Technical Summary TSSM Supplementary Material Coordinating Lead Authors: Thomas F. Stocker (Switzerland), Qin Dahe (China), Gian-Kasper Plattner (Switzerland) Lead Authors: Lisa V. Alexander (Australia), Simon K. Allen (Switzerland/New Zealand), Nathaniel L. Bindoff (Australia), François-Marie Bréon (France), John A. Church (Australia), Ulrich Cubasch (Germany), Seita Emori (Japan), Piers Forster (UK), Pierre Friedlingstein (UK/Belgium), Nathan Gillett (Canada), Jonathan M. Gregory (UK), Dennis L. Hartmann (USA), Eystein Jansen (Norway), Ben Kirtman (USA), Reto Knutti (Switzerland), Krishna Kumar Kanikicharla (India), Peter Lemke (Germany), Jochem Marotzke (Germany), Valérie Masson-Delmotte (France), Gerald A. Meehl (USA), Igor I. Mokhov (Russian Federation), Shilong Piao (China), Venkatachalam Ramaswamy (USA), David Randall (USA), Monika Rhein (Germany), Maisa Rojas (Chile), Christopher Sabine (USA), Drew Shindell (USA), Lynne D. Talley (USA), David G. Vaughan (UK), Shang-Ping Xie (USA) Contributing Authors: Myles R. Allen (UK), Olivier Boucher (France), Don Chambers (USA), Jens Hesselbjerg Christensen (Denmark), Philippe Ciais (France), Peter U. Clark (USA), Matthew Collins (UK), Josefino C. Comiso (USA), Viviane Vasconcellos de Menezes (Australia/Brazil), Richard A. Feely (USA), Thierry Fichefet (Belgium), Gregory Flato (Canada), Jesús Fidel González Rouco (Spain), Ed Hawkins (UK), Paul J. Hezel (Belgium/USA), Gregory C. Johnson (USA), Simon A. Josey (UK), Georg Kaser (Austria/Italy), Albert M.G. Klein Tank (Netherlands), Janina Körper (Germany), Gunnar Myhre (Norway), Timothy Osborn (UK), Scott B. Power (Australia), Stephen R. Rintoul (Australia), Joeri Rogelj (Switzerland/Belgium), Matilde Rusticucci (Argentina), Michael Schulz (Germany), Jan Sedláček (Switzerland), Peter A. Stott (UK), Rowan Sutton (UK), Peter W. Thorne (USA/Norway/UK), Donald Wuebbles (USA) Review Editors: Sylvie Joussaume (France), Joyce Penner (USA), Fredolin Tangang (Malaysia) This supplementary material should be cited as: Stocker, T.F., D. Qin, G.-K. Plattner, L.V. Alexander, S.K. Allen, N.L. Bindoff, F.-M. Bréon, J.A. Church, U. Cubasch, S. Emori, P. Forster, P. Friedlingstein, N. Gillett, J.M. Gregory, D.L. Hartmann, E. Jansen, B. Kirtman, R. Knutti, K. Krishna Kumar, P. Lemke, J. Marotzke, V. Masson-Delmotte, G.A. Meehl, I.I. Mokhov, S. Piao, V. Ramaswamy, D. Randall, M. Rhein, M. Rojas, C. Sabine, D. Shindell, L.D. Talley, D.G. Vaughan and S.-P. Xie, 2013: Technical Sum- mary Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www.climatechange2013.org and www.ipcc.ch. TSSM-1 Table of Contents TS.SM.1 Notes and Technical Details on Observed Global Surface Temperature Figures in the Summary for Policymakers Figure SPM.1.................... TS-SM-3 TS.SM.2 Notes and Technical Details on Observed Change in Precipitation Over Land Figures in the Summary for Policymakers Figure SPM.2............. TS-SM-3 TS.SM.3 Notes and Technical Details on Observed Indicators of a Changing Global TSSM Climate Figures for the Summary for Policymakers Figure SPM.3............. TS-SM-3 TS.SM.4 Notes and Technical Details on Observed Changes in the Global Carbon Cycle Figures in the Summary for Policymakers Figure SPM.4............. TS-SM-5 TS.SM.5 Notes and Technical Details on Radiative Forcing Estimates Figure in the Summary for Policy Makers Figure SPM.5.................................................. TS-SM-6 TS.SM.6 Notes and Technical Details on Comparison of Observed and Simulated Climate Change Figures for the Summary for Policymakers Figure SPM.6............. TS-SM-6 TS.SM.7 Notes and Technical Details on CMIP5 Simulated Time Series Figures in the Summary for Policymakers Figure SPM.7.................................................. TS-SM-7 TS.SM.8 Notes and Technical Details on Maps Showing CMIP5 Results in the Summary for Policymakers Figure SPM.8................................................ TS-SM-11 TS.SM.9 Notes and Technical Details on the Sea Level . Projection Figure for the Summary for Policymakers Figure SPM.9........... TS-SM-15 TS.SM.10 Notes and Technical Details on the Summary . for Policymakers Figure Plotting Global Mean Temperature Increase as a Function of Cumulative Total Global CO2 Emissions Figure SPM.10...................... TS-SM-15 References ......................................................................... TS-SM-17 TSSM-2 Technical Summary Supplementary Material TS.SM.1 Notes and Technical Details on Observed TS.SM.2 Notes and Technical Details on Global Surface Temperature Figures in Observed Change in Precipitation the Summary for Policymakers Over Land Figures in the Summary Figure SPM.1 for Policymakers Figure SPM.2 Data and programming code (IDL) used to create Summary for Policy- Data and programming code (IDL) used to create Summary for Policy- makers and Technical Summary figures originating from Sections 2.4 makers and Technical Summary figures originating from Sections 2.4 and 2.5 of Chapter 2 can be obtained from the IPCC WGI AR5 website and 2.5 of Chapter 2 can be obtained from the IPCC WGI AR5 website www.climatechange2013.org. www.climatechange2013.org. TS.SM.1.1 Annual and Decadal Global Surface TS.SM.2.1 Map of Observed Changes in Precipitation Temperature Anomalies Figure SPM.1a Over Land Figure SPM.2 Global Mean Surface Temperature (GMST) anomalies as provided by Maps of observed changes in annual precipitation over land show the dataset producers are given normalized relative to a 1961 1990 trends calculated from 3 datasets: TSSM climatology from the latest version (as at 15 March 2013) of three combined Land-Surface Air Temperature (LSAT) and Sea Surface Tem- CRU TS 3.10.01 (updated from Mitchell and Jones, 2005) perature (SST) datasets. These combined datasets and the correspond- GHCN V2 (updated through 2011; Vose et al., 1992) ing colours used in Figure SPM.1a are: GPCC V6 (Becker et al., 2013) HadCRUT4 (version 4.1.1.0) black Trends in annual precipitation are expressed per decade, and are calcu- NASA GISS blue lated for the time periods 1901 2010 and 1951 2010. See the Supple- NCDC MLOST (version 3.5.2) orange. mentary Material of Chapter 2 for a detailed description of the meth- odology used for trend and uncertainty calculations (Section 2.SM.3.3). An overview of methodological diversity between these three temper- Trends have been calculated only for those grid boxes with greater ature datasets is provided in Table 2.SM.6 of the Supplementary Mate- than 70% complete records and more than 20% data availability in rial to Chapter 2, and full comprehensive details on the construction first and last 10% of the time period. White areas indicate incomplete process for these datasets are provided in the references cited in this or missing data. Black plus signs (+) indicate grid boxes where trends table. For time-series of LSAT only, and SST only, the reader is referred are significant at the 2-tailed 10% significance level (i.e., a trend of to Figure TS.1. zero lies outside the 90% confidence interval). For the decadal anomalies, 90% confidence intervals are shown for the The Technical Summary provides maps for all 3 datasets (TS TFE.1, HadCRUT4 dataset (based on Morice et al., 2012). Figure 2), while the Summary for Policymakers provides a map based on GPCC only (Figure SPM.2). TS.SM.1.2 Maps of Observed Changes in Surface Temperature Figure SPM.1b TS.SM.3 Notes and Technical Details on Maps of observed changes in surface temperature are based on trends Observed Indicators of a Changing calculated from the 3 datasets listed above for the period 1901 2012. Global Climate Figures for the Summary See the Supplementary Material of Chapter 2 for a detailed description for Policymakers Figure SPM.3 of the methodology used for trend and uncertainty calculations (Sec- tion 2.SM.3.3). Trends have been calculated only for those grid boxes This material documents the provenance of the data and plotting with greater than 70% complete records and more than 20% data procedures that were used to create Figure SPM.3 in the IPCC WGI availability in the first and last 10% of the time period. White areas Fifth Assessment Report. This figure is closely derived from Figure TS.1 indicate incomplete or missing data. Black plus signs (+) indicate grid and FAQ 2.1, Figure 2 (see Chapter 2 Supplementary Material Section boxes where trends are significant at the 2-tailed 10% significance 2.SM.5), but includes fewer observed indicators. In addition, Figure level (i.e., a trend of zero lies outside the 90% confidence interval). SPM.3 includes an estimate of uncertainty for those datasets where this is available and has been assessed, illustrated with shading. Figure The Technical Summary provides maps for all 3 datasets (Figure TS.2), SPM.3 includes datasets and parameters assessed in Chapters 3 (ocean while the Summary for Policymakers provides a map based on NCDC heat content, sea level), and 4 (snow cover, sea ice). MLOST only (Figure SPM.1b). TSSM-3 Technical Summary Supplementary Material TS.SM.3.1 Northern Hemisphere Spring Snow Cover sea ice cover during the satellite era (since 1979) can be quantified Figure SPM.3a accurately because of global coverage at good temporal resolution and the high contrast in the signature of ice free and ice covered oceans. TS.SM.3.1.1 Datasets The uncertainty (shaded range) that is shown is 1 standard deviation of the more than 30 years of satellite data, assuming a Gaussian dis- Green: Northern Hemisphere annual March-April average snow-cover tribution. The standard deviation is calculated after the data have been extent based on an updated series from Brown and Robinson (2011), linearly detrended. 1922 2012. For the pre-satellite data (pre 1979), the true interannual variability is Shaded uncertainty estimate indicated by the 95% confidence interval. not known because available data are sparse and limited to only a few locations. Based on the expected quality of the Walsh and Chapman TS.SM.3.1.2 Plotting Techniques (2001) data and because of the lack of a better procedure, we use 1.75 standard deviations for the period 1880 to 1952 when data were Annual values are plotted. sparse and 1.5 standard deviation for the period 1953 to 1978 when significantly more data were available. For the HadISST1.2 data set, TSSM TS.SM.3.2 Arctic Summer Sea Ice Extent Figure SPM.3b which includes both pre- and post-satellite data (Rayner et al., 2003), we use 1 standard deviation for the entire period since 1900, calculat- All datasets provide Arctic annual July-August-September average sea ed after the data has been linearly detrended. ice extent. TS.SM.3.2.2 Plotting Techniques Green: Updated from Walsh and Chapman (2001). Annual values are from 1900 1978. Annual values are plotted. Blue: Hadley Centre Sea Ice and Sea Surface Temperature dataset (Had- TS.SM.3.3 Global Average Upper Ocean Heat Content ISST1.2) (Rayner et al., 2003). Annual values are from 1900 1939 and Figure SPM.3c 1953 2012. Values are excluded for the period 1940 1952 because the available data showed no change. It was a period when in situ data TS.SM.3.3.1 Datasets were very sparse and the gaps were filled in for completeness with climatology. For this assessment, this was not considered sufficiently All datasets provide global annual upper-ocean (0 to 700 m depth) robust and therefore the data during the period were excluded from heat content anomalies. the time series. Blue: Updated from Palmer et al. (2007). Annual values are from 1950 Red: Bootstrap algorithm (SBA) applied to data from the Scanning Mul- 2011. tichannel Microwave Radiometer (SMMR) (updated from Comiso and Nishio, 2008). Annual values are from 1979 2012. Green: Updated from Domingues et al. (2008). Annual values, smoothed with a 3-year running mean, are from 1950 2011. Black: NASA Team algorithm (NT1) applied to data from the Special Sensor Microwave/Imager (SSM/I) (Cavalieri et al., 1984) updated Yellow: Updated from Ishii and Kimoto (2009). Annual values are from in Cavalieri and Parkinson (2012) and Parkinson and Cavalieri (2012). 1950 2011. Annual values are from 1979 2011. Orange: Updated from Smith and Murphy (2007). Annual values are Yellow: Bootstrap algorithm (ABA) applied to data from the Advanced from 1950 2010. Microwave Scanning Radiometer - Earth Observing System (AMSR- E) (updated from Comiso and Nishio, 2008). Annual values are from Black: Updated from Levitus et al. (2012). Annual values are from 2002 2011. 1955 2011. Orange: Revised NASA Team algorithm (NT2) applied to data from the Uncertainty estimates are as reported in the cited publications. These Advanced Microwave Scanning Radiometer - Earth Observing System are one standard error of the mean, except for Levitus et al. (2012) (AMSR-E) (updated from Markus and Cavalieri, 2000). Annual values which provide one standard deviation. No uncertainty estimate is are from 2002 2011. available for Smith and Murphy (2007). Uncertainty estimates for each data point in the plots have been cal- TS.SM.3.3.2 Plotting Techniques culated based on the interannual variability of the ice extents. The sys- tematic errors are not considered because they are generally unknown The published ocean heat content anomaly datasets are relative to dif- and are expected to be approximately constant from one year to ferent climatological reference periods. Therefore, the datasets have another and would not change the results of trend analyses signifi- been aligned in Figure SPM.3c for the period 2006 2010, five years cantly. The interannual variability of the extent and actual area of the that are well measured by Argo, and then plotted relative to the result- TSSM-4 Technical Summary Supplementary Material ing mean of all curves for 1970, a time when the increasing availability TS.SM.4 Notes and Technical Details on of annual data from XBTs causes the uncertainty estimates to reduce Observed Changes in the Global considerably. Specifically the alignment procedure for Figure SPM.3c Carbon Cycle Figures in the Summary involved the following steps: for Policymakers Figure SPM.4 Obtain all five upper ocean heat content anomaly time series. TS.SM.4.1 Atmospheric Concentrations of Carbon 1. Recognize that all the time-series values are annual values, cen- Dioxide Figure SPM.4a tered on the middle of calendar years. 2. Find the average value of each time series for the years 2006 2010. The top panel of Figure TS.5, and panel (a) of Figure SPM.4 show time 3. Subtract the average 2006 2010 value for each time series from series of atmospheric concentrations of carbon dioxide (CO2). CO2 con- that specific time-series. centrations are expressed as a mole fraction in dry air, micromol/mol, 4. Find the value of each time series for the year 1970. abbreviated as ppm. Time series are shown for the Mauna Loa Obser- 5. Average these five values from the year 1970. vatory (red in Figure SPM.4a), and South Pole (black in Figure SPM.4a). 6. Subtract this 1970 average value from all of the time-series. Data were accessed from the following sources (active at the time of publication): TSSM TS.SM.3.4 Global Average Sea Level Figure SPM.3d 1. Mauna Loa Observatory ftp://ftp.cmdl.noaa.gov/ccg/co2/trends/co2_mm_mlo.txt. TS.SM.3.4.1 Datasets Monthly averages are plotted from March 1958 to August 2012. For Black: Church and White (2011) tide gauge reconstruction. Annual further details on the measurements see Keeling et al. (1976a) and values are from 1900 2009. Thoning et al. (1989). Yellow: Jevrejeva et al. (2008) tide gauge reconstruction. Annual values 2. South Pole are from 1900 2002. http://scrippsco2.ucsd.edu/data/flask_co2_and_isotopic/ monthly_co2/monthly_spo.csv Green: Ray and Douglas (2011) tide gauge reconstruction. Annual values are from 1900 2007. Monthly averages are plotted from June 1957 to February 2012. For further details on the measurements see Keeling et al. (1976b; 2001). Red: Nerem et al. (2010) satellite altimetry. A 1-year moving average boxcar filter has been applied to give annual values from 1993 2009. TS.SM.4.2 Ocean Surface Carbon Dioxide and In Situ pH Figure SPM.4b Shaded uncertainty estimates are one standard error as reported in the cited publications. The one standard error on the 1-year averaged The top panel of Figure TS.5, and panel (b) of Figure SPM.4 show time altimetry data (Nerem et al., 2010) is estimated at +/-1 mm, and thus series of observed partial pressure of dissolved CO2 (pCO2 given in considerably smaller than for all other datasets. uatm) at the ocean surface, together with time series of ocean surface in situ pH (total scale). All ocean time series are plotted as 12-month TS.SM.3.4.2 Plotting Techniques running means (6 months before to 6 months after the sample date) for each 6-month period centered on 1 January and 2 July of each year. The published Global Mean Sea Level (GMSL) datasets use arbitrary Data for both pCO2 and in situ pH were measured at the following and different reference periods where they start from zero. Further- stations and obtained from the following sources (active at the time more, the altimetry data begins only in 1993. Therefore, the datasets of publication): have been aligned in Figure SPM.3d to a common reference period of time using the following steps: 1. Hawaii Ocean Time-Series program (HOT) from the station ALOHA (updated from, Dore et al., 2009) 1. The longest running record (Church and White, 2011) is taken as the http://hahana.soest.hawaii.edu/hot/products/HOT_surface_CO2.txt reference to which all other datasets are aligned. 2. GMSL from Church and White (2011) is calculated relative to the Shown as light green and light blue time series in Figure SPM.4b, average for the period 1900 1905, and the resulting value for the for in situ pH and pCO2 respectably. Data were plotted for the period year 1993 (127 mm) is identified. 1988 2011. 3. All other records are then adjusted to give the same value of 127 mm in 1993 (i.e., for each dataset the offset required to give 127 Further technical details regarding the data are available from the mm in 1993 is applied to all annual values in that dataset). readme file: http://hahana.soest.hawaii.edu/hot/products/HOT_ surface_CO2_readme.pdf. TSSM-5 Technical Summary Supplementary Material 2. Bermuda Atlantic Time-Series Study (BATS): rapid adjustments are less well characterized and assumed to be small, http://bats.bios.edu/bats_form_bottle.html and thus the traditional RF is used. Shown as green and blue time series in Figure SPM.4b, for in situ pH The level of confidence given in Figure SPM.5 is based on Table 8.5. and pCO2, respectively, but not shown in Figure TS.5. Data were plotted for the period 1991 2011. For the main emitted compounds of CO2, CH4, Halocarbons, N2O, CO, NMVOC and NOx, the underlying values, their sources, and uncertain- Measured dissolved inorganic carbon (DIC) and total alkalinity (TA) at ties can be found in the Chapter 8 Supplementary Material, Tables in situ temperature were used to calculate pH on the total scale as well 8.SM.6 and 8.SM.7. as pCO2 in atm. The value of 0.27 W m 2 for aerosols and precursors shown in Figure Further technical details are described in Bates (2007). SPM.5 results from 0.35 W m 2 from RFari (Table 8.6) with the addi- tion of 0.04 W m 2 from BC-on-snow and the subtraction of the small 3. European Station for Time series in the Ocean (ESTOC; see nitrate contribution from NOx of 0.04 W m 2 (Table 8.SM.6). González-Dávila and Santana-Casiano, 2009): TSSM http://cdiac.ornl.gov/ftp/oceans/ESTOC_data The value of 0.55 W m 2 for cloud adjustments due to aerosols given in Figure SPM.5 results from the combination of ERFaci 0.45 [ 1.2 to Shown as dark green and dark blue time series in Figure SPM.4b, for in 0.0] W m 2 and rapid adjustment of ari 0.1 [ 0.3 to +0.1] W m 2 as situ pH and pCO2, respectively, but not shown in Figure TS.5. Data were reported in Figure TS.7. Detailed information can be found in Chapter 8 plotted for the period 1996 2009. and the Chapter 8 Supplementary Material, Table 8.SM.6. Further technical details regarding the data are available from The values for albedo changes due to land use and changes in solar González-Dávila (2010). irradiance come from Table 8.6 of Chapter 8. Note that the data for Figure SPM.4 (and Figure TS.5) provided at Total anthropogenic RF relative to 1750 is based on values given in the external sources cited above may be subject to revision based on Table 8.6 (for 2011) and Figure 8.18 (values for 1950 and 1980 given recalibration, and other quality control procedures conducted over in the caption). time by the data providers. TS.SM.6 Notes and Technical Details on TS.SM.5 Notes and Technical Details on Radiative Comparison of Observed and Simulated Forcing Estimates Figure in the Summary Climate Change Figures for the Summary for Policy Makers Figure SPM.5 for Policymakers Figure SPM.6 This material documents the underlying traceability for values that Figure SPM.6 and the related Figure TS.12 are reduced versions of were used to create Figure SPM.5 in the IPCC WG1 Fifth Assessment Figure 10.21 in Chapter 10. The reader is therefore referred to the Report. This figure is closely related to Figures TS.6 and TS.7 and Chap- detailed description of the main components of Figure 10.21 for data- ter 8, Figures 8.14 to 8.18. The reader is therefore referred to the Sup- sets and methods used (see the Chapter 10 Supplementary Material, plementary Material of Chapter 8 for detailed information on methods Section 10.SM.1). Here, mainly the differences of Figure SPM.6 and and sources used to estimate forcing values. TS.12 from Figure 10.21 are listed. Figure SPM.5 (and Figure TS.7) plots Radiative Forcing (RF) estimates in Figures SPM.6 and TS.12 show time series of decadal average, plotted 2011 relative to 1750 and aggregated uncertainties for the main drivers on a common axis and at the centre of each decade. The decadal aver- of climate change. This figure is different from similar figures shown in ages are taken from the annual time series that Figure 10.21 is based previous IPCC report SPMs (though an analogous figure was shown in on. Figure TS.12 features the multi-model mean as dark blue and dark Chapter 2 of AR4) as it evaluates the RF based on the emissions rather red line, while Figure SPM.6 only features the 5 95% confidence inter- than the concentration changes. An emitted compound changes the val. Note that the precipitation plot from Figure 10.21 are not included atmospheric concentration of the same substance but may also impact in the Technical Summary and SPM versions of this figure. that of other atmospheric constituents through chemistry processes. TS.SM.6.1 Continental Temperatures Values are global average RF, partitioned according to the emitted compounds or processes that result in a combination of drivers. In cal- The same model simulations and observational data sets are used as culations of RF for well-mixed greenhouse gases and aerosols in this for Figure 10.21. Continental land areas are based on the IPCC Special report, physical variables, except for the ocean and sea ice, are allowed Report on Managing the Risks of Extreme Events and Disasters to to respond to perturbations with rapid adjustments. The resulting forc- Advance Climate Change Adaptation (SREX) defined regions (IPCC, ing is called Effective Radiative Forcing (ERF) in the underlying report. 2012) shown pictorially in the bottom right most panel of Figure 10.7. For all drivers other than well-mixed greenhouse gases and aerosols, Temperature anomalies in Figure SPM.6 are with respect to 1880 1919 (except for Antarctica where anomalies are relative to 1950 2010). TSSM-6 Technical Summary Supplementary Material TS.SM.6.2 Ocean Heat Content TS.SM.7.1 Global Average Surface Temperature Change (Figure SPM.7a) and Global Ocean Surface pH The same model simulations and observational data sets are used as (Figure SPM.7c) for Figure 10.21. Step 1 Analyzed simulations TS.SM.6.3 Sea Ice The simulations considered are annual or monthly mean fields from different model simulations carried out as part of the CMIP5 project The same model simulations and observational data sets are used as (when applicable the variable name as given in the CMIP5 archive is for Figure 10.21. indicated in square brackets). The time series between 1850 and 2005 originate from the historical simulations. The two time series of the TS.SM.6.4 Data Quality future projections are from RCP2.6 and RCP8.5. The box plots show- ing the change at the end of the century additionally use RCP4.5 end For land and ocean surface temperatures panels, solid lines indicate RCP6.0. Table TS.SM.1 lists the models and ensemble simulations used where data spatial coverage of areas being examined is above 50% for panels (a) and panel (c). Only one ensemble simulation per model coverage and dashed lines where coverage is below 50%. For example, is used. All models are weighted equally except for sea ice (panel (b)) TSSM data coverage of Antarctica never goes above 50% of the land area of where a subset of models is considered. the continent. For ocean heat content and sea-ice panels, the solid line is where the coverage of data is good and higher in quality, and the Step 2a Interpolation dashed line is where the data coverage is only adequate, based respec- For panel (a), the monthly temperature fields [tas] are re-gridded to a tively on the spatial coverage and instrument type and on the presence 2.5° × 2.5° grid using bilinear interpolation. No special treatment is of satellite measurements. used at the land-sea border. For panel (c), the monthly temperature [tos] and salinity [sos] fields are TS.SM.7 Notes and Technical Details on CMIP5 first averaged to yield annual means. Then, annual-mean temperature, Simulated Time Series Figures in the salinity, dissolved inorganic carbon [dissic] and alkalinity [talk] fields Summary for Policymakers are re-gridded to a 1° × 1° using bilinear interpolation. For the model Figure SPM.7 MIROC-ESM-CHEM the upper-most layers of the 3-dimensional fields of monthly sea water potential temperature [thetao] and monthly sea This material documents the provenance of the data and plotting water salinity [so] are used. procedures that were used to create Figure SPM.7, based on Climate Model Intercomparison Project Phase 5 (CMIP5) model results as of Step 2b Derivation of pH March, 2013. This figure is closely derived from Figures 12.5 and TS.15 For each model, surface pH was computed from simulated DIC, alka- (global average surface temperature), 12.28 and TS.17 (sea ice), 6.28 linity, temperature, and salinity. Before computation each simulated and TS.20a (ocean surface pH), but includes fewer model scenarios. The input field was corrected for its decadal mean bias relative to modern reader is referred to the ­ echnical Summary and the Chapters 12 and 6 T observations, using the approach of Orr et al. (2005) and Orr (2011). where all RCP scenarios are given for the respective quantity. That is, pH was computed after first removing from each model field, the average difference between the model mean during 1989 1998 Table TS.SM.1 | Models and ensembles used for panels (a) and (c). Model Ensemble Member Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 ACCESS1.0 r1i1p1 (a) (a) (a) ACCESS1.3 r1i1p1 (a) (a) (a) BCC-CSM1.1 r1i1p1 (a) (a) (a) (a) (a) BCC-CSM1.1(m) r1i1p1 (a) (a) (a) (a) BNU-ESM r1i1p1 (a) (a) (a) (a) CanESM2 r1i1p1 (a) (c) (a) (c) (a) (c) (a) (c) CCSM4 r1i1p1 (a) (a) (a) (a) (a) CESM1(BGC) r1i1p1 (a) (a) (a) CESM1(CAM5) r1i1p1 (a) (a) (a) (a) (a) CMCC-CM r1i1p1 (a) (a) (a) CMCC-CMS r1i1p1 (a) (a) (a) CNRM-CM5 r1i1p1 (a) (a) (a) CSIRO-Mk3.6.0 r1i1p1 (a) (a) (a) (a) (a) EC-EARTH r8i1p1 (a) (a) (a) (a) (continued on next page) TSSM-7 Technical Summary Supplementary Material Table TS.SM.1 (continued) Model Ensemble Member Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 FGOALS-g2 r1i1p1 (a) (a) (a) (a) FIO-ESM r1i1p1 (a) (a) (a) (a) (a) GFDL-CM3 r1i1p1 (a) (a) (a) (a) (a) GFDL-ESM2G r1i1p1 (a) (c) (a) (c) (a) (c) (a) (c) (a) (c) GFDL-ESM2M r1i1p1 (a) (c) (a) (c) (a) (c) (a) (c) (a) (c) GISS-E2-H r1i1p1 (a) (a) (a) (a) (a) GISS-E2-H r1i1p2 (a) (a) (a) (a) (a) GISS-E2-H r1i1p3 (a) (a) (a) (a) (a) GISS-E2-H-CC r1i1p1 (a) (a) GISS-E2-R r1i1p1 (a) (a) (a) (a) (a) GISS-E2-R r1i1p2 (a) (a) (a) (a) (a) TSSM GISS-E2-R r1i1p3 (a) (a) (a) (a) (a) GISS-E2-R-CC r1i1p1 (a) (a) HadGEM2-AO r1i1p1 (a) (a) (a) (a) (a) HadGEM2-CC r1i1p1 (a) (c) (a) (c) (a) HadGEM2-ES r2i1p1 (a) (a) (a) (a) (a) INM-CM4 r1i1p1 (a) (a) (a) IPSL-CM5A-LR r1i1p1 (a) (c) (a) (c) (a) (c) (a) (c) (a) (c) IPSL-CM5A-MR r1i1p1 (a) (c) (a) (c) (a) (c) (a) (a) (c) IPSL-CM5B-LR r1i1p1 (a) (c) (a) (a) (c) MIROC5 r1i1p1 (a) (a) (a) (a) (a) MIROC-ESM r1i1p1 (a) (c) (a) (c) (a) (c) (a) (a) (c) MIROC-ESM-CHEM r1i1p1 (a) (c) (a) (c) (a) (c) (a) (c) (a) (c) MPI-ESM-LR r1i1p1 (a) (c) (a) (c) (a) (c) (a) (c) MPI-ESM-MR r1i1p1 (a) (c) (a) (c) (a) (c) (a) (c) MRI-CGCM3 r1i1p1 (a) (a) (a) (a) (a) NorESM1-M r1i1p1 (a) (a) (a) (a) (a) NorESM1-ME r1i1p1 (a) (c) (a) (a) (c) (a) (a) and the observational reference. For observed fields, the GLODAP grid- Step 5 Mean and standard deviation ded data product (Key et al., 2004) for DIC and alkalinity along with The mean and standard deviation over all the models is calculated. For the 2009 World Ocean Atlas climatology for temperature, salinity, and the time period after 2006 all the possible models that are listed in concentrations of phosphate and silica (Locarnini et al., 2010; Antonov Table TS.SM.1 are used. If a model provided several RCPs based on the et al., 2010; Garcia et al., 2010) were used. Changes to the concentra- same historical simulation, that historical simulation is counted only tions of phosphate and silica were assumed to be zero, because not once. all models provided those variables. pH was computed using routines based on the standard OCMIP carbonate chemistry adapted for earlier Step 6 Uncertainty estimates studies (Orr, 2011) to compute all carbonate system variables and use First, for each model the average from 2081 to 2100 is computed from recommended constants from the Guide to Best Practices for Ocean the above mentioned time series. Then, in a second step, the mul- CO2 Measurements (Dickson et al., 2007). ti-model average and standard deviation over all model averages are calculated. The likely ranges on the right of the figure show the mean Step 3 Global and annual mean plus/minus 1.64 times the standard deviation across the model averag- The monthly (temperature) or annual (pH) surface fields are averaged es. The shading on the time series indicates the mean value plus/minus (weighted by the cosine of the latitude) to obtain the global mean 1.64 times the standard deviation across the models for each year. values. The monthly global mean temperature values are averaged to annual means. Step 7 Graphical display To close the multi-model mean time series at the year 2005 when the Step 4 Reference period historical simulation ends and the RCP begins, the value at year 2005 The average from 1986 to 2005 of the annual means for each model is is assigned to belong to both the historical time series and also to the computed and is subtracted from the respective model time series to corresponding RCP. obtain the corresponding temperature anomalies. TSSM-8 Technical Summary Supplementary Material TS.SM.7.2 Northern Hemisphere September Sea Ice is catenated with the respective RCP scenario ensemble member to Extent Figure SPM.7b create a continuous time series from 1850 2100. Step 1 Analyzed simulations Step 2 Time series of NH September sea ice extent Table TS.SM.2 provides the model and RIP ensemble member included Using the sea ice concentration field, a mask of the sea ice concentra- from each RCP to create the multi-model mean time series of the NH tion >15% for each month of data for the Northern Hemisphere was September sea ice extent [sic] shown in Figure SPM.7b. In most cases, created. For each month, the sea ice extent is the sum of the area of the first ensemble member (r1i1p1) was used. A selection algorithm the ocean [areacello] times the ocean fraction [sftof] times the mask produces a subset of models that most closely match observations, of sic >15% at each grid point. The time series are computed on the and is detailed below. The corresponding historical ensemble member original model grids, which is usually the ocean grid. In some cases, Table TS.SM.2 | Models and ensemble members used. Model Ensemble Member RCP2.6 Historical/RCP4.5 RCP6.0 RCP8.5 ACCESS1.0 r1i1p1 x x ACCESS1.3 r1i1p1 x x TSSM BCC-CSM1.1 r1i1p1 x x x x BCC-CSM1.1(m) r1i1p1 x x x x BNU-ESM r1i1p1 x x x CanESM2 r1i1p1 x x x CCSM4 r1i1p1 x x x x CESM1(BGC) r1i1p1 x x CESM1(CAM5) r1i1p1 x x x x CESM1(WACCM) r2i1p1 x x x CMCC-CM r1i1p1 x x CMCC-CMS r1i1p1 x x CNRM-CM5 r1i1p1 x x x CSIRO-Mk3.6.0 r1i1p1 x x x x EC-EARTH r1i1p1 x x r8i1p1 x FGOALS-g2 r1i1p1 x x x FIO-ESM r1i1p1 x x x x GFDL-CM3 r1i1p1 x x x x GFDL-ESM2G r1i1p1 x x x x GFDL-ESM2M r1i1p1 x x x x GISS-E2-H r1i1p1 x x x x GISS-E2-H-CC r1i1p1 x GISS-E2-R r1i1p1 x x x x GISS-E2-R-CC r1i1p1 x HadGEM2-AO r1i1p1 x x x x HadGEM2-CC r1i1p1 x x HadGEM2-ES r2i1p1 x x x x INM-CM4 r1i1p1 x x IPSL-CM5A-LR r1i1p1 x x x x IPSL-CM5A-MR r1i1p1 x x x x IPSL-CM5B-LR r1i1p1 x x MIROC5 r1i1p1 x x x x MIROC-ESM r1i1p1 x x x x MIROC-ESM-CHEM r1i1p1 x x x x MPI-ESM-LR r1i1p1 x x x MPI-ESM-MR r1i1p1 x x x MRI-CGCM3 r1i1p1 x x x x NorESM1-M r1i1p1 x x x x NorESM1-ME r1i1p1 x x x x TSSM-9 Technical Summary Supplementary Material sea ice concentration is on the atmospheric grid. In cases where the to provide estimates for sea ice volume for comparison to the models. grid area was not available for regular grids, a regular lat-lon grid was constructed based on the grid dimensions following (c) The amplitude of the seasonal cycle of Arctic sea ice extent is com- puted for each model from a climatology of monthly sea ice extent areacello=((dlat*2/360)*R_earth) .* ((dlon*2 /360).*(R_earth*- for 1986 2005. The amplitude is the difference between the maximum cos(LAT))), (March) and minimum (September) sea ice extent for each model ensemble member. Amplitude of seasonal cycle for observations are with R_earth being the radius of Earth (6,371,000 m), dlat and dlon computed in the same way from Comiso and Nishio (2008, updated being the differentials in lat/lon in each dimension, and LAT being the 2012). latitude in radians. (d) The linear trend in September sea ice extent is computed for the If the ocean fraction was unavailable, it was assumed that the ocean period 1979 2012. Again observations are taken from Comiso and fraction was 1 where the sea ice concentration was greater than 0%. Nishio (2008, updated 2012). Step 3 Create multi-model mean time series Step 4b Estimation of natural variability for model ensembles TSSM The multi-model mean time series of sea ice extent is computed For models with multiple ensemble members, a standard deviation across all model members in Table TS.SM.2. A five-year running mean is computed for each of the diagnostics for each ensemble member. is applied to this time series. This is plotted as the dotted line in the Then the mean of all the standard deviations is computed, and using figure. Some time series start later than 1850 or end earlier than 2100, this value, a +/-2 standard deviation interval is constructed around the and these are treated as missing values for those years. ensemble mean or single realization of each diagnostic for each model. Step 4 Select models that most closely match observations Step 4c Model selection - Comparison of modeled diagnostics The selection process is done in a series of steps which compare the to observed/reanalyzed diagnostic models to observed/reanalyzed data. This selection process is based on For each of the observed/reanalyzed diagnostics, a +/- 20% interval is the underlying assessment of Chapter 12 and referenced therein. The constructed around the mean value for the given period. A model is method proposed by Massonnet et al. (2012) is applied here to the full retained in the selection if, for each diagnostic, either the +/-2 standard set of models that provided sea ice output fields to the CMIP5 data- deviation around the model ensemble mean diagnostic overlaps the base. For the model selection, all available ensemble members are used +/-20% interval around the observed/reanalysed value of the diagnostic for all of the models that provide simulations for Historical and RCP4.5. OR at least one ensemble member from that model gives a value for These ensemble members are listed in Table TS.SM.3. the diagnostic that falls within +/-20% of the observed/reanalysed data. A model is selected only if all four diagnostic values meet this criterion. Four diagnostics from the models are compared to the same quantities in observations or reanalyses. The diagnostics are: (a) September Arctic Results of the selection sea ice extent (1986 2005), (b) Annual mean Arctic sea ice volume The model diagnostics are calculated using RCP4.5 which has the (1986 2005), (c) Amplitude of the seasonal cycle of Arctic sea ice largest number of models. Five models are selected by this process: extent (1986 2005), and (d) Trend in September Arctic sea ice extent ACCESS1.0, ACCESS1.3, GFDL-CM3, IPSL-CM5A-MR, MPI-ESM-MR, (1979 2012). Computation of each diagnostic is described and then and all five models have simulations for both RCP8.5 and RCP4.5. For the method for comparison is described below. RCP2.6 only three of this subset have simulations (GFDL-CM3, IPSL- CM5A-MR, MPI-ESM-MR), and for RCP6.0, only two models have sim- Step 4a Computation of diagnostic quantities ulations (GFDL-CM3, IPSL-CM5A-MR). (a) Sea ice extent is computed for each model ensemble member as outlined above to get the total area where sea ice concentration is Step 5 Time series of sea ice extent for the selected models >15%. For each ensemble member, an average September sea ice The multi-model mean time series of September sea ice extent is cal- extent is then computed for the years 1986 2005. Observations for culated for the selected models. The solid line shows the multi-model sea ice extent use the monthly mean sea ice extents from Comiso and mean smoothed with a five-year running mean, and the shading rep- Nishio (2008, updated 2012). The observations were computed in the resents the minimum and maximum range of the selected model time same way as in the models (i.e., these are the monthly mean extents series, also smoothed by the same five year running mean. computed from the observed monthly mean sea ice concentration). The shaded bars on the right are the multi-model mean and the mean (b) Sea ice volume is computed as the sum of the sea ice thickness field of the maximum and minimum range for the selected models for the [sit] times the ocean area [areacello] times the ocean fraction [sftof], period 2081 2100. since the sea ice thickness is given as thickness averaged over the entire ocean grid cell. Caveats for the grids are the same as discussed in Step 2 above. The time series of monthly sea ice volume for each ensemble member is then annually averaged for the period 1986 2005. The bias-adjusted PIOMAS (Pan-Arctic Ice-Ocean Modelling and Assimilation System) reanalysis data (Schweiger et al., 2011) is used TSSM-10 Technical Summary Supplementary Material Table TS.SM.3 | Models and ensembles used for model selection, RCP4.5. Model Ensemble Member RCP4.5 Model Ensemble Member RCP4.5 ACCESS1.0 r1i1p1 GISS-E2-R r1i1p1 r2i1p1 ACCESS1.3 r1i1p1 r3i1p1 BCC-CSM1.1 r1i1p1 r4i1p1 r5i1p1 BCC-CSM1.1(m) r1i1p1 r6i1p1 BNU-ESM r1i1p1 GISS-E2-R-CC r1i1p1 CanESM2 r1i1p1 HadGEM2-AO r1i1p1 r2i1p1 r3i1p1 HadGEM2-CC r1i1p1 r4i1p1 HadGEM2-ES r2i1p1 r5i1p1 r3i1p1 CCSM4 r1i1p1 r4i1p1 r2i1p1 INM-CM4 r1i1p1 r3i1p1 r4i1p1 IPSL-CM5A-LR r1i1p1 r5i1p1 r2i1p1 TSSM r6i1p1 r3i1p1 r4i1p1 CESM1(BGC) r1i1p1 IPSL-CM5A-MR r1i1p1 CESM1(CAM5) r1i1p1 r2i1p1 IPSL-CM5B-LR r1i1p1 r3i1p1 MIROC5 r1i1p1 CESM1(WACCM) r2i1p1 r2i1p1 r3i1p1 CMCC-CM r1i1p1 MIROC-ESM r1i1p1 CMCC-CMS r1i1p1 MIROC-ESM-CHEM r1i1p1 CNRM-CM5 r1i1p1 MPI-ESM-LR r1i1p1 CSIRO-Mk3.6.0 r1i1p1 r2i1p1 r2i1p1 r3i1p1 r3i1p1 r4i1p1 MPI-ESM-MR r1i1p1 r5i1p1 r2i1p1 r6i1p1 r3i1p1 r7i1p1 MRI-CGCM3 r1i1p1 r8i1p1 r9i1p1 NorESM1-M r1i1p1 10i1p1 NorESM1-ME r1i1p1 EC-EARTH r1i1p1 r2i1p1 r3i1p1 r6i1p1 TS.SM.8 Notes and Technical Details on Maps r7i1p1 Showing CMIP5 Results in the Summary r8i1p1 r9i1p1 for Policymakers Figure SPM.8 10i1p1 11i1p1 12i1p1 This material documents the provenance of the data and plotting 13i1p1 procedures that were used to create Figure SPM.8, based on CMIP5 14i1p1 model results as of March, 2013. This figure is closely derived from Fig- FGOALS-g2 r1i1p1 ures 12.11 and TS.15 (global average surface temperature), TS.16 (pre- FIO-ESM r1i1p1 cipitation), 12.29 and TS.17 (sea ice), 6.28 and TS.20b (ocean surface r2i1p1 r3i1p1 pH), but includes fewer model scenarios. The reader is referred to the Technical Summary or the Chapters 12 and 6 where all RCP scenarios GFDL-CM3 r1i1p1 r3i1p1 are given for the respective quantity. r5i1p1 GFDL-ESM2G r1i1p1 TS.SM.8.1 Change in Average Surface Temperature GFDL-ESM2M r1i1p1 (Figure SPM.8a) and Change in Average GISS-E2-H r1i1p1 Precipitation (Figure SPM.8b) r2i1p1 r3i1p1 r4i1p1 Step 1 Analyzed simulations r5i1p1 The simulations considered are monthly mean fields of surface tem- GISS-E2-H-CC r1i1p1 perature [tas] and precipitation [pr] from different model simulations carried out as part of the CMIP5 project (when applicable the variable name as given in the CMIP5 archive is indicated in square brackets). Table TS.SM.4 lists the models and ensemble members used for these panels. Only one ensemble member per model is used. TSSM-11 Technical Summary Supplementary Material Step 2 Interpolation Step 3 Annual average and period In a first step the monthly fields are re-gridded to a 2.5° × 2.5° grid The monthly mean values are averaged to annual means. Then in a using bilinear interpolation. No special treatment is used at the land- second step the time mean is computed over the 20-year period of sea border. interest. Table TS.SM.4 | Models and ensemble members used. Model Ensemble Member RCP2.6 Historical/RCP4.5 RCP6.0 RCP8.5 ACCESS1.0 r1i1p1 x x ACCESS1.3 r1i1p1 x x BCC-CSM1.1 r1i1p1 x x x x BCC-CSM1.1(m) r1i1p1 x x x BNU-ESM r1i1p1 x x x CanESM2 r1i1p1 x x x TSSM CCSM4 r1i1p1 x x x x CESM1(BGC) r1i1p1 x x CESM1(CAM5) r1i1p1 x x x x CMCC-CM r1i1p1 x x CMCC-CMS r1i1p1 x x CNRM-CM5 r1i1p1 x x CSIRO-Mk3.6.0 r1i1p1 x x x x EC-EARTH r8i1p1 x x x FGOALS-g2 r1i1p1 x x x FIO-ESM r1i1p1 x x x x GFDL-CM3 r1i1p1 x x x x GFDL-ESM2G r1i1p1 x x x x GFDL-ESM2M r1i1p1 x x x GISS-E2-H r1i1p1 x x x x GISS-E2-H r1i1p2 x x x x GISS-E2-H r1i1p3 x x x x GISS-E2-H-CC r1i1p1 x GISS-E2-R r1i1p1 x x x x GISS-E2-R r1i1p2 x x x x GISS-E2-R r1i1p3 x x x x GISS-E2-R-CC r1i1p1 x HadGEM2-AO r1i1p1 x x x x HadGEM2-CC r1i1p1 x x HadGEM2-ES r2i1p1 x x x x INM-CM4 r1i1p1 x x IPSL-CM5A-LR r1i1p1 x x x x IPSL-CM5A-MR r1i1p1 x x x x IPSL-CM5B-LR r1i1p1 x x MIROC5 r1i1p1 x x x x MIROC-ESM r1i1p1 x x x x MIROC-ESM-CHEM r1i1p1 x x x x MPI-ESM-LR r1i1p1 x x x MPI-ESM-MR r1i1p1 x x x MRI-CGCM3 r1i1p1 x x x x NorESM1-M r1i1p1 x x x x NorESM1-ME r1i1p1 x x x x TSSM-12 Technical Summary Supplementary Material Step 4 Time average and anomalies for each model the standard deviation is computed over the different The average from 1986 to 2005 of the annual means for each model 20-year periods and for each grid point. is computed as the reference value and the annual mean from 2081 to 2100 are computed as the future period for the two RCPs. For each model To obtain the final value of the natural variability the median of the the reference value is then subtracted from the future period value. standard deviations of the different models is multiplied with the square root of 2 (the natural variability characterizes the typical dif- Step 5 Calculation of the significance ference between two 20-year periods, rather than the difference of one period from the long-term mean, the former being larger than the Step 5a Natural variability latter by the square root of two). To compute the natural variability all the models that provide more than 500 years of pre-industrial control simulation [piControl] are Step 5b Testing for significance used. A list of these models is given in Table TS.SM.5. For each model For each model the projected change is taken relative to its reference the first 100 years are discarded to minimize problems with model period and then the multi-model average at every grid point is com- initialization. Re-gridding and calculation of annual means is done as puted. In a second step, at each grid point the number of models with described in steps 2 and 3. The control runs are divided into 20-year positive and negative change are counted. TSSM non-overlapping periods. If the available data are not a multiple of 20-year the remaining years after the last 20-year period are not used If more than 90% of the models agree on the sign of the change and in the calculation. the multi-model mean change is larger than 2 times the natural var- iability (as defined above) this grid point is said to be significant and Averages over the 20-year periods are computed for every grid point. robust across models. A quadratic trend is subtracted from this time series of 20-year aver- aged periods to remove potential model drift at each grid point. Finally Step 5c Check for non-significance Again, for each model the projected change is taken relative to the Table TS.SM.5 | Models and ensemble members from the piControl experiments used reference period and then the multi-model average at every grid point for the calculation of the natural variability. is computed. Model Ensemble Member If the multi-model mean change at one grid point is less than the natu- ACCESS1.0 r1i1p1 ral variability (as defined above) the value is said to be non-significant. ACCESS1.3 r1i1p1 BCC-CSM1.1 r1i1p1 Step 6 Graphical display BNU-ESM r1i1p1 For each model the projected change is taken relative to the reference period and then the multi-model average at every grid point is comput- CanESM2 r1i1p1 ed. The locations that are significant and robust (as described in step CCSM4 r1i1p1 5b) are marked by small black dots and the locations that are non-sig- CESM1(BGC) r1i1p1 nificant (as described in step 5c) are marked by hatching. CMCC-CMS r1i1p1 CNRM-CM5 r1i1p1 For panel b, all calculations are performed as absolute changes. To CSIRO-Mk3-6-0 r1i1p1 show the relative changes, the multi-model mean precipitation change FGOALS-g2 r1i1p1 is divided by the multi-model mean of the reference period. FIO-ESM r1i1p1 GFDL-CM3 r1i1p1 TS.SM.8.2 Northern Hemisphere September Sea Ice GFDL-ESM2G r1i1p1 Extent (Figure SPM.8c) GFDL-ESM2M r1i1p1 Step 1 Analyzed simulations and subset of models GISS-E2-H r1i1p2 The simulations analyzed here are the same as those listed for Figure GISS-E2-H r1i1p3 SPM.7b. The subset of models are the same that are selected for Figure GISS-E2-R r1i1p2 SPM.7b outlined in the following Step 4. Only one ensemble member GISS-E2-R r1i1p3 from each model is used to create these figures. INM-CM4 r1i1p1 IPSL-CM5A-LR r1i1p1 Step 2 Computation of mean sea ice concentration MIROC5 r1i1p1 For each model ensemble member, the mean sea ice concentration [sic] MIROC-ESM r1i1p1 is calculated for the two periods, 1986 2005 and 2081 2100, on the MPI-ESM-LR r1i1p1 native model grid (see also recipe for Figure SPM.7b). MPI-ESM-MR r1i1p1 Step 3 Regrid sea ice concentration to common grid MPI-ESM-P r1i1p1 SOSIE (http://sosie.sourceforge.net/) is used to regrid the mean sea MRI-CGCM3 r1i1p1 ice concentration to a common 1° × 1° grid, applying the bilinear NorESM1-M r1i1p1 TSSM-13 Technical Summary Supplementary Material i ­nterpolation scheme (SOSIE: cmethod = bilin ). Further, the regridded TS.SM.8.3 Change in Ocean Surface pH (Figure SPM.8d) sea ice concentrations are drowned across the land-sea boundary to eliminate low-biased interpolated values in the area of land-sea tran- Step 1 Analyzed simulations sition (SOSIE: ldrown = T). With this approach, interpolation artifacts The simulations considered are annual or monthly mean fields from can occur throughout the Canadian Archipelago, since each model rep- different model simulations carried out as part of the CMIP5 project resents this area quite differently. Comparison of individual models on (when applicable the variable name as given in the CMIP5 archive their native grid allows to identify and mask such areas. Note that, for is indicated in square brackets). Table TS.SM.6 lists the models and these reasons the interpolated sea ice concentrations shall not be used ensemble members used for these panels. Only one ensemble member for quantitative interpretation, but only for visualization purposes. For per model is used. visualization the MATLAB land-ocean mask is overlaid. Step 2a Interpolation Step 4 Calculate multi-model mean sea ice concentration In a first step, the monthly temperature [tos] and salinity [sos] fields For each RCP, RCP2.6 and RCP8.5, and each period, 1986 2005 and are first averaged to yield annual means. For the model MIROC-ESM- 2081 2100, the mean sea ice concentration is calculated in each grid CHEM the upper-most layer of the 3-dimensional fields of monthly sea cell on the common grid. The same is done for the subset of models water potential temperature [thetao] and monthly sea water salinity TSSM for each period. For RCP2.6 this subset is GFDL-CM3, IPSL-CM5A-MR, [so] are used. Then, annual-mean temperature, salinity, dissolved inor- MPI-ESM-MR. For RCP8.5 this subset is ACCESS1.0, ACCESS1.3, GFDL- ganic carbon [dissic] and alkalinity [talk] fields are re-gridded to a 1° × CM3, IPSL-CM5A-MR, MPI-ESM-MR. 1° using bilinear interpolation. Step 5 Contour the multi-model mean sea ice concentration Step 2b Derivation of pH of 15% For each model, surface pH was computed from simulated DIC, alkalini- The multi-model mean sea ice concentration is contoured at 15% ty, temperature, and salinity. Before computation each simulated input according to the following: field was corrected for its decadal mean bias relative to modern obser- vations, using the approach used in Orr et al. (2005) and Orr (2011). 