands such events often disrupt most of the territory, especially on single-island
states, and have a very large negative impact on the state s GDP, in comparison with larger and more populous
states where individual events generally only affect a small proportion of the country and have a small impact on its
GDP (Anthoff et al., 2010). Moreover, the dependence of many small islands on a limited number of economic
sectors such as tourism, fisheries and agricultural crops, all of which are climate-sensitive, means that on the one
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hand climate change adaptation is integral to social stability and economic vitality but that government adaptation
efforts are constrained because of the high cost on the other.
29.3.4. Detection and Attribution of Observed Impacts of Climate Change on Small Islands
While exceptional vulnerability of many small islands to future climate change is widely accepted, the foregoing
analysis indicates that the scientific literature on observed impacts is quite limited. Detection of past and recent
climate change impacts is challenging due to the presence of other anthropogenic drivers, especially in the
constrained environments of small islands. Attribution is further challenged by the strong influence of natural
climate variability compared to gradual incremental change of climate drivers. Notwithstanding these limitations a
summary of the relationship between detection and attribution to climate change of several of the phenomena
described in the above sections has been prepared. Figure 29-2 reflects the degree of confidence in the linkage
between observed changes in several components of the coastal, terrestrial and human systems of small islands and
the drivers of climate change.
[INSERT FIGURE 29-2 HERE
Figure 29-2: A comparison of the degree of confidence in the detection of observed impacts of climate change on
tropical small islands with the degree of confidence in attribution to climate change drivers at this time. For
example, the blue symbol No. 2 (Coastal Systems), indicates there is very high confidence in both the detection of
sea-level rise consistent with global means and its attribution to climate change drivers; whereas the red symbol
No. 17 (Human Systems) indicates whilst detection of casualties and damage during extreme events is very high,
there is presently low confidence in the attribution to climate change. It is important to note that low confidence in
attribution frequently arises due to the limited research available on small island environments.]
29.4. Projected Integrated Climate Change Impacts
Small islands face many challenges in using climate change projections for policy development and decision-
making (Keener et al., 2012). Among these is the inaction inherent in the mismatch of the short-term time scale on
which government decisions are generally taken compared with the long-term time scale required for decisions
related to climate change. This is further magnified by the general absence of credible regional socio-economic
scenarios relevant at the spatial scale at which most decisions are taken. Scenarios are an important tool to help
decision makers disaggregate vulnerability to the direct physical impacts of the climate signal from the vulnerability
associated with socio-economic conditions and governance. There is however a problem in generating formal
climate scenarios at the scale of small islands since they are generally much smaller than the resolution of the global
climate models. This is because the grid squares in the Global Circulation Models (GCMs) used in the SRES
scenarios over the last decade, were between 200 and 600 km2 that provides inadequate resolution over the land
areas of most small islands. This has recently improved with the new RCP scenario GCMs with grid boxes generally
between 100 and 200 km2 in size.
The scale problem has been usually addressed by the implementation of statistical downscaling models that relate
GCM output to the historical climate of a local small island datapoint. The limitation of this approach is the need for
observed data ideally for at least three decades for a number of representative points on the island, in order to
establish the statistical relationships between GCM data and observations. In most small islands long-term quality-
controlled climate data are generally sparse, so that in widely dispersed islands such as in the Pacific, observational
records are usually supplemented with satellite observations combined with dynamical downscaling computer
models (Australian Bureau of Meteorology [ABoM] and CSIRO, 2011a; Keener et al., 2012). However where
adequate local data are available for several stations for at least 30 years, downscaling techniques have demonstrated
that they can provide projections at fine scales ranging from about 10 25 km2 (e.g. Charlery and Nurse, 2010;
ABoM and CSIRO, 2011a). Even so, most projected changes in climate for the Caribbean, Pacific, Indian Ocean
and Mediterranean islands, generally apply to the region as a whole and this may be adequate to determine general
trends in regions where islands are close together.
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29.4.1. Non-Formal Scenario-Based Projected Impacts
Scenarios are often constructed by using a qualitative or broad order of magnitude climate projections approach
based on expected changes in some physical climate signal from literature review rather than projections based on
direct location specific modelling. Usually this is proposed as a what if question which is then quantified using a
numerical method. For example in the Pacific, digital elevation models of Fiji s islands have been used to identify
high risk areas for flooding based on six scenarios for sea-level rise from 0.09 to 0.88 m in combination with six
scenarios for storm surge with return intervals from 1 to 50 years (Gravelle and Mimura, 2008). Another example of
qualitative modelling from the Pacific is a case study from Nauru which uses local data and knowledge of climate to
assess the GCM projections. It suggests that Nauru should plan for continued ENSO variability in the future with
dry years during La Nina and an overall increase in mean rainfall and extreme rainfall events. Climate adaptation
concerns which arise include water security and potential changes in extreme wet events which affect infrastructure,
and human health (Brown et al., 2013a). Climate change also poses risks for food security in the Pacific islands,
including agriculture and fisheries (Barnett, 2011) Projections have also been used in the islands of the Republic of
Baharain to estimate proness to inundation for sea-level rise of 0.5, 1.0 and 1.5 m (Al-Jeneid et al., 2008). Similarly,
in the Caribbean the elevation equivalent of a projected sea-level rise of 1 m has been superimposed on topographic
maps to estimate that 49-60% of tourist resort properties would be damaged, potentially transforming the
competitive position and sustainability of coastal tourism destinations in the region (Scott et al., 2012c). This
method has also been used to quantify the area loss for over 12 900 islands and over 3000 terrestrial vertebrates in
the tropical Pacific region for three sea-level rise scenarios. The study estimated that for sea-level rise of 1 m, 37
island endemic species in this region risk complete inundation (Wetzel et al., 2013).
29.4.2. Projected Impacts for Islands based on Scenario Projections
Another approach to scenario development is to use the region specific projections more directly. It is worth noting
that the broad synthesis in the AR4 of medium emissions climate scenario projections for small island regions
(Mimura et al., 2007) shows concordance with the new RCP scenarios (see Table 29-1 and new RCP projections in
Figure 29-3). For example, the SRES A1B medium emissions scenario suggests about a 1.8 to 2.3 °C median annual
increase in surface temperature in the Caribbean, Indian Ocean and Pacific Ocean small islands regions by 2100
compared to a 1980-1999 baseline, with an overall annual decrease in precipitation of about 12% in the Caribbean
(Table 11.1 in AR4 WG1; AR5 WG 1, 14.7.4) and a 3-5 per cent increase in the Indian and Pacific Oceans small
island regions. Comparative projections for the new RCP4.5 scenario suggests about a 1.2 to 2.3 °C increase in
surface temperature by 2100 compared to a 1986-2005 baseline and a decrease in precipitation of about 5 or 6 per
cent in the Caribbean and Mediterranean respectively signaling potential future problems for agriculture and water
availability compared to a 1-9 per cent increase in the Indian Ocean and Pacific Ocean small islands regions (Table
29.1). However, there are important spatial and high-island topography differences. Thus for example, among the
more dispersed Pacific islands where the equatorial regions are likely to get wetter and the sub-tropical high pressure
belts drier (as reported by AR5 WG I) in regions directly affected by the South Pacific Convergent Zone (SPCZ) and
western portion of the Inter-Tropical Convergent Zone (ITCZ), the rainfall outlook is uncertain (AR5 WG1,
14.7.13). Projections for the Mediterranean islands also differ from those for the tropical small islands. Throughout
the Mediterranean region, the length, frequency, and/or intensity of warm spells or heat waves are very likely to
increase to the year 2100 (AR5 WGI, 14.7.6). Sea-level rise projections in the small islands regions for RCP4.5 are
similar to the global projections of 0.41 to 0.71m (WGI AR5 13.5.1) ranging from 0.5-0.6 m by 2100 compared to
1986-2005 in the Caribbean, Pacific and Indian Ocean to 0.4-0.5 m in the Mediterranean and North Indian Ocean
(Table 29-1).
[INSERT TABLE 29-1 HERE
Table 29-1: Climate change projections for the intermediate low (500-700 ppm CO2-e) RCP4.5 scenario for six
Small Islands regions. The table shows the 25th, 50th (median) and 75th percentiles for surface air temperature and
precipitation based on averages from 42 CMIP5 global models (adapted from WGI AR5 Table 14.1). Mean net
regional sea-level change is evaluated from 21 CMIP5 models and includes regional non-scenario components
(adapted from WGI AR5 Figure 13-20).]
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In the main regions in which most tropical or sub-tropical small island states are located, there are few independent
peer reviewed scientific publications providing downscaled climate data projections, and even less illustrating the
experience gained from their use for policy making. A possible 2 °C temperature increase by the year 2100 has
potentially far reaching consequences for sentinel ecosystems such as coral reefs that are important to tropical
islands (see Chapter 6.2.2.4.4.). This is because Degree Heating Months (DHM) >2 °C-month are the determining
threshold for severe coral bleaching (Donner, 2009). For example in a study of sea surface temperature (SST) across
all coral reef regions using GCM ensemble projections forced with five different SRES future emissions scenarios,
Donner (2009) concluded that even warming in the future from the current accumulation of greenhouse gases in the
atmosphere could cause over half of the world s coral reefs to experience harmfully frequent thermal stress by 2080.
Further, this timeline could be brought forward to as early as 2030 under the A1B medium emissions scenario. He
further stated that thermal adaptation of 1.5 °C would only delay the thermal stress forecast by 50 80 years. Donner
(2009) also estimated the year of likelihood of a severe mass coral bleaching event due more than once every 5
years, to be 2074 in the Caribbean, 2088 in the western Indian Ocean, 2082 in the central Indian Ocean, 2065 in
Micronesia, 2051 in the central Pacific, 2094 in Polynesia and 2073 in the eastern Pacific small islands regions.
Using the new RCP scenarios by comparison, van Hooidonk et al. (2013) found that the onset of annual bleaching
conditions is associated with about 510 ppm CO2 equivalent. The conclusion based on outputs from a wide range of
emissions scenarios and models is that preserving >10 per cent of coral reefs worldwide would require limiting
warming to less than 1.5+/-1.3 °C compared to pre-industrial levels (Frieler et al., 2013).
Small island economies can also be objectively shown to be at greater risk from sea-level rise in comparison to other
geographic areas since most of their population and infrastructure are in the coastal zone. This is demonstrated in a
study using the Climate Framework for Uncertainty, Negotiation and Distribution (FUND) model to assess the
economic impact of substantial sea-level rise in a range of socio-economic scenarios downscaled to the national
level, including the four SRES storylines (Anthoff et al., 2010). Although this study showed that in magnitude, a
few regions will experience most of the absolute costs of sea-level rise by 2100, especially East Asia, North
America, Europe and South Asia, these same results when expressed as percent of GDP showed that most of the top
ten and four of the top five most impacted are small islands from the Pacific (Federated States of Micronesia, Palau,
Marshall Islands, Nauru) and Caribbean (Bahamas). The point is made that the damage costs for these small island
states are enormous in relation to the size of their economies (Nicholls and Tol, 2006) and that together with deltaic
areas they will find it most difficult to locally raise the finances necessary to implement adequate coastal protection
(Anthoff et al., 2010).
In the Caribbean, downscaled climate projections have been generated for some islands using the Hadley Centre
PRECIS regional model (Taylor et al., 2007; Stephenson et al., 2008). For the SRES A2 and B2 scenarios the
PRECIS regional climate model projects an increase in temperature across the Caribbean of 1 4 °C compared to a
1960-1990 baseline, with increasing rainfall during the latter part of the wet season from November January, in the
northern Caribbean (i.e. north of 22°N) and drier conditions in the southern Caribbean linked to changes in the
Caribbean Low Level Jet (CLLJ) with a strong tendency to drying in the traditional wet season from June October
(Whyte et al., 2008; Campbell et al., 2011; Taylor et al., 2013). Projected lengthening seasonal dry periods, and
increasing frequency of drought are expected to increase demand for water throughout the region under the SRES
A1B scenario (Cashman et al., 2010). Decrease in crop yield is also projected in Puerto Rico for the SRES B1 (low),
A2 (mid-high) and A1F1 scenarios during September although increased crop yield is suggested during February
(Harmsen et al., 2009). Using a tourism demand model linked to the SRES A1F1 A2 B1 and B2 scenarios, the
projected climate change heating and drying impacts are also linked to potential aesthetic, physical and thermal
effects that are estimated to cause a change in total regional tourist expenditure of about +321, +356, -118 and -146
million US dollars from the least to the most severe emissions scenario respectively (Moore, 2010).
In the Indian Ocean, representative downscaled projections have been generated for Australia s two Indian Ocean
territories, the Cocos (Keeling) Islands and Christmas Island using the CSIRO Mark 3.0 climate model with the
SRES A2 high emissions scenario (Maunsell Australia Pty Ltd., 2009). Future climate change projections for the
two islands for 2070 include an approximate 1.8 °C increase in air temperature by 2070, probable drier dry seasons
and wet seasons, about a 40 cm rise in sea-level and a decrease in the number of intense tropical cyclones.
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In the western tropical Pacific, extensive climate projections have been made for several Pacific Island Countries
based on downscaling from an ensemble of models (ABoM and CSIRO, 2011b). The temperature projections in this
region dominated by oceans seem less than those seen globally, ranging from +1.5 to 2.0 °C for the B1 low
emissions scenario to +2.5 to 3.0 °C for the A2 high emissions scenario by the year 2090 relative to a 20 year period
centred on 1990. Notably, extreme rainfall events that currently occur once every 20 years on average are generally
simulated to occur four times per 20-year period, on average, by 2055 and seven times per 20-year period, on
average, by 2090 under the A2 (high emissions) scenario (ABoM and CSIRO, 2011b). The results are not very
different from the tropical Pacific RCP4.5 projections with projected temperature increases of about +1.2 to 1.4 °C
by 2100 and an increase in rainfall of about 4% (Table 29-1). A comprehensive assessment of the vulnerability of
the fisheries and aquaculture sectors to climate change in 22 Pacific island countries and territories focused on two
future time-frames (2035 and 2100) and two SRES emissions scenarios, B1 (low emissions) and A2 (high
emissions) (Bell et al., 2013). Many anticipated changes in habitat and resource availability such as coral reef-based
fisheries are negative. By contrast, projected changes in tuna fisheries and freshwater aquaculture/fisheries can be
positive with implications for government revenue and island food security (Bell et al., 2013). Simulation studies on
changes in stocks of skipjack and bigeye tuna in the tropical Pacific area summarized in Table 29-2 and also
discussed in 7.4.2.1 and 30.6.2.1.1. Some of these projected changes may favour the large international fishing fleets
that can shift operations over large distances compared to local, artisanal fishers (Polovina et al., 2011).
[INSERT TABLE 29-2 HERE
Table 29-2: Summary of projected percentage changes in tropical Pacific tuna catches by 2035 and 2100 relative to
1980-2000 and the estimated resulting percentage changes to government revenue (After Bell et al., 2011).]
In the Mediterranean islands of Mallorca, Corsica, Sardinia, Crete and Lesvos, Gritti et al. (2006) simulated the
terrestrial vegetation biogeography and distribution dynamics under the SRES A1F1 and B1 scenarios to the year
2050. The simulations indicate that the effects of climate change are expected to be negligible within most
ecosystems except for mountainous areas. These areas are projected to be eventually occupied by exotic vegetation
types from warmer, drier conditions. Cruz et al. (2009) report similar results for the terrestrial ecosystems of
Madeira Island in the Atlantic. Downscaled SRES A2 and B2 scenarios for the periods 2040 2069 and 2070
2099 suggest that the higher altitude native humid forest called the Laurissilva, may expand upwards in altitude,
which could lead to a severe reduction of the heath woodland which because it has little upward area to shift may
reduce in range or disappear at high altitudes resulting in the loss of rare and endemic species within this ecosystem.
29.4.3. RCP Projections and Implications for Small Islands
Utilizing updated historical greenhouse gas emissions data the scientific community has produced future projections
for four plausible new global Representative Concentration Pathways (RCPs) in order to explore a range of global
climate signals up to the year 2100 and beyond (e.g. Moss et al., 2010). Typical model ensemble representations of
low, intermediate low, intermediate high and high RCP projections for annual temperature and precipitation in some
small islands regions are presented in Figure 29-3. Highlighted in Figure 29-3 is the ensemble mean of each RCP. A
more comprehensive compilation of quarterly global RCP projections can be found in the Annex I Atlas of Global
and Regional Climate Projections in the WGI AR5 Report.
[INSERT FIGURE 29-3 HERE
Figure 29-3: Time series of RCP scenarios annual projected temperature and precipitation change relative to 1986-
2005 for six small islands regions (using regions defined in AR5 WG1, Annex 1: Atlas of Global and Regional
Climate Projections).] Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean.
On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-yr mean
changes are given for 2081 2100 in the four RCP scenarios. Note that the model ensemble averages in the figure are
for grid points over wide areas and encompass many different climate change signals. To get projections for a
specific location and time period use the maps in the Atlas or the online interactive version at but please note that in
regions with small islands the models basically simulate the climate of the surrounding ocean and local conditions
on land may differ.]
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During negotiations towards a new multi-lateral climate change regime Small Island Developing States (SIDS) have
advocated that any agreement should be based on Global Mean Surface Temperature (GMST) increase well below
1.5 °C above pre-industrial levels (Hare et al., 2011; Riedy and McGregor, 2011). Inspection of column 1 in Figure
29-3 suggests that for the Caribbean, Indian Ocean and Pacific SIDS in the tropics, the median projected regional
increase is in the range 0.5-0.9 °C by 2100 compared to 1986-2005. This together with the temperature change that
has already occurred since the industrial revolution suggests that a temperature well below 1.5 °C is unlikely to be
achieved with the lowest RCP2.6 projection (Peters et al., 2013). By comparison temperature projections for the
intermediate low RCP4.5 scenario, Table 29-1 and Figure 29-3 suggest possible 1.2-1.5 °C temperature increases in
Caribbean, Indian Ocean and Pacific SIDS by 2100 compared to 1986-2005. Similarly, the projections for the
Mediterranean would be about a 2.3 °C increase by 2100 compared to 1986-2005 that would represent a 2.7 °C
increase compared to pre-industrial temperatures. Associated with this change, the Caribbean and Mediterranean
regions may experience a noticeable decrease in mean rainfall while the Indian and Pacific Ocean SIDS may
experience increased rainfall. These trends accelerate moderately for RCP 6.0 and steeply for RCP 8.5 (Table 29-1).
29.5. Inter- and Intra-Regional Trans-Boundary Impacts on Small Islands
Available literature since AR4 has highlighted previously less well understood impacts on small islands that are
generated by processes originating in another region or continent well beyond the borders of an individual
archipelagic nation or small island. These are inter-regional trans-boundary impacts. Intra-regional trans-boundary
impacts originate from a within-region source (e.g. within the Caribbean). Some trans-boundary processes may have
positive effects on the receiving small island or nation, though most that are reported have negative impacts.
Deciphering a climate change signal in inter- and intra-regional trans-boundary impacts on small islands is not easy
and usually involves a chain of linkages tracing back from island-impact to a distant climate or climate-related bio-
physical or human process. Some examples are given below.
29.5.1. Large Ocean Waves from Distant Sources
Unusually large deep ocean swells, generated from sources in the mid- and high-latitudes by Extra-Tropical
Cyclones (ETC) cause considerable damage on the coasts of small islands thousands of kilometres away in the
tropics. Impacts include sea-flooding and inundation of settlements, infrastructure and tourism facilities as well as
severe erosion of beaches (see also 5.4.3.4). Examples from small islands in the Pacific and Caribbean are common
though perhaps the most significant instance, in terms of a harbinger of climate change and sea-level rise, occurred
in the Maldives in April 1987 when long period swells originating from the Southern Ocean some 6000 km away
caused major flooding, damage to property, destruction of sea defences and erosion of reclaimed land and islands
(Harangozo, 1992). The Maldives and several other island groups in the Indian Ocean have been subject to similar
ocean swell events more recently, most notably in May 2007 (Department of Meteorology, 2007).
In the Caribbean, northerly swells affecting the coasts of islands have been recognized as a significant coastal hazard
since the 1950s (Donn and McGuinness, 1959). They cause considerable seasonal damage to beaches, marine
ecosystems and coastal infrastructure throughout the region (Cambers, 2009; Bush et al., 2009). These high-energy
events manifest themselves as long period high-amplitude waves that occur during the northern hemisphere winter
and often impact the normally sheltered, low-energy leeward coasts of the islands. Such swells have even reached
the shores of Guyana on the South American mainland as illustrated by a swell event in October 2005 that caused
widespread flooding and overtopping and destruction of sea defences (van Ledden et al., 2009).
Distant origin swells differ from the normal wave climate conditions experienced in the Caribbean, particularly
with respect to direction of wave approach, wave height and periodicity and in their morphological impact (Cooper
et al., 2013). Swells of similar origin and characteristics also occur in the Pacific (Fletcher et al., 2008; Keener et al.,
2012). These events frequently occur in the Hawaiian island where there is evidence of damage to coral growth by
swell from the north Pacific, especially during years with a strong El Nino signal (Fletcher et al., 2008). Hoeke et al.
(2013) describe inundation from mid-high latitude north and south Pacific waves respectively at Majuro (Marshall
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Islands) in November and December 1979 and along the Coral Coast (Fiji) in May 2011. They also describe in detail
an inundation event in December 2008 that was widespread throughout the western and central Pacific and resulted
in waves surging across low-lying islands causing severe damage to housing and infrastructure and key natural
resources that affected about 100,000 people across the region. The proximate cause of this event was swell
generated in mid-latitudes of the North Pacific Ocean, more than 4000 km from the furthest affected island (Hoeke
et al., 2013).
Whereas the origin of the long period ocean swells that impact small islands in the tropical regions come from the
mid-and high-latitudes in the Pacific, Indian and Atlantic oceans, there are also instances of unusually large waves
generated from tropical cyclones that spread into the mid- and high- latitudes. One example occurred during 1999
when tide gauges at Ascension and St. Helena Islands in the central south Atlantic recorded unusually large deep-
ocean swell generated from distant Hurricane Irene (Vassie et al., 2004). The impacts of increasing incidence or
severity of storms or cyclones is generally considered from the perspective of direct landfall of such systems,
whereas all of these instances serve to show the potential importance of swells to communities on distant, low-lying
coasts, particularly if the climatology of swells is modified under future climate change (Vassie et al., 2004). From
the perspective of those islands that suffer damage from this coastal hazard on an annual basis, this is an area that
warrants further investigation. Projected changes in global wind-wave climate to 2070-2100, compared to a base
period 1979-2009, show considerable regional and seasonal differences with both decreases and increases in annual
mean significant wave height. Of particular relevance in the present context is the projected increase in wave
activity in the Southern Ocean which influences a large portion of the global ocean as swell waves propagate
northwards into the Pacific, Indian and Atlantic oceans (Hemer et al., 2013).
Deep ocean swell waves and elevated sea-levels resulting from ETCs are examples of inter-regional trans-boundary
processes; locally generated Tropical Cyclones (TCs) provide examples of intra-regional trans-boundary processes.
Whilst hurricane force winds, heavy rainfall and turbulent seas associated with TCs can cause massive damage to
both land and coastal systems in tropical small islands, the impacts of sea waves and inundation associated with far
distant ETCs are limited to the coastal margins. Nevertheless both storm types result in a range of impacts covering
island morphology, natural and ecological systems, island economies, settlements and human well-being (see Figure
29-4).
[INSERT FIGURE 29-4 HERE
Figure 29-4: Tropical and extra-tropical cyclone impacts on the coasts of small islands. Four types of impacts are
distinguished here, black arrows showing the connections between them, based on the existing literature. An
example of the chain of impacts associated with two extra-tropical cyclones centred to the east of Japan is illustrated
by the red arrows. Swell waves generated by these events in December 2008 reached islands in the southwest Pacific
and caused extensive flooding (3) that impacted soil quality (8), freshwater resources (9), and damaged crops (10),
buildings (15), and transport facilities (16) in the region (Example based on Hoeke et al., 2013).
Examples of tropical cyclone impacts on small island coasts with reference
1. Society Islands, French Polynesia, February 2010 (Etienne, 2012); 2. Taveuni, Fiji, March 2010 (Etienne and
Terry, 2012); 3. Cook Islands (de Scally, 2008); Society and Autral Islands, French Polynesia, February 2010
(Etienne, 2012); 4. Viti Levu, Fiji, March 1997 (Terry et al., 2002); 5. Society Islands, French Polynesia, February
2010 (Etienne, 2012); 6. Curacao, Bonaire, Netherlands Antilles, November 1999 (Scheffers and Scheffers, 2006);
Hawaiian Islands (Fletcher et al., 2008); 7. Bay Islands, Honduras, October 1998 (Cahoon et al., 2003); 8. Marshall
Islands, June 1905 (Spennemann, 1996); 9. Pukapuka atoll, Cook Islands, February 2005 (Terry and Falkland,
2010); 10. Vanuatu, February 2004 (Richmond and Sovacool, 2012); 11. 12. 13. Tuamotu Islands, French Polynesia,
1982-83 (Dupon, 1987); 14. Grenada, September 2004 (OECS, 2004); 15. Grenada, September 2004 (OECS, 2004);
Tubuai, Austral Islands, French Polynesia, February 2010 (Etienne, 2012); 16. Vanuatu, February 2004 (Richmond
and Sovacool, 2012); Guadeloupe Island, October 2008 (Dorville and Zahibo, 2010); 17. Bora Bora, Raiatea,
Maupiti, Tahaa, Huahine, Society Islands, February 2010 (Etienne, 2012); 18. Vanuatu, February 2004 (Richmond
and Sovacool, 2012); 19. Tuamotu, French Polynesia, 1982-83 (Dupon, 1987).
Examples of extra-tropical cyclone impacts on small island coasts with reference
1. Maldives, April 1987 (Harangozo, 1992); 2. Maldives, January 1955 (Maniku, 1990); 3. Maldives, April 1987
(Harangozo, 1992); 9. Solomon Islands, December 2008 (Hoeke et al., 2013); 10. Chuck, Pohnpei, Kosrae,
Federated States of Micronesia, December 2008 (Hoeke et al., 2013); 15. Majuro, Marshall Islands, November 1979
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(Hoeke et al., 2013); 16. Coral Coast, Viti Levu, Fiji, May 2011 (Hoeke et al., 2013); 17. Majuro, Kwajalein, Arno,
Marshall Islands, December 2008 (Hoeke et al., 2013); 18. Bismark Archipelago, Papua New Guinea, December
2008 (Hoeke et al., 2013).]
29.5.2. Trans-Continental Dust Clouds and their Impact
The transport of airborne Saharan dust across the Atlantic and into the Caribbean has engaged the attention of
researchers for some time. The resulting dust clouds are known to carry pollen, microbes, insects, bacteria, fungal
spores and various chemicals and pesticides (Prospero et al., 2005; Middleton et al., 2008; Monteil, 2008; López-
Villarrubia et al., 2010; Garrison et al., 2006). During major events, dust concentrations can exceed 100 ug m-3
(Prospero, 2006). Independent studies using different methodologies have all found a strong positive correlation
between dust levels in the Caribbean and periods of drought in the Sahara, while concentrations show a marked
decrease during periods of higher rainfall. Consequently, it is argued that higher dust emissions due to increasing
aridity in the Sahel and other arid areas could enhance climate change effects over large areas, including the eastern
Caribbean and the Mediterranean (Prospero and Lamb, 2003). Similar findings have been reported at Cape Verde
where dust emission levels were found to be a factor of nine lower during the decade of the 1950s when rainfall was
at or above normal, compared to the 1980s, a period of intense drought in the Sahel region (Nicoll et al., 2011). Dust
from the Sahara has also reached the eastern Mediterranean (e.g. Santese et al., 2010) whilst dust from Asia has
been transported across the Pacific and Atlantic oceans and around the world (Uno et al., 2009).
There is also evidence that the trans-boundary movement of Saharan dust into the island regions of the Caribbean,
Pacific and Mediterranean is associated with various human health problems (Griffin, 2007) including asthma
admissions in the Caribbean (Monteil, 2008; Monteil and Antoine, 2009; Prospero et al., 2008), cardiovascular
morbidity in Cyprus in the Mediterranean (Middleton et al., 2008) and is found to be a risk factor in respiratory and
obstructive pulmonary disease in the Cape Verde islands (Martins et al., 2009). These findings underscore the need
for further research into the link between climate change, airborne aerosols and human health in localities such as
oceanic islands far distant from the continental source of the particulates.
29.5.3. Movement and Impact of Introduced and Invasive Species across Boundaries
Invasive species are coloniser species that establish populations outside their normal distribution ranges. The spread
of invasive alien species is regarded as a significant trans-boundary threat to the health of biodiversity and
ecosystems, and has emerged as a major factor in species decline, extinction and loss of biodiversity goods and
services worldwide. This is particularly true of islands, where both endemicity and vulnerability to introduced
species tend to be high (Kenis et al., 2009; Reaser et al., 2007; Westphal et al., 2008; Rocha et al., 2009; Kueffer et
al., 2010). The extent to which alien invasive species successfully establish themselves at new locations in a
changing climate will be dependent on many variables, but non-climate factors such as ease of access to migration
pathways, suitability of the destination, ability to compete and adapt to new environments, and susceptibility to
invasion of host ecosystems are deemed to be critical. This is borne out for example by Le Roux et al. (2008) who
studied the effect of the invasive weed Miconia calvescens in New Caledonia, Society Islands and Marquesas
Islands; by Gillespie et al. (2008) in an analysis of the spread of Leucaena leucocephala, Miconia calvescens,
Psidium sp. and Schinus terebinthifolius in the Hawaiian islands; and by Christenhusz and Toivonen (2008) who
showed the potential for rapid spread and establishment of the oriental vessel fern, Angiopteris evecta, from the
South Pacific throughout the tropics. Mutualism between an invasive ant and locally honeydew-producing insects
has been strongly associated with damage to the native and functionally important tree species, Pisonia grandis on
Cousine Island, Seychelles (Gaigher, et al., 2011).
Whilst invasive alien species constitute a major threat to biodiversity in small islands, the removal of such species
can result in recovery and return of species richness. This has been demonstrated in Mauritius by Baider and Florens
(2011) where some forested areas were weeded of alien plants and after a decade the forest had recovered close to
its initial condition, They concluded, given the severity of alien plant invasion in Mauritius, that their example can
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be seen as a relevant model for a whole swath of other island nations and territories around the world particularly in
the Pacific and Indian Oceans (Baider and Florens, 2011).
The movement of aquatic and terrestrial invasive fauna within and across regions will almost certainly exacerbate
the threat posed by climate change in island regions, and could impose significant environmental, economic and
social costs. Recent research has shown that the invasion of the Caribbean Sea by the Indo-Pacific lionfish (Pterois
volitans), a highly efficient and successful predator, is a major contributor to observed increases in algal dominance
in coral and sponge communities in the Bahamas and elsewhere in the region. The consequential damage to these
ecosystems has been attributed to a significant decline in herbivores due to predation by lionfish (Schofield, 2010;
Albins and Hixon, 2008; Lesser and Slattery, 2011; Green et al., 2011). While there is no evidence that the lionfish
invasion is climate-related, the concern is that when combined with pre-existing stress factors the natural resilience
of Caribbean reef communities will decrease (Albins and Hixon, 2013; Green et al., 2012), making them more
susceptible to climate change effects such as bleaching. Englund (2008) has documented the negative effects of
invasive species on native aquatic insects on Hawai i and French Polynesia, and their potential role in the extirpation
of native aquatic invertebrates in the Pacific. Similarly, there is evidence that on the island of Oahu introduced slugs
appear to be skewing species abundance in favour of certain non-native and native plants , by altering the rank
order of seedling survival rates , thereby undermining the ability of preferred species (e.g. the endangered C.
Superba) to compete effectively (Joe and Daehler, 2008).
29.5.4. Spread of Aquatic Pathogens within Island Regions
The mass mortality of the black sea urchin, Diadema antillarum, in the Caribbean basin during the early 1980s
demonstrates the ease with which ecological threats in one part of a region can be disseminated to other jurisdictions
thousands of kilometres away. The die-off was first observed in the waters off Panama around January 1983, and
within 13 months the disease epidemic had spread rapidly through the Caribbean Sea affecting practically all island
reefs, as far away as Tobago some 2000 km to the south and Bermuda, some 4000 km to the east. The diadema
population in the wider Caribbean declined between 90-95 per cent as a consequence of this single episode (Lessios,
1988, 1995) As D. antillarum is one of the principal grazers that removes macroalgae from reefs and thus promotes
juvenile coral recruitment, the collateral damage was severe, as the region s corals suffered from high morbidity and
mortality for decades thereafter (Carpenter and Edmunds, 2006; Idjadi et al., 2010).
There are other climate-sensitive diseases such as yellow, white and black band, white plague and white pox that
travel across national boundaries and infect coral reefs directly. This is variously supported by examples from the
Indo-Pacific and Caribbean relating to the role of bacterial infections in white syndrome and yellow band disease
(Piskorska et al., 2007; Cervino et al., 2008), the impact of microbial pathogens as stressors on benthic communities
in the Mediterranean associated with warming seawater (Danovaro et al., 2009, and an increasing evidence of white,
yellow and black band disease associated with Caribbean and Atlantic reefs (Rosenberg et al., 2009; Brandt and
McManus, 2009; Miller et al., 2009a; Weil and Croquer, 2009; Weil and Rogers, 2011).
29.5.5. Trans-Boundary Movements and Human Health
For island communities the trans-boundary implications of existing and future human health challenges are
projected to increase in a changing climate. For instance, the aggressive spread of the invasive giant African snail,
Achatina fulica, throughout the Caribbean, Indo-Pacific islands and Hawai i is not only assessed to be a severe
threat to native snails and other fauna (e.g. native gastropods), flora and crop agriculture, but is also identified as a
vector for certain human diseases such as meningitis (Reaser et al., 2007; Meyer et al., 2008; Thiengo et al., 2010).
Like other aquatic pathogens, ciguatoxins that cause ciguatera fish poisoning may be readily dispersed by currents
across and within boundaries in tropical and sub-tropical waters. Ciguatoxins are known to be highly temperature-
sensitive and may flourish when certain sea water temperature thresholds are reached, as has been noted in the South
Pacific (Llewellyn, 2010), Cook Islands (Rongo and van Woesik, 2011), Kiribati (Chan et al., 2011), the Caribbean
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and Atlantic (Otero et al., 2010; Tester et al., 2010) and Mediterranean (Aligizaki and Nikolaidis, 2008) (see also
29.3.3.2).
29.6. Adaptation and Management of Risks
Islands face risks from both climate-related hazards that have occurred for centuries, as well as new risks from
climate change. There have been extensive studies of the risks associated with past climate-related hazards and
adaptations to these, such as tropical cyclones, drought, and disease, and their attendant impacts on human health,
tourism, fisheries and other areas (Bijlsma et al., 1996; Cronk 1997; Solomon and Forbes 1999; Pelling and Uitto
2001). There have also been many studies that have used a variety of vulnerability, risk and adaptation assessment
methods particularly in the Pacific that have recently been summarized by Hay et al., (2013). But for most islands,
there is very little published literature documenting the probability, frequency, severity or consequences of climate
change risks such as sea-level rise, ocean acidification, and salinisation of freshwater resources or associated
adaptation measures. Projections of future climate change risks are limited by: the lack of model skill in projecting
the climatic variables that matter to small islands, notably: tropical cyclone frequency and intensity, wind speed and
direction, precipitation, sea-level, ocean temperature and ocean acidification (Brown et al., 2013b); inadequate
projections of regional sea levels (Willis and Church, 2012), and a lack of long term baseline monitoring of changes
in climatic risk, or to ground-truth models (Voccia, 2012), such as risk of saline intrusion, risk of invasive species,
risk of biodiversity loss, or risk of large ocean waves. In their absence, qualitative studies have documented
perceptions of change in current risks (Fazey et al., 2011; Lata and Nunn, 2012), reviewed effective coping
mechanisms for current stressors (Bunce et al. 2009; Campbell et al., 2011) and have considered future scenarios of
change (Weir and Virani 2011). These studies highlight that change is occurring, but they do not quantify the
probability, speed, scale or distribution of future climate risks. The lack of quantitative published assessments of
climate risk for many small islands means that future adaptation decisions have to rely on analogues of responses to
past and present weather extremes and climate variability, or assumed/hypothesised impacts of climate change based
on island type (Table 29-3). Differences in island type and differences in exposure to climate forcing and hazards
vary with island form that provides a framework for consideration of vulnerability and adaptation strategies. Critical
is a place-based understanding of island landscapes and of processes operating on individual islands (Forbes et al.,
2013).
[INSERT TABLE 29-3 HERE
Table 29-3: Summary of island types in the Pacific region and implications for hydro-meteorological hazards.]
29.6.1. Addressing Current Vulnerabilities on Small Islands
Islands are heterogeneous in geomorphology, culture, ecosystems, populations and hence also in their vulnerability
to climate change Vulnerabilities and adaptation needs are as diverse as the variety of islands between regions and
even within nation states (e.g. in Solomon Islands, Rasmussen et al., 2011), often with little climate adaptation
occurring in peripheral islands, for example in parts of the Pacific (Nunn et al. 2013). Quantitative comparison of
vulnerability is difficult due to the paucity of vulnerability indicators. Generic indices of national level vulnerability
continue to emerge (Cardona, 2007) but only a minority are focused on small islands (e.g. Blancard and Hoarau,
2013). The island-specific indicators that exist often suffer from lack of data (Hughes et al., 2012; Peduzzi et al.,
2009), use indicators that are not relevant in all islands (Barnett and Campbell, 2010), or use data of limited quality
for islands, such as sea-level rise (as used in Wheeler, 2011). As a result indicators of vulnerability for small islands
often misrepresent actual vulnerability Recent moves towards participatory approaches that link scientific
knowledge with local visions of vulnerability (see Park et al., 2012) offers an important way forward to
understanding island vulnerability in the absence of certainty in model-based scenarios.
Island vulnerability is often a function of four key stressors: socio-economic, physical, socio-ecological and climate-
induced , whose reinforcing mechanisms are important in determining the magnitude of impacts. Socio-economic
vulnerabilities are related to on-going challenges of managing urbanisation, pollution and sanitation, both in small
island states and non-sovereign islands as highlighted by Storey and Hunter (2010) in Kiribati, López-Marrero and
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Yarnal (2010) in Puerto Rico, and in Mayotte (France) (Le Masson and Kelman, 2011). Geo-physical characteristics
of islands (see Table 29-2; Figure 29-1) create inherent physical vulnerabilities. Thus, for example the Azores
(Portugal) face seismic, landslide and tsunami risks (Coutinho et al., 2009). Socio-ecological stresses, such as
habitat loss and degradation, invasive species (described in Sax and Gaines, 2008), overexploitation, pollution,
human encroachment and disease can harm biodiversity (Caujape-Castells et al., 2010; Kingsford et al., 2009), and
reduce the ability of socio-ecological systems to bounce back after shocks. To understand climate vulnerability on
islands, it is necessary to assess all of these dimensions of vulnerability (Rasmussen et al., 2011). For example, with
individual ecosystems, such as coral reef ecosystems those already under stress from non-climate factors are more
at risk from climate change than those that are unstressed (Maina et al., 2011; Hughes et al., 2003). Evidence is
starting to emerge that shows the same applies at the island scale. In Majuro atoll (Marshall Islands), 34-37 years of
aerial photography shows that socio-ecological stress is exacerbating shoreline change associated with sea-level rise,
especially on the lagoon-side of islands (Ford, 2012; see also 29.3.1.1). Islands faced with multiple stressors can
therefore be assumed to be more at risk from climate impacts.
Despite the limited ability of continental scale models to predict climate risks for specific islands, or the limited
capacity of island vulnerability indicators, scenario based damage assessments can be undertaken. Storm surge risks
have been effectively modeled for the Andaman and Nicobar Islands (Kumar et al., 2008). Rainfall induced
landslide risk maps have been produced for both Jamaica (Miller et al., 2009b) and the Chuuk Islands (Federated
States of Micronesia) (Harp et al., 2009). However the probability of change in frequency and severity of extreme
rainfall events and storm surges remains poorly understood for most small islands. Other risks, such as the climate
change driven health risks from the spread of infectious disease, loss of settlements and infrastructure, and decline of
ecosystems that affect island economies, livelihoods and human well-being also remain under-researched.
Nevertheless, it is possible to consider these risks along with the threat of rising sea level and suggest a range of
contemporary and future adaptation issues and prospects for small islands (Table 29-4).
[INSERT TABLE 29-4 HERE
Table 29-4: Selected key risks and potential for adaptation for small islands from the present-day to the long-term.]
29.6.2. Practical Experiences of Adaptation on Small Islands
There is disagreement about whether islands and islanders have successfully adapted to past weather variability and
climate change. Nunn (2007) argues that past climate changes have had a crisis effect on prehistoric societies in
much of the Pacific Basin. In contrast a variety of studies argue that past experiences of hydro-meteorological
extreme events have enabled islands to become resilient to weather extremes (Barnett, 2001). Resilience appears to
come from both a belief in their own capacity (Kuruppu and Liverman, 2011; Adger and Brown, 2009), and a
familiarity with their environment and understanding of what is needed to adapt (Tompkins et al., 2009; Le Masson
and Kelman, 2011). For example, compared to communities in the larger countries of Madagascar, Tanzania and
Kenya, the Indian Ocean islands the Seychelles and Mauritius were found to have: comparatively high capacity
to anticipate change and prepare strategies; self-awareness of human impact on environment; willingness to change
occupation; livelihood diversity; social capital; material assets; access to technology and infrastructure, all of which
produced high adaptive capacity (Cinner et al., 2012). Despite this resilience, islands are assumed to be generically
vulnerable to long term future climate change (Parks and Roberts, 2006; Myers, 2002).
There are many ways in which in-situ climate adaptation can be undertaken: reducing socio-economic
vulnerabilities, building adaptive capacity, enhancing disaster risk reduction, or building longer term climate
resilience (e.g. see McGray et al., 2007; Eakin et al., 2009). Figure 29-5 highlights the implications of the various
options. Not all adaptations are equally appropriate in all contexts. Understanding the baseline conditions and
stresses (both climate and other) are important in understanding which climate change adaptation option will
generate the greatest benefits. On small islands where resources are often limited, recognising the starting point for
action is critical to maximising the benefits from adaptation. The following section considers the benefits of
pursuing the various options.
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[INSERT FIGURE 29-5 HERE
Figure 29-5: The impact of alternative climate change adaptation actions or policies.]
29.6.2.1. Building Adaptive Capacity with Traditional Knowledge, Technologies, and Skills on Small Islands
As in previous IPCC Assessments, there is continuing strong support for the incorporation of indigenous knowledge
into adaptation planning. However this is moderated by the recognition that current practices alone may not be
adequate to cope with future climate extremes or trend changes The ability of a small island population to deal with
current climate risks may be positively correlated with the ability to adapt to future climate change, but evidence
confirming this remains limited (such as Lefale, 2010). Consequently, this section focuses on evidence for adaptive
capacity that reduces vulnerability to existing stressors, enables adaptation to current stresses, and supports current
disaster risk management.
Traditional knowledge has proven to be useful in short term weather forecasting (e.g. Lefale, 2010) although
evidence is inconclusive on local capacity to observe long-term climate change (e.g. Hornidge and Scholtes, 2011).
In Solomon Islands, Lauer and Aswani (2010) found mixed ability to detect change in spatial cover of seagrass
meadows. In Jamaica, Gamble et al. (2010) reported a high level of agreement between farmers perception of
increasing drought incidence and statistical analysis of precipitation and vegetation data for the area. In this case
farmers perceptions clearly validated the observational data and vice versa. Despite some claims that vulnerability
reduction in indigenous communities in small islands may be best tackled by combining indigenous and Western
knowledge in a culturally compatible and sustainable manner (Mercer et al., 2007), given the small number of
studies in this area, there is not sufficient evidence to determine the effectiveness and limits to the use of traditional
methods of weather forecasting under climate change on small islands.
Traditional technologies and skills can be effective for current disaster risk management but there is currently a lack
of supporting evidence to suggest that they will be equally appropriate under changing cultural conditions and future
climate changes on islands. Campbell (2009) identified that traditional disaster reduction measures used in Pacific
islands focused around maintaining food security, building community cooperation, and protecting settlements and
inhabitants. Examples of actions to maintain food security include: the production and storage of food surpluses
such as yam and breadfruit buried in leaf-lined pits to ferment; high levels of agricultural diversity to minimise
specific damage to any one crop; and the growth of robust famine crops unused in times of plenty which could be
used in emergencies (Campbell, 2009). Two discrete studies from Solomon Islands highlight the importance of
traditional patterns of social organisation within communities to support food security under social and
environmental change (Reenberg et al., 2008; Mertz et al., 2010). In both studies the strategy of relying on
traditional systems of organization for farming and land use management have been shown to work effectively
largely as there has been little cultural and demographic change. Nonetheless there are physical and cultural limits to
traditional disaster risk management. In relation to the ability to store surplus production on atoll islands, on
Rongelap in the Marshall Islands, surpluses are avoided, or are redistributed to support community bonds (Bridges
and McClatchey, 2009). Further, traditional approaches that Pacific island communities have used for survival for
millennia (such as building elevated settlements and resilient structures; and working collectively), have been
abandoned or forgotten due to processes of globalisation, colonialism and development (Campbell, 2009). Ongoing
processes of rapid urbanization, and loss of language and tradition suggest that traditional approaches may not
always be efficacious in longer-term adaptation.
Traditional construction methods have long been identified across the Pacific as a means of reducing vulnerability to
tropical cyclones and floods in rural areas. In Solomon Islands traditional practices include: elevating concrete floors
on Ontong Java to keep floors dry during heavy rainfall events; building low, aerodynamic houses with sago palm
leaves as roofing material on Tikopia as preparedness for tropical cyclones; and in Bellona local perceptions are
that houses constructed from modern materials and practices are more easily destroyed by tropical cyclones,
implying that traditional construction methods are perceived to be more resilient in the face of extreme weather
(Rasmussen et al., 2009). In parallel, Campbell (2009) documents the characteristics of traditional building styles (in
Fiji, Samoa and Tonga) where relatively steep hipped roofs, well bound connections and joints, and airtight spaces
with few windows or doors offer some degree of wind resistance. Traditional building measures can also reduce
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damages associated with earthquakes as evidenced in Haiti (Audefroy, 2011). By reducing damage caused by
other stresses (such as earthquakes), adaptive capacity is more likely to be maintained. The quality of home
construction is critical to its wind-resistance. If inadequately detailed, home construction will fail irrespective of
method. While some traditional measures could be challenged as potentially risky for example using palm leaves,
rather than metal roofs as a preparation for tropical cyclone impacts the documentation of traditional approaches,
with an evaluation of their effectiveness remains urgently needed. Squatter settlements in urban areas, especially on
steep hillsides in the Caribbean often use poor construction practices frequently driven by poverty and inadequate
building code enforcement (Prevatt et al., 2010).
Traditional systems appear less effective when multiple civilization-nature stresses are introduced. For example in
Reunion and Mayotte, population growth, and consequent rises in land and house prices have led low-income
families to settle closer to hazardous slopes that are prone to landslides and to river-banks which are prone to
flooding (Le Masson and Kelman, 2011). Traditional belief systems can also limit adaptive capacity. Thus, for
example in two Fijian villages, approximately half of survey respondents identified divine will as the cause of
climate change (Lata and Nunn, 2012). These findings reinforce earlier studies in Tuvalu (Mortreux and Barnett,
2009), and more widely across the Pacific (Barnett and Campbell, 2010). The importance of taking into account
local interests and traditional knowledge in adaptation in small islands is emphasised by Kelman and West (2009)
and McNamara and Westoby (2011), yet evidence does not yet exist that reveals the limits to such knowledge, such
as in the context of rapid socio-ecological change, or the impact of belief systems on adaptive capacity.
While there is clear evidence that traditional knowledge networks, technologies and skills can be used effectively to
support adaptation in certain contexts, the limits to these tools are not well understood. To date research in the
Pacific and Caribbean dominates small island climate change work. More detailed studies on small islands in the
central and western Indian Ocean, the Mediterranean and the central and eastern Atlantic would improve
understanding on this topic.
29.6.2.2. Addressing Risks on Small Islands
Relative to other areas, small islands are disproportionately affected by current hydro-meteorological extreme
events, both in terms of the percentage of the population affected, and losses as a percentage of GDP (Anthoff et al.,
2010; Table 29-5). Under climate change the risks of damage and associated losses are expected to continue to rise
(Nicholls and Cazenave, 2010). Yet much of the existing literature on climate risk in small islands does not consider
how to address high future risks, but instead focuses on managing present day risks through risk transfer, risk
spreading or risk avoidance. Risk transfer is largely undertaken through insurance; risk spreading through access to
and use of common property resources, livelihood diversification, or mutual support through networks (see
29.6.2.3); and risk avoidance through structural engineering measures or migration (see 29.6.2.4).
[INSERT TABLE 29-5 HERE
Table 29-5: Top ten countries in the Asia-Pacific region based on absolute and relative physical exposure to storms
and impact on GDP (between 1998 and 2009).]
Risk transfer through insurance markets has had limited uptake in small islands, as insurance markets do not
function as effectively as they do in larger locations, in part due to a small demand for the insurance products (Heger
et al., 2008). In the case of insurance for farmers, researchers found that a lack of demand for insurance products (in
their study countries: Grenada, Jamaica, Fiji and Vanuatu) meant an undersupply of customized food insurance
products, which in turn contributed to a lack of demand for insurance (Angelucci and Conforti, 2010). Alternatives
exist such as index-based schemes that provide payouts based on the crossing of a physical threshold, e.g. when
rainfall drops below a certain level, rather than on drought damage sustained (Linnerooth-Bayer and Mechler, 2009).
The potential for index-based insurance for climate stressors on islands is under-researched and there remains
limited evidence of the long-term effectiveness of index-based or pooled-risk insurance in supporting household
level adaptation. Small island governments also face expensive climate risk insurance. The Caribbean Catastrophe
Risk Insurance Facility (CCRIF) which has been operating since 2007 pools Caribbean-wide country-level risks into
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a central, more diversified risk portfolio offering lower premiums for participating national governments (CCRIF,
2008). The potential for a similar scheme in the Pacific is being explored (ADB, 2009; Cummins and Mahul, 2009).
Risk can be spread socially e.g. through social networks and familial ties (see also 29.6.2.3), or ecologically, e.g. by
changing resource management approach. Social networks can be used to spread risk among households. In Fiji,
after Tropical Cyclone Ami in 2003, households whose homes were not affected by the cyclone increased their
fishing effort to support those whose homes were damaged (Takasaki, 2011) mutual support formed a central
pillar for community-based adaptation. In the case of natural systems, risks can be spread through enhancing
representation of habitat types and replication of species e.g. through the creation of marine protected areas, around
key refuges that protect a diversity of habitat, that cover an adequate proportion of the habitat and that protect
critical areas such as nursery grounds and fish spawning aggregation areas (McLeod et al., 2009). Locally Managed
Marine Areas which involve the local community in the management and protection of their local marine
environment have proven to be effective in increasing biodiversity, and in reducing poverty in areas dependent on
marine resources in several Pacific islands (Techera, 2008; Game et al., 2011). By creating a network of protected
areas supported by local communities the risks associated with some forms of climate change can be spread and
potentially reduced (Mills et al., 2010) although such initiatives may not preserve thermally sensitive corals in the
face of rising SST.
Risk avoidance through engineered structures can reduce risk from some climate-related hazards (medium evidence,
medium agreement). In Jamaica, recommendations to reduce rainfall-driven land surface movements resulting in
landslides include: engineering structures such as soil nailing, gabion baskets (i.e. cages filled with rocks), rip
rapped surfaces (i.e. permanent cover with rock) and retaining walls together with engineered drainage systems
(Miller et al., 2009b). Engineering principles to reduce residential damage from hurricanes have been identified,
tested, and recommended for decades in the Caribbean. However, expected levels of success have often not been
achieved due to inadequate training of construction workers, minimal inspection of new buildings, and lack of
enforcement of building code requirements (Prevatt et al., 2010). Some island states do not even have the technical
or financial capacity to build effective shore protection structures as highlighted by a recent assessment in south
Tarawa, Kiribati (Duvat, 2013). In addition not all engineered structures are seen as effective risk avoidance
mechanisms. In the Azores archipelago, a proliferation of permanent engineered structures along the coastline to
prevent erosion have resulted in a loss of natural shoreline protection against wave erosion (Calado et al., 2011)). In
Barbados it is recognized that seawalls can protect human assets in areas prone to high levels of erosion, however
they can also cause sediment starvation in other areas, interfere with natural processes of habitat migration and cause
coastal squeeze which may render them less desirable for long term adaptation (Mycoo and Chadwick, 2012) (see
also 5.4.2.1). To reduce erosion risk an approach with less detrimental downstream effects that also supports tourism
is beach nourishment. This is increasingly being recommended, for example in the Caribbean (Mycoo and
Chadwick, 2012), the Mediterranean (Anagnostou et al., 2011), and western Indian Ocean (Duvat, 2009). Beach
nourishment however is not without its challenges, as requirements such as site-specific oceanographic and wave
climate data, adequate sand resources and critical engineering design skills may not be readily available in some
small islands.
29.6.2.3. Working Collectively to Address Climate Impacts on Small Islands
More attention is being focused on the relevance and application of community-based adaptation (CBA) principles
to island communities, to facilitate adaptation planning and implementation (Warrick, 2009; Kelman et al., 2011)
and to tackle rural poverty in resource dependent communities (Techera, 2008). CBA research is focusing on
empowerment that helps people to help themselves e.g. through marine catch monitoring (Breckwoldt and Seidel,
2012), while addressing local priorities and building on local knowledge and capacity. This approach to adaptation
is being promoted as an appropriate strategy for small islands, since it is something done with rather than to
communities (Warrick, 2009). Nonetheless externally driven programs to encourage community-level action have
produced some evidence of effective adaptation. Both Limalevu et al. (2010) and Dumaru (2010) describe the
outcomes of externally-led pilot CBA projects (addressing water security and coastal management) implemented in
villages across Fiji, notably: more effective management of local water resources through capacity building;
enhanced knowledge of climate change; and, the establishment of mechanisms to facilitate greater access to
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technical and financial resources from outside the community. More long term monitoring and evaluation of the
effectiveness of community level action is needed.
Collaboration between stakeholders can lessen the occurrence of simple mistakes that can reduce the effectiveness
of adaptation actions (medium evidence, medium agreement). Evidence from the Eastern Caribbean suggests that
adaptations taken by individual households to reduce landslide risk building simple retaining walls can be
ineffective compared to community level responses (Anderson et al., 2011). Landslide risk can be significantly
reduced through better hillside drainage. In the Eastern Caribbean, community groups, with input from engineers,
have constructed these networks of drains to capture surface runoff, household roof-water and grey water. Case
studies from Fiji and Samoa in which multi-stakeholder and multi-sector participatory approaches were used to help
enhance resilience of local residents to the adverse impacts of disasters and climate change (Gero et al., 2011)
further support this view. In the case of community based disaster risk reduction (CBDRR), Pelling (2011) notes that
buy-in from local and municipal governments is needed, as well as strong pre-existing relationships founded on
routine daily activities, to make CBDRR effective. Research from both Solomon Islands and the Cayman Islands
reinforce the conclusion that drivers of community resilience to hazard maps closely onto factors driving successful
governance of the commons, that is: community cohesion; effective leadership; and, community buy-in to collective
action (Schwarz et al., 2011; Tompkins et al., 2008). Where community organisations are operating in isolation, or
where there is limited coordination and collaboration community vulnerability is expected to increase (Ferdinand et
al., 2012). Strong local networks, and trusting relationships between communities and government appear to be key
elements in adaptation, in terms of maintaining sustainable agriculture and in disaster risk management (medium
evidence, high agreement).
All of these studies reinforce the earlier work of Barnett (2001), providing empirical evidence that supporting
community-led approaches to disaster risk reduction and hazard management may contribute to greater community
engagement with anticipatory adaptation. However, it is not yet possible to identify the extent to which climate
resilience is either a coincidental benefit of island lifestyle and culture, or a purposeful approach, such as the
community benefits gained from reciprocity among kinship groups (Campbell, 2009).
29.6.2.4. Addressing Long-Term Climate Impacts and Migration on Small Islands
Sea-level rise poses one of the most widely recognized climate change threats to low-lying coastal areas on islands
(29.3.1). However long term climate impacts depend on the type of island (see Figure 29-1) and the adaptation
strategy adopted. Small island states have 16% of their land area in low elevation coastal areas (<10m) as opposed to
a global average of 2%, and the largest proportion of low elevation coastal urban land area: 13% (along with
Australia and New Zealand), in contrast to the global average of 8% (McGranahan et al., 2007). Statistics like these
underpin the widely held view about small islands being overwhelmed by rising seas associated with sea-level rise
(Loughry and McAdam, 2008; Yamamoto and Esteban, 2010; Gordon-Clark, 2012; Berringer, 2012; Dema, 2012;
Lazrus, 2012; Laczko and Aghazarm, 2009). Yet there remains limited evidence as to which regions (Caribbean,
Pacific, Indian Ocean, west African islands) will experience the largest sea-level rise (Willis and Church, 2012) and
which islands will experience the worst climate impacts. Nicholls et al. (2011) have modeled impacts of 4°C
warming, producing a 0.5 to 2.0m sea-level rise, to assess the impacts on land loss and migration. With no
adaptation occurring, they estimate that this could produce displacement of between 1.2 and 2.2 million people from
the Caribbean, Indian Ocean and Pacific Ocean. More research is needed to produce robust agreement on the impact
of sea-level rise on small islands, and on the range of adaptation strategies that could be appropriate for different
island types under those scenarios. Research into the possible un-inhabitability of islands has to be undertaken
sensitively to avoid short-term risks (i.e. to avoid depopulation and ultimately island abandonment) associated with a
loss of confidence in an islands future (McNamara and Gibson, 2009; McLeman, 2011).
Due to the high costs of adapting on islands it has been suggested that there will be a need for migration (Nicholls et
al., 2011; Gemenne, 2011; Biermann and Boas, 2010; Voccia 2012). Relocation and displacement are frequently
cited as outcomes of sea-level rise, salinisation and land loss on islands (Byravan and Rajan, 2006; Kolmannskog
and Trebbi, 2010; see also 29.3.3.3). Climate stress is occurring at the same time as the growth in rural to urban
migration. The latter is leading to squatter settlements that strain urban infrastructure notably: sewerage, waste
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management, transport and electricity (Jones, 2005; Connell and Lea, 2002). Urban squatters on islands often live in
highly exposed locations, lacking basic amenities, leaving them highly vulnerable to climate risks (Baker, 2012).
However, a lack of research in this area makes it difficult to draw clear conclusions on the impact of climate change
on the growing number of urban migrants in islands.