1986 2005: multi-model mean all models: white line That is, pH was computed after first removing from each model field, 1986 2005: subset models: light blue line the average difference between the model mean during 1989 1998 2081 2100: multi-model mean all models: white filled patch and the observational reference. For observed fields, we used the 2081 2100: subset models: light blue filled patch GLODAP gridded data product (Key et al., 2004) for DIC and alkalinity along with the 2009 World Ocean Atlas climatology for temperature, Note for RCP8.5 there is no sea ice concentration >15% for the subset salinity, and concentrations of phosphate and silica (Locarnini et al., of models. 2010; Antonov et al., 2010; Garcia et al., 2010). Changes to the con- centrations of phosphate and silica were assumed to be zero, because The decision was taken to contour the 15% contour of mean sea ice all models did not provide those variables. pH was computed using concentration to make this figure consistent with Figure 12.29, which routines based on the standard OCMIP carbonate chemistry adapted shows a contour plot of the multi-model mean sea ice concentrations. for earlier studies (Orr, 2011) to compute all carbonate system varia- It is also possible to make binary fields of sea ice concentration >15%, bles and use recommended constants from the Guide to Best Practices take the mean of those binary fields (for both 20 year averages and for Ocean CO2 Measurements (Dickson et al., 2007). then in multi-model averages), and contour the 50% contour of the mean binary field as the mean sea ice extent. This option was not Step 3 Average of 20-year period chosen here. The time mean is computed over the 20-year period of interest. Table TS.SM.6 | Models and ensemble members used. Model Ensemble Member Historical RCP2.6 RCP4.5 RCP6.0 RCP8.5 CanESM2 r1i1p1 d d d d GFDL-ESM2G r1i1p1 d d d d d GFDL-ESM2M r1i1p1 d d d d d HadGEM2-CC r1i1p1 d d d IPSL-CM5A-LR r1i1p1 d d d d d IPSL-CM5A-MR r1i1p1 d d d d IPSL-CM5B-LR r1i1p1 d d d MIROC-ESM r1i1p1 d d d d d MIROC-ESM-CHEM r1i1p1 d d d d d MPI-ESM-LR r1i1p1 d d d d MPI-ESM-MR r1i1p1 d d d d NorESM1-ME r1i1p1 d d TSSM-14 Technical Summary Supplementary Material Step 4 Time average and anomalies TS.SM.10.1 Part A CO2 Only Runs The average from 1986 to 2005 of the annual means for each model is computed as the reference value and the annual mean from 2081 The thin black line represents the multi-model mean of the decadal to 2100 is computed as the future period for the two RCPs. For each averaged global-mean temperature response of the models listed in model the reference value is then subtracted. Table TS.SM.7 to a global 1% CO2 only forcing increase as performed as part of CMIP5, as a function of the decadal averaged global-mean Step 5 Graphical display diagnosed carbon emissions. For each model the projected change is taken relative to the reference period and the multi-model mean at every grid point is computed. The dark grey patch represents the 90% range surrounding the dec- adal averaged model response of the CMIP5 models listed in Table TS.SM.7 and is calculated as follows: Diagnosed carbon emissions and TS.SM.9 Notes and Technical Details on the Sea temperature response data of the above-defined CMIP5 models (com- Level Projection Figure for the Summary puted as in Gillett et al., 2013) is scaled, respectively, by dividing by the for Policymakers Figure SPM.9 standard deviation over all available decadal-averaged data points for a specific scenario. The 90% range is computed in polar coordinates. TSSM A full and comprehensive description of the methods used in the pro- The radius stretches along the x-axis (cumulative emissions) and the jections of global mean sea level for the 21st century is provided in the angle is the one between the slope from (0, 0) to a respective scaled Supplementary Material to Chapter 13 (see Section 13.SM.1). Further point (cumulative emissions, temperature anomaly) and the x-axis. For plotting details used to produce Figure SPM.9, and the related Figure each scaled point the radius and angle are computed. A number of TS.22 are provided here. n (n = 20) segments are defined by regularly spaced steps along the maximum radius of all available decadal-averaged data points of a TS.SM.9.1 Projected Global Mean Sea Level Rise specific scenario (scaled as described earlier). From all points that fall within the boundaries of each respective radius segment, the 5th and Projections are given from process-based models of global mean 95th percentiles in terms of available angles is computed. These per- sea level rise relative to 1986 2005 for the four emissions scenarios centiles are then assigned to the radius corresponding to the middle RCP2.6, RCP4.5, RCP6.0 and RCP8.5. of the current radius segment. Each of these mid-segment radii and its corresponding pair of angles are then transformed back to Cartesian The likely range for each RCP timeseries is delimited by the data in files coordinates. Finally, the 90% range is drawn by connecting all 5th and rcpXX_sumlower and rcpXX_sumupper, while the median timeseries 95th percentile points of a specific scenario in a hull. is the data in file rcpXX_summid, where XX stands for the respective RCP scenario. These data files are available from the WGI AR5 website Table TS.SM.7 | Models that were included in the shown results of the CO2 only 1% www.climatechange2013.org. The coloured vertical bars with horizon- increase CMIP5 runs (dark grey patch and thin black line). tal lines for the four RCP scenarios indicate the likely ranges and medi- ans for these scenarios as given in Table 13.5 of Chapter 13. Model Ensemble Member GFDL-ESM2G r1i1p1 Note that in Figure SPM.9, projected time series are shown only for INM-CM4 r1i1p1 RCP2.6 and RCP8.5. Figure TS.22 include time series for all four RCP GFDL-ESM2M r1i1p1 scenarios. IPSL-CM5B-LR r1i1p1 Projected contributions to sea level rise in 2081 2100 relative to BCC-CSM1.1 r1i1p1 1986 2005 for the four RCP scenarios are provided in Figure TS.21. MPI-ESM-MR r1i1p1 IPSL-CM5A-MR r1i1p1 IPSL-CM5A-LR r1i1p1 TS.SM.10 Notes and Technical Details on the MPI-ESM-LR r1i1p1 Summary for Policymakers Figure NorESM1-ME r1i1p1 Plotting Global Mean Temperature CESM1(BGC) r1i1p1 Increase as a Function of Cumulative HadGEM2-ES r1i1p1 Total Global CO2 Emissions Figure SPM.10 MIROC-ESM r1i1p1 CanESM2 r1i1p1 Figure SPM.10 contains data from CO2 only simulations and the RCP BNU-ESM r1i1p1 simulations. This figure is closely derived from TS TFE.8, Figure 1. CO2 only simulations are represented by grey-shaded patches and thin black lines, RCP data by coloured lines and patches. CMIP5 results are taken from the archive as of March 15, 2013. Note that the thick black line represents the historical time period of the RCP runs. TSSM-15 Technical Summary Supplementary Material TS.SM.10.2 Part B RCP Runs Land-use change emission estimated for each RCP are added to all EMICs, and to the ESMs that diagnose fossil-fuel emission only Data of the RCP runs (coloured lines and patches) is prepared with (see Table TS.SM.8). Land-use change emissions are obtained from the same methodology as the data for the CO2 only runs as described http://www.pik-potsdam.de/~mmalte/rcps/ for each RCP, respec- in the previous section. Note that markers show decadal time steps, tively. Note that the data for Figure SPM.10 provided at the exter- and that the labels in Figure SPM.10 (and TS TFE.8, Figure 1) denote nal sources cited above may be subject to changes in the future by the cumulative global carbon emissions from 1870 until (but not the owners. Furthermore, no guarantee is provided that the web- including) that year (i.e., label 2050 is placed next to the marker of the links cited above remain active. 2040 2049 decade). The 90% range is computed for n (n = 12) regu- Decadal-mean cumulative emissions are computed from cumula- larly spaced steps along the maximum radius available for each RCP tive carbon emissions relative to 1870. (scaled as described earlier). Available Earth System Models (ESM) for Each RCP range is drawn as long as data is available for all models the respective RCP are listed in Table TS.SM.8, available Earth System or until temperatures have peaked. The encompassing range shown Models of Intermediate Complexity (EMIC) in Table TS.SM.9. in Figure SPM.10 (and TS TFE.8, Figure 1) is constructed by con- necting the outer last points of each single RCP range and is filled Following operations are carried out onto the data: as long as data are available for all models for RCP8.5. Beyond TSSM this point, the range is illustratively extended by further progressing Decadal means of global-mean temperature change are computed along the radius while keeping the angles fixed at those available relative to the 1861 1880 base period. at the last point with data from all models for RCP8.5. The fading Emissions from the ESMs for the different scenarios are computed out of the range is illustrative. as in Jones et al. (2013). Table TS.SM.8 | Overview of RCP model runs available in the CMIP5 archive, as used in Figure SPM.10 (and TS TFE.8, Figure 1). Model Ensemble Member RCP2.6 RCP4.5 RCP6.0 RCP8.5 BCC-CSM1.1 r1i1p1 x* x* x* x* CanESM2 r1i1p1 x x x CESM1(BGC) r1i1p1 x x GFDL-ESM2G r1i1p1 x x x x GFDL-ESM2M r1i1p1 x x x x HadGEM2-CC r1i1p1 x x HadGEM2-ES r2i1p1 x x x x INM-CM4 r1i1p1 x* x* IPSL-CM5A-LR r1i1p1 x x x x IPSL-CM5A-MR r1i1p1 x x x IPSL-CM5B-LR r1i1p1 x x MIROC-ESM r1i1p1 x x x x MIROC-ESM-CHEM r1i1p1 x x x x MPI-ESM-LR r1i1p1 x x x NorESM1-ME r1i1p1 x x x x Notes: * runs do not include explicit land-use change modelling. Models diagnose fossil-fuel and land-use change emissions jointly and therefore do not require adding land-use change emissions. Table TS.SM.9 | Overview of EMIC RCP model runs from (Eby et al. 2013; Zickfeld et al. 2013), as used in Figure SPM.10 (and TS TFE.8, Figure 1). EMICs output is available from http://www.climate.uvic.ca/EMICAR5. Model RCP2.6 RCP4.5 RCP6.0 RCP8.5 Bern3D x x x x DCESS x x x x GENIE x x x x IGSM x x x x UVic x x x x TSSM-16 Technical Summary Supplementary Material References Antonov, J. I., et al., 2010: World Ocean Atlas 2009, Volume 2: Salinity [Levitus, S. Key, R. M., et al., 2004: A global ocean carbon climatology: Results from Global Data (Ed.)]. NOAA Atlas NESDIS 69, 184 pp. Analysis Project (GLODAP). Glob. Biogeoch. Cycles, 18, GB4031. Bates , N .R ., 2007: Interannual variability of the oceanic CO2 sink in the subtropical Le Quéré, C., et al., 2013: The global carbon budget 1959 2011. Earth Syst. Sci. Data, gyre of the North Atlantic Ocean over the last two decades. J. Geophys. Res. 5, 165 185. Oceans, 112, C09013. Levitus, S., et al., 2012: World ocean heat content and thermosteric sea level change Becker, A., et al., 2013: A description of the global land-surface precipitation (0-2000 m), 1955 2010. Geophys. Res. Lett., 39, L10603. data products of the Global Precipitation Climatology Centre with sample Locarnini, R. A., et al., 2010: World Ocean Atlas 2009, Volume 1: Temperature applications including centennial (trend) analysis from 1901 present. Earth Sys. [Levitus, S. (Ed.)]. NOAA Atlas NESDIS 68, 184 pp. Sci. Data, 5, 71 99. Markus, T., and D. J. Cavalieri, 2000: An enhancement of the NASA Team sea ice Brown, R. D., and D. A. Robinson, 2011: Northern Hemisphere spring snow cover algorithm. IEEE Trans. Geosci. Remote Sens., 38, 1387 1398. variability and change over 1922 2010 including an assessment of uncertainty. Massonnet, F., T. Fichefet, H. Goosse, C. M. Bitz, G. Philippon-Berthier, M. M. Holland, Cryosphere, 5, 219 229. and P.-Y. Barriat, 2012: Constraining projections of summer Arctic sea ice. Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979- Cryosphere, 6, 1383 1394. 2010. Cryosphere, 6, 957 979. Mitchell, T. D., and P. D. Jones, 2005: An improved method of constructing a database Cavalieri, D. J., P. Gloersen, and W. J. Campbell, 1984: Determination of sea ice of monthly climate observations and associated high-resolution grids. Int. J. parameters with the Nimbus-7 SMMR. J. Geophys. Res. Atmos., 89, 5355 5369. Climatol., 25, 693 712. TSSM Church, J. A., and N. J. White, 2011: Sea-Level Rise from the Late 19th to the Early Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying 21st Century. Surv. Geophys., 32, 585 602. uncertainties in global and regional temperature change using an ensemble of Comiso, J. C., and F. Nishio, 2008: Trends in the sea ice cover using enhanced and observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res. Oceans, 113, 22. C02S07. Nerem, R. S., D. P. Chambers, C. Choe, and G. T. Mitchum, 2010: Estimating Mean Dickson, A.G., C. L. Sabine, and J. R. Christian, J.R., (eds.), 2007: Guide to best Sea Level Change from the TOPEX and Jason Altimeter Missions. Mar. Geod., practices for ocean CO2 measurements. PICES Special Publication 3, 191 pp. 33, 435 446. Domingues, C. M., J. A. Church, N. J. White, P. J. Gleckler, S. E. Wijffels, P. M. Barker, Orr, J.C. et al., 2005: Anthropogenic ocean acidification over the twenty-first century and J. R. Dunn, 2008: Improved estimates of upper-ocean warming and multi- and its impact on calcifying organisms. Nature, 437, 681-686. decadal sea-level rise. Nature, 453, 1090 1094. Orr, J. C., 2011: Recent and future changes in ocean carbonate chemistry. In: Ocean Dore, J. E., R. Lukas, D. W. Sadler, M. J. Church, and D. M. Karl, 2009: Physical and Acidification [Gattuso, S.-P., and L. Hansson (eds.)]. Oxford University Press, biogeochemical modulation of ocean acidification in the central North Pacific. Oxford, United Kingdom and New York, NY, USA, 352 pp. Proc. Natl. Acad. Sci. U.S.A., 106, 12235 12240. Palmer, M. D., K. Haines, S. F. B. Tett, and T. J. Ansell, 2007: Isolating the signal of Eby, M., et al., 2013: Historical and idealized climate model experiments: an ocean global warming. Geophys. Res. Lett., 34, L23610. intercomparison of Earth system models of intermediate complexity. Clim. Past, Parkinson, C. L., and D. J. Cavalieri, 2012: Antarctic Sea Ice Variability and Trends, 9, 1111 1140. 1979-2010. Cryosphere, 6, 871 880. Garcia, H. E., R. A. Locarnini, T. P. Boyer, J. I. Antonov, O. K. Baranova, M. M. Zweng, Ray, R. D., and B. C. Douglas, 2011: Experiments in reconstructing twentieth-century and D. R. Johnson, 2010: World Ocean Atlas 2009, Volume 3: Dissolved Oxygen, sea levels. Prog. Oceanogr., 91, 496 515. Apparent Oxygen Utilization, and Oxygen Saturation [ Levitus, S. (Ed.)]. NOAA Rayner, N. A., et al., 2003: Global analyses of sea surface temperature, sea ice, and Atlas NESDIS 70, 344 pp. night marine air temperature since the late nineteenth century. J. Geophys. Res. Gillett, N. P., V. K. Arora, D. Matthews, and M. R. Allen, 2013: Constraining the Ratio Atmos., 108, 4407. of Global Warming to Cumulative CO2 Emissions Using CMIP5 Simulations. J. Schweiger, A., R. Lindsay, J. L. Zhang, M. Steele, H. Stern, R. Kwok, 2011: Uncertainty Clim., 26, 6844 6858. in modeled Arctic sea ice volume. J. Geophys. Res. Oceans, 116, C00D06. González-Dávila, M., and J. M. Santana-Casiano, 2009: Sea Surface and Atmospheric Smith, D. M., and J. M. Murphy, 2007: An objective ocean temperature and salinity fCO2 data measured during the ESTOC Time Series cruises from 1995 2009. Oak analysis using covariances from a global climate model. J. Geophys. Res. Oceans, Ridge National Laboratory, US Department of Energy, Oak Ridge, Tennessee. 112, C02022. http://cdiac.ornl.gov/ftp/oceans/ESTOC_data/. Thoning, K. W., P. P. Tans, and W. D. Komhyr, 1989: Atmospheric carbon dioxide at González-Dávila, M., J. M. Santana-Casiano, J. M. Rueda, and O. Llinás, 2010: Water Mauna Loa Observatory 2. Analysis of the NOAA GMCC data, 1974-1985. J. column distribution of the carbonate system variables in the ESTOC site from Geophys. Res., 94, 8549 8565. 1995 to 2004. Biogeosciences, 7, 3067 3081. Vose, R. S., Oak Ridge National Laboratory. Environmental Sciences Division., IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance U.S. Global Change Research Program, United States. Dept. of Energy. Office Climate Change Adaptation. A Special Report of Working Groups I and II of the of Health and Environmental Research., Carbon Dioxide Information Analysis Intergovernmental Panel on Climate Change [Field, C.B., et al., (eds.)]. Cambridge Center (U.S.), and Martin Marietta Energy Systems Inc., 1992: The global University Press, Cambridge, UK, and New York, NY, USA, 582 pp. historical climatology network: long-term monthly temperature, precipitation, Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content sea level pressure, and station pressure data. Carbon Dioxide Information variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., Analysis Center. Available to the public from N.T.I.S., 1 v. 65, 287 299. Walsh, J. E., and W. L. Chapman, 2001: 20th-century sea-ice variations from Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth, 2008: Recent global sea observational data. Ann. Glaciol., 33, 444 448. level acceleration started over 200 years ago? Geophys. Res. Lett., 35, L08715. Zickfeld, K., et al., 2013: Long-Term Climate Change Commitment and Reversibility: Jones, C., et al., 2013: Twenty-First Century Compatible CO2 Emissions and Airborne An EMIC Intercomparison. J. Clim., 26, 5782 5809. Fraction Simulated by CMIP5 Earth System Models under Four Representative Concentration Pathways. J. Clim., 26, 4398 4413. Please note that all external web-links cited in this document were active at the time Keeling, C., R. Bacastow, A. Bainbridge, C. Ekdahl, P. Guenther, L. Waterman, and J. of publication, but no guarantee is provided that these links remain active. Chin, 1976a: Atmospheric Carbon-Dioxide Variations at Mauna-Loa Observatory, Hawaii. Tellus, 28, 538 551. Keeling, C. D., J. A. Adams, and C. A. Ekdahl, 1976b: Atmospheric Carbon-Dioxide Variations at South Pole. Tellus, 28, 553 564. Keeling, C. D., S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and H. A. Meijer, 2001: Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and oceans from 1978 to 2000. I. Global aspects, SIO Reference Series, No. 01 06. Scripps Institution of Oceanography, San Diego, 88 pp. TSSM-17 Observations: 2SM Atmosphere and Surface Supplementary Material Coordinating Lead Authors: Dennis L. Hartmann (USA), Albert M.G. Klein Tank (Netherlands), Matilde Rusticucci (Argentina) Lead Authors: Lisa V. Alexander (Australia), Stefan Brönnimann (Switzerland), Yassine Abdul-Rahman Charabi (Oman), Frank J. Dentener (EU/Netherlands), Edward J. Dlugokencky (USA), David R. Easterling (USA), Alexey Kaplan (USA), Brian J. Soden (USA), Peter W. Thorne (USA/Norway/UK), Martin Wild (Switzerland), Panmao Zhai (China) Contributing Authors: Robert Adler (USA), Richard Allan (UK), Robert Allan (UK), Donald Blake (USA), Owen Cooper (USA), Aiguo Dai (USA), Robert Davis (USA), Sean Davis (USA), Markus Donat (Australia), Vitali Fioletov (Canada), Erich Fischer (Switzerland), Leopold Haimberger (Austria), Ben Ho (USA), John Kennedy (UK), Elizabeth Kent (UK), Stefan Kinne (Germany), James Kossin (USA), Norman Loeb (USA), Carl Mears (USA), Christopher Merchant (UK), Steve Montzka (USA), Colin Morice (UK), Cathrine Lund Myhre (Norway), Joel Norris (USA), David Parker (UK), Bill Randel (USA), Andreas Richter (Germany), Matthew Rigby (UK), Ben Santer (USA), Dian Seidel (USA), Tom Smith (USA), David Stephenson (UK), Ryan Teuling (Netherlands), Junhong Wang (USA), Xiaolan Wang (Canada), Ray Weiss (USA), Kate Willett (UK), Simon Wood (UK) Review Editors: Jim Hurrell (USA), Jose Marengo (Brazil), Fredolin Tangang (Malaysia), Pedro Viterbo (Portugal) This chapter supplementary material should be cited as: Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L. Alexander, S. Brönnimann, Y. Charabi, F. Dentener, E. Dlugo- kencky, D. Easterling, A. Kaplan, B. Soden, P. Thorne, M. Wild and P.M. Zhai, 2013: Observations: Atmosphere and Surface Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www.climatechange2013.org and www.ipcc.ch. 2SM-1 Table of Contents 2.SM.1 Introduction....................................................... 2SM-3 2.SM.2 Changes in Atmospheric Composition...... 2SM-3 2.SM.3 Quantifying Changes in the Mean: Trend Models and Estimation in Box 2.2............ 2SM-10 2.SM.4 Changes in Temperature.............................. 2SM-13 2.SM.5 FAQ 2.1, Figure 2............................................ 2SM-19 2SM 2.SM.6 Changes in Hydrological Cycle.................. 2SM-20 2.SM.7 Changes in Extreme Events........................ 2SM-20 2.SM.8 Box 2.5: Patterns and Indices of Climate Variability......................................... 2SM-22 References ............................................................................ 2SM-26 2SM-2 Observations: Atmosphere and Surface Supplementary Material Chapter 2 2.SM.1 Introduction and are unlikely to accumulate to levels that can significantly affect radiative forcing either directly or indirectly through destruction of The Chapter 2 Supplementary Material includes data or methods for stratospheric ozone, if current emission projections are followed which there was not space in the printed document, but that are (WMO, 2011). regarded as being valuable documentation for the main report or for subsequent scientific studies. 2.SM.2.2 Near-Term Climate Forcers Figure 2.SM.1 shows ozone trends based on yearly average ozone 2.SM.2 Changes in Atmospheric Composition values at the surface or within the lower troposphere beginning at different starting points since 1970. Most of the surface sites are in 2.SM.2.1 Long-Lived Greenhouse Gases rural locations so that they are representative of regional air quality; however, many of the Asian sites are urban. Trend values are from the Table 2.SM.1 contains the full list of species compiled for Chapter 8 peer-reviewed literature listed in Table 2.SM.2. to use for radiative forcing calculations. Following are discussions of additional species not discussed in Section 2.2.1 of the main text. (a) 1970 - 2010 2SM 2.SM.2.1.1 Hydrofluorocarbons New measurements of several hydrofluorocarbons (HFCs) have been reported since AR4: HFC-365mfc (Stemmler et al., 2007), HFC-245fa (Vollmer et al., 2006), HFC-227ea (Laube et al., 2010) and HFC-236fa (Vollmer et al., 2011). Observation-based estimates of emissions show a mix of poor to good agreement with bottom-up inventories (Vollmer et al., 2011). Atmospheric abundances of these four minor HFCs were <2 ppt in 2011, but their atmospheric burdens are increasing rapidly, with relative increases >8% yr 1. (b) 1980 - 2010 (b) 1980 - 2010 2.SM.2.1.2 Perfluorocarbons Atmospheric measurements of high molecular weight perfluorocar- (c) 1990 - 2010 bons (PFCs) have also been reported, including fully fluorine-substi- tuted alkanes (C3 to C8) (Saito et al., 2010; Ivy et al., 2012); and octa- fluorocyclobutane (c-C4F8) (Saito et al., 2010; Oram et al., 2012). All are currently <2 ppt, except when pollution events are observed at the air sampling sites. (c) 1990 - 2010 2.SM.2.1.3 Nitrogen Trifluoride and Sulfuryl Fluoride Since AR4, atmospheric observations of two new species were reported: NF3 and SO2F2. Prather and Hsu (2008) reported the potential impor- tance of NF3 for radiative forcing. It is a substitute for PFCs as a plasma source in the semiconductor industry, has a lifetime of 500 years, and a GWP100 = 16,100 (GWPs are described in Chapter 8). Arnold et al. (2013) determined 0.59 ppt for its global annual mean mole fraction in 2008, growing from almost zero in 1978. In 2011, NF3 was at 0.86 ppt, increasing by 0.49 ppt since 2005. Initial bottom-up inventories 1 0.5 underestimated its emissions; based on the atmospheric observations, O3 rate of change (ppb yr-1) 0 -1-0.5 NF3 emissions were 1.18 +/- 0.21 Gg in 2011. SO2F2 replaces CH3Br as a fumigant. Its GWP100 4740, is comparable to CFC-11. A new esti- Figure 2.SM.1 | Ozone trends based on yearly average ozone values at the surface mate of its lifetime, 36 +/- 11 year (Muhle et al., 2009), is significantly or within the lower troposphere (a) beginning between 1970 and 1979 and ending between 2000 and 2010. (b) Beginning between 1980 and 1989 and ending between longer than previous estimates. Its global annual mean mole fraction 2000 and 2010 and (c) beginning between 1990 and 1999 and ending between 2000 in 2011 was 1.71 ppt and it increased by 0.36 ppt from 2005 to 2011. and 2010. Measurements were made at the surface below 1 km (circles), at the surface above 1 km (triangles), in the lower troposphere by ozonesondes (squares) and in the 2.SM.2.1.4 Halons lower troposphere by aircraft (diamonds). Vectors indicate the ozone rate of change as shown in the legend. Colors indicate ozone trends that are statistically significant and positive (red), statistically non-significant and positive (pink), statistically nonsignificant Atmospheric abundances of halons, except for halon-1301, have been and negative (light blue) and statistically significant and negative (blue). Trend values decreasing. All have relatively small atmospheric abundances, 5 ppt, are from the peer-reviewed literature listed in Table 2.SM.2. 2SM-3 Chapter 2 Observations: Atmosphere and Surface Supplementary Material Table 2.SM.1 | Global annual mean mole fractions for long-lived greenhouse gases (LLGHGs) for use in calculating radiative forcing in Chapter 8, indication if significant natural source exists, references to the data used to calculate global means and summary of standard scales used. 2011 Global Relative Natural Species Annual Mean Data Sourcec References Scale Differenceb Source (ppt)a CO2 (ppm) 390.46 Negligible NOAA SIO   Keeling et al. (1976); Zhao and Tans (2006) N07; 08A CH4 (ppb) 1803.15 Negligible Yes Dlugokencky et al. (2005); Rigby et al. (2008) TU; N04 N2O (ppb) 324.15 0.1% Yes Prinn et al. (1990); Hall et al. (2007) S98; N06A C2F6 4.16 AGAGE Muhle et al. (2010) S07 C3F8 0.55 AGAGE Muhle et al. (2010) S07 CCl4 85.7 1.7% Simmonds et al. (1998); Thompson (2004) e S05; N08 CF4 79.0 AGAGE Yes Muhle et al. (2010) S05 CFC-11 237.7 0.7% Cunnold et al. (1997); Thompson (2004)e S05; N92 CFC-113 74.3 0.1% Fraser et al. (1996); Thompson (2004) ; Miller et al. (2008) e S05; N03 2SM CFC-115 8.37 AGAGE Miller et al. (2008) S05; N08 CFC-12 528.4 0.4% Cunnold et al. (1997); Thompson (2004)e S05; N08 CH2Cl2 25.9 18.7% Thompson (2004) e UB98 CH3Br 7.11 1.7% Yes Thompson (2004)e S05; N03 CH3CCl3 6.31 0.1% Thompson (2004)e; Prinn et al. (2005); Montzka et al. (2011) S05; N03 CH3Cl 534.1 1.6% Yes Thompson (2004); Miller et al. (2008) e S98; N03 CHCl3 7.41 AGAGEd Yes Prinn et al. (2000); Miller et al. (2008) S05; N03 H-1211 4.07 2.2% Thompson (2004)e; Miller et al. (2008) S05; N06 H-1301 3.23 2.8% Thompson (2004)e; Miller et al. (2008) S05; N06 H-2402 0.45 NOAA Butler et al. (1998) N92 HCFC-141b 21.4 0.3% O Doherty et al. (2004); Thompson (2004); Miller et al. (2008); Montzka et al. (2009) S05; N94 HCFC-142b 21.1 1.9% O Doherty et al. (2004); Thompson (2004); Miller et al. (2008); Montzka et al. (2009) S05; N94 HCFC-22 213.0 0.4% O Doherty et al. (2004); Montzka et al. (1993); Miller et al. (2008) S05; N06 HFC-125 9.58 AGAGEd O Doherty et al. (2009) UB98; N08 HFC-134a 62.5 0.3% Montzka et al. (1996); O Doherty et al. (2004); Miller et al. (2008) S05; N00 HFC-143a 12.0 AGAGE d Miller et al. (2008) S07; N08 HFC-152a 6.42 AGAGE Greally et al. (2007); Miller et al. (2008) S05 HFC-227ea 0.65 AGAGEd Laube et al. (2010); Vollmer et al. (2011) E05; N11 HFC-23 24.0 AGAGE d Miller et al. (2010) S07; N08 HFC-236fa 0.08 AGAGE Vollmer et al. (2011) E09 HFC-245fa 1.24 AGAGE Vollmer et al. (2006) E05 HFC-32 4.92 AGAGE d Miller et al. (2008) S07; N08 HFC-365mfc 0.58 AGAGEd Vollmer et al. (2011); Stemmler et al. (2007) E03; N11 SF6 7.28 0.6% Rigby et al. (2010); Hall et al. (2011) S05; N06 SO2F2 1.71 AGAGE Muhle et al. (2009) S07 NF3 0.86 AGAGE Weiss et al. (2008); Arnold et al. (2013) S12 Notes: AGAGE = Advanced Global Atmospheric Gases Experiment; NOAA = National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Global Monitoring Division; SIO = Scripps Institution of Oceanography, University of California, San Diego. CFC-11 = CCl3F; CFC-113 = CClF2CCl2F; CFC-12 = CCl2F2; HCFC-22 = CHClF2; HCFC-141b = CH3CCl2F; HCFC-142b = CH3CClF2; HFC-125 = CHF2CF3; HFC-134a = CH2FCF3; HFC-143a = CH3CF3; HFC-152a = CH3CHF2; HFC-23 = CHF3, CFC-115 = CClF2CF3, H-1211 = CBrClF2, H-1301 = CBrF3, H-2402 = CBrF2CBrF2, HFC-227ea = CF3CHFCF3, HFC-236fa = CF3CH2CF3, HFC-245fa = CHF2CH2CF3, HFC-32 = CH2F2, HFC-365mfc = CH3CF2CH2CF3. a Global surface annual mean dry-air mole fraction. b Relative difference between AGAGE and NOAA 2011 global annual mean values (AGAGE NOAA)/average). c Source of data. Blank space indicated NOAA + AGAGE. d Value listed from AGAGE data only, but NOAA maintains a scale and has unpublished data. e Updated information about NOAA standard scales available at: http://www.esrl.noaa.gov/gmd/ccl/summary_table.html. Scale: Standard gas scales used to calibrate instrument response. AGAGE/ SIO: TU = Tohoku University CH4 scale; SXX = Scripps Institution of Oceanography (SIO) trace gas scale developed in year 1998 (e.g., S98 = SIO-98); Sp = SIO-provisional; UB98 = Bristol University scale developed in 1998; E03 = Empa-2003; E09 = Empa-2009-p (provisional); 08A = Scripps Institution of Oceanography 08A CO2 standard scale. NOAA: N08 = NOAA scale developed in year 2008. 2SM-4 Observations: Atmosphere and Surface Supplementary Material Chapter 2 Table 2.SM.2 | Ozone trends reported in the literature, using data sets with at least 10 years of measurements. To understand ozone trends in air masses that are representative of regional or baseline conditions, measurements are from rural sites. However, in East Asia data are so limited that trends are also assessed in urban areas. Unless otherwise noted, trends are reported in ppb yr 1 with 95% confidence limits. Trends that are statistically significant at the 95% confidence level are shown in bold font. Trends are based on annual data unless seasons are specified. *Units are not listed for trend values reported in units of ppb yr 1, but units are reported for trend values reported in percent per year. Trend, ppb yr 1 Site or Seasonal Measurement Region (or percent Period Reference Remarks Information per year)* Europe Alpine high elevation A composite of 0.87 +/- 0.13 1978 1989 Logan et al. 2012) Unfiltered data, although data from January to surface sites, 3.0 3.6 Zugspitze, Jungfrau- 0.33 +/- 0.10 1990 1999 May, 1982 at Zugspitze were dropped. Quadratic km above sea level joch and Sonnblick 0.16 +/- 0.14 2000 2009 fit to seasonal time series for 1978 2009. Alpine high eleva- Zugspitze 0.39 +/- 0.06 1978 2010 Oltmans et al. (2013) Annual trend is determined from monthly means tion surface site, 3.0 0.14 +/- 0.06 1981 2010 using an autoregressive model that incorporates km above sea level 0.05 +/- 0.08 1991 2010 explanatory variables and a cubic polynomial fit. Surface, rural central Europe Hohenpeissenberg 0.26 +/- 0.07 1971 2010 Parrish et al. (2012) Filtered to remove very local contamination. The trend reported here is based on yearly averages and linear regression following the methods of Parrish et al. (2012). 2SM Surface, west coast Mace Head 0.31 +/- 0.10 1989 2010 Parrish et al. (2012) See entry above. In addition the data were filtered of Ireland to represent baseline transport conditions. Surface, west coast Mace Head 0.09 +/- 0.08 1988 2010 Oltmans et al. (2013) Daytime unfiltered data. Annual trend calculated from of Ireland 0.01 +/- 0.10 1991 2010 monthly means using an autoregressive model that incor- porates explanatory variables and a cubic polynomial fit. Surface, rural north- Arkona-Zingst 0.32 +/- 0.05 1957 2010 Parrish et al. (2012) Trend reported here is based on unfiltered yearly averages ern German coast and linear regression following methods of Parrish et al. (2012). Surface, alpine valley Arosa 0.40 +/- 0.09 1950 2000 Parrish et al. (2012) See entry above. No measurements were made from the late 1950s through 1988. Surface, rural elevated Kislovodsk High 0.65 +/- 0.01 1991 2006 Tarasova et al. (2009) Unfiltered data. Linear ­ egression of r site in southeast Europe Mountain Station all available hourly data. Northern Europe mid- Composite of ozone- 0.25 +/- 0.21 1990 2006 Hess and Zbinden Unfiltered data. Linear regression of 12-month running troposphere, 500 hPa sondes from Ny Alesund (2013) mean of monthly ozone deviations. This 17-year trend was and Sodankyla calculated by P. Hess using the same method as for the 1990 2000 and 2000 2006 trends reported in the paper. Central Europe lower MOZAIC 0.15 +/- 0.15 1995 2008 Logan et al. (2012) Unfiltered data; see entry below. Trends at alpine free troposphere, 2.6 3.8 MOZAIC 0.21 +/- 0.20 1998 2008 sites for 1998 2008 show similar rates. km above sea level Hohenpeissenberg 0.20 +/- 0.16 1998 2008 Payerne 0.25 +/- 0.17 1998 2008 Central Europe mid- free MOZAIC 0.33 +/- 0.21 1995 2008 Logan et al. (2012) Unfiltered data. Linear regression, with annual trend cal- troposphere, 5 6.1 MOZAIC 0.08 +/- 0.30 1998 2008 culated from four seasonal trends; trends and annual cycle km above sea level Hohenpeissenberg 0.1 +/- 0.17 1998 2008 fit to monthly means. MOZAIC is a composite of aircraft Payerne 0.43 +/- 0.19 1998 2008 flights to five European airports. Others are sonde stations. North America Northeastern USA, Whiteface Mountain 0.09 +/- 0.06 1973 2010 Oltmans et al. (2013) Annual trend is determined from monthly means rural mountaintop Summit, New York 0.07 +/- 0.08 1981 2010 using an autoregressive model that incorporates 0.22 +/- 0.12 1991 2010 explanatory variables and a cubic polynomial fit. Eastern USA, rural Winter, 36 sites 0.12 (44%, 0%) 1990 2010 Cooper et al. (2012) Mid-day data only. Linear regression of seasonal surface sites Spring, 40 sites 0.03 (5%, 8%) medians at a site. The reported trend is the average of Summer, 41 sites 0.45 (0%, 66%) the individual trends in the region. Values in paren- theses indicate the percent of sites with statistically significant positive or negative trends, respectively. Western USA, rural Winter, 11 sites 0.12 (36%, 0%) 1990 2010 Cooper et al. (2012) See entry above. surface sites Spring, 12 sites 0.19 (50%, 0%) Summer, 12 sites 0.10 (17%, 8%) USA west coast, marine Composite of several sites 0.27 +/- 0.13 1988 2010 Parrish et al. (2012) Trend reported here is based on yearly averages and linear boundary layer regression following methods of Parrish et al. (2012). Data were filtered to represent baseline transport conditions. High latitudes, surface Denali, 0.04 +/- 0.08 1987 2010 Parrish et al. (2012) Annual trend is determined from monthly means central Alaska 0.15 +/- 0.10 1991 2010 using an autoregressive model that incorporates explanatory variables and a cubic polynomial fit. Arctic, surface Barrow, Alaska 0.09 +/- 0.06 1973 2010 Oltmans et al. (2013) See entry above. 0.03 +/- 0.06 1981 2010 0.13 +/- 0.10 1991 2010 Eastern USA, free Annual composite of 0.41 +/- 0.32 1994 2006 Hess and Zbinden Unfiltered data. Linear regression of annually averaged troposphere, 500 hPa Wallops Island ozone- (2013) values for years when both ozonesonde and MOZAIC sondes and MOZAIC profiles were available. This 13-year trend was calculated aircraft profiles. by P. Hess using the same method as for the 1994 2000 and 2000 2006 trends reported in the paper. 2SM-5 Chapter 2 Observations: Atmosphere and Surface Supplementary Material Table 2.SM.2 (continued) Trend, ppb yr 1 Site or Seasonal Measurement Region (or percent Period Reference Remarks Information per year)* Mid-Atlantic USA, coastal Wallops Island 0.16 +/- 0.12 1971 2010 Oltmans et al. (2013) Annual trend is determined from monthly means Virginia, surface 850 hPa ozonesondes 0.02 +/- 0.16 1981 2010 using an autoregressive model that incorporates 0.27 +/- 0.22 1991 2010 explanatory variables and a cubic polynomial fit. 850 700 hPa Wallops Island 0.08 +/- 0.10 1971 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.01 +/- 0.12 1981 2010 0.33 +/- 0.14 1991 2010 700 500 hPa Wallops Island 0.09 +/- 0.10 1971 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.01 +/- 0.14 1981 2010 0.27 +/- 0.14 1991 2010 500 300 hPa Wallops Island 0.00 +/- 0.18 1971 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.20 +/- 0.20 1981 2010 0.09 +/- 0.32 1991 2010 Western USA, sur- Boulder 0.24 +/- 0.14 1981 2010 Oltmans et al. (2013) See entry above. 2SM face 700 hPa ozonesondes 0.19 +/- 0.16 1991 2010 700 500 hPa Boulder 0.36 +/- 0.10 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.06 +/- 0.12 1991 2010 500 300 hPa Boulder 0.38 +/- 0.18 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.12 +/- 0.26 1991 2010 Eastern Canada, Goose Bay ozonesondes 0.04 +/- 0.10 1981 2010 Oltmans et al. 2013) See entry above. surface 850 hPa 0.32 +/- 0.12 1991 2010 850 700 hPa Goose Bay ozonesondes 0.05 +/- 0.12 1981 2010 Oltmans et al. (2013) See entry above. 0.40 +/- 0.16 1991 2010 700 500 hPa Goose Bay ozonesondes 0.10 +/- 0.12 1981 2010 Oltmans et al. (2013) See entry above. 0.51 +/- 0.16 1991 2010 500 300 hPa Goose Bay ozonesondes 0.14 +/- 0.28 1981 2010 Oltmans et al. (2013) See entry above. 0.68 +/- 0.32 1991 2010 Central Canada, Churchill 0.18 +/- 0.08 1981 2010 Oltmans et al. (2013) See entry above. surface 850 hPa ozonesondes 0.09 +/- 0.12 1991 2010 850 700 hPa Churchill 0.12 +/- 0.10 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.10 +/- 0.16 1991 2010 700 500 hPa Churchill 0.06 +/- 0.10 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.31 +/- 0.16 1991 2010 500 300 hPa Churchill 0.05 +/- 0.30 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.55 +/- 0.40 1991 2010 Western Canada, Edmonton 0.05 +/- 0.10 1981 2010 Oltmans et al. (2013) See entry above. surface 850 hPa ozonesondes 0.02 +/- 0.16 1991 2010 850 700 hPa Edmonton 0.13 +/- 0.10 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.31 +/- 0.12 1991 2010 700 500 hPa Edmonton 0.13 +/- 0.10 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.45 +/- 0.12 1991 2010 500 300 hPa Edmonton 0.21 +/- 0.20 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.69 +/- 0.26 1991 2010 Arctic Canada, sur- Resolute 0.09 +/- 0.12 1981 2010 Oltmans et al. (2013) See entry above. face 850 hPa ozonesondes 0.21 +/- 0.16 1991 2010 850 700 hPa Resolute 0.03 +/- 0.12 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.39 +/- 0.18 1991 2010 700 500 hPa Resolute 0.00 +/- 0.14 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 0.40 +/- 0.18 1991 2010 500 300 hPa Resolute 0.00 +/- 0.42 1981 2010 Oltmans et al. (2013) See entry above. ozonesondes 1.17 +/- 0.64 1991 2010 Western North America Spring 0.52 +/- 0.20 1984 2011 Cooper et al. (2012) Unfiltered data. Linear regression based on median free troposphere (3 8 km) 0.41 +/- 0.27 1995 2011 values of all available measurements (lidar, ozonesonde and aircraft) in the 3 8 km range during April to May. Asia Mountaintop site in Mt. Happo, 1.85 km 0.65 +/- 0.32 1991 2011 Parrish et al. 2012) Trend reported here is based on unfiltered western Japan above sea level yearly averages and linear regression fol- lowing methods of Parrish et al., 2012. Surface, rural eastern Japan Ryori 0.22 +/- 0.90 1991 2010 Oltmans et al. 2013) Annual trend is determined from monthly means using an autoregressive model that incorporates explanatory variables and a cubic polynomial fit. (continued on next page) 2SM-6 Observations: Atmosphere and Surface Supplementary Material Chapter 2 Table 2.SM.2 (continued) Trend, ppb yr 1 Site or Seasonal Measurement Region (or percent Period Reference Remarks Information per year)* Japanese marine Composite of 3 sites 0.31 +/- 0.34 1998 2011 Parrish et al. 2012) Trend reported here is based on unfiltered boundary layer in western Japan yearly averages & linear regression follow- ing methods of Parrish et al., 2012. Beijing, China, Annual ~1 1997 2004 Ding et al. 2008) The rate of change was derived from a com- boundary layer Summer afternoons ~3 parison of mean MOZAIC aircraft profiles during the periods 1995 1999 and 2000 2005. Northern Taiwan, China, YangMing 0.54 +/- 0.21 1994 2007 Lin et al. 2010) Unfiltered data. Linear regression of annual elevated surface site means, using data from all times of day. Taiwan, China Composite of 3 coastal 0.52 +/- 0.10 1994 2007 Lin et al. 2010) Unfiltered data. Linear regression of annual Surface sites near urban emissions. means, using data from all times of day. Taiwan, China Composite of 12 urban 0.75 +/- 0.07 1994 2007 Lin et al. 2010) Unfiltered data. Linear regression of annual surface sites in the north means, using data from all times of day. of the country. 2SM Taiwan, China, surface Composite of 4 sites in ~1.5 1997 2006 Li et al. 2010) Unfiltered data. Linear regression using monthly southern Taiwan, near means. The reported trend was inferred from the urban emissions. regression line in Figure 2. Significance at the 95% confidence limit was not specified. Hong Kong, surface Hok Tsui coastal site 0.58 1994 2007 Wang et al. 2009b) Unfiltered data. Linear regression of monthly means, on southern tip of using all months and all times of day. This site is Hong Kong Island often upwind of the Hong Kong urban area. Northern Japan, Sapporo 0.35 +/- 0.10 1971 2010 Oltmans et al. 2013) Annual trend is determined from monthly means surface 850 hPa ozonesondes 0.63 +/- 0.12 1981 2010 using an autoregressive model that incorporates 0.15 +/- 0.14 1991 2010 explanatory variables and a cubic polynomial fit. 850 700 hPa Sapporo 0.23 +/- 0.10 1971 2010 Oltmans et al. 2013) See entry above ozonesondes 0.48 +/- 0.12 1981 2010 0.02 +/- 0.12 1991 2010 700 500 hPa Sapporo 0.23 +/- 0.10 1971 2010 Oltmans et al. 2013) See entry above ozonesondes 0.38 +/- 0.12 1981 2010 0.07 +/- 0.14 1991 2010 500 300 hPa Sapporo 0.16 +/- 0.20 1971 2010 Oltmans et al. 2013) See entry above ozonesondes 0.10 +/- 0.20 1981 2010 0.03 +/- 0.28 1991 2010 Central Japan, sur- Tsukuba 0.08 +/- 0.14 1971 2010 Oltmans et al. 2013) See entry above face 850 hPa ozonesondes 0.23 +/- 0.22 1981 2010 0.09 +/- 0.30 1991 2010 850 700 hPa Tsukuba 0.16 +/- 0.12 1971 2010 Oltmans et al. 2013) See entry above ozonesondes 0.21 +/- 0.16 1981 2010 0.09 +/- 0.24 1991 2010 700 500 hPa Tsukuba 0.16 +/- 0.10 1971 2010 Oltmans et al. 2013) See entry above ozonesondes 0.23 +/- 0.14 1981 2010 0.21 +/- 0.20 1991 2010 500 300 hPa Tsukuba 0.34 +/- 0.20 1971 2010 Oltmans et al. 2013) See entry above ozonesondes 0.41 +/- 0.26 1981 2010 0.92 +/- 1.12 1991 2010 Southern Japan, Naha 0.17 +/- 0.20 1991 2010 Oltmans et al. 2013) See entry above surface 850 hPa ozonesondes 850 700 hPa Naha 0.09 +/- 0.20 1991 2010 Oltmans et al. 2013) See entry above ozonesondes 700 500 hPa Naha 0.21 +/- 0.20 1991 2010 Oltmans et al. 2013) See entry above ozonesondes 500 300 hPa Naha 0.22 +/- 0.22 1991 2010 Oltmans et al. 2013) See entry above ozonesondes South Asia, tropo- A broad region including 0.3 0.7 % yr 1 1979 2005 Beig and Singh, 2007) The decadal trend was calculated using a spheric column ozone as much of India, southeast multifunctional regression model. Here the measured by satellites Asia and Indonesia trend is converted to percent yr 1. North Pacific Ocean tropics and subtropics Minamitorishima, Japan Remote Japanese 0.29 +/- 0.14 1994 2010 Oltmans et al. 2013) Annual trend is determined from monthly means island 4000 km east using an autoregressive model that incorporates of southern China explanatory variables and a cubic polynomial fit. Mauna Loa, Hawaii 3.4 km above sea level 0.16 +/- 0.08 1974 2010 Oltmans et al. 2013) See entry above. Only night time data were 0.14 +/- 0.08 1981 2010 used to ensure downslope flow conditions. 