Recent examples of environmental stress driven relocation and displacement provide contemporary analogues of
climate-induced migration. Evidence of post-natural disaster migration has been documented in the Caribbean in
relation to hurricanes (McLeman and Hunter, 2010), and in the Carteret Islands, Papua New Guinea where during
an exceptionally high inundation event in 2008 (see 29.5.1.1) islanders sought refuge on neighbouring Bougainville
island (Jarvis, 2010). Drawing any strong conclusions from this literature is challenging as there is little
understanding of how to measure the effect of the environmental signal in migration patterns (Afifi et al., 2013;
Krishnamurthy, 2012). While the example of the Carteret Islands cannot be described as evidence of adaptation to
climate change, it suggests that under some extreme scenarios island communities may need to consider relocating
in the future (Gemenne, 2011). In reality, financial and legal barriers are expected to inhibit significant levels of
international environmentally induced migration in the Pacific (Barnett and Chamberlain, 2010).
29.6.3. Barriers and Limits to Adaptation in Small Island Settings
Since publication of the IPCC SAR in 1996, significant barriers to climate change adaptation strategies in island
settings have been discussed in considerable detail. Barriers include inadequate access to financial, technological
and human resources, issues related to cultural and social acceptability of measures, constraints imposed by the
existing political and legal framework, the emphasis on island development as opposed to sustainability, and a
tendency to focus on addressing short term climate variability rather than long term climate change, and community
preferences for hard adaptation measures such as seawalls instead of soft measures such as beach nourishment
(Sovacool, 2012). Heger et al. (2008) recognised that more diversified economies have more robust responses to
climate stress, yet most small islands lack economies of scale in production, thus specialising in niche markets and
developing monocultures (e.g. sugar or bananas). Non-sovereign island states face additional exogenous barriers to
adaptation. For example, islands like Réunion and Mayotte benefit from the provision of social services somewhat
similar to what obtains in the Metropole, but not the level of enforcement of building codes and land use planning as
in France (Le Masson and Kelman, 2011). Owing to their nature and complexity, these constraints will not be easily
eliminated in the short term and will require on-going attention if their impact is to be minimized over time.
Exogenous factors, such as the comparatively few assessments of social vulnerability to climate change, adaptation
potential or resilience for island communities (Barnett, 2010) limit current understanding. In part this is due to the
particularities of islands both their heterogeneity and their difference from mainland locations as well as the
limitations of climate models in delivering robust science for small islands. It remains the case that thirteen years
after Nurse et al. (2001) noted that downscaled global climate models do not provide a complete or necessarily
accurate picture of climate vulnerabilities on islands, there is still little climate impacts research that reflects local
concerns and contexts (Barnett et al., 2008).
While lack of access to adequate financial, technological and human resources is often cited as the most critical
constraint, experience has shown that endogenous factors such as culture, ethics, knowledge and attitudes to risk are
important in constraining adaptation. Translating the word climate into Marshallese implies cosmos, nature and
culture as well as weather and climate (Rudiak-Gould, 2012). Such cultural misunderstandings can create both
barriers to action and novel ways of engaging with climate change. The lack of local support (due to encroachment
on traditional lands) for the development of new infiltration galleries to augment freshwater supply on Tarawa atoll,
Kiribati, highlights the importance of social acceptability (Moglia et al., 2008a, 2008b). Such considerations have
led to the conclusion that there is still much to be learned about the drivers of past adaptation and how
mainstreaming into national programs and policies, widely acclaimed to be a virtually indispensable strategy, can
practically be achieved (Mercer et al., 2007; Adger et al., 2009; Mertz et al., 2009).
Notwithstanding the extensive and ever-growing body of literature on the subject, there is still a relatively low level
of awareness and understanding at the community level on many islands about the nature of the threat posed by
climate change (Nunn, 2009). Even where the threat has been identified, it is often not considered an urgent issue, or
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a local priority, as exemplified in Malta (Akerlof et al., 2010) and Funafuti, Tuvalu (Mortreux and Barnett, 2009).
Lack of awareness, knowledge and understanding can function as an effective barrier to the implementation and
ultimate success of adaptation programs. This is borne out in both Fiji and Kiribati where researchers found that
spiritual beliefs, traditional governance mechanisms, and a short term approach to planning were barriers to
community engagement and understanding of climate change (Kuruppu, 2009; Lata and Nunn, 2012). Although
widely acknowledged to be critical in small islands, few initiatives pay little more than perfunctory attention to the
importance of awareness, knowledge and understanding in climate change adaptation planning. Hence, the renewed
call for adaptation initiatives to include and focus directly on these elements on an ongoing basis (e.g., Crump, 2008;
Kelman and West, 2009; Kelman, 2010; Kuruppu and Liverman, 2011; Gero et al., 2011) is timely, if these barriers
are to be eventually removed.
29.6.4. Mainstreaming and Integrating Climate Change into Development Plans and Policies
There is a growing body of literature that discusses the benefits and possibilities of mainstreaming or integrating
climate change policies in development plans. Various mechanisms through which development agencies as well as
donor and recipient countries can seek to capitalize on the opportunities to mainstream are beginning to emerge (see
for example Klein et al., 2007; Mertz et al., 2009). Agrawala and van Aalst (2008) provide examples from Fiji and
elsewhere, of where synergies (and trade-offs) can be found in integrating adaptation to climate change into
development cooperation activities, notably in the areas of: disaster risk reduction, community-based approaches to
development, and building adaptive capacity. Boyd et al. (2009) support the need for more rapid integration of
adaptation into development planning, to ensure that adaptation is not side-lined, or treated separately from sectoral
policies. Although there are synergies and benefits to be derived from the integration of climate change and
development policies, care is needed to avoid institutional overlaps, and differences in language and approach
which can give rise to conflict (Schipper and Pelling, 2006). Overall, there appears to be an emerging consensus
around the views expressed by Swart and Raes (2007) that climate change and development strategies should be
considered as complementary, and that some elements such as land and water management and urban, peri-urban
and rural planning provide important adaptation, development and mitigation opportunities. While the potential to
deliver such an integrated approach may be reasonably strong in urban centres on islands, there appears to be limited
capacity to mainstream climate change adaptation into local decision making in out-lying islands or peripheral areas
(Nunn et al., 2013).
29.7. Adaptation and Mitigation Interactions
Greenhouse gas emissions from most small islands are negligible in relation to global emissions, yet small islands
will most probably be highly impacted by climate change (Srinivasan, 2010). However, many small island
governments and communities have chosen to attempt to reduce their greenhouse gas emissions because of the cost
andthe potential co-benefits and synergies. Malta and Cyprus are obliged to do so in line with EU climate and
energy policies. This section considers some of the inter-linkages between adaptation and mitigation on small
islands and the potential synergies, conflicts, trade-offs and risks. Unfortunately there is relatively little research on
the emissions reduction potential of small islands, and far less on the inter-linkages between climate change
adaptation and emissions reduction in small islands. Therefore in this section a number of assumptions are made
about how and where adaptation and mitigation actions interact.
29.7.1. Assumptions/Uncertainties Associated with Adaptation and Mitigation Responses
Small islands are not homogeneous. Rather they have diverse geo-physical characteristics and economic structures
(see Table 29-2, Figure 29-1). Following Nunn (2009) the combination of island geography and economic types
informs the extent to which adaptation and mitigation actions might interact. The geography and location of islands
affect their sensitivity to hydro-meteorological and related hazards such as cyclones, floods, droughts, invasive alien
species, vector borne disease, and landslides. On the other hand the capacity of island residents to cope is often
related to income levels, resources endowment, technology and knowledge (see 29.6.2).
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The potential for mitigation and emissions reductions in islands depends to a large extent on their size and stage of
economic development. In the small and less developed islands key mitigation sectors including energy, transport,
industry, built environment, agriculture, forestry, or waste management sectors are generally relatively small (Metz
et al., 2007; Swart and Raes, 2007). Hence opportunities for emissions reductions are usually quite limited and are
mostly associated with electricity generation and utilization of vehicles. More mitigation opportunities should exist
in more economically advanced and larger islands that rely on forms of production that utilize fossil fuels, including
manufacturing, and where vehicle usage is extensive and electricity driven home appliances, such as air conditioners
and water heaters, are extensively used.
In the absence of significant mitigation efforts at the global scale, adaptation interventions could become very costly
and difficult to implement, once certain thresholds of change are reached (Nelson, 2011; Birkmann, 2011). Nicholls
et al. (2011) make a similar observation with respect to coastal protection as a response to sea-level rise. They
suggest that if global mean temperatures increase by around 4 0C (which may lead to sea-level rise between 0.5 m
and 2 m) the likelihood of successful coastal protection in some locations such as low-lying small islands, will be
low. Consequently, it is argued that the relocation of communities would be a likely outcome in such circumstances
(Nicholls et al., 2011).
29.7.2. Potential Synergies and Conflicts
Metz et al. (2007) suggest that adaptation and mitigation interactions occur in one of four main ways: adaptations
that result in greenhouse emissions reduction; mitigation options that facilitate adaptation; policy decisions that
couple adaptation and mitigation effects; and, trade-offs and synergies between adaptation and mitigation. Each of
these opportunities is considered using three examples: coastal forestry, energy supply, and tourism.
Small islands have relatively large coastal zones (in comparison to land area) and most development (as well as
potential mitigation and adaptation activities) are located in the coastal zone. Coastal ecosystems (coral reefs, sea
grasses and mangroves) play an important role in protecting coastal communities from wave erosion, tropical
cyclones, storm surges, and even moderate tsunami waves (Cochard et al., 2008). Whilst coastal forests including
both endemic and exotic species especially mangroves are seen as effective adaptation options ( bioshields Feagin
et al., 2010) in the coastal zones, they also play an important role in mitigation as carbon sinks (van der Werf et al.,
2009). Thus, the management and conservation of mangrove forests has the potential to generate synergies between
climate change adaptation and mitigation. However, despite this knowledge population, development and
agricultural pressures have constrained the expansion of island forest carbon stocks (Fox et al., 2010) while Gilman
et al. (2008) note that such pressures can also reduce the buffering capacity of coastal vegetation systems.
Renewable energy resources on small islands have only recently been considered within the context of long-term
energy security (Praene et al., 2012; Chen et al., 2007). Stuart (2006) speculates that the lack of uptake of renewable
technologies to date might be due to historical commitments to conventional fossil fuel-based infrastructure, and a
lack of resources to undertake research and development of alternatives. Those islands that have introduced
renewable energy technologies have often done so with support from international development agencies (Dornan,
2011). Despite this, there remain significant barriers to the wider institutionalization of renewable technologies in
small islands. Research in Europe and the United States has shown the mitigation and cost savings benefits of
Energy Service Companies (ESCOs): companies that enter into medium-to-long term performance-based contracts
with energy users, invest in energy efficiency measures in buildings and firms, and profit from the ensuing energy
savings measures for the premises (see for example Steinberger et al., 2009). Potential benefits exist in creating the
opportunity for ESCOs to operate in small islands. Preliminary evidence from Fiji suggests that if the incentive
mechanisms can be resolved, and information asymmetries between service providers and users can be aligned,
ESCOs could provide an opportunity to expand renewable technologies (Dornan, 2009). IPCC (2011) presents
examples of opportunities for renewable energy, including wind energy sources, as deployed in the Canary Islands.
The transition towards renewable energy sources away from fossil fuel dependence has been partly driven by
economic motives, notably to avoid oil price volatility and its impact. The development of hydro-power (in Fiji for
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example) necessitates protection and management of the water catchment zones, and thus could lead to improved
management of the water resources a critical adaptation consideration for areas expected to experience a decrease
in average rainfall as a result of climate change. Whilst the cost effectiveness of renewable technologies is critical,
placing it within the context of water adaptation could enhance project viability (Dornan, 2009). Cost-benefit
analyses have shown that in southeast Mediterranean islands photovoltaic generation and storage systems may be
more cost-effective than existing thermal power stations (Kaldellis, 2008; Kaldellis et al., 2009).
Energy prices in small islands are among the highest anywhere in the world, mainly due to their dependence on
imported fossil fuel, and limited ability to reap the benefits of economies of scale including bulk buying. Recent
studies show that the energy sectors in small islands may be transformed into sustainable growth entities mainly
through the judicious exploitation of renewable energy sources, combined with the implementation of energy
efficiency measures (van Alphen et al., 2008; Banuri, 2009; Mohanty, 2012; Rogers et al., 2012). Realising the
potential for such transformation, the countries comprising the Alliance of Small Island States (AOSIS) launched
SIDS Dock, which is intended to function as a docking station to connect the energy sector in SIDS with the
international finance, technology and carbon markets with the objective of pooling and optimizing energy efficiency
goods and services for the benefit of the group. This initiative seeks to decrease energy dependence in SIDS, while
generating financial resources to support low carbon growth and adaptation interventions.
Many small islands rely heavily on the foreign exchange from tourism to expand and develop their economies,
including the costs of mitigation and adaptation. Tourism, particularly in small islands, often relies on coastal and
terrestrial ecosystems to provide visitor attractions and accommodation space. Recognising the relationship between
ecosystem services and tourism in Jamaica, Thomas-Hope and Jardine-Comrie (2007) suggest that sustainable
tourism planning should include activities undertaken by the industry, that is tertiary treatment of waste, and re-use
of water, as well as composting organic material and investing in renewable energy. Gössling and Schumacher
(2010) and others who have examined the linkages between greenhouse gas emissions and sustainable tourism argue
that the tourism sector (operators and tourists) should pay to promote sustainable tourism, especially where they
benefit directly from environmental services sustained by these investments.
29.8. Facilitating Adaptation and Avoiding Maladaptation
While there is a clear consensus that adaptation to the risks posed by global climate change is necessary and urgent
in small islands, the implementation of specific strategies and options is a complex process that requires critical
evaluation of multiple factors, if expected outcomes are to be achieved (Kelman and West, 2009; Barnett and
O Neill, 2012). These considerations may include, inter alia, prior experience with similar or related threats,
efficacy of the strategies or options and their co-benefits, costs (monetary and non-monetary), availability of
alternatives and social acceptability. In addition, previous work (e.g. Adger et al., 2005) has emphasised the
relevance of scale as a critical factor when assessing the efficacy and value of adaptation strategies, as the extent to
which an option is perceived to be a success, failure or maladaptive may be conditioned by whether it is being
assessed as a response to climate variability (shorter-term) or climate change (longer-term).
As in other regions, adaptation in islands is locally delivered and context specific (Tompkins et al., 2010). Yet,
sectors and communities on small islands are often so intricately linked that there are many potential pathways that
may lead to maladaptation, be it via increased greenhouse gas emissions, foreclosure of future options, or
burdensome opportunity costs on local communities. There is also a concern that some types of interventions may
actually be maladaptive. For example, Barnett and O Neill (2012) suggest that strategies such as resettlement and
migration should be regarded as options of last resort on islands, as they may actually discourage viable adaptation
initiatives, by fostering over-dependence on external support. They further argue that a priori acceptance of
adaptation as an efficacious option for places like the Pacific islands, may also act as a disincentive for reducing
greenhouse gas emissions (Barnett and O Neill, 2012).
Notwithstanding the observations of Barnett and O Neill (2012), there is a concern that early foreclosure of this
option might well prove maladaptive, if location-specific circumstances show such action to be efficacious in the
longer-term. For example, Bunce et al. (2009) have shown that as an adaptive response to poverty, young fishers
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from Rodrigues Island periodically resort to temporary migration to the main capital island, Mauritius, where greater
employment prospects exist. The case study of the residents of Nauru who contemplated resettlement in Australia
after the collapse of phosphate mining (their only revenue source) in the1950s, provides helpful insight about the
complex social, economic and cultural challenges associated with environmentally triggered migration (Tabucanon
and Opeskin, 2011). Negotiations with the Government of Australia collapsed before a mutually acceptable
agreement was reached, and the Nauruans opted to abandon the proposal to relocate (Tabucanon and Opeskin,
2011). Overall however, it is suggested that states contemplating long term, off-island migration may wish to
consider early proactive planning, as resettlement of entire communities might prove to be socially, culturally and
economically disruptive (Campbell, 2010; McMichael et al., 2012; refer also to 29.3.3.3). A related challenge facing
small islands is the need to find the middle ground between resettlement and objective assessment of other
appropriate adaptation choices.
Similarly, while insurance is being promoted as an element of the overall climate change response strategy in some
island regions, e.g. the Caribbean, concerns have been expressed about possible linkages to maladaptation. The
potential consequences include the imposition of exorbitant premiums that are beyond the capacity of resource-
scarce governments as the perception of climate change risks increase, discriminatory coverage of sectors that may
not align with local priorities, and tacit encouragement for the state, individuals and the private sector to engage in
behavior that is not risk-averse, e.g. development in hazard-prone areas (Herweijer et al., 2009; Linnerooth-Bayer et
al., 2011; van Nostrand and Nevius, 2011; Thomas and Leichenko, 2011). Likewise, although the exploitation of
renewable energy is vital to the sustainable development of small islands, more attention needs to be paid to the
development of energy storage technologies, if rapid transition from conventional fuels is to be achieved in an
efficient manner. This is especially important in the case of intermittent energy sources (e.g. solar and wind), as the
cost of current storage technologies can frustrate achievement of full conversion to renewable energy. Thus to avoid
the possibility of maladaptation in the sector, countries may wish to consider engaging in comprehensive planning,
including considerations relating to energy storage (Krajaèiæ et al., 2010; Bazilian et al., 2011).
Recent studies have demonstrated that opportunities exist in island environments for avoiding maladaptation.
Studies have shown for example that decisions about adaptation choices and their implementation are best facilitated
where there is constructive engagement with the communities at risk, in a manner that fosters transparency and trust
(López-Marrero, 2010; van Aalst et al., 2008). Further, some analysts argue that adaptation choices are often
subjective in nature and suggest that participatory stakeholder involvement can yield valuable information about the
priorities and expectations that communities attach to the sector for which adaptation is being sought. The point is
underscored by Moreno and Becken (2009) whose study of the tourism sector on the Mamanuca islands (Fiji),
which clearly demonstrates that approaches which explicitly integrate stakeholders into each step of the process
from vulnerability assessment right through to consideration of alternatives measures can provide a sound basis for
assisting destinations with the implementation of appropriate adaptation interventions. This view is supported by
Dulal et al. (2009), who argue that the most vulnerable groups in the Caribbean - the poor, elderly, indigenous
communities and rural children will be at greater risk of being marginalized, if adaptation is not informed by
equitable and participatory frameworks.
Other studies reveal that new paradigms whose adoption can reduce the risk of maladaptation in island
environments, are emerging across various sectors. In the area of natural resource management, Hansen et al. (2010)
suggest that the use of protected areas for climate refugia, reduction of non-climate stressors on ecosystems,
adoption of adaptive management approaches combined with reduction of greenhouse gas emissions wherever
possible, may prove to be more effective response strategies than traditional conservation approaches. Other
strategic approaches, including the implementation of multi-sectoral and cross-sectoral measures, also facilitate
adaptation in a more equitable, integrated and sustainable manner. Similarly, no-regret measures such as
wastewater recycling, trickle irrigation, conversion to non-fossil fuel based energy and transportation which offer
collateral benefits with or without the threat of climate change, and low-regret strategies, which may only increase
existing operational costs marginally, are becoming increasingly attractive options to island governments (Gravelle
and Mimura, 2008; Heltberg et al., 2009; Howard et al., 2010). Together, these constitute valid risk management
approaches, as they are designed to assist communities in making prudent, but necessary decisions in the face of an
uncertain future.
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Some authors suggest that caution is needed to ensure that donors are not driving the adaptation and mitigation
agenda in small islands, as there is a risk that donor-driven adaptation or mitigation may not always address the
salient challenges on small islands, and may lead to inadequate adaptation or a waste of scarce resources (Barnett,
2010; Nunn, 2009). Others argue that donor-led initiatives may unintentionally cause enhanced vulnerability by
supporting adaptation strategies that are externally derived, rather than optimizing the benefits of local practices that
have proven to be efficacious through time (Reenberg et al., 2008; Kelman and West, 2009; Campbell and
Beckford, 2009).
29.9. Research and Data Gaps
Several advances have taken place in our understanding of the observed and potential effects of climate change on
small islands since the AR4. These cover a range of themes including: dynamic downscaling of scenarios
appropriate for small islands; impacts of trans-boundary processes generated well beyond the borders of an
individual nation or island; barriers to adaptation in small islands and how they may be overcome; the relationships
between climate change adaptation and disaster risk reduction; and, the relationships between climate change
adaptation, maladaptation and sustainable development.
It is also evident that much further work is required on these themes in small island situations, especially
comparative research. Important information and data gaps and many uncertainties still exist on impacts,
vulnerability and adaptation in small islands. These include:
Lack of climate change and socio-economic scenarios and data at the required scale for small islands.
Although some advances have been made (ABoM and CSIRO, 2011a, 2011b; Taylor et al., 2007), much of
the work in the Caribbean, Pacific, Indian Ocean and Mediterranean islands, is focused at the regional scale
rather than being country specific. Since most socio-economic decisions are taken at the local level, there is
need for a more extensive database of simulations of future small island climates and socio-economic
conditions at smaller spatial scales.
Difficulties in detecting and attributing past impacts on small islands to climate change processes.
Further investigation of the observed impacts of weather, climate and ocean events that may be related to
climate change is required to clarify the relative role of climate change and non-climate change drivers.
Uncertainty in the projections is not a sufficiently valid reason to postpone adaptation planning in
small islands. In several small islands adaptation is being progressed without a full understanding of past
or potential impacts and vulnerability. Whilst assessment of future impacts is hampered because of
uncertainty in climate projections at the local island level, alternative scenarios based on a general
understanding of broad trends could be used in vulnerability and sensitivity studies to guide adaptation
strategies.
Need for a range of climate change-related projections beyond temperature and sea-level. Generally
climate-model projections of temperature and sea-level have been satisfactory, but there are strong
requirements for projections for other variables that are of critical importance to small islands. These
include rainfall and drought, wind direction and strength, tropical storms and wave climate, and recognition
that trans-boundary processes are also significant in a small island context. While some such work has been
undertaken for some parts of the Pacific (ABoM and CSIRO, 2011a, 2011b), similar work still needs to be
carried out in other small island regions. In addition, the reliability of existing projections for some of the
other parameters needs to be improved and the data should be in suitable formats for use in risk
assessments.
Need to acknowledge the heterogeneity and complexity of small island states and territories. Although
small islands have several characteristics in common, neither the variety nor complexity of small islands is
sufficiently reflected in the literature. Thus, transferring data and practices from a continental situation, or
from one small island state to another, needs to be done with care and in a manner that takes full
cognizance of such heterogeneity and complexity.
Within country/territory differences need to be better understood. Many of the environmental and
human impacts reported in the literature on islands have been attributed to the whole country, when in fact
they refer only to the major centre or town or region. There is need for more work on rural areas, outer
islands and secondary communities. Several examples of such research have been cited in this chapter. Also
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it should be noted that some small island states are single islands and others highly fragmented multiple
islands.
Lack of investment and attention to climate and environmental monitoring frameworks in small
islands. A fundamental gap in the ability to improve empirical understanding of present and future climate
change impacts is the lack of climate and environmental monitoring frameworks that in turn hampers the
level of confidence with which adaptation responses can be designed and implemented.
Economic and social costs of climate change impacts and adaptation options are rarely known. In
small island states and territories the costs of past weather, climate and ocean events is poorly known and
further research is required to identify such costs, and to determine the economic and societal costs of
climate change impacts and the costs of adaptation options to minimize those impacts.
The foregoing list is a sample of the gaps, needs and research agenda that urgently need to be filled for small islands.
While some countries have begun to fill these gaps, this work needs to be replicated and expanded across all island
regions to improve the database available for ongoing climate change assessments. Such information would raise the
level of confidence in the adaptation planning and implementation process in small islands.
Frequently Asked Questions
FAQ 29.1: Why is it difficult to detect and attribute changes on small islands to climate change?
[to be inserted in Section 29.3.1.1]
In the last two or three decades many small islands have undergone substantial changes in human settlement patterns
and in socio-economic and environmental conditions. Those changes may have masked any clear evidence of the
effects of climate change. For example, on many small islands coastal erosion has been widespread and has
adversely affected important tourist facilities, settlements, utilities and infrastructure. But specific case studies from
islands in the Pacific, Indian and Atlantic oceans and the Caribbean have shown that human impacts play an
important role in this erosion, as do episodic extreme events that have long been part of the natural cycle of events
affecting small islands. So while coastal erosion is consistent with models of sea-level rise resulting from climate
change, determining just how much of this erosion might have been caused by climate change impacts is difficult.
Given the range of natural processes and human activities that could impact the coasts of small islands in the future,
without more and better empirical monitoring the role of climate change-related processes on small islands may
continue to be difficult to identify and quantify.
FAQ 29.2: Why is the cost of adaptation to climate change so high in small islands?
[to be inserted after Section 29.3.3.4]
Adaptation to climate change that involves infrastructural works generally require large up-front overhead costs,
which in the case of small islands cannot be easily downscaled in proportion to the size of the population or
territory. This is a major socio-economic reality that confronts many small islands, notwithstanding the benefits that
could accrue to island communities through adaptation. Referred to as indivisibility in economics, the problem can
be illustrated by the cost of shore protection works aimed at reducing the impact of sea-level rise. The unit cost of
shoreline protection per capita in small islands is substantially higher than the unit cost for a similar structure in a
larger territory with a larger population. This scale-reality applies throughout much of a small island economy
including the indivisibility of public utilities, services and all forms of development. Moreover, the relative impact
of an extreme event such as a tropical cyclone that can affect most of a small island s territory has a disproportionate
impact on that state s GDP, compared to a larger country where an individual event generally affects a small
proportion of its total territory and its GDP. The result is relatively higher adaptation and disaster risk reduction
costs per capita in countries with small populations and areas, especially those that are also geographically isolated,
have a poor resource base and high transport costs
FAQ 29.3: Is it appropriate to transfer adaptation and mitigation strategies between and within small island
countries and regions? [to be inserted after Section 29.7.2]
While lessons learned from adaptation and mitigation experiences in one island or island region may offer some
guidance, caution must be exercised to ensure that the transfer of such experiences is appropriate to local
biophysical, social, economic, political, and cultural circumstances. If this approach is not purposefully incorporated
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into the implementation process, it is possible that maladaptation and inappropriate mitigation may result. It is
therefore necessary to carefully assess the risk profile of each individual island so as to ensure that any investments
in adaptation and mitigation are context specific. The varying risk profiles between individual small islands and
small island regions have not always been adequately acknowledged in the past.
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Table 29-1: Climate change projections for the medium (500-700 ppm CO2-e) RCP4.5 scenario for the main Small
Islands regions. The table shows the 25th, 50th (median) and 75th percentiles for surface temperature and
precipitation based on averages from 42 CMIP5 global models (adapted from WGI AR5 Table 14.1). Mean net
regional sea level change is evaluated from 21 CMIP5 models and includes regional non-scenario components
(adapted from WGI AR5 Figure 13-20).
RCP4.5 Annual Projected Change for 2081-2100 compared to 1986-2005
Small Island Temperature (OC) Precipitation (%) Sea Level (m)
Region 25% 50% 75% 25% 50% 75% Range
Caribbean 1.2 1.4 1.9 -10 -5 -1 0.5 0.6
Mediterranean 2.0 2.3 2.7 -10 -6 -3 0.4 - 0.5
Northern 1.2 1.4 1.7 0 1 4 0.5 0.6
tropical Pacific
Southern 1.1 1.2 1.5 0 2 4 0.5 0.6
tropical Pacific
North 1.3 1.5 2.0 5 9 20 0.4 0.5
Indian Ocean
West 1.2 1.4 1.8 0 2 5 0.5 0.6
Indian Ocean
Table 29-2: Summary of projected percentage changes in tropical Pacific tuna catches by 2036 and 2100 relative to
1980-2000 and the estimated resulting percentage change to government revenue (after Bell et al., 2011).
Year and SRES Scenario
Tuna Fishery 2035 2100
B1/A2 B1 A2
Western fishery + 11% -0.2% -21%
Skipjack tuna
Eastern fishery +37% +43% +27%
Western fishery -2% -12% -24%
Bigeye tuna
Eastern fishery +3% -4% -18%
Total
Skipjack tuna +19% +12% -7%
Total
Bigeye tuna +0.3% -9% -27%
Total Federated States of
0.8 to 1.7% -0.9 to -1.9%
Change to Micronesia
Government Solomon Islands 0.01 to 0.16% -0.03 to 0.77%
Revenue Kiribati +11 to 18.4% +7.2 to 12.0%
(%) Tuvalu +3.7 to 9.2% +2.5 to 6.2%
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Table 29-3: Type of island in the Pacific region and implications for hydro-meteorological hazards (after Campbell,
2009).
Island type and size Island elevation, slope, Implications for hazard
rainfall
Continental
- Large - High elevations River flooding more likely to be a problem
- High biodiversity - River flood plains than in other island types. In Papua New
- Well developed soils - Orographic rainfall Guinea high elevations expose areas to frost
(extreme during El Nino).
Volcanic High Islands
- Relatively small land area - Steep slopes Because of size few areas are not exposed to
- Barrier reefs - Less well developed river tropical cyclones. Streams and rivers subject
- Different stages of erosion systems to flash flooding. Barrier reefs may ameliorate
- Orographic rainfall storm surge.
Atolls
- Very small land area - Very low elevations Exposed to storm surge, king tides and high
- Small islets surround a lagoon - Convectional rainfall waves. Narrow resource base. Exposed to
- Larger islets on windward side - No surface (fresh) water fresh water shortages and drought. Water
- Shore platform on windward side - Ghyben-Herzberg problems may lead to health hazards.
- No or minimal soil (freshwater) lens
Raised Limestone Islands
- Concave inner basin - Steep outer slopes Depending on height may be exposed to storm
- Narrow coastal plains - Sharp karst topography surge. Exposed to fresh water shortages and
- No or minimal soil - No surface water drought. Water problems may lead to health
hazards.
Table 29-4: Selected key risks and potential for adaptation for small islands from the present-day to the long-term.
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Table 29-5: Top ten countries in the Asia-Pacific region based on absolute and relative physical exposure to storms
and impact on GDP (between 1998 and 2009) (after ESCAP and UNISDR, 2010).
Rank Absolute exposure Relative exposure (% of the Absolute GDP loss Loss (as a % of GDP)
(millions affected) population affected) ($billions)
1 Japan (30.9) North Mariana Islands Japan (1,226.7) North Mariana Islands
(58.2) (59.4)
2 Philippines (12.1) Niue (25.4) Rep. of Korea (35.6) Vanuatu (27.1)
3 China (11.1) Japan (24.2) China (28.5) Niue (24.9)
4 India (10.7) Philippines (23.6) Philippines (24.3) Fiji (24.1)
5 Bangladesh (7.5) Fiji (23.1) Hong Kong (13.3) Japan (23.9)
6 Rep. of Korea (2.4) Samoa (21.4) India (8.0) Philippines (23.9)
7 Myanmar (1.2) New Caledonia (20.7) Bangladesh (3.9) New Caledonia (22.4)
8 Viet Nam (0.8) Vanuatu (18.3) North Mariana Islands Samoa (19.2)
(1.5)
9 Hong Kong (0.4) Tonga (18.1) Australia (0.8) Tonga (17.4)
10 Pakistan (0.3) Cook Islands (10.5) New Caledonia (0.7) Bangladesh (5.9)
Note: Small islands are highlighted in bold
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Figure 29-1: Representative tropical island typologies. From top-left: a young, active volcanic island (with
altitudinal zones) and limited living perimeter reefs (purple zone at outer reef edge), through to an atoll (centre
bottom) and raised limestone island (bottom right) dominated by ancient reef deposits (brown + white fleck). Atolls
have limited, low-lying land areas but well developed reef/lagoon systems. Islands composed of continental rocks
are not included in this figure, but see Table 29-3]
Figure 29-2: A comparison of the degree of confidence in the detection of observed impacts of climate change on
tropical small islands with the degree of confidence in attribution to climate change drivers at this time. For
example, the blue symbol No. 2 (Coastal Systems), indicates there is very high confidence in both the detection of
sea-level rise consistent with global means and its attribution to climate change drivers; whereas the red symbol
No. 17 (Human Systems) indicates whilst detection of casualties and damage during extreme events is very high,
there is presently low confidence in the attribution to climate change. It is important to note that low confidence in
attribution frequently arises due to the limited research available on small island environments.
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Figure 29-3: Time series of RCP scenarios annual projected temperature and precipitation change relative to 1986-
2005 for six small islands regions (using regions defined in AR5 WG1, Annex 1: Atlas of Global and Regional
Climate Projections).] Thin lines denote one ensemble member per model, thick lines the CMIP5 multi-model mean.
On the right-hand side the 5th, 25th, 50th (median), 75th and 95th percentiles of the distribution of 20-yr mean
changes are given for 2081 2100 in the four RCP scenarios. Note that the model ensemble averages in the figure are
for grid points over wide areas and encompass many different climate change signals. To get projections for a
specific location and time period use the maps in the Atlas or the online interactive version at but please note that in
regions with small islands the models basically simulate the climate of the surrounding ocean and local conditions
on land may differ. [Illustration to be redrawn to conform to IPCC publication specifications.]
Figure 29-4: Tropical and extra-tropical cyclone impacts on the coasts of small islands. Four types of impacts are
distinguished here, black arrows showing the connections between them, based on the existing literature. An
example of the chain of impacts associated with two extra-tropical cyclones centred to the east of Japan is illustrated
by the red arrows. Swell waves generated by these events in December 2008 reached islands in the southwest Pacific
and caused extensive flooding (3) that impacted soil quality (8), freshwater resources (9), and damaged crops (10),
buildings (15), and transport facilities (16) in the region (Example based on Hoeke et al., 2013).
Examples of tropical cyclone impacts on small island coasts with reference
1. Society Islands, French Polynesia, February 2010 (Etienne, 2012); 2. Taveuni, Fiji, March 2010 (Etienne and
Terry, 2012); 3. Cook Islands (de Scally, 2008); Society and Autral Islands, French Polynesia, February 2010
(Etienne, 2012); 4. Viti Levu, Fiji, March 1997 (Terry et al., 2002); 5. Society Islands, French Polynesia, February
2010 (Etienne, 2012); 6. Curacao, Bonaire, Netherlands Antilles, November 1999 (Scheffers and Scheffers, 2006);
Hawaiian Islands (Fletcher et al., 2008); 7. Bay Islands, Honduras, October 1998 (Cahoon et al., 2003); 8. Marshall
Islands, June 1905 (Spennemann, 1996); 9. Pukapuka atoll, Cook Islands, February 2005 (Terry and Falkland,
2010); 10. Vanuatu, February 2004 (Richmond and Sovacool, 2012); 11. 12. 13. Tuamotu Islands, French Polynesia,
1982-83 (Dupon, 1987); 14. Grenada, September 2004 (OECS, 2004); 15. Grenada, September 2004 (OECS, 2004);
Tubuai, Austral Islands, French Polynesia, February 2010 (Etienne, 2012); 16. Vanuatu, February 2004 (Richmond
and Sovacool, 2012); Guadeloupe Island, October 2008 (Dorville and Zahibo, 2010); 17. Bora Bora, Raiatea,
Maupiti, Tahaa, Huahine, Society Islands, February 2010 (Etienne, 2012); 18. Vanuatu, February 2004 (Richmond
and Sovacool, 2012); 19. Tuamotu, French Polynesia, 1982-83 (Dupon, 1987).
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Examples of extra-tropical cyclone impacts on small island coasts with reference
1. Maldives, April 1987 (Harangozo, 1992); 2. Maldives, January 1955 (Maniku, 1990); 3. Maldives, April 1987
(Harangozo, 1992); 9. Solomon Islands, December 2008 (Hoeke et al., 2013); 10. Chuck, Pohnpei, Kosrae,
Federated States of Micronesia, December 2008 (Hoeke et al., 2013); 15. Majuro, Marshall Islands, November 1979
(Hoeke et al., 2013); 16. Coral Coast, Viti Levu, Fiji, May 2011 (Hoeke et al., 2013); 17. Majuro, Kwajalein, Arno,
Marshall Islands, December 2008 (Hoeke et al., 2013); 18. Bismark Archipelago, Papua New Guinea, December
2008 (Hoeke et al., 2013).
Figure 29-5. The impact of alternative climate change adaptation actions or policies.
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Chapter 30. The Ocean
Coordinating Lead Authors
Ove Hoegh-Guldberg (Australia), Rongshuo Cai (China)
Lead Authors
Peter G. Brewer (USA), Victoria J. Fabry (USA), Karim Hilmi (Morocco), Sukgeun Jung (Republic of Korea),
Elvira Poloczanska (Australia), Svein Sundby (Norway)
Contributing Authors
Johann Bell (New Caledonia), Christopher J. Brown (Australia), Michael T. Burrows (UK), Long Cao (USA),
Simon Donner (Canada), C. Mark Eakin (USA), Arne Eide (Norway), Benjamin Halpern (USA), Charles R.
McClain (USA), Skip McKinnell (Canada), Mary O Connor (Canada), Camille Parmesan (USA), R. Ian Perry
(Canada), Anthony J. Richardson (Australia), David Schoeman (Australia), Sergio Signorini (USA), William
Skirving (USA / Australia), Dáithí Stone (Canada / South Africa / USA), William Sydeman (USA), Rui Zhang
(China), Ruben van Hooidonk (USA)
Review Editors
Ly Omar (Senegal), Carol Turley (UK)
Volunteer Chapter Scientists
Jo Davy (New Zealand), Sarah Lee (USA)
Contents
Executive Summary
30.1. Introduction
30.1.1. Major Sub-Regions within the Ocean
30.1.2. Detection and Attribution of Climate Change and Ocean Acidification in Ocean Sub-Regions
30.2. Major Conclusions from Previous Assessments
30.3. Regional Changes and Projections of Future Ocean Conditions
30.3.1. Physical Changes
30.3.1.1. Heat Content and Temperature
30.3.1.2. Sea Level
30.3.1.3. Ocean Circulation, Surface Wind and Waves
30.3.1.4. Solar Insolation and Clouds
30.3.1.5. Storm Systems
30.3.1.6. Thermal Stratification
30.3.2. Chemical Changes
30.3.2.1. Surface Salinity
30.3.2.2. Ocean Acidification
30.3.2.3. Oxygen Concentration
30.4. Global Patterns in the Response of Marine Organisms to Climate Change and Ocean Acidification
30.5. Regional Impacts, Risks, and Vulnerabilities: Present and Future
30.5.1. High Latitude Spring Bloom Systems
30.5.1.1. Observed Changes and Potential Impacts
30.5.1.2. Key Risks and Vulnerabilities
30.5.2. Equatorial Upwelling Systems
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30.5.2.1. Observed Changes and Potential Impacts
30.5.2.2. Key Risks and Vulnerabilities
30.5.3. Semi-Enclosed Seas
30.5.3.1. Observed Changes and Potential Impacts
30.5.3.2. Key Risks and Vulnerabilities
30.5.4. Coastal Boundary Systems
30.5.4.1. Observed Changes and Potential Impacts
30.5.4.2. Key Risks and Vulnerabilities
30.5.5. Eastern Boundary Upwelling Ecosystems
30.5.5.1. Observed Changes and Potential Impacts
30.5.5.2. Key Risks and Vulnerabilities
30.5.6. Sub-Tropical Gyres
30.5.6.1. Observed Changes and Potential Impacts
30.5.6.2. Key Risks and Vulnerabilities
30.5.7. Deep Sea (>1000 m)
30.5.7.1. Observed Changes and Potential Impacts
30.5.7.2. Key Risks and Vulnerabilities
30.5.8. Detection and Attribution of Climate Change Impacts with Confidence Levels
30.6. Sectorial Impacts, Adaptation, and Mitigation Responses
30.6.1. Natural Ecosystem Services
30.6.2. Economic Sectors
30.6.2.1. Fisheries and Aquaculture
30.6.2.2. Tourism
30.6.2.3. Shipping
30.6.2.4. Offshore Energy and Mineral Resource Extraction and Supply
30.6.3. Human Health
30.6.4. Ocean-based Mitigation
30.6.4.1. Deep Sea Carbon Sequestration
30.6.4.2. Offshore Renewable Energy
30.6.5. Maritime Security and Related Operations
30.7. Synthesis and Conclusions
30.7.1. Key Vulnerabilities and Risks
30.7.2. Global Frameworks for Decision Making
30.7.3. Emerging Issues, Data Gaps, and Research Needs
30.7.3.1. Changing Variability and Marine Impacts
30.7.3.2. Surface Wind, Storms, and Upwelling
30.7.3.3. Declining O2 Concentrations
30.7.3.4. Ocean Acidification
30.7.3.5. Net Primary Productivity
30.7.3.6. Movement of Marine Organisms and Ecosystems
30.7.3.7. Understanding Cumulative and Synergistic Impacts
30.7.3.8. Reorganization of Ecosystems and Food Webs
30.7.3.9. Socio-ecological Resilience
References
Frequently Asked Questions
30.1: Can we reverse the climate change impacts on the ocean?
30.2: How can we use non-climate factors to manage climate change impacts on the ocean?
30.3: Does slower warming mean less impact on plants and animals?
30.4: How will marine primary productivity change?
30.5: Will climate change cause dead zones in the ocean?
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Executive Summary
The Ocean plays a central role in Earth s climate and has absorbed 93% of the extra energy from the
enhanced greenhouse effect and approximately 30% of anthropogenic CO2 from the atmosphere. Regional
responses are addressed here by dividing the Ocean into seven sub-regions: High Latitude Spring Bloom Systems
(HLSBS), Eastern Boundary Upwelling Ecosystems (EBUE), Coastal Boundary Systems (CBS), Equatorial
Upwelling Systems (EUS), Sub-Tropical Gyres (STG), Semi-Enclosed Seas (SES), and the Deep Sea (DS; >1000
m). The eighth region, Polar Seas, is dealt with by Chapter 28 [Figure 30-1; WGI 3.2.5, Box 3.1, 3.8].
Global average sea surface temperatures have increased since both the beginning of the 20th Century and the
1950s (virtually certain). The average sea surface temperature (SST) of the Indian, Atlantic and Pacific
Oceans has increased by 0.65, 0.41 and 0.31°C respectively over the period 1950 2009 (very likely, p-
value<0.05). Changes in the surface temperatures of the ocean basins are consistent with temperature trends
simulated by ocean-atmosphere models with anthropogenic greenhouse gas forcing over the past century (high
confidence). Sub-regions within the Ocean also show robust evidence of change, with the influence of long-term
patterns of variability (e.g., Pacific Decadal Oscillation, PDO; Atlantic Multi-decadal Oscillation, AMO)
contributing to variability at regional scales, and making changes due to climate change harder to distinguish and
attribute [30.3.1, Figure 30-2e g, Table 30-1; WGI 2.4.2-3, 3.2 3.8, 10.3.4, 14].
Uptake of CO2 has decreased ocean pH (approximately 0.1 unit over 100 years), fundamentally changing
ocean carbonate chemistry in all ocean sub-regions, particularly at high latitudes (high confidence). The
current rate of ocean acidification is unprecedented within the last 65 Ma (high confidence) if not the last 300 Ma
(medium confidence). Warming temperatures, declining pH and carbonate ion concentrations represent risks to the
productivity of fisheries and aquaculture, and the security of regional livelihoods given the direct and indirect effects
of these variables on physiological processes (e.g., skeleton formation, gas exchange, reproduction, growth, and
neural function) and ecosystem processes (e.g., primary productivity, reef building, and erosion) (high confidence)
[6.2, 6.3, 30.3.1, 30.3.2; 6.1.2; WGI 3.8.2, Box 3.2, 5.3.1].
Regional changes observed in winds, surface salinity, stratification, ocean currents, nutrient availability, and
oxygen depth profile in many regions may be a result of anthropogenic greenhouse gas emissions (low to
medium confidences). Marine organisms and ecosystems are likely to change in response to these regional changes,
although evidence is limited and responses uncertain [30.3, 30.5; 6.2, 6.3; WGI 2.5, 2.7 3.3 3.8, 10.4.2, 10.4.4].
Most, if not all, of the Ocean will continue to warm and acidify, although the rates will vary regionally (high
confidence). Differences between Representative Concentration Pathways (RCPs) are very likely to be minimal until
2040 (high confidence). Projected temperatures of the surface layers of the Ocean, however, diverge as the 21st
Century unfolds and will be 1 3°C higher by 2100 under RCP8.5 than RCP2.6 across most ocean sub-regions. The
projected changes in ocean temperature pose serious risks and vulnerabilities to ocean ecosystems and dependent
human communities (robust evidence, high agreement, high confidence) [6.5, 30.3.1, 30.3.2, 30.7.1 Figure 30-2e g,
Table 30-3].
Rapid changes in physical and chemical conditions within ocean sub-regions have already affected the
distribution and abundance of marine organisms and ecosystems. Responses of species and ecosystems to
climate change have been observed from every ocean sub-region (high confidence). Marine organisms are moving to
higher latitudes consistent with warming trends (high confidence), with fish and zooplankton migrating at the fastest
rates, particularly in HLSBS regions. Changes to sea temperature have also altered the phenology or timing of key
life-history events such as plankton blooms, and migratory patterns and spawning in fish and invertebrates over
recent decades (medium confidence). There is medium to high agreement that these changes pose significant
uncertainties and risks to fisheries, aquaculture and other coastal activities. Ocean acidification maybe driving
similar changes (low confidence), although there is limited evidence and low agreement at present. The associated
risks will intensify as ocean warming and acidification continue [Box CC-MB, 30.4, 30.5, 6.3, 6.4].
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Regional risks and vulnerabilities to ocean warming and acidification can be compounded by non-climate
related stressors such as pollution, nutrient runoff from land, and over-exploitation of marine resources, as
well as natural climate variability (high confidence). These influences confound the detection and attribution of
the impacts of climate change and ocean acidification on ecosystems yet may also represent opportunities for
reducing risks through management strategies aimed at reducing their influence, especially in CBS, SES, and
HLSBS [30.1.2, 30.5, 5.3.4, 18.3.3 4].
Recent changes to wind and ocean mixing within the highly productive HLSBS, EBUE, and EUS are likely to
influence energy transfer to higher trophic levels and microbial processes. There is, however, limited evidence
and low agreement on the direction and magnitude of these changes and their relationship to ocean warming and
acidification (low confidence). In cases where NPP increases or is not consumed (e.g., Benguela EBUE, low
confidence), the increased transfer of organic carbon to deep regions can stimulate microbial respiration and reduce
O2 levels (medium confidence). Oxygen concentrations are also declining in the tropical Pacific, Atlantic, and Indian
Oceans (particularly EUS) due to reduced O2 solubility at higher temperatures, and changes in ocean ventilation and
circulation [Box CC-PP, 30.3.1, 30.3.2, 30.5.5 6, 6.3.3; WGI 3.8.3].
Global warming will result in more frequent extreme events and greater associated risks to ocean ecosystems
(high confidence). In some cases (e.g., mass coral bleaching and mortality), projected increases will eliminate
ecosystems, and increase risks and vulnerabilities to coastal livelihoods and food security (e.g., CBS in SE Asia;
SES, CBS, and STG in the Indo-Pacific) (medium to high confidence). Reducing stressors not related to climate
change represents an opportunity to strengthen the ecological resilience within these regions, which may help them
survive some projected changes in ocean temperature and chemistry [5.4, 30.6.1, 30.5.3 4, 30.5.6, Figure 30-4, Box
CC-CR, IPCC 2012].
The highly productive HLSBS in the North-eastern Atlantic has changed in response to warming oceans
(medium evidence, high agreement), with a range of consequences for fisheries. These ecosystems are
responding to recent warming, with the greatest changes being observed since the late 1970s in the phenology,
distribution and abundance of plankton assemblages, and the reorganization of fish assemblages (high confidence).
There is medium confidence that these changes will have both positive and negative implications depending on the
particular HLSBS fishery and the time frame [Box CC-MB, Box 6-1, 6.4.1.2, 30.5.1, 30.6.2.1].
EUS, which support highly productive fisheries off equatorial Africa and South America, have warmed over
the past 60 years (Pacific EUS: 0.43oC, Atlantic EUS: 0.54oC; very likely, p-value<0.05). While warming is
consistent with changes in upwelling intensity, there is low confidence in our understanding of how EUS will
change, especially in how El Nino-Southern Oscillation (ENSO) and other patterns of variability will interact in a
warmer world. The risk, however, of changes to upwelling increases with average global temperature, posing
significant uncertainties for dependent ecosystems, communities, and fisheries [30.5.2; WGI 14.4,].
The surface waters of the SES show significant warming from 1982 and most CBS show significant warming
since 1950. Warming of the Mediterranean has led to the recent spread of tropical species invading from the Atlantic
and Indian Oceans. Projected warming increases the risk of greater thermal stratification in some regions, which can
lead to reduced O2 ventilation and the formation of hypoxic zones, especially in the Baltic and Black Seas (medium
confidence). In some CBS, such as the East China Sea and Gulf of Mexico, these changes are further influenced by
the contribution of nutrients from coastal pollution contributing to the expansion of hypoxic (low O2) zones. These
changes are likely to influence regional ecosystems as well as dependent industries such as fisheries and tourism,
although there is low confidence in the understanding of potential changes and impacts [Table 30-1, 30.5.3, 5.3.4.3].
Coral reefs within CBS, SES, and STG are rapidly declining as result of local (i.e., coastal pollution,
overexploitation), and climate change (high confidence). Elevated sea temperatures drive impacts such as mass
coral bleaching and mortality (very high confidence), with an analysis of the CMIP5 ensemble projecting the loss of
coral reefs from most sites globally by 2050 under mid to high rates of ocean warming (very likely) [Figure 30-10,
30.5.3 4, 30.5.6, Box CC-CR].
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The productive EBUE and EUS involve upwelling waters that are naturally high in CO2 concentrations and
low in pH, and hence are potentially vulnerable to ocean warming and acidification (medium confidence).
There is limited evidence and low agreement, as to how upwelling systems are likely to change (low confidence).
Declining O2 and shoaling of the aragonite saturation horizon through ocean acidification increases the risk of
upwelling water being low in pH and O2 with impacts on coastal ecosystems and fisheries, as has been seen already
(e.g., California Current EBUE). These risks and uncertainties are likely to involve significant challenges for
fisheries and livelihoods along the west coasts of South America, Africa, and North America (low to medium
confidence) [30.3.2.2, 30.5.2, 30.5.5, Box CC-UP, Box CC-PP].
Chlorophyll concentrations measured by satellites have decreased in the STG of the North Pacific, Indian and
North Atlantic Oceans by 9%, 12% and 11%, respectively, over and above the inherent seasonal and
interannual variability from 1998 2010 (high confidence; p-value<0.05). Significant warming over this period
has resulted in increased water column stratification, reduced mixed layer depth and possibly declines in nutrient
availability and ecosystem productivity (limited evidence, medium agreement). The short timeframe of these studies
against well-established patterns of long-term variability lead to the conclusion that these changes are about as likely
as not due to climate change [30.5.6, Table 30-1, Box CC-UP, 6.3.4].
The world s most abundant yet difficult to access habitat, the DS, is changing (limited evidence, medium
agreement), with warming between 700 2000 m from 1957 2010 likely to involve a significant anthropogenic
signal (medium confidence). Decreased primary productivity of surface waters (e.g., STG) is likely to reduce the
availability of organic carbon to DS ecosystems. Understanding of the risks of climate change and ocean
acidification to the DS is important given the size of the DS region but is limited (low confidence) [30.5.7, Figure
30-2; WGI 3.2.4, Figure 3.2, 3.9].
Changes to surface wind and waves, sea level, and storm intensity will increase the vulnerability of ocean-
based industries such as shipping, energy and mineral extraction (medium confidence). Risks to equipment and
people may be reduced through the design and use of ocean-based infrastructure, together with the evolution of
policy (medium agreement). Risks and uncertainties will increase with further climate change. New opportunities as
well as risks for shipping, energy and mineral extraction, and international issues over access and vulnerability, may
accompany warming waters, particularly at high latitudes [30.3.1, 30.6.2, 28.2.2, 28.2.5, 28.3.4, 10.2.2, 10.4.4, IPCC
2012].
Changes to ocean temperature, chemistry and other factors are generating new challenges for fisheries, as
well as benefits (high agreement). Climate change is a risk to the sustainability of capture fisheries and aquaculture
development, adding to the threats of over-fishing and other non-climate stressors. In EUS and STG, shifts in the
distribution and abundance of large pelagic fish stocks will have the potential to create winners and losers among
island nations and economies. There has been a boost in fish stocks of high latitude fisheries in the HLSBS of the
North Pacific and North Atlantic, partly as a result of 30 years of increase in temperature. This is very likely to
continue, although some fish stocks will eventually decline. A number of practical adaptation options and supporting
international policies can minimize the risks and maximize the opportunities [30.6, 30.7, 7.4.2, 29.4].
Adaptation strategies for ocean regions beyond coastal waters are generally poorly developed but will benefit
from international legislation and expert networks, as well as marine spatial planning (high agreement).
Fisheries and aquaculture industries with high-technology and/or large investments, as well as marine shipping and
oil and gas industries, have high capacities for adaptation due to greater development of environmental monitoring,
modeling and resource assessments. For smaller-scale fisheries and developing nations, building social resilience,
alternative livelihoods, and occupational flexibility represent important strategies for reducing the vulnerability of
ocean-dependent human communities. Building strategies that include climate forecasting and early-warning
systems can reduce impacts of warming and ocean acidification in the short term. Overall, there is a strong need to
develop ecosystem-based monitoring and adaptation strategies to mitigate rapidly growing risks and uncertainties to
the coastal and oceanic industries, communities and nations (high agreement) [30.6, 7.3.2.4].
Significant opportunity exists within the Ocean and its sub-regions for reducing the CO2 flux to the
atmosphere (limited evidence, medium agreement). Ecosystems such as mangroves, seagrass and salt marsh offer
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important carbon storage and sequestration opportunities (e.g., Blue Carbon; limited evidence, medium agreement).
Blue Carbon strategies can also be justified in terms of the ecosystem services provide by coastal vegetated habitats
such as protection against coastal erosion and storm damage, and maintenance of habitats for fisheries species.
Sequestration of anthropogenic CO2 into deep ocean areas still faces considerable hurdles with respect to the
expense, legality and vulnerability of storage sites and infrastructure. There are also significant opportunities with
the Ocean for the development of offshore renewable energy such as wind and tidal power [30.6.1, 30.6.4].
International frameworks for collaboration and decision-making are critically important for coordinating
policy that will enable mitigation and adaptation by the Ocean sectors to global climate change (e.g., United
Nations Convention on the Law of the Sea, UNCLOS). These international frameworks offer an opportunity to
solve problems collectively, including improving fisheries management across national borders (e.g., reducing
illegal, unreported and unregulated fishing, IUU), responding to extreme events, and strengthening international
food security. Given the importance of the Ocean to all countries, there is a need for the international community to
progress rapidly to a whole of ocean strategy for responding to the risks and challenges posed by anthropogenic
ocean warming and acidification [30.7.2].
30.1. Introduction
The Ocean exerts a profound influence as part of the Earth, interacting with its atmosphere, cryosphere, land, and
biosphere to produce planetary conditions. It also directly influences human welfare through the provision and
transport of food and resources, as well as by providing cultural and economic benefits, and indirectly through the
regulation of atmospheric gas content and the distribution of heat and water across the planet. Chapter 30 examines
the extent to which regional changes to the Ocean can be accurately detected and attributed to anthropogenic climate
change and ocean acidification, building on the conclusions of Chapter 6, which focuses on the marine physiological
and ecological responses to climate change and ocean acidification. Detailed assessment of the role of recent
physical and chemical changes within the Ocean to anthropogenic climate change is provided in WGI (particularly
Chapters 2, 3, 13, and 14). In Chapter 30, impacts, risks, and vulnerabilities associated with climate change and
ocean acidification are assessed for seven ocean sub-regions, and the expected consequences and adaptation options
for key ocean-based sectors are discussed. Polar oceans (defined by the presence of sea ice in the north and by the
Polar Front in the south) are considered in Chapter 28.
While climate change affects coastal and low-lying sub-regions of multiple nations, detailed discussion of potential
risks and consequences for these regions occurs in the relevant chapters of this report (e.g., WGII Chapters 5 and 29
as well as other regional sections).
30.1.1. Major Sub-Regions within the Ocean
The Ocean represents a vast region that stretches from the high tide mark to the deepest oceanic trench (11,030 m)
and occupies 71% of the earth's surface. The total volume of the Ocean is approximately 1.3 billion km3, with
approximately 72% of this volume being below 1000 m (Deep Sea (DS), 30.5.7). There are considerable challenges
in assessing the regional impacts of climate change on the Ocean. Devising an appropriate structure in order to
explore the influence of climate change across the entire Ocean region and the broad diversity of life forms and
habitats is challenging. [Longhurst, 1998] identified over 50 distinct ecological provinces in the Ocean, defined by
physical characteristics and the structure and function of phytoplankton communities. Longhurst s scheme, however,
yields far more sub-regions than could be sensibly discussed in the space allocated within AR5. Consequently, we
have used comparable principles but have divided the non-polar ocean into seven larger sub-regions similar to
Barber [1988]. We recognize that these sub-regions do not always match physical-chemical patterns or specific
geographies, and that they interact strongly with terrestrial regions through weather systems and the exchange of
materials. Different ocean sub-regions may also have substantially different primary productivities and fishery catch.
Notably, over 80% of fishery catch is associated with three ocean sub-regions: Northern Hemisphere High Latitude
Spring Bloom Systems (HLSBS), Coastal Boundary Systems (CBS), and Eastern Boundary Upwelling Ecosystems
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(EBUE; Table SM30-1, Figure 30-1). The DS (>1000m) is included as a separate category that overlaps with the six
other ocean sub-regions dealt with in Chapter 30.
[INSERT FIGURE 30-1 HERE
Figure 30-1: (a) Separation of the world s non-polar oceans into seven major sub-regions (excluding the polar
oceans, which are considered in Chapter 28). The chlorophyll-a signal measured by SeaWiFS and averaged over the
period from Sep 4, 1997 to 30 Nov 2010 (NASA) is provides a proxy for differences in marine productivity (with
the caveats provided in Box CC-PP). Ecosystem structure and functioning, as well as key oceanographic features
provided the basis for separating the Ocean into the sub-regions shown. The map insert shows the distribution of
Deep Sea (DS) habitat (>1000 m; Bathypelagic and Abyssopelagic habitats combined). Numbers refer to: 1 = High
Latitude Spring Bloom Systems (HLSBS); 2 = Equatorial Upwelling Systems (EUS); 3 = Semi-Enclosed Seas
(SES); 4 = Coastal Boundary Systems (CBS); 5 = Eastern Boundary Upwelling Ecosystems (EBUE); 6 = Sub-
Tropical Gyres (STG); and 7 = DS (>1000 m). (b) Relationship between fish catch and area for each ocean sub-
region is shown in (a). Red columns: average fish catch (as millions tons yr-1) for the period 1970 2006. Blue
columns: area (millions km2). The four left-hand columns (sub-regions HLSBS-North, CBS, EBUE, and SES) cover
20 % of the world oceans area and deliver 80% of the world s fish catches. The values for the percent area of the
Ocean, primary productivity, and fishery catch for the major sub-regions are listed in Table SM30-1.].
30.1.2. Detection and Attribution of Climate Change and Ocean Acidification in Ocean Sub-Regions
The central goal of Chapter 30 is to assess the recent literature on the Ocean as a region for changes that can be
attributed to climate change and/or ocean acidification. Detailed assessments of recent physical and chemical
changes in the Ocean are outlined in WGI Chapters 2, 3, 6, 10, 13, and 14 (AR5). The detection and attribution of
climate change and ocean acidification on marine organisms and ecosystems is addressed in Chapter 6. Chapter 30
draws on these chapters to investigate regional changes in the physical, chemical, ecological, and socio-economic
aspects of the Ocean and the extent to which they can be attributed to climate change and ocean acidification.
Generally, successful attribution to climate change occurs when the full range of possible forcing factors is
considered and those related to climate change are found to be the most probable explanation for the detected
change in question [18.2.1.1]. Comparing detected changes with the expectations of well-established scientific
evidence also plays a central role in the successful attribution of detected changes. We attempt to do this for seven
sub-regions of the Ocean. There are a number of general limitations to the detection and attribution of impacts to
climate change and ocean acidification that are discussed elsewhere [18.2.1] along with challenges [18.2.2].
Different approaches and best practice guidelines are discussed in WGI Chapters 10 and 18 as well as in several
other places [Hegerl et al., 2007; Hegerl et al., 2010; Stott et al., 2010]. The fragmentary nature of ocean observing,
structural uncertainty in model simulations, the influence of long-term variability, and confounding factors unrelated
to climate change (e.g., pollution, introduced species, overexploitation of fisheries) represent major challenges
[Halpern et al., 2008; Hoegh-Guldberg et al., 2011b; Parmesan et al., 2011]. Different factors may also interact
synergistically or antagonistically with each other and climate change, further challenging the process of detection
and attribution [Hegerl et al., 2007; Hegerl et al., 2010].
30.2. Major Conclusions from Previous Assessments
An integrated assessment of the impacts of climate change and ocean acidification on the Ocean as a region was not
included in recent IPCC assessments, although a chapter devoted to the Ocean in the Second Assessment Report
(SAR) did attempt to assess the impacts of projected regional and global climate changes on the oceans [Ittekkot et
al., 1996]. The fact that assessments for ocean and coastal systems are spread throughout previous IPCC assessment
reports reduces the opportunity for synthesizing the detection and attribution of climate change and ocean
acidification across the physical, chemical, ecological, and socio-economic components of the Ocean and its sub-
regions. The IPCC Fourth Assessment Report (AR4) concluded, however, that while terrestrial sub-regions are
warming faster than the oceans, Observations since 1961 show that the average temperature of the global ocean has
increased to depths of at least 3000 m and that the ocean has been taking up over 80% of the heat being added to the
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climate system. AR4 also concluded that sea levels had risen due to the thermal expansion of the Ocean but
recognized that our understanding of the dynamics of glaciers and ice sheets was too limited to assess their
likelihood or provide a best estimate or an upper boundary for sea level rise (AR4, SPM). Changes to ocean
temperature and density have been identified as having the potential to alter large-scale ocean circulation. AR4
concluded that with respect to the Meridional Overturning Circulation (MOC) it is very likely that up to the end of
the 20th Century the MOC was changing significantly at interannual to decadal time scales (AR4, WGI Chapter 5,
Box 5.1), despite limited evidence of a slowing MOC.
According to AR4, sea-level rise over the last 100 150 years is probably contributing to coastal erosion in many
places , including the east coast of the United States and the United Kingdom (AR4, WGII Chapter 1). The AR4
assessment was virtually certain that rising atmospheric CO2 had changed carbonate chemistry of the ocean (i.e.,
buffering capacity, carbonate and bicarbonate concentrations), and that a decrease in surface pH of 0.1 had occurred
over the global ocean (calculated from the uptake of anthropogenic CO2 between 1750 and 1994 ([Sabine et al.,
2004; Raven et al., 2005]; AR4, 5.4.2.3, WGI Table 7.3). Large-scale changes in ocean salinity were also observed
from 1955 1998 and were characterized by a global freshening in sub-polar latitudes and salinification of
shallower parts of the tropical and subtropical oceans . In this case, freshening was observed in the Pacific, with
increased salinity being observed in the Atlantic and Indian Oceans (AR4, WGI 5.3.2 5.3.5). These changes in
surface salinity were qualitatively consistent with expected changes to surface freshwater flux. Freshening of mid
and high latitude waters together with increased salinity at low latitudes were seen as evidence of changes in
precipitation and evaporation over the oceans .
Substantial evidence indicated that changing ocean conditions have extensively influenced marine ecosystems (AR4,
WGII Table 1.5). AR4 noted that there is an accumulating body of evidence to suggest that many marine
ecosystems, including managed fisheries, are responding to changes in regional climate caused predominately by
warming of air and sea surface temperatures (SST) and to a lesser extent by modification of precipitation regimes
and wind patterns (AR4, WGII 1.3.4.2). Observed changes in marine ecosystems and managed fisheries reported
within AR4 included: changes to plankton community structure and productivity, the phenology and biogeography
of coastal species, intertidal communities on rocky shores and kelp forests, and the distribution of pathogens and
invasive species. Changes were also observed in coral reefs (primarily increased mass coral bleaching and
mortality), migratory patterns and trophic interactions of marine birds, reptiles, and mammals, as well as of a range
of other marine organisms and ecosystems (AR4, WGII Table 1.5), although a separate exercise in detection and
attribution of changes to climate change (as done for terrestrial studies) was not done as part of AR4.
30.3. Recent Changes and Projections of Future Ocean Conditions
Evidence that increasing concentrations of atmospheric CO2 have resulted in the warming and acidification of the
upper layers of the Ocean has strengthened since AR4. Understanding the full suite of physical and chemical
changes to the Ocean is critical to the interpretation of the past and future responses of marine organisms and
ecosystems, especially with respect to the implications for coastal and low-lying areas.
30.3.1. Physical Changes
30.3.1.1. Heat Content and Temperature
The Ocean has absorbed 93% of the extra heat arising from the enhanced greenhouse effect (1971 2010), with most
of the warming (64%) occurring in the upper (0 700 m) ocean (1971 2010; WGI Section 3.2.3, Figure 3.2, Box
3.1). It is virtually certain that global average sea surface temperatures (SST) have increased since the beginning of
the 20th Century, with improvements and growth of data sets and archives, and the understanding of errors and
biases since AR4 (WGI 2.4.2). It is virtually certain that the upper ocean (0 700m depth) has warmed from 1971
2010 (Figure 30-2a), while it is likely that the surface layers of the Ocean have warmed from the 1870s to 1971.
Rates of increase in temperature are highest near the surface of the Ocean (>0.1°C decade-1 in the upper 75 m from
1971 to 2010) decreasing with depth (0.015°C decade-1 at 700 m; Figure 30-2b, c). It is very likely that the
intensification of this warming near the surface has increased thermal stratification of the upper ocean by about 4%
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between 0 200 m depth from 1971 2010 in all oceans north of 40°S. It is likely that the Ocean has warmed between
700 2000 m from 1957 2010, with the warming signal becoming less apparent or non-existent at deeper depths
(WGI 3.2.1 3.2.3, Figures 3.1 3.2, Figure 3.9). These changes include a significant anthropogenic signal (virtually
certain) [Gleckler et al., 2012; Pierce et al., 2012], with the surface waters of all three ocean basins warming at
different rates, that all exceed those expected if there were no changes to greenhouse gas forcing over the past
century (Figure 30-2e-g). In this respect, the observed record also falls within the range of historical model outputs
that include observed increases in the concentration of greenhouse gases as opposed to models that do not (Figure
30-2 e-g).
Data archives such as HadISST1.1 contain sea surface temperatures (SST) reconstructed from a range of sources,
allowing an opportunity to explore mean monthly, gridded, global SST from 1870 to the present [Rayner et al.,
2003]. We used the published HadISST1.1 data set (higher temporal and spatial resolution than HadSST3) to
explore trends in historic SST within our sub-regions (Figure 30-1a; see definition of regions in Figure SM30-1 and
Table SM30-2, column 1). The median SST for 1871 1995 from the Comprehensive Ocean-Atmosphere Data Set
(COADS) were merged with data from the UK Met Office Marine Data Bank (MDB) to produce monthly globally-
complete fields of SST on a 1° latitude-longitude SST grid from 1870 to date.
The surface layers of the three ocean basins have warmed (p-value<0.05, very likely), with the Indian Ocean (0.11°C
decade-1) warming faster than the Atlantic (0.07°C decade-1) and Pacific (0.05°C decade-1) Oceans (high confidence)
(Table 30-1). This is consistent with the depth-averaged (0 700 m) temperature trend observed from 1971 2010
(Figure 30-2a).
While some regions (e.g., North Pacific) did not show a clear warming trend, most regions showed either significant
warming in the average temperature, or significant warming in either/or the warmest and coolest months of the year,
over the period 1950 2009 (HadISST1.1 data, Table 30-1). Trends in SST show considerable sub-regional
variability (Table 30-1, Figure 30-2a). Notably, the average temperature of most HLSBS did not increase
significantly from 1950 2009 (except in the Indian Ocean; Table 30-1) yet the temperatures of the warmest month
(North and South Atlantic, and South-eastern Pacific) and of the coolest month (North and South Atlantic, and
South Pacific) showed significant upward trends over this period (p-value<0.05; Table 30-1).
The two EUS warmed from 1950 2009 (Pacific EUS, 0.07°C decade-1 and Atlantic EUS, 0.09°C decade-1; Table 30-
1). The average monthly SST of the SES did not warm significantly, although the temperature of the coolest month
increased significantly within the Baltic Sea (0.35°C decade-1 or 2.11°C from 1950 2009), as did the temperatures
of the warmest months in the Black (0.14°C decade-1 or 0.83°C from 1950 2009), Mediterranean (0.11°C decade-1
or 0.66°C from 1950 2009) and Red (0.05°C decade-1 or 0.28°C from 1950 2009) Seas over the period 1950 2009
(very likely) (Table 30-1). Studies over shorter periods (e.g., 1982 2006, [Belkin, 2009]) report significant increases
in average SST of the Baltic (1.35°C), Black (0.96°C), Red (0.74°C), and Mediterranean (0.71°C) Seas. Such
studies are complicated by the influence of patterns of long-term variability and by the small size and land-locked
nature of SES. Coastal Boundary Systems (except the Caribbean and Gulf of Mexico) all showed highly significant
(p-value<0.05) warming (0.09°C 0.13°C.decade-1, Table 30-1). Among the EBUE, the Canary and Californian
current regions exhibited a significant rate of change in the average SST (0.09°C decade-1 and 0.12°C decade-1,
respectively; p-value<0.05), while the Benguela and Humboldt currents did not show significant temperature
changes from 1950 2009 (p-value>0.05; Table 30-1). There was some variability between current systems in terms
of the behavior of the coolest and warmest months. The temperature of the coolest month increased significantly
from 1950 2009 in the case of the Benguela and California currents (0.06°C decade-1 and 0.12°C decade-1
respectively, p-value<0.05), while there was a significant increase in the temperature of the warmest month in the
case of the Canary and Humboldt currents (0.11°C decade-1 and 0.10°C decade-1, respectively, Table 30-1).
The average temperature of STG showed complex patterns with increasing temperatures (1950 2009) in the Indian,
South Atlantic, and South Pacific Oceans (very likely) (0.11, 0.08, and 0.06°C decade-1, respectively; p-value<0.05),
but not in the North Atlantic or North Pacific Ocean (p-value>0.05). These rates are half the value reported over
shorter periods (e.g., 1998 2010, Table 1 in [Signorini and McClain, 2012] and based on NOAA_OI_SST_V2
data). Given the sensitivity of coral reefs to temperature ([Eakin et al., 2010; Strong et al., 2011; Lough, 2012]; Box
CC-CR), trends in key coral reef regions were also examined using the World Resources Institute s Reefs at Risk
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report (www.wri.org) to identify HadISST1.1 grid cells containing coral reefs (Figure 30-4b). Grouping the results
into six major coral reef regions, we found that coral reef waters (with the notable exception of the Gulf of Mexico
and Caribbean) have shown strong increases in average temperature (0.07 0.13°C decade-1) as well as the
temperature of the coolest (0.07 0.14°C decade-1) and warmest months (very likely) (0.07 0.12°C decade-1; Table
30-1). These trends in temperature have resulted in an absolute increase in sea temperature of 0.44 0.79°C from
1950 2009.
[INSERT FIGURE 30-2 HERE
Figure 30-2: (a) Depth-averaged 0 700 m temperature trend for 1971 2010 (longitudinal versus latitude, colors and
gray contours in °C per decade). (b) Zonally averaged temperature trends (latitude versus depth, colors and gray
contours in °C per decade) for 1971 2010, with zonally averaged mean temperature over plotted (black contours in
°C). (c) Globally-averaged temperature anomaly (Time versus depth, colors and grey contours in °C) relative to the
1971 2010 mean. (d) Globally-averaged temperature difference between the Ocean surface and 200 m depth (Black:
annual values; red: five year running mean). Panels (a) (d) from WGI Figure 3.1. (e) (g) Observed and simulated
variations in past and projected future annual average SST over three ocean basins (excluding regions within 300 km
of the coast). The black line shows estimates from HadISST1.1 observational measurements. Shading denotes the 5
95 percentile range of climate model simulations driven with historical changes in anthropogenic and natural
drivers (62 simulations), historical changes in natural drivers only (25), and the Representative Concentration
Pathways: Dark Blue: RCP2.6; Light Blue: RCP4.5; Green: RCP6.0, and Red: RCP8.5). Data are anomalies from
the 1986 2006 average of the HadISST1.1 data (for the HadISST1.1 time series) or of the corresponding historical
all-forcing simulations. Further details are given in Box 21-2.]
[INSERT FIGURE 30-3 HERE
Figure 30-3: Velocity at which sea surface temperature (SST) isotherms shifted (km decade-1) over the period 1960
2009 calculated using HaDISST1.1, with arrows indicating the direction and magnitude of shifts. Velocity of
climate change is obtained by dividing the temperature trend in °C decade-1 by the local spatial gradient °C km-1.
The direction of movement of SST is denoted by the direction of the spatial gradient and the sign of the temperature
trend: towards locally cooler areas with a local warming trend or towards locally warmer areas where temperatures
are cooling. Adapted from [Burrows et al., 2011].]
[INSERT TABLE 30-1 HERE
Table 30-1: Regional changes in sea surface temperature (SST) over the period 1950 2009 using the Ocean
regionalization specified in Figure 30-1a (for further detail of regions defined for analysis, see Figure SM30-1 and
Table 30-2, column 1). A linear regression was fitted to the average of all 1×1 degree monthly SST data extracted
from the HadISST1.1 data set [Rayner et al., 2003] for each sub-region over the period 1950 2009. All SST values
less than -1.8oC, together with all SST pixels that were flagged as being sea ice, were reset to the freezing point of
seawater (-1.8oC) to reflect the sea temperature under the ice. Separate analyses were also done to explore trends in
the temperatures extracted from the coldest-ranked and the warmest-ranked month of each year (Table SM30-2).
The table includes the slope of the regression (°C decade-1), the p-value for the slope being different from zero and
the total change over 60 years (i.e., the slope of linear regression multiplied by 6 decades) for each category. The p-
values that exceed 0.05 plus the associated slope and change values have a gray background, denoting the lower
statistical confidence in the slope being different from zero (no slope). Note, changes with higher p-values may still
describe informative trends although the level of confidence is lower that the slope is different from zero.]
Given the essential role that temperature plays in the biology and ecology of marine organisms (Box CC-MB, 6.2,
6.3, [Pörtner, 2002; Poloczanska et al., 2013]), the speed of isotherm migration ultimately determines the speed at
which populations must either move, adapt or acclimate to changing sea temperatures [Pörtner, 2002; Burrows et
al., 2011; Hoegh-Guldberg, 2012]. Burrows et al. [2011] calculated the rate at which isotherms are migrating as the
ratio of the rate of SST change (°C yr-1) to the spatial gradient of temperature (°C km-1) over the period 1960 2009
(Figure 30-3). While many of these temperature trajectories are towards the polar regions, some are not and are
influenced by features such as coastlines. This analysis and others (e.g., North Atlantic, González-Taboada and
Anadón [2012]) reveals that isotherms in the Ocean are moving at high velocities (up to 200 km decade-1), especially
at low latitudes (high confidence) (Figure 30-3). Other sub-regions showed smaller velocities with contracting
isotherms (cooling) in some areas (e.g., the Central and North Pacific, and Atlantic Oceans, Figure 30-3). There are
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also changes in the timing of seasonal temperatures in both spring and fall/autumn [Burrows et al., 2011;
Poloczanska et al., 2013] which, together with other variables (e.g., light, food availability, geography), are likely to
affect biological processes such as the migration of species to higher latitudes, and the timing and synchrony of
reproductive and other seasonal behaviors.
Significant excursions of sea temperature above long-term summer temperature maxima (or below long-term
temperature minima) significantly affect marine organisms and ecosystems [Hoegh-Guldberg, 1999; Bensoussan et
al., 2010; Crisci et al., 2011; Harley, 2011]. Consequently, calculating heat stress as a function of exposure time and
size of a particular temperature anomaly has proven useful in understanding recent changes to organisms and
ecosystems (e.g., coral reefs and thermal anomalies, [Strong et al., 2011]). The total heat stress accumulated over the
period 1981 2010 was calculated using the methodology of [Donner et al., 2007] and a reference climatology based
on 1985 2000 in which the highest monthly SST was used to define the thermal threshold, above which
accumulated thermal stress was calculated as exposure time multiplied by stress or Degree Heating Months
(DHM) as the running total over four consecutive months. While most sub-regions of the Ocean experienced an
accumulation of heat stress (relative to a climatology based on the period 1985 2000), equatorial and high latitude
sub-regions in the Pacific and Atlantic Oceans have the greatest levels of the accumulated heat stress (Figure 30-4a).
These are areas rich in thermally-sensitive coral reefs (Figure 30-4b, [Strong et al., 2011]). There was also a higher
proportion of years that have had at least one stress event (DHM>1) in the last 30 years (1981 2010, Figure 30-4c)
than in the preceding 30 years (1951 1980, Figure 30-4c, d.
[INSERT FIGURE 30-4 HERE
Figure 30-4: Recent changes in thermal stress calculated using HadISST1.1 data. A monthly climatology was
created by averaging the HadISST monthly SST values over the period 1985 2000 to create twelve averages, one
for each month of the year. The Maximum Monthly Mean (MMM) climatology was created by selecting the hottest
month for each pixel. Anomalies were then created by subtracting this value from each SST value, but only allowing
values to be recorded if they were greater than zero [Donner et al., 2007]. Two measures of the change in thermal
stress were calculated as a result: (a) The total thermal stress for the period 1981 2010, calculated by summing all
monthly thermal anomalies for each grid cell. (b) The location of coral reef grid cells used in Table 30-1 and for
comparison to regional heat stress here. Each dot is positioned over a 1×1 degree grid cell within which lies at least
one carbonate coral reef. The latitude and longitude of each reef is derived from data provided by the World
Resources Institute s Reefs at Risk report (http://www.wri.org). The six regions are as follows: Red Western
Pacific Ocean; Yellow Eastern Pacific Ocean; Dark Blue Caribbean & Gulf of Mexico; Green Western Indian
Ocean; Pink Eastern Indian Ocean; and Light Blue Coral Triangle & SE Asia. (c) Proportion of years with
thermal stress, which is defined as any year that has a thermal anomaly, for the periods 1951 1980 and (d) 1981
2010.]
The three ocean basins will continue warming under moderate (RCP4.5) to high (RCP8.5) emission trajectories
(high confidence) and will only stabilize over the second half of the century in the case of low range scenarios such
as RCP2.6 (Figure 30-2 e-g; WGI, AI.4 AI.8). Projected changes were also examined for specific ocean sub-regions
using ensemble averages from AOGCM simulations available in the CMIP5 archive (Table SM30-3) for the four
scenarios of the future (Representative Concentration Pathways: RCP2.6, RCP4.5, RCP6.0 and RCP8.5; [van
Vuuren et al., 2011]). Ensemble averages for each RCP are based on simulations from 10 16 individual models
(Table SM30-3). The subset of CMIP5 models were chosen because each has historic runs enabling the derivation of
the MMM climatology from 1985 2000, ensuring that all anomalies were comparable across time periods and
across RCPs (Figure 30-10). Model hind-cast changes matched those observed for ocean sub-regions for the period
1980 2009 (HadISST1.1; Figure 30-2), with the model ensemble slightly overestimating the extent of change across
the different ocean sub-regions (slope of observed/model = 0.81, r2 = 0.76, p-value<0.001). In this way, the absolute
amount of change projected to occur in the ocean sub-regions was calculated for near-term (2010 2039) and long-
term (2070 2099) periods (Table SM30-4). In the near-term, changes in the temperature projected for the surface
layers of the Ocean are largely indistinguishable between the different RCP pathways due to the similarity in forcing
until 2040. By the end of the century, however, SST across the ocean sub-regions were 1.8 3.3°C higher under
RCP8.5 than those projected to occur under RCP2.6 (Table SM30-4; Figure 30-2 e g). The implications of these
projected changes on the structure and function of oceanic systems are discussed below.
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30.3.1.2. Sea Level
The rate of sea level rise since the mid-19th Century has been larger than the mean rate during the previous two
millennia (high confidence). Over the period 1901 2010, Global Mean Sea Level (GMSL) rose by 0.19 (0.17 0.21)
m (WGI Figure SPM, 3.7, 5.6, 13.2). It is very likely that the mean rate of global averaged sea level rise was 1.7
[1.5 1.9] mm yr 1 between 1901 and 2010, 2.0 [1.7 2.3] mm yr 1 between 1971 and 2010 and 3.2 [2.8 3.6] mm yr 1
between 1993 and 2010 (WGI SPM, 3.7). These observations are consistent with thermal expansion of the Ocean
due to warming plus the addition of water from loss of mass by melting glaciers and ice sheets. Current rates of sea
level rise vary geographically, and can be higher or lower than the GMSL for several decades at time due to
fluctuations in natural variability and ocean circulation (Figure 30-5). For example, rates of sea level rise are up to
three times higher than the GMSL in the Western Pacific and South-east Asian region, and decreasing in many parts
of the Eastern Pacific for the period 1993 2012 as measured by satellite altimetry (Figure 30-5; WGI 13.6.5).
Sea level rise under increasing atmospheric greenhouse gas concentrations will continue for hundreds of years, with
the extent and rate of the increase in GMSL being dependent on the emission scenario followed. Central to this
analysis is the millennial-scale commitment to further sea level rise that is likely to arise from the loss of mass of the
Greenland and Antarctic ice sheets (WGI 13.5.4, Figure 13.13). Sea level rise is very likely to increase during the
21st Century relative to the period 1971 2010 due to increased ocean warming and the continued contribution of
water from loss of mass from glaciers and ice sheets. There is medium confidence that median sea level rise by
2081 2100 relative to 1986-2005 will be (5 95% range of process-based models): 0.44 m for RCP2.6, 0.53 m for
RCP4.5, 0.55 m for RCP6.0, and 0.74 m for RCP8.5. Higher values of sea level rise are possible but are not backed
by sufficient evidence to enable reliable estimates of the probability of specific outcomes. Many semi-empirical
model projections of GMSL rise are higher than process-based model projections (up to about twice as large), but
there is no consensus in the scientific community about their reliability and there is thus low confidence in their
projections (WGI 13.5.2 3; Table 13.6, Figure 13.12).
It is considered very likely that increases in sea level will result in greater levels of coastal flooding and more
frequent extremes by 2050 (WGI 13.7.2; [IPCC, 2012]). It is about as likely as not that the frequency of the most
intense storms will increase in some ocean basins, although there is medium agreement that the global frequency of
tropical cyclones is likely to decrease or remain constant (WGI 14.6, 14.8). While understanding of associated risks
is relatively undeveloped, coastal and low-lying areas, particularly in the southern Asia, Pacific Ocean and North
Atlantic regions, face increased flood risk (5.3.3.2, 8.2.3.4, 9.3.4.4. Future impacts of sea level rise include
increasing penetration of storm surges into coastal areas and changing patterns of shoreline erosion (5.3), as well as
the inundation of coastal aquifers by saltwater (5.4.2.5, 29.3.2). Regionally, some natural ecosystems may reduce in
extent (e.g., mangroves), although examples of habitat expansion have been reported [Brown et al., 2011]. Overall,
changes to sea level are very likely to modify coastal ecosystems such as beaches, salt marshes, coral reefs and
mangroves (5.4.2, Box CC-CR), especially where rates of sea level rise are highest (e.g., South-east Asia and the
Western Pacific).
[INSERT FIGURE 30-5 HERE
Figure 30-5. Map of the rate of change in sea surface height (geocentric sea level) for the period 1993 2012 derived
from satellite altimetry. Also shown are relative sea level changes (gray lines) from selected tide gauge stations for
the period 1950 2012. For comparison, an estimate of global mean sea level change is shown (red lines) with each
tide gauge time series. The relatively large short-term oscillations in local sea level (gray lines) are due to the natural
climate variability and ocean circulation. For example, the large regular deviations at Pago Pago are associated with
the El Nino-Southern Oscillation. Figure originally presented in WGI (FAQ 13.1, Figure 1).
30.3.1.3. Ocean Circulation, Surface Wind, and Waves
Circulation of atmosphere and ocean (and their interactions) drives much of the chemical, physical, and biological
characteristics of the Ocean, shaping phenomena such as ocean ventilation, coastal upwelling, primary production,
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and biogeochemical cycling. Critical factors for transporting nutrients from deep waters to the marine primary
producers in the upper layers of the ocean include wind-driven mixing and upwelling.
There has been a poleward movement of circulation features, including a widening of the tropical belt, contraction
of the northern polar vortex, and a shift of storm tracks and jet streams to higher latitudes (medium confidence, WGI
2.7.5, 2.7.6, 2.7.8, Box 2.5). Long-term patterns of variability (years to decades) continue to prevent robust
conclusions regarding long-term changes in atmospheric circulation and winds in many cases (WGI 2.7.5). There is
high confidence, however, that the increase in northern mid-latitude westerly winds from the 1950s to the 1990s, and
the weakening of the Pacific Walker circulation from the late 19th Century to the 1990s have been largely offset by
recent changes (WGI 2.7.5, 2.7.8, Box 2.5). Wind stress has increased since the early 1980s over the Southern
Ocean (medium confidence) (WGI 3.4.4), and tropical Pacific since 1990 (medium confidence), while zonal mean
wind stress may have declined by 7% in the equatorial Pacific from 1862 1990 due to weakening of the tropical
Walker circulation (medium confidence) (WGI 3.4.4; [Vecchi et al., 2006]). For example, it is very likely that the
sub-tropical gyres of the major ocean basins have expanded and strengthened since 1993. However, the short-term
nature of observing means that these changes are as likely as not to be due to decadal variability and/or due to longer
term trends in wind forcing associated with climate change (WGI 3.6). Other evidence of changes in ocean
circulation is limited to relatively short-term records that suffer from low temporal and spatial coverage. Therefore,
there is very low confidence that multi-decadal trends in ocean circulation can be separated from decadal variability
(WGI 3.6.6). There is no evidence of a long-term trend in large-scale currents such as the Atlantic Meridional
Overturning Circulation (AMOC), Indonesian Throughflow (ITF), the Antarctic Circumpolar Current (ACC), or the
transport of water between the Atlantic Ocean and Nordic Seas [WGI 3.6, Figures 3.10, 3.11].
Winds speed may have increased within the regions of EBUE (low confidence in attribution to climate; e.g.
California Current, WGI 2.7.2). Changing wind regimes have the potential to influence mixed layer depth (MLD)
and upwelling intensity in highly productive sub-regions of the world s oceans, although there is low agreement as
to whether or not upwelling will intensify or not under rapid climate change ([Bakun, 1990; Bakun et al., 2010]; Box
CC-UP). Surface waves are influenced by wind stress, although understanding trends remains a challenge due to
limited data. There is medium confidence that Significant Wave Height (SWH) has increased since the mid-1950s
over much of the North Atlantic north of 45°N, with typical winter season trends of up to 20 cm per decade (WGI
3.4.5). There is low confidence in the current understanding of how SWH will change over the coming decades and
century for most of the Ocean. It remains an important knowledge gap (WGI 3.4).
30.3.1.4. Solar Insolation and Clouds
Solar insolation plays a crucially important role in the biology of many marine organisms, not only as a source of
energy for photosynthesis but also as a potential co-stressor in the photic zone (with temperature), as is seen during
mass coral bleaching and mortality events (e.g., [Hoegh-Guldberg, 1999]). Global surface solar insolation (from the
NCEP/NCAR Reanalysis Project, Kalnay et al. [1996]) decreased by 4.3 W m-2 decade-1 from the 1950s until 1991,
after when it increased at 3.3 W m-2 decade-1 until 1999 [Ohmura, 2009; Wild, 2009], matching a broad suite of
evidence from many land-based sites (WGI, 2.3.3). While there is consistency between independent data sets for
particular regions, there is substantial ambiguity and therefore low confidence in observations of global-scale cloud
variability and trends (WGI 2.5.7). There is also low confidence in projections of how cloudiness, solar insolation
and precipitation will change as the planet warms due to the large interannual and decadal variability (ENSO, PDO),
short observation time series and uneven spatial sampling, particularly in the early record (before 1950; WGI 2.5.8).
30.3.1.5. Storm Systems
As agents of water column mixing, storms (from small atmospheric disturbances to intense tropical cyclones) can
remix nutrients from deeper areas into the photic zone of the Ocean, stimulating productivity. Storms can also
reduce local sea temperatures and associated stress by remixing heat into the deeper layers of the Ocean [Carrigan
and Puotinen, 2011]. Large storms can destroy coastal infrastructure and coastal habitats such as coral reefs and
mangrove forests, which can take decades to recover [Lotze et al., 2011; De ath et al., 2012]. While there is low
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confidence for long-term trends in tropical cyclone activity globally (largely due to the lack of reliable long-term
data sets), it is virtually certain that the frequency and intensity of the strongest tropical cyclones in the North
Atlantic have increased since the 1970s (WGI 2.6.3). There is medium agreement that the frequency of the most
intense cyclones in the Atlantic has increased since 1987 (WGI 2.6.3) and robust evidence of interdecadal changes
in the storm track activity within the North Pacific and North Atlantic [Lee et al., 2012]. It is also very likely that
there has been a decrease in the number of land-falling tropical cyclones along the East Australian coast since the
19th Century (WGI 2.6.3, [Callaghan and Power, 2011]). It is likely that these patterns are influenced by interannual
variability such as ENSO, with land-falling tropical cyclones being twice as common in La Nina versus El Nino
years (high confidence) [Callaghan and Power, 2011]. There has been an increase in the number of intense
wintertime extra-tropical cyclone systems since the 1950s in the North Pacific. Similar trends have been reported for
the Asian region, although analyzes are limited in terms of the spatial and temporal coverage of reliable records
(WGI 2.6.4). There is low confidence, however, in large-scale trends in storminess or storminess proxies over the
last century due to the lack of long-term data and inconsistencies between studies (WGI 2.6.4).
30.3.1.6. Thermal Stratification
As heat has accumulated in the Ocean there has been a 4% increase in thermal stratification of the upper layers in
most ocean regions (0 200 m, 40-year record) north of 40°S (WGI 3.2.2). Increasing thermal stratification has
reduced ocean ventilation and the depth of mixing in many ocean sub-regions (medium confidence) WGI 3.8.3).
This in turn reduces the availability of inorganic nutrients and consequently primary productivity (medium
confidence) (6.3.4). In the STG, which dominate the three major ocean basins (30.5.6), satellite-derived estimates of
surface chlorophyll and primary production decreased between 1999 and 2007 (Box CC-PP). In contrast, however,
in situ observations at fixed stations in the North Pacific and North Atlantic Oceans (Hawaii Ocean Time-series or
HOT, and Bermuda Atlantic Time-series Study, BATS), showed increases in nutrient and chlorophyll levels and
primary production over the same period, suggesting that other processes (e.g., ENSO, PDO, NAO, winds, eddies,
advection) can counteract broad-scale trends at local scales (Box CC-PP). The continued warming of the surface
layers of the Ocean will very likely further enhance stratification and potentially limit the nutrient supply to the
euphotic zone in some areas. The response of upwelling to global warming is likely to vary between regions and
represents a complex interplay between local and global variables and processes (Box CC-UW).
30.3.2. Chemical Changes
30.3.2.1. Surface Salinity
The global water cycle is dominated by evaporation and precipitation occurring over ocean regions, with surface
ocean salinity varying with temperature, solar radiation, cloud cover, and ocean circulation [Deser et al., 2004].
Changes in salinity influence stratification of water masses and circulation. Ocean salinity varies regionally (Figure
30-6a) and is an outcome of the balance between evaporation and precipitation ([Durack and Wijffels, 2010]; WGI
3.3). Evaporation-dominated regions (Figure 30-6b) such as the STG, and Atlantic and Western Indian Oceans
(WGI 3.3.3) have elevated salinity, while areas of high precipitation such as the North Pacific, North-eastern Indian
Ocean, South-east Asia, and the eastern Pacific have relatively low salinities (WGI 3.3.3, Figure 30-6a). It is very
likely that large-scale trends in salinity have also occurred in the Ocean interior, deriving from changes to salinity at
the surface and subsequent subduction (WGI 3.3.2 3.3.4).
Salinity trends are consistent with the amplification of the global hydrological cycle [Durack et al., 2012; Pierce et
al., 2012], a consequence of a warmer atmosphere very likely producing the observed trend in greater precipitation,
evaporation, atmospheric moisture (Figure 30-6b), and extreme events (WGI 2.6.2.1, 3.3.4; [IPCC, 2012]). Spatial
patterns in salinity and evaporation-precipitation are similar, providing indirect evidence that these processes have
been enhanced since the 1950s [WGI 3.3.2 3.3.4, Figures 3.4, 3.5 and 3.20d, FAQ 3.3]. These trends in salinity are
very likely to have a discernible contribution from anthropogenic climate change (WGI 10.4.2). The combined
changes in surface salinity and temperature are consistent with changes expected due to anthropogenic forcing of the
climate system and are inconsistent with the effects of natural climate variability, either internal to the climate
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system (e.g., ENSO, PDO; Figure 30-6c, d) or external to it (e.g., solar forcing or volcanic eruptions; [Pierce et al.,
2012]). There is high confidence between climate models that the observed trends in ocean salinity will continue as
average global temperature increases [Durack and Wijffels, 2010; Terray et al., 2012]. Ramifications of these
changes are largely unknown but are of interest given the role of ocean salinity and temperature in fundamental
processes such as the Atlantic Meridional Overturning Circulation (AMOC).
[INSERT FIGURE 30-6 HERE
Figure 30-6: (a) The 1955 2005 climatological-mean sea surface salinity [Antonov et al., 2010] color contoured at
0.5 PSS78 intervals (black lines). (b) Annual mean evaporation-precipitation averaged over the period 1950 2000
(NCEP) color contoured at 0.5 m yr 1 intervals (black lines). (c) The 58-year (2008 minus 1950) sea surface salinity
change derived from the linear trend (PSS78), with seasonal and ENSO signals removed [Durack and Wijffels,
2010] color contoured at 0.116 PSS78 intervals (black lines). (d) The 30-year (2003 2007 average centered at 2005,
minus the 1960 1989 average centered at 1975) sea surface salinity difference (PSS78) color contoured at 0.06
PSS78 intervals (black lines). Contour intervals in (c) and (d) are chosen so that the trends can be easily compared,
given the different time intervals in the two analyzes. White areas in (c) and (d) are marginal seas where the
calculations are not carried out. Regions where the change is not significant at the 99% confidence level are stippled
in gray. Figure originally presented as WGI Figure 3.4 in WGI.]
30.3.2.2. Ocean Acidification
The Ocean has absorbed approximately 30% of atmospheric CO2 from human activities resulting in decreased ocean
pH and carbonate ion concentrations, and increased bicarbonate ion concentrations (Box CC-OA, WG1 Box 3.2;
Figure SM30-2). The chemical response to increased CO2 dissolving into the Ocean from the atmosphere is known
with very high confidence (WGI 6.4.4). Factors such as temperature, biological processes, and sea ice (WGI 6.4)
play significant roles in determining the saturation state of seawater for polymorphs (i.e. different crystalline forms)
of calcium carbonate. Consequently, pH and the solubility of aragonite and calcite are naturally lower at high
latitudes and in upwelling areas (e.g., eastern Pacific upwelling, Californian Current ), where organisms and
ecosystems may be relatively more exposed to ocean acidification as a result ([Feely et al., 2012; Gruber et al.,
2012]; Figure 30-7a, b; Figure SM30-2). Aragonite and calcite concentrations vary with depth, with under-saturation
occurring at deeper depths in the Atlantic (calcite: 3500 4500 m, aragonite: 400 3000 m) as opposed to the Pacific
and Indian Oceans (calcite: 100 3000 m, aragonite: 100 1200 m; [Feely et al., 2004; Orr et al., 2005; Feely et al.,
2009]; Figure 30-8).
[INSERT FIGURE 30-7 HERE
Figure 30-7: Projected ocean acidification from 11 CMIP5 Earth System models under RCP8.5 (other RCP
scenarios have also been run with the CMIP5 models): (a) Time series of surface pH shown as the mean (solid line)
and range of models (filled), given as area-weighted averages over the Arctic Ocean (green), the tropical oceans
(red) and the Southern Ocean (blue). (b) Maps of the median model s change in surface pH from 1850 2100. Panel
(a) also includes mean model results from RCP2.6 (dashed lines). Over most of the Ocean, gridded data products of
carbonate system variables are used to correct each model for its present-day bias by subtracting the model-data
difference at each grid cell following [Orr et al., 2005]. Where gridded data products are unavailable (Arctic Ocean,
all marginal seas and the Ocean near Indonesia), the results are shown without bias correction. The bias correction
reduces the range of model projections by up to a factor of 4, e.g., in panel (a) compare the large range of model
projections for the Arctic (without bias correction) to the smaller range in the Southern Ocean (with bias correction).
Figure originally presented in WGI Figure 6.28 in WGI.]
[INSERT FIGURE 30-8 HERE
Figure 30-8: Projected aragonite saturation state from 11 CMIP5 Earth System models under RCP8.5 scenario: (a)
time series of surface carbonate ion concentration shown as the mean (solid line) and range of models (filled), given
as area weighted averages over the Arctic Ocean (green), the tropical oceans (red), and the Southern Ocean (blue);
maps of the median model's surface A in (b) 2010, (d) 2050, and (f) 2100; and zonal mean sections (latitude versus
depth) of A in 2100 over (c) the Atlantic Ocean and (e) the Pacific Ocean, while the ASH (Aragonite Saturation
Horizon) is shown for 2010 (dotted line) and 2100 (solid line). Panel (a) also includes mean model results from
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RCP2.6 (dashed lines). As for Figure 30-7, gridded data products of carbonate system variables [Key et al., 2004]
are used to correct each model for its present-day bias by subtracting the model-data difference at each grid cell
following [Orr et al., 2005]. Where gridded data products are unavailable (Arctic Ocean, all marginal seas and the
Ocean near Indonesia), results are shown without bias correction. Reprinted from Figure 6.29 in WGI.]
Surface ocean pH has decreased by approximately 0.1 pH units since the beginning of the Industrial Revolution
(high confidence) (Figure 30-7a; WGI 3.8.2, Box 3.2), with pH decreasing at the rate of -0.0013 and -0.0024 pH
units yr-1 (WGI 3.8.2, Table 3.2). The presence of anthropogenic CO2 diminishes with depth. The saturation horizons
of both polymorphs of calcium carbonate, however, are shoaling rapidly (1 2 m yr-1, and up to 5 m yr-1 in regions
such as the California Current [Orr et al., 2005; Feely et al., 2012]. Further increases in atmospheric CO2 are
virtually certain to further acidify the Ocean and change its carbonate chemistry (Figures S30.2, 30.7 and 30.8).
Doubling atmospheric CO2 (~RCP4.5; [Rogelj et al., 2012]) will decrease ocean pH by another 0.1 units and
decrease carbonate ion concentrations by approximately 100 umol kg-1 in tropical oceans (Figure 30-8a) from the
present day average of 250 umol kg-1 (high confidence). Projected changes for the open Ocean by 2100 (Figures
30.7, 30.8) range from a pH change of -0.14 unit with RCP2.6 (421 ppm CO2, +1C, 22% reduction of carbonate ion
concentration) to a pH change of -0.43 unit with RCP8.5 (936 ppm CO2, +3.7C, 56% reduction of carbonate ion
concentration). The saturation horizons will also become significantly shallower in all oceans (with the aragonite
saturation horizon between 0 and 1500 m in the Atlantic Ocean and 0 and 600 m (poles versus equator) in the
Pacific Ocean ([Sabine et al., 2004; Orr et al., 2005]; WGI 6.4, Figure 6.28). Trends towards under-saturation of
aragonite and calcite will also partly depend on ocean temperature, with surface polar waters expected to become
seasonally under-saturated with respect to aragonite and calcite within a couple of decades (Figure 30-8c f, Box
CC-OA[McNeil and Matear, 2008]).
Overall, observations from a wide range of laboratory, mesocosm and field studies reveal that marine macro-
organisms and ocean processes are sensitive to the levels of ocean acidification projected under elevated
atmospheric CO2 (high confidence) (Box CC-OA, 6.3.2, [Munday et al., 2009; Kroeker et al., 2013]). Ecosystems
that are characterized by high rates of calcium carbonate deposition (e.g., coral reefs, calcareous plankton
communities) are sensitive to decreases in the saturation states of aragonite and calcite (high confidence). These
changes are very likely to have broad consequences such as the loss of three-dimensional coral reef frameworks
[Hoegh-Guldberg et al., 2007; Manzello et al., 2008; Fabricius et al., 2011; Andersson and Gledhill, 2013; Dove et
al., 2013] and restructuring of food webs at relatively small (~50 ppm) additional increases in atmospheric CO2.
Projected shoaling of the aragonite and calcite saturation horizons are likely to impact deep water (100 2000 m)
communities of scleractinian corals and other benthic organisms as atmospheric CO2 increases ([Orr et al., 2005;
Guinotte et al., 2006]; WGI 6.4), although studies from the Mediterranean and of seamounts off SW Australia report
that some deep water corals may be less sensitive [Thresher et al., 2011; Maier et al., 2013]. Organisms are also
sensitive to changes in pH with respect to physiological processes such as respiration and neural functions (6.3.2).
Due to the relatively short history, yet growing effort, to understand the implications of rapid changes in pH and
ocean carbonate chemistry, there are a growing number of organisms and processes reported to be sensitive. The
impacts of ocean acidification on marine organisms and ecosystems continues to raise serious scientific concern,
especially given that the current rate of ocean acidification (at least 10 100 faster than the recent glacial transitions
[Caldeira and Wickett, 2003; Hoegh-Guldberg et al., 2007]) is unprecedented within the last 65 Ma (high
confidence) [Ridgwell and Schmidt, 2010] and possibly 300 Ma of Earth history (medium confidence) ([Hönisch et
al., 2012]; 6.1.2).
30.3.2.3. Oxygen Concentration
Dissolved O2 is a major determinant of the distribution and abundance of marine organisms (6.3.3). Oxygen
concentrations vary across ocean basins and are lower in the eastern Pacific and Atlantic basins, and northern Indian
Ocean (Figure 30-9b, 6.1.1.3). In contrast, some of the highest concentrations of O2 are associated with cooler high
latitude waters (Figure 30-9b). There is high agreement among analyzes providing medium confidence that O2
concentrations have decreased in the upper layers of the Ocean since the 1960s, particularly in the equatorial Pacific
and Atlantic Oceans (WGI Figure 3.20, 3.8.3). A formal fingerprint analysis undertaken by Andrews et al. [2013]
concluded that recent decreases in oceanic O2 are due to external influences (very likely). Conversely, O2 has
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increased in the North and South Pacific, North Atlantic and Indian Oceans, consistent with greater mixing and
ventilation due to strengthening wind systems (WGI 3.8.3). The reduction in O2 concentration in some areas of the
Ocean is consistent with that expected from higher ocean temperatures and a reduction in mixing (increasing
stratification) (WGI 3.8.3). Analysis of ocean O2 trends over time [Helm et al., 2011b] reveals that the decline in O2
solubility with increased temperature is responsible for no more than 15% of the observed change. The remaining
85%, consequently, is associated with increased deep-sea microbial respiration and reduced O2 supply due to
increased ocean stratification (WGI Box 6.5 Figure 1). In coastal areas, eutrophication can lead to increased
transport of organic carbon into adjacent ocean habitats where microbial metabolism is stimulated, resulting in a
rapid drawdown of O2 [Weeks et al., 2002; Rabalais et al., 2009; Bakun et al., 2010].
The development of hypoxic conditions (generally defined as O2 concentrations below ~60 u mol kg-1) over recent
decades has been documented across a wide array of ocean sub-regions including some SES (e.g., Black and Baltic
Seas), the Arabian Sea, and the California, Humboldt, and Benguela Current systems, where eruptions of hypoxic,
sulfide-laden water have also occurred in some cases [Weeks et al., 2002]. Localized, seasonal hypoxic dead zones
have emerged in economically valuable coastal areas such as the Gulf of Mexico [Turner et al., 2008; Rabalais et
al., 2010], the Baltic Sea [Conley et al., 2009] and the Black Sea [Kideys, 2002; Ukrainskii and Popov, 2009] in
connection with nutrient fluxes from land. Over a vast region of the eastern Pacific stretching from southern Chile to
the Aleutian Islands, the minimum O2 threshold (less than 2 mg l-1 or ~60 mmol-1) is found at 300 m depth and
upwelling of increasingly hypoxic waters is well documented [Karstensen et al., 2008]. Hypoxic waters in the
northern Arabian Sea and Bay of Bengal are located close to continental shelf areas. Long-term measurements
reveal that O2 concentrations are declining in these waters, with medium evidence that economically significant
mesopelagic fish populations are being threatened by a reduction in suitable habitat as respiratory stress increases
[Koslow et al., 2011]. It should be noted that hypoxia profiles based on a critical threshold of 60 umol kg-1 can
convey an overly simplistic message given that critical concentrations of O2 in this regard are very much species,
size, temperature, and life history stage specific. This variability in sensitivity is, however, a critical determinant for
any attempt to understand how ecosystems will respond to changing future O2 levels (6.3.3).
There is high agreement among modeling studies that O2 concentrations will continue to decrease in most parts of
the Ocean due to the effect of temperature on O2 solubility, microbial respiration rates, ocean ventilation, and ocean
stratification (Figure 30-9c, d; WGI Table 6.14 [Andrews et al., 2013]), with implications for nutrient and carbon
cycling, ocean productivity, marine habitats, and ecosystem structure (6.3.5). The outcomes of these global changes
are very likely to be influenced by regional differences such as wind stress, coastal processes, and the supply of
organic matter.
[INSERT FIGURE 30-9 HERE
Figure 30-9: (a) Simulated changes in dissolved O2 (mean and model range as shading) relative to 1990s for RCP2.6,
RCP4.5, RCP6.0, and RCP8.5. (b) Multi-model mean dissolved O2 (mmol m 3) in the main thermocline (200 600 m
depth average) for the 1990s, and changes in the 2090s relative to 1990s for RCP2.6 (c) and RCP8.5 (d). To indicate
consistency in the sign of change, regions are stippled when at least 80% of models agree on the sign of the mean
change. These diagnostics are detailed in [Cocco et al., 2013] in a previous model inter-comparison using the SRES-
A2 scenario and have been applied to CMIP5 models here. Models used: CESM1-BGC, GFDL-ESM2G, GFDL-
ESM2M, HadGEM2-ES, IPSL-CM5A-LR, IPSL-CM5A-MR, MPI-ESM-LR, MPI-ESM-MR, NorESM1. Figure
originally presented in WGI Figure 6.30 in WGI.]
30.4. Global Patterns in the Response of Marine Organisms to Climate Change and Ocean Acidification
Given the close relationship between organisms and ecosystems with the physical and chemical elements of the
environment, changes are expected in the distribution and abundance of marine organisms in response to ocean
warming and acidification (6.3, Box CC-MB, Box CC-OA). Our understanding of the relationship between ocean
warming and acidification reveals that relatively small changes in temperature and other variables can result in often
large biological responses that range from simple linear trends to more complex non-linear outcomes. There has
been a rapid increase in studies that focus on the influence and consequences of climate change for marine
ecosystems since AR4 ([Hoegh-Guldberg and Bruno, 2010; Poloczanska et al., 2013], representing an opportunity
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to examine, and potentially attribute, detected changes within the Ocean to climate change. Evidence of global and
regional responses of marine organisms to recent climate change have been shown through assessments of multiple
studies focused on single-species, populations, and ecosystems [Tasker, 2008; Thackeray et al., 2010; Przeslawski et
al., 2012; Poloczanska et al., 2013]. The most comprehensive assessment, in terms of geographic spread and
number of observed responses, is that of Poloczanska et al. [2013]. This study reveals a coherent pattern in observed
responses of ocean life to recent climate change across regions and taxonomic groups, with 81% of responses by
organisms and ecosystems being consistent with expected changes to recent climate change (high confidence) (Box
CC-MB). On average, spring events in the Ocean have advanced by 4.4 +/- 0.7 days decade-1 (mean +/- SE) and the
leading edges of species distributions have extended (generally poleward) by 72.0 +/- 0.35 km decade-1. Values were
calculated from data series ranging from the 1920s to 2010, although all series included data after 1990. The fastest
range shifts generally occurred in regions of high thermal velocity (the speed and direction at which isotherms move
[Burrows et al., 2011], 30.3.1.1). Subsequently, [Pinsky et al., 2013], using a database of 360 fish and invertebrate
species and species groups from coastal waters around North America, showed differences in the speed and
directions that species shift can be explained by differences in local climate velocities (Box CC-MB).
30.5. Regional Impacts, Risks, and Vulnerabilities: Present and Future
This section explores the impacts, risks, and vulnerabilities of climate change for the seven sub-regions within the
Ocean. There is considerable variability from region to region, especially in the extent and interaction of climate
change and non-climate change stressors. While the latter may complicate attribution attempts in many sub-regions,
interactions between the two groups of stressors may also represent opportunities to reduce the overall effects on
marine organisms and processes by environmental changes being driven by climate change (including ocean
acidification) [Crain et al., 2008; Griffith et al., 2012].
30.5.1. High Latitude Spring Bloom Systems
High Latitude Spring Bloom Systems (HLSBS) stretch from 35N to the edge of the winter sea ice (and from 35S to
the polar front) and provide 36% of world s fish catch (Figure 30-1b). Although much of the North Pacific is iron
limited [Martin and Fitzwater, 1988] and lacks a classical spring bloom [McAllister et al., 1960], strong seasonal
variability of primary productivity is pronounced at all high latitudes because of seasonally varying photoperiod and
water column stability [Racault et al., 2012]. Efficient transfer of marine primary and secondary production to
higher trophic levels, including commercial fish species, is influenced by both the magnitude and the spatial and
temporal synchrony between successive trophic production peaks [Hjort, 1914; Cushing, 1990; Beaugrand et al.,
2003; Beaugrand and Reid, 2003].
30.5.1.1. Observed Changes and Potential Impacts
30.5.1.1.1. North Atlantic
The average temperature of the surface waters of the North Atlantic HLSBS has warmed by 0.07°C decade-1,
resulting in an increase in sea temperature of 0.44°C between 1950 and 2009 (likely) (p-value = 0.15; Table 30-1).
Over the same period, both winter and summer temperatures have increased significantly (0.05°C decade-1 and
0.12°C decade-1 respectively, p-value<0.05). Since the 1970s, the Atlantic Ocean has warmed more than any other
ocean basin (0.3C decade-1; Figure 30-2a, WGI 3.2.2), with greatest warming rates over European continental shelf
areas such as the southern North Sea, the Gulf Stream front, the sub-polar gyres and the Labrador Sea [MacKenzie
and Schiedek, 2007b; a; Levitus et al., 2009; Lee et al., 2011; González-Taboada and Anadón, 2012]. Basin-wide
warming in the North Atlantic since the mid-1990s has been driven by global warming and the current warm phase
of the Atlantic Multidecadal Oscillation (AMO) ([Wang and Dong, 2010]; WGI 14.7.6 ).
The North Atlantic is one of the most intensively fished ocean sub-regions. The major areas for harvesting marine
living resources span the eastern North American, European and Icelandic shelves [Livingston and Tjelmeland,
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2000]. In addition, the deep regions of the Nordic Seas and the Irminger Sea contain large populations of pelagic
fish such as herring, blue whiting and mackerel, and mesopelagic fish such as pearlsides and redfish. The region
covers a wide latitudinal range from 35 80N and, hence, a large span in thermal habitats. This is reflected in the
latitudinal gradient from subtropical/temperate species along the southern fringe to boreal/arctic species along the
northern fringe.
Climate change is virtually certain to drive major changes to the northern fringes of the Atlantic HLSBS by 2100.
For the Barents Sea region, which borders the HLSBS and Arctic regions, modeling projections from 1995 2060
(SRES B2 scenario) gave an increase in phytoplankton production of 8%, an increase in Atlantic zooplankton
production of 20%, and a decrease of Arctic zooplankton production of 50% [Ellingsen et al., 2008]. These changes
result in a total increase in zooplankton production in the HLSBS section of the Barents Sea and a decrease in the
Arctic section. Together with poleward shifts of fish species, a substantial increase in fish biomass and catch is also
very likely at the northern fringes of the HLSBS [Cheung et al., 2011]. However, for some species like capelin,
which feeds in summer at the ice edge and spawns in spring at the southern Atlantic Norwegian/Murman coast of
the Barents Sea, the continuous temperature increase is very likely to cause discontinuous changes in conditions. The
limited migration potential for this small pelagic fish is also likely to drive an eastwards shift in spawning areas to
new spawning grounds along the Novaja Semlja coast[Huse and Ellingsen, 2008].
Observations of fish and other species moving to higher latitudes [Beare et al., 2005; Perry et al., 2005; Collie et al.,
2008; Lucey and Nye, 2010] within the North Atlantic HLSBS are consistent with results of modeling exercises
[Stenevik and Sundby, 2007; Cheung et al., 2011]. Examples from the Barents (28.2.2.1, Nordic, and North Seas
(Box 6-1; 23.4.6) show how warming from the early 1980s influenced North Atlantic ecosystems, where substantial
biological impacts such as large-scale modification of the phenology, abundance and distribution of plankton
assemblages and reorganization of fish assemblages have been observed [Beaugrand et al., 2002; Edwards, 2004;
Edwards and Richardson, 2004; Tasker, 2008; Nye et al., 2009; Head and Pepin, 2010; Simpson et al., 2011]. The
ranges of some cold-water zooplankton assemblages in the North-east Atlantic have contracted towards the Arctic
since 1958, and were replaced by warm-water zooplankton assemblages (specifically copepods) (high confidence),
which moved up to 1000 km northward [Beaugrand et al., 2002; Beaugrand, 2009]. Although changes to surface
circulation may have played a role [Reid et al., 2001], the primary driver of the shift was shown to be regional
warming [Beaugrand et al., 2002; Beaugrand, 2004]. Reorganization of zooplankton communities and an observed
decline in mean size has implications for energy transfer to higher trophic levels including commercial fish stocks
([Beaugrand et al., 2003; Kirby and Beaugrand, 2009; Lindley et al., 2010], 23.4.6). Warm-water species of fish
have increased in abundance on both sides of the North Atlantic (medium confidence) [Beare et al., 2005; Collie et
al., 2008; Genner et al., 2010; Hermant et al., 2010; Lucey and Nye, 2010; Simpson et al., 2011]. Diversity of
zooplankton and fish has increased as more diverse warm-water assemblages extend northward in response to
changing environmental conditions (high confidence) ([Kane, 2007; Hiddink and ter Hofstede, 2008; Beaugrand,
2009; Mountain and Kane, 2010; ter Hofstede et al., 2010], Box 6-1, 23.6.5).
The past decade has been the warmest decade ever recorded in the Barents Sea, resulting in large populations of krill
shrimp, and pelagic and demersal fish stocks linked to the Atlantic and boreal ecosystem of the Barents Sea (high
confidence) ([Johannesen et al., 2012]; 28.2.2.1). Recruitment to boreal fish stocks such as cod, haddock, and
herring has increased [Eriksen et al., 2012]. The relatively warm Atlantic waters have advanced northward and
eastward [Arthun et al., 2012] and sea-ice has retreated along with the Arctic water masses. As a result, boreal
euphausiids, which are mainly confined to Atlantic water, have increased in biomass and distribution [Dalpadado et
al., 2012] enhancing growth of young cod Gadus morhua (boreal) as well as the more Arctic (arcto-boreal) capelin
(Mallotus villosus). The abundance of amphipods of more Arctic origin has decreased, resulting in poorer feeding
conditions for polar zooplankton predators such as polar cod (Boreogadus saida). Blue whiting (Micromesistius
poutassou), which spawns west of the British Isles and feeds on zooplankton in the Norwegian Sea during the
summer, extended their summer feeding distribution into the Barents Sea during the recent warm period.
The Norwegian Sea is one of the two core regions for the herbivore copepod Calanus finmarchicus, an important
prey species for pelagic fish and early life-stages of all fish around the rim of this high latitude sea including the
North Sea and the Barents Sea [Sundby, 2000]. C. finmarchicus is the main food item for some of the world s largest
fish stocks such as the Norwegian spring-spawning herring (Clupea harengus), blue whiting (M. poutassou), and
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northeast Atlantic mackerel (Scomber scombrus). These stocks have increased considerably during the recent
warming that started in the early 1980s [Huse et al., 2012]. The individual size of herring has also increased,
enabling longer feeding migrations to utilize boreal zooplankton occurring closer to distant Arctic water masses.
Mackerel (Scomber scombrus) has advanced northward and westward into Icelandic waters [Astthorsson et al.,
2012] and was even observed in East Greenland water in summer 2013 [Nttestad et al., 2013]. Since 2004, the sum
of spawning stock biomass of the three pelagic fish species (herring, blue whiting, and mackerel) leveled out at
around 16 million tons.