0.31 +/- 0.14 1991 2010 (continued on next page) 2SM-7 Chapter 2 Observations: Atmosphere and Surface Supplementary Material Table 2.SM.2 (continued) Trend, ppb yr 1 Site or Seasonal Measurement Region (or percent Period Reference Remarks Information per year)* Hawaii, USA sur- Hilo 0.38 +/- 0.16 1982 2010 Oltmans et al. 2013) See entry above face 850 hPa ozonesondes 0.25 +/- 0.18 1991 2010 850 700 hPa Hilo 0.04 +/- 0.16 1982 2010 Oltmans et al. 2013) See entry above ozonesondes 0.15 +/- 0.22 1991 2010 700 500 hPa Hilo 0.11 +/- 0.16 1982 2010 Oltmans et al. 2013) See entry above ozonesondes 0.14 +/- 0.24 1991 2010 500 300 hPa Hilo 0.09 +/- 0.16 1982 2010 Oltmans et al. 2013) See entry above ozonesondes 0.05 +/- 0.26 1991 2010 Northern Hemisphere Atlantic Ocean Marine boundary layer, east- 40°N 60°N 0.05 1977 2002 Lelieveld et al. 2004) Unfiltered measurements from ships traversing the ern North Atlantic Ocean 20°N 40°N 0.51 indicated regions. The 95% confidence limits were 0° 20°N 0.42 not reported, although statistical significance was. 2SM Marine boundary Bermuda 0.31 +/- 0.25 (winter) 1989 2010 Parrish et al. 2012) Unfiltered data. Linear regression of seasonal averages. layer, western North 0.27 +/- 0.29 (spring) There is a data gap of 5 years in the middle of the record. Atlantic Ocean 0.30 +/- 0.16 (summer) 0.05 +/- 0.33 (autumn) Canary Islands, sub- Izana, Tenerife, 0.14 +/- 0.10 1991 2010 Oltmans et al. 2013) Annual trend is determined from monthly means using tropical marine location 2800 m above sea level. an autoregressive model that incorporates explanatory west of North Africa variables and a cubic polynomial fit. Only night time data were used to ensure downslope flow conditions. Seasons with a significant Northern Hemisphere upper troposphere increase in ozone Western USA None Ozone Schnadt Poberaj Ozone changes were calculated for various regions Northeast USA Winter, spring change et al. 2009) of the northern hemisphere upper troposphere that N. Atlantic Ocean Winter between were sampled by the NASA GASP aircraft program Europe Spring 1975 1979 in the 1970s and by the European MOZAIC pro- Middle East Spring, summer and gram in the 1990s. No region had a statistically Northern India Spring, summer 1994 2001 significant decrease in ozone, in any season. South China Summer Northern Japan Summer, autumn Southern Japan All seasons Southern Hemisphere Tropical South Pacific Ocean, Samoa 0.01 +/- 0.04 1976 2010 Oltmans et al. 2013) Annual trend is determined from monthly means marine boundary layer 0.05 +/- 0.04 1981 2010 using an autoregressive model that incorporates 0.02 +/- 0.68 1991 2010 explanatory variables and a cubic polynomial fit. Marine boundary 40°S 60°S 0.17 1977 2002 Lelieveld et al. 2004) Unfiltered measurements from ships traversing the layer, western South 20°S 40°S 0.24 indicated regions. The 95% confidence limits were Atlantic Ocean 0°S 20°S 0.12 not reported, although statistical significance was. Marine boundary layer, east- 20°S 40°S 0.68 1977 2002 Lelieveld et al. 2004) See entry above ern South Atlantic Ocean 0° 20°S 0.37 Mid-latitude marine Cape Point, 0.13 +/- 0.02 1983 2010 Oltmans et al. 2013) Annual trend is determined from monthly means boundary layer South Africa 0.17 +/- 0.04 1991 2010 using an autoregressive model that incorporates explanatory variables and a cubic polynomial fit. Mid-latitude marine Cape Grim, 0.06 +/- 0.02 1982 2010 Oltmans et al. 2013) See entry above boundary layer Tasmania, Australia 0.09 +/- 0.04 1991 2010 Mid-latitude rural Baring Head, New Zealand 0.01 +/- 0.06 1991 2010 Oltmans et al. 2013) See entry above coastal site Antarctica, Ekström ice Neumayer 0.13 +/- 0.16 1995 2005 Helmig et al. 2007) Unfiltered data. Linear regression based shelf, 10 km from the ocean on annual median values. Antarctica, 2.8 km South Pole 0.01 +/- 0.04 1975 2010 Oltmans et al. 2013) Annual trend is determined from monthly means above sea level 0.01 +/- 0.41 1981 2010 using an autoregressive model that incorporates 0.20 +/- 0.04 1991 2010 explanatory variables and a cubic polynomial fit. Tropical Indian Ocean, 2 4 km a.s.l 0.01 +/- 0.69 % yr 1 1992 2008 Clain et al. 2009) Unfiltered ozonesonde measurements. Linear regres- La Reunion Island 4 10 km a.s.l. 0.44 +/- 0.58 % yr 1 sion of all available year-round measurements. ozonesonde profiles 10 16 km a.s.l. 1.23 +/- 0.58 % yr 1 Subtropical South Africa, 2 4 km a.s.l 1.44 +/- 0.40 % yr 1 1990 2008 Clain et al. 2009) Unfiltered ozonesonde measurements. Linear Irene ozonesonde profiles 4 10 km a.s.l. 0.40 +/- 0.33 % yr 1 regression of all available year-round mea- 10 16 km a.s.l. 0.19 +/- 0.35 % yr 1 surements. No data for 1994 1997. Southern New Zealand Lauder, 0.15 +/- 0.06 1986 2010 Oltmans et al. 2013) Annual trend is determined from monthly means using surface 850 hPa ozonesondes 0.12 +/- 0.08 1991 2010 an autoregressive model that incorporates explanatory variables and a cubic polynomial fit. (continued on next page) 2SM-8 Observations: Atmosphere and Surface Supplementary Material Chapter 2 Table 2.SM.2 (continued) Trend, ppb yr 1 Site or Seasonal Measurement Region (or percent Period Reference Remarks Information per year)* 850 700 hPa Lauder, 0.14 +/- 0.06 1986 2010 Oltmans et al. 2013) See entry above ozonesondes 0.10 +/- 0.06 1991 2010 700 500 hPa Lauder, 0.16 +/- 0.08 1986 2010 Oltmans et al. 2013) See entry above ozonesondes 0.20 +/- 0.08 1991 2010 500 300 hPa Lauder, 0.06 +/- 0.12 1986 2010 Oltmans et al. 2013) See entry above ozonesondes 0.12 +/- 0.14 1991 2010 Coastal Antarctica, Syowa, 0.15 +/- 0.06 1971 2010 Oltmans et al. 2013) See entry above surface 850 hPa ozonesondes 0.10 +/- 0.06 1981 2010 0.02 +/- 0.08 1991 2010 850 700 hPa Syowa, 0.06 +/- 0.06 1971 2010 Oltmans et al. 2013) See entry above ozonesondes 0.03 +/- 0.06 1981 2010 0.06 +/- 0.06 1991 2010 700 500 hPa Syowa, 0.05 +/- 0.04 1971 2010 Oltmans et al. 2013) See entry above 2SM ozonesondes 0.01 +/- 0.06 1981 2010 0.12 +/- 0.08 1991 2010 500 300 hPa Syowa, 0.10 +/- 0.10 1971 2010 Oltmans et al. 2013) See entry above ozonesondes 0.07 +/- 0.14 1981 2010 0.18 +/- 0.16 1991 2010 Central Antarctica, South Pole, 0.05 +/- 0.04 1986 2010 Oltmans et al. 2013) See entry above 700 500 hPa ozonesondes 0.08 +/- 0.06 1991 2010 500 300 hPa South Pole, 0.12 +/- 0.14 1986 2010 Oltmans et al. 2013) See entry above ozonesondes 0.09 +/- 0.18 1991 2010 2.SM.2.3 Aerosols PM2.5 and PM10 (particulate matter with aerodynamic diameters <2.5 and <10 m, respectively) mass, total aerosol composition and vis- Comprehensive, long-term and high-quality observations of aerosols ibility at about 60 regional stations since 1989 (Hand et al., 2011). were initiated mainly after 2000, and are currently available only at a Canadian CAPMoN network results are summarized in Canada (2012). few locations and regions. The monitoring and observations of aero- sols are still to a large degree uncoordinated on the continental and In Asia, the Acid Deposition Network East Asia (EANET, 2011) has mea- global scale, despite the crucial importance of aerosols as short-lived sured particulate matter and deposition since 2001, but thus far no climate forcers. A few long-term background measurements of aerosol trend studies have been published. In China, CAWNET (China Atmo- properties are performed within the framework of WMO GAW (World sphere Watch Network) and CARSNET (Calibration campaign of the Meteorological Organisation Global Atmosphere Watch program); China Aerosol Remote Sensing NETwork) recently began systematic however, the data coverage is low. An overview and critical evaluation aerosol observations (Zhang et al., 2012); however, only a few years of worldwide, quality assured, aerosol trend measurements does not of data are available. An analysis of population weighted PM2.5 mea- exist at present. For studies of aerosol climate interactions, it is crucial surements reported in Brauer et al. (2012) showed that China has that the sites are representative for regional/rural conditions, with low the worlds highest average PM2.5 (55 g m 3), more than twice the influence of local pollution and that the measurements are harmonised global average, indicating a strong influence of pollutant emissions. among sites and networks, and provided as homogeneous time series. An analysis (Qu et al., 2010) of reconstructed urban PM10 time series (2000 2006) from reported air pollution indices in 86 Chinese cities Regional air pollution networks in Europe and North America are suggests that median aerosol concentrations declined from 108 to 95 the most reliable source of information on long-term surface aerosol g m 3 in 16 northern cities and increased slightly from 52 to 60 g m 3 trends in these parts of the world. In Europe, the European Monitoring in 12 southern cities. Quan et al. (2011) report strong declines in vis- and Evaluation Programme (EMEP) network provides regionally repre- ibility commencing in the 1970s in the eastern provinces of China, and sentative measurements of aerosol composition since the 1980s; these continuing through the 2000s. They link these reduced visibility levels measurements are described in annual reports, and they are available to emission changes and high PM levels. See the discussion of visibility via www.emep.int. Torseth et al. (2012) provide an overview of results measurements in Section 2.2.3.1 and in Wang et al. (2009a, 2012). from two or three decades of EMEP measurements, as discussed in Section 2.2.3. In some other Asian regions long-term measurements from individual research groups or small networks are becoming available, but it is In North America, the U.S. Clean Air Status and Trends Network (CAST- often difficult to assess the significance of these measurements for NET) and the Canadian Air and Precipitation Monitoring Network larger regions. (CAPMoN) provide regionally representative long-term measurements of major ions in aerosols, including sulphate (Hidy and Pennell, 2010); In India, the Central Pollution Control Board (CPCB), Government of these networks do not report PM2.5. The U.S. Interagency Monitoring India, is executing a nation-wide programme of ambient air quality of Protected Visual Environments (IMPROVE) Network has measured 2SM-9 Chapter 2 Observations: Atmosphere and Surface Supplementary Material monitoring known as the National Air Quality Monitoring Programme In Europe, long-term EC and organic carbon (OC) data have been avail- (NAMP). The network consists of 342 monitoring stations covering 127 able at two stations (in Norway and Italy) starting in 2001 (Yttri et al., cities/towns in 26 States and 4 Union Territories. The State of Envi- 2011). Torseth et al. (2012) report slight decreases over these 9 years, ronment Report (Ministery of Environment and Forest, 2009) reported but with no assessment of statistical significance. In North America, annual average levels of respirable particulate matter (approximately the combined IMPROVE and CSN network (Hand et al., 2011) is mea- PM10) in residential areas of major cities ranging from 120 to 160 g suring elemental and organic carbon. However, trend analysis of long- m 3 (Delhi), 80 to 120 g m 3 (Mumbai), 30 to 90 g m 3 (Chennai), and term data are reported only (Hand et al., 2011) for total carbon (TC = 120 to 140 g m 3 (Kolkata); in these cities trends are mostly stable or black carbon + organic carbon), as an upgrade in sampling techniques increasing for 2000 2007. No details on the robustness of trends are around 2005 led to a different measured ratio of EC and OC. These TC given, and the validity of these trends for rural regions is not reported. measurements indicate highly significant (95% confidence) downward trends of total carbon between 2.5 and 7.5% yr 1 along the east and Surface-based remote sensing of aerosols, as discussed in Section west coasts of the USA, and smaller and less significant (p < 0.15) 2.2.3.1, is based mainly on results from the global AERONET net- trends in other USA regions from 1989 to 2008. Sharma et al. (2006) work (Holben et al., 1998). However, coverage of AERONET over sev- published long-term measurements of equivalent BC at Alert, Canada eral regions is poor. Since AR4, several other regional networks were and Barrow, Alaska, USA. Decreases were 54% at Alert and 27% at 2SM established such as ARFINET covering India(Krishna Moorthy et al., Barrow for 1989 2003; part of the trend difference was associated 2013); AEROCAN over Canada (http://www.aerocanonline.com/), and with changes in circulation patterns, that is, the phase of North Atlantic SKYNET over Japan (Kim et al., 2004); these data are not included in Oscillation (NAO). this analysis. In China, constant EC concentrations until the late 1970s have been 2.SM.2.3.1 North American Sulphate Trends derived from sediments at Chaohu and Lake Taihu in Eastern China (Han et al., 2011), followed by a sharp increase afterwards, correspond- In Section 2.2.3.2 overall declines of SO42 from the IMPROVE (Hand ing to the rapid industrialization of China in the last three decades. et al., 2011) network are on the order of 2 to 4% yr 1, but slightly An analysis of broadband radiometer data from 1957 to 2007 (Wang larger (about 6% yr 1) along the east coast of the USA. SO42 declines and Shi, 2010) showed a slight decrease in absorption of aerosol after in winter were somewhat larger than in other seasons. These trends 1990, likely due to LAC, while there was no significant change in the are consistent with average trends reported by CASTNET (2010) of scattering fraction of aerosol. 0.045 g S m 3 yr 1 for the period 1990 2008 in the eastern US, and a decrease of CASTNET aerosol sulphate concentrations by 21% in In India, downward trends in BC of 250 ng m 3 yr 1 (from 4000 to 2000 the East and Northeast, 22% in the Midwest, and 20% in the South ng m 3) in the period 2001 2009 were observed at the southern sta- between the two periods 1990 1994 and 2000 2004 (Sickles and tion of Trivandrum, with the largest changes occurring in 2007 2009 Shadwick, 2007a). Indirect evidence for declining sulphate particulate (Krishna Moorthy et al., 2009). At the northern Kanpur station increas- concentrations is found in an analysis of SO42 wet deposition by 20 to es of Aerosol Optical Depth (AOD) during the post-monsoon period 30% over a time period of 15 years (Sickles and Shadwick, 2007b), cor- and winter were observed for 2001 2010, attributed to anthropogenic responding to a trend of about 1.4 to 2.1% yr 1. In Canada, aerosol emission changes, with declining trends during the pre-monsoon and sulphate concentrations declined by 30 to 45% between 1991 1993 monsoon seasons, attributed to changes in natural emissions (Kaska- and 2004 2006 at non-urban CAPMoN sites in the eastern half of the outis et al., 2012). country. These declines are consistent with the trends of inorganic aerosol components reported by Quinn et al. (2009) at Barrow, Alaska, 2.SM.2.4 Carbon Monoxide Surface Measurements ranging between 2.3% yr 1 for SO42 to 6.4% for NH4. Hidy and Pen- nell (2010) show remarkable agreement of PM2.5 and SO42 declines Analysis of carbon monoxide (CO) data from the NOAA ESRL GMD in Canada, pointing to common emission sources of PM2.5 and SO42 . global cooperative air sampling network (data path: ftp://ftp.cmdl. noaa.gov/ccg/co/flask/) indicates a small decrease in globally averaged 2.SM.2.3.2 Black (Light Absorbing) and Elemental Carbon CO from 2006 to 2010. These findings are corroborated by analysis of Trends 1994 2012 AGAGE baseline CO measurements at Mace Head, Ireland (updated from Prinn et al. (2000), http://agage.eas.gatech.edu/data. The terms black carbon (BC), also referred to as light absorbing carbon htm) which showed large variability until 2005, and smaller variability (LAC), and elemental carbon (EC) refer to the analysis method: opti- together with stable or slightly decreasing CO from 2006 2012. The cal methods (aerosol light absorption) or filter measurements using observations are consistent with estimates of a slight decline in global thermal methods, respectively. For a detailed discussion on methods, anthropogenic CO emissions over the same time, although East Asian see Bond et al. (2013). The measurements are associated with large emissions may have increased (Granier et al., 2011). uncertainties; intercomparisons show differences of a factor of 2 to 3 for optical methods, and a factor of 4 for thermal methods (Vignati et al., 2010) which also renders quantitative comparison of LAC time 2.SM.3 Quantifying Changes in the Mean: Trend series uncertain. In addition, although there is a general lack of BC/EC Models and Estimation in Box 2.2 measurements, long-term time series are even scarcer. The Supplementary Material provides a detailed description of the method used to estimate linear trends in Chapter 2 and compares the 2SM-10 Observations: Atmosphere and Surface Supplementary Material Chapter 2 results of this relatively simple method with those of a wide variety of 1. Ordinary least squares (OLS) is the best known case of this kind other methods for fitting lines to data and estimating their uncertainty. of analysis. It assumes that all i are independent identically distributed It is demonstrated that the differences among the methods are rather (i.i.d.) random variables with normal distribution N (0, e2 ). While e is small compared to the uncertainty estimates of each method. Details usually considered unknown, in this case its unbiased estimate can be of the smoothing method used to produce the result shown in Box 2.2, obtained from data residuals (2.SM.3) as Figure 1, are also provided. (2.SM.4) 2.SM.3.1 Methods of Estimating Linear Trends and Uncertainties Note that N   2 appears in the denominator instead of N because two degrees of freedom out of the original N were spent on fitting two Several different methods of calculating linear trends and their uncer- parameters a and b. tainties are illustrated here by application to the annual mean time series of globally averaged Earth surface temperatures from the Had- The trend slope b estimated by equation (2.SM.1) will also be normally CRUT4 data set (see Section 2.4.3 for details). The methods used are distributed: N (0, b2 ), and its standard deviation b can be estimated described briefly below. The conclusion of this analysis is that, for time using the e estimate: 2SM series like the one used here, the trend line slope and its uncertainty limits are very similar for most of the methods that take into account dependency in the data sets in the form of the first-order autoregres- (2.SM.5) sive model AR(1). These results are similar to those obtained by the Restricted Maximum Likelihood (REML) method used in AR4. The simi- Under the assumptions made about i, the random variable defined as larity of the AR4 method results to those of the methods investigated here was determined by applying these methods to AR4 data sets and obtaining similar results for linear trends and their uncertainties (not shown). has a known probability distribution, a Student s t with N   2 degrees of freedom. To form a confidence interval for b such that it contains the 2.SM.3.2 Comparison of Trend Slope Calculation Methods true value of b with probability p, define We would like to fit a straight line to a given time series of observa- (2.SM.6) tions {yi} that correspond to an independent variable (instants of time) 1+p {xi}: that is, the 2 -quantile of Student s t(N   2) distribution. Random- yi = a + bxi + ei , i = 1, ..., N variables with this distribution lie in the interval ( q, q) with probabil- where a and b are constant parameters to be determined, while {ei} ity p. From this statement applied to U, it is inferred that the interval represents residual variability in observations (with regard to the (b q sb,  b + q sb) contains b with probability p, or, as it is usually stated, straight line y = a + bx). Without any additional assumptions, one can find the least squares solution for the trend line, that is, â and b that b = b +/- q sb minimizes the overall squared error 1N e i2 in the equation above: (2.SM.7) where b, sb, and q are given by formulas (2.SM.1) to (2.SM.6). (2.SM.1) 2. OLS with reduced number of degrees of freedom by Santer where mx and my are sample means of x and y, respectively: et al. (2008), hereafter S2008. The standard OLS assumption about independence of the residual deviations of data from the straight line is often unrealistic. A better approximation to reality is a model for (2.SM.2) serially correlated error, a.k.a. first-order autoregressive model AR(1): Data residuals (or errors in the linear fit) are i + 1 = r i + di , i = 1, ..., N 1 (2.SM.8) ei = yi (â +  b xi ) , i = 1, ..., N (2.SM.3) where di , not i, are now thought of as independent random variables. For a certain class of statistical estimation problems, this kind of data To estimate uncertainty in â and b, it is useful to view {ei} as a realiza- interdependence acts as if the sample size was reduced to Nr: tion of some random process { i}. Then the estimates of â and b can be interpreted as random variables and inferences can be made about their uncertainties, that is, deviations from their true values. Assump- (2.SM.9) tions made about { i} affect, in general, the estimates of â and b, and, usually to a larger extent, the uncertainties (confidence intervals) for For example, if calculations by formulas (2.SM.1) to (2.SM.5) are car- these estimates. ried through for a large sample with data dependency due to the AR(1) 2SM-11 Chapter 2 Observations: Atmosphere and Surface Supplementary Material model (2.SM.8), replacing N   2 by Nr   2 in the denominator of (4) Let E be a random vector from the multivariate normal distribution N results in a correct estimate of the trend error s standard deviation b (0, V ), where V is a covariance matrix. The optimal estimator of c is by formula (2.SM.5). Based on these theoretical considerations, Santer et al. (2008) employed a heuristic procedure that carries this calcula- c = (XT V 1 X) 1 XT V 1 Y tion ahead using the value of r estimated from the sample of the OLS data residuals {ei}. Estimated r is the correlation coefficient between and the covariance matrix for c is two N   1-long subsamples lagged by one time step: P = (XT V 1 X) 1 (2.SM.10) For the practical implementation of this method, V is unknown. Here we assume that V is a covariance matrix of an AR(1) process: V = (vij), vij = e2 r|i j| where e2 and r are estimated as variance and lag-1 auto- (2.SM.11) correlation coefficient respectively from data residuals of the initial OLS fit, as described in equations (2.SM.4) and (2.SM.10) to (2.SM.12). 2SM (2.SM.12) 4. Prewhitening. First OLS is performed, and r is estimated as in (2.SM.10) above. Then the time series is prewhitened as (It is assumed that the timeseries are avalable on a uniform time grid without any gaps). (2.SM.13) Furthermore, S2008 used Nr   2 in place of N   2 as a degree-of-free- dom parameter for Student s t in (2.SM.6). Even though in case of AR(1) The OLS is applied to timeseries {yi } and corresponding times {xi, i = error the sampling distribution of U is not that of Student s t, S2008 1, ..., N 1}. The prewhitening scheme (2.SM.13) does not change the have calculated confidence intervals for b using formulas (2.SM.1) to value of the true trend coefficient b. ( 2.SM.7), with (2.SM.4) and (2.SM.6) modified by the replacement of N   2 by Nr   2, with Nr computed by (2.SM.9) using r estimated 5. Sen Theil trend estimator, or median slope method: Nonpara- according to (2.SM.10) to ( 2.SM.12). Their extensive numerical experi- metric estimate of the linear trend based on Kendall s t, from Sen ments suggested that this heuristic strategy results in reliable, conser- (1968). Relaxes the usual requirement of normal distribution of { i}, vative uncertainty estimates for the trend slope. but does assume i.i.d { i}. No reduction of effective sample size is done. 3. Generalized Least Squares (GLS). Rewrite the same problem as 6. Wang and Swail (2001) iterative method (WS2001). A method discussed above in matrix notation. Let X = [X0 X1] be an N × 2 matrix, of trend calculation iterating between computing Sen Theil trend and Y and E N-dimensional column-vectors such that slope for time series prewhitened as in equation (2.SM.13), comput- ing data residuals of the original time series with regards to the line X0 = [1...1]T , X1 = [x1...xN]T, Y = [yi...yN]T, E = [e1...eN]T with this new slope, estimating r from these residuals (as in Equa- tions (2.SM.10) to (2.SM.12)), prewhitening the original time series Let also cT = [a b]. Then the linear trend estimation problem becomes using this r value, etc. Zhang and Zwiers (2004) compared this method with other approaches, including Maximum Likelihood for linear trends Y = Xc + E with AR(1) error, and found it to perform best, especially for short time series. Table 2.SM.3 | Trends (degrees Celsius per decade) and 90% confidence intervals for HadCRUT4 global mean annual time series for periods 1901 2011, 1901 1950 and 1951 2011 calculated by methods described in the Supplementary Material. Effective sample size Nr and lagged by one time step correlation coefficient for residuals are given for methods that compute them. Note differences in the width of confidence intervals between methods that assume independence of data deviations from the straight line (OLS and Sen Theil methods) and those that allow AR(1) dependence in the data (all other methods). Two of these methods use non-parametric trend estimation (Sen Theil and WS2001). 1901 2011 1901 1950 1951 2011 Method Trend Nr Trend Nr Trend Nr OLS 0.075 +/- 0.006 0.107 +/- 0.016 0.107 +/- 0.015 S2008 0.075 +/- 0.013 28 0.599 0.107 +/- 0.026 21 0.407 0.107 +/- 0.028 21 0.494 GLS 0.073 +/- 0.012 0.599 0.100 +/- 0.023 0.407 0.104 +/- 0.025 0.494 Prewhitening 0.077 +/- 0.013 0.594 0.113 +/- 0.022 0.362 0.111 +/- 0.026 0.488 Sen Theil 0.075 ( 0.006, +0.007) 0.113 ( 0.019, +0.019) 0.109 ( 0.017, +0.019) WS2001 0.079 ( 0.014, +0.012) 0.596 0.114 ( 0.026, +0.023) 0.352 0.110 ( 0.028, +0.029) 0.487 2SM-12 Observations: Atmosphere and Surface Supplementary Material Chapter 2 2.SM.3.3 Method for Calculating Linear Trends and Their which is then used to estimate the variance of data deviations from Uncertainties for General Use Within Chapter 2 the trend line: The method applied in this chapter is a slight modification of the S2008 method. The sample size is not reduced (Nr = N), if the estimated r is negative. The method was also modified for use with time series where Therefore the variance of trend slope estimator is obtained: some data is missing. The formula (2.SM.9) for the effective sample size is still used. This formula was designed to give precise results for trend error when used for long time series of fully available data. In the presence of missing data (and shorter time series) this formula To construct a confidence interval for probability level p, let underestimates Nr further and thus results in wider (more conservative) confidence intervals (compared to the cases without missing data). The final procedure is as follows. 1+p be the 2 -quantile of Student s t(Nr   2) distribution. Finally The time series of observations {yi} corresponds to instants of time {xi, i 2SM = 1, ..., N} that form a uniform grid. In some cases, observations yi are b = b +/- q sb missing. Formally, two sets of indices Ia and Im are introduced that cor- respond to available and missing observations, respectively. Obviously, where b, sb, and q are given by formulas above. the union of the two sets includes all the possible data locations and the two sets do not intersect, 2.SM.3.4 Smoothing Spline Method {1, ..., N} = Ia Im, Ia Im = An alternative approach is to estimate local trends using non-para- metric trend models obtained by penalized smoothing of time series The size of Ia is Na. (e.g., Wahba, 1990; Wood, 2006; Section 6.7.2). The value in any year is considered to be the sum of a non-parametric smooth trend and a First, OLS is performed for available observations: low-order autoregressive noise term. The trend is represented locally by cubic spline polynomials (Scinocca et al., 2010) and the smoothing parameter is estimated using REML allowing for serial correlation in the residuals. where mx and my are sample means of x and y over Ia, respectively: 2.SM.4 Changes in Temperature Data residuals (or trend line misfits) are 2.SM.4.1 Change in Surface In Situ Observations Over Time ei = yi (â +  b xi ) , i Ia Observations are available for much of the global land surface starting Lag-one correlation coefficient of {ei} can be estimated over the subset in the mid-1800s or early 1900s. Availability is reduced in the most of indices Ic = { i : i Ia & ( i + 1) Ia}. Let Nc be the size of Ic. Then recent years owing in large part to international data exchange delays for monthly data summaries, although these have improved from many countries since AR4. Synoptic reports (used in reanalyses), and daily reports (used to analyse extremes) are also exchanged, and there has been no such decrease in their exchange. Non-digitized temperature records continue to be found in various country archives and are being digitized (Allan et al., 2011; Brunet and Jones, 2011). Efforts to create a single comprehensive raw digital data holding with provenance A provision is made for not raising the effective sample size if esti- tracking and version control have advanced (Thorne et al., 2011; Law- mated r is negative: rimore et al., 2013). Most historical sea surface temperature (SST) observations arise from ships, with buoy measurements and satellite r+ = max ( r, 0) data becoming a significant contribution in the 1980s. Digital archives such as the International Comprehensive Ocean-Atmosphere Data Set The resulting r+ is used to obtain the effective sample size of the set of (ICOADS, currently version 2.5, Woodruff et al., 2011) are constantly available observations: augmented as paper archives are imaged and digitized (Brohan et al., 2009). Despite substantial efforts in data assembly, the total number of available SST observations and the percentage of the Earth s sur- face area that they cover remain very low for years before 1850 and 2SM-13 Chapter 2 Observations: Atmosphere and Surface Supplementary Material drop drastically during the two World Wars. The sampling of land and All use monthly average temperature series from stations around the marine records through time which form the basis for the in situ land- globe. Global Historical Climatology Network (GHCN) V3 improvements surface air temperature (LSAT) and SST records detailed in the chapter (Lawrimore et al., 2011) included elimination of duplicate time series are summarized in Figure 2.SM.2. for many stations, updating more station data with the most recent data, the application of enhanced quality assurance procedures (Durre 100 et al., 2010) and a new pairwise homogenization approach for indi- 90 (a) LSAT vidual station time series (Menne and Williams, 2009). Two version 80 increments to this V3 product to fix coding issues have since accrued Land area sampled (%) 70 that have served to slightly increase the centennial time-scale trends. 60 Goddard Institute of Space Studies (GISS) continues to provide an estimate based primarily on GHCN but with different station inclusion 50 criteria, additional night-light-based urban adjustments and a distinct 40 gridding and infilling method (Hansen et al., 2010). CRUTEM4 (Jones 30 et al., 2012) incorporates additional series above and beyond those in 20 CRUTEM3 and also newly homogenized versions of the records for a 2SM 10 number of stations and countries. It continues the model of incorporat- 0 ing the best available estimates for each station arising from research 100 papers or individual national meteorological services with access to 90 (b) SST the best metadata on the assumption that such efforts have had most Ocean area sampled (%) 80 attention paid to them. In contrast, all other products considered in AR5 70 undertake a globally consistent homogenization processing of a given 60 set of input data, although those data may well have been processed 50 and adjusted at source. A new data product from a group based pre- 40 dominantly at Berkeley (Rohde et al., 2013) uses a kriging technique, 30 commonly used in geostatistics, to create a global mean timeseries 20 accounting for time-varying station biases by treating each apparently homogeneous segment as a unique record. This is substantially method- 10 ologically distinct from earlier efforts and so helps us to better explore 0 1800 1830 1860 1890 1920 1950 1980 2010 structural uncertainty (Box 2.1) in LSAT estimates. Figure 2.SM.2 | Change in percentage of possible sampled area for land records (top 2.SM.4.3 Sea Surface Temperature Data Improvements panel) and marine records (lower panel). Land data come from GHCNv3.2.0 and marine data from the ICOADS in situ record. and Data Set Innovations 2.SM.4.3.1 In Situ Sea Surface Temperature Data Records 2.SM.4.2 Land Surface Air Temperature Data Set Innovations Because of the irregular nature of sampling in space and time, when a large portion of observations are made from moving platforms (ships Improvements have been made to the historical global data sets of land- and drifting buoys), it is customary to use statistical summaries of based station observations used in AR4. Basic descriptions of the meth- binned observations (most commonly, by grid boxes) rather than ods for the current versions of all data sets are given in Table 2.SM.4. individual observed values (Table 2.SM.5). Means or medians of all SST Table 2.SM.4 | Summary of methods used by producers of global land-surface air temperature (LSAT) products. Basic methodological details are included to give a flavour of the methodological diversity. Further details can be found in the papers describing the data set construction processes cited in the text. Start of Quality Control and Dataset Number of Stations Infilling Averaging Procedure Record Homogeneity Adjustments CRUTEM4 1856 5696 (4891 used Source specific QC and homogeneity applied None Average of the two hemispheric (Jones et in gridding) generally to source data prior to collation averages (derived by area weight- al., 2012) ed average of grid boxes) weight- ed 2/3 Northern Hemisphere and 1/3 Southern Hemisphere GHCNv3 1880 7280 Outlier and neighbour QC and pairwise Limited infilling by eigenvectors Average of grid boxes (Lawrimore comparison based adjustments (for global mean calculations area weighted et al., 2011) only; Smith et al., 2008) GISS 1880 c.6300 Night lights based adjustments for urban influences Averages to 40 large scale bins Average of the bins with (Hansen et areal weighting. al., 2010) Berkeley (Rohde 1753 39028 Individual outliers are implicitly down-weighted. No gridding, but kriging Kriged field estimate limited to et al., 2013) Neighbour-based test to identify breaks and each produces field estimate maximum 1500 km distance from apparently homogeneous segment treated separately. based on the station con- any station straint at each timestep. 2SM-14 Observations: Atmosphere and Surface Supplementary Material Chapter 2 values in a given bin that pass quality control procedures are generally sets have been widely used in climate analyses so far. The progress on used. Standard deviations and numbers of observations in individual the analytical correction of solar heating biases in recent daytime MAT bins are useful for estimating uncertainties. These procedures usually data (Berry et al., 2004) allowed their use in a recent analysis (Berry serve as an initial step for producing more sophisticated gridded SST and Kent, 2009). Table 2.SM.5 gives a brief description of well-known products which involve bias correction and, for analyzed products, historical SST and NMAT products, organized by their type. interpolation and smoothing. Since AR4, many marine observations have been digitized (Brohan et al., 2009; Allan et al., 2011; Wilkinson 2.SM.4.3.2 Comparing Different Types of Data and Their Errors et al., 2011), substantially improving the coverage of the latest ICOADS Release 2.5 (Woodruff et al., 2011) and of the newer data sets based Comparisons are complicated because different measurement tech- on it (e.g., HadSST3, HadNMAT2). nologies target somewhat different physical characteristics of the surface ocean. Infrared (IR) and microwave (MW) radiometers sense Since AR4, major innovations have primarily been around understand- water temperature of the top 10 to 20 um and 1 to 2 mm respec- ing of post-1940 biases. Since 1940, ships making measurements of tively, whereas in situ SST measurements are made in the depth range SST have used a variety of methods (Kent et al., 2010), each with char- between 10 cm and several meters; these are often called bulk SST, acteristic biases (Kennedy et al., 2011a). These offsets have varied over with an implicit assumption that the ocean surface layer is well-mixed. 2SM time (Kent and Kaplan, 2006; Kent and Taylor, 2006), and for the period This assumption is valid only for nighttime conditions or when sur- 2002 2007 ship SSTs are overall biased warm by 0.12°C to 0.18°C on face winds are strong. Otherwise, the surface layer is stratified and its average compared to the buoy data (Reynolds et al., 2010; Kennedy temperature exhibits diurnal variability (Kawai and Wada, 2007; Ken- et al., 2011b, 2012 ). Since the 1980s, drifting and moored buoys have nedy et al., 2007), such that measured temperature values vary with been producing an increasingly large fraction of global SST observa- the depth and time of day of observation (Donlon et al., 2007). Aside tions and these have tended to be colder than ship-based measure- from the diurnal variability, an independent phenomenon of a thermal ments. skin layer takes place in the top 1 mm or so of the ocean surface and results in a strong temperature gradient across this layer (usually, cool- Although more variable than SSTs, marine air temperatures (MATs) are ing towards the surface) which is especially enhanced in the top 100 assumed to be physically constrained to track SST variability because um. Although all in situ and satellite measurements might be affected of the continuous air sea heat exchange, at least on large spatial and by diurnal variability, only IR satellite data are subject to the thermal temporal scales (monthly to annual, ocean basin to hemispheric). How- skin effect. IR radiometers are said to measure skin temperature. ever, longer-term variations noted in some locations and periods, for Temperature at the bottom of the thermal skin layer is called subskin example, Christy et al. (2001) and Smith and Reynolds (2002), neces- temperature. MW radiometer measurements are close to this variable. sitate a degree of caution. Regardless, MAT data provide a useful addi- To estimate error variance or to verify uncertainty estimates for SST tional record of marine region temperature changes. Adjustments have observations by comparison of different kinds of SST data, data values been applied to account for the change in observing height and for the have ideally to be adjusted for time and depth differences by modelling use of non-standard practices during World War II (Rayner et al., 2003) the skin effect and diurnal variability; in lieu of a model, geophysical and the 19th century (Bottomley et al., 1990). Because of biases due errors are reduced by constraining the comparison to the nighttime to solar heating, only Nighttime Marine Air Temperature (NMAT) data data only which minimizes the diurnal variability effects. Table 2.SM.5 | Data Sets of SST and NMAT Observations Used in Section 2.4.2. These data sets belong to the following categories: a database of individual in situ observations, gridded data sets of climate anomalies (with bucket and potentially additional bias corrections applied) and globally complete interpolated data sets based on the latter products. Space Time Grid Bucket/ Bias Data Set Period Resolution Corrections Applied Historical Database of In Situ Observations International Comprehensive Ocean Atmosphere Data 1662 present; Individual reports; None Set (ICOADS), Release 2.5 (Woodruff et al., 2011) 1800 present, 2° × 2° monthly summaries; 1960 present 1° × 1° monthly summaries Gridded Data Sets of Observed Climate Anomalies U.K.M.O. Hadley Centre SST, v.2 (HadSST2) 1850 present 5° × 5° monthly Bucket correction for pre-1941 period (Rayner et al., 2006) U.K.M.O. Hadley Centre SST, v.3 (HadSST3) 1850 present 5° × 5° monthly Bias correction for the entire period based on (Kennedy et al., 2011a; Kennedy et al., 2011b) percentages of different types of observations U.K.M.O. Hadley Centre NMAT, v.2 (HadNMAT2) 1886 2010 5° × 5° monthly Adjustments for changes in observation heights (Kent et al., 2013) and for non-standard observing practices Globally Complete Objective Analyses (Interpolated Products) of Historical SST Records U.K.M.O. Hadley Centre Interpolated SST, v.1 (HadISST) 1870 present 1° × 1° monthly Bucket corrections for pre-1941 period (Rayner et al., 2003) JMA Centennial in situ Observation Based Estimates of SST (COBE SST) 1891 present 1° × 1° monthly Bucket corrections for pre-1941 period (Ishii et al., 2005) NOAA Extended Reconstruction of SST, v. 3b (ERSSTv3b) 1854 present 2° × 2° monthly Bucket corrections for pre-1941 period (Smith et al., 2005, 2008) 2SM-15 Chapter 2 Observations: Atmosphere and Surface Supplementary Material Comparisons between in situ measurements and different satellite Temperature difference between the periods of 1986-2005 and 1850- instruments have been used to assess the uncertainties in the indi- 1900: vidual measurement techniques. Random error magnitudes on Along HadCRUT4: 0.61°C +/- 0.06°C (90% confidence interval) Track Scanning Radiometer (ATSR) measurements have been estimat- GISTEMP: N/A ed (O Carroll et al., 2008; Embury et al., 2012; Kennedy et al., 2012) MLOST: N/A to lie between 0.1°C and 0.2°C. The uncertainties associated with random errors for Advanced Along Track Scanninr Radiometer (AATSR) Temperature difference between the periods of 2003-2012 and 1850- measurements are therefore much lower than for ships (about 1°C to 1900: 1.5°C: Kent and Challenor (2006); Kent et al. (1999); Kent and Berry HadCRUT4: 0.78°C +/- 0.06°C (90% confidence interval) (2005) Reynolds et al. (2002); Kennedy et al. (2012)) or drifting buoys GISTEMP: N/A (0.15°C to 0.65°C: Kennedy et al. (2012); Reynolds et al. (2002); Emery MLOST: N/A et al. (2001); O Carroll et al. (2008)). Temperature difference between the periods of 1986-2005 and 1886- Characterizing relative mean biases between different systems informs 1905: the procedures for homogenizing and combining different kinds of HadCRUT4: 0.66°C +/- 0.06°C (90% confidence interval) 2SM measurements. Embury et al. (2012) found average biases of less than GISTEMP: 0.66°C 0.1°C between reprocessed AATSR retrievals and drifting buoy obser- MLOST: 0.66°C vations and of about 0.1°C between ATSR2 retrievals and buoys. Using an earlier AATSR data set, Kennedy et al. (2012) found that ship mea- Temperature difference between the periods of 1986-2005 and 1961- surements were warmer relative to matched satellite SSTs than drifting 1990: buoys, suggesting ship measurements were biased relative to drifting HadCRUT4: 0.30°C +/- 0.03°C (90% confidence interval) buoy measurements by 0.18°C. They hypothesized that HadSST2 con- GISTEMP: 0.31°C tained an increasing cool bias because of a decrease in the relative pro- MLOST: 0.30°C portion of warm-biased ship observations. They applied a time-varying adjustment to the HadSST2 global means in the form of 0.18°C times Temperature difference between the periods of 1986-2005 and 1980- the fraction of drifting buoys compared to the 1991 1995 period. This 1999: correction improved the consistency between trends in global average HadCRUT4: 0.11°C +/- 0.02°C (90% confidence interval) anomalies from the in situ and ATSR data sets. However, Kennedy et al. GISTEMP: 0.11°C (2011b) found a smaller relative bias between ships and drifting buoys MLOST: 0.11°C and found that changes in the biases associated with ship measure- ments might have been as large, or larger than, this effect. 2.SM.4.4 Technical Developments in Combined Land and SST Products 2.SM.4.3.3 Differences in Long-Term Average Temperature Anomalies Used in Other Chapters Table 2.SM.6 summarizes current methodological approaches. For Had- CRUT4 both the land and the ocean data sources have been updated Figure 2.SM.3 shows the differences between selected periods that are and the product now consists of 100 equi-probable solutions (Morice utilized in other chapters of the report analysed in a consistent manner et al., 2012). The post-1990s period is now more consistent with the for those three data sets considered in Section 2.4.3. Uncertainty remaining products it exhibits a greater rate of warming than the estimates have been calculated for HadCRUT4 using the HadCRUT4 previous version over this period. NOAA s Merged Land-Ocean Sur- uncertainty model (Morice et al., 2012). To allow estimates of coverage face Temperature (MLOST) analysis product has incorporated GHCNv3 uncertainty to be made for these differences between long-term aver- and ERSST3b and reinstated high-latitude land data but is otherwise ages, HadCM3 control run fields (which are much longer) were used methodologically unchanged from the version considered in AR4 (Vose in place of the National centers for Environmental Prediction (NCEP) et al., 2012). Since AR4, NASA GISS have undertaken updates and a reanalysis as the globally complete reference data. It was verified that published sensitivity analysis focussed primarily around their urban this does not greatly alter the uncertainty estimates when a subset of heat island adjustments approach (Section 2.4.1.3) and choice of prod- HadCM3 control of the same length as NCEP is used so it should not uct and method for merging pre-satellite era and satellite era SSTs be a first-order effect. (Hansen et al., 2010). For SST several alternative data sets or combina- tions of data sets were considered and these choices had an impact Temperature difference between the periods of 1946-2012 and 1880- of the order 0.04°C for the net change over the period of record. 