Observed changes in the phenology of plankton groups in the North Sea over the past 50 years are driven by climate
forcing, in particular regional warming (high confidence) [Edwards and Richardson, 2004; Wiltshire and Manly,
2004; Wiltshire et al., 2008; Lindley et al., 2010; Lindley and Kirby, 2010; Schluter et al., 2010], although responses
are species-specific with substantial variation within functional groups ([Edwards and Richardson, 2004]; Box 6-1).
For example, the peak maximum abundance of the copepod C. finmarchicus advanced by 10 days from the 1960s to
the 2000s, but its warm-water equivalent, C. helgolandicus, did not advance [Bonnet et al., 2005]. In the North Sea,
bottom temperatures in winter have warmed by 1.6°C (1980 2004; [Dulvy et al., 2008]). The whole demersal fish
community shifted deeper by 3.6 m decade-1 over the period 1980 2004, although mean latitude of the whole
community did not show net displacement [Dulvy et al., 2008]. Within the community, cool-water specialists
generally shifted northward while abundant warm-water species shifted southward, reflecting winter warming of the
southern North Sea. The cold winter temperatures of the shallow regions of the southern North Sea have acted to
exclude species with warm-water affinities. Trawl survey data from the rapidly-warming southern North Sea
suggests waves of immigration by southern species such as red mullet (Mullus surmuletus), anchovy (Engraulis
encrasicholus), and sardines (Sardina pilchardus), linked to increasing population sizes and warming temperatures
[Beare et al., 2004; Beare et al., 2005].
In the North-east Atlantic, range expansions and contractions linked to changing climate have also been observed in
benthic crustaceans, bivalves, gastropods, and polychaetes (medium confidence) [Mieszkowska et al., 2007;
Beukema et al., 2009; Berke et al., 2010]. For example, the southern range limit of the common intertidal barnacle
Semibalanus balanoides contracted northward along European coastlines at a rate of 15 50 km decade-1 since 1872,
and its retreat is attributed to reproductive failure as winter temperatures warm [Southward et al., 2005; Wethey and
Woodin, 2008]. Chthamalus montagui, its warm-water competitor, increased in abundance to occupy the niche
vacated by S. balanoides (high confidence) [Southward et al., 1995; Poloczanska et al., 2008].
Many of the longest and most comprehensive time series used to investigate the ecological consequences of climate
fluctuations and fishing, that span periods of cooling and warming over the past century, are from the North-east
Atlantic [Toresen and Ostvedt, 2000; Southward et al., 2005; Sundby and Nakken, 2008; Edwards et al., 2010;
Poloczanska et al., 2013]. Meta-analysis of 288 long-term datasets (spanning up to 90 years) of zooplankton,
benthic invertebrates, fish, and seabirds from the OSPAR Commission Maritime Area in the North-east Atlantic
showed widespread changes in distribution, abundance, and seasonality that were consistent (77%) with
expectations from enhanced greenhouse warming [Tasker, 2008]. The study bought together evidence of changes in
ocean climate and ecological responses across a range of species that encompassed both exploited and unexploited
species from a variety of information types including peer-reviewed reports from International Council for the
Exploration of the Sea (ICES) Working Groups. In particular, observations indicated poleward shifts in zooplankton
communities, increasing abundance of fish species in the northern part of their ranges and decreases in southern
parts, and the expansion of benthic species into more northerly or less coastal areas (high confidence).
The major portion of the literature on the influence of climate change on the North Atlantic region covers time spans
that are longer than for most other sub-regions of the Ocean. Even here, however, the bulk of the literature is limited
to the last 30 50 years. The few publications covering the first half of the 20th Century represent an important
longer-term perspective on the influence of climate change [Toresen and Ostvedt, 2000; Drinkwater, 2006; Sundby
and Nakken, 2008; Banón, 2009; Astthorsson et al., 2012]. For example, distinct changes in fauna were associated
with a pronounced warming period over 1920 1940 [Wood and Overland, 2010], when fish and other fauna shifted
northward [Iversen, 1934; Southward et al., 2005; Drinkwater, 2006; Hátún et al., 2009]. The major lesson from
these reports is that a rapid large-scale temperature increase occurred in the high latitude North Atlantic between the
1920s and 1940s, with basin-scale consequences for marine ecosystems that are comparable to warming and
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observed impacts over the last 30 years. The former event was of great concern within the scientific community,
particularly during the late 1940s and early 1950s [Iversen, 1934; Taning, 1949; Taning, 1953; Southward, 1980].
However, with the subsequent long-term cooling in the 1970s, discussion around climate responses was
discontinued [Southward, 1980]. The centennial-long perspective indicates that multi-decadal variability has played
a major role in changes observed over the past 30 years. The 150-yr instrumental record shows distinct warm phases
of the Atlantic Multidecadal Oscillation (AMO) during approximately 1930 1965 and from 1995, and cool phases
between approximately 1900 1930 and 1960 1995 (WGI 14.7.6). However, it is virtually certain that the enhanced
warming in recent decades cannot be explained without external forcing (WGI 10.3.1.1.3). Understanding the
changes in interdecadal variability over the next century is particularly important. The current warm phase of the
AMO is likely to terminate in the next few decades, leading to a cooling influence in the North Atlantic and
potentially offsetting some of the effects of global warming (WGI 14.7.6, 11.3.2.4.1). Over the transition period, the
climate of the North Atlantic is likely to change more rapidly than during previous transitions since 1900.
30.5.1.1.2. North Pacific
Sub-decadal variability in the North Pacific HLSBS is dominated by ENSO ([Trenberth, 1990]; WGI 14.4). Unlike
the North Atlantic HLSBS, the North Pacific HLSBS does not show any significant trends in temperature over time,
very likely as a consequence of climate variability influences on long-term warming patterns (1950 2009; Table 30-
1). Decadal and longer periods of variability in the North Pacific are reflected in the principal mode, the Pacific
Decadal Oscillation (PDO; WGI 14.7.3), with periodicities in SST of both 15 25 y and 50 70 y [Minobe, 1997;
Mantua and Hare, 2002]. Further modes of climate variability include the North Pacific Gyre Oscillation (NPGO;
[Di Lorenzo et al., 2008; Chhak et al., 2009]. The PDO exhibits SST anomalies of one sign along the eastern
boundary and the opposite sign in western and central Pacific. The PDO has been reported to have an anthropogenic
component [Bonfils and Santer, 2011] but confidence in this is very low (limited evidence, low agreement) (WGI
10.3.3). The interplay of the phases of these modes of variability has strong influence on high latitude Pacific
ecosystems (very high confidence). In the space of three years, the eastern North Pacific fluctuated from one of the
warmest years in the past century (2005) to one of the coldest (2008) [McKinnell et al., 2010; McKinnell and Dagg,
2010]. This rapid change was accompanied by large changes in primary productivity, zooplankton communities, and
fish and seabird populations [McKinnell et al., 2010; McKinnell and Dagg, 2010; Batten and Walne, 2011; Bi et al.,
2011; Keister et al., 2011].
Climate transitions among phases of variability tend to be characterized by abrupt reorganization of the ecosystems
as dynamic trophic relationships among species alter [Hunt et al., 2002; Peterson and Schwing, 2003; Litzow and
Ciannelli, 2007; Litzow et al., 2008; Alheit, 2009]. Periods of broad-scale environmental change were observed
across high latitude ecosystems in the North Pacific HLSBS (eastern Bering Sea and Gulf of Alaska) during 1976
78, 1987 89 and 1998 99. These periods were associated with regime shifts in foraging fish that occurred in 1979
82, 1988 92 and 1998 2001. The changes indicate how basin-scale variability such as the PDO can manifest across
distinct ecosystems [Overland et al., 2008; Link et al., 2009a; Link et al., 2009b]. Phenological shifts observed in the
zooplankton communities of the North Pacific were very likely in response to decadal climate variability, with
distinct changes noted after the climate shifts of the 1970s and 1990s [Mackas et al., 1998; Peterson and Schwing,
2003; Chiba et al., 2006]. Modeling evidence suggests a weak shift in PDO towards more occurrences of the
negative phase but credibility of projections remains uncertain (WGI 14.7.3). It is about as likely as not that the
PDO will change its form or behavior in the future (WGI 14.7.3).
The Kuroshio-Oyashio Extension (KOE) in the North-west Pacific displays pronounced decadal-scale variability
[Yatsu et al., 2008; Sugisaki et al., 2010]. Warm periods in the mid-1970s and late-1980s were accompanied by
dramatic changes in pelagic ecosystems and sardine and anchovy stocks [Chiba et al., 2008; Yatsu et al., 2008].
Observations and climate model simulations indicate that global warming is likely to further alter the dynamics of
the Kuroshio Current and the KOE over the coming century [McPhaden and Zhang, 2002; Sakamoto et al., 2005;
Wu et al., 2012; Zhang et al., 2013]. Alteration of the KOE will alter the timing, magnitude, and structure of spring
blooms in the western Pacific and have implications for pelagic fish recruitment, production, and biogeochemical
cycles [Ito et al., 2004; Hashioka et al., 2009; Yatsu et al., 2013].
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Commercial catches of salmon species in the North Pacific HLSBS follow decadal fluctuations in climate [Hare and
Mantua, 2000; Mantua and Hare, 2002]. Catches peaked in the warm periods of the 1930s 1940s and 1990s 2000s,
with 2009 yielding the highest catch to date, and warming trends are about as likely as not to have contributed to
recent peaks in some sub-regions [Morita et al., 2006; Irvine and Fukuwaka, 2011]. Poleward range shifts of some
large pelagic fish in the western North Pacific, such as yellowtail Seriola quinqueradiata and Spanish mackerel
Scomberomorus niphonius, were attributed, in part, to regional warming (high confidence) and these two species are
projected to shift 39 71 km poleward from the 2000s to 2030s under SRES A1B [Tian et al., 2012; Jung et al.,
2013]. Anticipating ecological responses to future anthropogenic climate change also requires evaluation of the role
of changes to climate beyond warming per se. For example, declining sea level pressure (SLP) in the North Pacific
is likely influenced by anthropogenic forcing [Gillett et al., 2003; Gillett and Stott, 2009] (WGI 10.3.3.4) and SLP in
turn is related to atmospheric climate parameters (e.g., turbulent mixing via wind stress) that regulate commercially
significant fish populations [Wilderbuer et al., 2002].
The northern fringe of the Bering Sea is among the most productive of marine sub-regions and includes the world s
largest single-species fishery, walleye pollock Theragra chalcogramma [Hunt et al., 2010]. This region underwent
major changes in recent decades as a result of climate variability, climate change, and fishing impacts ([Litzow et al.,
2008; Mueter and Litzow, 2008; Jin et al., 2009; Hunt et al., 2010]; 28.2.2.1). Seasonal sea ice cover declined since
the 1990s (to 2006), although there is no linear trend between 1953 and 2006, and the initiation of spring ice retreat
over the south-eastern Bering Sea shelf became earlier [Wang et al., 2007a; Wang et al., 2007b]. Concurrent with
the retreat of the cold pool , an area of reduced water temperature (<2°C) on the northern Bering Sea shelf that is
formed as a consequence of sea ice and is maintained over summer [Hunt et al., 2010], bottom trawl surveys of fish
and invertebrates show a significant community-wide northward distribution shift and a colonization of the former
cold pool areas by sub-arctic fauna (high confidence) [Wang et al., 2006a; Mueter and Litzow, 2008].
Over a vast region of the eastern Pacific stretching from southern Chile to the Aleutian Islands, waters low in
dissolved O2 (Oxygen Minimum Zone, OMZ) are found at 300 m depth [Karstensen et al., 2008]. Sporadic
upwelling of these low-O2 waters along the continental shelf is well documented, where biological respiration can
further reduce dissolved O2 levels and result in hypoxic or anoxic conditions that lead to mortality of coastal fishes
and invertebrates [Grantham et al., 2004; Chan et al., 2008]. The magnitude and severity of seasonal hypoxic
conditions in shallow-shelf waters of the eastern North Pacific HLSBS increased in recent decades [Bograd et al.,
2008; Chan et al., 2008]. In addition, minimum pH values in the water column usually occur near the depths of the
OMZ (WGI Box 3.2). A shoaling of the aragonite saturation horizon has likely resulted in low-aragonite conditions
within the density layers being upwelled on the shelf of the west coast of the USA, increasing the risk of seasonally-
upwelled water being relatively acidified [Feely et al., 2008] with observed impacts on Pacific oyster (Crassostrea
gigas) hatcheries [Barton et al., 2012]. In the time period 1991 2006, reductions in pH in the North Pacific between
800 m and ~100 m were attributed in approximately equal measure to anthropogenic and natural variations ([Byrne
et al., 2010]; WGI 3.8.2, Figure 3.19).
30.5.1.1.3. Southern Hemisphere
The seasonal peaks in phytoplankton productivity in the southern hemisphere are much less pronounced and of
smaller magnitude than those at northern hemisphere high latitudes [Yoder et al., 1993]. The southern hemisphere
HLSBS is broadly bounded by the sub-tropical front (STF) and the sub-Antarctic front. Associated with the STF is
intense biological activity of bloom-forming coccolithophores (phytoplankton) [Brown and Yoder, 1994]. The
calcifying plankton assemblages play a key role in carbon cycles in the region and the transport of carbon to deep
ocean sediments. The coccolithophore Emiliania huxleyi extended its range south of 60° in the South-west Pacific
(141 145°E) over the two decades since 1983 [Cubillos et al., 2007]. Although the drivers for this range extension
are not clear, it was proposed that the extension is facilitated by surface warming or changes in the abundance of
grazing zooplankton.
Large regions of the sub-Antarctic and Arctic surface waters are likely to become undersaturated with respect to
aragonite during winter by 2030, which will impact calcifying plankton and Southern Ocean ecosystems ([McNeil
and Matear, 2008; Bednar¹ek et al., 2012]; 28.2.2.2). Shell weights of the modern foraminifer Globigerina bulloides
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in the sediments of the sub-Antarctic region of the HLSBS south of Australia were observed to be 30 35% lower
than those from sediment cores representing pre-industrial periods, consistent with a recent decline in pH [Moy et
al., 2009]. Examination of the pteropod Limacina helicina antarctica captured from polar waters further south, show
severe levels of shell dissolution consistent with the shoaling of the aragonite saturation horizon and indicate that the
impact of ocean acidification is already occurring [Bednar¹ek et al., 2012].
While the South Pacific HLSBS has not shown warming overall, both the warmest and coolest months show a
slight, but significant, increase over time (both 0.05°C decade-1 from 1950 2009, p-value < 0.05, Table 30-1),
although some areas within this sub-region have warmed. For example, the western Tasman Sea has shown
enhanced warming since 1900 as compared to average global trends (high confidence). This has been driven by
changes in large-scale wind-forcing leading to a southward expansion of the South Pacific STG and intensification
of the southward-flowing East Australian Current (EAC; [Cai, 2006; Hill et al., 2008; Wu et al., 2012]; WG1 3.6.2).
Model simulations suggest both stratospheric ozone depletion and greenhouse forcing contribute to the observed
trend in wind stress [Cai and Cowan, 2007]. Coinciding with this warming and intensified EAC is the observation
that a number of benthic invertebrates, fish, and zooplankton are now found further south than they were in the mid-
20th Century [Ling, 2008; Pitt et al., 2010; Last et al., 2011]. Warming facilitated the establishment of the grazing
urchin Centrostephanus rodgersii in eastern Tasmania during the late 1970s (high confidence), which has resulted in
deleterious effects on macroalgal beds [Ling, 2008; Ling et al., 2008; Ling et al., 2009; Banks et al., 2010].
30.5.1.2. Key Risks and Vulnerabilities
Projected changes to the temperature of surface waters match those of the past 50 years, with average sea
temperatures in the HLSBS regions projected to increase by 0.35 1.17°C in the near-term (2010 2039) and by
1.70 4.84°C over the long-term (2010 2099) under the Business as usual (BAU) RCP8.5 scenario (Table SM30-
4). Under the lower-case scenario considered here (RCP2.6), projected rates of regional warming are much lower
(0.12-0.79°C) in the near-term, with slight cooling for some regions in the long-term (-0.16 1.46°C). Risks to
HLSBS from warming of surface waters include changes to primary production and carbon cycling, and the
reorganization of ecosystems in response to warmer and more acidified oceans. Both primary production and the
timing of the spring bloom in HLSBS are very sensitive to environmental change. Latitudinal shifts in the
distribution of phyto- and zooplankton communities will alter seasonality, community composition, and bloom
dynamics [Beaugrand, 2009; Ito et al., 2010; Shoji et al., 2011]. Alteration of the structure and composition of
plankton communities can propagate through high latitude food webs due to tight trophic linkages [Edwards and
Richardson, 2004; Beaugrand et al., 2010; Beaugrand and Kirby, 2010]. Mechanisms are complex, and tend to be
non-linear, with impacts on ecosystems, fisheries, and biogeochemical cycles being hard to project with any
certainty (Box CC-PP). A reorganization of commercial fish stocks, with attendant social and economic disruption,
is a key risk of ongoing climate change in HLSBS sub-regions. AR4 reported that the productivity of some marine
fisheries is likely to increase in the North Atlantic (AR4 WGII 10.4.1, 12.4.7). A large number of publications since
then has substantially extended such documentation and begun to elucidate the nuances in how marine ecosystems
and organisms respond [Sumaila et al., 2011].
An additional risk exists for sub-polar areas from the loss of seasonal sea ice. Decreases in seasonal sea ice are very
likely to lead to increases in the length of the growth season and the intensity of the light available to fuel
phytoplankton growth and, hence, enhance primary production and attending modifications of ecosystem structure
[Arrigo et al., 2008]. In the long-term, however, primary production may decrease due to the reduced supply of
nutrients to the surface layers (Box CC-PP). The decline in Arctic sea ice will open ecological dispersal pathways,
as well as new shipping routes (30.6.2.3), between the North Atlantic and the North Pacific; large numbers of the
Pacific diatom Neodenticula seminae were found in the North Atlantic in 1999 [Reid et al., 2007].
HLSBS are also vulnerable to rapid changes in the carbonate chemistry of ocean waters. Ocean acidification will
produce additional and large-scale challenges. There is medium agreement that calcifying organisms in these regions
will be negatively affected by ocean acidification with substantial impacts on higher trophic levels, although there is
limited evidence at this point.
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30.5.2. Equatorial Upwelling Systems
The largest upwelling systems are found in the equatorial regions of the eastern Pacific and Atlantic Oceans (Figure
30-1a). Equatorial Upwelling Systems (EUS) produce highly productive cold tongues that stretch westward across
equatorial areas, which is different to other upwelling systems (e.g., EBUE; 30.5.5). The associated upwelling is a
consequence of the Earth s rotation and easterly (westward) winds and currents, which drive water northwards and
southwards at the northern and southern edges of these sub-regions. As result, cold, nutrient-rich, and high CO2/low
pH waters are transported from the deeper layers of the Ocean to the surface, driving high levels of primary
productivity that support 4.7% of total global fisheries productivity (Table SM30-1, Figure 30-1b). Interannual
modes of variability (e.g., ENSO; WGI 14.4) dominate EUS, particularly in the Pacific [Barber et al., 1994;
McCarthy et al., 1996; Signorini et al., 1999; Le Borgne et al., 2002; Christian and Murtugudde, 2003; Mestas-
Nunez and Miller, 2006; Pennington et al., 2006; Wang et al., 2006b]. Upwelling of the Pacific EUS declines during
El Nino events, when the trade winds weaken, or even reverse, and is strengthened during La Nina events. ENSO
periodicity controls primary productivity and consequently has a strong influence over associated fisheries
production [Mestas-Nunez and Miller, 2006]. The Intertropical Convergence Zone (ITCZ; WGI 14.3.1.1), an
important determinant of regional ocean temperature, is located at the edges of the Indian and Pacific equatorial
upwelling zone and influences a range of variables including productivity, fisheries, and precipitation. The EUS are
also affected by interdecadal variability (e.g., Interdecadal Pacific Oscillation (IPO); [Power et al., 1999]; WGI
11.2.2, 14.3).
30.5.2.1. Observed Changes and Potential Impacts
The average sea temperature associated with the EUS has increased significantly (p-value<0.05) by 0.43°C and
0.54°C from 1950 2009 in the Pacific and Atlantic EUS, respectively [Table 30-1). In the Pacific, regional
variability in SST trends is driven by the temporal patterns in El Nino-Southern Oscillation and the more frequent El
Nino Modoki or Central Pacific El Nino events in recent decades (high confidence) ([Ashok et al., 2007; Yu and
Kao, 2007; Lee and McPhaden, 2010]; WGI 14.2.4.4). The faster warming of the Atlantic EUS is likely to be
associated with a weakening of upwelling [Tokinaga and Xie, 2011]. Sea level rise in the eastern equatorial Pacific
has been decreasing by up to 10 mm yr-1 since 1993 ([Church et al., 2006]; Figure 30-5).
Coral reefs in the EUS of the eastern Pacific (e.g., Galápagos and Cocos islands) have relatively low species
diversity and poorly developed carbonate reef frameworks, due to the low pH and aragonite saturation of upwelling
waters (high confidence) [Glynn, 2001; Manzello et al., 2008; Manzello, 2010]. Prolonged periods of elevated
temperature associated with El Nino have negatively affected corals, kelps and associated organisms, and induced
several possible local extinctions (high confidence) [Glynn, 2011]. Since 1985, coral reefs from west of South
America to the Gilbert Islands of Kiribati have experienced the highest levels of thermal stress relative to other areas
[Donner et al., 2010]. In 1982/1983, mass coral bleaching and mortality affected most of the reef systems within the
eastern equatorial Pacific [Glynn, 1984; Baker et al., 2008]. Subsequent canonical El Nino and Central Pacific El
Nino events in 1997/8, 2002/3, 2004/5, and 2009/10 (WGI 14.4.2, Figure 14.13) triggered mass coral bleaching by
adding to the background increases in sea temperatures (high confidence) [Donner et al., 2010; Obura and
Mangubhai, 2011; Vargas-Ángel et al., 2011]. In some locations, impacts of El Nino have also interacted with other
anthropogenic changes, such as those arising from changes to fishing pressure [Edgar et al., 2010], further
complicating the attribution of recent ecological changes to climate change.
30.5.2.2. Key Risks and Vulnerabilities
Climate models indicate that ENSO is virtually certain to continue to be a major driver of oceanic variability over
the coming century, although not all models can accurately replicate its behavior (WGI 9.5.3). Superposition of a
warming ocean on future ENSO activity (possibly modified in frequency and intensity) is likely to result in oceanic
conditions that are different from those experienced during past El Nino and La Nina events [Power and Smith,
2007]. Temperatures within EUS sub-regions are projected to continue to warm significantly (p-value<0.05). Under
RCP8.5, SST of the Atlantic EUS is projected to increase by 0.81°C over 2010 2039 and 2.56°C over 2010 2099,
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with similar increases projected for the Pacific EUS (Table SM30-4). Differences between RCPs for the two EUS
become clear beyond mid-century, with warming of SST over 2010 2099 being 0.43°C and 0.46°C under RCP2.6
and 3.01°C and 3.03°C under RCP8.5, for Pacific and Atlantic EUS respectively (Table SM30-4). These projected
increases in sea temperature will increase heat stress and ultimately irreversibly degrade marine ecosystems such as
coral reefs (very likely). Further increases in atmospheric CO2 will cause additional decrease in pH and aragonite
saturation of surface waters (adding to the low pH and aragonite saturation of upwelling conditions), with significant
differences between emission trajectories by the middle of the century. These changes in ocean carbonate chemistry
are very likely to negatively affecting some marine calcifiers, although many of the species from this region are
adapted to the low aragonite and calcite saturation states that result from equatorial upwelling, albeit with much
lower rates of calcification [Manzello, 2010; Friedrich et al., 2012]. A substantial risk exists with respect to the
synergistic interactions between sea temperature and declining pH, especially as to how they influence a large
number of key biological processes (Box CC-OA).
There is low confidence in the current understanding of how (or if) climate change will influence the behavior of
ENSO and other long-term climate patterns ([Collins et al., 2010]; WGI 12.4.4.2). There is also low agreement
between different CMIP5 GCMs on how ocean warming will affect ENSO, with no significant change to ENSO
amplitude in half the models examined, and both increasing and decreasing activity in others [Guilyardi et al.,
2012]. These differences appear to be a consequence of the delicate balance within ENSO between dampening and
amplifying feedbacks, and the different emphasis given to these processes within the different GCMs [Collins et al.,
2010]. Other studies have looked at the interaction between the STG and EUS, and warming of surface waters in the
Pacific, with at least one study projecting the possible expansion of the STG at the expense of the EUS [Polovina et
al., 2011]. In the latter case, the area of equatorial upwelling within the North Pacific would decrease by 28%, and
primary production and fish catch by 15%, by 2100. Many of the projected changes imply additional consequences
for pelagic fisheries due to the migration of fish stocks deriving from changing distribution of particular sea
temperatures [Lehodey et al., 2006; Lehodey et al., 2008; Cheung et al., 2010; Lehodey et al., 2011; Sumaila et al.,
2011; Bell et al., 2013b]. These projections suggest that fisheries within EUS will experience increased vulnerability
due to elevated variability in space and time as a result of climate change (low confidence).
30.5.3. Semi-Enclosed Seas
Semi-Enclosed Seas (SES) represent a subset of ocean sub-regions that are largely land-locked and consequently
heavily influenced by surrounding landscapes and climates [Healy and Harada, 1991]. In most cases, they support
small but regionally significant fisheries (3.3% of global production; Table SM30-1, Figure 30-1b) and opportunities
for other industries such as tourism. Five SES (all over 200,000 km with single entrances <120 km wide) are
considered here. This particular geography results in reduced circulation and exchange with ocean waters, and
jurisdictions for these water bodies are shared by two or more neighboring states. In many cases, the small volume
and disconnected nature of SES (relative to coastal and oceanic environments) makes them highly vulnerable to both
local and global stressors, especially with respect to the much reduced options for the migration of organisms as
conditions change.
30.5.3.1. Observed Changes and Potential Impacts
30.5.3.1.1. Arabian Gulf
The Arabian Gulf (also referred to as the Persian Gulf), along with Red Sea, is the world's warmest sea, with both
extreme negative and positive temperature excursions (annual temperature range of 12 35°C). Like other SES, the
Arabian Gulf is particularly vulnerable to changing environmental conditions as a result of its landlocked nature.
Trends in SST were not significant over the period 1950 2009 (Table 30-1), which is probably due to long-term
variability, and a consequence of regional and abrupt changes that occurred in the late 1980s [Conversi et al., 2010].
In keeping with this, recent (1985 2002) localized analyses (e.g., Kuwait Bay) show strong and significant warming
trends (based in this case on AVHRR (NOAA) satellite data) of 0.6°C decade-1 [Al-Rashidi et al., 2009]. There is
limited evidence and low agreement as to how this variability influences marine ecosystems and human activities
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within the Arabian Gulf, although impacts on some ecosystem components (e.g., coral reefs) have been defined to
some extent. The mass coral bleaching and mortality that occurred in 1996 and 1998 were a direct result of the
sensitivity of reef-building corals to elevated sea temperatures (high confidence) ([Riegl, 2002; 2003]; Box CC-CR).
These changes to coral reefs have resulted in a loss of fish species that feed on coral-associated invertebrates while
herbivores and planktivorous fish abundances have increased (medium confidence) [Riegl, 2002]. Despite coral
ecosystems in this sub-region being adapted to some of the highest temperatures in shallow seas on earth,
anthropogenic climate change is driving higher frequencies and intensities of mass coral bleaching and mortality
[Riegl et al., 2011]. Other biological changes (e.g., harmful algal blooms and fish kills, [Heil et al., 2001]) have been
associated with the increasing sea temperatures of the Arabian Gulf, although attribution to increasing temperatures
as opposed to other factors (e.g., water quality) is limited [Bauman et al., 2010].
30.5.3.1.2. Red Sea
Few studies have focused on attributing recent changes in the Red Sea ecosystems to climate change (including
ocean acidification). The Red Sea warmed by 0.74°C from 1982 2006 [Belkin, 2009], although trends in the average
SST, however, are not significant from 1950 2009 (p-value>0.05, Table 30-1) due to a high degree of variability
when longer periods were examined (supplementary material in [Belkin, 2009]). The temperature of the warmest
month of the year, however, showed a significant increase over the 60-year period (0.05°C decade-1; Table 30-1).
Regional trends within the Red Sea may also differ, with at least one other study reporting higher rates of warming
for the central Red Sea (1.46°C, relative to 1950 1997 NOAA Extended Reconstructed SST (ERSST) v3b
climatology [Cantin et al., 2010]).
Long-term monitoring of coral community structure and size over 20 years shows that average colony size of corals
has declined (high confidence) and species latitudinal limits may have changed (medium confidence). The decline
in average colony size is ascribed to heat-mediated bleaching as well as increases in coral diseases and Crown of
Thorns Starfish (Acanthaster sp.) predation [Riegl et al., 2012]. The patterns of this decline correlate well with the
pattern of recent heating in the Red Sea [Raitsos et al., 2011] with the biggest changes being seen in the southern
part of the Red Sea. Skeletal growth of the long-lived massive coral Diploastrea heliopora has declined
significantly, very likely due to warming temperatures (medium confidence) (p-value<0.05; [Cantin et al., 2010].
Cantin et al. [2010] proposed that the massive coral Diploastrea helipora will cease to grow in the central Red Sea
by 2070 under SRES A1B and A2 (medium confidence), although this may not hold for other coral species. For
example, an increase in linear extension of Porites corals, beginning in the 1980s, was recorded in the northern Red
Sea [Heiss, 1996], where temperatures have increased by 0.74C from 1982-2006 [Belkin, 2009] suggesting that
these corals were living in sub-optimal conditions (cooler waters). They may therefore benefit from elevated
temperature before reaching their thermal threshold, at which point growth rates would be predicted to decline, as
they are doing in other oceans. Riegl and Piller [2003] concluded that coral habitats at moderate depths in the Red
Sea might provide important refugia from some aspects of climate change in the future (limited evidence). Silverman
et al. [2007] quantified the sensitivity of net coral reef ecosystem calcification to changes in carbonate chemistry
(pH, aragonite saturation). Their results demonstrate a strong negative effect of ocean acidification on ecosystem-
scale calcification and decalcification, and show that small changes in carbonate dissolution could have large-scale
implications for the long-term persistence of carbonate coral reef systems within the Red Sea [Silverman et al.,
2007; Silverman et al., 2009].
30.5.3.1.3. Black Sea
The temperature of the surface waters of the Black Sea increased by 0.96C from 1982 2006 [Belkin, 2009], which
is consistent with other studies (high confidence) [Buongiorno Nardelli et al., 2010; Bozkurt and Sen, 2011]. As
with other SES (i.e., Arabian Gulf and Baltic, Mediterranean, and Red Seas), longer data sets do not reveal a
significant trend due to large-scale variability prior to 1982, which may be due to the influence of AMO, NAO, and
other long-term sources of variability (Table 30-1; supplementary material in Belkin, 2009). Buongiorno Nardelli et
al. [2010] observed that short-term SST variability (week-month) is strongly influenced by interactions with the
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overlying atmosphere, which itself is strongly influenced by the surrounding land temperatures. As with the
Mediterranean and Red Seas, however, a significant upward trend in the temperature is recorded in the warmest
month of the year over the period 1950 2009 (Table 30-1). Freshwater discharge from rivers draining into the Black
Sea has remained more or less constant since the early 1960s [Ludwig et al., 2009]. Increasing water temperature has
steadily eliminated the Cold Intermediate Layer (CIL; temperatures below 8°C) throughout the Black Sea basin over
1991 2003 (high confidence)[Oguz et al., 2003]. Reduced water column mixing and upwelling during warmer
winter periods has reduced the supply of nutrients to the upper layers of the Black Sea [Oguz et al., 2003] and
expanded areas of low O2 in the deeper parts of the Black Sea, which is the world's largest anoxic marine basin (high
confidence) [Murray et al., 1989]. These changes coincided with the collapse of fish stocks and the invasion by the
ctenophore Mnemiopsis leidyi in the 1980s [Oguz et al., 2008], while inputs of nutrients such as phosphate from the
Danube River decreased strongly since 1992 1993 [Oguz and Velikova, 2010]. Environmental perturbations explain
the declining levels of primary productivity, phytoplankton, bacterioplankton, and fish stocks in the Black Sea from
the mid-1990s [Yunev et al., 2007; Oguz and Velikova, 2010]. The Black Sea system is very dynamic and is strongly
affected by non-climate stressors in addition to climate change, making attribution of detected trends to climate
change difficult.
30.5.3.1.4. Baltic Sea
Temperatures in the highly dynamic Baltic Sea increased substantially since the early 1980s [Aleksandrov et al.,
2009; Belkin, 2009], with increases of 1.35°C (1982 2006) being among the highest for any SES [Belkin, 2009].
Increases of this magnitude are not seen in longer records throughout the Baltic Sea (1861 2001, [MacKenzie et al.,
2007; MacKenzie and Schiedek, 2007b; a]; 1900 1998, [Madsen and Hjerslev, 2009]). The salinity of the surface
and near bottom waters of the Baltic Sea (e.g., Gdansk Basin, [Aleksandrov et al., 2009]; central Baltic [Fonselius
and Valderrama, 2003; Möllmann et al., 2003] decreased from 1975 2000, due to changing rainfall and river run-
off, and a reduction in the pulses of sea water (vital for oxygenation and related chemical changes) from the North
Sea through its opening via the Kattegat (high confidence) [Samuelsson, 1996; Conley et al., 2009; Hänninen and
Vuorinen, 2011]. There is a strong vertical zonation within the Baltic Sea in terms of the availability of O2. The
shallow sub-regions of the Baltic are relatively well oxygenated. However, O2 levels are low in the deeper basins,
producing conditions where organisms and ecosystems are exposed to prolonged hypoxia.
The annual biomass of phytoplankton has declined almost threefold in the Baltic Transition Zone (Kattegat, Belt
Sea) and Western Baltic Sea since 1979 [Henriksen, 2009], reputedly due to changing nitrogen loads in the Danish
Straits (medium confidence) in addition to increasing sea temperature (very likely) [Madsen and Hjerslev, 2009].
Reduced phytoplankton production may have reduced the productivity of fisheries in the western Baltic Sea and the
Transition Zone (low to medium confidence) [Chassot et al., 2007]. Decreasing salinity in the Baltic deep basins
may also affect zooplankton reproduction, especially that of the copepod Pseudocalanus acuspes, contributing to
density-dependent decrease in growth of the commercially important herring and sprat stocks (high confidence)
[Möllmann et al., 2003; Möllmann et al., 2005; Casini et al., 2011]. The strong relationship between phytoplankton
and fish production, and increasing sea temperature, decreasing salinity and other environmental factors, suggests
that major changes in fisheries production will occur as sea temperatures increase and the hydrological cycle in the
Baltic region changes (high confidence) [MacKenzie et al., 2012]. A combination of climate change-induced
oceanographic changes (i.e., decreased salinity and increased temperatures), eutrophication, and overfishing have
resulted in major changes in trophic structure in the deep basins of the Baltic Sea [Möllmann et al., 2009]. This had
important implications for cod, a commercially important top-predator (medium confidence)[Lindegren et al., 2010].
30.5.3.1.5. Mediterranean Sea
The Mediterranean Sea is strongly linked to the climates of North Africa and Central Europe. SST within the
Mediterranean increased by 0.43°C from 1957 2008 (supplementary material, [Belkin, 2009]), although analysis of
data from 1950 2009 only detected a significant trend in summer temperature (0.11°C decade-1, p-value<0.05, Table
30-1) due to large fluctuations in SST prior to the 1980s. Surface temperatures increased in the Mediterranean Sea
consistent with significant increases in SST at a number of monitoring sites (high agreement, robust evidence) (e.g.
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[Coma et al., 2009; Conversi et al., 2010; Calvo et al., 2011]). It is likely that temperatures, along with salinity, have
also increased at depth (400 m or more) in the western Mediterranean Sea over the past 30-40 years which, when
analyzed in the context of heat budget and water flux of the Mediterranean, is consistent with anthropogenic
greenhouse warming [Bethoux et al., 1990; Rixen et al., 2005; Vargas-Yánez et al., 2010]. Large scale variability
such as the AMO and NAO can obscure or accentuate the overall warming trend ([Marullo et al., 2011]; WGI
14.5.1, 14.7.6). Relatively warm episodes in the 1870s, 1930 1970s and since the mid-1990s, for example, exhibit
an influence of the AMO [Kerr, 2000; Moron, 2003]. Reported temperature anomalies in the Mediterranean, often
locally manifesting themselves as periods of low wind, increased water column stratification, and a deepening
thermocline, are associated with positive phases of the NAO index [Molinero et al., 2005; Lejeusne et al., 2010].
Sea levels have increased rapidly in some areas over recent decades and are also strongly influenced by NAO
phases. The rate has been approximately 3.4 mm yr-1 (1990 2009) in the North-west Mediterranean (high
confidence) [Calvo et al., 2011]. These influences are reduced when measurements are pooled over longer time-
scales, resulting in a lower rate of sea level rise [Massuti et al., 2008]. If the positive phase of the NAO is more
frequent in the future ([Terray et al., 2004; Kuzmina et al., 2005]; WGI 14.4.2), then future sea level rise may be
slightly suppressed due to atmospheric influences (medium confidence) [Jorda et al., 2012]. As temperatures have
increased, the Mediterranean has become more saline (+0.035 0.040 psu from 1950 2000, [Rixen et al., 2005]) and
the length of the thermal stratification period persisted twice as long in 2006 as it did in 1974 [Coma et al., 2009].
Conditions within the Mediterranean Sea changed abruptly and synchronously with similar changes across the
North, Baltic, and Black Seas in the late 1980s [Conversi et al., 2010], which possibly explains the lack of trend in
SES SST when examined from 1950 2009 (Table 30-1). These changes in physical conditions (increased
temperature, higher sea level pressure, positive NAO index) also coincided with step-changes in the diversity and
abundance of zooplankton, decreases in stock abundance of anchovies, decreases in the frequency of red tides , and
increases in mucilage outbreaks [Conversi et al., 2010]. Mucilage outbreaks are strongly associated with warmer
and more stratified water columns (high confidence), and lead to a greater abundance and diversity of marine
microbes and potentially disease-causing organisms (likely) [Danovaro et al., 2009]. Increasing temperatures are
also driving the northward spread of warm-water species (medium confidence) such as the sardine Sardinella aurita
[Sabatés et al., 2006; Tsikliras, 2008], and have contributed to the fast spread of the invading Atlantic coral Oculina
patagonia [Serrano et al., 2013]. The recent spread of warm-water species that have invaded through the Straits of
Gibraltar and the Suez Canal into cooler northern areas is leading to the tropicalisation of Mediterranean fauna
(high confidence) [Bianchi, 2007; Ben Rais Lasram and Mouillot, 2008; CIESM, 2008; Galil, 2008; 2011]. Warming
since the end of the 1990s has accelerated the spread of tropical invasive species from the eastern Mediterranean
basin ([Raitsos et al., 2010]; 23.6.5).
In addition to general patterns of warming, periods of extreme temperatures have had large-scale and negative
consequences for Mediterranean marine ecosystems. Unprecedented mass mortality events, that affected at least 25
prominent invertebrate species, occurred during the summers of 1999, 2003, and 2006 across hundreds of kilometers
of coastline in the North-west Mediterranean Sea (very high confidence) [Cerrano et al., 2000; Garrabou et al.,
2009; Calvo et al., 2011; Crisci et al., 2011]. Events coincided with either short periods (2 5 days: 2003, 2006) of
high sea temperatures (27°C) or longer periods (30 40 days) of modestly high temperatures (24°C: 1999;
[Bensoussan et al., 2010; Crisci et al., 2011]). Impacts on marine organisms have been reported in response to the
extreme conditions during these events (e.g., gorgonian coral mortality [Coma et al., 2009], shoot mortality, and
anomalous flowering of seagrasses (high confidence) [Diaz-Almela et al., 2007; Marba and Duarte, 2010]. The
frequency and intensity of these types of heat stress events are expected to increase as sea temperatures increase
(high confidence).
Longer-term data series (over several decades) of changes in relative acidity of the Mediterranean Sea are scarce
[Calvo et al., 2011; MerMex-Group, 2011]. Recent re-analysis, however, has concluded that the pH of
Mediterranean waters has decreased by 0.05 0.14 pH units since the pre-industrial period (medium confidence)
[Luchetta et al., 2010; Touratier and Goyet, 2011]. Anthropogenic CO2 has penetrated the entire Mediterranean
water column, with the western basin being more contaminated than the eastern basin [Touratier and Goyet, 2011].
Studies that have explored the consequences of ocean acidification for the biology and ecology of the Mediterranean
Sea are rare [Martin and Gatusso, 2009; Rodolfo-Metalpa et al., 2010; Movilla et al., 2012], although insights have
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been gained by studying natural CO2 seeps at Mediterranean sites such as Ischia in Italy, where biodiversity
decreases with decreasing pH towards the vents, with a notable decline in calcifiers. [Hall-Spencer et al., 2008].
Transplants of corals, mollusks, and bryozoans along the acidification gradients around seeps reveal a low level of
vulnerability to CO2 levels expected over the next 100 years (low confidence) [Rodolfo-Metalpa et al., 2010 ;
Rodolfo-Metalpa et al., 2011]. However, periods of high temperature can increase vulnerability to ocean
acidification, thereby increasing the long-term risk posed to Mediterranean organisms and ecosystems as
temperatures warm. Significantly, some organisms such as seagrasses and some macroalgae appeared to benefit
from local ocean acidification [Hall-Spencer et al., 2008].
30.5.3.2. Key Risks and Vulnerabilities
SES are highly vulnerable to changes in global temperature on account of their small volume and landlocked nature.
Consequently, SES will respond faster than most other parts of the Ocean (high confidence). Risks to ecosystems
within SES are likely to increase as water columns become further stratified under increased warming, promoting
hypoxia at depth and reducing nutrient supply to the upper water column (medium evidence, high agreement). The
impact of rising temperatures on SES is exacerbated by their vulnerability to other human influences such as
overexploitation, pollution, and enhanced run-off from modified coastlines. Due to a mixture of global and local
human stressors, key fisheries have undergone fundamental changes in their abundance and distribution over the
past 50 years (medium confidence). A major risk exists for SES from projected increases in the frequency of
temperature extremes that drive mass mortality events, increasing water column stabilization leading to reduced
mixing, and changes to the distribution and abundance of marine organisms. The vulnerability of marine
ecosystems, fisheries, and human communities associated with the SES will continue to increase as global
temperatures increase.
Sea temperatures are very likely to increase in the five SES under moderate (RCP6.0) to high (RCP8.5) future
scenarios. Under BAU (RCP8.5; Table SM30-3), sea temperatures in the SES are projected to increase by 0.93
1.24°C over 2010 2039 (Table SM30-4). Increases of 3.45 4.37°C are projected over 2010 2099, with the greatest
increases projected for the surface waters of the Baltic Sea (4.37°C) and Arabian Gulf (4.26°C), and lower yet
substantial amounts of warming in the Red Sea (3.45°C) (Table SM30-4). The heat content added to these small
ocean regions is very likely to increase stratification, which will reduce the nutrient supply to the upper layers of the
water column, reducing primary productivity and driving major changes to the structure and productivity of
fisheries. Reduced mixing and ventilation, along with increased microbial metabolism, will very likely increase
hypoxia and expand the number and extent of dead zones . Changing rainfall intensity (23.3, WGI 12.4.) can exert
a strong influence on the physical and chemical conditions within SES, and in some cases will combine with other
climatic changes to transform these areas. These changes are likely to increase the risk of reduced bottom-water O2
levels to Baltic and Black Sea ecosystems (due to reduced solubility, increased stratification, and microbial
respiration), which is very likely to affect fisheries. These changes will increase the frequency and intensity of
impacts arising from heat stress, based on responses to temperature extremes seen over the past 30 years, such as the
mass mortality of benthic organisms that occurred in the Mediterranean Sea during the summers of 1999, 2003, and
2006, and the Arabian Gulf in 1996 and 1998. Extreme temperature events such as heat waves are projected to
increase (high confidence) (23.2, [IPCC, 2012]). Similar projections to those outlined in 30.5.4.2 can be applied to
the coral reefs of the Arabian Gulf and the Red Sea, where temperatures are very likely to increase above established
thresholds for mass coral bleaching and mortality (very high confidence) (Figure 30-10).
30.5.4. Coastal Boundary Systems
The Coastal Boundary Systems (CBS) are highly productive regions, comprising 10.6% of primary production and
28.0% of global fisheries production (Table SM30-1, Figure 30-1b). The CBS include the marginal seas of the
North-west Pacific, Indian, and Atlantic Oceans, encompassing: the Bohai/Yellow Sea, East China Sea, South China
Sea, and South-east Asian Seas (e.g., the Timor, Arafura, and Sulu Seas, and the northern coast of Australia) in the
Pacific; the Arabian Sea, Somali Current system, East Africa coast, Mozambique Channel, and Madagascar in the
Indian Ocean; and the Caribbean Sea and Gulf of Mexico in the Atlantic Ocean). Some CBS are dominated by
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powerful currents such as the Kuroshio (Pacific), or are strongly influenced by monsoons (e.g., Asian-Australian and
African monsoons).
30.5.4.1. Observed Changes and Potential Impacts
Many ecosystems within the CBS are strongly affected by the local activities of often-dense coastal human
populations. Activities such as the overexploitation of fisheries, unsustainable coastal development, and pollution
have resulted in the wide-spread degradation of CBS ecosystems [Burke et al., 2002; Burke et al., 2011]. These
influences have combined with steadily increasing ocean temperature and acidification to drive major changes to a
range of important ecosystems over the past 50 years. Understanding the interactions between climate change and
non-climate change drivers is a central part of the detection and attribution process within the CBS.
Overall, the CBS warmed by 0.14 0.80°C from 1950 2009 (Table 30-1), although changes within the Gulf of
Mexico/Caribbean Sea sub-region were not significant (p-value>0.05) over this period. Key sub-regions within the
CBS such as the Coral Triangle and Western Indian Ocean warmed by 0.79 and 0.60°C, respectively, from 1950
2009 (Table 30-1). Rates of sea level rise vary from decreasing sea levels (-5 to -10 mm yr-1) to low (2 3 mm yr-1,
Caribbean) to very high (10 mm yr-1, South-east Asia; Figure 30-5) rates of increase. Ocean acidification also varies
from region to region (Figure SM30-2), and is influenced by oceanographic and coastal processes, which often have
a large human component.
30.4.4.1.1. Bohai/Yellow Sea/East China Sea
The Bohai Sea, Yellow Sea and the East China Sea (ECS) are shallow marginal seas along the edge of the North-
west Pacific that are strongly influenced by the Kuroshio Current [Matsuno et al., 2009], the East Asian Monsoon
(EAM), and major rivers such as the Yellow (Huang He) River and Yangtze (Changjiang) River. Upwelling of the
Kuroshio sub-surface waters provides abundant nutrients that support high levels of primary productivity [Wong et
al., 2000; Wong et al., 2001]. The ecosystems of the ECS are heavily affected by human activities (e.g., overfishing
and pollution), which tend to compound the influence and consequences of climate change.
SST within the ECS has increased rapidly since the early 1980s (high confidence) [Lin et al., 2005; Jung, 2008; Cai
et al., 2011; Tian et al., 2012]. The largest increases in SST have occurred in the ECS in winter (1.96°C, 1955
2005) and in the Yellow Sea in summer (1.10°C, 1971 2006, [Cai et al., 2011]). These changes in SST are closely
linked to a weakening of the EAM (e.g., [Cai et al., 2006; Tang et al., 2009; Cai et al., 2011]) and increasing
warmth of the Kuroshio Current [Qi et al., 2010; Zhang et al., 2011; Wu et al., 2012]. At the same time, dissolved
O2 has decreased [Lin et al., 2005; Jung, 2008; Qi et al., 2010], with an associated increase in the extent of the
hypoxic areas in coastal areas of the Yellow Sea/ECS [Jung, 2008; Tang, 2009; Ning et al., 2011].
Primary productivity, biomass yields, and fish capture rates have experienced large changes within the ECS over the
past decades (limited evidence, medium agreement, low confidence) [Tang et al., 2003; Lin et al., 2005; Tang, 2009].
Fluctuations in herring abundance appear to closely track SST shifts within the Yellow Sea [Tang, 2009]. For
plankton and fish species, the proportions of warm-water species relative to warm-temperate species in the
Changjiang River Estuary (extending to the southern Taiwan Strait) have changed in past decades [Zhang et al.,
2005; Ma et al., 2009; Lin and Yang, 2011]. Northward shifts in catch distribution for some pelagic fish species in
Korean waters were driven, in part, by warming SST (medium confidence, [Jung et al., 2013]. The frequency of
harmful algal blooms (HAB) and blooms of the giant jellyfish Nemopilema nomurai in the offshore area of the ECS
have increased and have been associated with ocean warming and other factors such as eutrophication [Ye and
Huang, 2003; Tang, 2009; Cai and Tan, 2010]. While attribution of these changes to anthropogenic climate change
is complicated by the increasing influence of non-climate related human activities, many of these changes are
consistent with those expected as SST increases.
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30.5.4.1.2. South China Sea
The South China Sea (SCS) is surrounded by continental areas and a large number of islands, and is connected to the
Pacific, ECS, and Sulu Sea by straits such as the Luzon and Taiwan Strait. The region is greatly influenced by
cyclones/typhoons, and by the Pearl, Red, and Mekong Rivers. The region has a distinct seasonal circulation and is
greatly influenced by the southwest monsoon (in summer), the Kuroshio Current and northeast monsoon (in winter).
The SCS includes significant commercial fisheries areas and includes coral reefs, mangroves, and seagrasses.
The surface waters of the SCS have been warming steadily from 1945 1999 with the annual mean SST in the central
SCS increasing by 0.92°C (1950 2006, [Cai et al., 2009]), a rate similar to that observed for the entire Indo-
Pacific/SE Asian CBS from 1950 2009 (0.80°C, Table 30-1). Significant freshening in the SCS intermediate layer
since the 1960s has been observed [Liu et al., 2007]. The temperature change of the upper layers of the SCS has
made a significant contribution to sea level variation, which is spatially non-homogeneous and varies in time [Li et
al., 2002; Cheng and Qi, 2007; Liu et al., 2007].
Identifying the extent to which climate change is influencing the SCS is difficult due to confounding non-climate
change factors and their interactions (e.g., local human pollution, over-exploitation together with natural climate
variability such as EAM, ENSO, and PDO). Changing sea temperatures have influenced the abundance of
phytoplankton, benthic biomass, cephalopod fisheries, and the size of demersal trawl catches in the northern SCS
observed over the period 1976 2004 (limited evidence, medium agreement) [Ning et al., 2009]. Coral reefs and
mangroves are degrading rapidly as a result of both climate change and non-climate change related factors (very
likely) (Box CC-CR, [Chen et al., 2009; China-SNAP, 2011; Zhao et al., 2012]). Mass coral bleaching and mortality
of coral reefs within the SCS were triggered by elevated temperatures in 1998 and 2007 [Yu et al., 2006; Li et al.,
2011]. Conversely, warming enabled the establishment of a high latitude, non-carbonate, coral community in Daya
Bay in the northern SCS, although this community has recently degraded due to increasing anthropogenic stresses
[Chen et al., 2009; Qiu et al., 2010].
30.5.4.1.3. South-east Asian Seas
The South-east Asian Seas (SAS) include an archipelago of diverse islands that interact with the westward flow of
the North Equatorial Current and the Indonesian Throughflow (Figure 30-1a). A large part of this region is referred
to as the Coral Triangle [Veron et al., 2009]. The world's most biologically diverse marine area, it includes parts of
Malaysia, Indonesia, the Philippines, Timor Leste, the Solomon Islands, and Papua New Guinea. SST increased
significantly from 1985 2006 [Penaflor et al., 2009; McLeod et al., 2010], although with considerable spatial
variation. Trends examined over longer periods (1950 2009) show significant warming (+0.80°C, p-value<0.05,
Table 30-1). The sea level is rising by up to 10 mm yr-1 in much of this region [Church et al., 2004; Church et al.,
2006; Green et al., 2010]. Like other tropical areas in the world, coral reefs within SAS have experienced periods of
elevated temperature, which has driven several mass coral bleaching and mortality events since the early 1980s
(high confidence) [Hoegh-Guldberg et al., 2009; McLeod et al., 2010] (Figure 30-10a). The most recent occurred
during warm conditions in 2010 [Krishnan et al., 2011]. These changes are the result of increasing ocean
temperatures and are very likely to be a consequence of anthropogenic climate change (high confidence) (Box CC-
CR, WGI 10.4.1). Although calcification rates of some key organisms (e.g., reef-building corals; [Tanzil et al.,
2009]) have slowed over the past two decades, it is not possible to conclude that the changes are due to ocean
acidification. While a large part of the decline in coral reefs has been due to increasing local stresses (principally
destructive fishing, declining water quality, and overexploitation of key reef species), projected increases in SST
represent a major challenge for these valuable ecosystems (high agreement) [Burke et al., 2002; Burke and Maidens,
2004].
30.5.4.1.4. Arabian Sea and Somali Current
The Arabian Sea and Somali Current are relatively productive ocean areas, being strongly influenced by upwelling
and the monsoonal system. Wind-generated upwelling enhances primary production in the western Arabian Sea
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[Prakash and Ramesh, 2007]. Several key fisheries within this region are under escalating pressure from both
fishing and climate change. Sea surface temperature increased by 0.18°C and 0.26°C in the Arabian Sea and Somali
Current, respectively, from 1982 2006 (HadSST2, [Rayner et al., 2003; Belkin, 2009]), which is consistent with the
overall warming of the Western Indian Ocean portion of the CBS from 1950 2009 (0.60°C, Table 30-1). Salinity of
surface waters in the Arabian Sea increased by 0.5 1.0% over the past 60 years (Figure 30-6c), due to increased
evaporation from warming seas and contributions from the outflows of the saline Red Sea and Arabian Gulf. As in
other tropical sub-regions, increasing sea temperatures have increased the frequency of mass coral bleaching and
mortality within this region [Wilkinson and Hodgson, 1999; Goreau et al., 2000; Wilkinson, 2004].
The aragonite saturation horizon in both the Arabian Sea and Bay of Bengal is now 100 200 m shallower than in
pre-industrial times as a result of ocean acidification (medium confidence) [Feely et al., 2004]. Shoaling of the
aragonite saturation horizon is likely to affect a range of organisms and processes, such as the depth distribution of
pteropods (zooplankton) in the western Arabian Sea (medium confidence) [Hitchcock et al., 2002; Mohan et al.,
2006]. More than 50% of the area of oxygen minimum zones (OMZs) in the world s oceans occur in the Arabian
Sea and Bay of Bengal and long-term measurements reveal that O2 concentrations are declining in this region (high
confidence) [Helly and Levin, 2004; Karstensen et al., 2008; Stramma et al., 2010] (30.3.2.3). The information
regarding the consequences of climate change within this region is undeveloped and suggests that important
physical, chemical, and biological responses to climate change need to be the focus of further investigation.
30.5.4.1.5. East Africa coast and Madagascar
The Western Indian Ocean strongly influences the coastal conditions associated with Kenya, Mozambique,
Tanzania, Madagascar, La Réunion, Mayotte, and three archipelagos (Comoros, Mauritius, and the Seychelles). Sea
temperatures in the Western Indian Ocean have increased by 0.60°C over 1950 2009 (high confidence) (p-
value<0.05; Table 30-1), increasing the frequency of positive thermal anomalies which have triggered mass coral
bleaching and mortality events across the region over the past two decades (high confidence,[Baker et al., 2008;
Nakamura et al., 2011](CC-HS). Trends in changes in SST and surface salinity vary with location along the East
African coastline, with faster rates at higher latitudes (Figure 30-2). Periods of heat stress over the past 20 years
have triggered mass coral bleaching and mortality on coral reef ecosystems within this region [McClanahan et al.,
2007; McClanahan et al., 2009a; McClanahan et al., 2009c; McClanahan et al., 2009b; Ateweberhan and
McClanahan, 2010; Ateweberhan et al., 2011]. Steadily increasing sea temperatures have also produced anomalous
growth rates in long-lived corals such as Porites (high confidence) [McClanahan et al., 2009b]. Differences in the
susceptibility of reef-building corals to stress from rising sea temperatures has also resulted in changes to the
composition of coral (high confidence) (p-value<0.05; [McClanahan et al., 2007]) and benthic fish communities
(high confidence) (p-value<0.05; [Graham et al., 2008; Pratchett et al., 2011a]). These changes are very likely to
alter species composition and potentially the productivity of coastal fisheries (robust evidence, high agreement, high
confidence) [Jury et al., 2010], although there may be a significant lag between the loss of coral communities and
the subsequent changes in the abundance and community structure of fish (p-value<0.05, [Graham et al., 2007]).
Some of these potential changes can be adverted or reduced by interventions such as the establishment of marine
protected areas and changes to fishing management [McClanahan et al., 2008; Cinner et al., 2009; Jury et al., 2010;
MacNeil et al., 2010].
30.5.4.1.6. Gulf of Mexico and Caribbean Sea
The Gulf of Mexico and Caribbean Sea form a semi-contained maritime province within the Western Atlantic.
These areas are dominated by a range of activities including mineral extraction, fishing, and tourism, which provide
employment and opportunity for almost 75 million people who live in coastal areas of the US, Mexico, and a range
of other Caribbean nations [Adams et al., 2004]. The Gulf of Mexico and Caribbean Sea have warmed by 0.31°C
and 0.50°C and respectively from 1982 2006 (very likely) [Belkin, 2009]. Warming trends are not significant from
1950 2009 (Table 30-1), which may be partly due to spatial variability in warming patterns (30.5.3.1). The
Caribbean region has experienced a sustained decrease in aragonite saturation state from 1996 2006 (very likely)
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[Gledhill et al., 2008]. Sea levels within the Gulf of Mexico and Caribbean Sea have increased at the rate of 2 3 mm
y-1 from 1950 2000 [Church et al., 2004; Zervas, 2009].
Understanding influences of climate change on ocean ecosystems in this region is complicated by the confounding
influence of growing human populations and activities. The recent expansion of the seasonal hypoxic zone, and the
associated dead zone , in the Gulf of Mexico has been attributed to nitrogen inputs driven by land management
[Turner and Rabalais, 1994; Donner et al., 2004] and changes to river flows, wind patterns, and thermal
stratification of Gulf waters (high confidence) [Justiæ et al., 1996; Justiæ et al., 2007; Levin et al., 2009; Rabalais et
al., 2009; Rabalais et al., 2010]. The increases in coastal pollution and fishing have potentially interacted with
climate change to exacerbate impacts on marine ecosystems within this region (5.3.4, 29.3). These changes have
often been abrupt and non-linear [Taylor et al., 2012].
A combination of local and global disturbances has driven a large-scale loss of reef-building corals across the
Caribbean Sea since the late 1970s (high confidence) [Hughes, 1994; Gardner et al., 2003]. Record thermal stress in
2005 triggered the largest mass coral bleaching and mortality event on record for the region, damaging coral reefs
across hundreds of km2 in the eastern Caribbean Sea (high confidence) [Donner et al., 2007; Eakin et al., 2010].
Although conditions in 2010 were milder than 2005, elevated temperatures still occurred in some parts of the
Caribbean [Smith et al., 2013]. Increasing temperatures in the Caribbean have also been implicated in the spread of
marine diseases [Harvell et al., 1999; Harvell et al., 2002; Harvell et al., 2004] and some introduced species (likely)
[Firth et al., 2011]. As in other sub-regions, pelagic fish species are sensitive to changes in sea temperature and
modify their distribution and abundance [Muhling et al., 2011]. Fish and invertebrate assemblages in the Gulf of
Mexico have shifted deeper in response to SST warming over 1970s-2011 (medium confidence) [Pinsky et al.,
2013].
Coral ecosystems in the Caribbean Sea are at risk from ocean acidification (very likely) [Albright et al., 2010;
Albright and Langdon, 2011], although impacts are yet to be observed under field conditions. Ocean acidification
may also be altering patterns of fish recruitment to coral reefs, although direct evidence for how this has affected
Caribbean species is lacking (low confidence) [Dixson et al., 2008; Munday et al., 2009; Dixson et al., 2010].
30.5.4.2. Key Risks and Vulnerabilities
Worldwide, 850 million people live within 100 km of tropical coastal ecosystems such as coral reefs and mangroves
deriving multiple benefits including food, coastal protection, cultural services, and income from industries such as
fishing and tourism [Burke et al., 2011]. Marine ecosystems within the CBS are sensitive to increasing sea
temperatures (Figure 30-10), although detection and attribution is complicated by the significant influence and
interaction with non-climate change stressors (water quality, over-exploitation of fisheries, coastal degradation; Box
CC-CR). Warming is likely to have changed the primary productivity of ocean waters, placing valuable ecosystems
and fisheries within the ECS at risk (low to medium confidence). Other risks include the expansion of hypoxic
conditions and associated dead zones in many parts of the CBS. Given the consequences for coastal ecosystems and
fisheries, these changes are very likely to increase the vulnerability of coastal communities throughout the CBS.
Sea temperatures are increasing within many parts of the CBS ecosystems (1950 2009, Table 30-1), and will
continue to do so over the next few decades and century. Sea temperatures are projected to change by 0.34 0.50°C
over the near-term (2010 2039) and by 0.23 0.74°C over the long-term (2010 2099) under the lowest RCP scenario
(RCP2.6). Under BAU (RCP8.5), CBS sea temperatures are projected to increase by 0.62 0.85°C over tne near-term
and 2.44 3.32°C over the long-term (Table SM30-4). Given the large-scale impacts (e.g., mass coral bleaching and
mortality events) that have occurred in response to much smaller changes in the past over the CBS regions (0.14
0.80°C from 1950 2009, Table 30-1), the projected changes of 2.44 3.32°C over 2010-2099 are very likely to have
large-scale and negative consequences for the structure and function of many CBS ecosystems (virtually certain),
especially given the sensitivity of coral reefs to relatively small increases in temperature over the past three decades
[Hoegh-Guldberg, 1999; Eakin et al., 2010; Lough, 2012].
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It is very likely that coral-dominated reef ecosystems within the CBS (and elsewhere) will continue to decline and
will consequently provide significantly less ecosystem goods and services for coastal communities if sea
temperatures increase by more than 1°C above current temperatures (Box CC-CR, Figure 30-10). Combining the
known sensitivity of coral reefs within the Caribbean and Coral Triangle sub-regions [Strong et al., 1997; Hoegh-
Guldberg, 1999; Strong et al., 2011], with the exposure to higher temperatures that are projected under medium
(RCP4.5) to high (RCP8.5) scenarios, reveals that both coral reef-rich regions are virtually certain to experience
levels of thermal stress (DHM>1) that cause coral bleaching every 1 2 years by the mid to late part of this century
(robust evidence, high levels of agreement, very high confidence) (Figure 30-4b, c; Figure 30-10, Figure 30-12,
Figure SM30-3; [van Hooidonk et al., 2013]). The frequency of mass mortality events (DHM>5, Figure 30-10a-c)
also climbs towards events that occur every 1 2 years by the mid to late part of this century under low to high
climate change scenarios (robust evidence, high agreement, very high confidence) [Hoegh-Guldberg, 1999; Donner
et al., 2005; Frieler et al., 2012]. Mass mortality events that affect coral reefs will result in changes to community
composition in the near-term (2010 2039) [Berumen and Pratchett, 2006; Adjeroud et al., 2009] and a continuing
downward trend in reef-building coral stocks in the longer term [Gardner et al., 2003; Bruno and Selig, 2007; Baker
et al., 2008].
It is virtually certain that composition of fisheries catches [Graham et al., 2007; Pratchett et al., 2011a] [Pratchett
et al., 2008; Pratchett et al., 2011b] will change. The productivity of many fisheries will decrease (limited evidence,
medium agreement) as waters warm, acidify, and stratify, and as crucial habitat such as coral reefs degrades (low
confidence). These changes are very likely to increase the vulnerability of millions of people who live in coastal
communities and depend directly on fisheries and other ecological goods and services [Hoegh-Guldberg et al., 2009;
McLeod et al., 2010].
[INSERT FIGURE 30-10 HERE
Figure 30-10: Annual maximum proportions of reef pixels with Degree Heating Months [Donner et al., 2007];
DHM) 1 (used for projecting coral bleaching; [Strong et al., 1997; Strong et al., 2011]) and DHM 5 (associated
with bleaching across 100% of affected areas with significant mortality, [Eakin et al., 2010] for the period 1870
2009 for each of the six coral regions (Figure 30-4d) using the HadISST1.1 data set. The black line on each graph is
the maximum annual area value for each decade over the period 1870 2009. This value is continued through 2010
2099 using CMIP5 data and splits into the four Representative Concentration Pathways (RCP2.6, 4.5, 6.0, and 8.5).
DHM were produced for each of the four RCPs using the ensembles of CMIP models. From these global maps of
DHM, the annual percentage of grid cells with DHM 1 and DHM 5 were calculated for each coral region. These
data were then grouped into decades from which the maximum annual proportions were derived. The plotted lines
for 2010 2099 are the average of these maximum proportion values for each RCP. Monthly SST anomalies were
derived using a 1985 2000 maximum monthly mean (MMM) climatology derived in the calculations for Figure 30-
4. This was done separately for HadISST1.1, the CMIP5 models, and each of the four RCPs, at each grid cell for
every region. DHMs were then derived by adding up the monthly anomalies using a 4-month rolling sum. Figure
SM30-3 presents past and future sea temperatures for the six major coral reef provinces under historic, un-forced,
RCP4.5 and RCP8.5 scenarios.]
30.5.5. Eastern Boundary Upwelling Ecosystems
The Eastern Boundary Upwelling Ecosystems (EBUE) include the California, Peru/Humboldt, Canary/North-west
Africa, and Benguela Currents. They are highly productive sub-regions with rates of primary productivity that may
exceed 1000 g C m-2 yr-1. Although these provinces comprise less than 2% of the Ocean area, they contribute nearly
7% of marine primary production (Figure 30-1b) and more than 20% of the world s marine capture fisheries [Pauly
and Christensen, 1995]. Catches in the EBUE are dominated by planktivorous sardine, anchovy, and horse/jack
mackerel, and piscivorous benthic fish such as hake. Nutrient input from upwelling of cooler waters stimulates
primary production that is transferred to mid and upper trophic levels, resulting in substantial fish, seabird, and
marine mammal populations. As a result, the EBUE are considered hotspots of productivity and biodiversity
[Block et al., 2011]. The high level of productivity is a result of large-scale atmospheric pressure gradients and wind
systems that advect surface waters offshore leading to the upwelling of cold, nutrient-rich waters from depth (Box
CC-UP) [Chavez and Messie, 2009; Chavez et al., 2011]. Upwelling waters are typically low in pH and high in CO2,
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and are likely to continue to enhance changes in pH and CO2 resulting from rising atmospheric CO2 [Feely et al.,
2008; Gruber, 2011].
30.5.5.1. Observed Changes and Potential Impacts
There are extensive studies of the coupled climate-ecosystem dynamics of individual EBUE (e.g., California
Current). Decadal variability poses challenges to the detection and attribution of changes within the EBUE to
climate change, although there are a number of long-term studies that have been able to provide insight into the
patterns of change and their causes. Like other ocean sub-regions, EBUE are projected to warm under climate
change, with increased stratification and intensified winds as westerly winds shift poleward (likely). However,
cooling has also been predicted for some EBUE, resulting from the intensification of wind-driven upwelling [Bakun,
1990]. The California and Canary Currents have warmed by 0.73 and 0.53°C (very likely) (p-value<0.05, 1950-
2009, Table 30-1), respectively, while no significant trend was detected in the sea surface temperatures of the
Benguela (p-value = 0.44) and Humboldt Currents (p-value = 0.21) from 1950 2009 (Table 30-1). These trends
match shorter-term trends for various EBUE using Pathfinder version 5 data [Demarcq, 2009]. These differences are
likely to be the result of differences in the influence of long-term variability and the specific responses of coastal
wind systems to warming, although an analysis of wind data over the same period did not pick up clear trends (low
confidence, with respect to long-term wind trends) [Demarcq, 2009; Barton et al., 2013].
How climate change will influence ocean upwelling is central to resolving ecosystem and fishery responses within
each EBUE. There is considerable debate, however, as to whether or not climate change will drive an intensification
of upwelling (e.g., [Bakun et al., 2010; Narayan et al., 2010; Barton et al., 2013] in all regions. This debate is
outlined in Box CC-UP. EBUE are also areas of naturally low pH and high CO2 concentrations due to upwelling,
and consequently may be vulnerable to ocean acidification and its synergistic impacts [Barton et al., 2012]. A full
understanding of the consequences of ocean acidification for marine organisms and ecosystems is discussed
elsewhere (Box CC-OA, Box CC-UP, 6.2, 6.3.2, [Kroeker et al., 2013], WGI 6.4).
30.5.5.1.1. Canary Current
Part of the North Atlantic STG, the Canary Current extends from northern Morocco southwestward to the North
Atlantic Equatorial Current. It is linked with the Portugal Current (which is sometimes considered part of the Canary
Current) upstream and extends downstream to the Atlantic Equatorial Current. The coastal upwelling system,
however, is limited to a narrow belt along the Saharan west coast to the coast of Guinea, with the most intense
upwelling occurring centrally, along the coasts of Mauritania (15 20 N) and Morocco (21 26° N). Total fish
catches, comprising mainly coastal pelagic sardines, sardinellas, anchovies, and mackerel, have fluctuated around 2
million tons yr-1 since the 1970s (http://www.seaaroundus.org/lme/27.aspx). Contrasting with the other EBUE,
fishing productivity is modest, probably due to the legacy of uncontrolled fishing in the 1960s [Arístegui et al.,
2009].
Most observations suggest that the Canary Current has warmed since the early 1980s [Arístegui et al., 2009; Belkin,
2009; Demarcq, 2009; Barton et al., 2013], with analysis of HadISST1.1 data from 1950 2009 indicating warming
of 0.53°C from 1950 2009 (p-value<0.05, Table 30-1). Gómez-Gesteira et al. [2008] suggest a 20% and 45%
decrease in the strength of upwelling in winter and summer, respectively, from 1967 2006, consistent with a
decrease in wind strength and direction over the past 60 years. More recently, [Barton et al., 2013] show no clear
increasing or decreasing trend in wind strength over the past 60 years, and a lack of agreement among wind trends
and variability from different wind products, (e.g. PFEL, ICOADS, WASWind). This study presents no evidence for
changes in upwelling intensity, with the exception of upwelling off North-west Spain, where winds are becoming
slightly less favorable. Alteration of wind direction and strength influences upwelling and hence nutrient
concentrations, howevernutrient levels can also change in response to other variables such as the supply of iron-
laden dust from the Sahara [Alonso-Pérez et al., 2011]. There is medium evidence and medium agreement that
primary production in the Canary Current has decreased over the past two decades [Arístegui et al., 2009; Demarcq,
2009], in contrast to the nearby upwelling region off North-west Spain, where no significant trend was observed
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[Bode et al., 2011]. Satellite chlorophyll records (SeaWiFS, MODIS) are relatively short, making it difficult to
distinguish the influence of warming oceans from longer-term patterns of variability [Arístegui et al., 2009; Henson
et al., 2010]. Changing temperature has resulted in changes to important fisheries species. For example, Mauritanian
waters have become more suitable as feeding and spawning areas for some fisheries species (e.g., Sardinella aurita)
as temperatures increased [Zeeberg et al., 2008]. Clear attribution of these changes depends on the linkage between
the Azores High and global temperature, and on longer records for both physical and biological systems as pointed
out for data sets in general [Arístegui et al., 2009; Henson et al., 2010].
30.5.5.1.2. Benguela Current
The Benguela Current originates from the eastward-flowing, cold South Atlantic Current, flows northward along the
southwest coast of Africa, and is bounded north and south by the warm-water Angola and Agulhas Currents,
respectively. Upwelling is strongest and most persistent toward the center of the system in the Lüderitz-Orange
River upwelling cell [Hutchings et al., 2009]. Fish catch reached a peak in the late 1970s of 2.8 million tons yr-1
(http://www.seaaroundus.org/lme/29/1.aspx), before declines in the northern Benguela, due to overfishing and
interdecadal environmental variability, resulted in a reduced catch of around 1 million tons yr-1 (present) [Cury and
Shannon, 2004; Heymans et al., 2004; Hutchings et al., 2009]. Offshore commercial fisheries currently comprise
sardine, anchovy, horse mackerel, and hake, while the inshore artisanal and recreational fisheries comprise a variety
of fish species mostly caught by hook and line.
Most research on the Benguela Current has focused on fisheries and oceanography, with little emphasis on climate
change. As with the other EBUE, strong interannual and interdecadal variability in physical oceanography make the
detection and attribution of biophysical trends to climate change difficult. Nevertheless, the physical conditions of
the Benguela Current are highly sensitive to climate variability over a range of scales, especially to atmospheric
teleconnections that alter local wind stress [Hutchings et al., 2009; Leduc et al., 2010; Richter et al., 2010; Rouault
et al., 2010]. Consequently, there is medium agreement, despite limited evidence [Demarcq, 2009], that upwelling
intensity and associated variables (e.g., temperature, nutrient, and O2 concentrations) from the Benguela system will
change as a result of climate change (Box CC-UP).
The temperature of the surface waters of the Benguela Current did not increase from 1950 to 2009 (p-value>0.05,
Table 30-1), although shorter records show an decrease in the south-central Benguela Current (0.35 0.55 C decade-
1
[Rouault et al., 2010] or an increase for the whole Benguela region (0.24C, Belkin [2009]). These differences
between short versus long records indicate the substantial influence of long-term variability on the Benguela system
[Belkin, 2009]. Information on other potential consequences of climate-change within the Benguela system is sparse.
Sea-level rise is similar to the global mean, although it has not been measured rigorously within the Benguela
[Brundrit, 1995; Veitch, 2007]. Although upwelling water in the northern and southern portions of the Benguela
Current exhibits elevated and suppressed pCO2, respectively [Santana-Casiano et al., 2009]), the consequences of
changing upwelling intensity remain poorly explored with respect to ocean acidification. Finally, while periodic
hypoxic events in the Benguela system are largely driven by natural advective processes, these may be exacerbated
by future climate change [Monteiro et al., 2008; Bakun et al., 2010].
Despite its apparent sensitivity to environmental variability, there is limited evidence of ecological changes in the
Benguela Current EBUE due to climate change [Poloczanska et al., 2013]. For example, pelagic fish [Roy et al.,
2007], benthic crustaceans [Cockcroft et al., 2008], and seabirds [Crawford et al., 2008] have demonstrated general
eastward range shifts around the Cape of Good Hope. Although these may be associated with increased upwelling
along the South African south coast, specific studies that attribute these changes to anthropogenic climate change are
lacking. Trawl surveys of demersal fish and cephalopod species showed consistently predictable hotspots of
species richness over a 20 30 year study period (the earliest surveys since 1984 off South Africa) that were
associated with greater depths and cooler bottom waters [Kirkman et al., 2013]. However, major changes in the
structure and function of the demersal community have been shown in some parts of the Benguela in response to
environmental change e.g., due to predominately fishing pressure in the 1960s and environmental forcing in the
early 2000s in the southern Benguela [Howard et al., 2007], therefore changes driven by climate change may
eventually affect the persistence of these biodiversity hotspots [Kirkman et al., 2013].
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30.5.5.1.3. California Current
The California Current spans ~23° of latitude from central Baja California, Mexico, to central British Columbia,
Canada, linking the North Pacific Current (Westwind Drift) with the North Equatorial and Kuroshio Currents to
form the North Pacific Gyre. High productivity driven by advective transport and upwelling [Hickey, 1979; Chelton
et al., 1982; Checkley and Barth, 2009; Auad et al., 2011] supports well-studied ecosystems and fisheries. Fish
catches from the California Current have been approximately 0.6 million tonnes yr-1 since 1950
(http://www.seaaroundus.org/lme/3.aspx), which makes it the lowest catch of the four EBUE. The ecosystem
supports the foraging and reproductive activities of 2 6 million seabirds from around 100 species [Tyler et al.,
1993]. Marine mammals are diverse and relatively abundant, including recovering populations of humpback whales,
among others [Barlow et al., 2008].
The average temperature of the California Current warmed by 0.73°C from 1950 2009 (p-value <0.05, Table 30-1)
and by 0.14 0.80°C from 1985 2007 [Demarcq, 2009]. Like other EBUE, the California Current is characterized by
large-scale interannual and interdecadal climate-ecosystem variability [McGowan et al., 1998; Hare and Mantua,
2000; Chavez et al., 2003; Checkley and Barth, 2009]. During an El Nino, coastally-trapped Kelvin waves from the
tropics deepen the thermocline, thereby severely reducing upwelling and increasing ocean temperatures from
California to Washington [Peterson and Schwing, 2003; King et al., 2011]. Atmospheric teleconnections to the
tropical Pacific alter wind stress and coastal upwelling. Therefore, the ENSO is intimately linked with Bakun s
(1990) upwelling intensification hypothesis (Box CC-UP). Interdecadal variability in the California Current stems
from variability in the Pacific-North America pattern [Overland et al., 2010], which is influenced by the PDO
[Mantua et al., 1997; Peterson and Schwing, 2003] and the NPGO [Di Lorenzo et al., 2008]. The major effects of
the PDO and NPGO appear north of 39°N [Di Lorenzo et al., 2008; Menge et al., 2009].
There is robust evidence and medium agreement that the California Current has experienced a decrease in the
number of upwelling events (23 40%), but an increase duration of individual events resulting in an increase of the
overall magnitude of upwelling events from 1967 2010 (high confidence) [Demarcq, 2009; Iles et al., 2012]. This is
consistent with changes expected under climate change yet remains complicated by the influence of decadal-scale
variability (low confidence) [Iles et al., 2012]. Oxygen concentrations have also undergone large and consistent
decreases from 1984 2006 throughout the California Current, with the largest relative decreases occurring below the
thermocline (21% at 300 m). The hypoxic boundary layer (<60 umol kg-1) has also shoaled by up to 90 m in some
regions [Bograd et al., 2008]. These changes are consistent with the increased input of organic carbon into deeper
layers from enhanced upwelling and productivity, which stimulates microbial activity and results in the drawdown
of O2 (likely), [Bakun et al., 2010] but see also [McClatchie et al., 2010; Koslow et al., 2011]; WGI 3.8.3). These
changes are likely to have reduced the available habitat for key benthic communities as well as fish and other mobile
species [Stramma et al., 2010]. Increasing microbial activity will also increase the partial pressure of CO2,
decreasing pH and the carbonate chemistry of seawater. Together with the shoaling of the saturation horizon, these
changes have increased the incidence of low O2 and low pH water flowing onto the continental shelf (high
confidence) (40 120 m, [Feely et al., 2008]), causing problems for industries such as the shellfish aquaculture
industry [Barton et al., 2012].
30.5.5.1.4. Humboldt Current
The Humboldt Current is the largest of the four EBUE, covering an area larger than the other three combined. It
comprises the eastern edge of South Pacific Gyre, linking the northern part of the Antarctic Circumpolar Current
with the Pacific South Equatorial Current. Although the primary productivity per unit area is modest compared to
that of the other EBUE, the Humboldt Current system has very high levels of fish production. Current catches are in
line with a long-term average (since the 1960s) of 8 million tons yr-1 (http://www.seaaroundus.org/lme/13/1.aspx),
although decadal-scale variations range from 2.5 13 million tons yr-1. While the anchovies currently contribute 80%
of the total catch, they alternate with sardines on a multi-decadal scale, with their dynamics mediated by the
approach and retreat of sub-tropical waters to and from the coast [Alheit and Bakun, 2010]. This variability does not
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appear to be altering due to anthropogenic climate change. Thus, from the late 1970s to the early 1990s, sardines
were more important [Chavez et al., 2003]. The other major commercial fish species are jack mackerel among the
pelagic fish, and hake among the demersal fish.
The Humboldt Current EBUE did not show an overall warming trend in SST over the last 60 years (p-value>0.05,
Table 30-1), which is consistent with other data sets (1982 2006, HadISST1.1, [Belkin, 2009]; 1985-2007,
Pathfinder, [Demarcq, 2009]). Wind speed has increased in the central portions of the Humboldt Current, although
wind has decreased in its southern and northern sections [Demarcq, 2009]. The lack of a consistent warming signal
may be due to the strong influence of adjacent ENSO activity exerting opposing drivers on upwelling and which, if
they intensify, would decrease temperatures (limited evidence, medium agreement). Similar to the Canary Current
EBUE, however, there was a significant increase in the temperatures of the warmest month of the year over the
period 1950 2009 (p-value<0.05, Table 30-1).
Primary production is suppressed during warm El Nino events and amplified during cooler La Nina phases, these
changes then propagate through to higher trophic levels [Chavez et al., 2003; Tam et al., 2008; Taylor et al., 2008].
However, in addition to trophic changes, there is also a direct thermal impact on organisms, which varies depending
on the thermal adaptation window for each species (high confidence). A 37-year zooplankton time series for the
coast of Peru showed no persistent trend in abundance and diversity [Ayón et al., 2004], although observed shifts
coincided with the shifts in the regional SST. As for other EBUE, there is lack of studies that have rigorously
attempted to detect and attribute changes to anthropogenic climate change, although at least two studies
[Mendelssohn and Schwing, 2002; Gutierrez et al., 2011] provide additional evidence that the northern Humboldt
Current has cooled (due to upwelling intensification) since the 1950s, a trend matched by increasing primary
production. This is not entirely consistent with the lack of significant change over the period 1950 2009 (p-
value>0.05, Table 30-1). Nevertheless, these relationships are likely to be complex in their origin, especially in their
sensitivity to the long-term changes associated with ENSO and PDO, and the fact that areas within the Humboldt
Current EBUE may be showing different behaviors.
30.5.5.2. Key Risks and Vulnerabilities
EBUE are vulnerable to changes that influence the intensity of currents, upwelling, and mixing (and hence changes
in SST, wind strength and direction), as well as O2 content, carbonate chemistry, nutrient content, and the supply of
organic carbon to deep offshore locations (robust evidence, high agreement, high confidence). The extent to which
any particular EBUE is vulnerable to these factors depends on location (Figure 3 from Gruber [2011] and other
factors such as alternative sources of nutrient input and fishing pressure [Bakun et al., 2010]. This complex interplay
between regional and global drivers means that our understanding of how factors such as upwelling within the
EBUE will to respond to further climate change is uncertain (Box CC-UP, [Rykaczewski and Dunne, 2010]).
In the GCM ensembles examined (Table SM30-3), modest rates of warming (0.22 0.93°C) occur within the four
EBUEs in the near-term. Over 2010-2099, however, EBUE SSTs warm by 0.07 1.02C under RCP2.6, and 2.52
3.51C under RCP8.5 (Table SM30-4). These high temperatures have the potential to increase stratification of the
water column and substantially reduce overall mixing in some areas. In contrast, the potential strengthening of
coastal wind systems would intensify upwelling and stimulate primary productivity through the increased injection
of nutrients into the photic zone of the EBUE (Box CC-UP). Garreaud and Falvey [2009] explored how wind stress
along the South American coast would change by 2100 under B2 and A2 IPCC scenarios. Using an ensemble of 15
GCMs, southerly wind systems favoring upwelling increased along the sub-tropical coast of South America,
extending and strengthening conditions for upwelling.
Changes in the intensity of upwelling within the EBUE will drive fundamental changes to the abundance,
distribution, and viability of resident organisms, although an understanding of their nature and direction is limited.
In some cases, large-scale decreases in primary productivity and dependent fisheries are projected to occur for
EBUE ecosystems [Blanchard et al., 2012], while other projections question the strong connection between primary
productivity and fisheries production [Arístegui et al., 2009]. Increased upwelling intensity also has potential
disadvantages. Elevated primary productivity may lead to decreasing trophic transfer efficiency, thus increasing the
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amount of organic carbon exported to the seabed, where it is virtually certain to increase microbial respiration and
hence increase O2 stress [Weeks et al., 2002; Bakun et al., 2010]. Increased wind stress may also increase turbulence,
breaking up food concentrations (affecting trophic transfer), or causing excessive offshore advection, which could
remove plankton from shelf habitats. The central issue for the EBUE is therefore whether or not upwelling will
intensify and, if so, whether the negative consequences (e.g., reduced O2 and elevated CO2) associated with
upwelling intensification will outweigh potential benefits from increased primary production and fisheries catch.
30.5.6. Sub-Tropical Gyres
Sub-Tropical Gyres (STG) dominate the Pacific, Atlantic, and Indian Oceans (Figure 30-1a), and consist of large
stable water masses that circulate clockwise (northern hemisphere) and anticlockwise (southern hemisphere) due to
the Coriolis Effect. The oligotrophic areas at the core of the STG represent one of the largest habitats on Earth,
contributing 21.2% of ocean primary productivity and 8.3% of the global fish catch (Figure 30-1b, Table SM30-1).
A number of small island nations are found within this region. While many of the observed changes within these
nations have been described in previous chapters (e.g., 5.3-4, 29.3 5), region-wide issues and consequences are
discussed here due to the strong linkages between ocean and coastal issues.
30.5.6.1. Observed Changes and Potential Impacts
The central portions of the STG are oligotrophic (Figure SM30-1. Temperatures within the STG of the North Pacific
(NPAC), South Pacific (SPAC), Indian Ocean (IOCE), North Atlantic (NATL), and South Atlantic (SATL) have
increased at rates of 0.020, 0.024, 0.032, 0.025, and 0.027°C yr-1 from 1998 2010, respectively ([Signorini and
McClain, 2012]. This is consistent with increases observed from 1950 2009 (0.25 0.67C, Table 30-1). However
differences among studies done over differing time-periods emphasize the importance of long-term patterns of
variability. Salinity has decreased across the North and South Pacific STG (Figure 30-6c, WGI 3.3.3.1), consistent
with warmer sea temperatures and an intensification of the hydrological cycle [Boyer, 2005].
The North and South Pacific STGs have expanded since 1993 (high confidence), with these changes likely being the
consequence of a combination of wind forcing and long-term variability ([Parrish et al., 2000]; (WGI 3.6.3).
Chlorophyll levels, as determined by remote-sensing of ocean color (Box CC-UP), have decreased in the NPAC,
IOCE, and NATL by 9%, 12%, and 11%, respectively (p-value<0.5; [Signorini and McClain, 2012]) over and above
the inherent seasonal and interannual variability from 1998 2010 [Vantrepotte and Mélin, 2011]. Chlorophyll levels
did not change in the remaining two gyres (SPAC and SATL, and confirmed for SPAC by [Lee and McPhaden,
2010; Lee et al., 2010]). Furthermore, over the period 1998 2007, median cell diameter of key phytoplankton
species exhibited statistically significant linear declines of about 2% in the North and South Pacific, and 4% in the
North Atlantic Ocean [Polovina and Woodworth, 2012]. Changes in chlorophyll and primary productivity in these
sub-regions have been noted before [McClain et al., 2004; Gregg et al., 2005; Polovina et al., 2008] and are
influenced by seasonal and longer-term sources of variability (e.g., ENSO, PDO, 6.3.4, Figure 6.9). These changes
represent a significant expansion of the world s most unproductive waters, although caution must be exercised given
the limitations of satellite detection methods (Box CC-PP) and the shortness of records relative to longer-term
patterns of climate variability. There is high confidence that changes that reduce the vertical transport of nutrients
into the euphotic zone (e.g., decreased wind speed, increasing surface temperatures, and stratification) will reduce
the rate of primary productivity and hence fisheries.
30.5.6.1.1. Pacific Ocean STG
Pacific climate is heavily influenced by the position of the Intertropical Convergence Zone (ITCZ) and the South
Pacific Convergence Zone (SPCZ), which are part of the ascending branch of the Hadley circulation (WGI 14.3.1).
These features are also strongly influenced by interannual to interdecadal climate patterns of variability including
ENSO and PDO. The current understanding of how ENSO and PDO will change as average global temperatures
increase is not clear (low confidence) ([Collins et al., 2010], WGI 12.4.4.2). The position of both the ITCZ and
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SPCZ vary seasonally and with ENSO [Lough et al., 2011], with a northward migration during the northern
hemisphere summer and a southward migration during the southern hemisphere summer. These changes, along with
the West Pacific Monsoon, determine the timing and extent of the wet and dry seasons in SPAC and NPAC sub-
regions [Ganachaud et al., 2011]. Tropical cyclones are prominent in the Pacific (particularly the western Pacific),
and CBS sub-regions between 10° 30° north and south of the equator, although the associated storm systems may
occasionally reach higher latitudes. Spatial patterns of cyclones vary with ENSO, spreading out from the Coral Sea
to the Marquesas Islands during El Nino and contracting back to the Coral Sea, New Caledonia, and Vanuatu during
La Nina [Lough et al., 2011]. Historically, there have been almost twice as many land-falling tropical cyclones in La
Nina as opposed to El Nino years off the east coast of Australia, with a declining trend in the number of severe
tropical cyclones from 0.45 per year in the early 1870s to 0.17 per year in recent times [Callaghan and Power,
2011].
The Pacific Ocean underwent an abrupt shift to warmer sea temperatures in the mid-1970s as a result of both natural
(e.g., Interdecadal Pacific Oscillation (IPO) and climate forcing (high confidence)[Meehl et al., 2009]. This change
coincided with changes to total rainfall, rain days, and dry spells across the Pacific, with the direction of change
depending on the location relative to the SPCZ. Countries such as the Cook Islands, Tonga, Samoa and American
Samoa, and Fiji tend to experience drought conditions as the SPCZ (with cooler sea temperatures) moves toward the
northeast during El Nino (high confidence). The opposite is true during La Nina conditions. The consequences of
changing rainfall on the countries of the Pacific STG are discussed in greater detail elsewhere (5.4, 29.3, Table
29.1). While these changes are due to different phases of long-term variability in the Pacific, they illustrate the
ramifications and sensitivity of the Pacific to changes in climate change.
Elevated sea temperatures within the Pacific Ocean have increased the frequency of widespread mass coral
bleaching and mortality since the early 1980s (very high confidence, [Hoegh-Guldberg and Salvat, 1995; Hoegh-
Guldberg, 1999; Mumby et al., 2001; Baker et al., 2008; Donner et al., 2010]. There are few, if any, scientific
records of mass coral bleaching and mortality prior to this period (high confidence [Hoegh-Guldberg, 1999]. Rates
of decline in coral cover on coastal coral reef ecosystems range between 0.5 2.0% per year depending on the
location within the Indo-Pacific region (high confidence, [Bruno and Selig, 2007; Hughes et al., 2011; Sweatman et
al., 2011; De ath et al., 2012]. The reasons for this decline are complex and involve non-climate change related
factors (e.g., coastal pollution and overfishing) as well as global warming and possibly acidification. A recent
comprehensive analysis of the ecological consequences of coral bleaching and mortality concluded that bleaching
episodes have resulted in catastrophic loss of coral reefs in some locations, and have changed coral community
structure in many others, with a potentially critical influence on the maintenance of biodiversity in the marine
tropics (high confidence, [Baker et al., 2008]. Increasing sea levels have also caused changes in seagrass and
mangrove systems. Gilman et al. [2007] found a reduction in mangrove area with sea level rise, with the observed
mean landward recession of three mangrove areas over four decades being 25, 64, and 72 mm yr-1, 12 37 times
faster than the observed rate of sea level rise. Significant interactions exist between climate change and coastal
development, where migration shoreward depends on the extent to which coastlines have been modified or barriers
to successful migration have been established.
Changes in sea temperature also lead to changes in the distribution of key pelagic fisheries such as skipjack tuna
(Katsuwonus pelamis), yellowfin tuna (Thunnus albacares), big-eye tuna (T. obesus) and South Pacific albacore
tuna (T. alalunga), which make up the majority of key fisheries in the Pacific Ocean. Changes in distribution and
recruitment in response to changes in sea temperature as result of ENSO demonstrate the close association of pelagic
fish stocks and water temperature. The shift in habitat for top predators in the northeast Pacific was examined by
Hazen et al. [2012], who used tracking data from 23 marine species and associated environmental variables to
predict changes of up to 35% in core habitat for these species within the north Pacific. Potential habitats are
predicted to contract for the blue whale, salmon shark, loggerhead turtle, and blue and mako sharks, while potential
habitats for the sooty shearwater, black -footed albatross, leatherback turtle, white shark, elephant seal, and albacore,
bluefin and yellowfin tuna are predicted to expand [Hazen et al., 2012]. However, expansion of OMZs in the Pacific
STG is predicted to compress habitat (depth) for hypoxia-intolerant species such as tuna [Stramma et al., 2010;
Stramma et al., 2012].
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Reduction of ocean productivity of the STG [Sarmiento et al., 2004; Signorini and McClain, 2012] reduces the flow
of energy to higher trophic levels such as those of pelagic fish [Le Borgne et al., 2011]. The distribution and
abundance of fisheries stocks such as tuna are also sensitive to changes in sea temperature, and hence long-term
variability such as ENSO and PDO. The redistribution of tuna in the western central equatorial region has been
related to the position of the oceanic convergence zones, where the warm pool meets the cold tongue of the Pacific.
These changes have been reliably reproduced by population models that use temperature as a driver of the
distribution and abundance of tuna [Lehodey et al., 1997; Lehodey et al., 2006]. Projections of big-eye tuna (T.
obesus) distributions under SRES A2 show an improvement in spawning and feeding habitats to 2100 in the eastern
tropical Pacific and declines in the western tropical Pacific leading to an eastern displacement of tuna stocks
[Lehodey et al., 2008; Lehodey et al., 2010b].
30.5.6.1.2. Indian Ocean STG
Like the Pacific Ocean, the Indian Ocean plays a crucial role in global weather patterns, with teleconnections
throughout Africa, Australasia, Asia, and the Americas (e.g., [Clark et al., 2000; Manhique et al., 2011; Meehl and
Arblaster, 2011; Nakamura et al., 2011]. Increasing sea level, temperature, storm distribution and intensity, and
changing carbonate chemistry all influence the broad range of physical, chemical, and biological aspects of the
Indian Ocean. Coral reef ecosystems in the Indian Ocean gyre system were heavily affected by record positive sea
temperature anomalies seen in the southern hemisphere between February April 1998 (robust evidence, high
agreement, high confidence) [Ateweberhan et al., 2011]. Coral cover across the Western Indian Ocean declined by
an average of 37.7% after the 1998 heat stress event [Ateweberhan et al., 2011]. Responses to the anomalously hot
conditions in 1998 varied between sub-regions, with the central Indian Ocean islands (Maldives, Seychelles,
Chagos, and Lakshadweep) experiencing major decreases in coral cover directly after the 1998 event (from 40 53%
coral cover in 1977 1997 to 7% in 1999 2000) (high confidence) [Ateweberhan et al., 2011]. Coral reefs lining the
islands of southern India and Sri Lanka experienced similar decreases in coral cover (45%, 1977 1997 to 12%,
1999 2000). Corals in the southwestern Indian Ocean (Comoros, Madagascar, Mauritius, Mayotte, Réunion and
Rodrigues) showed less impact (44%, 1977 1997 to 40%, 1999 2000). Recovery from these increases in mortality
has been variable, with sites such as those around the central Indian Ocean islands exhibiting fairly slow recovery
(13% by 2001 2005) while those around southern India and Sri Lanka are showing much higher rates (achieving a
mean coral cover of 37% by 2001 2005, [Ateweberhan et al., 2011]). These changes to the population size of key
reef-building species will drive major changes in the abundance and composition of fish populations in coastal areas,
and affect other ecosystem services that are important for underpinning tourism and coastal protection (medium
confidence, Box CC-CR).
Fisheries that exploit tuna and other large pelagic species are very valuable to many small island states within the
Indian Ocean). As with Pacific fisheries, the distribution and abundance of large pelagic fish in the Indian Ocean is
greatly influenced by sea temperature. The anomalously high sea temperatures of 1997 98 (leading to a deepening
of the mixed layer in the west and a shoaling in the east) coincided with anomalously low primary production in the
western Indian Ocean and a major shift in tuna stocks (high confidence) ([Menard et al., 2007; Robinson et al.,
2010]. Fishing grounds in the Western Indian Ocean were deserted and fishing fleets underwent a massive shift
toward the eastern basin, which was unprecedented for the tuna fishery (high confidence). As a result of these
changes, many countries throughout the Indian Ocean lost significant tuna-related revenue [Robinson et al., 2010].
In 2007, tuna fishing revenue was again reduced by strong surface warming and deepening of the mixed layer, and
associated with a modest reduction in primary productivity in the west. These trends highlight the overall
vulnerability of tuna fishing countries in the Indian Ocean to climate variability, a situation similar to the other
major oceans of the world.
30.5.6.1.3. Atlantic Ocean STG
SST has increased within the two STG of the Atlantic Ocean over the last two decades [Belkin, 2009; Signorini and
McClain, 2012]. Over longer periods of time (1950 2009), trends in average temperature are not significant for the
North Atlantic STG (p-value>0.05) while they remain so for the South Atlantic STG (very likely) (0.08°C decade-1,
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p-value<0.05, Table 30-1). In both cases, however, temperatures in the coolest and warmest months increased
significantly (Table 30-1). The difference between these studies (i.e., over 10 30 years versus 60 years) emphasizes
the importance of long-term patterns of variability in the North Atlantic region. Variability in SST at a period of
about 60 80 years is associated with the Atlantic Multi-decadal Oscillation (AMO) [Trenberth and Shea, 2006]. Sea
surface temperatures influence hurricane activity (very likely) with recent record SST associated with record
hurricane activity in 2005 in the Atlantic [Trenberth and Shea, 2006] and mass coral bleaching and mortality in the
eastern Caribbean (high confidence, [Eakin et al., 2010]. In the former case, analysis concluded that 0.1°C of the
SST anomaly was attributable to the state of the AMO while 0.45°C was due to ocean warming as a result of
anthropogenic influences [Trenberth and Shea, 2006].
These changes have influenced the distribution of key fishery species as well the ecology of coral reefs in Bermuda
[Wilkinson and Hodgson, 1999; Baker et al., 2008] and in the eastern Caribbean [Eakin et al., 2010]. Small island
nations such as Bermuda depend on coral reefs for fisheries and tourism and are vulnerable to further increases in
sea temperature that cause mass coral bleaching and mortality (high confidence, Box CC-CR, Figure 30-10). As with
the other STG, phytoplankton communities and pelagic fish stocks are sensitive to temperature changes that have
occurred over the past several decades. Observation of these changes has enabled development of models that have a
high degree of accuracy in projecting the distribution and abundance of these elements within the Atlantic region in
general [Cheung et al., 2011].
30.5.6.2. Key Risks and Vulnerabilities
Sea surface temperatures of the vast STG of the Atlantic, Pacific, and Indian Oceans are increasing, which is very
likely to increase stratification of the water column. In turn, this is likely to reduce surface concentrations of nutrients
and, consequently, primary productivity (medium confidence) (Box CC-PP). Warming is projected to continue
(Table SM30-4), with substantial increases in the vulnerability and risk associated with systems that have been
observed to change so far (high confidence) (Figure 30-12). Under RCP2.6, the temperatures of the STG are
projected to increase by 0.17 0.56°C in the near-term (over 2010 2039) and between -0.03 0.90°C in the long-term
(over 2010-2099) (Table SM30-4). Under RCP8.5, however, surface temperatures of the world s STG are projected
to be 0.45 0.91°C warmer in the near-term and 1.90 3.44°C warmer in the long-term (Table SM30-4). These
changes in temperature are very likely to increase water column stability, reduce the depth of the mixed layer, and
influence key parameters such as nutrient availability and O2 concentrations. It is not clear as to how longer-term
sources of variability such as ENSO and PDO will change (WGI 14.4, 14.7.6) and ultimately influence these trends.
The world's most oligotrophic ocean sub-regions are likely to continue to expand over coming decades, with
consequences for ecosystem services such as gas exchange, fisheries, and carbon sequestration. Polovina et al.
[2011] explored this question for the North Pacific using a climate model that included a coupled ocean
biogeochemical component to investigate potential changes under an SRES A2 scenario (~RCP6.0 8.5; Figure 1.5
[Rogelj et al., 2012]). Model projections indicated the STG expanding by approximately 30% by 2100, driven by the
northward drift of the mid-latitude westerlies and enhanced stratification of the water column. The expansion of the
STG occurred at the expense of the equatorial upwelling and other regions within the North Pacific. In the North
Pacific STG, the total primary production is projected to decrease by 10-20% and large fish catch by 19 29% by
2100 under SRES A2 [Howell et al., 2013; Woodworth-Jefcoats et al., 2013]. However, our understanding of how
large-scale eddy systems will change in a warming world is incomplete, as are the implications for primary
productivity of these large and important systems (Box CC-PP, Box CC-UP).
Understanding how storm frequency and intensity will change represents a key question for many countries and
territories within the various STG. Projections of increasing sea temperature are likely to change the behavior of
tropical cyclones. At the same time, the maximum wind speed and rainfall associated with cyclones is likely to
increase, although future trends in cyclones and severe storms are very likely to vary from region to region (WGI
14.6). Patterns such as temporal clustering can have a strong influence on the impact of tropical cyclones on
ecosystems such as coral reefs [Mumby et al., 2011], although how these patterns will change within all STG is
uncertain at this point. However, an intensifying hydrological cycle is expected to increase precipitation in many
areas (high confidence WGI 2.5, 14.2), although longer droughts are also expected in other STG (medium
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confidence). Changes in the hydrological cycle impact on coastal ecosystems, increasing damage through coastal
flooding and physical damage from storm waves [Mumby et al., 2011]. Improving our understanding of how
weather systems associated with features such as the SPCZ (WGI 14.3.1) will vary is critical to climate change
adaptation of a large number of nations associated with the STG. Developing an understanding of how ocean
temperature, climate systems such as the SPCZ and ITCZ, and climate change and variability (e.g., ENSO, PDO)
interact will be essential in this regard. For example, variability in the latitude of the SPCZ is projected to increase,
possibly leading to more extreme events in Pacific island countries [Cai et al., 2012].
The consequences of projected sea temperatures on the frequency of coral bleaching and mortality within key sub-
regions of the STG are outlined in Box CC-CR, Figures 30.10 and Figure SM30-3. As with other sub-regions
(particularly CBS, STG, and SES) dominated by coral reefs, mass coral bleaching and mortality becomes an annual
risk under all scenarios, with mass mortality events beginning to occur every 1 2 years by 2100 (virtually certain,
Box CC-CR, Figure 30-10, Figure SM30-3). Coral-dominated reef ecosystems (areas with more than 30% coral
cover) are very likely to disappear under these circumstances by the mid part of this century [van Hooidonk et al.,
2013]. The loss of substantial coral communities has implications for the three-dimensional structure of coral reefs
(Box CC-CR) and the role of the latter as habitat for organisms such as fish [Hoegh-Guldberg, 2011; Hoegh-
Guldberg et al., 2011a; Pratchett et al., 2011a; Bell et al., 2013b]. The consequences of increasing sea temperature
can be exacerbated by increasing ocean acidification, with potential implications for reef calcification ([Kleypas et
al., 1999; Hoegh-Guldberg et al., 2007; Doney et al., 2009], medium confidence), reef metabolism and community
calcification [Dove et al., 2013], and other key ecological processes ([Pörtner et al., 2001; Pörtner et al., 2007;
Munday et al., 2009]. Ocean pH within the STG will continue to decrease as atmospheric CO2 increases, bringing
pH within the STG to 7.9 and 7.7 at atmospheric concentrations of 450 ppm and 800 ppm, respectively (Figure
SM30-2a, Box CC-OA). Aragonite saturation states will decrease to around 1.6 (800 ppm) and 3.3 (450 ppm; Figure
SM30-2b). Decreasing carbonate ion concentrations and saturation states pose serious risks to other marine calcifiers
such as encrusting coralline algae, coccolithophores (phytoplankton), and a range of benthic invertebrates [Doney et
al., 2009; Feely et al., 2009]. Increasing sea temperatures and sea level are also likely to influence other coastal
ecosystems (e.g., mangroves, seagrass meadows) in the Pacific, although significant gaps and uncertainties exist
(29.3.4 [Waycott et al., 2007; Waycott et al., 2011]. Many of the negative consequences for coral reefs, mangroves,
and seagrass meadows are likely to have negative consequences for dependent coastal fisheries (through destruction
of habitat) and tourism industries (medium confidence) ([Bell et al., 2011b; Pratchett et al., 2011a; Pratchett et al.,
2011b; Bell et al., 2013a].
Populations of key large pelagic fish are projected to move many hundreds of kilometers east of where they are
today in the Pacific STG (high confidence) [Lehodey et al., 2008; Lehodey et al., 2010a; Lehodey et al., 2011;
Lehodey et al., 2013], with implications for income, industry, and food security across multiple Pacific Island
nations (high confidence)[Cheung et al., 2010; McIlgorm et al., 2010; Bell et al., 2011b; Bell et al., 2013a], 7.4.2,
Table 29.2-29.3). These predictions of species range displacements, contractions, and expansions in response to
anticipated changes in the Ocean (Box CC-MB) present both a challenge and an opportunity for the development of
large-scale management strategies to preserve these valuable species. Our understanding of the consequences of
reduced O2 for pelagic fish populations is not clear, although there is high agreement on the potential physiological
outcomes (6.3.3). Those species that are intolerant to hypoxia, such as skipjack and yellowfin tuna [Lehodey et al.,
2011], will have their depth range compressed in the Pacific STG, which will increase their vulnerability to fisheries
and reduce overall fisheries habitat and productivity (medium confidence) [Stramma et al., 2010; Stramma et al.,
2011]. Despite the importance of these potential changes, our understanding of the full range of consequences is
limited at this point.
30.5.7. Deep Sea (>1000 m)
Assessments of the influence of climate change on the Deep Sea (DS) are challenging due to difficulty of access and
scarcity of long-term, comprehensive observations [Smith et al., 2009]. The size of this habitat is also vast, covering
well over 54% of the earth s surface and stretching from the top of the mid-oceanic ridges to the bottom of deep
ocean trenches [Smith et al., 2009]. The fossil record in marine sediments reveals that the DS has undergone large
changes in response to climate change in the past [Knoll and Fischer, 2011]. The paleo-skeletal record shows it is
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the rate, not just the magnitude, of climate change (temperature, O2, and CO2) that is critical to marine life in DS.
The current rate of change in key parameters very likely exceeds that of other major events in Earth history. Two
primary time scales are of interest. The first is the slow rate (century-scale) of ocean circulation and mixing, and
consequently the slow rate at which DS ecosystems experience physical climate change. The second is the rapid rate
at which organic matter enters the deep ocean from primary productivity generated at surface of the Ocean, which
represents a critical food supply to DS animals [Smith and Kaufmann, 1999; Smith et al., 2009]. It can also represent
a potential risk in some circumstances where the flux of organic carbon into the deep ocean, coupled with increased
sea temperatures, can lead to anoxic areas (dead zones) as metabolism is increased and O2 decreased [Chan et al.,
2008; Stramma et al., 2010].
30.5.7.1. Observed Changes and Potential Impacts
The greatest rate of change of temperature is occurring in the upper 700 m of the Ocean (WGI 3.2, very high
confidence), although smaller yet significant changes are occurring at depth. The DS environment is typically cold
(~-0.5 3°C; [Smith et al., 2008]), although abyssal temperatures in the SES can be higher (e.g., Mediterranean DS
~12°C, [Danovaro et al., 2010]. In the latter case, DS organisms can thrive in these environments as well,
illustrating the variety of temperature conditions that differing species of abyssal life have adapted to. Individual
species, however, are typically constrained within a narrow thermal and O2-demand window of tolerance [Pörtner,
2010] and therefore it is likely that shifts in the distribution of DS species and regional extinctions will occur.
Warming over multiple decades has been observed below 700 m [Levitus et al., 2005; Levitus et al., 2009], with
warming being minimal at mid-range depths (2000 3000 m), and increasing towards the sea floor in some sub-
regions (e.g., Southern Ocean, WGI Chapter 3). For the deep Atlantic Ocean, the mean age of deep waters (mean
time since last exposure to the atmosphere) is ~250 years; the oldest deep waters of the Pacific Ocean are >1000
years old. The patterns of ocean circulation are clearly revealed by the penetration of tracers and the signal of CO2
released from burning fossil-fuel penetrating into the abyss [Sabine et al., 2004]. It will take many centuries for full
equilibration of deep ocean waters and their ecosystems with recent planetary warming and CO2 levels [Wunsch and
Heimbach, 2008].
Temperature accounts for ~86% of the variance in the export of organic matter to the DS (medium confidence)
[Laws et al., 2000]. Consequently, upper ocean warming will reduce the export of organic matter to the DS (medium
confidence), potentially changing the distribution and abundance of DS organisms and associated food webs, and
ecosystem processes [Smith and Kaufmann, 1999]. Most organic matter entering the DS is recycled by microbial
systems at relatively shallow depths [Buesseler et al., 2007], at rates that are temperature dependent. Upper ocean
warming will increase the rate of sub-surface decomposition of organic matter (high confidence), thus intensifying
the intermediate depth oxygen minimum zones [Stramma et al., 2008; Stramma et al., 2010] and reducing food
supply to the abyssal ocean.
Particulate organic carbon is exported from the surface to deeper layers of the Ocean (>500m) with an efficiency of
between 20 50% [Buesseler et al., 2007], much of it being recycled by microbes before it reaches 1000m [Smith et
al., 2009]. The export of organic carbon is dependent on surface net primary productivity, which is likely to vary
(Box CC-PP), influencing the supply of food to DS [Laws et al., 2000; Smith et al., 2008]. Warming of intermediate
waters will also increase respiration at mid-water depths, reducing the flux of organic carbon. Our understanding of
other components of DS ecosystems is also relatively poor. For example, there is limited evidence and limited
agreement as to how ocean warming and acidification are likely to affect ecosystems such as those associated with
hydrothermal vents [Van Dover, 2012].
Oxygen concentrations are decreasing in the DS [Stramma et al., 2008; Helm et al., 2011a]. Although, the largest
signals occur at intermediate water depths < 1000 m [Nakanowatari et al., 2007; Whitney et al., 2007; Falkowski et
al., 2011], some waters >1000 m depth are also experiencing a decline [Jenkins, 2008]. The quantity of dissolved O2
throughout the Ocean will be reduced with warming due to direct effects on solubility (high confidence), with these
effects being widely distributed [Shaffer et al., 2009]. It is also virtually certain that metabolic rates of all animals
and microbial respiration rates will increase with temperature [Brown et al., 2004]. Thus, increased microbial
activity and reduced O2 solubility at higher temperatures will have additive consequences for the decline of O2 (high
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confidence) even in the DS. The DS waters are relatively well-oxygenated due to the higher solubility of O2 in
colder waters and the low supply rate of organic matter to great depths. The availability of oxygen to marine animals
is governed by a combination of concentration, temperature, pressure, and related properties such as diffusivity.
Analysis by [Hofmann et al., 2013] reveals that the supply potential of oxygen to marine animals in cold deep waters
is similar to that at much shallower depths (very high confidence).
Anthropogenic CO2 has penetrated to at least 1000 m in all three ocean basins (particularly the Atlantic; [Doney et
al., 2009]. Further declines of calcite and aragonite in already under-saturated DS water will presumably decrease
biological carbonate structure formation and increase dissolution, as has happened many times in Earth s past (high
confidence) [Zeebe and Ridgwell, 2011]. Some cold-water corals (reported down to 3500m) already exist in waters
under-saturated with respect to aragonite [Lundsten et al., 2009]. While initial investigations suggested that ocean
acidification (reduced by 0.15 and 0.30 pH units) would result in a reduction in the calcification rate of deep water
corals (30% and 6%, respectively [Maier et al., 2009]), there is accumulating evidence that ocean acidification may
have far less impact than previously anticipated on the calcification of some deep water corals (limited evidence,
medium agreement, low confidence) although it may reduce important habitats given that dead unprotected coral
mounds are likely to dissolve in under-saturated waters [Thresher et al., 2011; Form and Riebesell, 2012; Maier et
al., 2013].
30.5.7.2. Key Risks and Vulnerabilities
Rising atmospheric CO2 poses a risk to DS communities through increasing temperature, and decreasing O2,
carbonate chemistry and pH (high confidence) [Keeling et al., 2010]. Risks associated with the DS have implications
for the Ocean and planet given the high degree of inherent dependency and connectivity. The resulting changes to
the flow of organic carbon to some parts of the DS (e.g., STG) are very likely to affect DS ecosystems (medium
confidence) [Smith et al., 2008]. As with the Ocean generally, there is a need to fill in the substantial gaps that exist
in our knowledge and understanding of the world s largest habitat and its responses to rapid anthropogenic climate
change.
30.5.8. Detection and Attribution of Climate Change Impacts with Confidence Levels
The analysis in Chapter 30 and elsewhere in AR5 has identified a wide range of physical, chemical, and ecological
components that have changed over the last century (Box CC-MB). Figure 30-11 summarizes a number of examples
from the Ocean as a region together with the degree of confidence in both the detection and attribution steps. For
ocean warming and acidification, confidence is very high that changes are being detected and that they are due to
changes to the atmospheric greenhouse gas content. There is considerable confidence in both the detection (very
high confidence) and attribution (high confidence) of mass coral bleaching and mortality (Figure 30-11b), given the
well-developed understanding of environmental processes and physiological responses driving these events (Box
CC-CR, 6.3.1). For other changes, confidence is lower, either because detection of changes has been difficult, or
monitoring programs are not long-established (e.g., field evidence of declining calcification) or because detection
has been possible but models are in conflict (e.g., wind-driven upwelling). The detection and attribution of recent
changes is discussed in further detail in (18.3.3 4).
[INSERT FIGURE 30-11 HERE
Figure 30-11: Expert assessment of degree of confidence in detection and attribution of physical and chemical
changes (a) and ecological changes (b) across sub-regions, as designated in Figure 30-1a, and processes in the Ocean
(based on evidence explored throughout Chapter 30 and elsewhere in AR5). Further explanation of this figure is
given in 18.3.3 4 and 18.6.]
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30.6. Sectorial Impacts, Adaptation, and Mitigation Responses
Human welfare is highly dependent on ecosystem services provided by the Ocean. Many of these services are
provided by coastal and shelf areas, and are consequently addressed in other chapters (e.g., 5.4.3, 7.3.2.4, 22.3.2.3)
Oceans contribute provisioning (e.g., food, raw materials; see 30.6.4.1), regulating (e.g., gas exchange, nutrient
recycling, carbon storage, climate regulation, water flux), supporting (e.g., habitat, genetic diversity) and cultural
(e.g., recreational, cultural) services [Millennium-Ecosystem-Assessment, 2005; Tallis et al., 2013]. The
accumulating evidence indicating that fundamental ecosystem services within the Ocean are shifting rapidly should
be of major concern, especially with respect to the ability of regulating and supporting ecosystem services to
underpin current and future human population demands [Rockström et al., 2009; Ruckelshaus et al., 2013].
Discussion here is restricted to environmental, economic, and social sectors that have direct relevance to the Ocean,
namely natural ecosystems, fisheries and aquaculture, tourism, shipping, oil and gas, human health, maritime
security, and renewable energy. The influences of climate change on Ocean sectors will be mediated through
simultaneous changes in multiple environmental and ecological variables (Figure 30-12), and the extent to which
changes can be adapted to and/or risks mitigated (Table 30-3). Both short-term and longer-term adaptation is
necessary to address impacts arising from warming, even under the lowest stabilization scenarios assessed.
[INSERT FIGURE 30-12 HERE
Figure 30-12: (a) Examples of projected impacts and vulnerabilities associated with climate change in Ocean sub-
regions. (b) Examples of risks to fisheries from observed and projected impacts across Ocean sub-regions. Letters
indicate level of confidence: (vL): Very low, (L): Low, (M): Medium, (H): High and (vH): Very high. Details of
sub-regions are given in Table 30-1a and 30.1.1.
Sectorial approaches dominate resource management in the Ocean (e.g., shipping tends to be treated in isolation
from fishing within an area), yet cumulative and interactive effects of individual stressors are known to be
ubiquitous and substantial [Crain et al., 2008]. Climate change consistently emerges as a dominant stressor in
regional to global-scale assessments, although land-based pollution, commercial fishing, invasive species, coastal
habitat modification, and commercial activities such as shipping all rank high in many places around the world (e.g.,
30.5.3, 30.5.4, 5.3.4) [Halpern et al., 2009; Halpern et al., 2010]. Such cumulative effects pose challenges to
managing for the full suite of stressors to marine systems, but also present opportunities where mitigating a few key
stressors can potentially improve overall ecosystem condition (e.g., [Halpern et al., 2010; Kelly et al., 2011]. The
latter has often been seen as a potential strategy for reducing negative consequences of climate impacts on marine
ecosystems by boosting ecosystem resilience, thus buying time while the core issue of reducing greenhouse gas
emissions is tackled [West et al., 2009].