1945: An improved concatenation of pre-satellite era and satellite era SST HadCRUT4: 0.38°C +/- 0.04C (90% confidence interval) products removed a small apparent cooling bias in recent times. As of GISTEMP: 0.40°C December 2012 GISS changed the operational SST version they used to MLOST: 0.39°C ERSST3b. Following the release of their code the GISS method has been independently replicated in a completely different programming lan- guage (Barnes and Jones, 2011) which builds a degree of confidence in the veracity of the processing. 2SM-16 Observations: Atmosphere and Surface Supplementary Material Chapter 2 2SM Figure 2.SM.3 | Differences in multi-year average temperatures as calculated from HadCRUT4, GISS and NCDC MLOST for six pairs of periods. The median and 5 to 95% confi- dence interval for differences calculated from HadCRUT4 are shown in black. Period differences for GISS are shown in red. Period differences for NCDC MLOST are shown in blue. Table 2.SM.6 | Methodological details for the current global merged gridded surface temperature products. Only gross methodological details are included to give a flavour of the methodological diversity; further details can be found in the papers describing the data set construction processes. Start Merging of Land Dataset Land Data Set Marine Data Set Infilling Averaging Technique Date and Marine HadCRUT4 (100 versions) 1850 CRUTEM4 (100 versions) HadSST3 (100 versions) Weighted average based on None, spatial coverage Sum of area weighted (Morice et al., 2012) the percentage coverage incompleteness accounted grid box averages for for in error model Northern and South- ern Hemisphere / 2 MLOST 1880 GHCNv3 ERSST3b Weighted average based on Low-frequency component Area weighted average of (Vose et al., 2012) the percentage coverage filtered. Anomaly spatial available gridbox values covariance patterns for high-frequency compo- nent. Land and ocean interpolated separately. NASA GISS 1880 GHCNv3, USHCNv2 plus ERSST3b Priority given to land data Radius of influence up to After gridding, non-missing (Hansen et al., 2010) Antarctic SCAR data 1200 km for land data values are averaged over the zones 90°S 23.6S, 23.6°S 0°, 0° 23.6°N, 23.6°N 90°N; and the four means are averaged with 3:2:2:3 weighting to represent their area. 2.SM.4.5 Technical Advances in Radiosonde Records U ­ niversity of Vienna have produced RAOBCORE and RICH (Haimberger, 2007) using ERA reanalysis products (Box 2.3) as a basis for identify- There now exist five estimates of radiosonde temperature evolution, ing breaks. Given the relative sparseness of the observing network this which are based on a very broad range of methodological approaches may have advantageous properties in many regions compared to more to station selection, identification of artificial timeseries breaks and traditional intra-station or neighbour-based approaches. Breakpoints adjustments (Table 2.SM.7). HadAT and RATPAC were discussed in AR4 are identified through reanalysis background departures using a sta- and no further technical innovations have accrued for the operational tistical breakpoint test for both these products. Uncertainties in adjust- versions of these products. Development of an automated version of ments arising from the use of reanalyses fields to estimate the adjust- HadAT and discussion of efforts to characterize the resulting para- ments for RAOBCORE have been addressed by several variants and metric uncertainty are summarized in the main text. A group at the sensitivity studies (Haimberger, 2004, 2007; Haimberger et al., 2008). 2SM-17 Chapter 2 Observations: Atmosphere and Surface Supplementary Material Table 2.SM.7 | Summary of methodologies used to create the radiosonde products considered in this report. Except IUK (1960), all time series begin in 1958. Only gross meth- odological details are included to give a flavour of the methodological diversity; further details can be found in the papers describing the data set construction processes. Between these data set approaches a very broad range of processing choices have been considered. Temporal Number of Dataset Homogeneity Test Adjustment Method Resolution Stations HadAT2 Seasonal / monthly 676 KS-test on difference series from neighbour averages Target minus neighbour difference series based. (Thorne et al., 2005) together with metadata, manually interpreted RATPAC monthly 87 Multiple indicators and metadata assessed manually Manually based adjustments prior to 1996, (Free et al., 2005) by three investigators until 1996, first difference first difference derived breaks after 1995. method with t-test and metadata after 1995 IUK Individual launch 527 Derived hierarchically looking (1) for breaks Relaxation to an iterative solution minimum given (Sherwood et al., 2008) in 00Z-12Z series, (2) breaks in the series with breaks and set of spatial and temporal basis functions. twice daily measures, and (3) once daily ascents. Breakpoint detection was undertaken at the monthly time scale with no recourse to metadata RICH-obs Individual launch 2881 SNHT test on the difference between the observed Difference between station and a number of (64 member ensemble) data and ERA reanalysis product background apparently homogeneous neighbours 2SM (Haimberger et al., 2012) expectation field modified by metadata information. RICH-tau Individual launch 2881 As above Difference between station innovation (can- (64 member ensemble) didate station and reanalysis background (Haimberger et al., 2012) expectation field) and innovation estimates for apparently homogeneous neighbors. RAOBCORE Individual launch 2881 As above Difference between candidate station and (Haimberger et al., 2012) reanalysis background expectation field The RICH products use the same breakpoint locations but have only an changes were accounting for latitudinal error structure dependencies, indirect dependency on the reanalyses as the adjustments are neigh- and a more physical handling of instrument body temperature effect bour based. Two varieties of RICH have been developed (Haimberger issues in response to (Grody et al., 2004). In early 2011 version 3.3 et al., 2012). The first uses pairwise neighbour difference series to esti- was released which incorporated all the AMSU instruments and led mate the required adjustment. The second uses differences in station to a de-emphasising of the last MSU instrument which still remained innovations relative to the reanalyses fields. Both variants have been operational after 15 years, a trend reduction over the post-1998 period, run in ensemble mode and the resulting uncertainty estimates are dis- and a reduction in apparent noise. cussed in the main text. Sherwood and colleagues developed an itera- tive universal kriging approach for radiosonde data (Sherwood, 2007) The new NOAA STAR analysis used a fundamentally distinct approach and applied this to a global network (Sherwood et al., 2008) to create for the critical inter-satellite warm target calibration step (Zou et al., IUK (iterative universal kriging). The algorithm requires a set of break 2006). Satellites orbit in a pole-to-pole configuration with typically two locations and the raw data and then fits an optimal estimate of the satellites in operation at any time. Over most of the globe they never homogenized series based upon a number of basis functions includ- intersect. The exception is the polar regions where they quasi-regularly ing leading modes of variability. Breakpoint locations were defined by (typically once every 24 to 48 hours but this is orbital geometry depen- tests on the station series and without recourse to metadata. dent) sample in close proximity in space (<111 km) and time (<100 s). The STAR technique uses these Simultaneous Nadir Overpass (SNO) 2.SM.4.6 Advances in Microwave Sounding measures to characterize inter-satellite biases and the impact of instru- Unit Satellite Records ment body temperature effects before accounting for diurnal drift. SNO estimates remain two point comparisons between uncertain measures Gross methodological details of the microwave sounding unit (MSU) over a geographically limited domain, so cannot guarantee absolute products are summarized in Table 2.SM.8. The University of Alabama in accuracy. For humidity satellite measures the geographic domain Huntsville (UAH) data set removed an apparent seasonal cycle artefact has been shown to be an issue (John et al., 2012), but it is at present in the latter part of their record related to the introduction of Advanced unclear whether this extends to temperature measurements. Initially Microwave Sounding Unit (AMSU) in version 5.3 and changed the they produced Mid-Troposphere (MT) near-nadir measures since 1987 climatological baseline to 1981 2010 to produce version 5.4. Both over the oceans (Zou et al., 2006); then included more view angles changes had negligible impact on trend estimates. and additional channels including LS and multichannel recombinations (Zou et al., 2009); then extended back to 1979 and included land and Version 3.2 of the RSS product (Mears and Wentz, 2009a, 2009b) for residual instrument body temperature effects building upon the UAH the first time incorporated a subset of AMSU instruments. It was con- methodology and diurnal corrections based upon RSS (Zou and Wang, cluded that an instantaneous correction is required to merge MSU and 2010). In the latest version 2.0, STAR incorporated the AMSU observa- AMSU as they sense slightly different layers and that there will also tions inter-calibrated by the SNO method to extend to the present (Zou be a systematic long-term impact unless real-world trends are verti- and Wang, 2011). cally invariant (Mears et al., 2011). Using HadAT data this impact was estimated to be no more than 5% of the trend. Two more significant 2SM-18 Observations: Atmosphere and Surface Supplementary Material Chapter 2 Table 2.SM.8 | Summary of methodologies used to create the MSU products considered in this report. All time series begin in 1978 1979. Only gross methodological details are included to give a flavour of the methodological diversity, further details can be found in the papers describing the data set construction processes. Calibration Target MSU/AMSU Dataset Inter-Satellite Calibration Diurnal Drift Adjustments Temperature Effect Weighting Function Offsets UAH Backbone method adjusting Cross-scan differences used to Calibration target coefficients are No accounting for differences (Christy et al., 2003) all other satellites to a subset infer adjustments. Measure- determined as solution to system of beyond inter-satellite calibration. of long-lived satellites ments are adjusted to refer to the daily equations to explain the differ- measurement time at the begin- ence between co-orbiting satellites ning of each satellite s mission. RSS Stepwise pairwise adjustments of Climate model output used to infer Values of the target temperature Stepwise adjustment to account for (Mears and Wentz, all satellites based on difference in diurnal cycle. All measurements factors and scene temperature factors the change in weighting functions. 2009a, 2009b) means. Adjustments are a function adjusted to refer to local midnight. are obtained from a regression using of latitude and constant in time. all satellites of the same type together. STAR Simultaneous nadir overpass measures RSS adjustments are multiplied Largely captured in the SNO satellite Channel frequency shifts on each (Zou and by a constant factor to minimize intercomparison but residual artefacts satellite estimated and adjusted for. Wang, 2011) inter-satellite differences. are removed using the UAH method. 2SM 2.SM.4.7 Stratospheric Sounding Unit Data Background the directly measured quantity but rather inferred with the inference being dependent on the precision of available data for other dependent The Stratospheric Sounding Unit (SSU) instruments provide the only parameters and how the data are processed. GPS RO measurements long-term near-global temperature data above the lower stratosphere, have several attributes that make them suited for climate studies: (1) extending from the upper troposphere to the lower mesosphere they exhibit no satellite-to-satellite bias (Hajj et al., 2004; Ho et al., (Randel et al., 2009; Seidel, 2011), with the series terminating in 2006. 2009a), (2) they are of very high precision (Anthes et al., 2008; Foelsche In theory, five channels of AMSU should be able to continue this series et al., 2009; Ho et al., 2009a), (3) they are not affected by clouds and (Kobayashi et al., 2009) but despite incipient efforts at an AMSU-only precipitation, and (4) they are insensitive to retrieval error when used record (Mo, 2009) and plans to merge AMSU and SSU, the current long- to estimate interannual trends in the climate system (Ho et al., 2009b). term series ends in 2006. The raw record has three unique additional GPS RO observations can be used to derive atmospheric temperature issues to those encountered in MSU data set construction. The satellite profiles in the upper troposphere and lower stratosphere (UT/LS) (Hajj carries a cell of CO2 which tends to leak pressure through water egress et al., 2004; Kuo et al., 2004; Ho et al., 2009a). on the ground and degassing post-launch, causing a spurious increase in observed temperatures. Compounding this the CO2 content within the cells varies among SSU instruments (Kobayashi et al., 2009). At the 2.SM.5 FAQ 2.1, Figure 2 higher altitudes sensed, large diurnal and semi-diurnal tides (due to absorption of solar radiation) require substantial corrections (Brown- This material documents the provenance of the data that was input scombe et al., 1985). Finally, long-term temperature trends derived to FAQ 2.1, Figure 2 in the IPCC WG1 Fifth Assessment Report. The from SSU need adjustment for increasing atmospheric CO2 (Shine et code will also be archived at the website along with a static version of al., 2008) as this affects radiation transmission in this band. the data files when the final report is published. Two have been trun- cated (one marine air temperature and one sea surface temperature) 2.SM.4.8 Global Positioning System Radio Occultation for explicitly source documented and acknowledged significant issues. Data Background The FAQ includes datasets and parameters discussed in the remaining observational chapters. The data in each panel replicates that data uti- Global Positioning System (GPS) radio occultation (RO) fundamental lized in the underlying chapters. observations are time delay of the occulted signal s phase traversing the atmosphere. It is based on GPS radio signals that are bent and retarded Land surface air temperature anomalies relative to 1961 1990: by the atmospheric refractivity field, related mainly to pressure and Dark Grey: Berkeley (Rohde et al., 2013) temperature, during their propagation to a GPS receiver on a Low Earth Green: NCDC (Lawrimore et al., 2011) Orbit (LEO) satellite. An occultation event occurs whenever a GPS satel- Blue: GISS (Hansen et al., 2010) lite sets (or rises from) behind the horizon and its signals are occulted Red: CRUTEM4 (Jones et al., 2012) by the Earth s limb. The fundamental measurement is the signal phase which is based on precise timing with atomic clocks. Potential clock Global lower tropospheric MSU-equivalent temperature anomalies errors of GPS or LEO satellites are removed by differencing methods relative to 1981 2010 from satellites and radiosondes. using an additional GPS satellite as reference and by relating the mea- Black : HadAT2 (Thorne et al., 2005) surement to even more stable oscillators on the ground. Thus, GPS RO is Orange : RAOBCORE (Haimberger et al., 2012) anchored to the international time standard and currently the only self- Dark Grey: RICH-obs (Haimberger et al., 2012) calibrated raw satellite measurement with SI traceability, in principle Yellow: RICH-tau (Haimberger et al., 2012) (Leroy et al., 2006; Arndt et al., 2010). Subsequent analysis converts Green: RATPAC (Free et al., 2005) the time delay to temperature and other parameters, which inevitably Blue: RSS (Mears and Wentz, 2009a) adds some degree of uncertainty to the temperature data, which is not Red: UAH (Christy et al., 2003) 2SM-19 Chapter 2 Observations: Atmosphere and Surface Supplementary Material Sea-surface temperature anomalies relative to 1961 1990: Blue: HadISST1.2 (Rayner et al., 2003) Dark Grey: ERSSTv3b (Smith et al., 2008) Red: SMMR - SBA (Comiso and Nishio, 2008) Black: COBE (Ishii et al., 2005) Black: SSM/I - NT1 (Cavalieri et al., 1984) updated in Cavalieri and Green: HadISST (Rayner et al., 2006) Parkinson (2012) and Parkinson and Cavalieri (2012) Red: ICOADS (Worley et al., 2005) Yellow: AMSR2 ABA (Comiso and Nishio, 2008) Yellow: HadSST3 (Kennedy et al., 2011b) Orange: AMSR2 NT2 (Markus and Cavalieri, 2000) Ocean heat content anomalies (0 700 m). All data sets normalized Glacier mass balance relative to 1961 1970. relative to 2006 2010 and then rebased to be zero average across all Dark grey: Cogley area weighted as updated from (Cogley, 2009). data sets at 1971 as per Chapter 3, Figure 3.2. Area weighted extrapolation from directly and geodetically measured Blue: Palmer et al. (2007) g ­lacire mass balances. Updated to the complete Randoph Glacier Green: Domingues et al. (2008) Inventory [RGI] (Arendt et al., 2012) Yellow: Ishii and Kimoto (2009) Green: Leclercq et al. (2011) Orange: Smith and Murphy (2007) Blue: Marzeion et al. (2012) Black: Levitus et al. (2012) 2SM Marine air temperature anomalies relative to 1961 1990 2.SM.6 Changes in the Hydrological Cycle Red: HadNMAT2 (Kent et al., 2013) Blue: (Ishii et al., 2005). Series shown only after 1900 due to known but 2.SM.6.1 Precipitation Trends uncorrected biases in earlier data Figure 2.SM.4 shows the spatial variability of long-term trends (1901 Land surface specific humidity anomalies relative to 1981 2000 2010) and more recent trends (1951 2010) over land in annual pre- Green: HadCRUH (Willett et al., 2008) cipitation using the climate research unit (CRU), GHCN, and GPCC data Blue: (Dai, 2006) sets. Rather than absolute trends (in mm per year per decade, as in Red: ERA Interim Reanalyses (Dee et al., 2011) Figure 2.29) trends are calculated relative to local climatology. The Black: HadISDH (Willett et al., 2013) spatial patterns of these trends (which can be directly compared to the trends in model precipitation reported in later chapters) are broadly Sea level anomalies relative to 1961 1990: similar. Black: Church and White (2011) Yellow: Jevrejeva et al. (2008) 2.SM.6.2 Radiosonde Humidity Data Green: Ray and Douglas (2011) Red: Nerem et al. (2010) Since AR4 there have been three distinct efforts to homogenize the Orange: Ablain et al. (2009) tropospheric humidity records from operational radiosonde measure- Blue: Leuliette and Scharroo (2010) ments (Durre et al., 2009; McCarthy et al., 2009; Dai et al., 2011) (Table 2.SM.9). Northern Hemisphere March April snow cover anomalies relative to 1967 1990 Blue: Brown (2000) 2.SM.7 Changes in Extreme Events Red: Robinson and Frei (2000) Note: Figures 4.19 and SPM-2a (green line) show a combined record Although trends in extremes indices for temperature agree within of the above two data sets which includes an estimate of uncertainty uncertainty ranges (Table 2.11), note that there are differences in (updated from Brown and Robinson, 2011). the way that each data set has been constructed. These include (1) using different input station networks: HadGHCND and GHCNDEX use Summer (July August September) average Arctic sea ice extent (abso- almost identical input data, that is, from the Global Historical Climatol- lute values) ogy Network-Daily (GHCN-Daily) data set (Durre et al., 2010; Menne Green: Walsh and Chapman (2001) et al., 2012) but different averaging methods, while HadEX2 ­ rimarily p Table 2.SM.9 | Methodologically distinct aspects of the three approaches to homogenizing tropospheric humidity records from radiosondes. Region Time Resolution and First Data Set Neighbours Automated Variables Homogenized Considered Reporting Levels Guessa Durre et al. (2009) NH Monthly, mandatory and sig- Pairwise homogenization No Yes Column integrated water vapour nificant levels to 500 hPa McCarthy et NH Monthly, mandatory levels to 300 hPa All neighbour aver- Yes Yes Temperature, specific humid- al. (2009) age, iterative ity, relative humidity Dai et al. (2011) Globe Observation resolution, manda- None Yes Yes Dew-point depression tory levels to 100 hPa Notes: First guess refers to whether a manually imputed first guess for known metadata types was incorporated prior to formal homogenization efforts. a 2SM-20 Observations: Atmosphere and Surface Supplementary Material Chapter 2 2SM Trend (% per decade) Figure 2.SM.4 | Trends in precipitation over land from the CRU, GHCN and Global Precipitation Climatology Centre (GPCC) data sets for 1901 2010 (left hand panels) and 1951 2010 (right-hand panels) as in Figure 2.29, but now in percent per decade relative to local climatology rather than in mm yr 1 per decade. uses data from individual researchers or Meteororogical Services, from country to country. A subset of GHCN-Daily is used for the USA and (2) in one case the indices are calculated from a daily gridded but whereby only selected National Weather Service Cooperative and ­temperature data set (HadGHCND) while in the other two cases indices First-Order weather observing sites with reasonably long records are are first calculated at the station level and then gridded. This order used (Peterson et al., 2008) and where station time series were deter- of operation could be important to the physical interpretation of the mined (e.g., by the statistical analysis described in Menne and Williams result (Zhang et al., 2011) and its use in model evaluation for example (2005)) to be free of significant discontinuities after 1950 caused by, (Chapter 9). Comparison of these three data sets presents a measure of e.g., changes in station location, changes in time of observation. The the structural uncertainty that exists when estimating trends in global indices are usually pre-calculated at source before being combined into temperature extremes (Box 2.1) while still in all cases indicating a the data set using standard software (Zhang et al., 2011). In most cases robust warming trend over the latter part of the 20th century. the data have been carefully assessed for quality and homogeneity by researchers in the country of origin, for example, Canada (Mekis and A description of each data set is as follows. Vincent, 2011; Vincent et al., 2012), Australia (Trewin, 2012), and where data from regional workshops were used extensive post-processing and 2.SM.7.1 HadEX2 analysis was performed (e.g., Aguilar et al., 2009; Caesar et al., 2011) to ensure data quality and homogeneity. The number of stations used Unlike GHCNDEX (see later) most of the data for HadEX2 (Donat et al., in the gridding varies depending on the index being calculated (see Box 2013b) come from individual researchers or regional data sets. While 2.4, Table 1 for the types of indices calculated). For temperature indices HadEX2 updates a previous data set, HadEX (Alexander et al., 2006), it this ranges from about 6500 to 7400 stations and for precipitation is is not just an extension of that data set but rather represents the latest about 11,500 stations. Data are produced on a 3.75° × 2.5° longitude/ acquisition of station data. The level of quality control, however, varies latitude grid and are available from 1901 to 2010. 2SM-21 Chapter 2 Observations: Atmosphere and Surface Supplementary Material 2.SM.7.2 GHCNDEX The GHCN-Daily data set (Durre et al., 2010; Menne et al., 2012) on which GHCNDEX is based currently contains about 29,000 sta- tions with daily maximum and minimum temperature and more than 80,000 stations with daily precipitation (Donat et al., 2013a). These data have been obtained from numerous data sources that have been integrated and undergone extensive quality assurance reviews (Durre et al., 2010). Although the database is updated regularly over Europe, North America and Australia as well as at several hundred synoptic stations across numerous countries, many records from Asia, Africa and South America do not contain data from the most recent years. While many records are short or incomplete, many others, especially in North America, Europe and Australia, date back well into the 19th century. At present, however, there are no bias adjustments available for GHCN- 2SM Daily to account for historical changes in instrumentation, observing practice, station location or site conditions. Only stations with at least 40 years of valid data after 1950 are used to create GHCNDEX, as this helps to minimize the effect of varying station density. Subsequently this step reduces the number of stations used for gridding by a factor of six or seven. For example, there are approximately 4700 tempera- ture stations for gridding the warmest maximum temperature (TXx) and about 11,500 precipitation stations for gridding the maximum one-day precipitation total (Rx1day) (see Box 2.4, Table 1 for index definitions). However, because of the criteria limiting station length, the spatial distribution of stations is confined mostly to regions outside of Africa, South America and India. Data are produced on a 2.5° × 2.5° longitude/latitude grid and are available for years from 1951 to the present. 2.SM.7.3 HadGHCND Also uses GHCN-Daily as input (see earlier) but the order of operation is different, that is, in this case gridding of daily maximum and mini- mum temperatures is done first and then indices are calculated. Only temperature based indices are available. Data are produced on a 3.75° × 2.5° longitude/latitude grid and are available for years from 1951 to the present. 2.SM.8 Box 2.5: Patterns and Indices of Climate Variability Box 2.5, Table 1 lists some prominent modes of large-scale climate variability and indices used for defining them. Further characterization including comments for each index is provided in Table 2.SM.10. 2SM-22 Table 2.SM.10 | Established indices of climate variability with global or regional influence. Columns are: (1) name of a climate phenomenon, (2) name of the index, (3) index definition, (4) primary references, (5) comments, including when available, characterization of the index or its spatial pattern as a dominant variability mode. Primary Climate Phenomenon Index Name Index Definition Characterization / Comments References El Nino Traditional indices NINO3 SST anomaly averaged over Rasmusson and Traditional SST-based ENSO index, devised by the Climate Analysis Center Southern of ENSO-related [5°S 5°N, 150°W 90°W] Wallace (1983), of NOAA [now: Climate Prediction Center] because a warming in this region Oscillation Tropical Pacific Cane (1986) strongly influences the global atmosphere (Cane et al., 1986). (ENSO) climate variability NINO1 Same as above but for [10°S 5°S, 90°W 80°W] Introduced along with NINO3 by NOAA s Climate Analysis Center (now: Climate Prediction Center) about 1983 1984 to describe other details of ENSO-related tropical Pacific SST variability. NINO2 Same as above but for [5°S 0°, 90°W 80°W] NINO1+2 Same as above but for [10°S 0°, 90°W 80°W] NINO4 Same as above but for [5°S 5°N, 160°E 150°W] NINO3.4 Same as above but for Trenberth (1997) Used by W MO, NOAA to define El Nino/La Nina events. Detrended form is close to the [5°S 5°N, 170°W 120°W] first PC of linearly detrended global field of monthly SST anomalies (Deser et al., 2010) Troup Southern Oscil- Standardized for each calendar month SLP difference: Tahiti Troup (1965) Used by Australian Bureau of Meteorology lation Index (SOI) minus Darwin, × 10 SOI Standardized difference of standardized SLP anomalies: Trenberth (1984) Maximizes signal-to-noise ratio of linear combinations of Darwin/Tahiti records Tahiti minus Darwin Darwin SOI Standardized Darwin SLP anomaly Trenberth and Introduced to avoid use of the Tahiti record, considered suspicious before 1935. Hoar (1996) Equatorial SOI (EQSOI) Standardized difference of standardized SLP anomalies over Bell and Halpert equatorial [5°S 5°N] Pacific Ocean: [130°W 80°W] (1998) Observations: Atmosphere and Surface Supplementary Material minus [90°E 140°E] Indices of ENSO Trans-Nino Index (TNI) Standardized NINO1+2 minus standardized NINO4 Trenberth and Nearly uncorrelated with NINO3.4 events evolution and Stepaniak (2001) for identifying differ- El Nino Modoki SSTA: [165°E 140°W, 10°S 10°N] minus 1/2[110°W 70°W, Ashok et al. (2007) Defines typical El Nino Modoki events as those with the seasonal EMI value (JJAS ent types of events Index (EMI) 15°S 5°N] minus 1/2[125°E 145°E, 10°S 20°N] or DJF means) no less than 0.7, where is the seasonal EMI standard. Indices of Eastern Pacific EP Index: leading PC of the tropical Pacific SSTA with Kao and Yu (2009) (EP) and Central Pacific subtracted predictions from a linear regression on NINO4; CP (CP) types of ENSO events index: same as EP but with NINO1+2 used in place of NINO4. E and C Indices 45° orthogonal rotation of the two leading PCs of the Takahashi et Constructed to be mutually uncorrelated; many other SST-based ENSO indices are well equatorial Pacific SSTA. Approximate formulas: C = al. (2011) approximated by linear combinations of E and C. 1.7*NINO4 -0.1*NINO1+2, E = NINO1+2 0.5*NINO4 Pacific Decadal and Interdecadal Pacific Decadal First PC of monthly N. Pacific SST anomaly field Mantua et al. Variability Oscillation (PDO) [20°N 70°N] with subtracted global mean; sign (1997); Zhang Intedecadal Pacific Oscillation (IPO) is selected to anti-correlate with NPI et al. (1997) North Pacific Index (NPI) Projection of a global Folland et al. (1999); Power et al. (1999) ; Parker et al. (2007) IPO pattern was SST anomaly field onto the third EOF for the IPO pattern, which the 1911 1995 is found as one of the period and half leading EOFs of a low- power at 13.3 pass filtered global SST years; second EOF field; sign is selected to for 1891 2005 correlate with PDO data and 11 years half power. SLP [30°N 65°N; Trenberth and Hurrell (1994) 160°E 140°W] (continued on next page) Chapter 2 2SM-23 2SM 2SM Table 2.SM.10 (continued) Primary 2SM-24 Climate Phenomenon Index Name Index Definition Characterization / Comments References Chapter 2 NAO Lisbon/ Ponta Delgada- Lisbon/Ponta Delgada minus Stykkisholmur/ Hurrell (1995) Appears as a primary NH teleconnection pattern both in SLP and 500 hPa geopotential Stykkisholmur/ Reykjavik Reykjavik standardized SLP anomalies heigh (Z500) anomalies (Wallace and Gutzler, 1981); one of rotated PCs of NH Z700 (Barn- North Atlantic Oscil- ston and Livezey, 1987). SLP anomalies can be monthly, seasonal or annual averages, lation (NAO) Index resulting in the NAO index of corresponding temporal resolution (Hurrell, 1995). In Jones et al. (1997) definition, temporal averaging is applied to monthly NAO index values. NAO Gibraltar South-west Gibraltar minus south-west Iceland / Reykjavik stan- Jones et al. (1997) index is typically interpreted for boreal winter season (e.g., DJFM or NDJFM means). Iceland NAO Index dardized monthly surface pressure anomalies PC-based NAO Index Leading PC of SLP anomalies over the Atlantic sector Hurrell (1995) [20°N 80°N, 90oW 40oE]; sign is selected to cor- relate with station-based NAO indices. Summer NAO (SNAO) Leading PC of daily SLP anomalies for July and August over Folland et Calculations with daily, 10-day, or July August mean SLP data result in the same spatial pattern the North Atlantic region [25°N 70°N, 70°W 50°E]; sign is al. (2009) characterized by a more northerly location and smaller spatial scale than its winter counterpart. selected to correlate with station-based (winter) NAO indices. Model-oriented Difference between DJF SLP averages [90°W 60°E, Stephenson et NAO index which is less sensitive to climate models shifts of locations of maximum variability. NAO index 20°N 55°N] minus [90°W 60°E, 55°N 90°N]. al. (2006) Annular Northern Annular PC-based NAM (AO) index First PC of the monthly mean SLP anomalies poleward of Thompson Closely related to the NAO. modes Mode (NAM), 20°N; sign is selected to correlate with NAO indices. and Wallace a.k.a. Arctic (1998, 2000) Oscillation (AO) Southern Annular PC-based SAM index First PC of 850 hPa or 700 hPa height anoma- Thompson and Mode (SAM) a.k.a. lies south of 20°S; sign is selected to correlate Wallace (2000) Antarctic Oscil- with grid-based AAO and SAM indices lation (AAO), Grid-based SAM index: Difference between normalized zonal mean SLP Gong and 40°S 65°S difference at 40°S and 65°S, using gridded SLP fields Wang (1999) Grid-based SAM index: Same as above but uses latitudes 40°S and 70°S Nan and Li (2003) 40°S 70°S difference Station-based SAM Difference in normalized zonal mean SLP at Marshall (2003) index: 40°S 65°S 40°S and 65°S, using station data Pacific/North America (PNA) PNA index based on 1/4[Z(20°N, 160°W) Z(45°N, 165°W) + Z(55°N, Wallace and A primary NH teleconnection (Wallace and Gutzler, 1981) in SLP and in 500 hPa geopotential atmospheric teleconnection centers of action 115°W) Z(30°N, 85°W)], Z is the location s stan- Gutzler (1981) height anomalies (Z500); second leading rotated PC of the NH Z700 (Barnston and Livezey, dardized 500 hPa geopotential height anomaly 1987). CPC now uses this procedure for Z500 and provides monthly updates for the results. PNA from rotated PC Amplitude of the PNA pattern in the decomposition of the Barnston and (RPC) calculation 500 hPa geopotential (Z500) anomaly field into the set Livezey (1987). of leading rotated EOFs obtained from the RPCA analysis of the NH Z500 monthly anomalies; sign is selected for positive correlation with the centers of action PNA index Pacific/South America (PSA) PSA1 and PSA2 mode Second and third PCs respectively of SH 500 hPa Mo and Paegle Calculation was done with NCEP-NCAR reanalysis for January 1949 to March 2000. atmospheric teleconnection indices (PC-based) seasonal geopotential height anomaly (2001) First three PCs were explaining 20%, 13% and 11% of the total variance, respectively. There are many published variations on this procedure, involving temporal filtering, using austral winter data only, PC rotation, different variables (e.g., 200 hPa stream- function). PSA1 is positive during El Nino events (sign-selecting convention). PSA index based on [ Z(35°S, 150°W) + Z(60°S, 120°W) Z(45°S, 60°W)], Z is Karoly (1989) Approximates PSA1 of the previous definition. centers of action from the location s JJA 500 hPa geopotential height anomaly the 1972-1982 El Nino events composite PSA index based on [-Z(45°S, 170°W) + Z(67.5°S, 120°W) Z(50°S, 45°W)]/3, Yuan and Li (2008) Approximates ( 1)*PSA1 of the PC-based definition above. centers of action and Z is the location s 500 hPa geopotential height anomaly La Nina response sign Observations: Atmosphere and Surface Supplementary Material (continued on next page) Table 2.SM.10 (continued) Atlantic Ocean Multi- Atlantic Multidecadal 10-year running mean of de-trended Atlan- Enfield et al. Called virtually identical to the smoothed leading rotated N. Atlantic PC. decadal Variability Oscillation (AMO) index tic mean SST anomalies [0° 70°N] (2001) Revised AMO index As above, but subtracts global anomaly mean Trenberth and (60°S 60°N) instead of de-trending Shea (2006) Tropical Atlantic Ocean Nino ATL3 SST anomalies averaged over [3°S 3°N, 20°W 0°] Zebiak (1993) Identified as the two leading PCs of detrended tropical Atlantic monthly SSTA (20°S 20°N): Atlantic Mode (AONM) 38% and 25% variance respectively for HadISST1, 1900 2008 (Deser et al. 2010a). Ocean PC-based AONM First PC of the detrended tropical Atlantic monthly SSTA variability (20°S 20°N); sign is selected to correlate with ATL3 Tropical Atlan- PC-based AMM Index Second PC of the detrended tropical Atlan- Deser et al. (2010) tic Meridional tic monthly SSTA (20°S 20°N) Mode (AMM) Tropical Indian Ocean Basin Basin mean index (BMI) SST anomalies averaged over [40° 110°E, 20°S 20°N] Yang et al. (2007) Indian Ocean Mode (IOBM) IOBM, PC-based Index The first PC of the IO detrended SST anoma- Identified as the two leading PCs of detrended tropical Indian Ocean monthly SSTA (20°S 20°N): variability lies (40°E 110° E, 20°S 20°N); sign is selected 39% and 12% of the variance, respectively, for HadISST1, 1900 2008 (Deser et al. 2010a). by correlation with IOB basin mean index Deser et al. (2010) Indian Ocean Dipole PC-based IODM index The second PC of the IO detrended SST anomalies (40°E 110° Mode (IODM) E, 20°S 20°N); sign is selected to correlate with DMI Dipole Mode Index (DMI) SST anomalies difference: [50°E 70°E, Saji et al. (1999) 10°S 10°N)] [90°E 110°E, 10°S 0°] Observations: Atmosphere and Surface Supplementary Material Chapter 2 2SM-25 2SM Chapter 2 Observations: Atmosphere and Surface Supplementary Material References Ablain, M., A. Cazenave, G. Valladeau, and S. Guinehut, 2009: A new assessment of Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979 the error budget of global mean sea level rate estimated by satellite altimetry 2010. Cryosphere, 6, 957 979. over 1993 2008. Ocean Sci., 5, 193 201. Cavalieri, D. J., P. Gloersen, and W. J. Campbell, 1984: Determination of sea ice Aguilar, E., et al., 2009: Changes in temperature and precipitation extremes in parameters with the Nimbus-7 SMMR. J. Geophys. Res. Atmos., 89, 5355 5369. western central Africa, Guinea Conakry, and Zimbabwe, 1955 2006. J. Geophys. Christy, J. R., R. W. Spencer, W. B. Norris, W. D. Braswell, and D. E. Parker, 2003: Res. Atmos., 114, D02115. Error estimates of version 5.0 of MSU-AMSU bulk atmospheric temperatures. J. Alexander, L. V., et al., 2006: Global observed changes in daily climate extremes of Atmos. Ocean. Technol., 20, 613 629. temperature and precipitation. J. Geophys. Res. Atmos., 111, D05109. Christy, J. R., D. E. Parker, S. J. Brown, I. Macadam, M. Stendel, and W. B. Norris, 2001: Allan, R., P. Brohan, G. P. Compo, R. Stone, J. Luterbacher, and S. Bronnimann, 2011: Differential trends in tropical sea surface and atmospheric temperatures since The International Atmospheric Circulation Reconstructions over the Earth 1979. Geophys. Res. Lett., 28, 183 186. (ACRE) initiative. Bull. Am. Meteorol. Soc., 92, 1421-1425. Church, J. A., and N. J. White, 2011: Sea-level rise from the late 19th to the early 21st Anthes, R. A., et al., 2008: The COSMOC/FORMOSAT-3 - Mission early results. Bull. century. Surv. Geophys., 32, 585 602. Am. Meteorol. Soc., 89, 313 +. Clain, G., et al., 2009: Tropospheric ozone climatology at two Southern Hemisphere Arendt, A., et al., 2012: Randolph Glacier Inventory [v2.0]: A Dataset of Global tropical/subtropical sites (Reunion Island and Irene, South Africa) from Glacier Outlines. Global Land Ice Measurements from Space. Digital media. ozonesondes, LIDAR, and in situ aircraft measurements. Atmos. Chem. Phys., 2SM http://nsidc.org/data/docs/noaa/g01130_glacier_inventory 9, 1723 1734. Arndt, D. S., M. O. Baringer, and M. R. Johnson, 2010: State of the Climate in 2009. Cogley, J. G., 2009: Geodetic and direct mass-balance measurements: comparison Bull. Am. Meteorol. Soc., 91, S1 S224. and joint analysis. Ann. Glaciol., 50, 96 100. Ashok, K., S. K. Behera, S. A. Rao, H. Y. Weng, and T. Yamagata, 2007: El Nino Modoki Comiso, J. C., and F. Nishio, 2008: Trends in the sea ice cover using enhanced and and its possible teleconnection. J. Geophys. Res. Oceans, 112, C11007. compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res. Oceans, 113, Barnes, N., and D. Jones, 2011: Clear climate code: Rewriting legacy science software C02S07. for clarity. IEEE Software, 28, 36 42. Cooper, O. R., R. S. Gao, D. Tarasick, T. Leblanc, and C. Sweeney, 2012: Long-term Barnston, A. G., and R. E. Livezey, 1987: Classification, seasonality and persistence ozone trends at rural ozone monitoring sites across the United States, 1990 of low-frequency atmospheric circulation patterns. Mon. Weather Rev., 115, 2010. J. Geophys. Res., 117, D22307. 1083 1126. Cunnold, D., et al., 1997: GAGE/AGAGE measurements indicating reductions in Beig, G., and V. Singh, 2007: Trends in tropical tropospheric column ozone from global emissions of CCl3F and CCl2F2 in 1992 1994. J. Geophys. Res. Atmos., satellite data and MOZART model. Geophys. Res. Lett., 34, L17801. 102, 1259 1269. Bell, G. D., and M. S. Halpert, 1998: Climate assessment for 1997. Bull. Am. Meteorol. Dai, A., 2006: Recent climatology, variability, and trends in global surface humidity. Soc., 79, S1 S50. J. Clim., 19, 3589 3606. Berry, D. I., and E. C. Kent, 2009: A new air-sea interaction gridded dataset from Dai, A. G., J. H. Wang, P. W. Thorne, D. E. Parker, L. Haimberger, and X. L. L. Wang, ICOADS with uncertainty estimates. Bull. Am. Meteor. Soc., 90, 645-656. 2011: A new approach to homogenize daily radiosonde humidity data. J. Clim., Berry, D. I., E. C. Kent, and P. K. Taylor, 2004: An analytical model of heating errors in 24, 965 991. marine air temperatures from ships. J. Atmos. Ocean. Technol., 21, 1198 1215. Dee, D. P., et al., 2011: The ERA-Interim reanalysis: Configuration and performance of Bond, T. C., et al., 2013: Bounding the role of black carbon in the climate system: A the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553 597. scientific assessment. J. Geophys. Res. Atmos., 118, 5380 5552. Deser, C., M. A. Alexander, S. P. Xie, and A. S. Phillips, 2010: Sea surface temperature Bottomley, M., C. K. Folland, J. Hsiung, R. E. Newell, and D. E. Parker, 1990: Global variability: Patterns and mechanisms. Annu. Rev. Mar. Sci., 2, 115 143. ocean surface temperature atlas GOSTA . Meteorological Office, Bracknell, UK Ding, A. J., T. Wang, V. Thouret, J. P. Cammas, and P. Nedelec, 2008: Tropospheric and Department of Earth, Atmospheric and Planetary Sciences, Massachusetts ozone climatology over Beijing: analysis of aircraft data from the MOZAIC Institute of Technology, Cambridge, MA, USA, 20 pp. program. Atmos. Chem. Phys., 8, 1 13. Brauer, M., et al., 2012: Exposure assessment for estimation of the global burden of Dlugokencky, E., et al., 2005: Conversion of NOAA atmospheric dry air CH4 mole disease attributable to outdoor air pollution. Environ. Sci. Technol., 46, 652 660. fractions to a gravimetrically prepared standard scale. J. Geophys. Res. Atmos., Brohan, P., R. Allan, J. E. Freeman, A. M. Waple, D. Wheeler, C. Wilkinson, and S. 110, D18306. Woodruff, 2009: Marine observations of old weather. Bull. Am. Meteorol. Soc., Domingues, C. M., J. A. Church, N. J. White, P. J. Gleckler, S. E. Wijffels, P. M. Barker, 90, 219-230. and J. R. Dunn, 2008: Improved estimates of upper-ocean warming and multi- Brown, R. D., 2000: Northern Hemisphere snow cover variability and change, 1915 decadal sea-level rise. Nature, 453, 1090 1093. 97. J. Clim., 13, 2339 2355. Donat, M. G., L. V. Alexander, H. Yang, I. Durre, R. Vose, and J. Caesar, 2013a: Global Brown, R. D., and D. A. Robinson, 2011: Northern Hemisphere spring snow cover land-based datasets for monitoring climatic extremes. Bull. Am. Meteor. Soc., variability and change over 1922 2010 including an assessment of uncertainty. 94, 997-1006. Cryosphere, 5, 219 229. Donat, M. G., et al., 2013b: Updated analyses of temperature and precipitation Brownscombe, J. L., J. Nash, G. Vaughan, and C. F. Rogers, 1985: Solar tides in the extreme indices since the beginning of the twentieth century: The HadEX2 middle atmosphere. 1. Description of satellite-observations and comparison dataset. J. Geophys. Res. Atmos., 118, 2098-2118. with theoretical calculations at equinox. Q. J. R. Meteorol. Soc., 111, 677 689. Donlon, C., et al., 2007: The global ocean data assimilation experiment high- Brunet, M., and P. Jones, 2011: Data rescue initiatives: Bringing historical climate resolution sea surface temperature pilot project. Bull. Am. Meteorol. Soc., 88, data into the 21st century. Clim. Res., 47, 29 40. 1197 1213. Butler, J., S. Montzka, A. Clarke, J. Lobert, and J. Elkins, 1998: Growth and distribution Durre, I., C. N. Williams, X. G. Yin, and R. S. Vose, 2009: Radiosonde-based trends in of halons in the atmosphere. J. Geophys. Res. Atmos., 103, 1503 1511. precipitable water over the Northern Hemisphere: An update. J. Geophys. Res. Caesar, J., et al., 2011: Changes in temperature and precipitation extremes over the Atmos., 114, D05112. Indo-Pacific region from 1971 to 2005. Int. J. Climatol., 31, 791 801. Durre, I., M. J. Menne, B. E. Gleason, T. G. Houston, and R. S. Vose, 2010: Comprehensive Canada, 2012: Canadian Smog Science Assessment Highlights and Key Messages. automated quality assurance of daily surface observations. J. Appl. Meteorol. Environment Canada and Health Canada, 64 pp. Climatol., 49, 1615 1633. Cane, M. A., 1986: El-Nino. Annu. Rev. Earth Planet. Sci., 14, 43 70. EANET, 2011: EANET Data report on the acid deposition in the East Asian Region Cane, M. A., S. E. Zebiak, and S. C. Dolan, 1986: Experimental forecasts of El Nino. 2009. Acid Deposition Monitoring Network in East Asia, Nature, 321, 827 832. Embury, O., C. J. Merchant, and G. K. Corlett, 2012: A reprocessing for Climate of Sea CASTNET, 2010: Clean Air Status and Trends Network (CASTNET) 2008 Annual Surface Temperature from the Along-Track Scanning Radiometers: Preliminary Report. US Environmental Protection Agency, Washington, DC, 80 pp. validation, accounting for skin and diurnal variability. Remote Sens. Environ., 116, 62-78 . 