30.6.1. Natural Ecosystems
Adaptation in natural ecosystems may occur autonomously, such as shifts in species composition and distributions
[Poloczanska et al., 2013] or engineered by human intervention, such as assisted dispersal (4.4, [Hoegh-Guldberg et
al., 2008]. Currently, adaptation strategies for marine ecosystems included reducing additional stressors (e.g.
maintaining water quality, adapting fisheries management) and maintaining resilience ecosystems (e.g., Marine
Protected Areas) and are moving towards whole-of-ecosystem management approaches. Coral reefs, for example,
will recover faster from mass coral bleaching and mortality if healthy populations of herbivorous fish are maintained
(medium confidence, [Hughes et al., 2003], indicating that reducing overfishing will help maintain coral-dominated
reef systems while the international community reduces the emissions of greenhouse gases to stabilize global
temperature and ocean chemistry.
Approaches such as providing a formal valuation of ecological services from the Ocean have the potential to
facilitate adaptation by underpinning more effective governance, regulation, and ocean policy while at the same time
potentially improving the management of these often vulnerable services through the development of market
mechanisms and incentives [Beaudoin and Pendleton, 2012]. Supporting, regulating, and cultural ecosystem
services tend to transcend the immediate demands placed on provisioning services and are difficult to value in
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formal economic terms due to their complexity, problems such as double counting, and the value of non-market
goods and services arising from marine ecosystems generally [Fu et al., 2011; Beaudoin and Pendleton, 2012].
Blue Carbon is defined as the organic carbon sequestered by marine ecosystems such as phytoplankton, mangrove,
seagrass, and salt marsh ecosystems [Laffoley and Grimsditch, 2009; Nellemann and Corcoran, 2009]. In this
respect, Blue Carbon will provide opportunities for both adaptation to, and mitigation of, climate change if key
uncertainties in inventories, methodologies, and policies for measuring, valuing, and implementing Blue Carbon
strategies are resolved [McLeod et al., 2011]. Sediment surface levels in vegetated coastal habitats can rise several
meters over thousands of years, building carbon-rich deposits [Brevik and Homburg, 2004; Lo Iacono et al., 2008].
The degradation of coastal habitats not only liberates much of the carbon associated with vegetation loss, but can
release and oxidize buried organic carbon through erosion of cleared coastlines (high confidence) [Duarte et al.,
2005]. Combining data on global area, land-use conversion rates, and near-surface carbon stocks for marshes,
mangroves, and seagrass meadows, Pendleton et al. [2012] revealed that the CO2 emissions arising from destruction
of these three ecosystems was equivalent to 3 19% of the emissions generated by deforestation globally, with
economic damages estimated to be US$6 42 billion annually. Similarly, Luisetti et al. [2013] estimate the carbon
stock of seagrass and salt marshes in Europe, representing <4% of global carbon stocks in coastal vegetation, was
valued at US$180 million, at EU Allowance price of 8/tCO2 in June 2012. A reversal of EU Environmental
Protection Directives could result in economic losses of US$1 billion by 2060. Blue Carbon strategies can also be
justified in light of the numerous ecosystem services these ecosystems provide, such as protection against coastal
erosion and storm damage, and provision of habitats for fisheries species (17.4).
30.6.2. Economic Sectors
30.6.2.1. Fisheries and Aquaculture
The Ocean provided 64% of the production (in tonnes) supplied by world fisheries (capture and aquaculture) in
2010, amounting to 148.5 million tonnes of fish and shellfish [FAO, 2012]. This production, valued at US$217.5
billion, and supplied, on average, 18.6 kg of protein-rich food per person to an estimated population of 6.9 billion
[FAO, 2012]. Marine capture fisheries supplied 77.4 million tonnes with highest production from the northwest
Pacific (27%), west-central Pacific (15%), northeast Atlantic (11%) and southeast Pacific (10%)[FAO, 2012]. World
aquaculture production (59.9 million tonnes in 2010) is dominated by freshwater fishes, nevertheless marine
aquaculture supplied 18.1 million tonnes (30%)[FAO, 2012].
Marine capture fisheries production increased from 16.8 million tonnes in 1950 to a peak of 86.4 million tonnes in
1996, then declined before stabilising around 80 million tonnes [FAO, 2012]. The stagnation of marine capture
fisheries production is attributed to full exploitation of around 60% of the world s marine fisheries and
overexploitation of 30% (estimates for 2009) [FAO, 2012]. Major issues for industrial fisheries include illegal,
unreported and unregulated fishing, ineffective implementation of monitoring, control and surveillance, and
overcapacity in fishing fleets [Bank and FAO, 2008; FAO, 2012]. Such problems are being progressively addressed
in several developed and developing countries [Hilborn, 2007; Pitcher et al., 2009; Worm et al., 2009], where
investments have been made in stock assessment, strong management, and application of the FAO Code of Conduct
for Responsible Fisheries and the FAO Ecosystem Approach to Fisheries Management.
The significance of marine capture fisheries is illustrated powerfully by the number of people engaged in marine
small-scale fisheries (SSF) in developing countries. SSF account for around half of the fish harvested from the
Ocean, and provide jobs for more than 47 million people about 12.5 million fishers and another 34.5 million
people engaged in post-harvest activities [Mills et al., 2011]. SSF are often characterized by large numbers of
politically-weak fishers operating from decentralized localities, with poor governance, and insufficient data to
monitor catches effectively [Kurien and Willmann, 2009; Cochrane et al., 2011; Pomeroy and Andrew, 2011]. For
these SSF, management that aims to avoid further depletion of overfished stocks may be more appropriate in the
short-term than management aimed at maximizing sustainable production. These aims are achieved through adaptive
management by: (1) introduction of harvest controls (e.g., size limits, closed seasons and areas, gear restrictions, and
protection of spawning aggregations) to avoid irreversible damage to stocks in the face of uncertainty [Cochrane et
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al., 2011]; (2) flexible modification of these controls through monitoring [Plagányi et al., 2013]; and (3) investing in
the social capital and institutions needed for communities and governments to manage SSF [Makino et al., 2009;
Pomeroy and Andrew, 2011].
Changes to ocean temperature, chemistry, and other factors are generating new challenges for fisheries resulting in
loss of coastal and oceanic habitat [Hazen et al., 2012; Stramma et al., 2012], the movement of species [Cheung et
al., 2011], the spread and increase of disease and invading species [Ling, 2008; Raitsos et al., 2010; Chan et al.,
2011], and changes in primary production[Chassot et al., 2010]. There is medium evidence and medium agreement
that these changes will change both the nature of fisheries and their ability to provide food and protein for hundreds
of millions of people (7.2.1.2). The risks to ecosystems and fisheries vary from region to region (7.3.2.4). Dynamic
bioclimatic envelope models under SRES A1B project potential increases in fisheries production high latitudes, and
potential decreases at lower latitudes by the mid-21st century [Cheung et al., 2010] (6.5). Overall, warming
temperatures are projected to shift optimal environments for individual species polewards and redistribute
production, however changes will be region specific [Cheung et al., 2010; Merino et al., 2012].
Fisheries, in particular shellfish, are also vulnerable to declining pH and carbonate ion concentrations. As a result,
the global production of shellfish fisheries is likely to decrease, [Cooley and Doney, 2009; Pickering et al., 2011])
with further ocean acidification (medium confidence) (6.3.2, 6.3.5, 6.4.1.1, Box CC-OA). Impacts may be first
observed in EBUE where upwelled water is already relatively low in O2 and undersaturated with aragonite (30.5.5).
Seasonal upwelling of acidified waters onto the continental shelf in the California Current region, has recently
affected oyster hatcheries along the coast of Washington and Oregon (30.5.5.1.1[Barton et al., 2012]). Whether
declining pH and aragonite saturation due to climate change played a role is unclear, however future declines will
increase the risk of such events occurring.
Most marine aquaculture species are sensitive to changing ocean temperature (6.3.1.4, exposed through pens, cages
and racks placed directly in the sea, utilization of seawater in land-based tanks or collection of wild spat) and, for
mollusks particularly, changes in carbonate chemistry (6.3.2.4 [Turley et al., 2011; Barton et al., 2012]).
Environmental changes can therefore impact farm profitability, depending on target species and farm location. For
example, a 1°C rise in SST is projected to shift production of Norwegian salmonids further north but may increase
production overall [Hermansen and Heen, 2012]. Industries for non-food products, which can be important for
regional livelihoods such as Black Pearl in Polynesia, are also affected by rising SST. Higher temperatures are
known to affect the quality of pearl nacre, and can increase levels of disease in adult oysters [Bell et al., 2011a;
Pickering et al., 2011; Bell et al., 2013b]. Aquaculture production is also vulnerable to extreme events such as
storms and floods (e.g., [Chang et al., 2013]). Flooding and inundation by seawater may be a problem to shore
facilities on low-lying coasts. For example, shrimp farming operations in the tropics will be challenged by rising sea
levels, which will be exacerbated by mangrove encroachment and reduce the ability for thorough-drying of ponds
between crops [Della Patrona et al., 2011].
The impacts of climate change on marine fish stocks are expected to affect the economics of fishing and livelihoods
in fishing nations through changes in the price and value of catches, fishing costs, income to fishers and fishing
companies, national labor markets, and industry re-organization [Sumaila et al., 2011] (6.4.1). A study of the
potential vulnerabilities of national economies to the effects of climate change on fisheries, in terms of exposure to
warming, relative importance of fisheries to national economies and diets, and limited societal capacity to adapt,
concluded that a number countries including Malawi, Guinea, Senegal, Uganda, Sierra Leone, Mozambique,
Tanzania, Peru, Columbia, Venezuela, Mauritania, Morocco, Bangladesh, Cambodia, Pakistan, Yemen, and Ukraine
are most vulnerable [Allison et al., 2009].
Aquaculture production is expanding rapidly [Bostock et al., 2010] and will play an important role in food
production and livelihoods as the human demand for protein grows. This may also add pressure on capture fisheries
(7.3.2.6) [FAO, 2012; Merino et al., 2012]. Two-thirds of farmed food fish production (marine and freshwater) is
achieved with the use of feed derived from wild-harvested, small, pelagic fish and shellfish. Fluctuations in the
availability and price of fishmeal and fish oil for feeds, as well as their availability, pose challenges for the growth
of sustainable aquaculture production, particularly given uncertainties in changes in EBUE upwelling dynamics to
climate change (30.5.5). Technological advances and management change such as increasing feed efficiencies, using
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alternatives to fishmeal and fish oil, and farming of herbivorous finfish, coupled with economic and regulatory
incentives will reduce the vulnerability of aquaculture to the impacts of climate change on small, pelagic fish
abundance [Naylor et al., 2009; Merino et al., 2010; FAO, 2012].
The challenges of optimizing the economic and social benefits of both industrial fisheries, SSF and aquaculture
operations, which often already include strategies to adapt to climatic variability [Salinger et al., 2013], are now
made more complex by climate change [Cochrane et al., 2009; Brander, 2010; 2013]. Nevertheless, adaptation
options include establishment of early-warning systems to aid decision-making, diversification of enterprises and
development of adaptable management systems [Chang et al., 2013]. Vulnerability assessments that link
oceanographic, biological, and socio-economic systems can be applied to identify practical adaptations to assist
enterprises, communities, and households to reduce the risks from climate change and capitalize on the opportunists
[Pecl et al., 2009; Bell et al., 2013b; Norman-López et al., 2013]. The diversity of these adaptation options, and the
policies needed to support them, are illustrated by the following examples.
30.6.2.1.1. Tropical fisheries based on large pelagic fish
Fisheries for skipjack, yellowfin, big-eye, and albacore tuna provide substantial economic and social benefits to the
people of Small Island Developing States (SIDS). For example, tuna fishing license fees contribute substantially (up
to 40%) to the government revenue of several Pacific Island nations [Gillett, 2009; Bell et al., 2013b]. Tuna fishing
and processing operations also contribute up to 25% of gross domestic product in some of these nations and employ
over 12,000 people [Gillett, 2009; Bell et al., 2013b]. Considerable economic benefits are also derived from fisheries
for top pelagic predators in the Indian and Atlantic Oceans [FAO, 2012; Bell et al., 2013a]. Increasing sea
temperatures and changing patterns of upwelling are projected to cause shifts in the distribution and abundance of
pelagic top predator fish stocks (30.5.2, 30.5.5, 30.5.6), with potential to create winners and losers among island
economies as catches of the trans-boundary tuna stocks change among and within their exclusive economic zones
(EEZs;[Bell et al., 2013b; Bell et al., 2013a].
A number of practical adaptation options and supporting policies have been identified to minimize the risks and
maximize the opportunities associated with the projected changes in distribution of the abundant skipjack tuna in the
tropical Pacific ([Bell et al., 2011; Bell et al., 2013a], Table 30-2). These adaptation and policy options include: (1)
full implementation of the regional vessel day scheme , designed to distribute the economic benefits from the
resource in the face of climatic variability, and other schemes to control fishing effort in subtropical areas; (2)
strategies for diversifying the supply of fish for canneries in the west of the region as tuna move progressively east;
(3) continued effective fisheries management of all tuna species; (4) energy efficiency programs to assist domestic
fleets to cope with increasing fuel costs and the possible need to fish further from port; and (5) the eventual
restructuring of regional fisheries management organizations to help coordinate management measures across the
entire tropical Pacific. Provision of operational-level catch and effort data from all industrial fishing operations to
improve models for projecting redistribution of tuna stocks and quotas under climate change [Salinger et al.
2013][Nicol et al., 2013]. Similar adaptation options and policy responses are expected to be relevant to the
challenges faced by tuna fisheries in the tropical and sub-tropical Indian and Atlantic Oceans.
[INSERT TABLE 30-2 HERE
Table 30-2: Examples of priority adaptation options and supporting policies to assist Pacific Island countries and
territories to minimize the threats of climate change to the socio-economic benefits derived from pelagic and coastal
fisheries and aquaculture, and to maximize the opportunities. These measures are classified as win-win (W-W)
adaptations, which address other drivers of the sector in the short-term and climate change in the long-term, or lose-
win (L-W) adaptations, where benefits exceed costs in the short-term but accrue under longer-term climate change
(modified from [Bell et al., 2013b]).
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30.6.2.1.2. Small-scale fisheries
Small-scale fisheries (SSF) account for 56% of catch and 91% of people working in fisheries in developing
countries [Mills et al., 2011]. SSF are fisheries that tend to operate at family or community level, have low levels of
capitalization, and make an important contribution to food security and livelihoods. They are often dependent on
coastal ecosystems, such as coral reefs, that provide habitats for a wide range of harvested fish and invertebrate
species. Despite their importance to many developing countries, such ecosystems are under serious pressure from
human activities including deteriorating coastal water quality, sedimentation, ocean warming, overfishing, and
acidification (7.2.1.2, 30.3, 30.5, Box CC-CR). These pressures are translating into a steady decline in live coral
cover, which is very likely to continue over the coming decades, even where integrated coastal zone management is
in place (30.5.4, 30.5.6. For example, coral losses around Pacific Islands are projected to be as high as 75% by 2050
[Hoegh-Guldberg et al., 2011a]. Even under the most optimistic projections (a 50% loss of coral by 2050), changes
to state of coral reefs (Box CC-CR, Figure 30-10, Figure 30-12) are very likely to reduce the availability of
associated fish and invertebrates that support many of the SSF in the tropics (high confidence). In the Pacific, the
productivity of SSF on coral reefs has been projected to decrease by at least 20% by 2050 [Pratchett et al., 2011b],
which is also likely to occur in other coral reef areas globally given the similar and growing stresses in these other
regions (Table SM30-1, 30.5.4).
Adaptation options and policies for building the resilience of coral reef fisheries to climate change suggested for the
tropical Pacific include: (1) strengthening the management of catchment vegetation to improve water quality along
coastlines; (2) reducing direct damage to coral reefs; (3) maintaining connectivity of coral reefs with mangrove and
seagrass habitats; (4) sustaining and diversifying the catch of coral reef fish to maintain their replenishment
potential; and (5) transferring fishing effort from coral reefs to skipjack and yellowfin tuna resources by installing
anchored fish-aggregating devices (FADs) close to shore [Bell et al., 2011b; Bell et al., 2013b; Bell et al., 2013a],
Table 30-2). These adaptation options and policies represent a no regrets strategy in that they provide benefits for
coral reef fisheries and fishers irrespective of climate change and ocean acidification.
30.6.2.1.3. Northern Hemisphere HLSBS fisheries
The high latitude fisheries in the northern hemisphere span from around 30/35°N to 60°N in the North Pacific and
80°N in the North Atlantic, covering a wide range of thermal habitats supporting subtropical/temperate species to
boreal/arctic species. The characteristics of these HLSBS environments, as well as warming trends, are outlined in
30.5.1 and Table 30-1. In part, as a result of 30 years of increase in temperature [Belkin, 2009; Sherman et al.,
2009], there has been an increase in the size of fish stocks associated with high latitude fisheries in the northern
hemisphere. This is particularly the case for the Norwegian spring-spawning herring, which has recovered from
near-extinction as a result of overfishing and a cooler climate during the 1960s [Toresen and Ostvedt, 2000]. The
major components of both pelagic and demersal high latitude fish stocks are boreal species located north of 50°N.
Climate change is projected to increase high latitude plankton production and displace zooplankton and fish species
poleward. As a combined result of these future changes, the abundance of fish (particularly boreal species) may
increase in the northernmost part of the high latitude region [Cheung et al., 2011], although increases will only be
moderate in some areas.
The changes in distribution and migration of the pelagic fishes shows considerable spatial and temporal variability,
which can increase tensions among fishing nations. In this regard, tension over the Atlantic mackerel fisheries has
led to what many consider the first climate-change related conflict between fishing nations ([Cheung et al., 2012],
30.6.5), and which has emphasized the importance of developing international collaboration and frameworks for
decision making [Miller et al., 2013](30.6.7, 15.4.3.3). The Atlantic mackerel has over the recent decades been a
shared stock between the EU and Norway. However, the recent advancement of the Atlantic mackerel into the
Icelandic EEZ during summer has resulted in Icelandic fishers operating outside the agreement between the EU and
Norway. Earlier records of mackerel from the first half of the 20th and second half of the 19th century show,
however, that mackerel was present in Icelandic waters during the earlier warm periods [Astthorsson et al., 2012]. In
the Barents Sea, the North-east Arctic cod, Gadus morhua, reached record-high abundance during 2012 and also
reached its northernmost-recorded distribution (82°N)[ICES, 2012]. A further northward migration is impossible
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since this would be into the Deep Sea Polar Basin, beyond the habitat of shelf species. A further advancement
eastwards to the Siberian shelf is, however, possible. The North-east Arctic cod stock is shared exclusively by
Norway and Russia, and to date there has been a good agreement between those two nations on the management of
the stock. These examples highlight the importance of international agreements and cooperation (Table 30-4).
The HLSBS fisheries constitute a large-scale high-tech industry, with large investments in highly mobile fishing
vessels, equipment, and land-based industries with capacity for adapting fisheries management and industries for
climate change [Frontiers-Economics-Ltd, 2013]. Knowledge of how climate fluctuations and change affect the
growth, recruitment, and distribution of fish stocks is presently not incorporated into fisheries management
strategies [Perry et al., 2010]. These strategies are vital for fisheries that hope to cope with the challenges of a
changing ocean environment, and are centrally important to any attempt to develop ecosystem-based management
and sustainable fisheries under climate change. The large pelagic stocks, with their climate-dependent migration
pattern, are shared among several nations. Developing equitable sharing of fish quotas through international treaties
(Table 30-4) is a necessary adaptation for a sustainable fishery. Factors presently taken into account in determining
the shares of quotas are the historical fishery, bilateral exchanges of quotas for various species, and occupation time
of the stocks in the various EEZs.
30.6.2.2. Tourism
Tourism recreation represents one of the world s largest industries, accounting for 9% (>US$6 trillion) of global
GDP and employing over 255 million people. It is expected to grow by an average of 4% annually and reach 10% of
global GDP within the next 10 years [WTTC, 2012]. As with all tourism, that which is associated with the Ocean is
heavily influenced by climate change, global economic and socio-political conditions, and their interactions ([Scott
et al., 2012b]; 10.6.1). Climate change, through impacts on ecosystems (e.g., coral reef bleaching), can reduce the
appeal of destinations, increase operating costs, and/or increase uncertainty in a highly sensitive business
environment [Scott et al., 2012b].
Several facets of the influence of climate change on the Ocean directly impact tourism (10.6.1, 10.6.2). Tourism is
susceptible to extreme events such as violent storms, long periods of drought, and/or extreme precipitation events
(5.3.3, 10.6.1, [IPCC, 2012]). Sea level rise through its influence on coastal erosion and submergence, salinization
of water supplies, and changes to storm surge, increases the vulnerability of coastal tourism infrastructure, tourist
safety, and iconic ecosystems (high confidence) (5.3.3.2 10.6.1, [IPCC, 2012], Table SPM.1). For example,
approximately 29% of resorts in the Caribbean are within 1 m of the high tide mark and 60% are at risk of beach
erosion from rapid sea level rise [Scott et al., 2012a].
Increasing sea temperatures (30.3.1.1) can change the attractiveness of locations and the opportunities for tourism
through their influence on the movement of organisms and the state of ecosystems such as coral reefs (10.6.2, Box
CC-CR, [UNWTO and UNEP, 2008]). Mass coral bleaching and mortality (triggered by elevated sea temperatures,
high confidence) can decrease the appeal of destinations for diving-related tourism, although the level of awareness
of tourists of impacts (e.g., <50% of tourists were concerned about coral bleaching during 1998) and expected
economic impacts have been found to be uncertain [Scott et al., 2012b]. Some studies, however, have noted reduced
tourist satisfaction and identified dead coral as one of the reasons for disappointment at the end of the holiday
[Westmacott et al., 2001]. Tourists respond to changes in factors such as weather and opportunity by expressing
different preferences. For example, preferred conditions and hence tourism are projected to shift towards higher
latitudes with climate change, or from summer to cooler seasons [Amelung et al., 2007] (10.6.2).
Options for adaptation by the marine tourism sector include: (1) identifying and responding to inundation risks with
current infrastructure, and planning for projected sea level rise when building new tourism infrastructure (5.5, [Scott
et al., 2012a]); (2) promoting shoreline stability and natural barriers by preserving ecosystems such as mangroves,
salt marshes and coral reefs (5.5, [Scott et al., 2012b]); (3) deploying forecasting and early-warning systems in order
to anticipate challenges to tourism and natural ecosystems [Strong et al., 2011; IPCC, 2012]; (4) preparation of risk
management and disaster preparation plans in order to respond to extreme events; (5) reducing the effect of other
stressors on ecosystems and building resilience in iconic tourism features such as coral reefs and mangroves; and (6)
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educating tourists to improve understanding the negative consequences of climate change over those stemming from
local stresses [Scott et al., 2012b; Scott et al., 2012a]. Adaptation plans for tourism industries need to address
specific operators and regions. For example, some operators may have costly infrastructure at risk while others may
have few assets but are dependent on the integrity of natural environments [Turton et al., 2010].
30.6.2.3. Shipping
International shipping accounts for >80% of world trade by volume [UNCTAD, 2009a; b] and ~3% of global CO2
emissions from fuel combustion although CO2 emissions are expected to increase 2-3 fold by 2050 [Heitmann and
Khalilian, 2010], WGIII 8.1, 8.2). Changes in shipping routes [Borgerson, 2008], variation in the transport network
due to shifts in grain production and global markets, as well as new fuel and weather-monitoring technology, may
alter these emission patterns (WGIII 8.3, 8.5). Extreme weather events, intensified by climate change, may interrupt
ports and transport routes more frequently, damaging infrastructure and introducing additional dangers to ships,
crews, and the environment [UNCTAD, 2009a; b; Pinnegar et al., 2012](10.4.4). These issues have been assessed by
some countries which have raised concerns over the potential for costly delays and cancellation of services, and the
implications for insurance premiums as storminess and other factors change increase risks [Thornes et al., 2012].
Climate change may benefit maritime transport by reducing Arctic sea ice and consequently shorten travel distances
between key ports [Borgerson, 2008] thus also decreasing total GHG emissions from ships (WGIII 8.5.2). Currently,
reliability of this route limits its use [Schyen and Brathen, 2011], and the potential full operation of the Northwest
Passage and Northern Sea Route would require a transit management regime, regulation (e.g., navigation,
environmental, safety, and security), and a clear legal framework to address potential territorial claims that may
arise, with a number of countries having direct interest in the Arctic. Further discussion of issues around melting
Arctic sea ice and the Northern Sea Route are given in Chapter 28 (28.2.5, 28.3.4).
30.6.2.4. Offshore Energy and Mineral Resource Extraction and Supply
The marine oil and gas industry face potential impacts from climate change on its ocean-based activities. Over 100
oil and gas platforms were destroyed in the Gulf of Mexico by the unusually strong hurricanes Katrina and Rita in
2005. Other consequences for oil pipelines and production facilities ultimately reduced US refining capacity by 20%
[IPCC, 2012]. The increasing demand for oil and gas has pushed operations to waters 2000 m deep or more, far
beyond continental shelves. The very large-scale moored developments required are exposed to greater hazards and
higher risks, most of which are not well understood by existing climate/weather projections. Although there is a
strong trend towards seafloor well completions with a complex of wells, manifolds, and pipes that are not exposed to
surface forcing, these systems face different hazards from instability and scouring of the unconsolidated sediments
by DS currents [Randolph et al., 2010]. The influence of warming oceans on sea floor stability is widely debated
due largely to uncertainties about the effects of methane and methane hydrates [Sultan et al., 2004; Archer et al.,
2009; Geresi et al., 2009]. Declining sea ice is also opening up the Arctic to further oil and gas extraction.,
Discussion of the potential expansion of oil and mineral production in the Arctic is made in Chapter 28 (28.2.5,
28.2.6, 28.3.4).
The principal threat to oil and gas extraction and infrastructure in maritime settings is the impact of extreme weather
[Kessler et al., 2011], which is likely to increase given that future storm systems are expected to have greater energy
[Emanuel, 2005; Trenberth and Shea, 2006; Knutson et al., 2010]. Events such as Hurricane Katrina have illustrated
challenges which will arise for this industry with projected increases in storm intensity [Cruz and Krausmann,
2008]. In this regard, early warning systems and integrated planning offer some potential to reduce the effect of
extreme events [IPCC, 2012].
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30.6.3. Human Health
The major threats to public health due to climate change include diminished security of water and food supplies,
extreme weather events, and changes in the distribution and severity of diseases, including those due to marine
biotoxins ([Costello et al., 2009], 5.4.3.5, 6.4.2.3, 11.2). The predominately negative impacts of disease for human
communities are expected to be more serious in low-income areas such as South-east Asia, southern and east Africa,
and various sub-regions of South America [Patz et al., 2005], which also have under-resourced health systems
[Costello et al., 2009]. Many of the influences are directly or indirectly related to basin-scale changes in the Ocean
(e.g., temperature, rainfall, plankton populations, sea level rise, and ocean circulation [McMichael et al., 2006]).
Climate change in the Ocean may influence the distribution of diseases like cholera (11.5.2.1), and the distribution
and occurrence of harmful algal blooms (HAB). The frequency of cholera outbreaks induced by Vibrio cholerae and
other enteric pathogens are correlated with sea surface temperatures, multidecadal fluctuations of ENSO, and
plankton blooms, which may provide insight into how this disease may change with projected rates of ocean
warming [Colwell, 1996; Pascual et al., 2000; Rodó et al., 2002; Patz et al., 2005; Myers and Patz, 2009; Baker-
Austin et al., 2012]. The incidence of diseases such as ciguatera also shows a link to ENSO, with ciguatera
becoming more prominent after periods of elevated sea temperature. This indicates that ciguatera may become more
frequent in a warmer climate [Llewellyn, 2010], particularly given the higher prevalence of ciguatera in areas with
degraded coral reefs (low confidence) [Pratchett et al., 2011a].
30.6.4. Ocean-based Mitigation
30.6.4.1 Deep Sea Carbon Sequestration
Carbon dioxide capture and storage into the deep sea and geologic structures are also discussed in WGIII Chapter 7
(7.5.5, 7.8.2, 7.10, 7.12) The economic impact of deliberate CO2 sequestration beneath the sea floor has previously
been reviewed [IPCC, 2005]. Active CO2 sequestration from co-produced CO2 into sub-sea geologic formations is
being instigated in the North Sea and in the Santos Basin offshore from Brazil. These activities will increase as
offshore oil and gas production increasingly exploits fields with high CO2 in the source gas and oil. Significant risks
from the injection of high levels of CO2 into deep ocean waters have been identified for DS organisms and
ecosystems although chronic effects have not yet been studied. These risks are similar to those discussed previously
with respect to ocean acidification and could further exacerbate declining O2 levels and changing trophic networks
in deep water areas [Seibel and Walsh, 2001] (6.4.2.2).
There are significant issues within the decision frameworks regulating these activities. Dumping of any waste or
other matter in the sea, including the seabed and its subsoil, is strictly prohibited under the 1996 London Protocol
(LP) except for those few materials listed in Annex I. Annex 1 was amended in 2006 to permit storage of CO2 under
the seabed. Specific Guidelines for Assessment of Carbon Dioxide Streams for Disposal into Sub-Seabed
Geological Formations were adopted by the parties to the LP in 2007. The Guidelines take a precautionary
approach to the process, requiring Contracting Parties under whose jurisdiction or control such activities are
conducted to issue a permit for the disposal subject to stringent conditions being fulfilled [Rayfuse and Warner,
2012].
30.6.4.2 Offshore renewable energy
Renewable energy supply from the Ocean includes ocean energy and offshore wind turbines. The global technical
potential for ocean and wind energy is not as high as solar energy although considerable potential still remains.
Detailed discussion of the potential of renewable energy sources are given in WGIII Chapter 7 (7.4.2, 7.5.3, 7.8.2).
There is an increasing trend in the renewable energy sector to offshore wind turbines (10.2.2). At present, there is
high uncertainty about how changes in wind intensity and patterns, and extreme events, will impact the offshore
wind energy sector. Given the design and engineering solutions available to combat climate change impacts (Table
10.1, Table 10.7), it is unlikely that this sector will face insurmountable challenges from climate change.
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30.6.5. Maritime Security and Related Operations
Climate change and its influence on the Ocean has become an area of increasing concern in terms of the
maintenance of national security and the protection of citizens. These concerns have arisen as Nation States
increasingly engage in operations ranging from humanitarian assistance in climate-related disasters to territorial
issues exacerbated by changing coastlines, human communities, resource access, and new seaways [Kaye, 2012;
Rahman, 2012], 12.6.1). In this regard, increasing sea levels along gently sloping coastlines can have the seemingly
perverse outcome that the territorial limits to the maritime jurisdiction of the State might be open to question as the
distance from national baselines to the outer limits of the EEZ increases beyond 200 nm over time [Schofield and
Arsana, 2012].
Changes in coastal resources may also be coupled with decreasing food security to compound coastal poverty and
lead, in some cases, to increased criminal activities such as piracy, IUU fishing, and human, arms and drug
trafficking [Kaye, 2012]. While the linkages have not been clearly defined in all cases, it is possible that changes in
the Ocean as result of climate change will increase pressure on resources aimed at maintaining maritime security and
countering criminal activity, disaster relief operations, and freedom of navigation (12.6.2). National maritime
security capacity and infrastructure may also require rethinking as new challenges present themselves as a result of
climate change and ocean acidification (12.6.1-2) [Allen and Bergin, 2009; Rahman, 2012]. Opportunities may also
arise from changes to international geography such as formation of new ice-free seaways through the Arctic, which
may benefit some countries in terms of maintaining maritime security and access (28.2.6). Conversely, such new
features may also lead to increasing international tensions as States perceive new vulnerabilities from these changes
to geography.
Like commercial shipping (30.6.2.3), naval operations in many countries result in significant greenhouse emissions
(e.g., the US Navy emits around 2% of the national greenhouse gas emissions, [Mabus, 2010]). As a result, there are
a number of programs being implemented by navies around the world to try and reduce their carbon footprint and air
pollution such as improving engine efficiency, reducing fouling of vessels, increasing the use of biofuels, and using
nuclear technology for power generation, amongst other initiatives.
30.7. Synthesis and Conclusions
Evidence that human activities are fundamentally changing the Ocean is virtually certain. Sea temperatures have
increased rapidly over the past 60 years at the same time as pH has declined, consistent with the expected influence
of rising atmospheric concentrations of CO2 and other greenhouse gases (very high confidence). The rapid rate at
which these fundamental physical and chemical parameters of the Ocean are changing is unprecedented within the
last 65 Ma (high confidence) and possibly 300 Ma (medium confidence). As the heat content of the Ocean has
increased, the Ocean has become more stratified (very likely), although there is considerable regional variability. In
some cases, changing surface wind has influenced the extent of mixing and upwelling, although our understanding
of where and why these differences occur regionally is uncertain. The changing structure and function of the Ocean
has led to changes in parameters such as O2, carbonate ions, and inorganic nutrients concentrations (high
confidence). Not surprisingly, these fundamental changes have resulted in responses by key marine organisms,
ecosystems and ecological processes, with negative implications for hundreds of millions of people that depend on
the ecosystem goods and services provided by the Ocean (very likely). Marine organisms are migrating at rapid rates
towards higher latitudes, fisheries are transforming, and many organisms are shifting their reproductive and
migratory activity in concert with the changes in temperature and other parameters. Ecosystems such as coral reefs
are declining rapidly (high confidence). An extensive discussion of these changes is provided in previous sections
and in other chapters of AR5.
[INSERT TABLE 30-3 HERE
Table 30-3 Key risks to ocean and coastal issues from climate change and the potential for risk reduction through
mitigation and adaptation. Key risks are identified based on assessment of the literature and expert judgments made
by authors of the various WGII AR5 chapters, with supporting evaluation of evidence and agreement in the
referenced chapter sections. Each key risk is characterized as very low, low, medium, high, or very high. Risk levels
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are presented for the near-term era of committed climate change (here, for 2030 2040), in which projected levels of
global mean temperature increase do not diverge substantially across emissions scenarios. Risk levels are also
presented for the longer-term era of climate options (here, for 2080 2100), for global mean temperature increases of
2°C and 4°C above pre-industrial levels. For each timeframe, risk levels are estimated for the current state of
adaptation and for a hypothetical highly adapted state. As the assessment considers potential impacts on different
physical, biological, and human systems, risk levels should not necessarily be used to evaluate relative risk across
key risks. Relevant climate variables are indicated by symbols.
30.7.1. Key Risks and Vulnerabilities
The rapid changes in the physical, chemical, and biological state of the Ocean pose a number of key risks and
vulnerabilities for ecosystems, communities, and nations worldwide. Table 30-3 and Figure 30-12 summarize risks
and vulnerabilities from climate change and ocean acidification, along with adaptation issues and prospects, and a
summary of expert opinion on how these risks will change under further changes in environmental conditions.
Rising ocean temperatures are changing the distribution, abundance, and phenology of many marine species and
ecosystems, and consequently represent a key risk to food resources, coastal livelihoods, and industries such as
tourism and fishing, especially for HLSBS, CBS, STG, and EBUE (Table 30-3, Figure 30-12a-b, 30.5, Box CC-MB,
6.3.1 6.3.4, 7.3.2.4). Key risks involve changes in the distribution and abundance of key fishery species (Figure 30-
12a 2, 4; Figure 30-12b 2; high confidence) as well as the spread of disease and invading organisms, each of which
has the potential to impact ecosystems as well as aquaculture and fishing (Table 30-3, 6.3.5, 6.4.1.1, 6.5.3, 7.3.2.4,
7.4.2, 29.5.3, 29.5.4). Adaptation to these changes may be possible in the short-term through dynamic fisheries
policy and management (i.e., relocation of fishing effort, Table 30-3), as well as monitoring and responding to
potential invading species in coastal settings. The increasing frequency of thermal extremes (Box CC-HS) will also
increase the risk that the thermal threshold of corals and other organisms is exceeded on a more frequent basis
(especially in CBS, STG, SES, HLSBS, and EUS ocean regions; 30.5, Box CC-CR, 6.2). These changes pose a key
risk to vulnerable ecosystems such as mangroves and coral reefs, with potential to have a series of serious impacts
on fisheries, tourism, and coastal ecosystem services such as coastal protection (Table 30-3, 30.5, Box CC-CR,
5.4.2.4, 6.3.2. 6.3.5, 6.4.1.3, 7.2.1.2, 29.3.1.2). Genetic adaptation of species to increasing levels of stress may not
occur fast enough given fairly long generation times of organisms such as reef building corals and many other
invertebrates and fish (Table 30-3). In this case, risks may be reduced by addressing non-climate change related
stresses (e.g., pollution, overfishing), although this strategy could have minimal impact if further increases in sea
temperature occur (high confidence). The loss of these important coastal ecosystems is associated with the emerging
risks associated with the collapse of some coastal fisheries along with livelihoods, food, and regional security
(medium confidence). These changes are likely to be exacerbated by other key risks such as coastal inundation and
habitat loss due to sea level rise, as well is intensified precipitation events (high confidence) (5.4, Box CC-CR).
Adaptation options in this case include engineered coastal defences, re-establishing coastal vegetation such as
mangroves, protecting water supplies from salination, and developing strategies for coastal communities to
withdraw to less vulnerable locations over time (5.5).
The recent decline in O2 concentrations has been ascribed to warming through the effect on ocean mixing and
ventilation, as well as the solubility of O2 and its consumption by marine microbes (30.3.2.3, 30.5.7, 6.1.1.3, 6.3.3).
This represents a key risk to ocean ecosystems (Figure 30-12a 6, Figure 30-12b 3; medium confidence). These
changes increase the vulnerability of marine communities, especially those below the euphotic zone, to hypoxia and
ultimately lead to a restriction of suitable habitat (Figure 30-12a 7, high confidence). In the more extreme case, often
exacerbated by the contribution of organic carbon from land-based sources, dead zones may form . Decreasing
oxygen, consequently, is very likely to increase the vulnerability of fisheries and aquaculture (Figure 30-12b 1, 3;
medium confidence), and consequently puts livelihoods at risk, particularly in EBUE (e.g., California and Humboldt
Current ecosystems; 30.5.5), SES (e.g., Baltic and Black Seas; 30.5.3) and CBS (e.g., Gulf of Mexico, NE Indian
Ocean; 30.5.4, 30.3.2.3). It is very likely that the warming of surface waters has also increased the stratification of
the upper ocean by about 4% between 0 and 200 m from 1971 2010 in all oceans north of about 40°S. In many
cases, there is significant adaptation opportunity to reduce hypoxia locally by reducing the flow of organic carbon
and hence microbial activity within these coastal systems (30.5.4). Relocating fishing effort, and modifying
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procedures associated with industries like aquaculture, may offer some opportunity to adapt to these changes
(likely). Declining O2 concentrations is likely to have significant impacts on DS habitats, where organisms are
relatively sensitive to environmental changes of this nature due to the very constant conditions under which they
have evolved (30.5.7).
Ocean acidification has increased the vulnerability of ocean ecosystems by affecting key aspects of the physiology
and ecology of marine organisms (particularly in CBS, STG, and SES regions; Table 30-3; 6.3.2, Box CC-OA).
Decreasing pH and carbonate ion concentrations reduce the ability of marine organisms to produce shells and
skeletons, and may interfere with a broad range of important processes such as reproduction, navigation, and neural
function in a broad range of marine organisms which show minor to major influences of ocean acidification on their
biology (30.3.2.2, 6.3.2, Box CC-OA). Natural variability in ocean pH can interact with ocean acidification to create
damaging periods of extremes (i.e., high CO2, low O2 and pH), which can have a strong effect on coastal activities
such as aquaculture (medium confidence Figure 30-12b 1; Box CC-UP, 6.2). There may be opportunity to adapt
aquaculture to increasingly acidic conditions by monitoring natural variability and restricting water intake to periods
of optimal conditions. Reducing other non-climate change or ocean acidification associated stresses also represents
an opportunity to build greater ecological resilience against the impacts of changing ocean carbonate chemistry.
Ocean acidification is also an emerging risk for DS habitats as CO2 continues to penetrate the Ocean, although the
impacts and adaptation options are poorly understood and explored. Ocean acidification has heightened importance
for some groups of organisms and ecosystems (Box CC-OA). In ecosystems that are heavily dependent on the
accumulation of calcium carbonate over time (e.g., coral reefs, Halimeda beds), increasing ocean acidification puts
at risk ecosystems services which are critical for hundreds of thousands of marine species, plus people and
industries, particularly within CBS, STG and SES (high confidence). Further risks may emerge from the non-linear
interaction of different factors (e.g., increasing ocean temperature may amplify effects of ocean acidification, and
vice versa) and via the interaction of local stressors with climate change (e.g., interacting changes may lead to
greater ecosystems disturbances than each impact on its own). There is an urgent need to understand these types of
interactions and impacts, especially given the long time it will take to return ocean ecosystems to pre-industrial pH
and carbonate chemistry (i.e., tens of thousands of years, FAQ 30.1, should CO2 emissions continue at the current
rate).
It is very likely that surface warming has increased stratification of the upper ocean is contributing to the decrease in
O2 along with the temperature related decreases in oxygen solubility (WGI 3.8.3). Changes to wind speed, wave
height, and storm intensity influence the location and rate of mixing within the upper layers of the Ocean and hence
the concentration of inorganic nutrients (e.g., in EBUE, EUS; Figure 30-12a 1, 3). These changes to ocean structure
increase the risks and vulnerability of food webs within the Ocean. However, our understanding of how primary
productivity is going to change in a warming and more acidified ocean is limited, as is our understanding of how
upwelling will respond to changing surface wind as the world continues to warm (Box CC-PP, Box CC-UP). As
already discussed, these types of changes can have implications for the supply of O2 into the Ocean and the upward
transport of inorganic nutrients to the euphotic zone. While our understanding is limited, there is significant
potential for regional increases in wind speed to result in greater rates of upwelling and the supply of inorganic
nutrients to the photic zone. While this may increase productivity of phytoplankton communities and associated
fisheries, greater rates of upwelling can increase the risk of hypoxic conditions developing at depth as excess
primary production sinks into the Ocean and stimulates microbial activity at depth (Table 30-3, 30.3.2.3, 30.5.5,
6.1.1.3). Changes in storm intensity can increase the risk of damage to shipping and industrial infrastructure, which
increases the risk of accidents and delays to the transport of products between countries, security operations, and the
extraction of minerals from coastal and oceanic areas (30.6.2, [IPCC, 2012].
The proliferation of key risks and vulnerabilities to the goods and services provided by ocean ecosystems as a result
of ocean warming and acidification generate a number of key risks for the citizens of almost every nation. Risks to
food security and livelihoods are expected to increase over time, aggravating poverty and inequity (Table 30-3). As
these problems increase, regional security is likely to deteriorate as disputes over resources increase, along with
increasing insecurity of food and nutrition (Table 30-3, [IPCC, 2012], 30.6.5, 12.4-12.6, 29.3).
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30.7.2. Global Frameworks for Decision-Making
Global frameworks for decision-making are central to management of vulnerability and risk at the scale and
complexity of the world s oceans. General frameworks and conventions for policy development and decision-
making within oceanic and coastal regions are important in terms of the management of stressors not directly due to
ocean warming or acidification, but which may influence the outcome of these two factors. Table 30-3 and Table
30-4 outlines a further set of challenges arising from multiple interacting stressors, as well as potential risks and
vulnerabilities, ramifications, and adaptation options. In the latter case, examples of potential global frameworks and
initiatives for initiating and managing these adaptation options are described. These frameworks represent
opportunities for global cooperation and the development of international, regional, and national policy responses to
the challenges posed by the changing ocean [Kenchington and Warner, 2012; Tsamenyi and Hanich, 2012; Warner
and Schofield, 2012].
[INSERT TABLE 30-4 HERE
Table 30-4: Ramifications, adaptation options and frameworks for decision-making for ocean regions. Symbols are
as follows: T = sea temperature; UW = upwelling; OA = ocean acidification; NU = nutrient concentration; IC = ice
cover; SS = storm strength; SLR = sea level rise ( = Increased; = decreased; italics = uncertain). Acronyms are:
CBD (Convention on Biological Diversity); CTI (Coral Triangle Initiative); GEF (Global Environment Facility);
IHO (International Hydrographic Organization); ILO (International Labor Organization); IOM (International
Organization of Migration); ISPS (International Ship and Port Facility Security); MARPOL (International
Convention for the Prevention of Pollution From Ships); PACC (Pacific Adaptation to Climate Change Project);
PEMSEA (Partnerships in Environmental Management for the Seas of East Asia); RFMO (Regional Fisheries
Management Organizations); SPREP (Secretariat of the Pacific Regional Environment Programme); UNCLOS
(United Nations Convention on the Law of the Sea); UNHCR (United Nations High Commissioner for Refugees);
UNSFSA (Straddling Fish Stocks Agreement); and WHO (World Health Organization).]
The United Nations Convention on the Law of the Sea (UNCLOS) was a major outcome of the third UN Conference
on the Law of the Sea (UNCLOS III). The European Union and 164 countries have joined in the Convention.
UNCLOS replaced earlier frameworks that were built around the freedom of the seas concept and which limited
territorial rights to 3 nm off a coastline. UNCLOS provides a comprehensive framework for the legitimate use of the
Ocean and its resources, including maritime zones, navigational rights, protection and preservation of the marine
environment, fishing activities, marine scientific research, and mineral resource extraction from the seabed beyond
national jurisdiction. The relationship between climate change and UNCLOS is not clear and depends on
interpretation of the key elements within the UNFCCC (United Nations Framework Convention for Climate
Change) and Kyoto Protocol [Boyle, 2012]. However, UNCLOS provides mechanisms to help structural adaptation
in response to challenges posed by climate change. In a similar way, there is a wide range of other policy and legal
frameworks that structure and enable responses to the outcomes of rapid anthropogenic climate change in the Ocean.
There are many existing international conventions and agreements that explicitly recognize climate change (Table
30-4). The United Nations Straddling Fish Stocks Agreement (UNSFSA) aims at enhancing international
cooperation of fisheries resources, with an explicit understanding under article 6 that management needs to take
account existing and predicted oceanic, environmental and socio-economic conditions and to undertake relevant
research, including surveys of abundance, biomass surveys, hydro-acoustic surveys, research on environmental
factors affecting stock abundance, and oceanographic and ecological studies (Annex one, article 3). International
conventions such as these will become increasingly important as changes to the distribution and abundance of
fisheries are modified by climate change and ocean acidification.
Global frameworks for decision-making are increasingly important in the case of the Ocean, most of which falls
outside national boundaries [Elferink, 2012; Warner, 2012]. Approximately 64% of the Ocean (40% of the Earth s
surface) is outside EEZs and continental shelves of the world's nations (high seas and seabed beyond national
jurisdiction). With rapidly increasing levels of exploitation, there are increasing calls for more effective decision
frameworks aimed at regulating fishing and other activities (e.g., bio-prospecting) within these ocean commons .
These international frameworks will become increasingly valuable as nations respond to impacts on fisheries
resources that stretch across national boundaries. One such example is the multilateral cooperation that was driven
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by President Yudhoyono of Indonesia in August 2007 and led to the Coral Triangle Initiative on Coral Reefs,
Fisheries, and Food Security (CTI), which involves region-wide (involving 6.8 million km2 including 132,800 km of
coastline) cooperation between the governments of Indonesia, the Philippines, Malaysia, Papua New Guinea, the
Solomon Islands, and Timor Leste on reversing the decline in coastal ecosystems such as coral reefs [Clifton, 2009;
Hoegh-Guldberg et al., 2009; Veron et al., 2009]. Partnerships, such as the CTI, have the potential to provide key
frameworks to address issues such as interaction between the over-exploitation of coastal fishing resources and the
recovery of reefs from mass coral bleaching and mortality, and the implications of the movement of valuable fishery
stocks beyond waters under national jurisdiction. An initiative called the Global Partnership for Oceans set out
(www.globalpartnershipforoceans.org, March 28, 2012) to establish a global framework with which to share
experience, resources and expertise, as well as to engage governments, industry, civil, and public sector interests in
both understanding and finding solutions to key issues such as overfishing, pollution, and habitat destruction [GPO,
2012]. Similarly, the Areas Beyond National Jurisdiction (ABNJ, Global Environment Facility) Initiative has been
established to promote the efficient, collaborative, and sustainable management of fisheries resources and
biodiversity conservation across the Ocean.
Global partnerships are also essential for providing support to the many nations that often do not have the scientific
or financial resources to solve the challenges that lie ahead [Busby, 2009; Mertz et al., 2009]. In this regard,
international networks and partnerships are particularly significant in terms of assisting nations in developing local
adaptation solutions to their ocean resources. By sharing common experiences and strategies through global
networks, nations have the chance to tap into a vast array of options with respect to responding to the negative
consequences of climate change and ocean acidification on the world s ocean and coastal resources.
30.7.3. Emerging Issues, Data Gaps, and Research Needs
While there has been an increase in the number of studies being undertaken to understand the physical, chemical,
and biological changes within the Ocean in response to climate change and ocean acidification, the number of
marine studies still lag significantly behind terrestrial and atmospheric studies [Hoegh-Guldberg and Bruno, 2010;
Poloczanska et al., 2013]. Rectifying this gap should be a major international objective given the importance of the
Ocean in terms of understanding and responding to future changes and consequences from ocean warming and
acidification.
30.7.3.1. Changing Variability and Marine Impacts
Understanding the long-term variability of the Ocean is critically important in terms of the detection and attribution
of changes to climate change (30.3, 30.5.8), but also in terms of the interaction between variability and
anthropogenic climate change. Developing instrument systems that expand the spatial and temporal coverage of the
Ocean and key processes will be critical to documenting and understanding its behavior under further increases in
average global temperature and changes the atmospheric concentration of CO2. International collaborations such as
the Argo network of oceanographic floats are rapidly improving our understanding of the physical behavior of the
Ocean and will provide important insight into its long-term subsurface variability [Schofield et al., 2013].
30.7.3.2. Surface Wind, Storms, and Upwelling
Improving our understanding of the potential behavior of surface wind in a warming world is needed for improving
our understanding of how upwelling will change in key regions (e.g., EUS, EBUE; Box CC-UP). Understanding
these changes will provide important information for future fisheries management but will also illuminate the
potential risks of intensified upwelling leading to hypoxia at depth and the potential expansion of dead zones
(30.3.2; 30.5.2-30.5.4). Understanding surface wind in a warming climate will also yield important information on
surface mixing as well as how surface wave height might also vary, improving our understanding of potential
interactions in coastal areas between wind, waves, and sea level rise (30.3.1). Given the importance of mixing and
upwelling to the supply of inorganic nutrients to the surface layers of the ocean, understanding these important
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phenomena at the ocean-atmosphere interface will provide important insight into how ocean warming and
acidification are likely to impact ecosystems, food webs and ultimately central important fisheries such as those
found along the west coasts of Africa and the Americas.
30.7.3.3. Declining O2 Concentrations
The declining level of O2 in the Ocean is an emerging issue of major importance (30.3.2). Developing a better
understanding of the role and temperature sensitivity of microbial systems in determining O2 concentrations will
enable a more coherent understanding of the changes and potential risks to marine ecosystems. Given the
importance of microbial systems to the physical, chemical, and biological characteristics of the Ocean, it is
extremely important that these systems receive greater focus, especially with regards to their response to ocean
warming and acidification. This is particularly important for the DS (>1000 m), which is the most extensive habitat
on the planet. In this respect, increasing our understanding of DS habitats and how they may be changing under the
influence of climate change and ocean acidification is of great importance. Linkages between changes occurring in
the surface layers and those associated with the DS are particularly important in light of our need to understand how
rapidly changes are occurring and what the implications are for the metabolic activity and O2 content of DS habitats.
30.7.3.4. Ocean Acidification
The rapid and largely unprecedented changes to ocean acidification represent an emerging issue given the central
importance of pH and the concentration of ions such as carbonate in the biology of marine organisms (Box CC-OA).
Despite the relatively short history of research on this issue, there are already a large number of laboratories and
field studies that demonstrate a large range of effects across organisms, processes, and ecosystems. Key gaps
[Gattuso et al., 2011] remain in our understanding of how ocean acidification will interact with other changes in the
Ocean, and whether or not biological responses to ocean acidification are necessarily linear. The vulnerability of
fishery species (e.g., mollusks) to ocean acidification represents an emerging issue, with a need for research to
understand and develop strategies for industry to minimize the impacts. Understanding of how carbonate structures
like coral reefs and Halimeda beds, will respond to a rapidly acidifying ocean represents a key gap and research
need, especially in understanding the rate at which consolidated carbonate structures and related habitats are likely
to erode and dissolve. Interactions between ocean acidification, upwelling, and decreasing O2 represent additional
areas of concern and research. There is also a need to improve our understanding of the socio-economic
ramifications of ocean acidification [Turley et al., 2011; Hilmi et al., 2013].
30.7.3.5. Net Primary Productivity
Oceanic phytoplankton are responsible for 50% of global net primary productivity. However, our understanding of
how oceanic primary production is likely to change in a warmer and more acidified ocean is uncertain (Box CC-PP;
Box CC-UP). Changes in net primary productivity will resonate through food webs and ultimately affect fish
production. Given the central role that primary producers and their associated ecological processes play in ocean
ecosystem functioning, it is crucial that we improve our understanding of how net primary productivity is likely to
vary at global and regional levels (30.5.2, 30.5.5). At the same time, understanding how plankton communities will
vary spatially and temporarily will be important in any attempt to understand how fish populations will fare in a
warmer and more acidified ocean. The research challenge is to determine when and where net primary production is
expected to change, coupled with research on adaptation strategies for changes to the global distribution of seafood
procurement, management and food security.
30.7.3.6. Movement of Marine Organisms and Ecosystems
Marine organisms are moving, generally towards higher latitudes or deeper waters, consistent with the expectation
of a warming ocean. Our current understanding of which organisms and ecosystems are moving, and the
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ramifications for reorganisation of ecosystems and communities, and the implications for nations is uncertain at best.
Given the implications for fisheries, invasive species, and the spread of disease, it is an imperative that our
understanding of the movement of ecosystems is improved. Documentation of species responses and a deeper
understanding of the processes that lead to persistent range shifts, and a focus on the ecosystem, social and
economic implications of range shifts is a research need.
30.7.3.7. Understanding Cumulative and Synergistic Impacts
Understanding cumulative and synergistic impacts is poorly developed for ocean systems. Much of our
understanding has been built on experimental approaches that are focused on single stressors that respond gradually
without interaction or have impacts that accumulate over time (Table 30-3). Multifactorial experiments exploring the
impact of combined variables (e.g., elevated temperature and acidification at the same time) will enable more
realistic projections of the future to be established. Equally, developing a better understanding of how biological and
ecological responses change in relation to key environmental variables should also be a goal of future research. In
this regard, assumptions that responses are likely to be gradual and linear over time ultimately have little basis, yet
are widespread within the scientific literature.
30.7.3.8. Reorganization of Ecosystems and Food Webs
The pervasive influence of ocean warming and acidification on the distribution, abundance, and function of
organisms and processes has and will continue to drive the reorganization of ecosystems and food webs (virtually
certain) ([Hoegh-Guldberg and Bruno, 2010; Poloczanska et al., 2013], Box CC-MB). One of the inevitable
outcomes of differing tolerances and responses to climate change and ocean acidification is the development of
novel assemblages of organisms in the near future. Such communities are likely to have no past or contemporary
counterparts, and will consequently require new strategies for managing coastal areas and fisheries. Changes to a
wide array of factors related or not related to climate change have the potential to drive extremely complex changes
in community structure and, consequently, food web dynamics. Developing a greater capability for detecting and
understanding these changes will be critical for future management of ocean and coastal resources.
30.7.3.9. Socio-ecological Resilience
Many communities depend on marine ecosystems for food and income yet our understanding of the consequences of
environmental degradation is poor. For example, while there is high confidence that coral reefs will continue to
deteriorate at current rates of climate change and ocean acidification [Gardner et al., 2003; Bruno and Selig, 2007;
De ath et al., 2012], there is relatively poor understanding of the implications for the hundreds of millions of people
who depend on these important coastal ecosystems for food and livelihoods. Improving our understanding of how to
reinforce socio-ecological resilience in communities affected by the deterioration of key coastal and oceanic
ecosystems is central to developing effective adaptation responses to these growing challenges (30.6 Table 30-3,
Table 30-4).
Frequently Asked Questions
FAQ 30.1: Can we reverse the climate change impacts on the ocean? [to be inserted after Section 30.3.2]
In less than 150 years, greenhouse gas emissions have resulted in such major physical and chemical changes in our
oceans that it will take thousands of years to reverse them. There are a number of reasons for this. Given its large
mass and high heat capacity, the ability of the Ocean to absorb heat is 1000 times larger than that of the atmosphere.
The Ocean has absorbed at least nine tenths of the Earth s heat gain between 1971 and 2010. To reverse that heating,
the warmer upper layers of the Ocean have to mix with the colder deeper layers. That mixing can take up to 1000
years. This means it will take centuries to millennia for deep ocean temperatures to warm in response to today s
surface conditions, and at least as long for ocean warming to reverse after atmospheric greenhouse gas
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concentrations decrease (virtually certain). But climate change-caused alteration of basic conditions in the Ocean is
not just about temperature. The Ocean becomes more acidic as more CO2 enters it and will take tens of thousands of
years to reverse these profound changes to the carbonate chemistry of the ocean (virtually certain). These enormous
physical and chemical changes are producing sweeping and profound changes in marine ecosystems. Large and
abrupt changes to these ecosystems are unlikely to be reversible in the short to medium term (high confidence).
FAQ30.2: Does slower warming mean less impact on plants and animals? [to be inserted in Section 30.4]
The greater thermal inertia of the Ocean means that temperature anomalies and extremes are lower than those seen
on land. This does not necessarily mean that impacts of ocean warming are less for the ocean than for land. A large
body of evidence reveals that small amounts of warming in the Ocean can have large effects on ocean ecosystems.
For example, relatively small increases in sea temperature (as little as 1 2°C) can cause mass coral bleaching and
mortality across hundreds of square kilometers of coral reef (high confidence). Other analyses have revealed that
increased temperatures are spreading rapidly across the world s oceans (measured as the movement of bands of
equal water temperature or isotherms). This rate of warming presents challenges to organisms and ecosystems as
they try to migrate to cooler regions as the Ocean continues to warm. Rapid environmental change also poses steep
challenges to evolutionary processes, especially where long-lived organisms such as corals and fish are concerned
(high confidence).
FAQ30.3: How will marine primary productivity change? [to be inserted after Section 30.5.2.2]
Drifting microscopic plants known as phytoplankton are the dominant marine primary producers, at the base of the
marine food chain. Their photosynthetic activity is critically important to life in general. It provides oxygen,
supports marine food webs, and influences global biogeochemical cycles. Changes in marine primary productivity in
response to climate change remain the single biggest uncertainty in predicting the magnitude and direction of future
changes in fisheries and marine ecosystems (low confidence). Changes have been reported to a range of different
ocean systems (e.g., High Latitude Spring Bloom Systems, Sub-tropical Gyre Systems, Equatorial Upwelling
Systems, and Eastern Boundary Upwelling Ecosystems), some of which are consistent with changes in ocean
temperature, mixing, and circulation. However, direct attribution of these changes to climate change is made
difficult by long-term patterns of variability that influence productivity of different parts of the Ocean (e.g., Pacific
Decadal Oscillation). Given the importance of this question for ocean ecosystems and fisheries, longer time series
studies to understand how these systems are changing as a result of climate change are a priority (high agreement).
FAQ30.4: Will climate change cause dead zones in the oceans? [to be inserted after Section 30.5.5.2]
Dissolved oxygen is a major determinant of the distribution and abundance of marine organisms. Dead zones are
persistent hypoxic conditions where the water doesn t have enough dissolved oxygen to support oxygen-dependent
marine species. These areas exist all over the world and are expanding, with impacts on coastal ecosystems and
fisheries (high confidence). Dead zones are caused by several factors, particularly eutrophication where too many
nutrients run off coastal cities and agricultural areas into rivers that carry these materials out to sea. This stimulates
primary production leading to a greater supply of organic carbon, which can sink into the deeper layers of the ocean.
As microbial activity is stimulated, there is a sharp reduction in dissolved oxygen levels and an increased risk of
dead zones (high confidence). Climate change can influence the distribution of dead zones by increasing water
temperature and hence microbial activity, as well as reducing mixing of the ocean (i.e., increasing layering or
stratification) of the Ocean which have different temperatures, densities, salinities and reducing mixing of
oxygen-rich surface layers into the deeper parts of the Ocean. In other areas, increased upwelling can lead to
stimulated productivity, which can also lead to more organic carbon entering the deep ocean, where it is consumed,
decreasing oxygen levels (medium confidence). Managing local factors such as the input of nutrients into coastal
regions can play an important role in reducing the rate at which dead zones are spreading across the world s oceans
(high agreement).
FAQ30.5: How can we use non-climate factors to manage climate change impacts on the oceans?
[to be inserted after Section 30.7.1]
Like most natural system, the Ocean is exposed to a range of stresses that may or may not be related to climate
change. Human activities can result in pollution, eutrophication (too many nutrients), habitat destruction, invasive
species, destructive fishing, and over-exploitation of marine resources. Sometimes, these activities can increase the
impacts of climate change, although they can, in a few circumstances, dampen the effects as well. Understanding
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how these factors interact with climate change and ocean acidification is important in its own right. However,
reducing the impact of these non-climate factors may reduce the overall rate of change within ocean ecosystems.
Building ecological resilience through ecosystem-based approaches to the management of the marine environment,
for example, may pay dividends in terms of reducing and delaying the effects of climate change (high confidence).
Cross-Chapter Boxes
Box CC-CR. Coral Reefs
[Jean-Pierre Gattuso (France), Ove Hoegh-Guldberg (Australia), Hans-Otto Pörtner (Germany)]
Coral reefs are shallow-water ecosystems that consist of reefs made of calcium carbonate which is mostly secreted
by reef-building corals and encrusting macroalgae. They occupy less than 0.1% of the ocean floor yet play multiple
important roles throughout the tropics, housing high levels of biological diversity as well as providing key
ecosystem goods and services such as habitat for fisheries, coastal protection and appealing environments for
tourism (Wild et al., 2011). About 275 million people live within 30 km of a coral reef (Burke et al., 2011) and
derive some benefits from the ecosystem services that coral reefs provide (Hoegh-Guldberg, 2011) including
provisioning (food, livelihoods, construction material, medicine), regulating (shoreline protection, water quality),
supporting (primary production, nutrient cycling) and cultural (religion, tourism) services. This is especially true for
the many coastal and small island nations in the world s tropical regions (29.3.3.1).
Coral reefs are one of the most vulnerable marine ecosystems (high confidence; 5.4.2.4, 6.3.1, 6.3.2, 6.3.5, 25.6.2,
and 30.5) and more than half of the world s reefs are under medium or high risk of degradation (Burke et al., 2011).
Most human-induced disturbances to coral reefs were local until the early 1980s (e.g., unsustainable coastal
development, pollution, nutrient enrichment and overfishing) when disturbances from ocean warming (principally
mass coral bleaching and mortality) began to become widespread (Glynn, 1984). Concern about the impact of ocean
acidification on coral reefs developed over the same period, primarily over the implications of ocean acidification
for the building and maintenance of the calcium carbonate reef framework (Box CC-OA).
[INSERT FIGURE CR-1 HERE
Figure CR-1: A and B: the same coral community before and after a bleaching event in February 2002 at 5 m depth,
Halfway Island, Great Barrier Reef. Coral cover at the time of bleaching was 95% bleached almost all of it severely
bleached, resulting in mortality of 20.9% (Elvidge et al., 2004). Mortality was comparatively low due in part
because these coral communities were able to shuffle their symbiont to more thermo-tolerant types (Berkelmans and
van Oppen, 2006; Jones et al., 2008). C and D: three CO2 seeps in Milne Bay Province, Papua New Guinea show
that prolonged exposure to high CO2 is related to fundamental changes in the ecology of coral reefs (Fabricius et al.,
2011), including reduced coral diversity (-39%), severely reduced structural complexity (-67%), lower density of
young corals (-66%) and fewer crustose coralline algae (-85%). At high CO2 sites (panel D; median pHT ~7.8), reefs
are dominated by massive corals while corals with high morphological complexity are underrepresented compared
with control sites (D; median pH ~8.0). Reef development ceases at pHT values below 7.7. pHT: pH on the total
scale. E: temporal trend in coral cover for the whole Great Barrier Reef over the period 1985 2012 (N, number of
reefs, mean +/- 2 standard errors; De'ath et al., 2012). F: composite bars indicate the estimated mean coral mortality
for each year, and the sub-bars indicate the relative mortality due to crown-of-thorns starfish, cyclones, and
bleaching for the whole Great Barrier Reef (De'ath et al., 2012). Photo credit: R. Berkelmans (A and B) and K.
Fabricius (C and D).]
A wide range of climatic and non-climatic drivers affect corals and coral reefs and negative impacts have already
been observed (5.4.2.4, 6.3.1, 6.3.2, 25.6.2.1, 30.5.3, 30.5.6). Bleaching involves the breakdown and loss of
endosymbiotic algae, which live in the coral tissues and play a key role in supplying the coral host with energy (see
6.3.1. for physiological details and 30.5 for a regional analysis). Mass coral bleaching and mortality, triggered by
positive temperature anomalies (high confidence), is the most widespread and conspicuous impact of climate change
(Figure CR-1A and B, Figure 5-3; 5.4.2.4, 6.3.1, 6.3.5, 25.6.2.1, 30.5 and 30.8.2). For example, the level of thermal
stress at most of the 47 reef sites where bleaching occurred during 1997-98 was unmatched in the period 1903 to
1999 (Lough, 2000). Ocean acidification reduces biodiversity (Figure CR-1C and D) and the calcification rate of
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corals (high confidence; 5.4.2.4, 6.3.2, 6.3.5) while at the same time increasing the rate of dissolution of the reef
framework (medium confidence; 5.2.2.4) through stimulation of biological erosion and chemical dissolution. Taken
together, these changes will tip the calcium carbonate balance of coral reefs towards net dissolution (medium
confidence; 5.4.2.4). Ocean warming and acidification have synergistic effects in several reef-builders (5.2.4.2,
6.3.5). Taken together, these changes will erode habitats for reef-based fisheries, increase the exposure of coastlines
to waves and storms, as well as degrading environmental features important to industries such as tourism (high
confidence; 6.4.1.3, 25.6.2, 30.5).
A growing number of studies have reported regional scale changes in coral calcification and mortality that are
consistent with the scale and impact of ocean warming and acidification when compared to local factors such as
declining water quality and overfishing (Hoegh-Guldberg et al., 2007). The abundance of reef building corals is in
rapid decline in many Pacific and SE Asian regions (very high confidence, 1-2% per year for 1968-2004; Bruno and
Selig, 2007). Similarly, the abundance of reef-building corals has decreased by over 80% on many Caribbean reefs
(1977 to 2001; Gardner et al., 2003), with a dramatic phase shift from corals to seaweeds occurring on Jamaican
reefs (Hughes, 1994). Tropical cyclones, coral predators and thermal stress-related coral bleaching and mortality
have led to a decline in coral cover on the Great Barrier Reef by about 51% between 1985 and 2012 (Figure CR-1E
and F). Although less well documented, benthic invertebrates other than corals are also at risk (Przeslawski et al.,
2008). Fish biodiversity is threatened by the permanent degradation of coral reefs, including in a marine reserve
(Jones et al., 2004).
Future impacts of climate-related drivers (ocean warming, acidification, sea level rise as well as more intense
tropical cyclones and rainfall events) will exacerbate the impacts of non-climate related drivers (high confidence).
Even under optimistic assumptions regarding corals being able to rapidly adapt to thermal stress, one-third (9 to
60%, 68% uncertainty range) of the world s coral reefs are projected to be subject to long-term degradation (next
few decades) under the RCP3-PD scenario (Frieler et al., 2013). Under the RCP4.5 scenario, this fraction increases
to two-thirds (30 to 88%, 68% uncertainty range). If present day corals have residual capacity to acclimate and/or
adapt, half of the coral reefs may avoid high frequency bleaching through 2100 (limited evidence, limited
agreement; Logan et al., 2013). Evidence of corals adapting rapidly, however, to climate change is missing or
equivocal (Hoegh-Guldberg, 2012).
Damage to coral reefs has implications for several key regional services:
Resources: Coral reefs account for 10 to 12% of the fish caught in tropical countries, and 20 to 25% of the
fish caught by developing nations (Garcia and Moreno, 2003). Over half (55%) of the 49 island countries
considered by Newton et al. (2007) are already exploiting their coral reef fisheries in an unsustainable way
and the production of coral reef fish in the Pacific is projected to decrease 20% by 2050 under the SRES
A2 emissions scenario (Bell et al., 2013).
Coastal protection: Coral reefs contribute to protecting the shoreline from the destructive action of storm
surges and cyclones (Sheppard et al., 2005), sheltering the only habitable land for several island nations,
habitats suitable for the establishment and maintenance of mangroves and wetlands, as well as areas for
recreational activities. This role is threatened by future sea level rise, the decrease in coral cover, reduced
rates of calcification and higher rates of dissolution and bioerosion due to ocean warming and acidification
(5.4.2.4, 6.4.1, 30.5).
Tourism: More than 100 countries benefit from the recreational value provided by their coral reefs (Burke
et al., 2011). For example, the Great Barrier Reef Marine Park attracts about 1.9 million visits each year
and generates A$ 5.4 billion to the Australian economy and 54,000 jobs (90% in the tourism sector; Biggs,
2011).
Coral reefs make a modest contribution to the Global Domestic Product but their economic importance can be high
at the country and regional scales (Pratchett et al., 2008). For example, tourism and fisheries represent 5% of the
GDP of South Pacific islands (average for 2001-2011; Laurans et al., 2013). At the local scale, these two services
provided in 2009-2011 at least 25% of the annual income of villages in Vanuatu and Fiji (Pascal, 2011; Laurans et
al., 2013).
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Isolated reefs can recover from major disturbance, and the benefits of their isolation from chronic anthropogenic
pressures can outweigh the costs of limited connectivity (Gilmour et al., 2013). Marine protected areas (MPAs) and
fisheries management have the potential to increase ecosystem resilience and increase the recovery of coral reefs
after climate change impacts such as mass coral bleaching (McLeod et al., 2009). Although they are key
conservation and management tools, they are unable to protect corals directly from thermal stress (Selig et al., 2012)
suggesting that they need to be complemented with additional and alternative strategies (Rau et al., 2012; Billé et
al., 2013). While MPA networks are a critical management tool, they should be established considering other forms
of resource management (e.g., fishery catch limits and gear restrictions) and integrated ocean and coastal
management to control land-based threats such as pollution and sedimentation. There is medium confidence that
networks of highly protected areas nested within a broader management framework can contribute to preserving
coral reefs under increasing human pressure at local and global scales (Salm et al. 2006). Locally, controlling the
input of nutrients and sediment from land is an important complementary management strategy (Mcleod et al.,
2009) because nutrient enrichment can increase the susceptibility of corals to bleaching (Wiedenmann et al., 2012)
and coastal pollutants enriched with fertilizers can increase acidification (Kelly et al., 2011). In the long term,
limiting the amount of ocean warming and acidification is central to ensuring the viability of coral reefs and
dependent communities (high confidence; 5.2.4.4, 30.5).
CC-CR References
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R.J. Matear, T.D. Pickering, M.S. Pratchett, A. Sen Gupta, I. Senina I. and M. Waycott . 2013: Mixed responses of tropical Pacific fisheries
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Frieler K., M. Meinshausen, A. Golly, M. Mengel, K. Lebek, S.D. Donner and O. Hoegh-Guldberg, 2013: Limiting global warming to 2 °C is
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Garcia S. M. and I. de Leiva Moreno, 2003: Global overview of marine fisheries. In: Responsible fisheries in the marine ecosystem, [Sinclair M.
and Valdimarsson G. (eds.)], Wallingford: CABI pp. 1-24.
Gardner T. A., I.M. Cote, J.A. Gill, A. Grant and A.R. Watkinson, 2003: Long-term region-wide declines in Caribbean corals. Science
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Gilmour J. P., LD. Smith, A.J. Heyward, A.H. Baird and M.S. Pratchett M. S., 2013: Recovery of an isolated coral reef system following severe
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Hoegh-Guldberg O., 2011: Coral reef ecosystems and anthropogenic climate change. Regional Environmental Change 11, 215-227.
Hoegh-Guldberg O., 2012: The adaptation of coral reefs to climate change: is the Red Queen being outpaced? Scientia Marina 76, 403-408.
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N. Knowlton, C. M. Eakin, R. Iglesias-Prieto, N. Muthiga, R. H. Bradbury, A. Dubi, and M. E. Hatziolos, 2007: Coral reefs under rapid
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Jones A. M., R. Berkelmans, M.J. van Oppen, J.C. Mieog and W. Sinclair, 2008: A community change in the algal endosymbionts of a
scleractinian coral following a natural bleaching event: field evidence of acclimatization. In: Proceedings of the Royal Society of London.
Series B: Biological Sciences 275, 1359-1365.
Jones G. P., M.I. McCormick, M. Srinivasan and J.V. Eagle, 2004: Coral decline threatens fish biodiversity in marine reserves. In: Proceedings
of the National Academy of Science U.S.A. 101, 8251-8253.
Kelly R. P., M.M. Foley, W.S. Fisher, R.A. Feely, B.S. Halpern, G.G. Waldbusser and M.R. Caldwell, 2011: Mitigating local causes of ocean
acidification with existing laws. Science 332, 1036-1037.
Laurans Y., N. Pascal, T. Binet, L. Brander, E. Clua, G. David, D. Rojat and A. Seidl, 2013: Economic valuation of ecosystem services from
coral reefs in the South Pacific: taking stock of recent experience. Journal of Environmental Management 116C, 135-144.
Logan C. A., J.P. Dunne, C.M. Eakin and S.D. Donner, 2013: Incorporating adaptation and acclimatization into future projections of coral
bleaching. Global Change Biology, doi:10.1111/gcb.12390.
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McLeod E., R. Salm, A. Green and J. Almany, 2009: Designing marine protected area networks to address the impacts of climate change.
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Newton K., I. M. Côté, G.M. Pilling G, S. Jennings and N.K. Dulvy, 2007: Current and future sustainability of island coral reef fisheries. Current
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Box CC-HS. Heat Stress and Heat Waves
[Lennart Olsson (Sweden), Dave Chadee (Trinidad and Tobago), Ove Hoegh-Guldberg (Australia), John Porter (Denmark), Hans-O. Pörtner
(Germany), Kirk Smith (USA), Maria Isabel Travasso (Argentina), Petra Tschakert (USA)]
Heat waves are periods of abnormally and uncomfortably hot weather during which the risk of heat stress on people
and ecosystems is high. The number and intensity of hot days have increased markedly in the last three decades
(Coumou et al., 2013) (high confidence). According to WG I, it is likely that the occurrence of heat waves has more
than doubled in some locations due to human influence and it is virtually certain that there will be more frequent hot
extremes over most land areas in the latter half of the 21st century. Coumou et al. (2013) predicted that, under a
medium warming scenario, the number of monthly heat records will be over 12 times more common by the 2040s
compared to a non-warming world. In a longer time perspective, if the global mean temperature increases to +10C
or more, the habitability of large parts of the tropics and mid-latitudes will be at risk (Sherwood and Huber, 2010).
Heat waves affect natural and human systems directly, often with severe losses of lives and assets as a result, and
they may act as triggers for tipping points (Hughes et al., 2013). Consequently, heat waves play an important role in
several key risks noted in Chapter 19 and CC-KR.
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Economy and Society [Ch 10, 11, 12, 13]
Environmental heat stress has already reduced the global labor capacity to 90% in peak months with a further
predicted reduction to 80% in peak months by 2050. Under a high warming scenario (RCP8.5), labor capacity is
expected to be less than 40% of present day conditions in peak months by 2200 (Dunne et al., 2013). Adaptation
costs for securing cooling capacities and emergency shelters during heat waves will be substantial.
Heat waves are associated with social predicaments such as increasing violence (Anderson, 2012) as well as overall
health and psychological distress and low life satisfaction (Tawatsupa et al., 2012). Impacts are highly differential
with disproportional burdens on poor people, elderly people, and those who are marginalized (Wilhelmi et al.,
2012). Urban areas are expected to suffer more due to the combined effect of climate and the urban heat island effect
(Fischer et al., 2012). In LICs and MICs, adaptation to heat stress is severely restricted for most people in poverty
and particularly those who are dependent on working outdoors in agriculture, fisheries, and construction. In small-
scale agriculture, women and children are particularly at risk due to the gendered division of labor (Croppenstedt et
al., 2013). The expected increase in wildfires as a result of heat waves (Pechony and Shindell, 2010) is a concern for
human security, health, and ecosystems. Air pollution from wildfires already causes an estimated 339,000 premature
deaths per year worldwide (Johnston et al., 2012).
Human Health [Ch 11]
Morbidity and mortality due to heat stress is now common all over the world (Barriopedro et al., 2011; Rahmstorf
and Coumou, 2011; Nitschke et al., 2011; Diboulo et al., 2012; Hansen et al., 2012). People in physical work are at
particular risk as such work produces substantial heat within the body, which cannot be released if the outside
temperature and humidity is above certain limits (Kjellstrom et al., 2009). The risk of non-melanoma skin cancer
from exposure to UV radiation during summer months increases with temperature (van der Leun, Jan C et al., 2008).
Increase in ozone concentrations due to high temperatures affects health (Smith et al., 2010), leading to premature
mortality, e.g. cardiopulmonary mortality (Smith et al., 2010). High temperatures are also associated with an
increase in air-borne allergens acting as a trigger for respiratory illnesses such as asthma, allergic rhinitis,
conjunctivitis, and dermatitis (Beggs, 2010).
Ecosystems [Ch 4, 5, 6, 30]
Tree mortality is increasing globally (Williams et al., 2012) and can be linked to climate impacts, especially heat and
drought (Reichstein et al., 2013), even though attribution to climate change is difficult due to lack of time series and
confounding factors. In the Mediterranean region, higher fire risk, longer fire season, and more frequent large,
severe fires are expected as a result of increasing heat waves in combination with drought (Duguy et al., 2013), Box
4.2.
Marine ecosystem shifts attributed to climate change are often caused by temperature extremes rather than changes
in the average (Pörtner and Knust, 2007). During heat exposure near biogeographical limits, even small (<0.5°C)
shifts in temperature extremes can have large effects, often exacerbated by concomitant exposures to hypoxia and/or
elevated CO2 levels and associated acidification (Hoegh-Guldberg et al., 2007), Figure 6-5, (medium confidence)
[Ch 6.3.1, 6.3.5; 30.4; 30.5; CC-MB]
Most coral reefs have experienced heat stress sufficient to cause frequent mass coral bleaching events in the last 30
years, sometimes followed by mass mortality (Baker et al., 2008). The interaction of acidification and warming
exacerbates coral bleaching and mortality (very high confidence).Temperate seagrass and kelp ecosystems will
decline with the increased frequency of heat waves and through the impact of invasive subtropical species (high
confidence). [Ch 5, 6, 30.4-30.5, CC-CR, CC-MB]
Agriculture [Ch 7]
Excessive heat interacts with key physiological processes in crops. Negative yield impacts for all crops past +3C of
local warming without adaptation, even with benefits of higher CO2 and rainfall, are expected even in cool
environments (Teixeira et al., 2011). For tropical systems where moisture availability or extreme heat limits the
length of the growing season, there is a high potential for a decline in the length of the growing season and
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suitability for crops (medium evidence, medium agreement) (Jones and Thornton, 2009). For example, half of the
wheat-growing area of the Indo-Gangetic Plains could become significantly heat-stressed by the 2050s.
There is high confidence that high temperatures reduce animal feeding and growth rates (Thornton et al., 2009).
Heat stress reduces reproductive rates of livestock (Hansen, 2009), weakens their overall performance (Henry et al.,
2012), and may cause mass mortality of animals in feedlots during heat waves (Polley et al., 2013). In the U.S.,
current economic losses due to heat stress of livestock are estimated at several billion USD annually (St-Pierre et al.,
2003).
Box CC-HS References
Anderson, C.A., 2012: Climate Change and Violence. In: The Encyclopedia of Peace Psychology. [Christie, D.J. (ed.)]. Wiley Online Library
Baker, A.C., P.W. Glynn, and B. Riegl, 2008: Climate change and coral reef bleaching: An ecological assessment of long-term impacts, recovery
trends and future outlook. Estuarine, Coastal and Shelf Science, 80(4), 435-471.
Barriopedro, D., E.M. Fischer, J. Luterbacher, R.M. Trigo, and R. García-Herrera, 2011: The hot summer of 2010: redrawing the temperature
record map of Europe. Science, 332(6026), 220-224.
Beggs, P.J., 2010: Adaptation to impacts of climate change on aeroallergens and allergic respiratory diseases. International Journal of
Environmental Research and Public Health, 7(8), 3006-3021.
Coumou, D., A. Robinson, and S. Rahmstorf, 2013: Global increase in record-breaking monthly-mean temperatures. Climatic Change, 118(3-4),
771-782.
Croppenstedt, A., M. Goldstein, and N. Rosas, 2013: Gender and agriculture: inefficiencies, segregation, and low productivity traps. The World
Bank Research Observer, 28(1), 79-109.
Diboulo, E., A. Sie, J. Rocklöv, L. Niamba, M. Ye, C. Bagagnan, and R. Sauerborn, 2012: Weather and mortality: a 10 year retrospective
analysis of the Nouna Health and Demographic Surveillance System, Burkina Faso. Global Health Action, 5(19078).
Duguy, B., S. Paula, J.G. Pausas, J.A. Alloza, T. Gimeno, and R.V. Vallejo, 2013: Effects of climate and extreme events on wildfire regime and
their ecological impacts. In: Regional Assessment of Climate Change in the Mediterranean. Springer, pp. 101-134.
Dunne, J.P., R.J. Stouffer, and J.G. John, 2013: Reductions in labour capacity from heat stress under climate warming. Nature Climate Change,
published on-line 24 February 2013, 1-4.
Fischer, E., K. Oleson, and D. Lawrence, 2012: Contrasting urban and rural heat stress responses to climate change. Geophysical Research
Letters, 39(3), L03705.
Hansen, J., M. Sato, and R. Ruedy, 2012: Perception of climate change. Proceedings of the National Academy of Sciences, 109(37), E2415-
E2423.
Hansen, P.J., 2009: Effects of heat stress on mammalian reproduction. Philosophical Transactions of the Royal Society B: Biological Sciences,
364(1534), 3341-3350.
Henry, B., R. Eckard, J.B. Gaughan, and R. Hegarty, 2012: Livestock production in a changing climate: adaptation and mitigation research in
Australia. Crop and Pasture Science, 63(3), 191-202.
Hoegh-Guldberg, O., P. Mumby, A. Hooten, R. Steneck, P. Greenfield, E. Gomez, C. Harvell, P. Sale, A. Edwards, and K. Caldeira, 2007: Coral
reefs under rapid climate change and ocean acidification. Science, 318(5857), 1737-1742.
Hughes, T.P., S. Carpenter, J. Rockström, M. Scheffer, and B. Walker, 2013: Multiscale regime shifts and planetary boundaries. Trends in
Ecology & Evolution, 28(7), 389-395.
Johnston, F.H., S.B. Henderson, Y. Chen, J.T. Randerson, M. Marlier, R.S. DeFries, P. Kinney, D.M. Bowman, and M. Brauer, 2012: Estimated
global mortality attributable to smoke from landscape fires. Environmental Health Perspectives, 120(5), 695.
Jones, P.G. and P.K. Thornton, 2009: Croppers to livestock keepers: livelihood transitions to 2050 in Africa due to climate change.
Environmental Science & Policy, 12(4), 427-437.
Kjellstrom, T., R. Kovats, S. Lloyd, T. Holt, and R. Tol, 2009: The direct impact of climate change on regional labor productivity. Archives of
Environmental & Occupational Health, 64(4), 217-227.
Nitschke, M., G.R. Tucker, A.L. Hansen, S. Williams, Y. Zhang, and P. Bi, 2011: Impact of two recent extreme heat episodes on morbidity and
mortality in Adelaide, South Australia: a case-series analysis. Environ Health, 10, 42.
Pechony, O. and D. Shindell, 2010: Driving forces of global wildfires over the past millennium and the forthcoming century. Proceedings of the
National Academy of Sciences, 107(45), 19167-19170.
Polley, H.W., D.D. Briske, J.A. Morgan, K. Wolter, D.W. Bailey, and J.R. Brown, 2013: Climate Change and North American Rangelands:
Trends, Projections, and Implications. Rangeland Ecology & Management, 66(5), 493-511.
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Pörtner, H.O. and R. Knust, 2007: Climate change affects marine fishes through the oxygen limitation of thermal tolerance. Science, 315(5808),
95-97.
Rahmstorf, S. and D. Coumou, 2011: Increase of extreme events in a warming world. Proceedings of the National Academy of Sciences, 108(44),
17905-17909.
Reichstein, M., M. Bahn, P. Ciais, D. Frank, M.D. Mahecha, S.I. Seneviratne, J. Zscheischler, C. Beer, N. Buchmann, and D.C. Frank, 2013:
Climate extremes and the carbon cycle. Nature, 500(7462), 287-295.
Sherwood, S.C. and M. Huber, 2010: An adaptability limit to climate change due to heat stress. Proceedings of the National Academy of Sciences,
107(21), 9552-9555.
Smith, K.R., M. Jerrett, H.R. Anderson, R.T. Burnett, V. Stone, R. Derwent, R.W. Atkinson, A. Cohen, S.B. Shonkoff, and D. Krewski, 2010:
Public health benefits of strategies to reduce greenhouse-gas emissions: health implications of short-lived greenhouse pollutants. The Lancet,
374(9707), 2091-2103.
St-Pierre, N., B. Cobanov, and G. Schnitkey, 2003: Economic losses from heat stress by US livestock industries. Journal of Dairy Science, 86,
E52-E77.
Tawatsupa, B., V. Yiengprugsawan, T. Kjellstrom, and A. Sleigh, 2012: Heat stress, health and well-being: findings from a large national cohort
of Thai adults. BMJ Open, 2(6).
Teixeira, E.I., G. Fischer, H. van Velthuizen, C. Walter, and F. Ewert, 2011: Global hot-spots of heat stress on agricultural crops due to climate
change. Agricultural and Forest Meteorology, 170, 206-215.
Thornton, P., J. Van de Steeg, A. Notenbaert, and M. Herrero, 2009: The impacts of climate change on livestock and livestock systems in
developing countries: A review of what we know and what we need to know. Agricultural Systems, 101(3), 113-127.
van der Leun, Jan C, R.D. Piacentini, and F.R. de Gruijl, 2008: Climate change and human skin cancer. Photochemical & Photobiological
Sciences, 7(6), 730-733.
Wilhelmi, O., A. de Sherbinin, and M. Hayden, 2012: 12 Exposure to heat stress in urban environments. Ecologies and Politics of Health, 41,
219.
Williams, A.P., C.D. Allen, A.K. Macalady, D. Griffin, C.A. Woodhouse, D.M. Meko, T.W. Swetnam, S.A. Rauscher, R. Seager, and H.D.
Grissino-Mayer, 2012: Temperature as a potent driver of regional forest drought stress and tree mortality. Nature Climate Change, 3, 292-
297.
Box CC-MB. Observed Global Responses of Marine Biogeography, Abundance, and
Phenology to Climate Change
[Elvira Poloczanska (Australia), Ove Hoegh-Guldberg (Australia), William Cheung (Canada), Hans O. Pörtner (Germany),
Michael Burrows (UK)]
WGII AR4 presented detection and attribution of a global climate change fingerprint on natural systems (AR4, Ch 1,
SPM Figure 1), but studies from marine systems were mostly absent. Since AR4, there has been a rapid increase in
studies that focus on climate change impacts on marine species, which represents an opportunity to move from more
anecdotal evidence to examining and potentially attributing detected changes within the Ocean to climate change
(6.3, Figure MB-1). Recent changes in populations of marine species and the associated shifts in diversity patterns
are resulting, at least partly, from climate change-mediated biological responses across ocean regions (6.2, Table 6.7,
30.5) (robust evidence, high agreement, high confidence).
Poloczanska et al. (2013) assess a potential pattern in responses of ocean life to recent climate change using a global
database of 208 peer-reviewed papers. Observed responses (n=1735) were recorded from 857 species or
assemblages across regions and taxonomic groups, from phytoplankton to marine reptiles and mammals (Figure
MB-1). Observations were defined as those where the authors of a particular paper assessed the occurrence change
in a biological parameter (including distribution, phenology, abundance, demography or community composition)
and, if change occurs, the consistency of the change with that expected under climate change. Studies from the peer-
reviewed literature were selected using three criteria: (1) authors inferred or directly tested for trends in biological
and climatic variables; (2) included data after 1990; and (3) observations spanned at least 19 years, to reduce bias
resulting from biological responses to short-term climate variability.
[INSERT FIGURE MB-1 HERE
Figure MB-1: 1735 observed responses to climate change from 208 single- and multi-species studies. Changes
attributed to climate change (blue), inconsistent with climate change (red) and are equivocal (yellow). Each circle
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represents the centre of a study area. Where points fall on land, it is because they are centroids of distribution that
surround an island or peninsula. Pie charts show the proportions within regions bounded by red squares and in the
Mediterranean; numbers indicate the total (consistent, opposite or equivocal) observations within each region. Note:
57% of the studies included were published since AR4 (from Poloczanska et al., 2013).]
The results of this meta-analysis show that climate change has already had widespread impacts on species
distribution, abundance, phenology, and subsequently, species richness and community composition across a broad
range of taxonomic groups (plankton to top predators). Of the observations that showed a response in either
direction, changes in phenology, distribution and abundance were overwhelmingly (81%) in a direction that was
consistent with theoretical responses to climate change (6.2). Knowledge gaps exist, especially in equatorial sub-
regions and the Southern Hemisphere (Figure MB-1).
The timing of many biological events (phenology) had an earlier onset. For example, over the last 50 years, spring
events shifted earlier for many species with an average advancement of 4.4 +/- 0.7 days decade-1 (mean +/- SE) and
summer events by 4.4 +/- 1.1 days decade-1 (robust evidence, high agreement, high confidence) (Figure MB-2).
Phenological observations included in the study, range from shifts in peak abundance of phytoplankton and
zooplankton, to reproduction and migration of invertebrates, fishes and seabirds (6.3.2, 30.5).
The distributions of benthic, pelagic and demersal species and communities have shifted by 10s to 1000s of km,
although the range shifts have not been uniform across taxonomic groups or ocean regions (6.3.2, 30.5) (robust
evidence, high agreement, high confidence). Overall, leading range edges expanded in a poleward direction at 72.0 +/-
13.5 km decade-1 and trailing edges contracted in a poleward direction at 15.8 +/- 8.7 km decade-1 (Figure MB-2)
revealing much higher current rates of migration than the potential maximum rates reported for terrestrial species
(Figure 4.6) despite slower warming of the Ocean than land surface (WG1 3.2).
[INSERT FIGURE MB-2 HERE
Figure MB-2. Rates of change in distribution (km decade-1) for marine taxonomic groups, measured at the leading
edges (red) and trailing edges (brown). Average distribution shifts calculated using all data, regardless of range
location, are in black. Distribution rates have been square-root transformed; standard errors may be asymmetric as a
result. Positive distribution changes are consistent with warming (into previously cooler waters, generally
poleward). Means +/- standard error are shown, along with number of observations (from Poloczanska et al., 2013).]
Poleward distribution shifts have resulted in increased species richness in mid to high latitude regions (Hiddink and
ter Hofstede, 2008) and changing community structure (Simpson et al., 2011) (28.2.2). Increases in warm-water
components of communities concurrent with regional warming have been observed in mid to high latitude ocean
regions including the Bering Sea, Barents Sea, Nordic Sea, North Sea, and Tasman Sea (Box 6.1, 30.5). Observed
changes in species composition of catches from 1970 2006 that is partly attributed to long-term ocean warming
suggest increasing dominance of warmer water species in sub-tropical and higher latitude regions, and reduction in
abundance of sub-tropical species in equatorial waters (Cheung et al., 2013), with implications for fisheries (6.5,
7.4.2, 30.6.2.1)
The magnitude and direction of distribution shifts can be related to temperature velocities (i.e., the speed and
direction at which isotherms propagate across the Ocean s surface (30.3.1.1, Burrows et al. 2011). Pinsky et al.
(2013) showed that shifts in both latitude and depth of benthic fish and crustaceans could be explained by climate
velocity with remarkable accuracy, using a database of 128 million individuals across 360 marine taxa from surveys
of North American coastal waters conducted over 1968 to 2011. Poloczanska et al. (2013) found that faster
distribution shifts generally occur in regions of highest surface temperature velocity, such as the North Sea and sub-
Arctic Pacific Ocean. Observed marine species shifts, since approximately 1950s, have generally been able to track
observed velocities (Fig MB-3), with phyto- and zooplankton distribution shifts vastly exceeding climate velocities,
but with considerable variability within and among taxonomic groups (Poloczanska et al. 2013).
Biogeographic shifts are also be influenced by other factors such as nutrient and stratification changes, species
interactions, habitat availability and fishing (6.3). Rate and pattern of biogeographic shifts in sedentary organisms
and benthic macroalgae are complicated by the influence of local dynamics and topographic features (islands,
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channels, coastal lagoons, e.g., of the Mediterranean (Bianchi, 2007), coastal upwelling e.g., Lima et al. (2007)).
Geographical barriers constrain range shifts and may cause a loss of endemic species (Ben Rais Lasram et al., 2010),
with associated niches filled by alien species, either naturally migrating or artificially introduced (Philippart et al.,
2011).
Whether marine species can continue to keep pace as warming rates, hence climate velocities, increase (Fig MB-3b)
is a key uncertainty. Climate velocities on land are expected to outpace the ability of many terrestrial species to track
climate velocities this century (4.3.2.5, Figure 4.6) For marine species, the observed rates of shift are generally much
faster than those land for land species, particularly for primary producers and lower trophic levels (Poloczanska et
al. 2013). Phyto- and zooplankton communities (excluding larval fish) have extended distributions at remarkable
rates (Figure MB-3b), such as in the North-east Atlantic (30.5.1) with implications for marine food webs.
Geographical range shifts and depth distribution vary between coexisting marine species (Genner et al., 2004; Perry
et al., 2005; Simpson et al., 2011) as a consequence of species-specific thermal window widths and associated
vulnerabilities (Figure 6.5). Warming therefore causes differential changes in growth, reproductive success, larval
output, early juvenile survival, and recruitment, implying shifts in the relative performance of animal species and,
thus, their competitiveness (Pörtner and Farrell, 2008; Figure 6.7A). Such effects may underlie abundance losses or
local extinctions, regime shifts between coexisting species, or critical mismatches between predator and prey
organisms. Changes in local and regional species richness, abundance, community composition, productivity, energy
flows and invasion resistance result. Even among Antarctic stenotherms such differences related to mode of life,
phylogeny and associated metabolic capacities exist (6.3.1.4). As a consequence, marine ecosystem functions may
be substantially reorganized at the regional scale, potentially triggering a range of cascading effects (Hoegh-
Guldberg and Bruno, 2010). A focus on understanding the mechanisms underpinning the nature and magnitude of
responses of marine organisms to climate change can help forecast impacts and the associated costs to society and
facilitate adaptive management strategies effective in mitigating these impacts (6.3, 6.4).
[INSERT FIGURE MB-3 HERE
Figure MB-3. A. Rate of climate change for the Ocean (sea surface temperature (SST) °C); B. corresponding climate
velocities for the Ocean and median velocity from land (adapted from Burrows et al., 2011); and C. observed rates
of displacement of marine taxonomic groups over several decades until 2010. The thin dotted red arrows give an
example of interpretation. Rates of climate change of 0.008 °C yr-1 correspond to ca. 2.4 km yr-1median climate
velocity in the Ocean. When compared to observed rates of displacement, many marine taxonomic groups have been
able to track these velocities, except phyto- and zooplankton where rates of displacement greatly exceed climate
velocity. All values are calculated for ocean surface with the exclusion of polar seas (Figure 30-1a). (A) Observed
rates of climate change for Ocean SST (Black dotted line) are derived from HadISST1.1 data set, all other rates are
calculated based on the average of the CMIP5 climate model ensembles (Table S30-3) for the historical period and
for the future based on the four RCP emissions scenarios. Data were smoothed using a 20-year sliding window. (B)
Median climate velocity calculated from HadISST1.1 dataset over 1960 2010 using the methods of Burrows et al.,
2011. The three axes represent estimated median climate velocities are representative of areas of slow velocities
such as Pacific subtropical gyre (STG) system (Purple line), the global Ocean surface (excluding polar seas, Blue
line), and areas of high velocities such as the Coral Triangle and North Sea (Orange line). Figure 30-3 shows climate
velocities over the ocean surface calculated over 1960 2010. The Red line corresponds to the median rate over
global land surface calculated using historical surface temperatures from the CMIP5 model ensemble (Table S30-3).
(C) Rates of displacement for marine taxonomic groups estimated by Poloczanska et al. 2013 using published
studies (Figure MB-2 Black data set). Note the displacement rates for phytoplankton exceed the axis, so values are
given.]
Box CC-MB References
Ben Rais Lasram, F.B., F. Guilhaumon, C. Albouy, S. Somot, W. Thuiller and D. Mouillot, 2010: The Mediterranean Sea as a cul-de-sac for
endemic fishes facing climate change, Global Change Biology, 16, 3233-3245.
Bianchi, C.N., 2007: Biodiversity issues for the forthcoming Mediterranean Sea, Hydrobiologia, 580, 7-21.
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Burrows, M.T., D. S. Schoeman, L.B. Buckley, P.J. Moore, E.S. Poloczanska, K. Brander, K, C.J. Brown, J.F. Bruno, C.M. Duarte, B.S.
Halpern, J. Holding, C.V. Kappel, W. Kiessling, M.I. O Connor, J.M. Pandolfi, C. Parmesan, F. Schwing, W.J. Sydeman and A.J. Richardson,
2011; The pace of shifting climate in marine and terrestrial ecosystems, Science, 334,652-655.
Cheung, W.W.L., J.L. Sarmiento, J. Dunne, T.L. Frölicher, V. Lam, M.L.D. Palomares, R. Watson and D. Pauly, 2013: Shrinking of fishes
exacerbates impacts of global ocean changes on marine ecosystems. Nature Climate Change 3, 254-258.
Genner, M.J., D.W. Sims, V.J. Wearmouth, E.J. Southall, A.J. Southward, P.A. Henderson and S.J. Hawkins, 2004: Regional climatic warming
drives long-term community changes of British marine fish. Proceedings of the Royal Society of London B: Biological Sciences, 271(1539),
655-661.
Hiddink, J. G., and R. ter Hofstede (2008), Climate induced increases in species richness of marine fishes, Global Change Biology, 14, 453-460.
Hoegh-Guldberg, O. and J.F. Bruno, 2010: The impact of climate change on the World s marine ecosystems, Science, 328, 1523-1528.
Lima, F.P., P.A. Ribeiro, N. Queiroz, S.J. Hawkins and A.M. Santos, 2007; Do distributional shifts of northern and southern species of algae
match the warming pattern? Global Change Biology, 13, 2592-2604.
Perry, A.L., P.J. Low, J.R. Ellis and J.D. Reynolds, 2005: Climate change and distribution shifts in marine fishes. Science, 308(5730), 1912-
1915.
Philippart, C.J.M., R. Anadon, R. Danovaro, J.W. Dippner, K.F. Drinkwater, S.J. Hawkins, T. Oguz, G. O Sullivan and P.C. Reid, 2011:
Impacts of climate change on European marine ecosystems: observations, expectations and indicators, Journal of Experimental Marine Biology
and Ecology, 400, 52-69.
Pinksy, M.L., B. Worm, M.J. Fogarty, J.L. Sarmiento, and S.A. Levin, 2013 Marine taxa track local climate velocities. Science 341, 1239-1242.
Pörtner, H.O. and A.P. Farrell, 2008: Ecology: Physiology and climate change. Science, 322(5902), 690-692.
Poloczanska, E.S., C.J. Brown, W.J. Sydeman, W. Kiessling, D.S. Schoeman, P.J. Moore, K. Brander, J.F. Bruno, L.B. Buckley, M.T. Burrows,
C.M. Duarte, B.S. Halpern, J. Holding, C.V. Kappel, M.I. O Connor, J.M. Pandolfi, C. Parmesan, F. Schwing, S.A.Thompson and A.J.
Richardson, 2013: Global imprint of climate change on marine life, Nature Climate Change, published online 4 August 2013, doi:
10.1038/NCLIMATE1958, 7 pp.
Simpson, S.D., S. Jennings, M.P. Johnson, J.L. Blanchard, P.J. Schon, D.W. Sims and M.J. Genner, 2011: Continental shelf-wide response of a
fish assemblage to rapid warming of the sea, Current Biology, 21, 1565-1570.
Box CC-OA. Ocean Acidification
[Jean-Pierre Gattuso (France), Peter Brewer (USA), Ove Hoegh-Guldberg (Australia), Joan A. Kleypas (USA), Hans-Otto Pörtner (Germany),
Daniela Schmidt (UK)]
Anthropogenic ocean acidification and global warming share the same primary cause, which is the increase of
atmospheric CO2 (Figure OA-1A; WGI, 2.2.1). Eutrophication, loss of sea ice, upwelling and deposition of
atmospheric nitrogen and sulphur all exacerbate ocean acidification locally (5.3.3.6, 6.1.1, 30.3.2.2).
[INSERT FIGURE OA-1 HERE
Figure OA-1: A: Overview of the chemical, biological, socio-economic impacts of ocean acidification and of policy
options (adapted from Turley and Gattuso, 2012). B: Multi-model simulated time series of global mean ocean
surface pH (on the total scale) from CMIP5 climate model simulations from 1850 to 2100. Projections are shown for
emission scenarios RCP2.6 (blue) and RCP8.5 (red) for the multi-model mean (solid lines) and range across the
distribution of individual model simulations (shading). Black (grey shading) is the modelled historical evolution
using historical reconstructed forcings. The models that are included are those from CMIP5 that simulate the global
carbon cycle while being driven by prescribed atmospheric CO2 concentrations. The number of CMIP5 models to
calculate the multi-model mean is indicated for each time period/scenario (WGI AR5 Figure 6.28). C: Effect of near
future acidification (seawater pH reduction of 0.5 unit or less) on major response variables estimated using weighted
random effects meta-analyses, with the exception of survival which is not weighted (Kroeker et al., 2013). The log-
transformed response ratio (LnRR) is the ratio of the mean effect in the acidification treatment to the mean effect in
a control group. It indicates which process is most uniformly affected by ocean acidification but large variability
exists between species. Significance is determined when the 95% bootstrapped confidence interval does not cross
zero. The number of experiments used in the analyses is shown in parentheses. * denotes a statistically significant
effect.]
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Chemistry and Projections
The fundamental chemistry of ocean acidification is well understood (robust evidence, high agreement). Increasing
atmospheric concentrations of CO2 result in an increased flux of CO2 into a mildly alkaline ocean, resulting in a
reduction in pH, carbonate ion concentration, and the capacity of seawater to buffer changes in its chemistry (very
high confidence). The changing chemistry of the surface layers of the open ocean can be projected at the global scale
with high accuracy using projections of atmospheric CO2 levels (Fig. CC-OA-1B). Observations of changing upper
ocean CO2 chemistry over time support this linkage (WGI Table 3.2 and Figure 3.18; Figures 30.8, 30.9). Projected
changes in open ocean, surface water chemistry for year 2100 based on representative concentration pathways
(WGI, Figure 6.28) compared to preindustrial values range from a pH change of -0.14 unit with RCP 2.6 (421 ppm
CO2, +1 C, 22% reduction of carbonate ion concentration) to a pH change of -0.43 unit with RCP 8.5 (936 ppm
CO2, +3.7 C, 56% reduction of carbonate ion concentration). Projections of regional changes, especially in the
highly complex coastal systems (5.3.3.6, 30.3.2.2), in polar regions (WGI 6.4.4), and at depth are more difficult but
generally follow similar trends.
Biological, Ecological, and Biogeochemical Impacts
Investigations of the effect of ocean acidification on marine organisms and ecosystems have a relatively short
history, recently analyzed in several metaanalyses (6.3.2.1, 6.3.5.1). A wide range of sensitivities to projected rates
of ocean acidification exists within and across diverse groups of organisms, with a trend for greater sensitivity in
early life stages (high confidence; 5.4.2.2, 5.4.2.4, 6.3.2). A pattern of positive and negative impacts emerges (high
confidence; Fig. OA-1C) but key uncertainties remain in our understanding of the impacts on organisms, life
histories and ecosystems. Responses can be influenced, often exacerbated by other drivers, such as warming,
hypoxia, nutrient concentration, and light availability (high confidence; 5.4.2.4, 6.3.5).
Growth and primary production are stimulated in seagrass and some phytoplankton (high confidence; 5.4.2.3,
6.3.2.2-3, 30.5.6). Harmful algal blooms could become more frequent (limited evidence, medium agreement). Ocean
acidification may stimulate nitrogen fixation (limited evidence, low agreement; 6.3.2.2). It decreases the rate of
calcification of most, but not all, sea-floor calcifiers (medium agreement, robust evidence) such as reef-building
corals (Box CC-CR), coralline algae, bivalves and gastropods reducing the competitiveness with non-calcifiers
(5.4.2.2, 5.4.2.4, 6.3.2.5). Ocean warming and acidification promote higher rates of calcium carbonate dissolution
resulting in the net dissolution of carbonate sediments and frameworks and loss of associated habitat (medium
confidence; 5.4.2.4, 6.3.2.5, 6.3.5.4-5). Some corals and temperate fishes experience disturbances to behavior,
navigation and their ability to tell conspecifics from predators (6.3.2.4). However, there is no evidence for these
effects to persist on evolutionary timescales in the few groups analyzed (6.3.2).
Some phytoplankton and mollusks displayed adaptation to ocean acidification in long-term experiments (limited
evidence, medium agreement; 6.3.2.1), indicating that the long-term responses could be less than responses obtained
in short-term experiments. However, mass extinctions in Earth history occurred during much slower rates of ocean
acidification, combined with other drivers changing, suggesting that evolutionary rates are not fast enough for
sensitive animals and plants to adapt to the projected rate of future change (medium confidence; 6.1.2).
Projections of ocean acidification effects at ecosystem level are made difficult by the diversity of species-level
responses. Differential sensitivities and associated shifts in performance and distribution will change predator-prey
relationships and competitive interactions (6.3.2.5, 6.3.5-6), which could impact food webs and higher trophic levels
(limited evidence, high agreement). Natural analogues at CO2 vents indicate decreased species diversity, biomass
and trophic complexity of communities (Box CC-CR; 5.4.2.3, 6.3.2.5, 30.3.2.2, 30.5). Shifts in community structure
have also been documented in regions with rapidly declining pH (5.4.2.2).
Due to an incomplete understanding of species-specific responses and trophic interactions the effect of ocean
acidification on global biogeochemical cycles is not well understood (limited evidence, low agreement) and
represents an important knowledge gap. The additive, synergistic or antagonistic interactions of factors such as
temperature, concentrations of oxygen and nutrients, and light are not sufficiently investigated yet.
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Risks, Socioeconomic Impacts and Costs
The risks of ocean acidification to marine organisms, ecosystems, and ultimately to human societies, include both
the probability that ocean acidification will affect fundamental physiological and ecological processes of organisms
(6.3.2.1), and the magnitude of the resulting impacts on ecosystems and the ecosystem services they provide to
society (Box 19-2). For example, ocean acidification under RCP4.5 to RCP8.5 will impact formation and
maintenance of coral reefs (high confidence; Box CC-CR, 5.4.2.4) and the goods and services that they provide such
as fisheries, tourism and coastal protection (limited evidence, high agreement; Box CC-CR, 6.4.1.1,19.5.2, 27.3.3,
30.5, 30.6). Ocean acidification poses many other potential risks, but these cannot yet be quantitatively assessed due
to the small number of studies available, particularly on the magnitude of the ecological and socioeconomic impacts
(19.5.2).
Global estimates of observed or projected economic costs of ocean acidification do not exist. The largest uncertainty
is how the impacts on lower trophic levels will propagate through the food webs and to top predators. However,
there are a number of instructive examples that illustrate the magnitude of potential impacts of ocean acidification.
A decrease of the production of commercially-exploited shelled mollusks (6.4.1.1) would result in a reduction of US
production of 3 to 13% according to the SRES A1FI emission scenario (low confidence). The global cost of
production loss of mollusks could be over 100 billion USD by 2100 (limited evidence, medium agreement). Models
suggest that ocean acidification will generally reduce fish biomass and catch (low confidence) and that complex
additive, antagonistic and/or synergistic interactions will occur with other environmental (warming) and human
(fisheries management) factors (6.4.1.1). The annual economic damage of ocean-acidification-induced coral reef
loss by 2100 has been estimated, in 2009, to be 870 and 528 billion USD, respectively for the A1 and B2 SRES
emission scenarios (low confidence; 6.4.1). Although this number is small compared to global GDP, it can represent
a very large GDP loss for the economies of many coastal regions or small islands that rely on the ecological goods
and services of coral reefs (25.7.5, 29.3.1.2).
Mitigation and Adaptation
Successful management of the impacts of ocean acidification includes two approaches: mitigation of the source of
the problem (i.e. reduce anthropogenic emissions of CO2), and/or adaptation by reducing the consequences of past
and future ocean acidification (6.4.2.1). Mitigation of ocean acidification through reduction of atmospheric CO 2 is
the most effective and the least risky method to limit ocean acidification and its impacts (6.4.2.1). Climate
geoengineering techniques based on solar radiation management will not abate ocean acidification and could
increase it under some circumstances (6.4.2.2). Geoengineering techniques to remove carbon dioxide from the
atmosphere could directly address the problem but are very costly and may be limited by the lack of CO2 storage
capacity (6.4.2.2). Additionally, some ocean-based approaches, such as iron fertilization, would only re-locate ocean
acidification from the upper ocean to the ocean interior, with potential ramifications on deep-water oxygen levels
(6.4.2.2; 30.3.2.3 and 30.5.7). A low-regret approach, with relatively limited effectiveness, is to limit the number
and the magnitude of drivers other than CO2, such as nutrient pollution (6.4.2.1). Mitigation of ocean acidification at
the local level could involve the reduction of anthropogenic inputs of nutrients and organic matter in the coastal
ocean (5.3.4.2). Some adaptation strategies include drawing water for aquaculture from local watersheds only when
pH is in the right range, selecting for less sensitive species or strains, or relocating industries elsewhere (6.4.2.1).
CC-OA References
Kroeker K., R.C. Kordas, A. Ryan, I. Hendriks, L.Ramajo, G. Singh, C. Duarte and J.-P. Gattuso, 2013: Impacts of ocean acidification on
marine organisms: quantifying sensitivities and interaction with warming. Global Change Biology 19, 1884-1896.
Turley C. and J.-P. Gattuso, 2012. Future biological and ecosystem impacts of ocean acidification and their socioeconomic-policy implications.
Current Opinion In Environmental Sustainability 4, 278-286.
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Box CC-PP. Net Primary Production in the Ocean
[Philip W. Boyd (New Zealand), Svein Sundby (Norway), Hans-Otto Pörtner (Germany)]
Net Primary Production (NPP) is the rate of photosynthetic carbon fixation minus the fraction of fixed carbon used
for cellular respiration and maintenance by autotrophic planktonic microbes and benthic plants (6.2.1, 6.3.1).
Environmental drivers of NPP include light, nutrients, micronutrients, carbon dioxide, and temperature (Panel A).
These drivers in turn, are influenced by oceanic and atmospheric processes, including cloud cover, sea-ice extent,
mixing by winds, waves and currents, convection, density stratification, and various forms of upwelling induced by
eddies, frontal activity and boundary currents. Temperature has multiple roles as it influences rates of phytoplankton
physiology and heterotrophic bacterial recycling of nutrients, in addition to stratification of the water column and
sea-ice extent (Panel A). Climate change is projected to strongly impact NPP through a multitude of ways that
depend on the regional and local physical settings (WGI, Ch. 3), and on ecosystem structure and functioning
(medium confidence, 6.3.4, 6.5.1). The influence of environmental drivers on NPP causes as much as a 10-fold
variation in regional productivity: from <50 g C m-2 year-1 in nutrient-poor subtropical waters and light-limited
Arctic waters to >> 300 g C m-2 year-1 in productive upwelling regions and highly eutrophic coastal regions (Panel
B).
The oceans currently provide ~50 x 1015 g C year-1, or about half of global NPP (Field et al. 1998). Global estimates
of NPP are mainly obtained from satellite remote-sensing (6.1.2), which provides unprecedented spatial and
temporal coverage, and may be validated regionally against oceanic measurements. Observations reveal significant
changes in rates of NPP when environmental controls are altered by episodic natural perturbations, such as volcanic
eruptions enhancing iron supply, as observed in high-nitrate low-chlorophyll waters of the NE Pacific (Hamme et
al., 2010). Climate variability can drive pronounced changes in NPP (Chavez et al., 2011), such as during El Nino to
La Nina transitions in Equatorial Pacific, when vertical nutrient and trace element supply are enhanced (Chavez et
al., 1999).
Multi-year time-series records of NPP have been used to assess spatial trends in NPP in recent decades. Behrenfeld
et al. (2006) using satellite data, reported a prolonged and sustained global NPP decrease of 190 x 1012 g C year-1,
for the period 1999 to 2005 - an annual reduction of ~0.4 % of global NPP. In contrast, a time-series of directly
measured NPP between 1988 to 2007 by Saba et al. (2010) (i.e. in situ incubations using the radiotracer 14C-
bicarbonate) revealed an increase (2 % year-1) in NPP for two low latitude open ocean sites. This discrepancy
between in situ and remotely-sensed NPP trends points to uncertainties in either the methodology used and/or the
extent to which discrete sites are representative of oceanic provinces (Saba et al., 2010, 2011). Modeling studies
have subsequently revealed that the <15 year archive of satellite-derived NPP is insufficient to distinguish climate-
change mediated shifts in NPP from those driven by natural climate variability (Henson et al., 2010; Beaulieu et al.,
2013). Although multidecadal, the available time-series of oceanic NPP measurements are also not of sufficient
duration relative to the timescales of climate variability modes (up to 60-70 years for AMO, for example, Figure 6-
1). Recent attempts to synthesize longer (i.e. centennial) records of chlorophyll as a proxy for phytoplankton stocks
(e.g., Boyce et al., 2010) have been criticized for relying on questionable linkages between different proxies for
chlorophyll over a century of records (e.g., Rykaczewski and Dunne, 2011).
Models in which projected climate-change alters the environmental drivers of NPP provide estimates of spatial
changes and of the rate of change of NPP. For example, four global coupled climate-ocean biogeochemical Earth
System Models (WGI Ch. 6) projected an increase in NPP at high latitudes as a result of alleviation of light and
temperature limitation of NPP particularly in Northern and Southern Hemisphere subpolar gyre biomes (Steinacher
et al., 2010). However, this regional increase in NPP was more than offset by decreases in NPP at lower latitudes
and at mid-latitudes due to the reduced input of macro-nutrients into the photic zone. The reduced mixed-layer depth
and reduced rate of circulation may cause a decrease in the flux of macronutrients to the photic zone (Figure 6-2).
These changes to oceanic conditions result in a reduction in global mean NPP by 2 to 13% by 2100 relative to 1860
under a high emission scenario (Polovina et al., 2011; SRES A2, between RCP6.0 and RCP8.5). This is consistent
with a more recent analysis based on 10 Earth System Models (Bopp et al., 2013), which project decreases in global
NPP by 8.6 (+/-7.9), 3.9 (+/-5.7), 3.6 (+/-5.7), 2.0 (+/-4.1) % in the 2090s relative to the 1990s, under the scenarios
RCP8.5, RCP6.0, RCP4.5 and RCP2.6, respectively. However, the magnitude of projected changes varies widely
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between models (e.g. from 0 to 20% decrease in NPP globally under RCP 8.5). The various models show very large
differences in NPP at regional (i.e. provinces, see panel B) scales.
Earlier model projections had predicted changes in global NPP from a decrease of > 10% (Field et al., 1998; Boyd
and Doney, 2002) to an increase of up to 8.1% under an intermediate scenario (SRES A1B, similar to RCP6.0)
(Sarmiento et al., 2004; Schmittner et al., 2008). These projections did not consider the potential contribution of
primary production derived from atmospheric nitrogen fixation in tropical and subtropical regions, favoured by
increasing stratification and reduced nutrient inputs from mixing. This mechanism is potentially important, although
such episodic increases in nitrogen fixation are not sustainable without the presence of excess phosphate (e.g. Moore
et al., 2009; Boyd et al., 2010). This may lead to an underestimation of NPP (Mohr et al., 2010; Mulholland et al.,
2012; Wilson et al., 2012), however, the extent of such underestimation is unknown (Luo et al., 2012).
Care must be taken when comparing global, provincial (e.g. low latitude waters, for example Behrenfeld et al.,
2006) and regional trends in NPP derived from observations, as some regions have additional local environmental
influences such as enhanced density stratification of the upper ocean from melting sea ice. For example, a longer
phytoplankton growing season, due to more sea-ice free days, may have increased NPP (based on a regionally
validated time-series of satellite NPP) in Arctic waters (Arrigo and van Dijken, 2011) by an average of 8.1 Tg C
year 1 between 1998 and 2009. Other regional trends in NPP are reported in 30.5.1-6. In addition, although future
model projections of global NPP from different models (Steinacher et al., 2010; Bopp et al., 2013) are comparable,
regional projections from each of the models differ substantially. This raises concerns as to which aspect(s) of the
different model NPP parameterizations are responsible for driving regional differences in NPP, and moreover, how
accurate model projections are of global NPP.