2SM-26 Observations: Atmosphere and Surface Supplementary Material Chapter 2 Emery, W. J., D. J. Baldwin, P. Schlussel, and R. W. Reynolds, 2001: Accuracy of in situ Ho, S. P., et al., 2009b: Estimating the uncertainty of using GPS radio occultation data sea surface temperatures used to calibrate infrared satellite measurements. J. for climate monitoring: Intercomparison of CHAMP refractivity climate records Geophys. Res. Oceans, 106, 2387 2405. from 2002 to 2006 from different data centers. J. Geophys. Res. Atmos., 114, Enfield, D. B., A. M. Mestas-Nunez, and P. J. Trimble, 2001: The Atlantic multidecadal D23107. oscillation and its relation to rainfall and river flows in the continental US. Holben, B. N., et al., 1998: AERONET A federated instrument network and data Geophys. Res. Lett., 28, 2077 2080. archive for aerosol characterization. Remote Sens. Environ., 66, 1 16. Foelsche, U., B. Pirscher, M. Borsche, G. Kirchengast, and J. Wickert, 2009: Assessing Hurrell, J. W., 1995: Decadal trends in the North Atlantic Oscillation: Regional the climate monitoring utility of radio occultation data: From CHAMP to temperatures and precipitation. Science, 269, 676 679. FORMOSAT-3/COSMIC. Terres. Atmos. Ocean. Sci., 20, 155 170. Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content Folland, C. K., D. E. Parker, A. Colman, and W. R., 1999: Large scale modes of ocean variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., surface temperature since the late nineteenth century. In: Beyond El Nino: 65, 287 299. Decadal and Interdecadal Climate Variability [A. Navarra (ed.)]. Springer-Verlag, Ishii, M., A. Shouji, S. Sugimoto, and T. Matsumoto, 2005: Objective analyses of sea- New York, NY, USA, and Heidelberg, Germany, pp. 73 102. surface temperature and marine meteorological variables for the 20th century Folland, C. K., J. Knight, H. W. Linderholm, D. Fereday, S. Ineson, and J. W. Hurrell, using icoads and the Kobe collection. Int. J. Climatol., 25, 865 879. 2009: The summer North Atlantic Oscillation: Past, present, and future. J. Clim., Ivy, D., et al., 2012: Atmospheric histories and growth trends of C4F10, C5F12, 22, 1082 1103. C6F14, C7F16 and C8F18. Atmos. Chem. Phys., 12, 4313 4325. Fraser, P., et al., 1996: Lifetime and emission estimates of 1,1,2 trichlorotrifluorethane Jevrejeva, S., J. C. Moore, A. Grinsted, and P. L. Woodworth, 2008: Recent global sea (CFC-113) from daily global background observations June 1982 June 1994. J. level acceleration started over 200 years ago? Geophys. Res. Lett., 35, L08715. 2SM Geophys. Res. Atmos., 101, 12585 12599. John, V. O., G. Holl, S. A. Buehler, B. Candy, R. W. Saunders, and D. E. Parker, 2012: Free, M., D. J. Seidel, J. K. Angell, J. Lanzante, I. Durre, and T. C. Peterson, 2005: Understanding intersatellite biases of microwave humidity sounders using Radiosonde Atmospheric Temperature Products for Assessing Climate (RATPAC): global simultaneous nadir overpasses. J. Geophys. Res. Atmos., 117, D02305. A new data set of large-area anomaly time series. J. Geophys. Res. Atmos., 110, Jones, P. D., T. Jonsson, and D. Wheeler, 1997: Extension to the North Atlantic D22101. Oscillation using early instrumental pressure observations from Gibraltar and Gong, D. Y., and S. W. Wang, 1999: Definition of Antarctic Oscillation Index. Geophys. south-west Iceland. Int. J. Climatol., 17, 1433 1450. Res. Lett., 26, 459 462. Jones, P. D., D. H. Lister, T. J. Osborn, C. Harpham, M. Salmon, and C. P. Morice, Granier, C., et al., 2011: Evolution of anthropogenic and biomass burning emissions 2012: Hemispheric and large-scale land-surface air temperature variations: An of air pollutants at global and regional scales during the 1980 2010 period. extensive revision and an update to 2010. J. Geophys. Res. Atmos., 117, 29. Clim. Change, 109, 163 190. Kao, H. Y., and J. Y. Yu, 2009: Contrasting Eastern-Pacific and Central-Pacific Types of Greally, B., et al., 2007: Observations of 1,1-difluoroethane (HFC-152a) at AGAGE ENSO. J. Clim., 22, 615 632. and SOGE monitoring stations in 1994 2004 and derived global and regional Karoly, D., 1989: Southern-Hemisphere circulation features associated with El emission estimates. J. Geophys. Res. Atmos., 112, D06308. Nino Southern Oscillation events. J. Clim., 2, 1239 1252. Grody, N. C., K. Y. Vinnikov, M. D. Goldberg, J. T. Sullivan, and J. D. Tarpley, 2004: Kaskaoutis, D. G., S. R. P, R. Gautam, M. Sharma, P. G. Kosmopoulos, and S. N. Tripathi, Calibration of multisatellite observations for climatic studies: Microwave 2012: Variability and trends of aerosol properties over Kanpur, northern India Sounding Unit (MSU). J. Geophys. Res. Atmos., 109, D24104. using AERONET data (2001 10). Environ. Res. Lett., 7, 024003. Haimberger, L., 2004: Checking the temporal homogeneity of radiosonde data in the Kawai, Y., and A. Wada, 2007: Diurnal sea surface temperature variation and its Alpine region using ERA-40 analysis feedback data. Meteorol. Z., 13, 123 129. impact on the atmosphere and ocean: A review. J. Oceanogr., 63, 721 744. Haimberger, L., 2007: Homogenization of radiosonde temperature time series using Keeling, C., R. Bacastow, A. Bainbridge, C. Ekdahl, P. Guenther, L. Waterman, and J. innovation statistics. J. Clim., 20, 1377 1403. Chin, 1976: Atmospheric carbon-dioxide variations at Mauna-Loa Observatory, Haimberger, L., C. Tavolato, and S. Sperka, 2008: Toward elimination of the warm Hawaii. Tellus, 28, 538 551. bias in historic radiosonde temperature records - Some new results from a Kennedy, J. J., P. Brohan, and S. F. B. Tett, 2007: A global climatology of the diurnal comprehensive intercomparison of upper-air data. J. Clim., 21, 4587 4606. variations in sea-surface temperature and implications for MSU temperature Haimberger, L., C. Tavolato, and S. Sperka, 2012: Homogenization of the global trends. Geophys. Res. Lett., 34, L05712. radiosonde temperature dataset through combined comparison with reanalysis Kennedy, J. J., N. A. Rayner, and R. O. Smith, 2012: Using AATSR data to assess the background series and neighboring stations. J. Clim., 25, 8108-8131. quality of in situ sea surface temperature observations for climate studies. Hajj, G. A., et al., 2004: CHAMP and SAC-C atmospheric occultation results and Remote Sens. Environ., 116, 79 92. intercomparisons. J. Geophys. Res. Atmos., 109, D06109. Kennedy, J. J., N. A. Rayner, R. O. Smith, D. E. Parker, and M. Saunby, 2011a: Hall, B., G. Dutton, and J. Elkins, 2007: The NOAA nitrous oxide standard scale for Reassessing biases and other uncertainties in sea surface temperature atmospheric observations. J. Geophys. Res. Atmos., 112, D09305. observations measured in situ since 1850: 2. Biases and homogenization. J. Hall, B., et al., 2011: Improving measurements of SF6 for the study of atmospheric Geophys. Res. Atmos., 116, 22. transport and emissions. Atmos. Meas. Tech., 4, 2441 2451. Kennedy, J. J., N. A. Rayner, R. O. Smith, D. E. Parker, and M. Saunby, 2011a: Han, Y. M., et al., 2011: Comparison of elemental carbon in lake sediments measured Reassessing biases and other uncertainties in sea surface temperature by three different methods and 150-year pollution history in eastern China. observations measured in situ since 1850: 2. Biases and homogenization. J. Environ. Sci. Technol., 45, 5287 5293. Geophys. Res. Atmos., 116, D14104. Hand, J. L., et al., 2011: IMPROVE, spatial and seasonal patterns and temporal Kent, E. C., and D. I. Berry, 2005: Quantifying random measurement errors in variability of haze and its constituents in the United States, Report V, ISSN 0737- voluntary observing ships meteorological observations. Int. J. Climatol., 25, 5352-87, CIRA, Fort Collins, Colorado. 843 856. Hansen, J., R. Ruedy, M. Sato, and K. Lo, 2010: Global surface temperature change. Kent, E. C., and P. G. Challenor, 2006: Toward estimating climatic trends in SST. Part Rev. Geophys., 48, RG4004. II: Random errors. J. Atmos. Ocean. Technol., 23, 476 486. Helmig, D., et al., 2007: A review of surface ozone in the polar regions. Atmos. Kent, E. C., and A. Kaplan, 2006: Toward estimating climatic trends in SST. Part III: Environ., 41, 5138 5161. Systematic biases. J. Atmos. Ocean. Technol., 23, 487 500. Hess, P. G., and R. Zbinden, 2013: Stratospheric impact on tropospheric ozone Kent, E. C., and P. K. Taylor, 2006: Toward estimating climatic trends in SST. Part I: variability and trends: 1990 2009. Atmos. Chem. Phys., 13, 649 674. Methods of measurement. J. Atmos. Ocean. Technol., 23, 464 475. Hidy, G. M., and G. T. Pennell, 2010: Multipollutant air quality management: 2010 Kent, E. C., P. G. Challenor, and P. K. Taylor, 1999: A statistical determination of the critical review. J. Air Waste Manage. Assoc., 60, 645 674. random observational errors present in voluntary observing ships meteorological Ho, S. P., M. Goldberg, Y. H. Kuo, C. Z. Zou, and W. Schreiner, 2009a: Calibration of reports. J. Atmos. Ocean. Technol., 16, 905 914. temperature in the Lower Stratosphere from microwave measurements using Kent, E. C., J. J. Kennedy, D. I. Berry, and R. O. Smith, 2010: Effects of instrumentation COSMIC radio occultation data: Preliminary results. Terres. Atmos. Ocean. Sci., changes on sea surface temperature measured in situ. Clim. Change, 1, 718 728. 20, 87 100. 2SM-27 Chapter 2 Observations: Atmosphere and Surface Supplementary Material Kent, E. C., N. A. Rayner, D. I. Berry, M. Saunby, B. I. Moat, J. J. Kennedy, and D. E. Mekis, É., and L. A. Vincent, 2011: An overview of the second generation adjusted Parker, 2013: Global analysis of night marine air temperature and its uncertainty daily precipitation dataset for trend analysis in Canada. Atmos. Ocean, 49, since 1880, the HadNMAT2 Dataset, J. Geophys. Res., 118, 1281-1298. 163 177. Kim, D.-H., B.-J. Sohn, T. Nakajima, T. Takamura, T. Takemura, B.-C. Choi, and S.-C. Menne, M. J., and C. N. Williams, 2005: Detection of undocumented changepoints Yoon, 2004: Aerosol optical properties over east Asia determined from ground- using multiple test statistics and composite reference series. J. Clim., 18, 4271 based sky radiation measurements. J. Geophys. Res., 109, D02209. 4286. Kobayashi, S., M. Matricardi, D. Dee, and S. Uppala, 2009: Toward a consistent Menne, M. J., and C. N. Williams, 2009: Homogenization of temperature series via reanalysis of the upper stratosphere based on radiance measurements from SSU pairwise comparisons. J. Clim., 22, 1700 1717. and AMSU-A. Q. J. R. Meteorol. Soc., 135, 2086 2099. Menne, M. J., I. Durre, B. G. Gleason, T. G. Houston, and R. S. Vose, 2012: An overview Krishna Moorthy, K., S. Suresh Babu, M. R. Manoj, and S. K. Satheesh, 2013: Buildup of the Global Historical Climatology Network-Daily database. J. Atmos. Ocean. of aerosols over the Indian Region. Geophys. Res. Lett., doi:10.1002/grl.50165, Technol., 29, 897 910. in press. Miller, B., R. Weiss, P. Salameh, T. Tanhua, B. Greally, J. Muhle, and P. Simmonds, Krishna Moorthy, K., S. S. Babu, S. K. Satheesh, S. Lal, M. M. Sarin, and S. 2008: Medusa: A sample preconcentration and GC/MS detector system for in Ramachandran, 2009: Climate implications of atmospheric aerosols and trace situ measurements of atmospheric trace halocarbons, hydrocarbons, and sulfur gases: Indian Scenario, Climate Sense. World Meteorological Organisation, compounds. Anal. Chem., 80, 1536-1545 . Geneva, Switzerland, pp. 157 160. Miller, B., et al., 2010: HFC-23 (CHF3) emission trend response to HCFC-22 (CHClF2) Kuo, Y. H., T. K. Wee, S. Sokolovskiy, C. Rocken, W. Schreiner, D. Hunt, and R. A. Anthes, production and recent HFC-23 emission abatement measures. Atmos. Chem. 2004: Inversion and error estimation of GPS radio occultation data. J. Meteorol. Phys., 10, 7875-7890. 2SM Soc. Jpn., 82, 507 531. Ministery of Environment and Forest, G. o. I., 2009: State of Environmnet Report, Laube, J., et al., 2010: Accelerating growth of HFC-227ea India 2009. Ministery of Environment and Forest, 194 pp. (1,1,1,2,3,3,3-heptafluoropropane) in the atmosphere. Atmos. Chem. Phys., 10, Mo, K., and J. Paegle, 2001: The Pacific-South American modes and their downstream 5903 5910. effects. Int. J. Climatol., 21, 1211 1229. Lawrimore, J., J. Rennie, and P. Thorne, 2013: Responding to the need for better Mo, T., 2009: A study of the NOAA-15 AMSU-A brightness temperatures from 1998 global temperature Ddata. EOS Trans. Am. Geophys. Union, 94, 61 62. through 2007. J. Geophys. Res. Atmos., 114. Lawrimore, J. H., M. J. Menne, B. E. Gleason, C. N. Williams, D. B. Wuertz, R. S. Vose, Montzka, S., B. Hall, and J. Elkins, 2009: Accelerated increases observed for and J. Rennie, 2011: An overview of the Global Historical Climatology Network hydrochlorofluorocarbons since 2004 in the global atmosphere. Geophys. Res. monthly mean temperature data set, version 3. J. Geophys. Res. Atmos., 116, Lett., 36, L03804. D19121. Montzka, S., R. Myers, J. Butler, J. Elkins, and S. Cummings, 1993: Global tropospheric Leclercq, P. W., J. Oerlemans, and J. G. Cogley, 2011: Estimating the glacier distribution and calibration scale of HCFC-22. Geophys. Res. Lett., 20, 703 706. contribution to sea-level rise for the period 1800 2005. Surv. Geophys., 32, Montzka, S., M. Krol, E. Dlugokencky, B. Hall, P. Jockel, and J. Lelieveld, 2011: Small 519 535. interannual variability of global atmospheric hydroxyl. Science, 331, 67-69. Lelieveld, J., J. van Aardenne, H. Fischer, M. de Reus, J. Williams, and P. Winkler, 2004: Montzka, S., R. Myers, J. Butler, J. Elkins, L. Lock, A. Clarke, and A. Goldstein, 1996: Increasing ozone over the Atlantic Ocean. Science, 304, 1483 1487. Observations of HFC-134a in the remote troposphere. Geophys. Res. Lett., 23, Leroy, S. S., J. G. Anderson, and J. A. Dykema, 2006: Testing climate models using GPS 169 172. radio occultation: A sensitivity analysis. J. Geophys. Res. Atmos, 111, D17105. Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying Leuliette, E. W., and R. Scharroo, 2010: Integrating Jason-2 into a multiple-altimeter uncertainties in global and regional temperature change using an ensemble of climate data record. Mar. Geodesy, 33, 504 517. observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, Levitus, S., et al., 2012: World ocean heat content and thermosteric sea level change 22. (0 2000 m), 1955 2010. Geophys. Res. Lett., 39, L10603. Muhle, J., et al., 2009: Sulfuryl fluoride in the global atmosphere. J. Geophys. Res. Li, H. C., K. S. Chen, C. H. Huang, and H. K. Wang, 2010: Meteorologically adjusted Atmos., 114, D05306. long-term trend of ground-level ozone concentrations in Kaohsiung County, Muhle, J., et al., 2010: Perfluorocarbons in the global atmosphere: Tetrafluoromethane, southern Taiwan. Atmos. Environ., 44, 3605 3608. hexafluoroethane, and octafluoropropane. Atmos. Chem. Phys., 10, 5145-5164. Lin, Y. K., T. H. Lin, and C. S.C., 2010: The changes in different ozone metrics and their Nan, S., and J. P. Li, 2003: The relationship between the summer precipitation in the implications following precursor reductions over northern Taiwan from 1994 to Yangtze River Valley and the boreal spring Southern Hemisphere annular mode. 2007. Environ. Monit. Assess., 169, 143 157. Geophys. Res. Lett., 30, 2266. Logan, J. A., et al., 2012: Changes in Ozone over Europe since 1990: analysis of ozone Nerem, R. S., D. P. Chambers, C. Choe, and G. T. Mitchum, 2010: Estimating mean measurements from sondes, regular Aircraft (MOZAIC), and alpine surface sites. sea level change from the TOPEX and Jason Altimeter Missions. Mar. Geodesy, J. Geophys. Res., 117, D09301. 33, 435 446. Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace, and R. C. Francis, 1997: A Pacific O Carroll, A. G., J. R. Eyre, and R. W. Saunders, 2008: Three-way error analysis interdecadal climate oscillation with impacts on salmon production. Bull. Am. between AATSR, AMSR-E, and in situ sea surface temperature observations. J. Meteorol. Soc., 78, 1069 1079. Atmos. Ocean. Technol., 25, 1197 1207. Markus, T., and D. J. Cavalieri, 2000: An enhancement of the NASA Team sea ice O Doherty, S., et al., 2004: Rapid growth of hydrofluorocarbon 134a and algorithm. Ieee Trans. Geosci. Remote Sens, 38, 1387 1398. hydrochlorofluorocarbons 141b, 142b, and 22 from Advanced Global Marshall, G. J., 2003: Trends in the southern annular mode from observations and Atmospheric Gases Experiment (AGAGE) observations at Cape Grim, Tasmania, reanalyses. J. Clim., 16, 4134 4143. and Mace Head, Ireland. J. Geophys. Res. Atmos., 109, D06310. Marzeion, B., A. H. Jarosch, and M. Hofer, 2012: Past and future sea-level change O Doherty, S., et al., 2009: Global and regional emissions of HFC-125 (CHF2CF3) from the surface mass balance of glaciers. Cryosphere, 6, 1295 1322. from in situ and air archive atmospheric observations at AGAGE and SOGE McCarthy, M. P., P. W. Thorne, and H. A. Titchner, 2009: An analysis of tropospheric observatories. J. Geophys. Res. Atmos., 114, D23304. humidity trends from radiosondes. J. Clim., 22, 5820 5838. Oltmans, S. J., et al., 2013: Recent tropospheric ozone changes A pattern dominated Mears, C. A., and F. J. Wentz, 2009a: Construction of the Remote Sensing Systems by slow or no growth. Atmos. Environ., 67, 331 351. V3.2 Atmospheric temperature records from the MSU and AMSU Microwave Oram, D., et al., 2012: Long-term tropospheric trend of octafluorocyclobutane Sounders. J. Atmos. Ocean. Technol., 26, 1040 1056. (c-C4F8 or PFC-318). Atmos. Chem. Phys., 12, 261 269. Mears, C. A., and F. J. Wentz, 2009b: Construction of the RSS V3.2 Lower-Tropospheric Palmer, M. D., K. Haines, S. F. B. Tett, and T. J. Ansell, 2007: Isolating the signal of temperature dataset from the MSU and AMSU Microwave Sounders. J. Atmos. ocean global warming. Geophys. Res. Lett., 34, 6. Ocean. Technol., 26, 1493 1509. Parker, D., C. Folland, A. Scaife, J. Knight, A. Colman, P. Baines, and B. Dong, 2007: Mears, C. A., F. J. Wentz, P. Thorne, and D. Bernie, 2011: Assessing uncertainty in Decadal to multidecadal variability and the climate change background. J. estimates of atmospheric temperature changes from MSU and AMSU using a Geophys. Res. Atmos., 112. D18115. Monte-Carlo estimation technique. J. Geophys. Res. Atmos., 116. Parkinson, C. L., and D. J. Cavalieri, 2012: Antarctic Sea ice variability and trends, 1979 2010. Cryosphere, 6, 871 880. 2SM-28 Observations: Atmosphere and Surface Supplementary Material Chapter 2 Parrish, D. D., et al., 2012: Long-term changes in lower tropospheric baseline ozone Sherwood, S. C., 2007: Simultaneous detection of climate change and observing concentrations at northern mid-latitudes. Atmos. Chem. Phys., 12, 11485 11504. biases in a network with incomplete sampling. J. Clim., 20, 4047 4062. Peterson, T. C., X. B. Zhang, M. Brunet-India, and J. L. Vazquez-Aguirre, 2008: Changes Sherwood, S. C., C. L. Meyer, R. J. Allen, and H. A. Titchner, 2008: Robust tropospheric in North American extremes derived from daily weather data. J. Geophys. Res. warming revealed by iteratively homogenized radiosonde data. J. Clim., 21, Atmos., 113, D07113. 5336 5350. Power, S., T. Casey, C. Folland, A. Colman, and V. Mehta, 1999: Inter-decadal Shine, K. P., J. J. Barnett, and W. J. Randel, 2008: Temperature trends derived from modulation of the impact of ENSO on Australia. Clim. Dyn., 15, 319 324. Stratospheric Sounding Unit radiances: The effect of increasing CO2 on the Prather, M., and J. Hsu, 2008: NF3, the greenhouse gas missing from Kyoto. Geophys. weighting function. Geophys. Res. Lett., 35, L02710. Res. Lett., 35, L12810. Sickles, J. E., II, and D. S. Shadwick, 2007a: Changes in air quality and atmospheric Prinn, R., et al., 1990: Atmospheric emissions and trends of nitrous oxide deduced deposition in the eastern United States: 1990 2004. J. Geophys. Res., 112, from 10 years of ALE-GAGE Data. J. Geophys. Res., 95, 18, 369 18, 385. D17301. Prinn, R., et al., 2000: A history of chemically and radiatively important gases in air Sickles, J. E., II, and D. S. Shadwick, 2007b: Seasonal and regional air quality and deduced from ALE/GAGE/AGAGE. J. Geophys. Res. Atmos., 105, 17751 17792. atmospheric deposition in the eastern United States. J. Geophys. Res., 112, Prinn, R., et al., 2005: Evidence for variability of atmospheric hydroxyl radicals over D17302. the past quarter century. Geophys. Res. Lett., L07809. Simmonds, P., et al., 1998: Global trends and emission estimates of CCl4 from in situ Qu, W. J., R. Arimoto, X. Y. Zhang, C. H. Zhao, Y. Q. Wang, L. F. Sheng, and G. Fu, 2010: background observations from July 1978 to June 1996. J. Geophys. Res. Atmos., Spatial distribution and interannual variation of surface PM10 concentrations 103, 31331 31331. over eighty-six Chinese cities. Atmos. Chem. Phys., 10, 5641 5662. Smith, D. M., and J. M. Murphy, 2007: An objective ocean temperature and salinity 2SM Quan, J., Q. Zhang, H. He, J. Liu, M. Huang, and H. Jin, 2011: Analysis of the formation analysis using covariances from a global climate model. J. Geophys. Res. Oceans, of fog and haze in North China Plain (NCP). Atmos. Chem. Phys., 11, 8205 8214. 112, C02022. Quinn, P. K., T. S. Bates, K. Schulz, and G. E. Shaw, 2009: Decadal trends in aerosol Smith, T. M., and R. W. Reynolds, 2002: Bias corrections for historical sea surface chemical composition at Barrow, Alaska: 1976 2008. Atmos. Chem. Phys., 9, temperatures based on marine air temperatures. J. Clim., 15, 73 87. 8883 8888. Smith, T. M., T. C. Peterson, J. H. Lawrimore, and R. W. Reynolds, 2005: New surface Randel, W. J., et al., 2009: An update of observed stratospheric temperature trends. J. temperature analyses for climate monitoring. Geophys. Res. Lett., 32, L14712. Geophys. Res. Atmos., 114, D02107. Smith, T. M., R. W. Reynolds, T. C. Peterson, and J. Lawrimore, 2008: Improvements Rasmusson, E. M., and J. M. Wallace, 1983: Meteorological aspects of the El Nino- to NOAA s historical merged land-ocean surface temperature analysis (1880 Southern Oscillation. Science, 222, 1195 1202. 2006). J. Clim., 21, 2283 2296. Ray, R. D., and B. C. Douglas, 2011: Experiments in reconstructing twentieth-century Stemmler, K., et al., 2007: European emissions of HFC-365mfc, a chlorine-free sea levels. Prog. Oceanogr., 91, 496 515. substitute for the foam blowing agents HCFC-141b and CFC-11. Environ. Sci. Rayner, N. A., et al., 2003: Global analyses of sea surface temperature, sea ice, and Technol., 41, 1145 1151. night marine air temperature since the late nineteenth century. J. Geophys. Res. Stephenson, D. B., V. Pavan, M. Collins, M. M. Junge, R. Quadrelli, and C. M. G. Atmos., 108, 37. Participating, 2006: North Atlantic Oscillation response to transient greenhouse Rayner, N. A., et al., 2006: Improved analyses of changes and uncertainties in sea gas forcing and the impact on European winter climate: A CMIP2 multi-model surface temperature measured in situ sice the mid-nineteenth century: The assessment. Clim. Dynam., 27, 401-420. HadSST2 dataset. J. Clim., 19, 446 469. Takahashi, K., A. Montecinos, K. Goubanova, and B. Dewitte, 2011: ENSO regimes: Reynolds, R. W., C. L. Gentemann, and G. K. Corlett, 2010: Evaluation of AATSR and Reinterpreting the canonical and Modoki El Nino. Geophys. Res. Lett., 38, TMI Satellite SST Data. J. Clim., 23, 152 165. L10704. Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes, W. Wang, and A. M. S. Ams, Tarasova, O. A., I. A. Senik, M. G. Sosonkin, J. Cui, J. Staehelin, and A. S. H. Prevot, 2002: An Improved In Situ and Satellite SST Analysis, J. Climate, 15, 1609-1625. 2009: Surface ozone at the Caucasian site Kislovodsk High Mountain Station Rigby, M., et al., 2008: Renewed growth of atmospheric methane. Geophys. Res. and the Swiss Alpine site Jungfraujoch: data analysis and trends (1990 2006). Lett., 35, L22805. Atmos. Chem. Phys., 9, 4157 4175. Rigby, M., et al., 2010: History of atmospheric SF6 from 1973 to 2008. Atmos. Chem. Thompson, D. W. J., and J. M. Wallace, 1998: The Arctic Oscillation signature in the Phys., 10, 10305-10320. wintertime geopotential height and temperature fields. Geophys. Res. Lett., 25, Robinson, D. A., and A. Frei, 2000: Seasonal variability of Northern Hemisphere snow 1297 1300. extent using visible satellite data. Prof. Geogr., 52, 307 315. Thompson, D. W. J., and J. M. Wallace, 2000: Annular modes in the extratropical Rohde, R., et al., 2013: Berkeley earth temperature averaging process. Geoinform. circulation. Part I: Month-to-month variability. J. Clim., 13, 1000 1016. Geostat. An Overview, 1:2, doi:10.4172/2327-4581.1000103. Thompson, T. A., et al., 2004: Halocarbons and other atmospheric trace species. Saito, T., Y. Yokouchi, A. Stohl, S. Taguchi, and H. Mukai, 2010: Large emissions Climate Monitoring and Diagnostics Laboratory Summary Report. NOAA CMDL, of perfluorocarbons in East Asia deduced from continuous atmospheric Boulder, Colorado, pp.115 135. measurements. Environ. Sci. Technol., 44, 4089 4095. Thorne, P. W., D. E. Parker, S. F. B. Tett, P. D. Jones, M. McCarthy, H. Coleman, and P. Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole Brohan, 2005: Revisiting radiosonde upper air temperatures from 1958 to 2002. mode in the tropical Indian Ocean. Nature, 401, 360 363. J. Geophys. Res. Atmos., 110, 17. Santer, B., et al., 2008: Consistency of modelled and observed temperature trends in Thorne, P. W., et al., 2011: Guiding the creation of a comprehensive surface the tropical troposphere. Int. J. Climatol., 28, 1703 1722. temperature resource for 21st century climate science. Bull. Am. Meteorol. Soc., Schnadt Poberaj, C., J. Staehelin, D. Brunner, V. Thouret, H. De Backer, and R. Stübi, 92, 40-47. 2009: Long-term changes in UT/LS ozone between the late 1970s and the 1990s Torseth, K., et al., 2012: Introduction to the European Monitoring and Evaluation deduced from the GASP and MOZAIC aircraft programs and from ozonesondes. Programme (EMEP) and observed atmospheric composition change during Atmos. Chem. Phys., 9, 5343 5369. 1972 2009. Atmos. Chem. Phys. Discuss., 12, 1733 1820. Scinocca, J. F., D. B. Stephenson, T. C. Bailey, and J. Austin, 2010: Estimates of past Trenberth, K. E., 1984: Signal versus noise in the Southern Oscillation. Mon. Weather and future ozone trends from multimodel simulations using a flexible smoothing Rev., 112, 326 332. spline methodology. J. Geophys. Res. Atmos., 115. Trenberth, K. E., 1997: The definition of El Nino. Bull. Am. Meteorol. Soc., 78, 2771 Seidel, D. J., Gillett, N. P., Lanzante, J. R., Shine, K. P., Thorne, P. W., 2011: Stratospheric 2777. temperature trends: Our evolving understanding. Trenberth, K. E., and J. W. Hurrell, 1994: Decadal atmosphere-ocean variations in the SEN, P., 1968: Estimates of regression coefficient based on Kendalls tau. J. Am. Stat. Pacific. Clim. Dyn., 9, 303 319. Assoc., 63, 1379-1389. Trenberth, K. E., and T. J. Hoar, 1996: The 1990 1995 El Nino Southern Oscillation Sharma, S., E. Andrews, L. A. Barrie, J. A. Ogren, and D. Lavoué, 2006: Variations and event: Longest on record. Geophys. Res. Lett., 23, 57 60. sources of the equivalent black carbon in the high Arctic revealed by long-term Trenberth, K. E., and D. P. Stepaniak, 2001: Indices of El Nino evolution. J. Clim., 14, observations at Alert and Barrow: 1989 2003. J. Geophys. Res., 111, D14208. 1697 1701. 2SM-29 Chapter 2 Observations: Atmosphere and Surface Supplementary Material Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in Zhang, X., and F. Zwiers, 2004: Comment on Applicability of prewhitening to 2005. Geophys. Res. Lett., 33, L12704. eliminate the influence of serial correlation on the Mann-Kendall test by Sheng Trewin, B., 2012: A daily homogenized temperature data set for Australia. Int. J. Yue and Chun Yuan Wang. Water Resourc. Res., 40, W03805. Climatol., 33, 1510-1529. Zhang, X., et al., 2011: Indices for monitoring changes in extremes based on daily Troup, A. J., 1965: Southern Oscillation. Q. J. R. Meteorol. Soc., 91, 490 . temperature and precipitation data. Wiley Interdis. Rev. Clim. Change, 2, 851- Vignati, E., M. Karl, M. Krol, J. Wilson, P. Stier, and F. Cavalli, 2010: Sources of 870. uncertainties in modelling black carbon at the global scale. Atmos. Chem. Phys., Zhang, X. Y., Y. Q. Wang, T. Niu, X. C. Zhang, S. L. Gong, Y. M. Zhang, and J. Y. Sun, 10, 2595 2611. 2012: Atmospheric aerosol compositions in China: Spatial/temporal variability, Vincent, L. A., X. L. L. Wang, E. J. Milewska, H. Wan, F. Yang, and V. Swail, 2012: A chemical signature, regional haze distribution and comparisons with global second generation of homogenized Canadian monthly surface air temperature aerosols. Atmos. Chem. Phys., 12, 779 799. for climate trend analysis. J. Geophys. Res. Atmos., 117, D18110. Zhang, Y., J. M. Wallace, and D. S. Battisti, 1997: ENSO-like interdecadal variability: Vollmer, M., S. Reimann, D. Folini, L. Porter, and L. Steele, 2006: First appearance and 1900 93. J. Clim., 10, 1004-1020. rapid growth of anthropogenic HFC-245fa (CHF2CH2CF3) in the atmosphere. Zhao, C., and P. Tans, 2006: Estimating uncertainty of the WMO mole fraction scale Geophys. Res. Lett., 33, L20806. for carbon dioxide in air. J. Geophys. Res. Atmos., 111, D08S09. Vollmer, M., et al., 2011: Atmospheric histories and global emissions of the Zou, C. Z., and W. H. Wang, 2010: Stability of the MSU-derived atmospheric anthropogenic hydrofluorocarbons HFC-365mfc, HFC-245fa, HFC-227ea, and temperature trend. J. Atmos. Ocean. Technol., 27, 1960 1971. HFC-236fa. J. Geophys. Res. Atmos., 116, D08304. Zou, C.-Z., and W. Wang, 2011: Intersatellite calibration of AMSU-A observations for Vose, R. S., et al., 2012b: NOAA s Merged Land-Ocean Surface Temperature Analysis. weather and climate applications. J. Geophys. Res. Atmos., D23113. 2SM Bull. Am. Meteor. Soc., 93, 1677 1685. Zou, C. Z., M. Gao, and M. D. Goldberg, 2009: Error structure and atmospheric Wahba, G., 1990: Spline Models for Observational Data. Society for Industrial and temperature trends in observations from the Microwave Sounding Unit. J. Clim., Applied Mathematics, Philadelphia, PA, 169pp. 22, 1661 1681. Wallace, J. M., and D. S. Gutzler, 1981: Teleconnections in the geopotential height Zou, C. Z., M. D. Goldberg, Z. H. Cheng, N. C. Grody, J. T. Sullivan, C. Y. Cao, and field during the Northern Hemisphere winter. Mon. Weather Rev., 109, 784 812. D. Tarpley, 2006: Recalibration of microwave sounding unit for climate studies Walsh, J. E., and W. L. Chapman, 2001: 20th-century sea-ice variations from using simultaneous nadir overpasses. J. Geophys. Res. Atmos., 111, D19114. observational data. Ann. Glaciol., 33, 444 448. Wang, B., and G. Shi, 2010: Long term trends of atmospheric absorbing and scattering optical depths over China region estimated from the routine observation data of surface solar irradiances. J. Geophys. Res. Atmos., 115, D00K28 Wang, K., R. E. Dickinson, and S. Liang, 2009a: Clear sky visibility has decreased over land globally from 1973 to 2007. Science, 323, 1468 1470. Wang, K. C., R. E. Dickinson, L. Su, and K. E. Trenberth, 2012: Contrasting trends of mass and optical properties of aerosols over the Northern Hemisphere from 1992 to 2011. Atmos. Chem. Phys., 12, 9387 9398. Wang, T., et al., 2009b: Increasing surface ozone concentrations in the background atmosphere of Southern China, 1994 2007. Atmos. Chem. Phys., 9, 6217 6227. Wang, X. L. L., and V. R. Swail, 2001: Changes of extreme wave heights in Northern Hemisphere oceans and related atmospheric circulation regimes. J. Clim., 14, 2204 2221. Weiss, R., J. Muhle, P. Salameh, and C. Harth, 2008: Nitrogen trifluoride in the global atmosphere. Geophys. Res. Lett., 35, L20821. Wilkinson, C., et al., 2011: Recovery of logbooks and international marine data: the RECLAIM project. Int. J. Climatol., 31, 968 979. Willett, K. M., P. D. Jones, N. P. Gillett, and P. W. Thorne, 2008: Recent changes in surface humidity: Development of the HadCRUH dataset. J. Clim., 21, 5364 5383. Willett, K. M., et al., 2013: HadISDH: An updateable land surface specific humidity product for climate monitoring. Clim. Past, 9, 657 677. WMO, 2011: Scientific assessment of ozone depletion: 2010, Global Ozone Research and Monitoring Project Report No. 52. World Meteorological Organization, Geneva, Switzerland. Wood, S. N., 2006: Generalized Additive Models: An Introduction with R. CRC/ Chapman & Hall, Boca Raton, FL, USA. Woodruff, S. D., et al., 2011: ICOADS Release 2.5: Extensions and Enhancements to the Surface Marine Meteorological Archive. Int. J. Climatol., 31, 951 967. Worley, S. J., S. D. Woodruff, R. W. Reynolds, S. J. Lubker, and N. Lott, 2005: ICOADS release 2.1 data and products. Int. J. Climatol., 25, 823 842. Yang, J., Q. Liu, S.-P. Xie, Z. Liu, and L. Wu, 2007: Impact of the Indian Ocean SST basin mode on the Asian summer monsoon. Geophys. Res. Lett., 34, L02708. Yttri, K. E., et al., 2011: Transboundary particulate matter in Europe, Status Report 2011. Co-operative Programme for Monitoring and Evaluation of the Long Range Transmission of Air Pollutants (Joint CCC, MSC-W, CEIP and CIAM report 2011). NILU - Chemical Coordinating Centre - CCC, http://www.nilu.no/projects/ ccc/reports/. Yuan, X., and C. Li, 2008: Climate modes in southern high latitudes and their impacts on Antarctic sea ice. J. Geophys. Res. Oceans, 113, C06S91. Zebiak, S. E., 1993: Air-sea interaction in the equatorial Atlantic region. J. Clim., 6, 1567 1568. 2SM-30 4SM Observations: Cryosphere Supplementary Material Coordinating Lead Authors: David G. Vaughan (UK), Josefino C. Comiso (USA) Lead Authors: Ian Allison (Australia), Jorge Carrasco (Chile), Georg Kaser (Austria/Italy), Ronald Kwok (USA), Philip Mote (USA), Tavi Murray (UK), Frank Paul (Switzerland/Germany), Jiawen Ren (China), Eric Rignot (USA), Olga Solomina (Russian Federation), Konrad Steffen (USA/Switzerland), Tingjun Zhang (USA/China) Contributing Authors: Anthony A. Arendt (USA), David B. Bahr (USA), Michiel van den Broeke (Netherlands), Ross Brown (Canada), J. Graham Cogley (Canada), Alex S. Gardner (USA), Sebastian Gerland (Norway), Stephan Gruber (Switzerland), Christian Haas (Canada), Jon Ove Hagen (Norway), Regine Hock (USA), David Holland (USA), Matthias Huss (Switzerland), Thorsten Markus (USA), Ben Marzeion (Austria), Rob Massom (Australia), Geir Moholdt (USA), Pier Paul Overduin (Germany), Antony Payne (UK), W. Tad Pfeffer (USA), Terry Prowse (Canada), Valentina Radić (Canada), David Robinson (USA), Martin Sharp (Canada), Nikolay Shiklomanov (USA), Sharon Smith (Canada), Sharon Stammerjohn (USA), Isabella Velicogna (USA), Peter Wadhams (UK), Anthony Worby (Australia), Lin Zhao (China) Review Editors: Jonathan Bamber (UK), Philippe Huybrechts (Belgium), Peter Lemke (Germany) This chapter supplementary material should be cited as: Vaughan, D.G., J.C. Comiso, I. Allison, J. Carrasco, G. Kaser, R. Kwok, P. Mote, T. Murray, F. Paul, J. Ren, E. Rignot, O. Solomina, K. Steffen and T. Zhang, 2013: Observations: Cryosphere Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Inter- governmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www.climatechange2013.org and www.ipcc.ch. 4SM-1 Table of Contents 4.SM.1 Supplementary Material for the Sea Ice Section................................................................ 4SM-3 4.SM.2 Details of Studies Using Determining Glacier Area Changes..................................... 4SM-5 References .............................................................................. 4SM-9 4SM 4SM-2 Observations: Cryosphere Chapter 4 Supplementary Material 4.SM.1 Supplementary Material for the Sea Comiso, 2008) in analyzed trends and variability. NT2 data, however, ­ Ice Section could not be used for the entire historical data because it requires the use of 89 GHz data, which is not available in SMMR and in the Most of the published studies on the large-scale variability and trends early part of the SSM/I time series. The time series that has been used of the global sea ice cover that have been published in recent years as an alternative to the NT2 time series has thus been the NT1 time were based primarily on results from analysis of passive microwave series which provides values different from NT2. In the meantime, the satellite data (Parkinson et al., 1999; e.g., Zwally et al., 2002; Stroeve et Hadley Centre, in collaboration with National Oceanic and Atmospher- al., 2007; Comiso and Nishio, 2008; Stammerjohn et al., 2008; Cavalieri ic Administration (NOAA), constructed another sea ice record referred and Parkinson, 2012; Parkinson and Cavalieri, 2012) to as HadISST_Ice. The data set has been assembled together with sea surface temperature (SST) at a relatively coarser resolution (1° latitude The first satellite-borne imaging passive microwave sensor was the by 1° longitude) and for a longer time series. The data set as described Nimbus-5/Electrically Scanning Microwave Radiometer (ESMR), which in Rayner et al., (2003) made use of atlases, in situ data and ice centre provided quantitative measurements of the extent and variability of data (as in, Walsh and Chapman, 2001) for the pre-satellite era. Start- the sea ice cover in both hemispheres from 1973 to 1976 (Zwally et ing in 1979, satellite data including those from NT1 have been used. al., 1983; Parkinson et al., 1987). However, with only one channel and The use of satellite data in the Hadley data set, however, apparently a system that scanned a wide field of view (between 50 and +50 has had some problems of consistency because, apparently spurious, degrees off-nadir, causing changes in incidence angle and footprint sudden increases in ice concentration from one year to another have size), gaps in the record from a few days to a few months and an been identified (e.g., Screen, 2011), making the data set difficult to unknown bias, these data have not been included in time series varia- use for variability and trend analysis. Also, as Screen (2011) pointed bility and trend analysis in Chapter 4. out, there are large differences in ice extent and ice area distributions derived from NT1 data compared with those derived from NT2 data. The data that are most frequently used are those from the multichan- nel, conically scanning (i.e., constant incident angle) and dual polar- The results presented in Section 4.2 make use of mainly SBA data for ized microwave radiometers that provide more accurate and more consistency with those presented in AR4. To assess the robustness consistent ice concentration and hence ice extent and ice area (see of the conclusions of Section 4.2 to changes in the data sets used, a Glossary for definitions) products. This series started with the Nimbus- comparative analysis of results from SBA, NT1 and Hadley (i.e., Had- 7 Scanning Multichannel Microwave Radiometer (SMMR) which was ISST1_Ice) data is presented. launched in October 1978 and provided, for the first time, measure- ments that allowed unambiguous determination of sea ice concentra- Time series plots of monthly anomalies in ice extent as derived from tion (Gloersen et al., 1993). SBA, NT1 and Hadley for the period November 1978 to December 2011 are presented in Figure 4.SM.1. Although data up to December 2012 Several algorithms for deriving sea ice concentration using different are presented in Chapter 4, data available for HadISST1_Ice and NT1 4SM techniques and utilizing different sets of channels have been devel- for this comparison goes up to December 2011 only. The NT1 data set oped (Svendsen et al., 1983; Cavalieri et al., 1984; Swift et al., 1985; used is an update version of that presented in Cavalieri and Parkinson Comiso, 1986; Steffen et al., 1992). The most commonly used tech- (2012) and Parkinson and Cavalieri (2012). The plots in Figure 4.SM.1a niques for sea ice studies are the Nimbus-7 National Aeronautics and 4.SM.1b for SBA and NT1, respectively show very similar patterns, and Space Administration (NASA) Team algorithm (NT1, Cavalieri et but the Hadley plot (Figure 4.SM.1c) shows deviations from the other al., 1984) and the Bootstrap algorithm (Comiso, 1986). SMMR was two, especially from 1984 to 1986 and from 2007 to 2012, where the eventually succeeded by a series of Special Sensor Microwave/Imager amplitudes of the interannual variation are significantly higher. Using (SSM/I) sensors and the two systems now provide a continuous set of linear regression, the derived trends are estimated to be 3.73% per data from November 1978 to the present. Subsequently, a more capa- decade for SBA, 4.22% per decade for NT1 and 2.0% per decade for ble and improved sensor, Advanced Microwave Scanning Radiometer Hadley. The trends from SBA and NT1 differ slightly but provide similar - Earth Observing System (AMSR-E), was launched on board the NASA/ trend information but that from Hadley is about half the other two, Aqua satellite and this has provided higher resolution and improved with a much more modest decline. The difference in the trend values sea ice data from May 2002 to October 2011. The algorithms currently for the Hadley data is likely due to the anomalous deviation of the data used for this sensor are the AMSR-E Bootstrap Algorithm (ABA) and from the other two data sets as indicated in the preceding text. The cor- the NASA Team Algorithm, Version 2 (NT2) as discussed in Markus and responding estimates for the trends in sea ice area data are 4.35%, Cavalieri (2000). With some enhancements ABA was also adapted and 4.71% and 2.8% per decade, respectively, all relatively higher than used to reprocess SMMR and SSM/I data, and called SSM/I (or SMMR) those for ice extent but again providing similar trend results for SBA Bootstrap Algorithm (SBA) as discussed in Comiso and Nishio (2008). and NT1 but a significantly lesser rate of decline for Hadley. Using the SBA, AMSR-E data were used as the baseline and basis for In the Antarctic, the trends are more modest and in the opposite direc- improving the SMMR and SSM/I ice data sets used in Chapter 4. NT2 tion as depicted in Figure 4.SM.2. Again, the patterns of the interannu- addressed some of the problems associated with NT1, such as erro- al variability are very similar for all three, with the Hadley data being neously low ice concentrations within the pack caused by unpredicta- the most different, exhibiting higher short-term fluctuations and a ble polarization ratios. Comparisons of NT2 and Bootstrap data have more positive trend. Trend results are +1.44%, +1.34% and +2.48% shown good agreement (Comiso and Parkinson, 2008; Parkinson and per decade for SBA, NT1 and Hadley, respectively. Again, the results 4SM-3 Chapter 4 Supplementary Material Observations: Cryosphere from SBA and NT1 are slightly different but provide similar trend i ­nformation while Hadley provides a considerably higher trend. The corresponding trends in ice area are +2.07%, +1.57% and +3.07% ) per decade, for SBA, NT1 and Hadley, respectively. The discrepancy in the trends for ice area between the SBA and NT1 results is in part due ( to lower concentration averages for NT1 compared with SBA data as indicated in the preceding text. However, they provide similar conclu- sions about the changes in the ice cover. The Hadley trend is again substantially higher than those of the other two. For a more detailed comparison, September monthly sea ice extents for SBA, NT1 and Hadley for all years from 1979 to 2011 are plot- ) ted together in Figure 4.SM.3a. For completeness, ABA and NT2 ice extents using AMSR-E data are also shown for the period 2002 2011. ( The values from all three primary data sources are very similar, with the SBA showing the highest values and Hadley normally lowest. Values from AMSR-E data using ABA and NT2 are relatively lower because of higher resolution as discussed in Comiso and Nishio (2008), but other- wise there is good consistency. There are greater discrepancies among the three data sources when sea ice areas are plotted (Figure 4.SM.3b), with NT1 and Hadley showing good agreement up to 1997 and signif- ) icant disagreement after that. The higher ice area values for SBA are associated with higher ice concentration values derived from the data ( than the other two, as discussed previously. The values for ABA and NT2 from AMSR-E (which have been used as the baseline) are in good Figure 4.SM.2 | Monthly anomalies of ice extent from November 1978 to December 2011 using (a) SBA, (b) NT1 and (c) Hadley data in the Southern Hemisphere. Trends are shown with uncertainty calculated at 1 standard deviation (1). 4SM agreement and also agree well with the SBA values. Large ­ ifference d between NT1 and NT2 values are evident, as has been observed by Screen (2011). Nevertheless, the trends in extent for SBA and NT1 are 10.2% and 10.5% per decade, respectively, basically providing the same conclusion, while the trend in extent for the Hadley data is 8.0% per decade. The trends for ice area are also similar enough at 11.3% (SBA) and 12.5% per decade (NT1) while the trend for Hadley data is 10.2% per decade. Another data set that is available and has been used for sea ice stud- ies is that from the Arctic Radiation and Turbulence Interaction Study (ARTIST) provided by the University of Bremen. The data make use of only the 89-GHz channels (horizontal and vertical polarized data) to generate relatively high resolution data from AMSR-E (5 to 6 km). High resolution is needed in many mesoscale studies. However, the 89-GHz radiation is very sensitive to weather and changes in the snow cover conditions, and the data should be used with care because they may be partly contaminated by incorrect values, especially under adverse weather conditions. Also, the ARTIST time series data from Aqua/ AMSR-E is, as yet, too short for meaningful sea ice variability and trend studies. Figure 4.SM.1 | Monthly anomalies of ice extent from November 1978 to December Of the three satellite data sets that are currently available for sea ice 2011 using (a) SBA, (b) NT1 and (c) Hadley data in the Northern Hemisphere. Trends are variability and trend analysis, SBA and NT1 provides basically the same shown with uncertainty calculated at 1 standard deviation (1). 4SM-4 Observations: Cryosphere Chapter 4 Supplementary Material Figure 4.SM.3 | Monthly estimates of (a) sea ice extent and (b) sea ice area for the month of September from 1979 to 2011 for the Northern Hemisphere. Trends are shown with uncertainty calculated at 1 standard deviation (1). patterns of interannual variability and approximately similar trends. 4SM The distributions are similar enough to provide basically the same information and conclusions about the trend of the changing sea ice cover. The patterns of variability provided by the Hadley data set are generally similar to those of SBA and NT1 but there are years when the data are suspect, as has been identified by Screen (2011). This is the primary reason for the discrepancies in the trends of the Hadley data compared with those of the SBA and NT1 data. 4.SM.2 Details of Studies of Glacier Area Change Table 4.SM.1 provides an overview of studies reporting glacier area changes over entire mountain ranges or larger regions. Where availa- ble, area change rates are also given for sub-periods that have been extracted from the respective publications, partly using a linear extrap- olation of given change rates to determine values for a common year in all sub-regions. In some studies, the glacier count is not given. 4SM-5 Chapter 4 Supplementary Material Observations: Cryosphere Table 4.SM.1 | Overview of studies presenting glacier area changes. Bold values in the column Change rate indicate values shown in Figure 4.10. Values in italics refer to the entire period with Area covered giving the value for the last year. Change rates are given for some regions to three decimals to avoid overlap of lines in Figure 4.10. For three regions (5, 9, and 12) studies on area changes were not found. Area Relative Change RGI Country / Sub-region / Start End Number Glacier Covered Change Rate Reference Region Region Mountain Range Year Year of Years Count (km2) (%) (% a 1) 1 USA, AK Chugach Mountains 1952 2007 55 347 1285.7 23 0.42 Le Bris et al. (2011) 2 USA North Cascades 1958 1998 40 321 117.3 7.0 0.18 Granshaw and Fountain (2006) 2 Canada Rocky Mountains 1985 2001 16 523 1056.7 7.6 0.48 Tennant et al. (2012) 2001 2006 5 523 976.5 9.9 1.98 1985 2006 21 523 880.0 16.7 0.80 2 USA Wind River Range 1966 2006 40 n/a 45.9 37.7 0.94 Thompson et al. (2011) 2 Canada Yukon 1959 2007 48 n/a 11622 21.9 0.456 Barrand and Sharp (2010) 2 Canada Rocky Mountainsa 1985 2005 20 14329 30063 11.1 0.555 Bolch et al. (2010) 2 Canada Clemenceau Icefield 1985 2001 16 123 313 13.4 0.84 Jiskoot et al. (2009) Chaba Group 1985 2001 16 53 97 28.9 1.81 2 Canada Rocky Mountains 1952 2001 49 59 40 15 0.31 Debeer and Sharp (2007) Columbia Mountains 1952 2001 49 403 397 5.0 0.10 Coast Mountains 1964 2002 38 1053 2397 5.0 0.13 3 Canada Queen Elizabeth Island 1960 2000 40 1274 107071 2.7 0.07 Sharp et al. (2013) 3 Canada North Ellesmere 1960 2000 40 473 27556 3.4 0.09 Sharp et al. (2013) Agassiz 1960 2000 40 296 21645 1.3 0.03 Axel/Meighen / 1960 2000 40 165 12231 1.7 0.04 Melville Prince of Wales 1960 2000 40 39 19558 0.9 0.02 South Ellesmere 1960 2000 40 187 10696 5.9 0.15 Devon Island 1960 2000 40 114 15344 4.0 0.10 4 Canada Bylot Island 1959 2001 42 n/a 5036 5.0 0.119 Dowdeswell et al. (2007) 4 Canada Barnes Ice Cap 1958 2000 42 n/a 5995 2.0 0.048 Sharp et al. (2013) 4SM Penny Ice Cap 1959 2000 41 n/a 6604 1.9 0.046 Terra Nivea 1958 2000 42 n/a 197 14.0 0.33 Grinnel Ice Cap 1958 2000 42 n/a 135 10.9 0.26 4 Canada Baffin Island 1975 2000 25 264 2187 12.5 0.16 Paul and Svoboda (2009) 5 Greenland n/a 6 Iceland Four ice caps 1998 2011 13 4 1004.5 7.6 0.58 Johannesson et al. (2013) 7 Svalbard Glaciers >1 km2 1990 2008 18 n/a 5204.7 4.6 0.26 König et al. (2013) 8 Norway Jostedalbreen 1966 2006 40 297 725.1 9 0.225 Paul et al. (2011) 8 Norway Jotunheimen 1965 2003 38 164 229.5 12.4 0.33 Andreassen et al. (2008) 8 Norway Svartisen 1968 1999 31 300 518.0 1.1 0.04 Paul and Andreassen (2009) 9 Russian Arctic n/a 10 Russian Federation Ural 1956 2000 44 30 9.17 22.3 0.51 Shahgedanova et al. (2012) 10 Russian Federation Kodar Mountains 1995 2001 6 34 11.72 18.7 3.11 Stokes et al. (2013) 2001 2010 9 34 9.53 26.4 2.94 1995 2010 15 34 7.01 40.2 2.68 10 Russian Federation Altai Chuya Ridges 1952 2004 52 126 284 19.7 0.38 Shahgedanova et al. (2010) 10 Russian Federation Altai 1952 2008 56 1030 805 10.2 0.182 Narozhniy and Zemtsov (2011) 11 Austria Alps 1969 1998 29 925 567 17.1 0.59 Lambrecht and Kuhn (2007) 11 Austria Ötztaler Alps 1997 2006 9 81 116 8.2 0.9 Abermann et al. (2009) 11 Switzerland Alps 1973 1999 26 938 1171.2 16.1 0.62 Paul et al. (2004) 1985 1998 13 471 372.2 18.0 1.38 11 Spain Pyrenees 1982 1994 12 10 6.08 20.9 1.97 Gonzales Trueba et al. (2008) 1994 2001 7 10 4.81 39.7 5.6 1982 2001 19 10 6.08 52.3 2.75 (continued on next page) 4SM-6 Observations: Cryosphere Chapter 4 Supplementary Material Table 4.SM.1 (continued) Area Relative Change RGI Country / Sub-region / Start End Number Glacier Covered Change Rate Reference Region Region Mountain Range Year Year of Years Count (km2) (%) (% a 1) 11 Italy Aosta Valley 1975 1999 24 174 163.9 16.7 0.7 Diolaiuti et al. (2012) 1999 2005 6 174 136.6 12.4 2.07 1975 2005 30 174 119.6 27.0 0.9 11 Italy South Tyrol 1983 1997 14 205 136.6 19.7 1.41 Knoll and Kerschner (2009) 1997 2006 9 302 109.7 11.9 1.32 1983 2006 23 205 136.6 31.6 1.37 11 Italy Lombardy 1992 1999 7 249 117.4 10.8 1.54 Citterio et al. (2007) 12 Caucasus n/a 13 Mongolia Altai Mountains 1989 2009 20 n/a 213 4.2 0.21 Krumwiede et al. (2013) 13 China Muztag Ata and 1962 1990 28 n/a 838.2 3.4 0.12 Shangguan et al. (2006) Konggur Mountains 1990 1999 9 n/a 809.9 4.6 0.52 (East Pamir) 1962 1999 37 772.2 7.9 0.214 13 China (Tarim Basin) Quarqan 1977 2001 24 399 752.6 3.4 0.14 Shangguan et al. (2009) Keliya 1970 1999 29 731 1306.5 3.1 0.11 Hotan 1968 2000 32 2487 5131.8 0.7 0.02 Yarkant 1974 2001 27 1421 3170.6 6.1 0.23 Pamir 1964 2001 37 880 2085.4 7.9 0.21 Tienshan 1964 2000 36 1249 4039.2 1.3 0.04 Kaidu 1964 2000 36 498 405.8 7.1 0.20 13 China (Tibet) Gongga Mountains 1966 1989 23 74 257.7 5.8 0.25 Pan et al. (2012b) 1989 2009 20 75 242.8 5.9 0.29 1966 2009 43 76 228.5 11.3 0.26 13 Kyrgistan Pskem 1968 2000 32 525 219.8 19.47 0.61 Narama et al. (2010) 2000 2007 7 525 177.0 6.69 0.96 Ili-Kungoy 1971 1999 28 735 672.2 12.18 0.44 4SM 1999 2007 8 735 590.3 4.12 0.52 At-Bashi 1968 2000 32 192 113.6 12.06 0.38 2000 2007 7 192 99.9 4.20 0.60 SE-Fergana 1968 2000 32 306 190.1 9.21 0.29 2000 2007 7 306 172.6 0.52 0.07 13 China Qilian Mountainsa 1956 2003 47 910 397.4 21.7 0.462 Wang et al. (2011) 13 China Karlik Shan 1971 1992 21 n/a 126 2.63 0.13 Wang et al. (2009) 1992 2001 9 n/a 122.7 2.67 0.27 1971 2001 30 n/a 119.4 5.2 0.17 13 China Lenglonglinga 1972 2007 35 179 86.2 28.3 0.81 Pan et al. (2012a) 13 Kyrgistan Akshiirak 1977 2003 26 178 406.8 8.6 0.33 Aizen et al. (2007) Ala Archa 1981 2003 22 48 40.62 10.6 0.48 14 Himalaya Ten basins meana 1962 2004 42 1868 6332 15.8 0.38 Kulkarni et al. (2011) 14 India Kang Yatze 1969 1991 23 121 96.4 13.0 0.56 Schmidt and Nusser (2012) 1991 2010 18 121 83.9 1.5 0.09 1969 2010 41 121 96.4 14.4 0.35 14 India Gharwal Himalayaa 1968 2006 38 82 600 4.6 0.121 Bhambri et al. (2011) 15 Nepal Khumbu Himal 1976 2006 30 n/a 3211.9 15.6 0.52 Nie et al. (2010) 15 Nepal Khumbu Himal 1962 2005 43 n/a 92.3 5.3 0.123 Bolch et al. (2008) 15 Nepal Sagarmatha 1962 2001 39 29 403.9 4.9 0.126 Salerno et al. (2008) National Park (continued on next page) 4SM-7 Chapter 4 Supplementary Material Observations: Cryosphere Table 4.SM.1 (continued) Area Relative Change RGI Country / Sub-region / Start End Number Glacier Covered Change Rate Reference Region Region Mountain Range Year Year of Years Count (km2) (%) (% a 1) 16 Peru Cordillera Coropuna 1955 2003 48 711 123 54 1.125 Silverio and Jaquet (2012)Peru 16 Peru Cordillera Blanca 1970 1990 20 n/a 190 12.8 0.64 Baraer et al. (2012) 1990 2009 19 165 17.4 0.92 1970 2009 39 136.3 28.0 0.72 16 Peru Cordillera Vilcanota 1985 1996 11 n/a 444 22.5 2.05 Salzmann et al. (2013) 1996 2006 10 344 13.7 1.37 1985 2006 21 297 33.2 1.58 16 Peru Quelcaya Ice Cap 1985 2000 15 n/a 55.7 17.6 1.17 Salzmann et al. (2013) 2000 2009 9 45.9 3.1 0.34 1985 2009 24 42.8 23.1 0.96 16 Indonesia Puncack Jaya 1942 1972 30 10 9.9 30.3 1.01 Klein and Kincaid (2006) 1972 2002 30 5 6.9 66.2 2.23 1942 2002 60 2.15 78.3 1.30 16 Columbia Six mountain ranges a 1959 1987 28 n/a 106.8 21.9 0.78 Ceballos et al. (2006) 1987 2002 15 83.5 33.5 2.23 1959 2002 43 45.6 48.1 1.18 16 Peru Cordillera Blanca 1970 2003 33 445 665.1 22.4 0.68 Racoviteanu et al. (2008) 16 Tansania Kilimandscharo a 1962 2011 49 n/a 7.32 76.0 1.55 Cullen et al. (2013) 17 Chile Gran Campo Nevado 1942 2002 60 81 252.6 14.4 0.24 Schneider et al. (2007) 17 Chile / Argentina San Lorenzo 1985 2000 15 213 239.0 9.9 0.66 Falaschi et al. (2013) Mountains 2000 2008 8 213 215.4 9.7 1.21 1985 2008 23 213 206.9 13.4 0.58 17 Chile / Argentina Patagonia 1986 2001 15 183 23743 2.2 0.14 Davies and Glasser (2012) 2001 2011 10 165 23229 2.2 0.22 4SM 1986 2011 25 183 22717 4.3 0.17 17 Chile Northern Pata- 1979 2001 22 >70b 4093 3.4 0.15 Rivera et al. (2007) gonia Icefield 17 Chile Aconcagua Basin 1955 2003 48 151 19.9 0.41 Bown et al. (2008) 18 New Zealand Southern Alps 1978 2002 24 n/a 513 16.6 0.69 Gjermundsen et al. (2011) 19 Antarctica Kerguelen Islanda 1963 2001 38 n/a 703 21 0.55 Berthier et al. (2009) 19 Antarctica King George Island 1956 1995 39 n/a 1250 7.0 0.179 Rückamp et al. (2011) 2000 2008 8 n/a 1.6 0.20 Notes: (a) More detailed analyses (e.g., sub-regions, other periods) are available in the respective papers. (b) Glaciers <0.5 km2 were not counted separately. 4SM-8 Observations: Cryosphere Chapter 4 Supplementary Material References Abermann, J., A. Lambrecht, A. Fischer, and M. Kuhn, 2009: Quantifying changes Gjermundsen, E. F., R. Mathieu, A. Kääb, T. Chinn, B. Fitzharris, and J. O. Hagen, 2011: and trends in glacier area and volume in the Austrian Otztal Alps (1969 1997 Assessment of multispectral glacier mapping methods and derivation of glacier 2006). Cryosphere, 3, 205 215. area changes, 1978 2002, in the central Southern Alps, New Zealand, from Aizen, V. B., V. A. Kuzmichenok, A. B. Surazakov, and E. M. Aizen, 2007: Glacier ASTER satellite data, field survey and existing inventory data. J. Glaciol., 57, changes in the Tien Shan as determined from topographic and remotely sensed 667 683. data. Global Planet. Change, 56, 328 340. Gloersen, P., W. J. Campbell, D. J. Cavalieri, J. C. Comiso, C. L. Parkinson, and H. J. Andreassen, L. M., F. Paul, A. Kaab, and J. E. Hausberg, 2008: Landsat-derived glacier Zwally, 1993: Satellite passive microwave observations and analysis of Arctic inventory for Jotunheimen, Norway, and deduced glacier changes since the and Antarctic sea-ice. Ann. Glaciol., 17, 149 154. 1930s. Cryosphere, 2, 131 145. Gonzales Trueba, J. J., R. Martin Moreno, E. Martinez de Pison, and E. Serrano, 2008: Baraer, M., et al., 2012: Glacier recession and water resources in Peru s Cordillera Little Ice Age glaciation and current glaciers in the Iberian Peninsula. Holocene, Blanca. J. Glaciol., 58, 134 150. 18, 551 568. Barrand, N. E., and M. J. Sharp, 2010: Sustained rapid shrinkage of Yukon glaciers Granshaw, F. D., and A. G. Fountain, 2006: Glacier change (1958 1998) in the North since the 1957 1958 International Geophysical Year. Geophys. Res. Lett., 37, Cascades National Park Complex, Washington, USA. J. Glaciol., 52, 251 256. L07501. Jiskoot, H., C. J. Curran, D. L. Tessler, and L. R. Shenton, 2009: Changes in Clemenceau Berthier, E., R. Le Bris, L. Mabileau, L. Testut, and F. Remy, 2009: Ice wastage on Icefield and Chaba Group glaciers, Canada, related to hypsometry, tributary the Kerguelen Islands (49 degrees S, 69 degrees E) between 1963 and 2006. J. detachment, length-slope and area-aspect relations. Ann. Glaciol., 50, 133 143. Geophys. Res. Earth Surf., 114, 11. Johannesson, T., et al., 2013: Ice-volume changes, bias estimation of mass-balance Bhambri, R., T. Bolch, R. K. Chaujar, and S. C. Kulshreshtha, 2011: Glacier changes in measurements and changes in subglacial lakes derived by lidar mapping of the the Garhwal Himalaya, India, from 1968 to 2006 based on remote sensing. J. surface of Icelandic glaciers. Ann. Glaciol., 54, 63 74. Glaciol., 57, 543-556. Klein, A. G., and J. L. Kincaid, 2006: Retreat of glaciers on Puncak Jaya, Irian Jaya, Bolch, T., B. Menounos, and R. Wheate, 2010: Landsat-based inventory of glaciers in determined from 2000 and 2002 IKONOS satellite images. J. Glaciol., 52, 65 79. western Canada, 1985 2005. Remote Sens. Environ., 114, 127 137. Knoll, C., and H. Kerschner, 2009: A glacier inventory for South Tyrol, Italy, based on Bolch, T., M. Buchroithner, T. Pieczonka, and A. Kunert, 2008: Planimetric and airborne laser-scanner data. Ann. Glaciol., 50, 46 52. volumetric glacier changes in the Khumbu Himal, Nepal, since 1962 using König, M., C. Nuth, J. Kohler, G. Moholdt, and R. Pettersson, 2013: A digital glacier Corona, Landsat TM and ASTER data. J. Glaciol., 54, 592 600. database for Svalbard. In: Global Land Ice Measurements form Space [J. S. Bown, F., A. Rivera, and C. Acuna, 2008: Recent glacier variations at the Aconcagua Kargel, G. J. Leonard, M. P. Bishop, A. Kääb and B. Raup (eds.)]. Springer Praxis, basin, central Chilean Andes. Ann. Glaciol., 48, 43 48. New York, NY, USA, 229-239. Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979 Krumwiede, B. S., U. Kamp, G. J. Leonard, A. Dashtseren, and M. Walther, 2013: Recent 2010. Cryosphere, 6, 957 979. glacier changes in the Mongolian Altai Mountains: Case studies from Munkh Cavalieri, D. J., P. Gloersen, and W. J. Campbell, 1984: Determination of sea ice Khairkhan and Tavan Bogd. In: Global Land Ice Measurements from Space [J. S. parameters with the Nimbus-7 SMMR. J. Geophys. Res. Atmos., 89, 5355 5369. Kargel, G. J. Leonard, M. P. Bishop, A. Kääb and B. Raup (eds.)]. Springer-Praxis, Ceballos, J. L., C. Euscategui, J. Ramirez, M. Canon, C. Huggel, W. Haeberli, and H. New York, NY, USA, 481-507. Machguth, 2006: Fast shrinkage of tropical glaciers in Colombia. Ann. Glaciol., Kulkarni, A. V., B. P. Rathore, S. K. Singh, and I. M. Bahuguna, 2011: Understanding 43, 194 201. changes in the Himalayan cryosphere using remote sensing techniques. Int. J. Citterio, M., G. Diolaiuti, C. Smiraglia, C. D Agata, T. Carnielli, G. Stella, and G. B. Remote Sens., 32, 601 615. 4SM Siletto, 2007: The fluctuations of Italian glaciers during the last century: A Lambrecht, A., and M. Kuhn, 2007: Glacier changes in the Austrian Alps during contribution to knowledge about Alpine glacier changes. Geograf. Annal. A, the last three decades, derived from the new Austrian glacier inventory. Ann. 89A, 167 184. Glaciol., 46, 177 184. Comiso, J. C., 1986: Characteristics of winter sea ice from satellite multispectreal Le Bris, R., F. Paul, H. Frey, and T. Bolch, 2011: A new satellite-derived glacier inventory microwave observations J. Geophys. Res. Oceans, 91, 975 994. for western Alaska. Ann. Glaciol., 52, 135-143. Comiso, J. C., and F. Nishio, 2008: Trends in the sea ice cover using enhanced and Markus, T., and D. J. Cavalieri, 2000: An enhancement of the NASA Team sea ice compatible AMSR-E, SSM/I, and SMMR data. J. Geophys. Res. Oceans, 113, algorithm. IEEE Trans. Geosci. Remote Sens., 38, 1387 1398. C02S07. Narama, C., A. Kaab, M. Duishonakunov, and K. Abdrakhmatov, 2010: Spatial Comiso, J. C., and C. L. Parkinson, 2008: Arctic sea ice parameters from AMSR-E variability of recent glacier area changes in the Tien Shan Mountains, Central using two techniques, and comparisons with sea ice from SSM/I. J. Geophys. Asia, using Corona (similar to 1970), Landsat (similar to 2000), and ALOS Res., 113, C02S05. (similar to 2007) satellite data. Global Planet. Change, 71, 42 54. Cullen, N. J., P. Sirguey, T. Moelg, G. Kaser, M. Winkler, and S. J. Fitzsimons, 2013: Narozhniy, Y., and V. Zemtsov, 2011: Current state of the Altai Glaciers (Russia) and A century of ice retreat on Kilimanjaro: The mapping reloaded. Cryosphere, 7, trends over the period of instrumental observations 1952 2008. Ambio, 40, 419 431. 575-588. Davies, B. J., and N. F. Glasser, 2012: Accelerating shrinkage of Patagonian glaciers Nie, Y., Y. L. Zhang, L. S. Liu, and J. P. Zhang, 2010: Glacial change in the vicinity of from the Little Ice Age (c. AD 1870) to 2011. J. Glaciol., 58, 1063 1084. Mt. Qomolangma (Everest), central high Himalayas since 1976. J. Geograph. Sci., Debeer, C. M., and M. J. Sharp, 2007: Recent changes in glacier area and volume 20, 667 686. within the southern Canadian Cordillera. Ann. Glaciol., 46, 215 221. Pan, B., B. Cao, J. Wang, G. Zhang, C. Zhang, Z. Hu, and B. Huang, 2012a: Glacier Diolaiuti, G. A., D. Bocchiola, M. Vagliasindi, C. D Agata, and C. Smiraglia, 2012: The variations in response to climate change from 1972 to 2007 in the western 1975 2005 glacier changes in Aosta Valley (Italy) and the relations with climate Lenglongling mountains, northeastern Tibetan Plateau. J. Glaciol., 58, 879 888. evolution. Prog. Phys. Geogr., 36, 764 785. Pan, B. T., G. L. Zhang, J. Wang, B. Cao, H. P. Geng, C. Zhang, and Y. P. Ji, 2012b: Glacier Dowdeswell, E. K., J. A. Dowdeswell, and F. Cawkwell, 2007: On the glaciers of Bylot changes from 1966 2009 in the Gongga Mountains, on the south-eastern Island, Nunavut, Arctic Canada. Arct. Antarct. Alp. Res., 39, 402-411. margin of the Qinghai-Tibetan Plateau and their climatic forcing. Cryosphere, Falaschi, D., C. Bravo, M. Masiokas, R. Villalba, and A. Rivera, 2013: First glacier 6, 1087 1101. inventory and recent changes in glacier area in the Monte San Lorenzo Region Parkinson, C. L., and J. C. Comiso, 2008: Antarctic sea ice parameters from AMSR-E (47°S), Southern Patagonian Andes, South America. Arct. Antarct. Alp. Res., 45, data using two techniques and comparisons with sea ice from SSM/I. J. Geophys. 19 28. Res. Oceans, 113, C02S06. Parkinson, C. L., and D. J. Cavalieri, 2012: Antarctic Sea Ice Variability and Trends, 1979 2010. Cryosphere, 6, 871 880. 4SM-9 Chapter 4 Supplementary Material Observations: Cryosphere Parkinson, C. L., D. J. Cavalieri, P. Gloersen, H. J. Zwally, and J. C. Comiso, 1999: Arctic Steffen, K., D. J. Cavalieri, J. C. Comiso, K. St Germain, P. Gloersen, J. Key, and I. sea ice extents, areas, and trends, 1978 1996. J. Geophys. Res. Oceans, 104, Rubinstein, 1992: The estimation of geophysical parameters using Passive 20837 20856. Microwave Algorithms. In: Microwave Remote Sensing of Sea Ice [F. D. Carsey Parkinson, C. L., J. C. Comiso, H. J. Zwally, D. J. Cavalieri, P. Gloersen, and W. Campbell, (ed.)]. American Geophysical Union, Washington, DC, pp. 201 231. 1987: Analysis of northern hemisphere sea ice from satellite passive microwave Stokes, C. R., M. Shahgedanova, I. S. Evans, and V. V. Popovnin, 2013: Accelerated loss data. Ann. Glaciol., 9, 1 8. of alpine glaciers in the Kodar Mountains, south-eastern Siberia. Global Planet. Paul, F., and L. M. Andreassen, 2009: A new glacier inventory for the Svartisen region, Change, 101, 82 96. Norway, from Landsat ETM plus data: challenges and change assessment. J. Stroeve, J., M. M. Holland, W. Meier, T. Scambos, and M. Serreze, 2007: Arctic sea ice Glaciol., 55, 607 618. decline: Faster than forecast. Geophys. Res. Lett., 34, L09501. Paul, F., and F. Svoboda, 2009: A new glacier inventory on southern Baffin Island, Svendsen, E., et al., 1983: Norvegian Remote-Sensing Experiment Evaluation Canada, from ASTER data: II. Data analysis, glacier change and applications. of the NIMBUS-7 Scanning Multichannel Microwave Radiometer for Sea Ice Ann. Glaciol., 50, 22 31. Research. J. Geophys. Res. Oceans Atmos., 88, 2781 2791. Paul, F., L. M. Andreassen, and S. H. Winsvold, 2011: A new glacier inventory for the Swift, C. T., L. S. Fedor, and R. O. Ramseier, 1985: An algorithm to measure sea ice Jostedalsbreen region, Norway, from Landsat TM scenes of 2006 and changes concentration with microwave radiometers. J. Geophys. Res. Oceans, 90, 1087 since 1966. Ann. Glaciol., 52, 153-162. 1099. Paul, F., A. Kaab, M. Maisch, T. Kellenberger, and W. Haeberli, 2004: Rapid Tennant, C., B. Menounos, R. Wheate, and J. J. Clague, 2012: Area change of glaciers disintegration of Alpine glaciers observed with satellite data. Geophys. Res. in the Canadian Rocky Mountains, 1919 to 2006. Cryosphere, 6, 1541 1552. Lett., 31, 4. Thompson, D., G. Tootle, G. Kerr, R. Sivanpillai, and L. Pochop, 2011: Glacier variability Racoviteanu, A. E., Y. Arnaud, M. W. Williams, and J. Ordonez, 2008: Decadal changes in the Wind River Range, Wyoming. J. Hydrol. Engng., 16, 798-805. in glacier parameters in the Cordillera Blanca, Peru, derived from remote Walsh, J. E., and W. L. Chapman, 2001: 20th-century sea-ice variations from sensing. J. Glaciol., 54, 499 510. observational data. Ann. Glaciol., 33, 444 448. Rayner, N. A., et al., 2003: Global analyses of SST, sea ice and night marine air Wang, P., Z. Li, and W. Gao, 2011: Rapid Shrinking of Glaciers in the Middle Qilian temperature since the late nineteenth century. J. Geophys. Res., 108, 4407. Mountain Region of Northwest China during the Last similar to 50 Years. J. Earth Rivera, A., T. Benham, G. Casassa, J. Bamber, and J. A. Dowdeswell, 2007: Ice elevation Sci., 22, 539 548. and areal changes of glaciers from the Northern Patagonia Icefield, Chile. Global Wang, Y. T., S. G. Hou, and Y. P. Liu, 2009: Glacier changes in the Karlik Shan, eastern Planet. Change, 59, 126 137. Tien Shan, during 1971/72 2001/02. Ann. Glaciol., 50, 39 45. Rückamp, M., M. Braun, S. Suckro, and N. Blindow, 2011: Observed glacial changes Zwally, H. J., C. L. Parkinson, and J. C. Comiso, 1983: Variability of Antarctic sea ice on the King George Island ice cap, Antarctica, in the last decade. Global Planet. and changes in carbon dioxide. Science, 220, 1005 1012. Change, 79, 99 109. Zwally, H. J., J. C. Comiso, C. L. Parkinson, D. J. Cavalieri, and P. Gloersen, 2002: Salerno, F., E. Buraschi, G. Bruccoleri, G. Tartari, and C. Smiraglia, 2008: Glacier Variability of Antarctic sea ice 1979 1998. J. Geophys. Res., 107, 1029 1047. surface-area changes in Sagarmatha national park, Nepal, in the second half of the 20th century, by comparison of historical maps. J. Glaciol., 54, 738 752. Salzmann, N., C. Huggel, M. Rohrer, W. Silverio, B. G. Mark, P. Burns, and C. Portocarrero, 2013: Glacier changes and climate trends derived from multiple sources in the data scarce Cordillera Vilcanota region, southern Peruvian Andes. Cryosphere, 7, 103 118. Schmidt, S., and M. Nusser, 2012: Changes of High Altitude Glaciers from 1969 to 4SM 2010 in the Trans-Himalayan Kang Yatze Massif, Ladakh, Northwest India. Arct. Antarct. Alp. Res., 44, 107 121. Schneider, C., M. Schnirch, C. Acuna, G. Casassa, and R. Kilian, 2007: Glacier inventory of the Gran Campo Nevado Ice Cap in the Southern Andes and glacier changes observed during recent decades. Global Planet. Change, 59, 87 100. Screen, J. A., 2011: Sudden increase in Antarctic sea ice: Fact or artifact? Geophys. Res. Lett., 38, L13702. Shahgedanova, M., G. Nosenko, T. Khromova, and A. Muraveyev, 2010: Glacier shrinkage and climatic change in the Russian Altai from the mid-20th century: An assessment using remote sensing and PRECIS regional climate model. J. Geophys. Res. Atmos., 115, 12. Shahgedanova, M., G. Nosenko, I. Bushueva, and M. Ivanov, 2012: Changes in area and geodetic mass balance of small glaciers, Polar Urals, Russia, 1950 2008. J. Glaciol., 58, 953 964. Shangguan, D., S. Liu, Y. Ding, L. Ding, J. Xu, and L. Jing, 2009: Glacier changes during the last forty years in the Tarim Interior River basin, northwest China. Prog. Nat. Sci., 19, 727 732. Shangguan, D., et al., 2006: Monitoring the glacier changes in the Muztag Ata and Konggur mountains, east Pamirs, based on Chinese Glacier Inventory and recent satellite imagery. Ann. Glaciol., 43, 79 85. Sharp, M., et al., 2013: Remote Sensing of Recent Glacier Changes in the Canadian Arctic. In: Global Land Ice Measurements from Space [J. S. Kargel, G. J. Leonard, M. P. Bishop, A. Kääb and B. Raup (eds.)]. Springer-Praxis, New York, NY, USA, in press. Silverio, W., and J.-M. Jaquet, 2012: Multi-temporal and multi-source cartography of the glacial cover of Nevado Coropuna (Arequipa, Peru) between 1955 and 2003. Int. J. Remote Sens., 33. Stammerjohn, S. E., D. G. Martinson, R. C. Smith, X. Yuan, and D. Rind, 2008: Trends in Antarctic annual sea ice retreat and advance and their relation to El Nino- Southern Oscillation and Southern Annular Mode variability. J. Geophys. Res. Oceans, 113, C03S90. 4SM-10 Carbon and Other Biogeochemical Cycles Supplementary Material Coordinating Lead Authors: 6SM Philippe Ciais (France), Christopher Sabine (USA) Lead Authors: Govindasamy Bala (India), Laurent Bopp (France), Victor Brovkin (Germany/Russian Federation), Josep Canadell (Australia), Abha Chhabra (India), Ruth DeFries (USA), James Galloway (USA), Martin Heimann (Germany), Christopher Jones (UK), Corinne Le Quéré (UK), Ranga B. Myneni (USA), Shilong Piao (China), Peter Thornton (USA) Contributing Authors: Anders Ahlström (Sweden), Alessandro Anav (UK/Italy), Oliver Andrews (UK), David Archer (USA), Vivek Arora (Canada), Gordon Bonan (USA), Alberto Vieira Borges (Belgium/Portugal), Philippe Bousquet (France), Lex Bouwman (Netherlands), Lori M. Bruhwiler (USA), Kenneth Caldeira (USA), Long Cao (China), Jérôme Chappellaz (France), Frédéric Chevallier (France), Cory Cleveland (USA), Peter Cox (UK), Frank J. Dentener (EU/Netherlands), Scott C. Doney (USA), Jan Willem Erisman (Netherlands), Eugenie S. Euskirchen (USA), Pierre Friedlingstein (UK/Belgium), Nicolas Gruber (Switzerland), Kevin Gurney (USA), Elisabeth A. Holland (Fiji/ USA), Brett Hopwood (USA), Richard A. Houghton (USA), Joanna I. House (UK), Sander Houweling (Netherlands), Stephen Hunter (UK), George Hurtt (USA), Andrew D. Jacobson (USA), Atul Jain (USA), Fortunat Joos (Switzerland), Johann Jungclaus (Germany), Jed O. Kaplan (Switzerland/Belgium/USA), Etsushi Kato (Japan), Ralph Keeling (USA), Samar Khatiwala (USA), Stefanie Kirschke (France/Germany), Kees Klein Goldewijk (Netherlands), Silvia Kloster (Germany), Charles Koven (USA), Carolien Kroeze (Netherlands), Jean-François Lamarque (USA/Belgium), Keith Lassey (New Zealand), Rachel M. Law (Australia), Andrew Lenton (Australia), Mark R. Lomas (UK), Yiqi Luo (USA), Takashi Maki (Japan), Gregg Marland (USA), H. Damon Matthews (Canada), Emilio Mayorga (USA), Joe R. Melton (Canada), Nicolas Metzl (France), Guy Munhoven (Belgium/Luxembourg), Yosuke Niwa (Japan), Richard J. Norby (USA), Fiona O Connor (UK/Ireland), James Orr (France), Geun-Ha Park (USA), Prabir Patra (Japan/ India), Anna Peregon (France/Russian Federation), Wouter Peters (Netherlands), Philippe Peylin (France), Stephen Piper (USA), Julia Pongratz (Germany), Ben Poulter (France/USA), Peter A. Raymond (USA), Peter Rayner (Australia), Andy Ridgwell (UK), Bruno Ringeval (Netherlands/ France), Christian Rödenbeck (Germany), Marielle Saunois (France), Andreas Schmittner (USA/Germany), Edward Schuur (USA), Stephen Sitch (UK), Renato Spahni (Switzerland), Benjamin Stocker (Switzerland), Taro Takahashi (USA), Rona L. Thompson (Norway/New Zealand), Jerry Tjiputra (Norway/Indonesia), Guido van der Werf (Netherlands), Detlef van Vuuren (Netherlands), Apostolos Voulgarakis (UK/Greece), Rita Wania (Austria), Sönke Zaehle (Germany), Ning Zeng (USA) Review Editors: Christoph Heinze (Norway), Pieter Tans (USA), Timo Vesala (Finland) This chapter supplementary material should be cited as: Ciais, P., C. Sabine, G. Bala, L. Bopp, V. Brovkin, J. Canadell, A. Chhabra, R. DeFries, J. Galloway, M. Heimann, C. Jones, C. Le Quéré, R.B. Myneni, S. Piao and P. Thornton, 2013: Carbon and Other Biogeochemical Cycles Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www. climatechange2013.org and www.ipcc.ch. 6SM-1 Table of Contents 6.SM.1 Supplementary Material to Section 6.4.6.1: Projections for Formation of Reactive Nitrogen by Human Activity......................... 6SM-3 References .............................................................................. 6SM-4 6SM 6SM-2 Carbon and Other Biogeochemical Cycles Chapter 6 Supplementary Material 6.SM.1 Supplementary Material to Section 6.4.6.1: Projections for Formation of Reactive Nitrogen by Human Activity SOx deposition (kgS km-2 yr-1) N deposition (kgN km-2 yr-1) 1990s 1990s a. 5000 b. 5000 1000 1000 500 500 100 100 50 50 10 10 0 0 2090s, changes from 1990s 2090s, changes from 1990s c. d. RCP2.6 RCP2.6 e. f. 1000 1000 100 100 RCP4.5 10 RCP4.5 10 -10 g. h. -10 -100 -100 -1000 -1000 RCP6.0 RCP6.0 i. j. RCP8.5 RCP8.5 6SM Figure 6.SM.1 | Spatial variability of nitrogen and SOx deposition in 1990s with projections to the 2090s (shown as difference relative to the 1990s), using the 2.6, 4.5, 6.0 and 8.5 Representative Concentration Pathway (RCP) scenarios, kg N km 2 yr 1, adapted from Lamarque et al. (2011). Note that no information on the statistical significance of the shown differences is available. This is of particular relevance for areas with small changes. 6SM-3 Chapter 6 Supplementary Material Carbon and Other Biogeochemical Cycles The change in dissolved inorganic nitrogen (DIN) discharge under References the Global Orchestration (GO) scenario of the Millennium Ecosystem Assessment (MEA) (the scenario with the most extreme pressures) was Lamarque, J. F., et al., 2011: Global and regional evolution of short lived radiatively active gases and aerosols in the Representative Concentration Pathways. Clim. assessed by taking the change between the base year 2000, and the Change, 109, 191 212. projection year, in this case 2050 (Figure 6.34b). Manure is the most Mayorga, E., et al., 2010: Global nutrient export from WaterSheds 2 (NEWS 2): Model important contributor as a result of assumed high per capita meat con- development and implementation. Environ. Model. Software, 25, 837 853. sumption, although there are considerable regional variations (Seitz- Seitzinger, S. P., et al., 2010: Global river nutrient export: A scenario analysis of past inger et al., 2010). At the other extreme is the projected change in the and future trends. Global Biogeochem. Cycles, 24, GB0A08. riverine flux between 2000 and 2050 for the Adapting Mosaic scenario, the most ambitious in terms of nutrient managements of the MEA sce- narios. These two scenarios provide a range of projections for future DIN riverine fluxes by the year 2050. DIN river discharge (kgN km-2 yr-1) 2000 a. 500 100 50 10 5 0 2050, changes from 2000 b. 50 10 5 Global Orchestration Scenario, MEA 1 -1 c. -5 -10 -50 Adapting Mosaic Scenario, MEA Figure 6.SM.2 | (a) Dissolved inorganic nitrogen (DIN) river discharge to coastal zone (mouth of rivers) in 2000, based on Global Nutrient Export from WaterSheds (NEWS) 2 model; change in DIN discharge from 2000 to 2050, based on the (b) Global Orchestra- tion and (c) Adapting Mosaic scenarios from the Millennium Ecosystem Assessment 6SM (MEA) (Mayorga et al., 2010; Seitzinger et al., 2010). Units are kg N km 2 yr 1 of water- shed area, as an average for each watershed. Global DIN export to the coastal zone in 2050 under the Global Orchestration and Adapting Mosaic scenarios changes by +5.5 and 0.4 TgN yr 1, respectively, relative to the export of 18.9 TgN yr 1 in 2000 (Seitzinger et al., 2010). 6SM-4 7SM Clouds and Aerosols Supplementary Material Coordinating Lead Authors: Olivier Boucher (France), David Randall (USA) Lead Authors: Paulo Artaxo (Brazil), Christopher Bretherton (USA), Graham Feingold (USA), Piers Forster (UK), Veli-Matti Kerminen (Finland), Yutaka Kondo (Japan), Hong Liao (China), Ulrike Lohmann (Switzerland), Philip Rasch (USA), S.K. Satheesh (India), Steven Sherwood (Australia), Bjorn Stevens (Germany), Xiao-Ye Zhang (China) Contributing Authors: Govindasamy Bala (India), Nicolas Bellouin (UK), Angela Benedetti (UK), Sandrine Bony (France), Ken Caldeira (USA), Anthony Del Genio (USA), Maria Cristina Facchini (Italy), Mark Flanner (USA), Steven Ghan (USA), Claire Granier (France), Corinna Hoose (Germany), Andy Jones (UK), Makoto Koike (Japan), Ben Kravitz (USA), Benjamin Laken (Spain), Matthew Lebsock (USA), Natalie Mahowald (USA), Gunnar Myhre (Norway), Colin O Dowd (Ireland), Alan Robock (USA), Bjrn Samset (Norway), Hauke Schmidt (Germany), Michael Schulz (Norway), Graeme Stephens (USA), Philip Stier (UK), Trude Storelvmo (USA), Dave Winker (USA), Matthew Wyant (USA) Review Editors: Sandro Fuzzi (Italy), Joyce Penner (USA), Venkatachalam Ramaswamy (USA), Claudia Stubenrauch (France) This chapter supplementary material should be cited as: Boucher, O., D. Randall, P. Artaxo, C. Bretherton, G. Feingold, P. Forster, V.-M. Kerminen, Y. Kondo, H. Liao, U. Lohmann, P. Rasch, S.K. Satheesh, S. Sherwood, B. Stevens and X.Y. Zhang, 2013: Clouds and Aerosols Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www. climatechange2013.org and www.ipcc.ch. 7SM-1 Table of Contents 7.SM.1 Supplementary Material to Section 7.2.7.1.................................................. 7SM-3 7.SM.2 Supplementary Material to Section 7.5.2.1.................................................. 7SM-4 References .............................................................................. 7SM-4 7SM 7SM-2 Clouds and Aerosols Chapter 7 Supplementary Material 7.SM.1 Supplementary Material to W m 2. The lower bound is also justified by a sensitivity study to ice Section 7.2.7.1 particle shape which rules out negative values for observed contrail optical depths (Markowicz and Witek, 2011a). The upper bound also Forster et al. (2007) estimated the 2005 radiative forcing (RF) from accounts for the potential effect of sub-visible contrails, noting that contrails as +0.01 ( 0.007 to +0.02) W m 2, but neglected any increase only one published estimate extends significantly beyond 0.03 W m 2. due to traffic increase for previous estimates and considered 2000 A medium confidence is attached to this estimate. An additional RF of estimates to be representative of 2005. Lee et al. (2009) scaled these +0.003 W m 2 is due to emissions of water vapour in the stratosphere estimates upward 18% to account for revised fuel use estimates, pro- by aviation as estimated by Lee et al. (2009). pulsive efficiency and flight routes for year 2005. Forster et al. (2007) quoted Sausen et al. (2005) to update the 2000 Estimates of the RF due to contrails published since AR4 are compiled forcing for aviation-induced cirrus (including linear contrails) to +0.03 in Table 7.SM.1. These have been scaled by scheduled air traffic dis- (+0.01 to +0.08) W m 2 but did not consider this to be a best estimate tance (in millions of kilometres) as provided by http://www.airlines. because of large uncertainties. In particular, observationally based esti- org/Pages/Annual-Results-World-Airlines.aspx (see Table 7.SM.2) to mates of aviation-induced cirrus forcing estimates may unintentionally produce RF estimates for the year 2011. This simple linear scaling include cirrus changes not directly caused by aviation. assumes non-scheduled air traffic distance increases at the same rate as scheduled traffic as well as a constant likelihood of persistent con- Only a few estimates of the RF due to aviation-induced cirrus have trail formation per kilometre flown despite the changing geographical been published since AR4 (Table 7.SM.3) and all focused on contrail distributions of flights. The trend in propulsive efficiency (which would cirrus. Schumann and Graf (2013) constrained their model with obser- increase the trend in contrail formation) and any saturation effect vations of the diurnal cycle of contrails and cirrus in a region with high (which would decrease the trend in contrail formation) are neglected. air traffic relative to a region with little air traffic, and estimated a It should be noted that the intervals provided by the individual studies RF of +0.05 (0.04 to +0.08) W m 2 for contrails and contrail-induced in Table 7.SM.1 generally correspond to minimum maximum values cirrus in 2006, but their model has a large shortwave contribution, from sensitivity studies rather than statistical uncertainty ranges. The suggesting that larger estimates are possible (Myhre et al., 2009). An lower and upper bounds for the Spangenberg et al. (2013) study cor- alternative approach was taken by Burkhardt and Kärcher (2011), who respond to the most conservative and most sensitive contrail masks of estimated a global forcing of +0.03 W m 2 from contrails and contrail Duda et al. (2013), respectively. cirrus within a climate model for the year 2002 (Burkhardt and Kärch- er, 2009). Their RF for contrails and contrail-cirrus (+0.0375 W m-2) is The average of RF estimates for the year 2011 since AR4 amounts to corrected here for the radiative impact due to the decrease in natu- +0.012 W m 2, which is rounded to +0.01 W m 2 to provide a central ral cirrus ( 0.007 W m-2). Based on these two studies we assess the estimate for this assessment. The 90% uncertainty range is estimat- combined contrail and contrail-induced cirrus ERF for the year 2011 ed empirically from the published sensitivity studies as 0.005 to 0.03 to be +0.05 W m 2 neglecting the possibility that rapid adjustments Table 7.SM.1 | Estimates of the contrail radiative forcing (RF) and their scaling to year 2011 (W m 2). The uncertainty of the estimate by Markowicz and Witek (2011b) is calculated by combining the uncertainties due to crystal shape and contrail optical depth. RF Due to Contrails Reference RF Due to Contrails Reference Year Scaled to Year 2011 Forster et al. (2007) - AR4 +0.01 ( 0.007 to +0.02) 2000 (2005) +0.015 ( 0.01 to +0.03) Rädel and Shine (2008) +0.006 2002 +0.009 Rap et al. (2010b) - offline +0.012 2002 +0.018 Rap et al. (2010b) - online +0.008 (+0.004 to 0.012) 2002 +0.012 (+0.006 to +0.018) Kärcher et al. (2010) +0.008 to +0.020 2000 +0.012 to +0.030 Burkhardt and Kärcher (2011) +0.0043 (young contrails) 2002 +0.007 Frömming et al. (2011) +0.0059 (+0.0049 to +0.0211) 2000 +0.009 (+0.007 to +0.032) Markowicz and Witek (2011b) +0.011 ( +0.006 to +0.016) 2002 +0.017 (+0.010 to +0.024) Voigt et al.(2011) +0.0159 (+0.0111 to +0.0477) 2005 +0.020 (+0.014 to +0.060) Yi et al. (2012) +0.0113 (+0.0098 to +0.0165) 2006 +0.014 (+0.012 to +0.020) Spengenberg et al. (2013) +0.0057 (+0.0028 to +0.0171) 2006 +0.007 (+0.003 to +0.021) This Assessment +0.01 (+0.005 to +0.03) Table 7.SM.2 | Scheduled air traffic distance (in millions of kilometres) as provided by http://www.airlines.org/Pages/Annual-Results-World-Airlines.aspx. 1992 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 15,690 25,517 25,612 25,418 26,264 29,163 30,862 32,099 34,109 35,368 34,039 36,833 38,530 7SM 7SM-3 Chapter 7 Supplementary Material Clouds and Aerosols Table 7.SM.3 | Estimates of the radiative forcing (RF)/effective radiative forcing (ERF) due to contrails and contrail cirrus and their scaling to year 2011 (W m 2). RF Due to Contrails and Reference RF/ERF Due to Contrails and Contrail Reference Contrail Cirrus Year Cirrus Scaled to Year 2011 Stordal et al. (2005) / Sausen et al. (2005) - AR4 +0.03 (+0.01 to +0.08) 2000 +0.045 (+0.015 to +0.12) Burkhardt and Kärcher (2011) +0.03 2002 +0.045 Schumann and Graf (2013) +0.05 (+0.04 to +0.08)a 2006 +0.060 (+0.040 to +0.119)b This Assessment +0.05 (+0.02 to +0.15) Notes: a The range is an expert judgment for a 1- interval. b The range corresponds to a 90% uncertainty range. References may reduce this estimate (Ponater et al., 2005; Rap et al., 2010a). We further assess the 90% uncertainty range to be +0.02 to +0.15 W m 2 Burkhardt, U., and B. Kärcher, 2009: Process-based simulation of contrail cirrus in a to take into account the large uncertainties associated with spreading global climate model. J. Geophys. Res., 114, D16201. rate, optical depth, ice particle shape and radiative transfer. A low con- Burkhardt, U., and B. Kärcher, 2011: Global radiative forcing from contrail cirrus. Nature Clim. Change, 1, 54-58. fidence is attached to this estimate. Duda, D. P., P. Minnis, K. Khlopenkov, T. L. Chee, and R. Boeke, 2013: Estimation of 2006 Northern Hemisphere contrail coverage using MODIS data. Geophys. Res. Lett., 40, 612-617. 7.SM.2 Supplementary Material Forster, P., et al., 2007: Changes in atmospheric constituents and in radiative forcing. to Section 7.5.2.1 In: Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Figure 7.SM.1 shows the annual zonal mean radiative forcing due to Averyt, M. Tignor and H. L. Miller (eds.)], pp. 129-234, Cambridge University aerosol radiation interactions (RFari, in W m 2) due to all anthropogen- Press, Cambridge, United Kingdom and New York, NY, USA. ic aerosols from the different AeroCom II models that were combined Frömming, C., M. Ponater, U. Burkhardt, A. Stenke, S. Pechtl, and R. Sausen, 2011: in Figure 7.17. Sensitivity of contrail coverage and contrail radiative forcing to selected key parameters. Atmos. Environ., 45, 1483-1490. Kärcher, B., U. Burkhardt, M. Ponater, and C. Frömming, 2010: Importance of 2 representing optical depth variability for estimates of global line-shaped contrail Multi model mean CAM4 Oslo radiative forcing. Proc. Natl. Acad. Sci. U.S.A., 107, 19181 19184. HadGEM2 ECHAM5 HAM Lee, D., et al., 2009: Aviation and global climate change in the 21st century. Atmos. OsloCTM2 SPRINTARS GISS MATRIX GISS modelE Environ., 43, 3520-3537. CAM5.1 BCC Markowicz, K. M., and M. L. Witek, 2011a: Simulations of contrail optical properties GMI GEOS Chem and radiative forcing for various crystal shapes. J. Appl. Meteorol. Climatol., 50, 1 GOCART NCAR CAM3.5 1740-1755. IMPACT INCA Markowicz, K. M., and M. Witek, 2011b: Sensitivity study of global contrail radiative TM5 RFari (Wm 2) forcing due to particle shape. J. Geophys. Res., 116, D23203. Myhre, G., et al., 2009: Intercomparison of radiative forcing calculations of stratospheric water vapour and contrails. Meteorol. Z., 18, 585-596. 0 Myhre, G., et al., 2013: Radiative forcing of the direct aerosol effect from AeroCom Phase II simulations. Atmos. Chem. Phys., 13, 1853-1877. Ponater, M., S. Marquart, R. Sausen, and U. Schumann, 2005: On contrail climate sensitivity. Geophys. Res. Lett., 32, L10706. Rädel, G., and K. P. Shine, 2008: Radiative forcing by persistent contrails and its dependence on cruise altitudes. J. Geophys. Res., 113, D07105. 1 Rap, A., P. M. Forster, J. M. Haywood, A. Jones, and O. Boucher, 2010a: Estimating the climate impact of linear contrails using the UK Met Office climate model. Geophys. Res. Lett., 37, L20703. Rap, A., P. M. Forster, A. Jones, O. Boucher, J. M. Haywood, N. Bellouin, and R. R. De 90oS 60oS 30oS 0o 30oN 60oN 90oN Leon, 2010b: Parameterization of contrails in the UK Met Office Climate Model. Latitude J. Geophys. Res., 115, D10205. Sausen, R., et al., 2005: Aviation radiative forcing in 2000: An update on IPCC (1999). Figure 7.SM.1 | Annual zonal mean radiative forcing due to aerosol radiation inter- Meteorol. Z., 14, 555-561. actions (RFari, in W m 2) due to all anthropogenic aerosols from the different AeroCom Schumann, U., and K. Graf, 2013: Aviation-induced cirrus and radiation changes at II models (Myhre et al., 2013). No adjustment for missing species in certain models has diurnal timescales. J. Geophys. Res., 118, 2404-2421. been applied. The forcings are for the 1850 2000 period. See also Figure 7.17. Spangenberg, D. A., P. Minnis, S. T. Bedka, R. Palikonda, D. P. Duda, and F. G. Rose, 2013: Contrail radiative forcing over the Northern Hemisphere from 2006 Aqua MODIS data. Geophys. Res. Lett., 40, 595-600. Stordal, F., G. Myhre, E. J. G. Stordal, W. B. Rossow, D. S. Lee, D. W. Arlander, and T. Svenby, 2005: Is there a trend in cirrus cloud cover due to aircraft traffic? Atmos. Chem. Phys., 5, 2155 2162. Voigt, C., et al., 2011: Extinction and optical depth of contrails. Geophys. Res. Lett., 7SM 38, L11806. Yi, B., P. Yang, K.-N. Liou, P. Minnis, and J. E. Penner, 2012: Simulation of the global contrail radiative forcing: A sensitivity analysis. Geophys. Res. Lett., 39, L00F03. 7SM-4 Anthropogenic and Natural 8SM Radiative Forcing Supplementary Material Coordinating Lead Authors: Gunnar Myhre (Norway), Drew Shindell (USA) Lead Authors: François-Marie Bréon (France), William Collins (UK), Jan Fuglestvedt (Norway), Jianping Huang (China), Dorothy Koch (USA), Jean-François Lamarque (USA), David Lee (UK), Blanca Mendoza (Mexico), Teruyuki Nakajima (Japan), Alan Robock (USA), Graeme Stephens (USA), Toshihiko Takemura (Japan), Hua Zhang (China) Contributing Authors: Borgar Aamaas (Norway), Olivier Boucher (France), Stig B. Dalsren (Norway), John S. Daniel (USA), Piers Forster (UK), Claire Granier (France), Joanna Haigh (UK), Oivind Hodnebrog (Norway), Jed O. Kaplan (Switzerland/Belgium/USA), George Marston (UK), Claus J. Nielsen (Norway), Brian C. O Neill (USA), Glen P. Peters (Norway), Julia Pongratz (Germany), Michael Prather (USA), Venkatachalam Ramaswamy (USA), Raphael Roth (Switzerland), Leon Rotstayn (Australia), Steven J. Smith (USA), David Stevenson (UK), Jean-Paul Vernier (USA), Oliver Wild (UK), Paul Young (UK) Review Editors: Daniel Jacob (USA), A.R. Ravishankara (USA), Keith Shine (UK) This chapter supplementary material should be cited as: Myhre, G., D. Shindell, F.-M. Bréon, W. Collins, J. Fuglestvedt, J. Huang, D. Koch, J.-F. Lamarque, D. Lee, B. Mendoza, T. Nakajima, A. Robock, G. Stephens, T. Takemura and H. Zhang, 2013: Anthropogenic and Natural Radiative Forc- ing Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www. climatechange2013.org and www.ipcc.ch. 8SM-1 Table of Contents 8.SM.1 Figures on Regional Emissions to 8.SM.14 Metric Values for Other Near-Term Support Section 8.2.2..................................... 8SM-3 Climate Forcers to Support Section 8.7.2................................................... 8SM-23 8SM 8.SM.2 Description of Hydroxyl Radical Feedback and Perturbation Lifetime for Methane 8.SM.15 Metric Values for Halocarbons Including to Support Section 8.2.3............................... 8SM-6 Climate Carbon Feedback for Carbon Dioxide to Support Section 8.7.2............. 8SM-24 8.SM.3 Well-Mixed Greenhouse Gas Radiative Forcing Formulae and Uncertainties 8.SM.16 Metric Values to Support Figure 8.32 to Support Table 8.3....................................... 8SM-7 and Figure 8.33.............................................. 8SM-39 8.SM.4 Total Solar Irradiance Reconstructions 8.SM.17 Metric Values for Sectors to Support from 1750 to 2012 to Support Section 8.7.2................................................... 8SM-40 Section 8.4.1..................................................... 8SM-7 8.SM.18 Further Information on Temperature 8.SM.5 Table with Estimates of Radiative Forcing Impact from Various Sectors to Support due to Solar Changes over the Industrial Section 8.7.2................................................... 8SM-41 Era to Support Section 8.4.1...................... 