From a global perspective, open ocean NPP will decrease moderately by 2100 under both low (SRES B1 or RCP4.5)
and high emission scenarios (A2 or RCP6.0 - 8.5, 6.3.4, 6.5.1, medium confidence), paralleled by an increase in NPP
at high latitudes and a decrease in the tropics (medium confidence). However, there is limited evidence and low
agreement on the direction, magnitude and differences of a change of NPP in various ocean regions and coastal
waters projected by 2100 (low confidence).
[INSERT FIGURE PP-1 HERE
Figure PP-1: A) Environmental factors controlling Net Primary Production (NPP). NPP is mainly controlled by
three basic processes: 1) Light conditions in the surface ocean, i.e. the photic zone where photosynthesis occurs, 2)
upward flux of nutrients and micronutrients from underlying waters into the photic zone, 3) Regeneration of
nutrients and micronutrients via the breakdown and recycling of organic material before it sinks out of the photic
zone. All three processes are influenced by physical, chemical and biological processes and vary across regional
ecosystems. In addition, water temperature strongly influences the upper rate of photosynthesis for cells that are
resource-replete. Predictions of alteration of primary productivity under climate change depend on correct
parameterizations and simulations of each of these variables and processes for each region. B) Annual composite
map of global areal NPP rates (derived from MODIS Aqua satellite climatology from 2003-2012; NPP was
calculated with the Carbon-based Production Model (CbPM, Westberry et al., 2008)). Overlaid is a grid of (thin
black lines) that represent 51 distinct global ocean biogeographical provinces (after Longhurst, 1998 and based on
Boyd and Doney, 2002). The characteristics and boundaries of each province are primarily set by the underlying
regional ocean physics and chemistry. Figure courtesy of Toby Westberry (OSU) and Ivan Lima (WHOI), satellite
data courtesy of NASA Ocean Biology Processing Group.]
Box CC-PP References
Arrigo, K.R. and G.L. van Dijken, 2011: Secular trends in Arctic Ocean net primary production. Journal of Geophysical Research, 116(C9),
C09011.
Beaulieu, C., S.A. Henson, J.L. Sarmiento, J.P. Dunne, S.C. Doney, R.R. Rykaczewski and L. Bopp, 2013: Factors challenging our ability to
detect long-term trends in ocean chlorophyll. Biogeosciences, 10(4), 2711-2724.
Behrenfeld, M.J., R.T. O'Malley, D.A. Siegel, C.R. McClain, J.L. Sarmiento, G.C. Feldman, A.J. Milligan, P.G. Falkowski, R.M. Letelier and
E.S. Boss, 2006: Climate-driven trends in contemporary ocean productivity. Nature, 444(7120), 752-755.
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Bopp, L., L. Resplandy, J.D. Orr, D. S.C., J.P. Dunne, M. Gehlen, P. Halloran, C. Heinze, T. Ilyina, R. Séférian, J. Tijiputra and M. Vichi, 2013:
Multiple stressors of ocean ecosystems in the 21st century: projections with CMIP5 models. Biogeosciences, 10(10), 6225-6245.
Boyce, D.G., M.R. Lewis and B. Worm, 2010: Global phytoplankton decline over the past century. Nature, 466(7306), 591-596.
Boyd, P.W. and S.C. Doney, 2002: Modelling regional responses by marine pelagic ecosystems to global climate change. Geophysical Research
Letters, 29(16), 1806.
Boyd, P.W., R. Strzepek, F.X. Fu and D.A. Hutchins, 2010: Environmental control of open-ocean phytoplankton groups: now and in the future.
Limnology and Oceanography, 55(3), 1353-1376.
Chavez, F.P., M. Messie and J.T. Pennington, 2011: Marine primary production in relation to climate variability and change. Annual Review of
Marine Science, 3(1), 227-260.
Chavez, F.P., P.G. Strutton, C.E. Friederich, R.A. Feely, G.C. Feldman, D.C. Foley and M.J. McPhaden, 1999: Biological and chemical response
of the equatorial Pacific Ocean to the 1997-98 El Nino. Science, 286(5447), 2126-2131.
Field, C.B., M.J. Behrenfeld, J.T. Randerson and P. Falkowski, 1998: Primary production of the biosphere: integrating terrestrial and oceanic
components. Science, 281(5374), 237-240.
Hamme, R.C., P.W. Webley, W.R. Crawford, F.A. Whitney, M.D. DeGrandpre, S.R. Emerson, C.C. Eriksen, K.E. Giesbrecht, J.F.R. Gower,
M.T. Kavanaugh, M.A. Pena, C.L. Sabine, S.D. Batten, L.A. Coogan, D.S. Grundle and D. Lockwood, 2010: Volcanic ash fuels anomalous
plankton bloom in subarctic northeast Pacific. Geophysical Research Letters, 37, L19604.
Henson, S.A., J.L. Sarmiento, J.P. Dunne, L. Bopp, I. Lima, S.C. Doney, J. John and C. Beaulieu, 2010: Detection of anthropogenic climate
change in satellite records of ocean chlorophyll and productivity. Biogeosciences, 7(2), 621-640.
Longhurst, A.R., 1998: Ecological Geography of the Sea. Academic Press, San Diego, CA, USA, 560 pp.
Luo, Y.-W., S.C. Doney, L.A. Anderson, M. Benavides, I. Berman-Frank, A. Bode, S. Bonnet, K.H. Boström, D. Böttjer, D.G. Capone, E.J.
Carpenter, Y.L. Chen, M.J. Church, J.E. Dore, L.I. Falcón, A. Fernández, R.A. Foster, K. Furuya, F. Gómez, K. Gundersen, A.M. Hynes,
D.M. Karl, S. Kitajima, R.J. Langlois, J. LaRoche, R.M. Letelier, E. Maranón, D.J. McGillicuddy Jr., P.H. Moisander, C.M. Moore, B.
Mourino-Carballido, M.R. Mulholland, J.A. Needoba, K.M. Orcutt, A.J. Poulton, E. Rahav, P. Raimbault, A.P. Rees, L. Riemann, T.
Shiozaki, A. Subramaniam, T. Tyrrell, K.A. Turk-Kubo, M. Varela, T.A. Villareal, E.A. Webb, A.E. White, J. Wu and J.P. Zehr, 2012:
Database of diazotrophs in global ocean: abundances, biomass and nitrogen fixation rates. Earth System Science Data, 5, 47-106.
Mohr, W., T. Grosskopf, D.W.R. Wallace and J. LaRoche, 2010: Methodological underestimation of oceanic nitrogen fixation rates. PLoS ONE,
5(9), e12583.
Moore, C.M., M.M. Mills, E.P. Achterberg, R.J. Geider, J. LaRoche, M.I. Lucas, E.L. McDonagh, X. Pan, A.J. Poulton, M.J.A. Rijkenberg, D.J.
Suggett, S.J. Ussher and E.M.S. Woodward, 2009: Large-scale distribution of Atlantic nitrogen fixation controlled by iron availability.
Nature Geoscience, 2(12), 867-871.
Mulholland, M.R., P.W. Bernhardt, J.L. Blanco-Garcia, A. Mannino, K. Hyde, E. Mondragon, K. Turk, P.H. Moisander and J.P. Zehr, 2012:
Rates of dinitrogen fixation and the abundance of diazotrophs in North American coastal waters between Cape Hatteras and Georges Bank.
Limnology and Oceanography, 57(4), 1067-1083.
Polovina, J.J., J.P. Dunne, P.A. Woodworth and E.A. Howell, 2011: Projected expansion of the subtropical biome and contraction of the
temperate and equatorial upwelling biomes in the North Pacific under global warming. ICES Journal of Marine Science, 68(6), 986-995.
Rykaczewski, R.R. and J.P. Dunne, 2011: A measured look at ocean chlorophyll trends. Nature, 472(7342), E5-E6.
Saba, V.S., M.A.M. Friedrichs, D. Antoine, R.A. Armstrong, I. Asanuma, M.J. Behrenfeld, A.M. Ciotti, M. Dowell, N. Hoepffner, K.J.W. Hyde,
J. Ishizaka, T. Kameda, J. Marra, F. Mélin, A. Morel, J. O'Reilly, M. Scardi, W.O. Smith Jr., T.J. Smyth, S. Tang, J. Uitz, K. Waters and
T.K. Westberry, 2011: An evaluation of ocean color model estimates of marine primary productivity in coastal and pelagic regions across
the globe. Biogeosciences, 8(2), 489-503.
Saba, V.S., M.A.M. Friedrichs, M.-E. Carr, D. Antoine, R.A. Armstrong, I. Asanuma, O. Aumont, N.R. Bates, M.J. Behrenfeld, V. Bennington,
L. Bopp, J. Bruggeman, E.T. Buitenhuis, M.J. Church, A.M. Ciotti, S.C. Doney, M. Dowell, J. Dunne, S. Dutkiewicz, W. Gregg, N.
Hoepffner, K.J.W. Hyde, J. Ishizaka, T. Kameda, D.M. Karl, I. Lima, M.W. Lomas, J. Marra, G.A. McKinley, F. Mélin, J.K. Moore, A.
Morel, J. O'Reilly, B. Salihoglu, M. Scardi, T.J. Smyth, S.L. Tang, J. Tjiputra, J. Uitz, M. Vichi, K. Waters, T.K. Westberry and A. Yool,
2010: Challenges of modeling depth-integrated marine primary productivity over multiple decades: a case study at BATS and HOT. Global
Biogeochemical Cycles, 24, GB3020.
Sarmiento, J.L., R. Slater, R. Barber, L. Bopp, S.C. Doney, A.C. Hirst, J. Kleypas, R. Matear, U. Mikolajewicz, P. Monfray, V. Soldatov, S.A.
Spall and R. Stouffer, 2004: Response of ocean ecosystems to climate warming. Global Biogeochemical Cycles, 18(3), GB3003.
Schmittner, A., A. Oschlies, H.D. Matthews and E.D. Galbraith, 2008: Future changes in climate, ocean circulation, ecosystems, and
biogeochemical cycling simulated for a business-as-usual CO2 emission scenario until year 4000 AD. Global Biogeochemical Cycles, 22(1),
GB1013.
Steinacher, M., F. Joos, T.L. Frölicher, L. Bopp, P. Cadule, V. Cocco, S.C. Doney, M. Gehlen, K. Lindsay, J.K. Moore, B. Schneider and J.
Segschneider, 2010: Projected 21st century decrease in marine productivity: a multi-model analysis. Biogeosciences, 7(3), 979-1005.
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Westberry, T., Behrenfeld, M.J., Siegel, D.A, and Boss, E. 2008. Carbon-based primary productivity modeling with vertically resolved
photoacclimation. Global Biogeochemical Cycles, 22(2): GB2024. DOI: 10.1029/2007GB003078
Wilson, S.T., D. Bottjer, M.J. Church and D.M. Karl, 2012: Comparative assessment of nitrogen fixation methodologies, conducted in the
oligotrophic North Pacific Ocean. Applied and Environmental Microbiology, 78(18), 6516-6523.
Box CC-UP. Uncertain Trends in Major Upwelling Ecosystems
[Salvador E. Lluch-Cota (Mexico), Ove Hoegh-Guldberg (Australia), David Karl (USA), Hans O. Pörtner (Germany), Svein Sundby (Norway),
Jean-Pierre Gatusso (France)]
Upwelling is the vertical transport of cold, dense, nutrient-rich, relatively low-pH and often oxygen-poor waters to
the euphotic zone where light is abundant. These waters trigger high levels of primary production and a high
biomass of benthic and pelagic organisms. The driving forces of upwelling include wind stress and the interaction of
ocean currents with bottom topography. Upwelling intensity also depends on water column stratification. The major
upwelling systems of the Planet, the Equatorial Upwelling System (EUS, 30.5.2, Figure 30.1A) and the Eastern
Boundary Upwelling Ecosystems (EBUE, 30.5.5, Figure 30.1A), represent only 10% of the ocean surface but
contribute nearly 25 % to global fish production (Figure 30.1B, Table S30.1).
Marine ecosystems associated with upwelling systems can be influenced by a range of bottom-up trophic
mechanisms, with upwelling, transport, and chlorophyll concentrations showing strong seasonal and interannual
couplings and variability. These, in turn, influence trophic transfer up the food chain, affecting zooplankton,
foraging fish, seabirds and marine mammals.
There is considerable speculation as to how upwelling systems might change in a warming and acidifying ocean.
Globally, the heat gain of the surface ocean has increased stratification by 4% (WGI 3.2, 3.4.4, 3.8), which means
that more wind energy is needed to bring deep waters to the surface. It is as yet unclear to what extent wind stress
can offset the increased stratification, due to the uncertainty in wind speed trends (WGI, 3.4.4). In the tropics,
observations of reductions in trade winds over several decades contrast more recent evidence indicating their
strengthening since the early 1990s (WGI, 9.4.1.3.4). Observations and modelling efforts in fact show diverging
trends in coastal upwelling at the eastern boundaries of the Pacific and the Atlantic. Bakun (1990) proposed that the
the difference in heat gaining rates between land and ocean causes an increase in the pressure gradient, which results
in increased alongshore winds and leads to intensified offshore transport of surface water through Ekman pumping,
and the upwelling of nutrient rich, cold waters (Figure CC-UP). Some regional records support this hypothesis,
others do not. There is considerable variability in warming and cooling trends over the past decades both within and
among systems making it difficult to predict changes in the intensity of all Eastern Boundary Upwelling Ecosystems
(30.5.5).
Understanding whether upwelling and climate change will impact resident biota in an additive, synergistic or
antagonistic manner is important for projections of how ecological goods and services provided for human society
will change. Even though upwellings may prove more resilient to climate change than other ocean ecosystems
because of their ability to function under extremely variable conditions (Capone and Hutchins, 2013), consequences
of their shifts are highly relevant since these are the most biologically active systems in the ocean. Increased
upwelling would enhance fisheries yields. However, the export of organic material from surface to deeper layers of
the ocean may increase and stimulate its decomposition by microbial activity, thereby enhancing oxygen depletion
and CO2 enrichment in deeper water layers. Once this water returns to the surface through upwelling benthic and
pelagic coastal communities will be exposed to acidified and deoxygenated water which may combine with
anthropogenic impact to negatively affect marine biota and ecosystem structure of the upper ocean (high confidence,
6.3.2, 6.3.3; 30.3.2.2, 30.3.2.3). Extreme hypoxia may result in abnormal mortalities of fishes and invertebrates
(Keller et al., 2010), reduce the fisheries catch potential and impact aquaculture in coastal areas (5.4.3.3, 6.3.7,
30.5.1.1.2, 30.5.5.1.3, Barton et al., 2012). Shifts in upwelling also coincide with an apparent increase in the
frequency of submarine eruptions of methane and hydrogen sulphide gas, caused by enhanced formation and sinking
of phytoplankton biomass to the hypoxic or anoxic sea floor . This combination of factors has been implicated in the
extensive mortality of coastal fishes and invertebrates (Bakun and Weeks, 2004), resulting in significant reductions
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in fishing productivity, such as Cape hake (Merluccius capensis), Namibia s most valuable fishery (Hamukuaya et
al., 1998).
Reduced upwelling would also reduce the productivity of important pelagic fisheries, such as for sardines, anchovies
and mackerel, with major consequences for the economies of several countries (6.4.1, Chp 7, Figure 30.1A, B, Table
S30.1). However, under projected scenarios of reduced upward supply of nutrients due to stratification of the open
ocean , upwelling of both nutrients and trace elements may become increasingly important to maintaining upper
ocean nutrient and trace metal inventories. It has been suggested that upwelling areas may also increase nutrient
content and productivity under enhanced stratification, and that upwelled and partially denitrified waters containing
excess phosphate may select for N2-fixing microorganisms (Deutsch et al., 2007; Deutsch and Weber, 2012), but
field observations of N2 fixation in these regions have not supported these predictions (Fernandez et al., 2011; Franz
et al., 2012). The role of this process in global primary production thus needs to be validated (low confidence).
The central question therefore is whether or not upwelling will intensify, and if so, whether the effects of intensified
upwelling on O2 and CO2 inventories will outweigh its benefits for primary production and associated fisheries and
aquaculture (low confidence). In any case increasing atmospheric CO2 concentrations will equilibrate with upwelling
waters that may cause them to become more corrosive, depending upon pCO2 of the upwelled water, and potentially
increasingly impact the biota of Eastern Boundary Upwelling Ecosystems.
[INSERT FIGURE UP-1 HERE
Figure UP-1: Upper panel: Schematic hypothetic mechanism of increasing coastal wind-driven upwelling at eastern
boundary systems, where differential warming rates between land and ocean results in increased land-ocean pressure
gradients (1) that produce stronger alongshore winds (2) and offshore movement of surface water through Ekman
transport (3), and increased upwelling of deep cold nutrient rich waters to replace it (4). Lower panel: potential
consequences of climate change in upwelling systems. Increasing stratification and uncertainty in wind stress trends
result in uncertain trends in upwelling. Increasing upwelling may result in higher input of nutrients to the euphotic
zone, and increased primary production, which in turn may enhance pelagic fisheries, but also decreased coastal
fisheries due to an augmented exposure of coastal fauna to hypoxic, low pH waters. Decreased upwelling may result
in lower primary production in these systems with direct impacts on pelagic fisheries productivity.]
Box CC-UP References
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Table 30-1: Regional changes in sea surface temperature (SST) over the period 1950 2009 using the Ocean regionalization specified in Figure 30-1a (for further
detail of regions defined for analysis, see Figure SM30-1 and Table 30-2, column 1). A linear regression was fitted to the average of all 1×1 degree monthly SST
data extracted from the HadISST1.1 data set [Rayner et al., 2003] for each sub-region over the period 1950 2009. All SST values less than -1.8oC, together with
all SST pixels that were flagged as being sea ice, were reset to the freezing point of seawater (-1.8oC) to reflect the sea temperature under the ice. Separate
analyses were also done to explore trends in the temperatures extracted from the coldest-ranked and the warmest-ranked month of each year (Table SM30-2).
The table includes the slope of the regression (°C decade-1), the p-value for the slope being different from zero and the total change over 60 years (i.e., the slope
of linear regression multiplied by 6 decades) for each category. The p-values that exceed 0.05 plus the associated slope and change values have a gray
background, denoting the lower statistical confidence in the slope being different from zero (no slope). Note, changes with higher p-values may still describe
informative trends although the level of confidence is lower that the slope is different from zero.
Regression slope Total change over 60 years p value, slope different from zero
°C/Decade °C/Decade °C/Decade Change Change Change °C/Decade °C/Decade °C/Decade
Coolest All months Warmest over 60 over 60 over 60 Coolest All months Warmest
Month Month years years (all years Month Month
Sub-region (coolest months) (warmest
month) month)
1. High Latitude Spring Bloom Systems (HLSBS)
Indian Ocean 0.056 0.087 0.145 0.336 0.522 0.870 0.000 0.003 0.000
North Atlantic 0.054 0.073 0.116 0.324 0.438 0.696 0.001 0.15 0.000
South Atlantic 0.087 0.063 0.097 0.522 0.378 0.582 0.000 0.098 0.000
North Pacific (west) 0.052 0.071 0.013 0.312 0.426 0.078 0.52 0.403 0.462
North Pacific (east) 0.016 0.04 0.016 0.096 0.24 0.096 0.643 0.53 0.444
Total North Pacific 0.033 0.055 0.015 0.198 0.33 0.09 0.284 0.456 0.319
South Pacific (west) 0.043 0.017 0.044 0.258 0.102 0.264 0.016 0.652 0.147
South Pacific (east) 0.047 0.031 0.052 0.282 0.186 0.312 0.000 0.396 0.003
Total South Pacific 0.046 0.027 0.050 0.276 0.162 0.300 0.000 0.467 0.000
2. Equatorial Upwelling Systems (EUS)
Atlantic Equatorial 0.101 0.090 0.079 0.606 0.540 0.474 0.000 0.000 0.000
Pacific Equatorial 0.079 0.071 0.065 0.474 0.426 0.39 0.096 0.001 0.071
3. Semi-Enclosed Seas (SES)
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Arabian Gulf 0.027 0.099 0.042 0.162 0.594 0.252 0.577 0.305 0.282
Baltic Sea 0.352 0.165 0.06 2.112 0.99 0.36 0.000 0.155 0.299
Black Sea -0.004 0.053 0.139 -0.024 0.318 0.834 0.943 0.683 0.009
Mediterranean Sea 0.035 0.084 0.110 0.21 0.504 0.660 0.083 0.32 0.006
Red Sea 0.033 0.07 0.047 0.198 0.42 0.282 0.203 0.138 0.042
4. Coastal Boundary Systems (CBS)
Western Atlantic 0.137 0.123 0.127 0.822 0.738 0.762 0.000 0.000 0.000
Caribbean/Gulf of Mexico 0.023 0.024 0.019 0.138 0.144 0.114 0.193 0.498 0.281
Western Indian Ocean 0.097 0.100 0.096 0.582 0.600 0.576 0.000 0.000 0.000
Eastern Indian Ocean 0.099 0.092 0.080 0.594 0.552 0.480 0.000 0.000 0.000
E Indian/SE Asia/W Pacific 0.144 0.134 0.107 0.864 0.804 0.642 0.000 0.000 0.000
5. Eastern Boundary Upwelling Ecosystems (EBUE)
Benguela Current 0.062 0.032 0.002 0.372 0.192 0.012 0.012 0.437 0.958
California Current 0.117 0.122 0.076 0.702 0.732 0.456 0.026 0.011 0.125
Canary Current 0.054 0.089 0.106 0.324 0.534 0.636 0.166 0.014 0.000
Humboldt Current 0.051 0.059 0.104 0.306 0.354 0.624 0.285 0.205 0.013
6. Sub-Tropical Gyres
Indian Ocean 0.141 0.112 0.103 0.846 0.672 0.618 0.000 0.000 0.000
North Atlantic 0.042 0.046 0.029 0.252 0.276 0.174 0.048 0.276 0.038
South Atlantic 0.079 0.083 0.098 0.474 0.498 0.588 0.000 0.017 0.000
North Pacific (west) 0.065 0.071 0.059 0.390 0.426 0.354 0.000 0.018 0.000
North Pacific (east) 0.008 0.042 0.051 0.048 0.252 0.306 0.617 0.133 0.014
Total North Pacific 0.034 0.055 0.051 0.204 0.33 0.306 0.001 0.053 0.000
South Pacific (west) 0.060 0.076 0.092 0.360 0.456 0.552 0.002 0.000 0.000
South Pacific (east) 0.055 0.056 0.088 0.330 0.336 0.528 0.000 0.058 0.000
Total South Pacific 0.056 0.060 0.089 0.336 0.360 0.534 0.000 0.027 0.000
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7. Coral Reef Provinces (Figure 30-3)
Caribbean/Gulf of Mexico 0.026 0.024 0.023 0.156 0.144 0.138 0.107 0.382 0.203
Coral Triangle & SE Asia 0.137 0.131 0.098 0.822 0.786 0.588 0.000 0.000 0.000
Eastern Indian Ocean 0.081 0.097 0.116 0.486 0.582 0.696 0.000 0.000 0.000
Western Indian Ocean 0.091 0.100 0.102 0.546 0.600 0.612 0.000 0.000 0.000
Eastern Pacific Ocean 0.079 0.094 0.101 0.474 0.564 0.606 0.106 0.000 0.023
Western Pacific Ocean 0.072 0.073 0.073 0.432 0.438 0.438 0.000 0.000 0.000
8. Basin Scale
North Atlantic (combined) 0.045 0.061 0.090 0.270 0.366 0.540 0.002 0.198 0.000
South Atlantic (combined) 0.076 0.074 0.101 0.456 0.444 0.606 0.000 0.041 0.000
Atlantic Ocean Basin 0.060 0.068 0.091 0.360 0.408 0.546 0.000 0.000 0.000
Total North Pacific 0.030 0.052 0.046 0.180 0.312 0.276 0.000 0.248 0.006
Total South Pacific 0.055 0.048 0.075 0.330 0.288 0.450 0.000 0.115 0.000
Pacific Ocean Basin 0.043 0.052 0.046 0.258 0.312 0.276 0.000 0.000 0.006
Indian Ocean Basin 0.130 0.108 0.106 0.780 0.648 0.636 0.000 0.000 0.000
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Table 30-2: Examples of priority adaptation options and supporting policies to assist Pacific Island countries and
territories to minimize the threats of climate change to the socio-economic benefits derived from pelagic and coastal
fisheries and aquaculture, and to maximize the opportunities. These measures are classified as win-win (W-W)
adaptations, which address other drivers of the sector in the short-term and climate change in the long-term, or lose-
win (L-W) adaptations, where benefits exceed costs in the short-term but accrue under longer-term climate change
(modified from [Bell et al., 2013b]).
Adaptation options Supporting policies
Economic development
Full implementation of the vessel day scheme Strengthen national capacity to administer the VDS.
(VDS) to control fishing effort by the Parties to the Adjust national tuna management plans and marketing
Nauru Agreements (W-W). strategies to provide flexible arrangements to buy and
Diversify sources of fish for canneries in the region sell tuna.
and maintain trade agreements, e.g., an Economic Include implications of climate change in management
Partnership Agreement with the European Union (W- objectives of the WCPFC.
W). Apply national management measures to address
Continued conservation and management measures climate change effects for subregional concentrations of
for all species of tuna to maintain stocks at healthy tuna in archipelagic waters beyond the mandate of
levels and make these valuable species more resilient WCPFC.
to climate change (W-W). Require all industrial tuna vessels to provide
Energy efficiency programmes to assist fleets to operational-level catch and effort data to improve the
cope with oil price rises and minimise CO2 emissions, models for redistribution of tuna stocks during climate
and reduce costs of fishing further afield as tuna change.
distributionsshift east (W-W).
Pan-Pacific tuna management through merger of the
Western and Central Pacific Fisheries Commission
(WCPFC) and Inter-American Tropical Tuna
Commission to coordinate management measures
across the tropical Pacific (L-W).
Food security
Manage catchment vegetation to reduce transfer of Strengthen governance for sustainable use of coastal
sediments and nutrients to coasts to reduce damage to fish habitats by: (1) building national capacity to
adjacent coastal coral reefs, mangroves and understand the threats of climate change; (2)
seagrasses that support coastal fisheries (W-W). empowering communities to manage fish habitats; and
Foster the care of coral reefs, mangroves and (3) changing agriculture, forestry and mining practices to
seagrasses by preventing pollution, managing waste prevent sedimentation and pollution.
and eliminating direct damage to these coastal fish Minimise barriers to landward migration of coastal
habitats (W-W). habitats during development of strategies to assist other
Provide for migration of fish habitats by prohibiting sectors to respond to climate change.
construction adjacent to mangroves and seagrasses Apply primary fisheries management to stocks of
and installing culverts beneath roads to help the coastal fish and shellfish to maintain their potential for
plants colonise landward areas as sea level rises; (L- replenishment.
W). Allocate the necessary quantities of tuna from total
Sustain and diversify catches of demersal coastal national catches to food security to increase access to
fish to maintain the replenishment potential of all fish for both urban and coastal populations.
stocks (L-W). Dedicate a proportion of the revenue from fishing
Increase access to tuna caught by industrial fleets licences to improve access to tuna for food security.
through storing and selling tuna and by-catch landed Include anchored inshore FADs as part of national
at major ports to provide inexpensive fish for rapidly infrastructure for food security.
growing urban populations (W-W).
Install fish aggregating devices (FADs) close to the
coast to improve access to fish for rural communities
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as human populations increase and demersal fish
decline (W-W).
Develop coastal fisheries for small pelagic fish
species, e.g. mackerel, anchovies, pilchards, sardines
and scads (W-W?).
Promote simple post-harvest methods, such as
traditional smoking, salting and drying, to extend the
shelf life of fish when abundant catches are landed
(W-W).
Livelihoods
Relocate pearl farming operations to deeper water Provide incentives for aquaculture enterprises to assess
and to sites closer to coral reefs and seagrass/algal risks to infrastructure so that farming operations and
areas where water temperatures and aragonite facilities can be climate-proofed and relocated if
saturation levels are likely to be more suitable for necessary.
good growth and survival of pearl oysters, and Strengthen environmental impact assessments for
formation of high-quality pearls (L-W). coastal aquaculture activities to include the additional
Raise the walls and floor of shrimp ponds so that risks posed by climate change.
they drain adequately as sea level rises (L-W). Develop partnerships with regional technical agencies
Identify which shrimp ponds may need to be to provide support for development of sustainable
rededicated to producing other commodities (L-W). aquaculture.
a = The Parties to the Nauru Agreement (PNA) are Palau, Federated States of Micronesia, Papua New Guinea,
Solomon Islands, Marshall Islands, Nauru, Kiribati, and Tuvalu.
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Table 30-3: Key risks to ocean and coastal issues from climate change and the potential for risk reduction through
mitigation and adaptation. Key risks are identified based on assessment of the literature and expert judgments made
by authors of the various WGII AR5 chapters, with supporting evaluation of evidence and agreement in the
referenced chapter sections. Each key risk is characterized as very low, low, medium, high, or very high. Risk levels
are presented for the near-term era of committed climate change (here, for 2030 2040), in which projected levels of
global mean temperature increase do not diverge substantially across emissions scenarios. Risk levels are also
presented for the longer-term era of climate options (here, for 2080 2100), for global mean temperature increases of
2°C and 4°C above pre-industrial levels. For each timeframe, risk levels are estimated for the current state of
adaptation and for a hypothetical highly adapted state. As the assessment considers potential impacts on different
physical, biological, and human systems, risk levels should not necessarily be used to evaluate relative risk across
key risks. Relevant climate variables are indicated by symbols.
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Table 30-4: Ramifications, adaptation options and frameworks for decision-making for ocean regions. Symbols are as follows: T = sea temperature; UW =
upwelling; OA = ocean acidification; NU = nutrient concentration; IC = ice cover; SS = storm strength; SLR = sea level rise ( = Increased; = decreased;
italics = uncertain). Acronyms are: CBD (Convention on Biological Diversity); CTI (Coral Triangle Initiative); GEF (Global Environment Facility); IHO
(International Hydrographic Organization); ILO (International Labor Organization); IOM (International Organization of Migration); ISPS (International Ship and
Port Facility Security); MARPOL (International Convention for the Prevention of Pollution From Ships); PACC (Pacific Adaptation to Climate Change Project);
PEMSEA (Partnerships in Environmental Management for the Seas of East Asia); RFMO (Regional Fisheries Management Organizations); SPREP (Secretariat
of the Pacific Regional Environment Programme); UNCLOS (United Nations Convention on the Law of the Sea); UNHCR (United Nations High Commissioner
for Refugees); UNSFSA (Straddling Fish Stocks Agreement); and WHO (World Health Organization).
Primary Biophysical Key risks and Ramifications Adaptation options Policy frameworks and Key
driver(s) change projected vulnerabilities initiatives (examples) References
and Chapter
sections
T, OA Spatial and Reduced fisheries Reduced national Increased international cooperation LOSC, PEMSEA, CTI, [Tsamenyi and
temporal variation production impacts income, increased over key fisheries. Improved RFMO agreements, Hanich, 2012]
in primary important sources of unemployment, plus understanding of linkages between UNSFSA, [Bell et al.,
productivity income to some countries increase in poverty. ocean productivity, recruitment and 2011; Bell et
(medium while others may see Potential increase in fisheries stock levels. Implementation al., 2013a]
confidence at increase productivity (e.g., disputes over national of the regional vessel day scheme , 6.4.1, 6.5.3,
global scales, Box as tuna stocks shift ownership of key fishery provide social and economic 30.6.2.1,
CC-PP) eastwards in the resources (likely) incentives to fisheries and fishersfor 30.7.2, Box
Pacific)(medium adaptation,. CC-PP,
confidence).
T, OA Ecosystem regime Reduced fisheries Decreased food and Strengthen coastal zone management PEMSEA, CTI, PACC, [Bell et al.,
shifts (e.g., coral to production as coastal employment security and to reduce contributing stressors (e.g., MARPOL, UNHCR, 2013a]; 5.4.3,
algal reefs; habitats and ecosystems human migration away coastal pollution, over-harvesting and CBD, IOM, GEF, ILO 6.3.1-2, 12.4,
structural shifts in such as coral reefs (medium from coastal zone (likely) physical damage to coastal resources). 30.5.2-4,
phytoplankton confidence). Promote Blue Carbon* initiatives. 29.3.1, 29.3.3,
communities, 30.5.6, 30.6.1,
medium 30.6.2.1, Box
confidence) CC-CR
Tourist appeal of coastal Increased levels of As above, strengthen coastal zone CBD, PEMSEA, CTI, [Kenchington
assets decreases as coastal poverty in some management and reduce additional PACC, UNHCR, and Warner,
ecosystems change to less countries as tourist stressors on tourist sites; implement MARPOL 2012], 5.5.4.1,
'desirable' state reducing income decreases education programs and awareness 6.4.1 2, 10.6,
income to some countries (likely). among visitors. Diversify tourism 30.6.2.2,
(low confidence). activities.
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Primary Biophysical Key risks and Ramifications Adaptation options Policy frameworks and Key
driver(s) change projected vulnerabilities initiatives (examples) References
and Chapter
sections
Increased risk of some Increased disease and Increased monitoring and education National policy [Llewellyn,
diseases (e.g., ciguatera, mortality; decreases in surrounding key risks (e.g., strategies as well as and 2010] 6.4.2.3,
harmful algal blooms) as coastal food resources ciguatera); develop alternate fisheries regional cooperation 10.6, 29.3.3.2,
temperatures increase shift and fisheries income and income for periods when disease needed 29.5.3, 30.6.3,
and ecosystems shift away (likely). incidence increases, develop or update
from coral dominance (low health response plans.
confidence)
Increased poverty and Increased population Develop alternative industries and UNCLOS, PEMSEA, [Kaye, 2012;
dislocation of coastal pressure on migration income for affected coastal people. CTI, ISPS, IMO, Bali Rahman,
people (particularly in the destinations (e.g. large Strengthen coastal security both Process on Transnational 2012]
tropics) as coastal regional cities), and nationally and across regions. Crime ASEAN Mutual 12.4-6,29.3.3,
resources such as fisheries reduced freedom to Increase cooperation over criminal legal Assistance treaty 29.6.2, 30.6.5
degrade (medium navigate in some areas activities. and bilateral extradition
confidence). (as criminal activity and MLA agreements
increases; likely).
T Migration of Reorganization of Social and economic Increased international cooperation UNCLOS, CBD, RFMO 7.4.2, 6.5,
organisms and commercial fish stocks and disruption (very likely) and improve understanding of regime agreements, UNSFSA 30.5, 30.6.2.1,
ecosystems to ecological regime shifts changes; early-detection monitoring Box CC-MB,
higher latitudes (medium to high of physical and biological variables
(high confidence). confidence). and regional seasonal forecasting;
include related uncertainties into
fisheries management; social and
economic incentives for industry.
Increased in abundance, Increased disease risk to Increase environmental monitoring; ; IMO, BWM, Anti 6.4.1.5,
growing season and aquaculture and fisheries. technological advances to deal with Fouling Convention 7.3.2.4,
distributional extent ofpests Income loss and pest and fouling organisms; increase 29.5.3-4,
and fouling species increased operating and vigilance and control related to 30.6.2.1, Box
(medium confidence). maintenance costs (very biosecurity. CC-MB
likely)
Threats to human health Increased disease and Reduce exposure through increased UNICEF, WHO, IHOs, [Myers and
increase due to expansion mortality in some coastal monitoring and education, adoption or and national Patz, 2009];
of pathogens distribution to communities (likely) update of health response plans to governments. 6.4.3, 10.8.2,
higher latitudes (low outbreaks 11.7, 29.3.3,
confidence). 30.6.3, Box
CC-MB
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Primary Biophysical Key risks and Ramifications Adaptation options Policy frameworks and Key
driver(s) change projected vulnerabilities initiatives (examples) References
and Chapter
sections
T, NU, Increased Increased threats to Reduced supply of Provide early-detection monitoring CTI, PEMSEA, WHO, [Llewellyn,
OA incidence of ecosystems, fisheries and marine fish and shellfish and improve predictive models, MARPOL 2010], 30.6.3,
harmful algal human health (medium and greater incidence of provide education and adoption or 11.7, 6.4.2.3
blooms (HABs, confidence). disease among some update of health response plans.
low confidence). coastal communities
(likely).
T Increased Increased freshwater, Increasing damage to Improve management of catchment CTI, PEMSEA, SPREP 3.4, 29.3.1,
precipitation as a sediment and nutrients flow coastal reef systems with and coastal processes; expand riparian 30.5.4, 30.6.1
result of into coastal areas, increase ecological regime shifts vegetation along creeks and rivers;
intensified in number and severity of in many cases (very improve agricultural retention of soils
hydrological cycle flood events(medium to likely). and nutrients.
in some coastal high confidence).
areas (medium
confidence)
T Changing weather Increased risk of damage to Increased damage and Adjust infrastructure specifications, IMO [IPCC, 2012],
patterns,, storm infrastructure such as that associated costs (likely) Developearly-warning systems and 10.4.4, 29.3,
frequency , involved in shipping, and update emergency response plans to 30.6.2.3-4
medium oil and gas exploration and extreme events.
confidence) extraction (medium to low
confidence).
SLR, Increased wave Exposure of coastal Increased costs to human Develop integrated coastal UNICEF, IHOs, and [Warner,
SS exposure of coastal infrastructure and towns and settlements, management that consider SLR in national governments. 2012]
areas and communities to damage numbers of displaced planning and decision-making; 5.5, 12.4.1,
increased sea level and inundation, increase people and human increase understanding of the issues 29.5.1,
(high confidence) coastal erosion (high migration (very likely). through education. 30.3.1.2,
confidence) 30.6.5
Inundation of coastal Reduced food and water Assist communities to find UNICEF, IHOs, and [Warner,
aquifers reduces water security leads to alternatives for food and water, or national governments. 2012], 5.4.3,
supplies and decreased increased coastal assist in relocation of populations and 12.4.1, 29.3.2,
coastal agricultural poverty, reduced food agriculture from vulnerable areas. 30.3.1.2
productivity (high security, and migration
confidence). (very likely).
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Primary Biophysical Key risks and Ramifications Adaptation options Policy frameworks and Key
driver(s) change projected vulnerabilities initiatives (examples) References
and Chapter
sections
SLR Risk of inundation UNCLOS defined limits of Lack of clarity increases Seek resolution of shifting national UNCLOS [IPCC, 2012;
and coastal maritime jurisdiction will as do disputes over baselines issue (retreat and Schofield and
erosion, especially contract as national maritime limits and redefinition, stabilization, or fixation Arsana, 2012;
in low-lying baselines shift inland. maritime jurisdiction. of EEZ and other currently defined Warner and
countries (high Potential uncertainty Some nations at risk of maritime jurisdiction limits. Schofield,
confidence). increases in some areas major losses to their 2012]
with respect to the territorial waters (very 5.5, 30.6.5
international boundaries to likely).
maritime jurisdiction (high
confidence).
T, IC Loss of summer Access to northern coasts Potential for increased Seek early resolution of areas in UNCLOS Chapter 28
sea ice (high of Canada, USA and tension on different dispute currently and in the future.
confidence) Russia increases security interpretations of access
concerns (high confidence). rights and boundaries
(likely to very likely).
New resources become Tensions over maritime International agreements need to be UNCLOS Chapter 28
available as ice retreats, claims and ownership of sorted.
increasing vulnerability of resources (likely).
international borders in
some cases (medium
confidence).
*Blue Carbon initiatives include conservation and restoration of mangroves, saltmarsh and seagrass beds as carbon sinks (30.6.1).
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Figure 30-1: (a) Separation of the world s non-polar oceans into seven major sub-regions (excluding the polar
oceans, which are considered in Chapter 28). The chlorophyll-a signal measured by SeaWiFS and averaged over the
period from Sep 4, 1997 to 30 Nov 2010 (NASA) is provides a proxy for differences in marine productivity (with
the caveats provided in Box CC-PP). Ecosystem structure and functioning, as well as key oceanographic features
provided the basis for separating the Ocean into the sub-regions shown. The map insert shows the distribution of
Deep Sea (DS) habitat (>1000 m; Bathypelagic and Abyssopelagic habitats combined). Numbers refer to: 1 = High
Latitude Spring Bloom Systems (HLSBS); 2 = Equatorial Upwelling Systems (EUS); 3 = Semi-Enclosed Seas
(SES); 4 = Coastal Boundary Systems (CBS); 5 = Eastern Boundary Upwelling Ecosystems (EBUE); 6 = Sub-
Tropical Gyres (STG); and 7 = DS (>1000 m). (b) Relationship between fish catch and area for each ocean sub-
region is shown in (a). Red columns: average fish catch (as millions tons yr-1) for the period 1970 2006. Blue
columns: area (millions km2). The four left-hand columns (sub-regions HLSBS-North, CBS, EBUE, and SES) cover
20 % of the world oceans area and deliver 80% of the world s fish catches. The values for the percent area of the
Ocean, primary productivity, and fishery catch for the major sub-regions are listed in Table SM30-1.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 30-2: (a) Depth-averaged 0 700 m temperature trend for 1971 2010 (longitudinal versus latitude, colors and
gray contours in °C per decade). (b) Zonally averaged temperature trends (latitude versus depth, colors and gray
contours in °C per decade) for 1971 2010, with zonally averaged mean temperature over plotted (black contours in
°C). (c) Globally-averaged temperature anomaly (Time versus depth, colors and grey contours in °C) relative to the
1971 2010 mean. (d) Globally-averaged temperature difference between the Ocean surface and 200 m depth (Black:
annual values; red: five year running mean). Panels (a) (d) from WGI Figure 3.1. (e) (g) Observed and simulated
variations in past and projected future annual average SST over three ocean basins (excluding regions within 300 km
of the coast). The black line shows estimates from HadISST1.1 observational measurements. Shading denotes the 5
95 percentile range of climate model simulations driven with historical changes in anthropogenic and natural
drivers (62 simulations), historical changes in natural drivers only (25), and the Representative Concentration
Pathways: Dark Blue: RCP2.6; Light Blue: RCP4.5; Green: RCP6.0, and Red: RCP8.5). Data are anomalies from
the 1986 2006 average of the HadISST1.1 data (for the HadISST1.1 time series) or of the corresponding historical
all-forcing simulations. Further details are given in Box 21-2.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 30-3: Velocity at which sea surface temperature (SST) isotherms shifted (km decade-1) over the period 1960
2009 calculated using HaDISST1.1, with arrows indicating the direction and magnitude of shifts. Velocity of
climate change is obtained by dividing the temperature trend in °C decade-1 by the local spatial gradient °C km-1.
The direction of movement of SST is denoted by the direction of the spatial gradient and the sign of the temperature
trend: towards locally cooler areas with a local warming trend or towards locally warmer areas where temperatures
are cooling. Adapted from [Burrows et al., 2011].
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 30-4: Recent changes in thermal stress calculated using HadISST1.1 data. A monthly climatology was
created by averaging the HadISST monthly SST values over the period 1985 2000 to create twelve averages, one
for each month of the year. The Maximum Monthly Mean (MMM) climatology was created by selecting the hottest
month for each pixel. Anomalies were then created by subtracting this value from each SST value, but only allowing
values to be recorded if they were greater than zero [Donner et al., 2007]. Two measures of the change in thermal
stress were calculated as a result: (a) The total thermal stress for the period 1981 2010, calculated by summing all
monthly thermal anomalies for each grid cell. (b) The location of coral reef grid cells used in Table 30-1 and for
comparison to regional heat stress here. Each dot is positioned over a 1×1 degree grid cell within which lies at least
one carbonate coral reef. The latitude and longitude of each reef is derived from data provided by the World
Resources Institute s Reefs at Risk report (http://www.wri.org). The six regions are as follows: Red Western
Pacific Ocean; Yellow Eastern Pacific Ocean; Dark Blue Caribbean & Gulf of Mexico; Green Western Indian
Ocean; Pink Eastern Indian Ocean; and Light Blue Coral Triangle & SE Asia. (c) Proportion of years with
thermal stress, which is defined as any year that has a thermal anomaly, for the periods 1951 1980 and (d) 1981
2010. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 30-5. Map of the rate of change in sea surface height (geocentric sea level) for the period 1993 2012 derived
from satellite altimetry. Also shown are relative sea level changes (gray lines) from selected tide gauge stations for
the period 1950 2012. For comparison, an estimate of global mean sea level change is shown (red lines) with each
tide gauge time series. The relatively large short-term oscillations in local sea level (gray lines) are due to the natural
climate variability and ocean circulation. For example, the large regular deviations at Pago Pago are associated with
the El Nino-Southern Oscillation. Figure originally presented in WGI FAQ 13.1, Figure 1).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 30-6: (a) The 1955 2005 climatological-mean sea surface salinity [Antonov et al., 2010] color contoured at
0.5 PSS78 intervals (black lines). (b) Annual mean evaporation-precipitation averaged over the period 1950 2000
(NCEP) color contoured at 0.5 m yr 1 intervals (black lines). (c) The 58-year (2008 minus 1950) sea surface salinity
change derived from the linear trend (PSS78), with seasonal and ENSO signals removed [Durack and Wijffels,
2010] color contoured at 0.116 PSS78 intervals (black lines). (d) The 30-year (2003 2007 average centered at 2005,
minus the 1960 1989 average centered at 1975) sea surface salinity difference (PSS78) color contoured at 0.06
PSS78 intervals (black lines). Contour intervals in (c) and (d) are chosen so that the trends can be easily compared,
given the different time intervals in the two analyzes. White areas in (c) and (d) are marginal seas where the
calculations are not carried out. Regions where the change is not significant at the 99% confidence level are stippled
in gray. Figure originally presented as WGI Figure 3.4 in WGI.
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Figure 30-7: Projected ocean acidification from 11 CMIP5 Earth System models under RCP8.5 (other RCP
scenarios have also been run with the CMIP5 models): (a) Time series of surface pH shown as the mean (solid line)
and range of models (filled), given as area-weighted averages over the Arctic Ocean (green), the tropical oceans
(red) and the Southern Ocean (blue). (b) Maps of the median model s change in surface pH from 1850 2100. Panel
(a) also includes mean model results from RCP2.6 (dashed lines). Over most of the Ocean, gridded data products of
carbonate system variables are used to correct each model for its present-day bias by subtracting the model-data
difference at each grid cell following [Orr et al., 2005]. Where gridded data products are unavailable (Arctic Ocean,
all marginal seas and the Ocean near Indonesia), the results are shown without bias correction. The bias correction
reduces the range of model projections by up to a factor of 4, e.g., in panel (a) compare the large range of model
projections for the Arctic (without bias correction) to the smaller range in the Southern Ocean (with bias correction).
Figure originally presented in WGI Figure 6.28 in WGI.
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Figure 30-8: Projected aragonite saturation state from 11 CMIP5 Earth System models under RCP8.5 scenario: (a)
time series of surface carbonate ion concentration shown as the mean (solid line) and range of models (filled), given
as area weighted averages over the Arctic Ocean (green), the tropical oceans (red), and the Southern Ocean (blue);
maps of the median model's surface A in (b) 2010, (d) 2050, and (f) 2100; and zonal mean sections (latitude versus
depth) of A in 2100 over (c) the Atlantic Ocean and (e) the Pacific Ocean, while the ASH (Aragonite Saturation
Horizon) is shown for 2010 (dotted line) and 2100 (solid line). Panel (a) also includes mean model results from
RCP2.6 (dashed lines). As for Figure 30-7, gridded data products of carbonate system variables [Key et al., 2004]
are used to correct each model for its present-day bias by subtracting the model-data difference at each grid cell
following [Orr et al., 2005]. Where gridded data products are unavailable (Arctic Ocean, all marginal seas and the
Ocean near Indonesia), results are shown without bias correction. Reprinted from Figure 6.29 in WGI.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 30-9: (a) Simulated changes in dissolved O2 (mean and model range as shading) relative to 1990s for
RCP2.6, RCP4.5, RCP6.0, and RCP8.5. (b) Multi-model mean dissolved O2 (umol m 3) in the main thermocline
(200 600 m depth average) for the 1990s, and changes in the 2090s relative to 1990s for RCP2.6 (c) and RCP8.5
(d). To indicate consistency in the sign of change, regions are stippled when at least 80% of models agree on the
sign of the mean change. These diagnostics are detailed in [Cocco et al., 2013] in a previous model inter-comparison
using the SRES-A2 scenario and have been applied to CMIP5 models here. Models used: CESM1-BGC, GFDL-
ESM2G, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, IPSL-CM5A-MR, MPI-ESM-LR, MPI-ESM-MR,
NorESM1. Figure originally presented in WGI Figure 6.30 in WGI.
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Figure 30-10: Annual maximum proportions of reef pixels with Degree Heating Months [Donner et al., 2007];
DHM) 1 (used for projecting coral bleaching; [Strong et al., 1997; Strong et al., 2011]) and DHM 5 (associated
with bleaching across 100% of affected areas with significant mortality, [Eakin et al., 2010] for the period 1870
2009 for each of the six coral regions (Figure 30-4d) using the HadISST1.1 data set. The black line on each graph is
the maximum annual area value for each decade over the period 1870 2009. This value is continued through 2010
2099 using CMIP5 data and splits into the four Representative Concentration Pathways (RCP2.6, 4.5, 6.0, and 8.5).
DHM were produced for each of the four RCPs using the ensembles of CMIP models. From these global maps of
DHM, the annual percentage of grid cells with DHM 1 and DHM 5 were calculated for each coral region. These
data were then grouped into decades from which the maximum annual proportions were derived. The plotted lines
for 2010 2099 are the average of these maximum proportion values for each RCP. Monthly SST anomalies were
derived using a 1985 2000 maximum monthly mean (MMM) climatology derived in the calculations for Figure 30-
4. This was done separately for HadISST1.1, the CMIP5 models, and each of the four RCPs, at each grid cell for
every region. DHMs were then derived by adding up the monthly anomalies using a 4-month rolling sum. Figure
SM30-3 presents past and future sea temperatures for the six major coral reef provinces under historic, un-forced,
RCP4.5 and RCP8.5 scenarios.]
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Figure 30-11: Expert assessment of degree of confidence in detection and attribution of physical and chemical
changes (a) and ecological changes (b) across sub-regions, as designated in Figure 30-1a, and processes in the Ocean
(based on evidence explored throughout Chapter 30 and elsewhere in AR5). Further explanation of this figure is
given in 18.3.3 4 and 18.6. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure 30-12: (a) Examples of projected impacts and vulnerabilities associated with climate change in Ocean sub-regions. (b) Examples of risks to fisheries from
observed and projected impacts across Ocean sub-regions. Letters indicate level of confidence: (vL): Very low, (L): Low, (M): Medium, (H): High and (vH):
Very high. Details of sub-regions are given in Table 30-1a and 30.1.1. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure CR-1: A and B: the same coral community before and after a bleaching event in February 2002 at 5 m depth,
Halfway Island, Great Barrier Reef. Coral cover at the time of bleaching was 95% bleached almost all of it severely
bleached, resulting in mortality of 20.9% (Elvidge et al., 2004). Mortality was comparatively low due in part
because these coral communities were able to shuffle their symbiont to more thermo-tolerant types (Berkelmans and
van Oppen, 2006; Jones et al., 2008). C and D: three CO2 seeps in Milne Bay Province, Papua New Guinea show
that prolonged exposure to high CO2 is related to fundamental changes in the ecology of coral reefs (Fabricius et al.,
2011), including reduced coral diversity (-39%), severely reduced structural complexity (-67%), lower density of
young corals (-66%) and fewer crustose coralline algae (-85%). At high CO2 sites (panel D; median pHT ~7.8), reefs
are dominated by massive corals while corals with high morphological complexity are underrepresented compared
with control sites (D; median pH ~8.0). Reef development ceases at pHT values below 7.7. pHT: pH on the total
scale. E: temporal trend in coral cover for the whole Great Barrier Reef over the period 1985 2012 (N, number of
reefs, mean +/- 2 standard errors; De'ath et al., 2012). F: composite bars indicate the estimated mean coral mortality
for each year, and the sub-bars indicate the relative mortality due to crown-of-thorns starfish, cyclones, and
bleaching for the whole Great Barrier Reef (De'ath et al., 2012). Photo credit: R. Berkelmans (A and B) and K.
Fabricius (C and D).
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Figure
MB-1: 1735 observed responses to climate change from 208 single- and multi-species studies. Changes attributed to
climate change (blue), inconsistent with climate change (red) and are equivocal (yellow). Each circle represents the
centre of a study area. Where points fall on land, it is because they are centroids of distribution that surround an
island or peninsula. Pie charts show the proportions within regions bounded by red squares and in the
Mediterranean; numbers indicate the total (consistent, opposite or equivocal) observations within each region. Note:
57% of the studies included were published since AR4 (from Poloczanska et al., 2013).
[Illustration to be redrawn to conform to IPCC publication specifications.]
Figure MB-2. Rates of change in distribution (km decade-1) for marine taxonomic groups, measured at the leading
edges (red) and trailing edges (brown). Average distribution shifts calculated using all data, regardless of range
location, are in black. Distribution rates have been square-root transformed; standard errors may be asymmetric as a
result. Positive distribution changes are consistent with warming (into previously cooler waters, generally
poleward). Means +/- standard error are shown, along with number of observations (from Poloczanska et al., 2013).
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure MB-3. A. Rate of climate change for the Ocean (sea surface temperature (SST) °C); B. corresponding climate
velocities for the Ocean and median velocity from land (adapted from Burrows et al., 2011); and C. observed rates
of displacement of marine taxonomic groups over several decades until 2010. The thin dotted red arrows give an
example of interpretation. Rates of climate change of 0.008 °C yr-1 correspond to ca. 2.4 km yr-1median climate
velocity in the Ocean. When compared to observed rates of displacement, many marine taxonomic groups have been
able to track these velocities, except phyto- and zooplankton where rates of displacement greatly exceed climate
velocity. All values are calculated for ocean surface with the exclusion of polar seas (Figure 30-1a). (A) Observed
rates of climate change for Ocean SST (Black dotted line) are derived from HadISST1.1 data set, all other rates are
calculated based on the average of the CMIP5 climate model ensembles (Table S30-3) for the historical period and
for the future based on the four RCP emissions scenarios. Data were smoothed using a 20-year sliding window. (B)
Median climate velocity calculated from HadISST1.1 dataset over 1960 2010 using the methods of Burrows et al.,
2011. The three axes represent estimated median climate velocities are representative of areas of slow velocities
such as Pacific subtropical gyre (STG) system (Purple line), the global Ocean surface (excluding polar seas, Blue
line), and areas of high velocities such as the Coral Triangle and North Sea (Orange line). Figure 30-3 shows climate
velocities over the ocean surface calculated over 1960 2010. The Red line corresponds to the median rate over
global land surface calculated using historical surface temperatures from the CMIP5 model ensemble (Table S30-3).
(C) Rates of displacement for marine taxonomic groups estimated by Poloczanska et al. 2013 using published
studies (Figure MB-2 Black data set). Note the displacement rates for phytoplankton exceed the axis, so values are
given. [Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure OA-1: A: Overview of the chemical, biological, socio-economic impacts of ocean acidification and of policy
options (adapted from Turley and Gattuso, 2012). B: Multi-model simulated time series of global mean ocean
surface pH (on the total scale) from CMIP5 climate model simulations from 1850 to 2100. Projections are shown for
emission scenarios RCP2.6 (blue) and RCP8.5 (red) for the multi-model mean (solid lines) and range across the
distribution of individual model simulations (shading). Black (grey shading) is the modelled historical evolution
using historical reconstructed forcings. The models that are included are those from CMIP5 that simulate the global
carbon cycle while being driven by prescribed atmospheric CO2 concentrations. The number of CMIP5 models to
calculate the multi-model mean is indicated for each time period/scenario (WGI AR5 Figure 6.28). C: Effect of near
future acidification (seawater pH reduction of 0.5 unit or less) on major response variables estimated using weighted
random effects meta-analyses, with the exception of survival which is not weighted (Kroeker et al., 2013). The log-
transformed response ratio (LnRR) is the ratio of the mean effect in the acidification treatment to the mean effect in
a control group. It indicates which process is most uniformly affected by ocean acidification but large variability
exists between species. Significance is determined when the 95% bootstrapped confidence interval does not cross
zero. The number of experiments used in the analyses is shown in parentheses. * denotes a statistically significant
effect.
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Figure PP-1: A) Environmental factors controlling Net Primary Production (NPP). NPP is mainly controlled by
three basic processes: 1) Light conditions in the surface ocean, i.e. the photic zone where photosynthesis occurs, 2)
upward flux of nutrients and micronutrients from underlying waters into the photic zone, 3) Regeneration of
nutrients and micronutrients via the breakdown and recycling of organic material before it sinks out of the photic
zone. All three processes are influenced by physical, chemical and biological processes and vary across regional
ecosystems. In addition, water temperature strongly influences the upper rate of photosynthesis for cells that are
resource-replete. Predictions of alteration of primary productivity under climate change depend on correct
parameterizations and simulations of each of these variables and processes for each region. B) Annual composite
map of global areal NPP rates (derived from MODIS Aqua satellite climatology from 2003-2012; NPP was
calculated with the Carbon-based Production Model (CbPM, Westberry et al., 2008)). Overlaid is a grid of (thin
black lines) that represent 51 distinct global ocean biogeographical provinces (after Longhurst, 1998 and based on
Boyd and Doney, 2002). The characteristics and boundaries of each province are primarily set by the underlying
regional ocean physics and chemistry. Figure courtesy of Toby Westberry (OSU) and Ivan Lima (WHOI), satellite
data courtesy of NASA Ocean Biology Processing Group.
[Illustration to be redrawn to conform to IPCC publication specifications.]
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Figure UP-1: Upper panel: Schematic hypothetic mechanism of increasing coastal wind-driven upwelling at eastern
boundary systems, where differential warming rates between land and ocean results in increased land-ocean pressure
gradients (1) that produce stronger alongshore winds (2) and offshore movement of surface water through Ekman
transport (3), and increased upwelling of deep cold nutrient rich waters to replace it (4). Lower panel: potential
consequences of climate change in upwelling systems. Increasing stratification and uncertainty in wind stress trends
result in uncertain trends in upwelling. Increasing upwelling may result in higher input of nutrients to the euphotic
zone, and increased primary production, which in turn may enhance pelagic fisheries, but also decreased coastal
fisheries due to an augmented exposure of coastal fauna to hypoxic, low pH waters. Decreased upwelling may result
in lower primary production in these systems with direct impacts on pelagic fisheries productivity.
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