8SM-10 References ............................................................................ 8SM-43 8.SM.6 Further Information on Total Solar Irradiance, Uncertainties and Change Since the Maunder Minimum to Support Section 8.4.1................................... 8SM-10 8.SM.7 Method Description to Support Figure 8.16....................................................... 8SM-11 8.SM.8 Table with Values and Uncertainties to Support Figure 8.17...................................... 8SM-13 8.SM.9 Description of Forcing Time Series to Support Figure 8.18................................. 8SM-14 8.SM.10 Uncertainties in Trends in Forcing to Support Figure 8.19...................................... 8SM-14 8.SM.11 Definition and Methods to Calculate Metric Values to Support Section 8.7.1................................................... 8SM-14 8.SM.12 Uncertainty Calculations for Global Warming Potential to Support Section 8.7.1................................................... 8SM-18 8.SM.13 Calculations of Metric Values for Halocarbons to Support Section 8.7.2................................................... 8SM-21 8SM-2 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material 8.SM.1 Figures on Regional Emissions to Support Section 8.2.2 Western South USA Europe China India America Africa Historical RCP2.6 RCP4.5 8SM RCP6.0 RCP8.5 Black Carbon (TgC year-1) CH4 (TgCH4 year-1) CO (TgCO year-1) NH3 (TgNH3 year-1) NMVOC (TgNMVOC year-1) NOx (TgNO2 year-1) Organic Carbon (TgC year-1) SO2 (TgSO2 year-1) Figure 8.SM.1 | Time evolution of regional anthropogenic and biomass burning emissions 1850 2100 used in Coupled Model Intercomparison Project Phase 5 (CMIP5)/Atmo- spheric Chemistry and Climate Model Intercomparison Project (ACCMIP) following each Representative Concentration Pathway (RCP). Historical (1850 2000) values are from (Lamarque et al., 2010). RCP values are from (van Vuuren et al., 2011). 8SM-3 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Methane Historical : 1850 1950 1980 2000 Scenarios : RCP 2.6 2050 RCP 4.5 2050 RCP 6.0 2050 RCP 8.5 2050 8SM RCP 2.6 2100 RCP 4.5 2100 RCP 6.0 2100 RCP 8.5 2100 (g[CH4] m-2 year-1) 0.1 0.2 0.5 1 2 5 10 20 Carbone Monoxide Historical : 1850 1950 1980 2000 Scenarios : RCP 2.6 2050 RCP 4.5 2050 RCP 6.0 2050 RCP 8.5 2050 RCP 2.6 2100 RCP 4.5 2100 RCP 6.0 2100 RCP 8.5 2100 (g[CO] m-2 year-1) 0.1 0.2 0.5 1 2 5 10 20 NMVOC Historical : 1850 1950 1980 2000 Scenarios : RCP 2.6 2050 RCP 4.5 2050 RCP 6.0 2050 RCP 8.5 2050 RCP 2.6 2100 RCP 4.5 2100 RCP 6.0 2100 RCP 8.5 2100 (g[NMVOC] m-2 year-1) 0.01 0.02 0.05 0.1 0.2 0.5 1 2 Figure 8.SM.2 | Time evolution of anthropogenic and biomass burning emissions 1850 2100 used in CMIP5/ACCMIP following each RCP. Historical (1850 2000) values are from Lamarque et al. (2010). RCP values are from van Vuuren et al. (2011). 8SM-4 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material NOX Historical : 1850 1950 1980 2000 Scenarios : RCP 2.6 2050 RCP 4.5 2050 RCP 6.0 2050 RCP 8.5 2050 8SM RCP 2.6 2100 RCP 4.5 2100 RCP 6.0 2100 RCP 8.5 2100 (g[N] m-2 year-1) 0.01 0.02 0.05 0.1 0.2 0.5 1 2 SO2 Historical : 1850 1950 1980 2000 Scenarios : RCP 2.6 2050 RCP 4.5 2050 RCP 6.0 2050 RCP 8.5 2050 RCP 2.6 2100 RCP 4.5 2100 RCP 6.0 2100 RCP 8.5 2100 (g[S] m-2 year-1) 0.01 0.02 0.05 0.1 0.2 0.5 1 2 NH3 Historical : 1850 1950 1980 2000 Scenarios : RCP 2.6 2050 RCP 4.5 2050 RCP 6.0 2050 RCP 8.5 2050 RCP 2.6 2100 RCP 4.5 2100 RCP 6.0 2100 RCP 8.5 2100 (g[N] m-2 year-1) 0.01 0.02 0.05 0.1 0.2 0.5 1 2 Figure 8.SM.2 | (continued) 8SM-5 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Black Carbon Historical : Scenarios : 8SM (g[C] m-2 year-1) 0.01 0.02 0.05 0.1 0.2 0.5 1 2 Organic Carbon Historical : 1850 1950 1980 2000 Scenarios : RCP 2.6 2050 RCP 4.5 2050 RCP 6.0 2050 RCP 8.5 2050 RCP 2.6 2100 RCP 4.5 2100 RCP 6.0 2100 RCP 8.5 2100 (g[C] m-2 year-1) 0.01 0.02 0.05 0.1 0.2 0.5 1 2 Figure 8.SM.2 | (continued) 8.SM.2 Description of Hydroxyl Radical and therefore gives a feedback factor f = 1/(1 s) = 1.34 +/- 0.06 (for Feedback and Perturbation Lifetime 1- range). The error estimate on f is estimated from the error estimate for Methane to Support Section 8.2.3 on s using error(f) = error(s) * df/ds. The methane lifetime with respect to tropospheric hydroxyl radical (OH) The perturbation lifetime is therefore calculated by combining the is estimated at 11.2 +/- 1.3 years, while the lifetime of methane (CH4) range of values for the CH4 lifetime with the range of values for the with respect to additional sinks is estimated at 120 +/- 24 years (bac- feedback factor, leading to a perturbation lifetime of 12.4 +/- 1.4 years terial uptake in soils), 150 +/- 50 years (stratospheric loss) and 200 +/- (for one sigma range) which is adopted for the metric calculations. 100 years (chlorine loss), respectively. This leads to a total CH4 lifetime Note that this value is slightly larger than the value obtained using the estimate of 9.25 +/- 0.6 years, calculated by computing the total lifetime mean estimates from all parameters (12.3 years). using the full range of each separate lifetime listed above. Note that adding the inverse values of the best estimates of the lifetimes gives 9.15 years, but the value based on full ranges is chosen here. Combin- ing this information with the OH-lifetime sensitivity (s) for CH4, s_OH (0.31 +/- 0.04) by scaling s_OH with the ratio between total lifetime and OH-lifetime (9.25/11.2) leads to an overall estimate of s of 0.25 +/- 0.03 8SM-6 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material 8.SM.3 Well-Mixed Greenhouse Gas Radiative Table 8.SM.1 | Supplementary for Table 8.3: RF formulae for CO2, CH4 and N2O. Forcing Formulae and Uncertainties to Support Table 8.3 Gas RF (in W m 2) Constant CO2 5.35 The formulae used to calculate the radiative forcings (RFs) from carbon dioxide (CO2), CH4 and nitrous oxide (N2O) are taken from Myhre et al. CH4 0.036 (1998) Table 3 as in Third Assessment Report (TAR) and Fourth Assess- 8SM ment Report (AR4). They are listed here for convenience. N2O 0.12 In calculating the uncertainties in the WMGHG RF we assume a +/-10% Notes: (5 to 95% confidence interval) uncertainty in the radiative transfer f (M , N) = 0.47 ln [1+2.01×10 5 (MN)0.75 + 5.31×10 15 M (MN)1.52] modeling that is correlated across all species. We assume the uncer- C is CO2 in ppm. tainties in the measurements of the 1750 and 2011 abundance levels M is CH4 in ppb. of the gases are uncorrelated. N is N2O in ppb. The subscript 0 denotes the unperturbed molar fraction for the species being evaluated. However, note that for the CH4 forcing N0 should refer to present-day N2O, and for the N2O forcing M0 should refer to present-day CH4. Table 8.SM.2 | Supplementary for Table 8.3: Uncertainties in WMGHG RF. CO2 CH4 N2O Halogens Total WMGHG Uncertainty in 1750 level 2 ppm 25 ppb 7 ppb 0 Uncertainty in 2011 level 0.16 ppm 2.5 ppb 0.1 ppb 0 dRF 1750 level (W m ) 2 0.039 0.01 0.023 0 0.047 dRF 2011 level (W m ) 2 0.003 0.00 0.000 0 0.003 dRF radiative transfer modeling (W m 2) 0.182 0.05 0.017 0.036 0.283 Total uncertainty (W m ) 2 0.186 0.05 0.029 0.036 0.287 8.SM.4 Total Solar Irradiance Reconstructions from 1750 to 2012 to Support Section 8.4.1 Table 8.SM.3 | Total Solar Irradiance (TSI, W m 2) reconstruction since 1750 based on Ball et al. (2012) and Krivova et al.(2010) (annual resolution series). The series are standard- ized to the Physikalisch-Meteorologisches Observatorium Davos (PMOD) measurements of solar cycle 23 (1996 2008) (PMOD is already standardized to Total Irradiance Monitor (TIM)). Year TSI (W m 2) Year TSI (W m 2) Year TSI (W m 2) 1740 1360.71 1841 1361.05 1942 1361.22 1741 1360.73 1842 1360.96 1943 1360.96 1742 1360.79 1843 1360.90 1944 1360.93 1743 1360.59 1844 1360.83 1945 1361.14 1744 1360.52 1845 1360.81 1946 1361.18 1745 1360.49 1846 1360.83 1947 1361.68 1746 1360.49 1847 1360.55 1948 1362.07 1747 1360.47 1848 1360.87 1949 1361.90 1748 1360.70 1849 1361.32 1950 1361.80 1749 1360.98 1850 1361.18 1951 1361.27 1750 1361.00 1851 1361.12 1952 1361.33 1751 1360.90 1852 1361.15 1953 1361.18 1752 1360.79 1853 1361.08 1954 1361.02 1753 1360.76 1854 1360.93 1955 1361.12 1754 1360.69 1855 1360.80 1956 1361.47 (continued on next page) 8SM-7 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Table 8.SM.3 (continued) Year TSI (W m 2) Year TSI (W m 2) Year TSI (W m 2) 1755 1360.61 1856 1360.73 1957 1361.95 1756 1360.60 1857 1360.73 1958 1362.43 1757 1360.65 1858 1360.86 1959 1362.17 8SM 1758 1360.64 1859 1360.95 1960 1362.11 1759 1360.26 1860 1361.22 1961 1361.81 1760 1360.19 1861 1361.26 1962 1361.37 1761 1360.89 1862 1361.10 1963 1361.28 1762 1360.95 1863 1361.04 1964 1361.14 1763 1360.87 1864 1360.88 1965 1361.06 1764 1360.84 1865 1360.87 1966 1361.09 1765 1360.61 1866 1360.85 1967 1361.40 1766 1360.65 1867 1360.73 1968 1361.63 1767 1360.74 1868 1360.72 1969 1361.57 1768 1361.12 1869 1360.96 1970 1361.68 1769 1361.37 1870 1360.80 1971 1361.60 1770 1361.59 1871 1361.19 1972 1361.56 1771 1361.41 1872 1361.09 1973 1361.32 1772 1361.38 1873 1361.11 1974 1361.17 1773 1361.12 1874 1361.00 1975 1361.05 1774 1360.99 1875 1360.89 1976 1360.98 1775 1360.72 1876 1360.79 1977 1361.29 1776 1360.67 1877 1360.76 1978 1361.95 1777 1360.74 1878 1360.70 1979 1362.23 1778 1361.12 1879 1360.68 1980 1362.10 1779 1360.75 1880 1360.71 1981 1362.08 1780 1360.50 1881 1360.95 1982 1361.69 1781 1360.58 1882 1360.86 1983 1361.67 1782 1360.80 1883 1360.78 1984 1361.12 1783 1360.52 1884 1361.13 1985 1361.09 1784 1360.57 1885 1361.02 1986 1361.09 1785 1360.66 1886 1360.90 1987 1361.11 1786 1360.84 1887 1360.76 1988 1361.70 1787 1361.00 1888 1360.73 1989 1362.11 1788 1361.25 1889 1360.70 1990 1361.86 1789 1360.76 1890 1360.70 1991 1361.93 1790 1360.58 1891 1360.86 1992 1362.00 1791 1360.59 1892 1361.03 1993 1361.46 1792 1360.63 1893 1361.26 1994 1361.20 1793 1360.53 1894 1361.53 1995 1361.15 1794 1360.53 1895 1361.38 1996 1361.02 1795 1360.80 1896 1361.17 1997 1361.12 1796 1360.76 1897 1360.98 1998 1361.46 1797 1360.69 1898 1360.91 1999 1361.76 1798 1360.68 1899 1360.88 2000 1361.93 (continued on next page) 8SM-8 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.3 (continued) Year TSI (W m 2) Year TSI (W m 2) Year TSI (W m 2) 1799 1360.66 1900 1360.80 2001 1361.84 1800 1360.60 1901 1360.69 2002 1361.79 1801 1360.85 1902 1360.65 2003 1361.31 1802 1360.93 1903 1360.74 2004 1361.09 8SM 1803 1360.76 1904 1361.08 2005 1360.92 1804 1360.71 1905 1360.89 2006 1360.88 1805 1360.67 1906 1361.21 2007 1360.88 1806 1360.74 1907 1361.00 2008 1360.82 1807 1360.58 1908 1361.15 2009 1360.81 1808 1360.53 1909 1360.99 2010 1361.01 1809 1360.53 1910 1360.96 2011 1361.22 1810 1360.49 1911 1360.77 2012 1361.42 1811 1360.48 1912 1360.67 1812 1360.48 1913 1360.70 1813 1360.50 1914 1360.76 1814 1360.53 1915 1361.10 1815 1360.55 1916 1361.50 1816 1360.62 1917 1361.63 1817 1360.65 1918 1361.89 1818 1360.61 1919 1361.53 1819 1360.60 1920 1361.29 1820 1360.57 1921 1361.09 1821 1360.53 1922 1360.90 1822 1360.52 1923 1360.82 1823 1360.50 1924 1360.79 1824 1360.57 1925 1360.89 1825 1360.62 1926 1361.15 1826 1360.68 1927 1361.47 1827 1360.87 1928 1361.24 1828 1360.95 1929 1361.21 1829 1360.96 1930 1361.35 1830 1361.01 1931 1361.07 1831 1361.01 1932 1360.89 1832 1360.86 1933 1360.79 1833 1360.75 1934 1360.80 1834 1360.72 1935 1360.95 1835 1360.76 1936 1361.50 1836 1361.13 1937 1361.65 1837 1361.40 1938 1361.59 1838 1361.38 1939 1361.69 1839 1361.21 1940 1361.51 1840 1361.20 1941 1361.42 8SM-9 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing 8.SM.5 Table with Estimates of Radiative Forcing due to Solar Changes over the Industrial Era to Support Section 8.4.1 Table 8.SM.4 | Comparison of RF estimates between 1745 and 2008 minima. Reference Assumptions RF (W m 2) Comments 8SM 0.019 (7-year rm) Use a flux transport model to simulate the evolution of total and open magnetic Wang et al. (2005) flux without a secularly varying background 0.013 (annual) 0.071 (7-year rm) Wang et al. (2005) Same as above but with a secularly varying background Used to estimate RF in AR4 0.065 (annual) Steinhilber et Use the solar modulation potential obtained from cosmogenic isotopes -0.02 (5-years resolutions) al. (2009) 0.048 (7-year rm) Krivova et al. (2010) Use the evolution of the solar surface magnetic field, relying on time constants representing Ball et al. (2012) the decay and conversion of different surfaces magnetic field structures 0.045 (annual) Notes: rm = running means. For the reconstructions based on solar surface magnetic structures, with annual resolution, the year of the minimum is 1745. However, for the Steinhilber et al. (2009) reconstruction, based on cosmogenic isotopes, the minimum is in 1765, because the resolution of the series is 5 years. 8.SM.6 Further Information on Total Solar 8.SM.6.1 Uncertainties Irradiance, Uncertainties and Change Since the Maunder Minimum to 1. PMOD RF and uncertainty between 1986 and 2008: Support Section 8.4.1 According to PMOD, 2009 is the year of the TSI minimum, but according to TIM it is 2008. We take the year 2008 as the year of the minimum. The absolute measurements of TSI are extremely difficult with an abso- lute accuracy better than 0.1%. All TSI instruments since 1979 have The PMOD TSI mean for September 2008 was 1365.26 +/- 0.16 W m 2, been calibrated, relatively or absolutely. In order to maintain a reason- whereas in the 1986 minimum it was 1365.57 +/- 0.01 W m 2 (Frohlich, able accuracy in the annual to multi-decadal timeframe it is essential 2009). to have at least three independent sensors operating in space simul- taneously. The fundamental difficulties of the absolute measurements Difference between 2008 and 1986 minima: are described in Butler et al. (2008). Fox et al. (2011) quantified how 1365.26 +/- 0.16 1365.57 +/- 0.01 the uncertainty in satellite TSI measurements could be improved by an order of magnitude by adding primary SI traceability on board. For Applying the error propagation formula: instance, to reduce from 3.60% for Moderate Resolution Imaging Spec- (a +/- x) (b +/- y) = (a b) +/- [x2 + y2)]1/2 trometer (MODIS)/Visible Infrared Imaging Radiometer Suite (VIIRS) to 0.30% for Traceable Radiometry Underpinning Terrestrial- and Helio- That for our case is: Studies (TRUTHS). This would reduce by 67 to 75% the time required (1365.26 1365.57) +/- [(0.16)2 + (0.01)2]1/2 = 0.31 +/- 0.16 to achieve trend accuracy. The RF is: The Spectral Irradiance Monitor (SIM) on board of the Solar Radia- [ 0.31 +/- 0.16] * 0.175 * 0.78 = 0.042 +/- 0.022 ~ 0.04 +/- 0.02 W m 2 tion and Climate Experiment (SORCE) measurements (Harder et al., 2009) suggest that over solar cycle (SC) 23 declining phase, the 200 to 8.SM.6.2 Standardization 400 nm ultraviolet (UV) flux decreased by two to six times more than expected from prior observations and model calculations and in phase We use the following expression to standardize the time series: with the TSI trend, whereas surprisingly the visible presents an oppo- [Si ] + site trend. However, SIM s solar spectral irradiance measurements from April 2004 to December 2008 and inferences of their climatic implica- Where Si is the annual TSI of the series that will be standardized. tions are incompatible with the historical solar UV irradiance database, coincident solar proxy data, current understanding of the sources of is the TSI average of the whole time span of series that will be solar irradiance changes and empirical climate change attribution standardized. results, but are consistent with known effects of instrument sensitivity drifts. Thus what seems to be needed is improved characterization of is the TSI average of the series we are using as the standard. In the SIM/SORCE observations and extreme caution in studies of climate our case the TIM TSI between 2003 and 2012 or the PMOD TSI for SC and atmospheric change (Haigh et al., 2010) until additional validation 23 (1996 2008). and uncertainty estimates are available (DeLand and Cebula, 2012; Lean and Deland, 2012). 8SM-10 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material For the RF estimates the years with minimum solar activity based on As these two sunspot number versions are quite different in the his- modern or historical observations are used as provided in the refer- torical period, using one or the other results in different trends since enced literature. These years may in some cases be slightly different the MM (Hathaway et al., 2002) and therefore different RF estimates. from the years with minimum annual mean TSI (see Table 8.SM.3), but Moreover, Svalgaard et al. (2012) have published some preliminary cor- these differences have a negligible impact on the RF estimates pro- rections to SGN that could imply a reduction in the RF since the MM. vided in Section 8.4.1. 8SM 8.SM.6.3 Total Solar Irradiance Variations Since the 8.SM.7 Method Description to Support Maunder Minimum Figure 8.16 For the Maunder minimum (MM)-to-present AR4 gives a RF positive In Figure 8.16, probability distributions are shown for the main climate range of 0.1 to 0.28 W m 2, equivalent to 0.08 to 0.22 W m 2 used drivers as well as for the total anthropogenic forcing. This paragraph here. The estimates based on irradiance changes in Sun-like stars were describes how it was built. included in this range but are not included in the Fifth Assessment Report (AR5) range because they are now considered incorrect: Bali- For each of the major forcing agents, a best estimate and a 90% uncer- unas and Jastrow (1990) found a bimodal separation between non- tainty range [P05; P95] was provided. The best estimate is the median cycling MM-like state stars with the lowest Ca II brightness, and the of the probability distribution. The values are available in Table 8.6 and higher emission Ca II cycling stars. More recent surveys have not repro- repeated below. For some forcing agents, the best estimate and the duced their results and suggest that the selection of the original set uncertainty range are provided for RF, and not for effective radiative was flawed (Wright (2004); also, stars in a MM-like state do not always forcing (ERF). In such a case, we assume that ERFBest=RFBest and we exhibit Ca II emission brightness below that of solar minimum (Hall assumed a quadratic 17% increase of the uncertainty range , that is: and Lockwood (2004). The reconstructions in Schmidt et al. (2011) indicate a MM-to-pres- (8.SM.1) ent RF range of 0.08 to 0.18 W m 2, which is within the AR4 range although narrower. Gray et al. (2010) point out that choosing the solar Most forcing agents considered here (WMGHG, ozone, stratospheric activity minima years of 1700 (Maunder) or 1800 (Dalton) would sub- H2O, land use change) have symmetrical uncertainty ranges (i.e., Best stantially increase the solar RF with respect to 1750-to-present while = (P05 + P95)/2). For these forcing agents, the probability distribution leaving the anthropogenic forcings essentially unchanged, and that is assumed to be Gaussian, with a standard deviation as these solar minima forcings would represent better the solar RF of the pre-industrial era. (8.SM.2) Other recent estimates give various MM-to-present RF values: The analysis of Shapiro et al. (2011) falls outside the range 0.08 to 0.18 W where f 1.645 is the factor to convert one standard deviation to the m 2 reported above: 0.78 W m 2. These authors used the semi-empir- 5-95% probability range. ical photosphere model A (supergranule cell interior) of Fontenla et al. (1999). But Judge et al. (2012) indicate that by using such model The other forcing agents (black carbon on snow, contrails, aerosols) Shapiro et al. (2011) overestimated the quiet-Sun irradiance variations have non-symmetrical uncertainty ranges. For black carbon and snow, by a factor of about two, then the RF would be 0.36 W m 2 , which is we assume a log-normal distribution as still outside the range of Schmidt et al. (2011). Studies of magnetic field indicators suggest that changes over the 19th and 20th centuries were more modest than those assumed in the Shapiro et al. (2011) recon- (8.SM.3) struction (Svalgaard and Cliver, 2010; Lockwood and Owens, 2011). Also, analysis by Feulner (2011) indicates that temperature simulations with x0 as the best estimate and adjusted to fit P05 and P50 (BC = driven by such a large solar forcing are inconsistent with reconstructed 0.5 ; Contrails = 0.65). and observed historical temperatures, although when a forcing in line with the range presented here is used they are consistent. Hence we For the aerosols, which have a non-symmetrical uncertainty range, we do not include this larger forcing within our assessed range. Schrijver build a probability distribution as et al. (2011) and Foukal et al. (2011) find a RF which is consistent with the RF range given above (0.08 to 0.18 W m 2). Almost all the TSI reconstructions since pre-industrial times are based on the Sunspot Group Number (SGN; Hoyt and Schatten (1998). The SGN is preferred by researchers respect to the International Sunspot (8.SM.4) Number (Clette et al., 2007) because SGN starts at 1610 and it is the longest time series based on direct solar observations. 8SM-11 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing x0, - and + are adjusted to fit the best estimates and 90% uncertainty ranges: 8SM and with (8.SM.5) The total anthropogenic ERF distribution was then derived through a Monte Carlo approach (106 independent shots), summing the random estimates of all components. This approach assumes that all forcing agent uncertainties are independent. The results are provided in Table 8.SM.5. Table 8.SM.5 | Best estimate values and 5 and 95% ranges for RF and ERF. Yellow are the input values, green the extrapolated values (from RF to ERF) and red is the result of the Monte Carlo addition. RF ERF Forcing agent Best P05 P95 Best P05 P95 Well-mixed greenhouse gases 2.83 2.26 3.40 Ozone 0.350 0.15 0.55 0.350 0.141 0.559 Stratospheric H2O 0.070 0.02 0.12 0.070 0.019 0.121 Surface albedo 0.15 0.25 0.05 0.150 0.253 0.047 Black carbon on snow 0.04 0.02 0.08 0.040 0.019 0.090 Contrails 0.05 0.02 0.15 Aerosols 0.90 1.90 0.10 Total 2.29 1.13 3.33 8SM-12 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material 8.SM.8 Table with Values and Uncertainties to Support Figure 8.17 Table 8.SM.6 | Radiative forcing (RF, in W m 2) by emitted components as shown in Figure 8.17. The RF values are made consistent with Table 8.6. For emissions of CO2, CH4, CO, NMVOCs and NOx the values for the influence on CO2, CH4 and ozone are based on Stevenson et al. (2013) and Shindell et al. (2009). The seven models altogether performing the calculations for these compounds (six models in Stevenson et al., (2013); and one model in Shindell et al., (2009) have been treated with equal weight. For CO, CH4 and NMVOC only fossil fuel emissions have been taken into account. The split between NOx and NH3 of 40/60 on the RF of nitrate is from Shindell et al. (2009). The BC and OC from biomass burning is set to +0.2 and 0.2, respectively and thus a net RF of biomass burning of 0.0, in line with Table 8.4. BC ari is RF of BC from aerosol radiation interaction, formerly denoted as 8SM direct aerosol effect. Unlike in AR4 (Table 2.13) the N2O influence on RF of ozone has been set to zero, due to insufficient quantification of this and particularly the vertical profile of the ozone change. ERFaci is effective radiative forcing of aerosol cloud interaction. HFCs/ BC CFCs/ Sul- CO2 CH4 N2O PFCs/ BC ari snow OC Ozone H2O(Str) Nitrate ERFaci Total HCFCs phate SF6 & ice Components emitted CO2 1.68 1.680 CH4 0.018 0.641 0.241 0.07 0.970 N2O 0.17 0 0.170 CFCs/HCFCs/ 0.33 0.15 0.180 halons HFCs/PFCs/SF6 0.03 0.030 CO 0.087 0.072 0.075 0.234 NMVOC 0.033 0.025 0.042 0.100 NOx 0.254 0.143 0.04 0.151 NH3 0.07 0.01 0.060 BC 0.60 0.04 0.640 OC 0.29 0.290 SO2 0.41 0.410 Aerosols 0.45 0.450 SUM 1.82 0.48 0.17 0.33 0.03 0.60 0.04 0.29 0.35 0.07 0.11 0.40 0.45 Table 8.SM.7 | Percentage uncertainty in values provided in Table 8.SM.6. Uncertainty (%) Source Components emitted CO2 10 10% uncertainty in the total RF of CO2 and combined with assumed 50% uncertainty for other contributions 14% uncertainty in CH4 contribution from Section 8.3.3, 55% uncertainty for contribution to ozone, CH4 17 71% for stratospheric water vapour and 50% assumed for contribution to CO2 N2O 17 CFCs/HCFCs/halons 85 10% uncertainty for direct effect and 100% for change in stratospheric ozone (see Section 8.3.3) HFCs/PFCs/SF6 10 30% uncertainty in CH4 contribution Section 8.3.3, 37% for ozone contribution (Section 8.3.3) assumed 50% CO 24 for contribution to CO2 100% uncertainty in CH4 contribution Section 8.3.3, 70% for ozone contribution and assumed 50% NMVOC 41 for contribution to CO2 58% uncertainty in CH4 contribution Section 8.3.3, 64% for ozone contribution and the range for nitrate NOx ( 124 to +116) as provided in Table 8.4 NH3 ( 172 to +73) Same uncertainty as nitrate in Table 8.4 See Table 8.4 and Table 8.6 for BC from fossil fuel and biofuel and BC on snow and ice, respectively. BC ( 61 to +70) BC from biomass burning is given as +0.2 (0.03 to 0.4); see Section 7.5.1.2 See Table 8.4 for OC from fossil fuel and biofuel. OC from biomass burning is given as 0.2 ( 0.4 to 0.03); OC ( 63 to +72) see Section 7.5.1.2 SO2 50 See Table 8.4 ERFaci ( 167 to +100) ERFaci 0.45 ( 1.2 to 0.0); see Table 8.6 8SM-13 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing 8.SM.9 Description of Forcing Time Series to Support Figure 8.18 Table 8.SM.8 | Supplementary for Figure 8.18: Time evolution forcing. Forcing Agent Data Sources for Time Evolution WMGHG WMGHG concentration as in Annex II. RF calculated based on formulas described in Section 8.3.2. Radiative efficiencies for halocarbons 8SM are given in Table 8.A.1. Tropospheric ozone Values for 1850, 1930, 1980 and 2000 from Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP; Stevenson et al., 2013) and combined with higher temporal resolution from Oslo Chemical Transport Model 2 (Oslo CTM2; Skeie et al., 2011a). Stratospheric ozone The stratospheric ozone RF follows the functional shape of the Effective Equivalent Stratospheric Chlorine assuming a 3 years age of air (Daniel et al., 2010). Stratospheric water vapour RF is 15% of the CH4 RF. Total aerosol ERF Values for 1850, 1930, 1980 and 2000 from ACCMIP (Shindell et al., 2013) combined with higher temporal results from Spectral Radiation- Transport Model for Aerosol Species (SPRINTARS) and Oslo CTM2 for the Industrial Era and Commonwealth Scientific and Industrial Research Organisation (CSIRO) and Geophysical Fluid Dynamics Laboratory (GFDL) models in addition for the 2000 2010 period. All four models included in Shindell et al. (2013). Note that Oslo CTM2 and CSIRO do not include rapid adjustment for the aerosol cloud interaction. Aerosol radiation interaction Values for 1850, 1930, 1980 and 2000 from ACCMIP (Shindell et al., 2013) combined with higher temporal results from Goddard Institute for Space Studies (GISS) and Oslo CTM2 models. Surface albedo (land use change) Based on an assessment of the time series from Skeie et al. (2011a), Hansen et al. (2011), Pongratz et al. (2009) and Schmidt et al. (2012). Time series scaled to fit the best estimate for 2011. Surface albedo (BC on snow) Values for 1850, 1930, 1980 and 2000 from ACCMIP (Lee et al., 2013) combined with higher temporal results from Oslo CTM2 (Skeie et al., 2011b). Contrails The best estimate for contrails (RF) or combined contrails and contrail induced cirrus (ERF) is scaled to aircraft kilometres flown in table downloaded from the following website: http://www.airlines.org/Pages/Annual-Results-World-Airlines.aspx. Solar TSI reconstructions (Krivova et al., 2010; Ball et al., 2012) standardized to Physikalisch-Meteorologisches Observatorium Davos (PMOD) and Total Irradiance Monitor (TIM) data is divided by 4 and multiplied by the Earth co-albedo (1 0.3) and multiplied with 0.78 to account for absorption in the stratosphere (see Section 8.4.1). TSI provided in the Supplementary Material Table 8.SM.3. Volcanic aerosols Mean of (Gao et al., 2008; Crowley and Unterman, 2013) between 1750 and 1850 and (Sato et al., 1993; updated version of April, 2013) from 1850 to present. RF is calculated as RF = AOD * ( 25.0) W m 2. 8.SM.10 Uncertainties in Trends in Forcing to 8.SM.11 Definition and Methods to Calculate Support Figure 8.19 Metric Values to Support Section 8.7.1 8.SM.11.1 Equations for the Global Warming Potential The Absolute Global Warming Potential (AGWP) is the time-integrated radiative forcing due to a 1 kg pulse emission of gas i (usually in W m 2 yr kg 1). The Global Warming Potential (GWP) for gas i is obtained by dividing the AGWPi by the AGWP of a reference gas, normally CO2: (8.SM.6) where H is the time horizon; RFi is the radiative forcing due to a pulse emission of a gas i given by ( ) (8.SM.7) where Ai is the RFi per unit mass increase in atmospheric abundance Figure 8.SM.3 | Linear trend in anthropogenic, natural and total forcing for the indi- cated years. The uncertainty ranges (90% confidence range) are combined from uncer- of species i (radiative efficiency (RE)), and Ri is the fraction of species tainties in the forcing values (from Table 8.6) (upward vertical lines) and the uncertain- i remaining in the atmosphere after the pulse emissions. The GWP are ties in selection of time period (downward vertical lines). Monte Carlo simulations were currently not defined using the Effective Radiative Forcing (ERF, Section performed to derive uncertainties in the forcing based on ranges given in Table 8.6 and 8.1.1.2), but this could be considered as a potential improvement of linear trends in forcing. The sensitivity to time periods has been derived from changing the concept. the time periods by +/-2 years. 8SM-14 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material For most species, Ri is based on a simple exponential decay, The simplest form of RT is a single response term (M = 1) (Shine et al., 2005; Olivié et al., 2012). A better representation of the climate (8.SM.8) response, however, is two or three terms (M = 2, 3) (Boucher and Reddy, 2008; Li and Jarvis, 2009; Olivié et al., 2012). We use RT from where i is the perturbation lifetime and thus, for these species, Boucher and Reddy (2008) which assumes two exponential terms and is based on the Hadley Centre Coupled Model version 3 (HadCM3) model (Table 8.SM.9). The climate sensitivity is 1.06 K (W m 2) 1, equiv- 8SM alent to a 3.9 K equilibrium response to 2 × CO2. (8.SM.9) Using the equations above, the AGTP with a time horizon H for the The atmospheric decay of a pulse consists of many different time scales non-CO2 greenhouse gases: (Prather, 1994). Nevertheless, for gases with atmospheric lifetimes larger than the mixing times of the major reservoirs (>3 years), the decay can be approximated as it is here with a single e-fold time equal to the perturbation lifetime. In this case the total integrated impacts are exact (Prather, 2007). For very short-lived gases (<1 year), the (8.SM.14) single e-fold also provides the correct integral, but the impacts occur over a longer time frame than expected from the perturbation lifetime. and the AGTP for CO2 is For CO2, Ri is more complicated because its atmospheric response time (or lifetime of a perturbation) cannot be represented by a simple expo- nential decay (Joos et al., 2013). The decay of a perturbation of atmo- spheric CO2 following a pulse emission at time t is usually approxi- mated by a sum of exponentials (Forster et al., 2007; Joos et al., 2013): (8.SM.15) (8.SM.10) Table 8.SM.9 | Parameter values for the response to a pulse of radiative forcing used The AGWPCO2 is then (Shine et al., 2005): in the AGTP calculations 1st Term 2nd Term cj (K(W m 2) 1) 0.631 0.429 dj (years) 8.4 409.5 (8.SM.11) 8.SM.11.2 Equations for the Global Temperature Change 8.SM.11.3 Updates of Metric Values Potential The metric values need updating as a result of new scientific knowl- The Absolute Global Temperature change Potential (AGTP) can be rep- edge, but also because of changes in lifetimes and REs caused by resented as (Boucher and Reddy, 2008; Fuglestvedt et al., 2010): changing atmospheric background conditions (Reisinger et al., 2011). For the reference gas CO2, changes in AGWPCO2 and AGTPCO2 will affect the GWP and GTP of all other gases. With increasing CO2 levels in the (8.SM.12) atmosphere the marginal RF is reduced, while at the same time the ocean uptake is reduced and airborne fraction increased (Caldeira and where RT is the climate response to a unit forcing and can be repre- Kasting, 1993). These changes are in opposite directions, but do not sented as a sum of exponentials, totally cancel, and hence lead to changes in AGWPCO2 (Figure 8.30) and AGTPCO2. (8.SM.13) To convert the RE values given per ppbv values to per kg (Shine et al., 2005), they must be multiplied by (MA/Mi)(109/TM) where MA is the where the parameters cj are the components of the climate sensitivity mean molecular weight of air (28.97 kg kmol 1), Mi is the molecular and dj are response times. The first term in the summation can crudely weight of species i and TM is the total mass of the atmosphere, 5.1352 be associated with the response of the ocean mixed layer to a forcing × 1018 kg (Trenberth and Smith, 2005). and the higher order terms the response of the deep ocean (Li and Jarvis, 2009). The equilibrium climate sensitivity is given by the equilib- rium response to a sustained unit forcing, = cj. 8SM-15 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing 8.SM.11.3.1 Metric Values for Carbon Dioxide The impulse response function (IRF) has been updated from AR4. Table 8.SM.10 shows the parameters of the IRF used in AR5 based on Joos The radiative forcing for CO2 can be approximated using the expression et al. (2013) and Figure 8.SM.4 shows the IRFs from the four previous based on radiative transfer models (Myhre et al., 1998): IPCC assessment reports together with the new IRF used in AR5. Table 8.SM.11 gives calculated values for integrated IRF and AGWPs for CO2. (8.SM.16) Table 8.SM.10 | Parameter values for the sum of exponentials (Equation 8.SM.10) 8SM describing the fraction of CO2 remaining in the atmosphere after a pulse emission of where = 5.35 W m 2, C0 is the reference concentration and C is the CO2 (Joos et al., (2013). change from the reference. The radiative efficiency is the change in RF 1st Term 2nd Term 3rd Term 4th Term for a change in the atmospheric abundance, Coefficient (unitless) 0.2173 0.2240 0.2824 0.2763 Time Scale (i, years) - 394.4 36.54 4.304 Table 8.SM.11 | Mean and uncertainty range for the time-integrated IRF and AGWP from Joos et al. (2013). The AGWP for AR5 uses the integrated IRF based on Equation (8.SM.17) 8.SM.10 and Table 8.SM.9 and a radiative efficiency for a 1 ppm change at 391 ppm. 20-Year 100-Year or if C 0 then the derivative can be used: Time-integrated IRF (year) Mean 14.2 52.4 (8.SM.18) 5 95% range 12.2 16.3 39.5 65.2 AGWP (10 15 W m 2 yr kg 1) At current CO2 levels (391 ppm) and for C = 1 ppm, the radiative effi- Mean 25.2 92.5 ciency (RE) of CO2 is 1.37 * 10 05 W m 2 ppb 1. The difference between 5 to 95% range 20.7 29.6 67.9 117 using C = 1 ppm and the derivate is 0.13%. For CO2, using a molecular weight of 44.01 kg kmol 1, the A becomes 1.7517 * 10 15 W m 2 kg 1. AR5 AGWP (10 15 W m 2 yr kg 1) 24.9 91.7 1 Fraction remaining in atmosphere after pulse emission IRF FAR* (1990) 0.9 IRF SAR (1995) IRF TAR (2001) 0.8 IRF AR4 (2007) Joos et al (2013) mean 0.7 Joos et al (2013) +/-2 0.6 0.5 0.4 0.3 0.2 0.1 0 0 50 100 150 200 250 300 350 400 450 500 Time (yrs) Figure 8.SM.4 | The impulse response functions (IRFs) from the five IPCC Assessment Reports. The First Assessment Report (FAR) IRF (dotted) is based on an unbalanced carbon- cycle model (ocean only) and thus is not directly comparable to the others. The Second Assessment Report (SAR) IRF is based the CO2 response of the Bern model (Bern-SAR), an early generation reduced-form carbon cycle model (Joos et al., 1996), and uses a 10 GtC pulse emission into a constant background without temperature feedbacks (Enting et al., 1994). The IRF was not updated for the Third Assessment Report (TAR), but a different parameterisation was used in World Meterological Organisation (WMO)/United Nations Environment Programme (UNEP) Scientific Assessment of Ozone Depletion: 1998 (WMO, 1999) The Fourth Assessment Report (AR4) IRF is based on the Bern 2.5CC Earth System Model of Intermediate Complexity (EMIC) (Plattner et al., 2008). A pulse size of 40 GtC is used and includes temperature feedbacks. The Fifth Assessment Report (AR5) IRF is based on a model intercomparison and uses a pulse size of 100 GtC and includes temperature feedbacks (Joos et al., 2013). Apart from FAR, the changing IRF in each assessment report represents increasing background concentrations and improved models. 8SM-16 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material 8.SM.11.3.2 Metric Values for Methane f1 + f2 = 0.65 to account for RF from both O3 and stratospheric H2O. We also present metric values for CH4 of fossil origin (based on Boucher et The RE of CH4 is scaled to include effects on ozone and stratospheric al., (2009); Table 1). If these metric values are used the carbon emitted H2O, so that the AGWP becomes as CH4 must not be included in the CO2 emissions (which are often based on total carbon content). 8.SM.11.3.3 Metric Values for Nitrous Oxide 8SM (8.SM.19) The indirect effect of increased N2O abundance on CH4 changes via where f1 is due to effects on ozone and f2 is due to stratospheric H2O. stratospheric ozone, UV fluxes and OH levels is included in GWPs and The AGTP is modified in a similar way. GTPs. The reduction in CH4 ( 36 molecules per +100 molecules N2O) offsets some of the climate impact from N2O emissions. The AGWP These indirect effects were included in AR4 by increasing the direct becomes RF from CH4 by 25% (due to tropospheric ozone) and 15% (due to stratospheric H2O). New studies provide updated values and include more effects. By accounting for aerosol responses, Shindell et al. (2009) found that the GWP for CH4 increased by about 40% while Collins et al. (2010) found that the GTP for CH4 increased by 5 to 30% when the (8.SM.20) effect of ozone on CO2 was included. Boucher et al. (2009) included the effect of CO2 from oxidation of CH4 from fossil sources and calculated a where f1 and f2 are the indirect effects for CH4. The AGTP is modified GWP100 higher than given in AR4 (27 to 28 versus 25). They found that in a similar way. CO2 oxidation had a larger effect on GTP values and this effect was larger than the direct CH4 effect for time horizons beyond 100 years. 8.SM.11.4 Time Horizons In AR5 we use updated estimates for the indirect effects of CH4 on In previous IPCC assessments, GWP values were given for 20-, 100- ozone based on recent studies (Shindell et al., 2005; Shindell et al., and 500-year time horizons, while here we only use 20 and 100 years. 2009; Collins et al., 2013; Holmes et al., 2013; Stevenson et al., 2013). Instead of using GWP values for 500 years we show the response to Based on these studies we assess the indirect effect on ozone (tropo- emissions of some extremely long-lived gases such as PFCs; see Figure spheric and stratospheric) to f1 = 0.5 (0.2 to 0.8) of the direct effect. The 8.SM.5. Once these gases are emitted they stay in the atmosphere and indirect RF from CH4 via changes in stratospheric H2O is retained as f2 contribute to warming on very long time scales (99% of an emission = 0.15 of the direct effect. Thus, we increase the direct effect of CH4 by of PFC-14 is still in the atmosphere after 500 years). For comparison 18 SF6 [3200 yrs] 16 HFC 23 [222 yrs] PFC 116 (C2F6) [10 000 yrs] Temperature change (10 12K kg 1) 14 PFC 14 (CF4) [50 000 yrs] CFC 12 [100 yrs] 12 HFC 134a [13.4 yrs] CO2 (x1000) 10 8 6 4 2 0 0 50 100 150 200 250 300 350 400 450 500 Year Figure 8.SM.5 | Temperature response due to 1-kg pulse emissions of greenhouse gases with a range of lifetimes (given in parentheses). Calculated with a temperature impulse response function taken from Boucher and Reddy (2008) which has a climate sensitivity of 1.06 K (W m 2) 1, equivalent to a 3.9 K equilibrium response to 2 × CO2 (unit for carbon dioxide is kg CO2). 8SM-17 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing we also include gases with lifetimes of the order of centuries down We estimate the uncertainty in AGWPCO2 using the uncertainty in ACO2 to a decade. A 1 kg pulse of SF6 has a temperature effect after 500 and ICO2, years that is of the order of 35,000 larger than that of CO2. The cor- responding numbers for CF4 and C2F6 are 11,000 and 18,000, respec- tively. There are large uncertainties related to temperature responses where (as well as the CO2 response) on time-scales of centuries, but these results nevertheless indicate the persistence and long-lived warming (8.SM.23) 8SM effects of these gases. In the case of the AGWP for non-CO2 species, the expression becomes One reason for not using a time horizon of 500 years is the increasing uncertainty in radiative efficiency, carbon uptake and ambiguity in the interpretation of GWP500, especially for gases with short adjustment times relative to the time scale of the CO2 perturbation. As explained in Section 8.7.1.2, the GWP gives the ratio of two integrals: one of a pulse for of a non-CO2 gas that decays to zero and that of the CO2 response for which 20 to 40% of a pulse remains in the atmosphere for cen- (8.SM.24) turies. Figure 8.SM.5 also shows that the temperature response to a pulse of the relatively short-lived HFC-134a is close to zero for several where the expressions for the AGWP are from Equations 8.SM.9, centuries before the 500-year time horizon, while the GWP500 is 371. 8.SM.19 and 8.SM.20. The uncertainty in the AGWP for CO2 is based This example highlights how the integrated nature of GWP means that on Equation 8.SM.23. the GWP value at a particular time may give misleading information about the climate impacts at that time, as the time scale used in the Table 8.SM.12 shows the uncertainty data and source used in the GWP becomes very different from the residence time of the emitted analysis. Many of the input parameters are given for a 1- range and compound. we scale the uncertainty by 1.645 to convert to 90% confidence for consistency with rest of AR5. In some cases this represents a strong and uncertain assumption since the high-end uncertainties are not 8.SM.12 Uncertainty Calculations for Global necessarily well defined. The estimated uncertainties should be seen Warming Potential to Support as a rough first order evaluation to get an impression of the order of Section 8.7.1 magnitude and the main contributions to total uncertainty. In the absence of detailed uncertainty assessment, a first estimate of Table 8.SM.13 shows the uncertainty for the AGWP of CO2, CH4, N2O, uncertainty for a given function, f, and input parameters, xi, can be CFC-11, CFC-12 and HFC-134a, Table 8.SM.14 shows the correspond- based on a first-order Taylor expansion of the variance in f leading ing uncertainty for the GWPs, and Figure 8.SM.6 shows the contri- to the well-known adding in quadrature approximation (Morgan and bution of each term i in Equation 8.SM.21 to the uncertainty. The Henrion, 1990), uncertainty in AGWP is generally dominated by the perturbation life- time, though this varies depending on the lifetime relative to the time horizon. The uncertainty in the AGWPCH4 has an important contribution from the indirect effects, particularly the forcing from ozone changes. (8.SM.21) Except for CH4, the uncertainty in the GWPs is dominated by the uncer- tainty in AGWPCO2. where f represents the uncertainty of each term, defined as the sen- sitivity to a marginal change multiplied by the error in the term. This approximation assumes that the uncertainties are small, xi >> xi, the uncertainties are normally distributed, f is smooth for the range of input values and, most importantly, the uncertainties are independent. If f is a product of two terms (f = xy), then it can be shown that (8.SM.22) 8SM-18 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.12 | Uncertainty data, assumptions and sources used for the analysis. Note that uncertainties are assumed to be normally distributed and further analysis is required to determine the correct distribution. Term Expected Value (x) Uncertainty (+/- x, 5 to 95%) Notes ACO2 Table 8.A.1 10%, Section 8.3.1 ICO2 Joos et al. (2013) Joos et al. (2013) ACH4 Table 8.A.1 10%, Section 8.3.1 Value before adjusting for ozone and stratospheric H2O 8SM CH4 Section 8.2.3.3 18.57%, Section 8.2.3.3 One standard deviation uncertainty of 1.4/12.4 scaled by 1.645 to convert to 90% confidence f1 0.5 Ozone, see Equation 8.SM.19 60% Uncertainty is 0.2 0.8 f2 0.15, see Equation 8.SM.19 71.43%, Table 8.6 Uncertainty is 0.02 0.12 AN2O Table 8.A.1 10%, Section 8.3.1 N2O Table 8.A.1 12.99%, Prather et al. (2012), Section 8.2.3.4 One standard deviation uncertainty of 7.9% scaled by 1.645 to convert to 90% confidence ACFC-11 Table 8.A.1 10%, Section 8.3.1 CFC-11 Table 8.A.1 22.55%, Rigby et al. (2013) One standard deviation uncertainty of 13.71% scaled by 1.645 to convert to 90% confidence ACFC-12 Table 8.A.1 10%, Section 8.3.1 CFC-12 Table 8.A.1 28.76%, Rigby et al. (2013) One standard deviation uncertainty of 17.49% scaled by 1.645 to convert to 90% confidence AHFC-134a Table 8.A.1 10%, Section 8.3.1 HFC-134a Table 8.A.1 17.9%, Prather et al. (2012) One standard deviation uncertainty of 10.9% scaled by 1.645 to convert to 90% confidence Table 8.SM.13 | The estimated uncertainty in the AGWP for CO2, CH4, CFC-11, CFC-12, and HFC-134a showing the results of the full uncertainty analysis ( Full ) and the effects of adding the uncertainty of different terms one at a time in the order (from left to right) of the next largest contributions. All values (+/- x) are percentages of the expected value, x, for a 90% confidence interval. Time Horizon AGWPCO2 AGWPCH4 AGWPN2O (years) +ICO2 +ACO2 Full +fi +CH4 +ACH4 Full +AN2O +CH4 +CH4 Full 20 14 18 18 19 22 24 24 11 11 11 11 100 25 26 26 19 27 29 29 11 12 12 12 500 28 30 30 19 27 29 29 11 26 16 16 Time Horizon AGWPCFC-11 AGWPCFC-12 AGWPHFC-134a (years) +CFC-11 +ACFC-11 Full +CFC-12 +ACFC-12 Full +HFC-134a +AHFC-134a Full 20 5 11 11 3 10 10 10 14 14 100 16 19 19 12 16 16 18 20 20 500 23 25 25 28 30 30 18 21 21 Table 8.SM.14 | The estimated uncertainty in the GWP for CH4, N2O, CFC-11, CFC-12, and HFC-134a showing the results of the full uncertainty analysis ( Full ) and the effects of adding the uncertainty of different terms one at a time in the order (from left to right) of the next largest contributions. All values (+/- x) are percentages of the expected value, x, for a 90% confidence interval. +CO2 represents the uncertainty in AGWPCO2. Time Horizon GWPCH4 GWPN2O (years) +CO2 +fi +CH4 +ACH4 Full +CO2 +AN2O +CH4 +CH4 Full 20 18 26 28 30 30 18 21 21 21 21 100 26 33 38 39 39 26 29 29 29 29 500 30 35 40 41 41 30 32 34 34 34 Time Horizon GWPCFC-11 GWPCFC-12 GWPHFC-134a (years) +CO2 +CFC-11 +ACFC-11 Full +CO2 +CFC-12 +ACFC-12 Full +CO2 +HFC-134a +AHFC-134a Full 20 18 18 21 21 18 18 20 20 18 20 23 23 100 26 31 33 33 26 29 31 31 26 32 33 33 500 30 37 39 39 30 41 42 42 30 35 36 36 8SM-19 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing 13 AGWPCO2 13 AGWPCH4 x 10 x 10 1 5 ICO2 ACO2 0.8 4 f = | x | x f = | x | x 0.6 3 d d 8SM 0.4 2 0.2 1 ACH4 f1 f2 CH4 0 0 0 100 200 300 400 500 0 100 200 300 400 500 12 AGWP 10 AGWP x 10 N2O x 10 CFC11 6 1.2 AN2O (ACH4,f1,f2) N2O 5 1 f = | x | x f = | x | x 4 0.8 d d 3 0.6 2 0.4 1 0.2 CFC11 ACFC11 0 0 0 100 200 300 400 500 0 100 200 300 400 500 10 AGWPCFC12 11 AGWPHFC134a x 10 x 10 5 2.5 4 2 f = | x | x f = | x | x 3 1.5 d d 2 1 1 0.5 CFC12 ACFC12 HFC134a AHFC134a 0 0 0 100 200 300 400 500 0 100 200 300 400 500 Time Horizon (years) Time Horizon (years) Figure 8.SM.6 | The contribution of each term to the uncertainty in the AGWP, AGWP is obtained by adding each term in quadrature according to Equation 8.SM.21. ICO2 has data available only for four data points. For AGWPN2O the contribution from the radiative efficiency and indirect effect of CH4 are combined in quadrature. In uncertainty analysis, the contributions are added in quadrature (Equation 8.SM.21), which will amplify the differences. 8SM-20 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material 8.SM.13 Calculations of Metric Values for and supplementary material to published papers. A table that lists the Halocarbons to Support Section 8.7.2 absorption cross-sections used to calculate the RE for each compound can be found at the following website: http://cicero.uio.no/halocar- The method used to calculate the radiative efficiencies (REs) and GWPs bonmetrics/. Experimental absorption cross-sections have been used in Table 8.A.1 is discussed briefly here. More details are available at the for the majority of compounds, but for a few compounds theoretical following website: http://cicero.uio.no/halocarbonmetrics/. spectra were used because of unavailability of experimental spectra. 8SM 8.SM.13.1 Lifetimes 8.SM.13.3 Instantaneous Radiative Efficiency The lifetime of each compound is taken from WMO (2011) when avail- The simple method from Pinnock et al. (1995) has been adopted here able. For some compounds, when WMO lifetimes are not available, for the calculation of RE, except that a revised version of the Pinnock et lifetimes are taken from the published literature (sources of lifetime al. curve has been used. This ensures a common method for deriving RE estimates are given here: http://cicero.uio.no/halocarbonmetrics/). For from absorption cross sections, and hence greater internal consistency, a few compounds, lifetimes could not be found in the literature and in contrast to the many different methods/assumptions used for cal- only the RE (and not the GWP) could be calculated. The REs of these culation of RE used in the literature. The new curve, at 1 cm 1 spectral compounds, assuming a homogeneous mixing in the atmosphere, are resolution (rather than the original 10 cm 1 resolution used in Pinnock given in Table 8.SM.15. et al., (1995) is based on calculations with the Oslo Line-by-Line (LBL) model (Myhre et al., 2006), and is shown in Figure 8.SM.7. 8.SM.13.2 Absorption Cross Sections The absorption cross sections used for the RE and GWP calculations come from a variety of sources, including the High-Resolution Trans- mission (HITRAN)-2008 (Rothman et al., 2009) and Gestion et Etude des Informations Spectroscopiques Atmosphériques (GEISA)-2011 (Jacquinet-Husson et al., 2011) databases, authors of published papers, Table 8.SM.15 | Calculated radiative efficiencies (REs) for compounds where lifetime estimates are unknown. Note that homogeneous mixing in the atmosphere is assumed; hence the REs presented here are probably upper estimates. Common Name or Chemical Name Chemical Formula Radiative Efficiency (W m 2 ppb 1) 1,1,1,3,3,3-Hexafluoro-2-(trifluoromethyl)-2-propanol (CF3)3COH 0.38 HG -10 CH3OCF2OCH3 0.26 HG -20 CH3O(CF2O)2CH3 0.72 HG -30 CH3O(CF2O)3CH3 1.14 HFE-338mec3 CF3CFHCF2OCF2H 0.51 Fluoromethyl carbonofluoridate FCOOCFH2 0.19 Difluoromethyl carbonofluoridate FCOOCF2H 0.33 Trifluoromethyl carbonofluoridate FCOOCF3 0.32 Perfluoroethyl carbonofluoridate FCOOCF2CF3 0.48 2,2,2-Trifluoroethyl carbonofluoridate FCOOCH2CF3 0.33 Perfluoropropyl carbonofluoridate FCOOCF2CF2CF3 0.53 Trifluoromethyl 2,2,2-trifluoroacetate CF3COOCF3 0.49 Perfluoroethyl 2,2,2-trifluoroacetate CF3COOCF2CF3 0.62 1,1,1,3,3,3-Hexafluoropropan-2-yl 2,2,2-trifluoroacetate CF3COOCH(CF3)2 0.49 Vinyl 2,2,2-trifluoroacetate CF3COOCH=CH2 0.39 Allyl 2,2,2-trifluoroacetate CF3COOCH2CHCH2 0.35 Phenyl 2,2,2-trifluoroacetate CF3COOPh 0.39 Methyl 2-fluoroacetate H2CFCOOCH3 0.08 Difluoromethyl 2,2-difluoroacetate HCF2COOCHF2 0.44 4,4,4-Trifluorobutanal CF3(CH2)2CHO 0.16 8SM-21 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing 3.5 cm molecule ] ) 1 1 1 OSLO Pinnock 1 (cm ) 2 3 8SM 2.5 18 cm [10 2 2 Wm 1.5 3 Radiative Forcing (10 1 0.5 0 0 500 1000 1500 2000 2500 3000 1 Wavenumber (cm ) Figure 8.SM.7 | Radiative forcing efficiency (for a 0 to 1 ppbv increase in mixing ratio) per unit cross section calculated with the Oslo Line-by-Line (LBL) model. 8.SM.13.4 Stratospheric Temperature Adjustment same models (Oslo CTM2, Svde et al., 2008); and Oslo Broadband model, (Myhre and Stordal, 1997) and a similar setup as in Sellevag et The revised Pinnock et al. curve shown in Figure 8.SM.7 applies for al. (2004). One fractional correction curve has been ­ alculated for the c instantaneous radiative forcing efficiency. To take into account strato- compounds dominated by loss through photolysis in the stratosphere, spheric temperature adjustment, a factor has been applied based on and one curve for compounds that are lost mainly by reaction with results from previous studies. For most compounds, the instantaneous OH. The first curve was calculated by applying an exponential curve REs have been increased by 10% (Pinnock et al., 1995; Myhre and fit which gives the formula f() = 1 0.1826 0.3339, where f is the Stordal, 1997; Jain et al., 2000; Naik et al., 2000; Forster et al., 2005) to fractional correction and is the lifetime in years. The empirical fit for account for stratospheric temperature adjustment. For a few selected the latter curve was constrained to form an S-shaped curve with the compounds, explicit model calculations have been carried out using the formula f() = (a)b / (1 + cd), and the constants have values a = Oslo LBL model (Myhre et al., 2006). These calculations show increases 2.962, b = 0.9312, c = 2.994 and d = 0.9302. The resulting two curves of 9.1%, 10.5%, and 10.5% for CFC-11, CFC-12 and CF4, respectively, are shown in Figure 8.SM.8 and have been applied when calculating when taking into account the stratospheric temperature adjustment, REs and GWPs for compounds where the lifetime is known. For shorter- while there is a reduction of 5.0% for HFC-41. The assumed increase lived compounds (less than about 2 to 3 years), the fractional correc- of 10% for the remaining compounds is considered a good approxima- tion depends on where the compound is emitted and so no unique tion, based on our calculations and the literature (e.g., Pinnock et al., curve can be defined. Here it has been assumed that the geographical 1995; Myhre and Stordal, 1997). distribution is similar to the approach in Sellevag et al. (2004). These fractional corrections have been made to the RE after the instanta- 8.SM.13.5 Lifetime Correction neous RE has been modified for stratospheric temperature adjustment as described in the paragraph above. Fractional correction factors to the RE, to take into account the non- uniform mixing in the atmosphere, have previously been presented in Freckleton et al. (1998) and Sihra et al. (2001). Here, the method of Sihra et al. (2001) has been extended by including the results of Sellevag et al. (2004), and by carrying out new calculations using essentially the 8SM-22 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material 1 HFC 143 0.9 0.8 HFC 32 HCFC 123 CH3Br HFC 152a 8SM 0.7 Fractional correction New S shaped fit 0.6 Sihra et al. (2001) Jain et al. (2000) 0.5 Acerboni et al. (2001) HFC 161 HFE 254eb2 Sellevag et al. (2004) HFE 356mmz1 HFC 1234yf 0.4 Various compounds New exponential fit 0.3 Jain et al. (2000) Halon 1211 CFC 11 0.2 CFC 12 0.1 0 3 2 1 0 1 2 3 4 10 10 10 10 10 10 10 10 Lifetime (years) Figure 8.SM.8 | Factor needed to correct radiative efficiency (RE) to account for non-uniform vertical and horizontal distribution versus atmospheric lifetime. The red symbols are for compounds whose main loss mechanism is stratospheric photolysis while the blue symbols are for compounds that are lost in the troposphere mainly by reaction with OH. Dark blue symbols have been used in the calculation of the S-shaped fit and dark red symbols have been used in the calculation of the exponential fit. Light blue and light red symbols are shown for comparison. The curve from Sihra et al. (2001) represents an empirical least squares fit to the fractional correction factors from Jain et al. (2000). For compounds where several different absorption bands have been used in the RF calculations, both the mean and the standard deviation of the fractional corrections are shown. 8.SM.14 Metric Values for Other Near-Term Climate Forcers to Support Section 8.7.2 Derwent et al. (2001) report a GWP100 of 5.8 for the effects of H2 emis- sions on CH4 and ozone. For global emissions of SO2 Fuglestvedt et al. (2010) calculated GWPs of 140 and 40 for 20 and 100 years, respectively. The GTPs are 41 and 6.9 for the same time horizons (for both metrics the values are given on an SO2 basis and account only for the aerosol radiation interaction of sulphate). For SO2 Shindell et al. (2009) calculated 22 +/- 20 (aerosol radiation interaction only) and 76 +/- 69 (aerosol radiation interaction and aerosol cloud inter- actions) for GWP100, and 78 +/- 70 and 268 +/- 241 for GWP20. For NH3 Shindell et al. (2009) calculated 19 +/- 22 (aerosol-radiation interaction only) and 15 +/- 18 (aerosol radiation interaction and aerosol cloud interactions ) for GWP100, and 65 +/- 76 and 53 +/- 62 for GWP20. Due to competition for ammonium between nitrate and sulphate, the net aerosol forcing from either SO2 or NH3 emissions is the residual of larger responses of opposite signs, which leads to the high uncertainty in their numbers. (These values are based on IRF for CO2 from AR4.) The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated values for the reference gas CO2. 8SM-23 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing 8.SM.15 Metric Values for Halocarbons Including Climate Carbon Feedback for Carbon Dioxide to Support Section 8.7.2 Table 8.SM.16 | GWP and GTP with climate carbon feedbacks included for halocarbons. The additional effect (delta) and the total effect are given. (Climate carbon feedbacks in response to the reference gas CO2 are always included). 8SM Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name CFC-11 Delta 3.04E-12 122 6.32E-11 689 1.29E-13 189 4.32E-13 701 6.32E-13 1156 Total 1.75E-10 7020 4.91E-10 5352 4.84E-12 7078 3.45E-12 5589 1.91E-12 3491 CFC-12 Delta 4.53E-12 182 1.20E-10 1308 1.94E-13 283 7.45E-13 1208 1.34E-12 2459 Total 2.74E-10 10,976 1.06E-09 11,547 7.90E-12 11,549 7.50E-12 12,160 5.96E-12 10,907 CFC-13 Delta 4.40E-12 177 1.43E-10 1558 1.89E-13 277 8.09E-13 1312 1.76E-12 3221 Total 2.75E-10 11,040 1.42E-09 15,451 8.18E-12 11,960 9.58E-12 15,530 1.05E-11 19,144 CFC-113 Delta 2.75E-12 110 6.99E-11 762 1.17E-13 171 4.41E-13 716 7.69E-13 1407 Total 1.65E-10 6600 6.04E-10 6586 4.72E-12 6902 4.29E-12 6963 3.22E-12 5880 CFC-114 Delta 3.18E-12 127 9.38E-11 1023 1.36E-13 199 5.55E-13 900 1.11E-12 2026 Total 1.96E-10 7839 8.82E-10 9615 5.74E-12 8385 6.12E-12 9922 5.79E-12 10,579 CFC-115 Delta 2.37E-12 95 7.81E-11 851 1.02E-13 149 4.39E-13 712 9.69E-13 1772 Total 1.49E-10 5954 7.81E-10 8516 4.42E-12 6463 5.25E-12 8517 5.88E-12 10,749 HCFC-21 Delta 4.72E-13 19 2.91E-12 32 1.71E-14 25 2.16E-14 35 2.03E-14 37 Total 1.40E-11 562 1.65E-11 179 1.49E-13 217 3.75E-14 61 3.14E-14 57 HCFC-22 Delta 2.88E-12 115 3.13E-11 342 1.18E-13 172 2.46E-13 399 2.44E-13 446 Total 1.35E-10 5395 1.93E-10 2106 2.99E-12 4368 7.59E-13 1230 3.87E-13 708 HCFC-122 Delta 1.99E-13 8 1.17E-12 13 7.06E-15 10 8.66E-15 14 8.13E-15 15 Total 5.63E-12 226 6.60E-12 72 5.51E-14 81 1.49E-14 24 1.26E-14 23 HCFC-122a Delta 7.28E-13 29 5.00E-12 54 2.75E-14 40 3.78E-14 61 3.54E-14 65 Total 2.43E-11 975 2.87E-11 312 3.19E-13 466 6.77E-14 110 5.50E-14 101 HCFC-123 Delta 2.61E-13 10 1.57E-12 17 9.35E-15 14 1.16E-14 19 1.09E-14 20 Total 7.54E-12 302 8.85E-12 96 7.64E-14 112 2.01E-14 33 1.69E-14 31 HCFC-123a Delta 9.98E-13 40 7.12E-12 78 3.82E-14 56 5.42E-14 88 5.08E-14 93 Total 3.47E-11 1390 4.10E-11 447 4.89E-13 715 9.86E-14 160 7.89E-14 144 (continued on next page) 8SM-24 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name HCFC-124 8SM Delta 1.24E-12 50 9.96E-12 109 4.87E-14 71 7.70E-14 125 7.24E-14 132 Total 4.79E-11 1920 5.82E-11 635 8.12E-13 1187 1.52E-13 246 1.13E-13 206 HCFC-132c Delta 8.93E-13 36 6.50E-12 71 3.43E-14 50 4.96E-14 80 4.65E-14 85 Total 3.16E-11 1268 3.75E-11 409 4.61E-13 674 9.10E-14 148 7.23E-14 132 HCFC-141b Delta 1.48E-12 59 1.43E-11 156 5.99E-14 88 1.12E-13 182 1.08E-13 197 Total 6.51E-11 2608 8.60E-11 938 1.33E-12 1941 2.80E-13 453 1.69E-13 309 HCFC-142b Delta 2.53E-12 101 3.33E-11 363 1.05E-13 153 2.56E-13 415 2.75E-13 502 Total 1.28E-10 5125 2.15E-10 2345 3.11E-12 4546 1.10E-12 1787 4.69E-13 858 HCFC-225ca Delta 4.02E-13 16 2.51E-12 27 1.47E-14 21 1.87E-14 30 1.75E-14 32 Total 1.21E-11 485 1.42E-11 155 1.31E-13 192 3.25E-14 53 2.72E-14 50 HCFC-225cb Delta 1.23E-12 49 9.92E-12 108 4.85E-14 71 7.67E-14 124 7.22E-14 132 Total 4.77E-11 1913 5.80E-11 633 8.09E-13 1183 1.51E-13 245 1.12E-13 205 (E)-1-Chloro-3,3,3-trifluoroprop-1-ene Delta 5.31E-15 <1 2.97E-14 <1 1.82E-16 <1 2.17E-16 <1 2.04E-16 <1 Total 1.42E-13 6 1.66E-13 2 1.28E-15 2 3.71E-16 1 3.16E-16 1 HFC-23 Delta 4.45E-12 178 1.34E-10 1459 1.91E-13 279 7.85E-13 1272 1.59E-12 2913 Total 2.75E-10 11,005 1.27E-09 13,856 8.07E-12 11,802 8.78E-12 14,232 8.54E-12 15,622 HFC-32 Delta 1.67E-12 67 1.29E-11 141 6.52E-14 95 9.91E-14 161 9.31E-14 170 Total 6.24E-11 2502 7.50E-11 817 9.97E-13 1457 1.88E-13 305 1.45E-13 265 HFC-41 Delta 3.43E-13 14 2.27E-12 25 1.28E-14 19 1.70E-14 28 1.60E-14 29 Total 1.10E-11 441 1.29E-11 141 1.34E-13 195 3.01E-14 49 2.48E-14 45 HFC-125 Delta 2.83E-12 113 4.79E-11 522 1.19E-13 174 3.49E-13 566 4.37E-13 798 Total 1.55E-10 6207 3.39E-10 3691 4.08E-12 5971 2.19E-12 3543 9.66E-13 1766 HFC-134 Delta 2.06E-12 82 2.03E-11 221 8.31E-14 122 1.59E-13 258 1.54E-13 282 Total 9.14E-11 3663 1.23E-10 1337 1.90E-12 2778 4.13E-13 670 2.41E-13 441 HFC-134a Delta 1.97E-12 79 2.27E-11 248 8.07E-14 118 1.78E-13 288 1.80E-13 329 Total 9.45E-11 3789 1.42E-10 1549 2.17E-12 3171 6.11E-13 991 2.90E-13 530 (continued on next page) 8SM-25 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name HFC-143 8SM Delta 9.18E-13 37 6.35E-12 69 3.48E-14 51 4.81E-14 78 4.51E-14 82 Total 3.09E-11 1239 3.64E-11 397 4.10E-13 600 8.63E-14 140 7.00E-14 128 HFC-143a Delta 3.05E-12 122 6.45E-11 703 1.29E-13 189 4.38E-13 710 6.50E-13 1189 Total 1.76E-10 7064 5.05E-10 5508 4.89E-12 7146 3.56E-12 5771 2.02E-12 3693 HFC-152 Delta 5.73E-14 2 3.27E-13 4 .1.99E-15 3 2.40E-15 4 2.25E-15 4 Total 1.56E-12 63 1.83E-12 20 1.45E-14 21 4.10E-15 7 3.49E-15 6 HFC-152a Delta 4.46E-13 18 2.71E-12 30 1.61E-14 23 2.01E-14 33 1.89E-14 35 Total 1.31E-11 524 1.53E-11 167 1.35E-13 198 3.49E-14 57 2.93E-14 54 HFC-161 Delta 1.29E-14 1 7.26E-14 1 4.44E-16 1 5.30E-16 1 4.99E-16 1 Total 3.46E-13 14 4.06E-13 4 3.14E-15 5 9.06E-16 1 7.72E-16 1 HFC-227ca Delta 2.36E-12 95 3.99E-11 435 9.91E-14 145 2.91E-13 472 3.64E-13 665 Total 1.29E-10 5175 2.82E-10 3077 3.41E-12 4978 1.82E-12 2954 8.05E-13 1472 HFC-227ea Delta 2.40E-12 96 4.69E-11 512 1.01E-13 148 3.27E-13 531 4.56E-13 835 Total 1.36E-10 5454 3.54E-10 3860 3.72E-12 5431 2.45E-12 3967 1.25E-12 2294 HFC-236cb Delta 1.85E-12 74 2.12E-11 231 7.59E-14 111 1.66E-13 269 1.67E-13 305 Total 8.86E-11 3550 1.32E-10 1438 2.02E-12 2953 5.58E-13 904 2.68E-13 490 HFC-236ea Delta 2.28E-12 92 2.39E-11 261 9.30E-14 136 1.88E-13 305 1.84E-13 337 Total 1.05E-10 4203 1.46E-10 1596 2.27E-12 3322 5.41E-13 878 2.91E-13 532 HFC-236fa Delta 2.84E-12 114 8.64E-11 942 1.22E-13 178 5.05E-13 818 1.03E-12 1890 Total 1.76E-10 7054 8.25E-10 8998 5.18E-12 7575 5.69E-12 9220 5.61E-12 10,267 HFC-245ca Delta 1.62E-12 65 1.35E-11 147 6.39E-14 93 1.04E-13 169 9.85E-14 180 Total 6.42E-11 2575 7.91E-11 863 1.14E-12 1663 2.13E-13 345 1.53E-13 281 HFC-245cb Delta 2.94E-12 118 6.20E-11 676 1.24E-13 182 4.21E-13 683 6.25E-13 1144 Total 1.70E-10 6795 4.86E-10 5298 4.70E-12 6875 3.42E-12 5552 1.94E-12 3553 HFC-245ea Delta 6.73E-13 27 4.57E-12 50 2.54E-14 37 3.45E-14 56 3.23E-14 59 Total 2.22E-11 890 2.61E-11 285 2.84E-13 415 6.14E-14 100 5.02E-14 92 (continued on next page) 8SM-26 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name HFC-245eb 8SM Delta 8.37E-13 34 5.64E-12 61 3.15E-14 46 4.25E-14 69 3.99E-14 73 Total 2.74E-11 1099 3.23E-11 352 3.46E-13 506 7.56E-14 123 6.19E-14 113 HFC-245fa Delta 1.79E-12 72 1.59E-11 174 7.14E-14 104 1.24E-13 202 1.18E-13 216 Total 7.46E-11 2992 9.47E-11 1032 1.42E-12 2079 2.76E-13 447 1.84E-13 337 HFC-263fb Delta 2.50E-13 10 1.49E-12 16 8.93E-15 13 1.10E-14 18 1.04E-14 19 Total 7.18E-12 288 8.42E-12 92 7.20E-14 105 1.91E-14 31 1.61E-14 29 HFC-272ca Delta 4.31E-13 17 2.82E-12 31 1.60E-14 23 2.11E-14 34 1.98E-14 36 Total 1.36E-11 547 1.60E-11 175 1.62E-13 236 3.72E-14 60 3.07E-14 56 HFC-329p Delta 2.09E-12 84 3.55E-11 387 8.79E-14 128 2.59E-13 420 3.24E-13 593 Total 1.15E-10 4594 2.52E-10 2742 3.03E-12 4423 1.63E-12 2638 7.21E-13 1318 HFC-365mfc Delta 1.57E-12 63 1.48E-11 161 6.33E-14 93 1.16E-13 188 1.11E-13 203 Total 6.79E-11 2724 8.86E-11 966 1.36E-12 1986 2.77E-13 450 1.73E-13 317 HFC-43-10mee Delta 2.20E-12 88 2.80E-11 305 9.09E-14 133 2.16E-13 351 2.28E-13 417 Total 1.10E-10 4403 1.79E-10 1952 2.63E-12 3851 8.78E-13 1424 3.82E-13 698 HFC-1132a Delta 1.51E-16 <1 8.44E-16 <1 5.18E-18 <1 6.16E-18 <1 5.79E-18 <1 Total 4.04E-15 <1 4.73E-15 <1 3.61E-17 <1 1.05E-17 <1 8.98E-18 <1 HFC-1141 Delta 6.04E-17 <1 3.38E-16 <1 2.07E-18 <1 2.47E-18 <1 2.32E-18 <1 Total 1.62E-15 <1 1.90E-15 <1 1.44E-17 <1 4.21E-18 <1 3.60E-18 <1 (Z)-HFC-1225ye Delta 8.31E-16 <1 4.65E-15 <1 2.85E-17 <1 3.39E-17 <1 3.19E-17 <1 Total 2.22E-14 1 2.60E-14 <1 1.99E-16 <1 5.80E-17 <1 4.95E-17 <1 (E)-HFC-1225ye Delta 2.81E-16 <1 1.57E-15 <1 9.64E-18 <1 1.15E-17 <1 1.08E-17 <1 Total 7.52E-15 <1 8.81E-15 <1 6.72E-17 <1 1.96E-17 <1 1.67E-17 <1 (Z)-HFC-1234ze Delta 1.01E-15 <1 5.68E-15 <1 3.48E-17 <1 4.14E-17 <1 3.90E-17 <1 Total 2.71E-14 1 3.18E-14 <1 2.43E-16 <1 7.08E-17 <1 6.04E-17 <1 HFC-1234yf Delta 1.25E-15 <1 7.02E-15 <1 4.31E-17 <1 5.12E-17 <1 4.82E-17 <1 Total 3.35E-14 1 3.93E-14 <1 3.00E-16 <1 8.75E-17 <1 7.47E-17 <1 (continued on next page) 8SM-27 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name (E)-HFC-1234ze 8SM Delta 3.40E-15 <1 1.90E-14 <1 1.17E-16 <1 1.39E-16 <1 1.30E-16 <1 Total 9.07E-14 4 1.06E-13 1 8.14E-16 1 2.37E-16 <1 2.02E-16 <1 (Z)-HFC-1336 Delta 5.98E-15 <1 3.35E-14 <1 2.06E-16 <1 2.45E-16 <1 2.30E-16 <1 Total 1.60E-13 6 1.87E-13 2 1.44E-15 2 4.18E-16 1 3.56E-16 1 HFC-1243zf Delta 5.31E-16 <1 2.97E-15 <1 1.82E-17 <1 2.17E-17 <1 2.04E-17 <1 Total 1.42E-14 1 1.66E-14 <1 1.27E-16 <1 3.70E-17 <1 3.16E-17 <1 HFC-1345zfc Delta 4.49E-16 <1 2.51E-15 <1 1.54E-17 <1 1.83E-17 <1 1.72E-17 <1 Total 1.20E-14 <1 1.41E-14 <1 1.07E-16 <1 3.13E-17 <1 2.67E-17 <1 3,3,4,4,5,5,6,6,6-Nonafluorohex-1-ene Delta 4.84E-16 <1 2.71E-15 <1 1.66E-17 <1 1.98E-17 <1 1.86E-17 <1 Total 1.29E-14 1 1.52E-14 <1 1.16E-16 <1 3.38E-17 <1 2.88E-17 <1 3,3,4,4,5,5,6,6,7,7,8,8,8-Tridecafluorooct-1-ene Delta 3.84E-16 <1 2.15E-15 <1 1.32E-17 <1 1.57E-17 <1 1.47E-17 <1 Total 1.03E-14 <1 1.20E-14 <1 9.19E-17 <1 2.68E-17 <1 2.29E-17 <1 3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,10-Heptadecafluorodec-1-ene Delta 3.31E-16 <1 1.85E-15 <1 1.14E-17 <1 1.35E-17 <1 1.27E-17 <1 Total 8.86E-15 <1 1.04E-14 <1 7.92E-17 <1 2.31E-17 <1 1.97E-17 <1 Methyl chloroform Delta 4.02E-13 16 3.05E-12 33 1.56E-14 23 2.34E-14 38 2.20E-14 40 Total 1.48E-11 594 1.77E-11 193 2.32E-13 339 4.42E-14 72 3.42E-14 63 Carbon tetrachloride Delta 1.64E-12 66 2.66E-11 290 6.86E-14 100 1.96E-13 318 2.39E-13 436 Total 8.86E-11 3550 1.85E-10 2019 2.31E-12 3378 1.16E-12 1887 5.01E-13 915 Methyl chloride Delta 4.09E-14 2 2.42E-13 3 1.45E-15 2 1.78E-15 3 1.67E-15 3 Total 1.16E-12 46 1.36E-12 15 1.14E-14 17 3.07E-15 5 2.59E-15 5 Methylene chloride Delta 3.12E-14 1 1.78E-13 2 1.08E-15 2 1.30E-15 2 1.22E-15 2 Total 8.49E-13 34 9.95E-13 11 7.86E-15 11 2.23E-15 4 1.90E-15 3 Chloroform Delta 5.73E-14 2 3.27E-13 4 1.99E-15 3 2.40E-15 4 2.25E-15 4 Total 1.56E-12 63 1.83E-12 20 1.45E-14 21 4.10E-15 7 3.49E-15 6 1,2-Dichloroethane Delta 3.18E-15 <1 1.79E-14 <1 1.10E-16 <1 1.31E-16 <1 1.23E-16 <1 Total 8.56E-14 3 1.00E-13 1 7.77E-16 1 2.24E-16 <1 1.91E-16 <1 (continued on next page) 8SM-28 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name Methyl bromide 8SM Delta 8.01E-15 <1 4.68E-14 1 2.82E-16 <1 3.44E-16 1 3.23E-16 1 Total 2.24E-13 9 2.63E-13 3 2.16E-15 3 5.91E-16 1 5.01E-16 1 Methylene bromide Delta 3.56E-15 <1 2.02E-14 <1 1.23E-16 <1 1.48E-16 <1 1.39E-16 <1 Total 9.66E-14 4 1.13E-13 1 8.90E-16 1 2.54E-16 <1 2.16E-16 <1 Halon-1201 Delta 9.29E-13 37 7.16E-12 78 3.62E-14 53 5.50E-14 89 5.17E-14 95 Total 3.47E-11 1390 4.16E-11 454 5.54E-13 809 1.05E-13 170 8.04E-14 147 Halon-1202 Delta 6.76E-13 27 4.50E-12 49 2.53E-14 37 3.38E-14 55 3.17E-14 58 Total 2.18E-11 875 2.57E-11 280 2.69E-13 393 5.99E-14 97 4.92E-14 90 Halon-1211 Delta 2.34E-12 94 2.97E-11 324 9.67E-14 141 2.30E-13 373 2.42E-13 442 Total 1.17E-10 4684 1.90E-10 2070 2.80E-12 4091 9.28E-13 1504 4.04E-13 739 Halon-1301 Delta 3.35E-12 134 7.91E-11 862 1.43E-13 208 5.15E-13 835 8.40E-13 1536 Total 1.98E-10 7935 6.56E-10 7154 5.61E-12 8194 4.68E-12 7581 3.12E-12 5703 Halon-2301 Delta 4.89E-13 20 3.36E-12 37 1.85E-14 27 2.54E-14 41 2.38E-14 44 Total 1.63E-11 655 1.93E-11 210 2.14E-13 313 4.55E-14 74 3.70E-14 68 Halon-2311/Halothane Delta 1.38E-13 6 8.15E-13 9 4.90E-15 7 6.01E-15 10 5.64E-15 10 Total 3.91E-12 157 4.59E-12 50 3.84E-14 56 1.04E-14 17 8.75E-15 16 Halon-2401 Delta 5.38E-13 22 3.58E-12 39 2.01E-14 29 2.69E-14 44 2.52E-14 46 Total 1.74E-11 696 2.04E-11 223 2.14E-13 312 4.77E-14 77 3.92E-14 72 Halon-2402 Delta 1.68E-12 68 2.40E-11 262 7.01E-14 102 1.82E-13 296 2.04E-13 373 Total 8.76E-11 3511 1.59E-10 1734 2.19E-12 3207 8.90E-13 1443 3.70E-13 676 Nitrogen trifluoride Delta 5.19E-12 208 1.66E-10 1815 2.23E-13 326 9.48E-13 1537 2.04E-12 3733 Total 3.24E-10 12,987,000 1.64E-09 17,885 9.61E-12 14,049 1.11E-11 18,041 1.20E-11 21,852 Sulphur hexafluoride Delta 7.06E-12 283 2.37E-10 2580 3.03E-13 444 1.32E-12 2139 2.96E-12 5416 Total 4.44E-10 17,783 2.39E-09 26,087 1.32E-11 19,348 1.60E-11 25,934 1.84E-11 33,631 (Trifluoromethyl)sulphur pentafluoride Delta 5.46E-12 219 1.78E-10 1946 2.35E-13 343 1.01E-12 1633 2.21E-12 4039 Total 3.42E-10 13,698 1.78E-09 19,396 1.02E-11 14,855 1.20E-11 19,443 1.33E-11 24,271 (continued on next page) 8SM-29 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name Sulphuryl fluoride 8SM Delta 3.08E-12 124 5.84E-11 637 1.30E-13 190 4.12E-13 668 5.60E-13 1024 Total 1.74E-10 6965 4.34E-10 4732 4.71E-12 6885 2.96E-12 4805 1.46E-12 2671 PFC-14 Delta 1.96E-12 79 6.64E-11 724 8.44E-14 123 3.69E-13 598 8.34E-13 1524 Total 1.24E-10 4954 6.74E-10 7349 3.69E-12 5396 4.49E-12 7286 5.23E-12 9563 PFC-116 Delta 3.31E-12 133 1.12E-10 1216 1.42E-13 208 6.20E-13 1006 1.40E-12 2560 Total 2.08E-10 8344 1.13E-09 12,340 6.21E-12 9085 7.55E-12 12,243 8.76E-12 16,016 PFC-c216 Delta 2.76E-12 111 9.26E-11 1010 1.19E-13 174 5.16E-13 837 1.16E-12 2119 Total 1.74E-10 6964 9.36E-10 10,208 5.18E-12 7576 6.26E-12 10,149 7.19E-12 13,151 PFC-218 Delta 2.68E-12 107 8.97E-11 978 1.15E-13 168 5.00E-13 812 1.12E-12 2051 Total 1.68E-10 6752 9.06E-10 9878 5.02E-12 7344 6.06E-12 9826 6.95E-12 12,705 PFC-318 Delta 2.87E-12 115 9.61E-11 1048 1.23E-13 180 5.36E-13 869 1.20E-12 2199 Total 1.80E-10 7221 9.71E-10 10,592 5.37E-12 7856 6.49E-12 10,530 7.47E-12 13,655 PFC-31-10 Delta 2.77E-12 111 9.27E-11 1011 1.19E-13 174 5.17E-13 839 1.16E-12 2121 Total 1.74E-10 6981 9.37E-10 10,213 5.19E-12 7594 6.27E-12 10,160 7.18E-12 13,137 Perfluorocyclopentene Delta 6.63E-15 <1 3.72E-14 <1 2.28E-16 <1 2.71E-16 <1 2.55E-16 <1 Total 1.77E-13 7 2.08E-13 2 1.60E-15 2 4.63E-16 1 3.95E-16 1 PFC-41-12 Delta 2.56E-12 103 8.59E-11 937 1.10E-13 161 4.79E-13 776 1.08E-12 1968 Total 1.61E-10 6448 8.70E-10 9484 4.80E-12 7017 5.81E-12 9422 6.70E-12 12,253 PFC-51-14 Delta 2.38E-12 95 7.97E-11 869 1.02E-13 149 4.44E-13 720 9.97E-13 1823 Total 1.49E-10 5988 8.05E-10 8780 4.46E-12 6514 5.38E-12 8730 6.19E-12 11,316 PFC-61-16 Delta 2.35E-12 94 7.88E-11 859 1.01E-13 148 4.39E-13 712 9.86E-13 1802 Total 1.48E-10 5922 7.96E-10 8681 4.41E-12 6443 5.32E-12 8631 6.12E-12 11,183 PFC-71-18 Delta 2.29E-12 92 7.67E-11 837 9.84E-14 144 4.28E-13 694 9.60E-13 1756 Total 1.44E-10 5769 7.76E-10 8456 4.29E-12 6276 5.19E-12 8408 5.96E-12 10,894 PFC-91-18 Delta 2.18E-12 87 7.26E-11 791 9.35E-14 137 4.05E-13 658 9.06E-13 1657 Total 1.37E-10 5476 7.32E-10 7977 4.07E-12 5954 4.90E-12 7943 5.59E-12 10,222 (continued on next page) 8SM-30 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name Perfluorodecalin(cis) 8SM Delta 2.19E-12 88 7.31E-11 797 9.42E-14 138 4.08E-13 662 9.13E-13 1669 Total 1.38E-10 5515 7.37E-10 8033 4.10E-12 5997 4.93E-12 8000 5.63E-12 10,295 Perfluorodecalin(trans) Delta 1.90E-12 76 6.35E-11 692 8.18E-14 120 3.55E-13 575 7.93E-13 1450 Total 1.20E-10 4792 6.40E-10 6980 3.56E-12 5211 4.29E-12 6951 4.89E-12 8946 PFC-1114 Delta 1.03E-17 <1 5.78E-17 <1 3.55E-19 <1 4.22E-19 <1 3.96E-19 <1 Total 2.77E-16 <1 3.24E-16 <1 2.47E-18 <1 7.21E-19 <1 6.15E-19 <1 PFC-1216 Delta 2.49E-16 <1 1.40E-15 <1 8.56E-18 <1 1.02E-17 <1 9.57E-18 <1 Total 6.67E-15 <1 7.82E-15 <1 5.97E-17 <1 1.74E-17 <1 1.48E-17 <1 Perfluorobuta-1,3-diene Delta 1.27E-17 <1 7.11E-17 <1 4.36E-19 <1 5.19E-19 <1 4.88E-19 <1 Total 3.40E-16 <1 3.99E-16 <1 3.04E-18 <1 8.86E-19 <1 7.57E-19 <1 Perfluorobut-1-ene Delta 3.25E-16 <1 1.82E-15 <1 1.11E-17 <1 1.33E-17 <1 1.25E-17 <1 Total 8.69E-15 <1 1.02E-14 <1 7.77E-17 <1 2.26E-17 <1 1.93E-17 <1 Perfluorobut-2-ene Delta 6.28E-15 <1 3.52E-14 <1 2.16E-16 <1 2.57E-16 <1 2.42E-16 <1 Total 1.68E-13 7 1.97E-13 2 1.51E-15 2 4.39E-16 1 3.74E-16 1 HFE-125 Delta 5.18E-12 208 1.42E-10 1549 2.21E-13 324 8.68E-13 1408 1.62E-12 2961 Total 3.15E-10 12,617 1.28E-09 13,951 9.13E-12 13,349 9.01E-12 14,615 7.59E-12 13,871 HFE-134 (HG-00) Delta 5.51E-12 221 8.69E-11 947 2.31E-13 337 6.46E-13 1047 7.69E-13 1406 Total 2.96E-10 11,857 5.97E-10 6512 7.65E-12 11,183 3.67E-12 5945 1.55E-12 2837 HFE-143a Delta 1.33E-12 53 1.00E-11 109 5.16E-14 75 7.67E-14 124 7.19E-14 132 Total 4.86E-11 1947 5.80E-11 632 7.47E-13 1091 1.43E-13 232 1.12E-13 205 HFE-227ea Delta 3.88E-12 156 8.49E-11 926 1.65E-13 241 5.70E-13 924 8.70E-13 1590 Total 2.26E-10 9058 6.77E-10 7377 6.31E-12 9224 4.79E-12 7773 2.85E-12 5217 HCFE-235ca2 (enflurane) Delta 1.54E-12 62 1.12E-11 122 5.92E-14 87 8.55E-14 139 8.01E-14 147 Total 5.45E-11 2185 6.47E-11 705 7.95E-13 1162 1.57E-13 254 1.25E-13 228 HCFE-235da2 (isoflurane) Delta 1.37E-12 55 9.51E-12 104 5.21E-14 76 7.20E-14 117 6.75E-14 123 Total 4.63E-11 1854 5.45E-11 595 6.14E-13 898 1.29E-13 209 1.05E-13 192 (continued on next page) 8SM-31 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name HFE-236ca 8SM Delta 4.72E-12 189 6.86E-11 748 1.97E-13 287 5.19E-13 842 5.87E-13 1074 Total 2.47E-10 9901 4.58E-10 4990 6.23E-12 9105 2.62E-12 4241 1.09E-12 1985 HFE-236ea2 (desflurane) Delta 3.10E-12 124 3.22E-11 351 1.26E-13 184 2.53E-13 410 2.48E-13 453 Total 1.42E-10 5678 1.97E-10 2143 3.05E-12 4463 7.17E-13 1163 3.90E-13 713 HFE-236fa Delta 2.07E-12 83 1.82E-11 199 8.24E-14 120 1.42E-13 230 1.35E-13 247 Total 8.56E-11 3431 1.08E-10 1177 1.61E-12 2358 3.10E-13 503 2.10E-13 384 HFE-245cb2 Delta 1.65E-12 66 1.25E-11 136 6.41E-14 94 9.58E-14 155 8.99E-14 164 Total 6.06E-11 2430 7.25E-11 790 9.41E-13 1376 1.80E-13 292 1.40E-13 256 HFE-245fa1 Delta 1.86E-12 74 1.55E-11 169 7.34E-14 107 1.21E-13 196 1.14E-13 208 Total 7.41E-11 2970 9.15E-11 997 1.32E-12 1932 2.48E-13 402 1.77E-13 324 HFE-245fa2 Delta 1.97E-12 79 1.54E-11 168 7.69E-14 112 1.19E-13 193 1.12E-13 204 Total 7.45E-11 2987 8.99E-11 981 1.22E-12 1786 2.29E-13 372 1.74E-13 318 2,2,3,3,3-Pentafluoropropan-1-ol Delta 6.60E-14 3 3.75E-13 4 2.29E-15 3 2.75E-15 4 2.58E-15 5 Total 1.79E-12 72 2.10E-12 23 1.65E-14 24 4.70E-15 8 4.00E-15 7 HFE-254cb1 Delta 9.07E-13 36 5.88E-12 64 3.36E-14 49 4.41E-14 71 4.13E-14 76 Total 2.85E-11 1141 3.35E-11 365 3.33E-13 487 7.74E-14 126 6.41E-14 117 HFE-263fb2 Delta 4.72E-15 <1 2.65E-14 <1 1.62E-16 <1 1.93E-16 <1 1.82E-16 <1 Total 1.26E-13 5 1.48E-13 2 1.13E-15 2 3.30E-16 1 2.81E-16 1 HFE-263m1 Delta 1.03E-13 4 5.87E-13 6 3.57E-15 5 4.30E-15 7 4.04E-15 7 Total 2.80E-12 112 3.29E-12 36 2.61E-14 38 7.37E-15 12 6.26E-15 11 3,3,3-Trifluoropropan-1-ol Delta 1.39E-15 <1 7.77E-15 <1 4.77E-17 <1 5.67E-17 <1 5.33E-17 <1 Total 3.71E-14 1 4.35E-14 <1 3.32E-16 <1 9.69E-17 <1 8.27E-17 <1 HFE-329mcc2 Delta 3.22E-12 129 4.88E-11 532 1.35E-13 197 3.66E-13 593 4.24E-13 776 Total 1.71E-10 6847 3.30E-10 3598 4.36E-12 6379 1.96E-12 3175 8.17E-13 1494 HFE-338mmz1 Delta 2.87E-12 115 4.22E-11 460 1.20E-13 175 3.19E-13 517 3.63E-13 663 Total 1.51E-10 6053 2.83E-10 3081 3.82E-12 5584 1.63E-12 2644 6.77E-13 1238 (continued on next page) 8SM-32 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name HFE-338mcf2 8SM Delta 1.96E-12 79 1.73E-11 189 7.82E-14 114 1.35E-13 219 1.28E-13 234 Total 8.13E-11 3258 1.03E-10 1118 1.53E-12 2239 2.95E-13 478 2.00E-13 365 Sevoflurane (HFE-347mmz1) Delta 6.66E-13 27 4.24E-12 46 2.45E-14 36 3.17E-14 51 2.97E-14 54 Total 2.05E-11 821 2.41E-11 262 2.31E-13 337 5.54E-14 90 4.61E-14 84 HFE-347mcc3 (HFE-7000) Delta 1.33E-12 53 1.01E-11 110 5.17E-14 76 7.77E-14 126 7.29E-14 133 Total 4.91E-11 1968 5.88E-11 641 7.69E-13 1125 1.46E-13 237 1.13E-13 207 HFE-347mcf2 Delta 1.91E-12 77 1.60E-11 175 7.57E-14 111 1.24E-13 202 1.18E-13 215 Total 7.64E-11 3063 9.43E-11 1028 1.36E-12 1993 2.55E-13 414 1.83E-13 335 HFE-347pcf2 Delta 2.08E-12 83 1.68E-11 183 8.17E-14 119 1.30E-13 211 1.22E-13 224 Total 8.07E-11 3236 9.83E-11 1072 1.38E-12 2015 2.57E-13 417 1.90E-13 348 HFE-347mmy1 Delta 1.00E-12 40 7.02E-12 77 3.82E-14 56 5.33E-14 86 4.99E-14 91 Total 3.42E-11 1370 4.04E-11 440 4.65E-13 680 9.61E-14 156 7.76E-14 142 HFE-356mec3 Delta 1.06E-12 42 7.47E-12 81 4.04E-14 59 5.67E-14 92 5.32E-14 97 Total 3.64E-11 1457 4.29E-11 468 5.01E-13 732 1.03E-13 166 8.26E-14 151 HFE-356mff2 Delta 5.90E-14 2 3.34E-13 4 2.04E-15 3 2.45E-15 4 2.30E-15 4 Total 1.60E-12 64 1.87E-12 20 1.46E-14 21 4.18E-15 7 3.56E-15 7 HFE-356pcf2 Delta 1.72E-12 69 1.36E-11 149 6.72E-14 98 1.05E-13 171 9.89E-14 181 Total 6.57E-11 2633 7.96E-11 867 1.10E-12 1601 2.05E-13 332 1.54E-13 281 HFE-356pcf3 Delta 1.25E-12 50 8.64E-12 94 4.74E-14 69 6.54E-14 106 6.13E-14 112 Total 4.20E-11 1685 4.96E-11 540 5.58E-13 816 1.17E-13 190 9.52E-14 174 HFE-356pcc3 Delta 1.13E-12 45 7.97E-12 87 4.31E-14 63 6.05E-14 98 5.67E-14 104 Total 3.88E-11 1555 4.58E-11 500 5.34E-13 781 1.09E-13 177 8.81E-14 161 HFE-356mmz1 Delta 4.79E-14 2 2.71E-13 3 1.66E-15 2 1.98E-15 3 1.86E-15 3 Total 1.29E-12 52 1.52E-12 17 1.18E-14 17 3.39E-15 5 2.89E-15 5 HFE-365mcf3 Delta 3.31E-15 <1 1.85E-14 <1 1.14E-16 <1 1.35E-16 <1 1.27E-16 <1 Total 8.84E-14 4 1.04E-13 1 7.93E-16 1 2.31E-16 <1 1.97E-16 <1 (continued on next page) 8SM-33 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name HFE-365mcf2 8SM Delta 2.01E-13 8 1.16E-12 13 7.05E-15 10 8.53E-15 14 8.01E-15 15 Total 5.56E-12 223 6.52E-12 71 5.24E-14 77 1.46E-14 24 1.24E-14 23 HFE-374pc2 Delta 1.57E-12 63 1.20E-11 130 6.11E-14 89 9.18E-14 149 8.62E-14 158 Total 5.80E-11 2326 6.95E-11 758 9.09E-13 1329 1.73E-13 281 1.34E-13 245 4,4,4-Trifluorobutan-1-ol Delta 6.72E-17 <1 3.76E-16 <1 2.31E-18 <1 2.74E-18 <1 2.58E-18 <1 Total 1.80E-15 <1 2.11E-15 <1 1.61E-17 <1 4.68E-18 <1 4.00E-18 <1 2,2,3,3,4,4,5,5-Octafluorocyclopentanol Delta 4.52E-14 2 2.56E-13 3 1.57E-15 2 1.88E-15 3 1.76E-15 3 Total 1.22E-12 49 1.44E-12 16 1.12E-14 16 3.21E-15 5 2.73E-15 5 HFE-43-10pccc124 (H-Galden 1040x, HG-11) Delta 4.23E-12 170 4.92E-11 536 1.74E-13 254 3.84E-13 623 3.90E-13 713 Total 2.04E-10 8176 3.07E-10 3353 4.69E-12 6854 1.33E-12 2156 6.28E-13 1149 HFE-449s1 (HFE-7100) Delta 1.08E-12 43 8.05E-12 88 4.17E-14 61 6.17E-14 100 5.79E-14 106 Total 3.91E-11 1568 4.66E-11 509 5.95E-13 870 1.15E-13 186 8.99E-14 164 n-HFE-7100 Delta 1.24E-12 50 9.29E-12 101 4.82E-14 70 7.12E-14 115 6.68E-14 122 Total 4.52E-11 1810 5.38E-11 587 6.87E-13 1004 1.33E-13 215 1.04E-13 190 i-HFE-7100 Delta 1.04E-12 42 7.79E-12 85 4.04E-14 59 5.96E-14 97 5.60E-14 102 Total 3.78E-11 1517 4.51E-11 492 5.76E-13 842 1.11E-13 180 8.70E-14 159 HFE-569sf2 (HFE-7200) Delta 1.93E-13 8 1.13E-12 12 6.81E-15 10 8.31E-15 13 7.80E-15 14 Total 5.40E-12 217 6.34E-12 69 5.20E-14 76 1.43E-14 23 1.21E-14 22 n-HFE-7200 Delta 2.20E-13 9 1.28E-12 14 7.74E-15 11 9.44E-15 15 8.86E-15 16 Total 6.14E-12 246 7.21E-12 79 5.91E-14 86 1.62E-14 26 1.37E-14 25 i-HFE-7200 Delta 1.51E-13 6 8.80E-13 10 5.31E-15 8 6.47E-15 10 6.08E-15 11 Total 4.21E-12 169 4.94E-12 54 4.05E-14 59 1.11E-14 18 9.42E-15 17 HFE-236ca12 (HG-10) Delta 5.21E-12 209 8.31E-11 907 2.18E-13 319 6.16E-13 999 7.39E-13 1352 Total 2.81E-10 11,248 5.74E-10 6260 7.28E-12 10,646 3.56E-12 5769 1.51E-12 2769 HFE-338pcc13 (HG-01) Delta 4.50E-12 181 5.11E-11 557 1.85E-13 270 4.00E-13 648 4.02E-13 736 Total 2.15E-10 8607 3.18E-10 3466 4.88E-12 7129 1.33E-12 2153 6.44E-13 1178 (continued on next page) 8SM-34 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name 1,1,1,3,3,3-Hexafluoropropan-2-ol 8SM Delta 5.73E-13 23 3.57E-12 39 2.09E-14 31 2.66E-14 43 2.50E-14 46 Total 1.72E-11 691 2.02E-11 221 1.87E-13 274 4.64E-14 75 3.87E-14 71 HG-02 Delta 4.22E-12 169 4.79E-11 523 1.73E-13 253 3.75E-13 608 3.77E-13 690 Total 2.01E-10 8072 2.98E-10 3250 4.57E-12 6686 1.25E-12 2019 6.04E-13 1105 HG-03 Delta 4.42E-12 177 5.01E-11 547 1.81E-13 265 3.92E-13 636 3.95E-13 722 Total 2.11E-10 8443 3.12E-10 3400 4.78E-12 6993 1.30E-12 2112 6.32E-13 1155 HG-20 Delta 5.16E-12 207 8.24E-11 898 2.16E-13 316 6.10E-13 990 7.32E-13 1339 Total 2.78E-10 11,143 5.69E-10 6201 7.21E-12 10,546 3.52E-12 5715 1.50E-12 2743 HG-21 Delta 5.84E-12 234 6.79E-11 740 2.40E-13 351 5.30E-13 860 5.38E-13 984 Total 2.82E-10 11,285 4.24E-10 4628 6.47E-12 9461 1.84E-12 2976 8.67E-13 1586 HG-30 Delta 7.14E-12 286 1.14E-10 1242 2.99E-13 437 8.44E-13 1369 1.01E-12 1852 Total 3.84E-10 15,408 7.86E-10 8575 9.98E-12 14,583 4.87E-12 7903 2.07E-12 3793 1-Ethoxy-1,1,2,2,3,3,3-heptafluoropropane Delta 2.07E-13 8 1.20E-12 13 7.28E-15 11 8.86E-15 14 8.32E-15 15 Total 5.77E-12 231 6.77E-12 74 5.52E-14 81 1.52E-14 25 1.29E-14 24 Fluoroxene Delta 1.93E-16 <1 1.08E-15 <1 6.62E-18 <1 7.88E-18 <1 7.41E-18 <1 Total 5.16E-15 <1 6.05E-15 <1 4.61E-17 <1 1.35E-17 <1 1.15E-17 <1 1,1,2,2-Tetrafluoro-1-(fluoromethoxy)ethane Delta 2.01E-12 80 1.64E-11 179 7.91E-14 116 1.27E-13 206 1.20E-13 219 Total 7.88E-11 3157 9.64E-11 1051 1.37E-12 1996 2.55E-13 413 1.87E-13 341 2-Ethoxy-3,3,4,4,5-pentafluorotetrahydro-2,5-bis[1,2,2,2-tetrafluoro-1-(trifluoromethyl)ethyl]-furan Delta 1.86E-13 7 1.10E-12 12 6.61E-15 10 8.12E-15 13 7.62E-15 14 Total 5.28E-12 212 6.19E-12 68 5.19E-14 76 1.40E-14 23 1.18E-14 22 Fluoro(methoxy)methane Delta 4.44E-14 2 2.51E-13 3 1.53E-15 2 1.83E-15 3 1.72E-15 3 Total 1.20E-12 48 1.40E-12 15 1.09E-14 16 3.13E-15 5 2.67E-15 5 Difluoro(methoxy)methane Delta 4.79E-13 19 2.85E-12 31 1.70E-14 25 2.10E-14 34 1.97E-14 36 Total 1.37E-11 547 1.60E-11 175 1.36E-13 198 3.62E-14 59 3.06E-14 56 Fluoro(fluoromethoxy)methane Delta 4.40E-13 18 2.59E-12 28 1.56E-14 23 1.91E-14 31 1.79E-14 33 Total 1.24E-11 497 1.45E-11 159 1.21E-13 176 3.28E-14 53 2.77E-14 51 (continued on next page) 8SM-35 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name Difluoro(fluoromethoxy)methane 8SM Delta 1.75E-12 70 1.20E-11 131 6.63E-14 97 9.05E-14 147 8.48E-14 155 Total 5.82E-11 2335 6.86E-11 748 7.55E-13 1103 1.62E-13 262 1.32E-13 241 Trifluoro(fluoromethoxy)methane Delta 1.97E-12 79 1.44E-11 157 7.58E-14 111 1.10E-13 179 1.03E-13 189 Total 7.02E-11 2812 8.33E-11 909 1.03E-12 1512 2.03E-13 329 1.61E-13 294 HG -01 Delta 6.94E-13 28 4.36E-12 48 2.54E-14 37 3.25E-14 53 3.05E-14 56 Total 2.10E-11 843 2.47E-11 269 2.31E-13 338 5.66E-14 92 4.72E-14 86 HG -02 Delta 7.38E-13 30 4.64E-12 51 2.70E-14 39 3.46E-14 56 3.24E-14 59 Total 2.24E-11 897 2.63E-11 287 2.46E-13 360 6.03E-14 98 5.03E-14 92 HG -03 Delta 6.91E-13 28 4.34E-12 47 2.53E-14 37 3.23E-14 52 3.03E-14 55 Total 2.09E-11 840 2.46E-11 268 2.30E-13 336 5.64E-14 91 4.70E-14 86 HFE-329me3 Delta 3.20E-12 128 6.34E-11 691 1.35E-13 198 4.40E-13 714 6.20E-13 1133 Total 1.82E-10 7299 4.81E-10 5241 4.98E-12 7286 3.33E-12 5406 1.73E-12 3173 3,3,4,4, 5,5,6,6,7,7,7-Undecafluoroheptan-1-ol Delta 1.52E-15 <1 8.51E-15 <1 5.22E-17 <1 6.21E-17 <1 5.84E-17 <1 Total 4.06E-14 2 4.76E-14 1 3.64E-16 1 1.06E-16 <1 9.04E-17 <1 3,3,4,4,5,5,6,6,7,7,8,8,9,9, 9-Pentadecafluorononan-1-ol Delta 1.17E-15 <1 6.53E-15 <1 4.01E-17 <1 4.77E-17 <1 4.48E-17 <1 Total 3.12E-14 1 3.65E-14 <1 2.80E-16 <1 8.14E-17 <1 6.95E-17 <1 3,3,4,4,5,5,6,6,7,7,8,8,9,9,10,10,11,11,11-Nonadecafluoroundecan-1-ol Delta 6.69E-16 <1 3.75E-15 <1 2.30E-17 <1 2.73E-17 <1 2.57E-17 <1 Total 1.79E-14 1 2.10E-14 <1 1.60E-16 <1 4.67E-17 <1 3.98E-17 <1 2-Chloro-1,1,2-trifluoro-1-methoxyethane Delta 3.99E-13 16 2.41E-12 26 1.43E-14 21 1.79E-14 29 1.68E-14 31 Total 1.16E-11 465 1.36E-11 149 1.19E-13 174 3.09E-14 50 2.60E-14 48 PFPMIE (perfluoropolymethylisopropyl ether) Delta 3.04E-12 122 9.93E-11 1083 1.30E-13 191 5.60E-13 908 1.23E-12 2247 Total 1.90E-10 7619 9.89E-10 10,789 5.65E-12 8263 6.67E-12 10,815 7.38E-12 13,501 HFE-216 Delta 7.45E-16 <1 4.17E-15 <1 2.56E-17 <1 3.04E-17 <1 2.86E-17 <1 Total 1.99E-14 1 2.34E-14 <1 1.78E-16 <1 5.20E-17 <1 4.44E-17 <1 Trifluoromethyl formate Delta 1.65E-12 66 1.14E-11 124 6.24E-14 91 8.62E-14 140 8.08E-14 148 Total 5.54E-11 2220 6.53E-11 712 7.36E-13 1075 1.55E-13 251 1.25E-13 229 (continued on next page) 8SM-36 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name Perfluoroethyl formate 8SM Delta 1.62E-12 65 1.12E-11 122 6.16E-14 90 8.50E-14 138 7.97E-14 146 Total 5.46E-11 2190 6.44E-11 703 7.26E-13 1061 1.53E-13 247 1.24E-13 226 Perfluoropropyl formate Delta 1.13E-12 45 7.34E-12 80 4.18E-14 61 5.51E-14 89 5.16E-14 94 Total 3.56E-11 1427 4.18E-11 456 4.22E-13 616 9.70E-14 157 8.01E-14 147 Perfluorobutyl formate Delta 1.14E-12 46 7.62E-12 83 4.27E-14 62 5.74E-14 93 5.38E-14 98 Total 3.70E-11 1485 4.36E-11 475 4.62E-13 675 1.02E-13 165 8.36E-14 153 2,2,2-Trifluoroethyl formate Delta 1.16E-13 5 6.66E-13 7 4.06E-15 6 4.88E-15 8 4.59E-15 8 Total 3.18E-12 128 3.73E-12 41 2.96E-14 43 8.36E-15 14 7.11E-15 13 3,3,3-Trifluoropropyl formate Delta 6.12E-14 2 3.47E-13 4 2.12E-15 3 2.54E-15 4 2.39E-15 4 Total 1.66E-12 66 1.94E-12 21 1.52E-14 22 4.34E-15 7 3.70E-15 7 1,2,2,2-Tetrafluoroethyl formate Delta 1.35E-12 54 9.12E-12 99 5.07E-14 74 6.89E-14 112 6.45E-14 118 Total 4.44E-11 1778 5.22E-11 569 5.67E-13 829 1.23E-13 199 1.00E-13 183 1,1,1,3,3,3-Hexafluoropropan-2-yl formate Delta 9.53E-13 38 6.46E-12 70 3.59E-14 52 4.88E-14 79 4.57E-14 84 Total 3.14E-11 1259 3.70E-11 403 4.02E-13 587 8.69E-14 141 7.10E-14 130 Perfluorobutyl acetate Delta 5.90E-15 <1 3.31E-14 <1 2.03E-16 <1 2.41E-16 <1 2.27E-16 <1 Total 1.58E-13 6 1.85E-13 2 1.42E-15 2 4.12E-16 1 3.52E-16 1 Perfluoropropyl acetate Delta 6.17E-15 <1 3.46E-14 <1 2.12E-16 <1 2.52E-16 <1 2.37E-16 <1 Total 1.65E-13 7 1.93E-13 2 1.48E-15 2 4.31E-16 1 3.67E-16 1 Perfluoroethyl acetate Delta 7.34E-15 <1 4.11E-14 <1 2.52E-16 <1 3.00E-16 <1 2.82E-16 1 Total 1.96E-13 8 2.30E-13 3 1.76E-15 3 5.12E-16 1 4.37E-16 1 Trifluoromethyl acetate Delta 7.39E-15 <1 4.14E-14 <1 2.54E-16 <1 3.02E-16 <1 2.84E-16 1 Total 1.97E-13 8 2.32E-13 3 1.77E-15 3 5.16E-16 1 4.40E-16 1 Methyl carbonofluoridate Delta 3.02E-13 12 1.88E-12 20 1.10E-14 16 1.40E-14 23 1.31E-14 24 Total 9.05E-12 363 1.06E-11 116 9.70E-14 142 2.43E-14 39 2.03E-14 37 1,1-Difluoroethyl carbonofluoridate Delta 9.41E-14 4 5.35E-13 6 3.26E-15 5 3.92E-15 6 3.68E-15 7 Total 2.55E-12 102 2.99E-12 33 2.35E-14 34 6.70E-15 11 5.70E-15 10 (continued on next page) 8SM-37 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name 1,1-Difluoroethyl 2,2,2-trifluoroacetate 8SM Delta 1.08E-13 4 6.15E-13 7 3.75E-15 5 4.50E-15 7 4.23E-15 8 Total 2.94E-12 118 3.44E-12 38 2.70E-14 40 7.70E-15 12 6.55E-15 12 Ethyl 2,2,2-trifluoroacetate Delta 4.88E-15 <1 2.74E-14 <1 1.68E-16 <1 2.00E-16 <1 1.88E-16 <1 Total 1.31E-13 5 1.53E-13 2 1.17E-15 2 3.41E-16 1 2.91E-16 1 2,2,2-Trifluoroethyl 2,2,2-trifluoroacetate Delta 2.43E-14 1 1.37E-13 1 8.37E-16 1 9.98E-16 2 9.38E-16 2 Total 6.52E-13 26 7.64E-13 8 5.90E-15 9 1.70E-15 3 1.45E-15 3 Methyl 2,2,2-trifluoroacetate Delta 1.80E-13 7 1.04E-12 11 6.32E-15 9 7.65E-15 12 7.18E-15 13 Total 4.98E-12 200 5.84E-12 64 4.71E-14 69 1.31E-14 21 1.11E-14 20 Methyl 2,2-difluoroacetate Delta 1.16E-14 0 6.54E-14 1 4.01E-16 1 4.77E-16 1 4.49E-16 1 Total 3.12E-13 12 3.66E-13 4 2.81E-15 4 8.15E-16 1 6.95E-16 1 Difluoromethyl 2,2,2-trifluoroacetate Delta 9.52E-14 4 5.40E-13 6 3.30E-15 5 3.95E-15 6 3.71E-15 7 Total 2.58E-12 103 3.02E-12 33 2.37E-14 35 6.76E-15 11 5.75E-15 11 2,2,3,3,4,4,4-Heptafluorobutan-1-ol Delta 1.17E-13 5 6.72E-13 7 4.08E-15 6 4.93E-15 8 4.63E-15 8 Total 3.21E-12 129 3.77E-12 41 3.02E-14 44 8.45E-15 14 7.18E-15 13 1,1,2-Trifluoro-2-(trifluoromethoxy)-ethane Delta 2.27E-12 91 2.26E-11 246 9.20E-14 134 1.77E-13 287 1.72E-13 314 Total 1.01E-10 4063 1.37E-10 1489 2.12E-12 3096 4.65E-13 754 2.69E-13 492 1-Ethoxy-1,1,2,3,3,3-hexafluoropropane Delta 8.16E-14 3 4.66E-13 5 2.84E-15 4 3.41E-15 6 3.20E-15 6 Total 2.22E-12 89 2.61E-12 28 2.06E-14 30 5.84E-15 9 4.96E-15 9 1,1,1,2,2,3,3-Heptafluoro-3-(1,2,2,2-tetrafluoroethoxy)-propane Delta 3.40E-12 136 8.11E-11 884 1.45E-13 212 5.26E-13 853 8.65E-13 1582 Total 2.01E-10 8075 6.76E-10 7371 5.71E-12 8353 4.82E-12 7812 3.26E-12 5960 2,2,3,3-Tetrafluoro-1-propanol Delta 4.58E-14 2 2.59E-13 3 1.59E-15 2 1.90E-15 3 1.78E-15 3 Total 1.24E-12 50 1.45E-12 16 1.13E-14 17 3.24E-15 5 2.76E-15 5 2,2,3,4,4,4-Hexafluoro-1-butanol Delta 6.00E-14 2 3.40E-13 4 2.07E-15 3 2.48E-15 4 2.33E-15 4 Total 1.62E-12 65 1.90E-12 21 1.48E-14 22 4.25E-15 7 3.62E-15 7 2,2,3,3,4,4,4-Heptafluoro-1-butanol Delta 5.72E-14 2 3.25E-13 4 1.98E-15 3 2.38E-15 4 2.23E-15 4 Total 1.55E-12 62 1.82E-12 20 1.43E-14 21 4.07E-15 7 3.46E-15 6 (continued on next page) 8SM-38 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material Table 8.SM.16 (continued) Acronym, AGWP AGWP Common AGTP AGTP AGTP 20-year GWP 100-year GWP GTP GTP GTP Name or 20-year 50-year 100-year (W m 2 20-year (W m 2 100-year 20-year 50-year 100-year Chemical (K kg 1) (K kg 1) (K kg 1) yr kg 1) yr kg 1) Name 1,1,2,2-Tetrafluoro-3-methoxy-propane 8SM Delta 1.87E-15 <1 1.05E-14 <1 6.43E-17 <1 7.65E-17 <1 7.19E-17 <1 Total 5.00E-14 2 5.86E-14 1 4.48E-16 1 1.31E-16 <1 1.11E-16 <1 Perfluoro-2-methyl-3-pentanone Delta 3.55E-16 <1 1.99E-15 <1 1.22E-17 <1 1.45E-17 <1 1.36E-17 <1 Total 9.49E-15 <1 1.11E-14 <1 8.49E-17 <1 2.48E-17 <1 2.11E-17 <1 3,3,3-Trifluoro-propanal Delta 3.83E-17 <1 2.14E-16 <1 1.31E-18 <1 1.56E-18 <1 1.47E-18 <1 Total 1.02E-15 <1 1.20E-15 <1 9.15E-18 <1 2.67E-18 <1 2.28E-18 <1 2-Fluoroethanol Delta 3.14E-15 <1 1.76E-14 <1 1.08E-16 <1 1.28E-16 <1 1.21E-16 <1 Total 8.39E-14 3 9.83E-14 1 7.53E-16 1 2.19E-16 <1 1.87E-16 <1 2,2-Difluoroethanol Delta 1.08E-14 <1 6.05E-14 1 3.71E-16 1 4.42E-16 1 4.15E-16 1 Total 2.88E-13 12 3.38E-13 4 2.60E-15 4 7.54E-16 1 6.43E-16 1 2,2,2-Trifluoroethanol Delta 7.01E-14 3 3.98E-13 4 2.43E-15 4 2.91E-15 5 2.74E-15 5 Total 1.90E-12 76 2.23E-12 24 1.75E-14 26 4.98E-15 8 4.24E-15 8 1,1 -Oxybis[2-(difluoromethoxy)-1,1,2,2-tetrafluoroethane Delta 4.65E-12 186 7.57E-11 825 1.95E-13 285 5.58E-13 905 6.78E-13 1240 Total 2.52E-10 10,096 5.27E-10 5741 6.57E-12 9609 3.31E-12 5367 1.42E-12 2603 1,1,3,3,4,4,6,6,7,7,9,9,10,10,12,12-hexadecafluoro-2,5,8,11-Tetraoxadodecane Delta 4.25E-12 170 6.91E-11 754 1.78E-13 260 5.10E-13 827 6.20E-13 1133 Total 2.30E-10 9223 4.81E-10 5245 6.00E-12 8778 3.02E-12 4903 1.30E-12 2378 1,1,3,3,4,4,6,6,7,7,9,9,10,10,12,12,13,13,15,15-Eicosafluoro-2,5,8,11,14-Pentaoxapentadecane Delta 3.43E-12 138 5.59E-11 609 1.44E-13 211 4.12E-13 668 5.01E-13 916 Total 1.86E-10 7456 3.89E-10 4240 4.85E-12 7095 2.44E-12 3963 1.05E-12 1923 8.SM.16 Metric Values to Support Figure 8.32 and Figure 8.33 Table 8.SM.17 | Metric values used for Figures 8.32 and 8.33. Species GWP10 GWP20 GWP50 GWP100 GTP10 GTP20 GTP50 GTP100 CO2 1 1 1 1 1 1 1 1 CH4 104.2 83.9 48.4 28.5 99.9 67.5 14.1 4.3 N2O 246.6 263.7 275.6 264.8 253.5 276.9 281.8 234.2 BC 4349.2 2421.1 1139.3 658.6 2398.2 702.8 110.0 90.7 OC 438.5 244.1 114.9 66.4 241.8 70.9 11.1 9.1 SO2 253.5 141.1 66.4 38.4 139.6 40.9 6.4 5.3 NOx 134.2 16.7 15.6 10.8 2.8 86.3 27.4 2.8 CO 8.6 5.9 3.2 1.9 6.8 3.7 0.7 0.3 8SM-39 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing 8.SM.17 Metric Values for Sectors to Support Section 8.7.2 Table 8.SM.18 | GWPs and GTPs for NOX, BC, OC and SO2 from various sectors (metrics for SO2 are given on SO2 basis, while for NOX they are given on a nitrogen basis). For the reference gas CO2, RE and IRF from AR4 are used in the calculations. The GWP100 and GTP100 values can be scaled by 0.94 and 0.92, respectively, to account for updated values for the reference gas CO2. For 20 years the changes are negligible. ari is aerosol radiation interaction. 8SM Sector and emission GWP GTP region (if sub-global) H = 20 H = 100 H = 20 H = 100 Aviation NOXa 92 to 338 21 to 67 396 to 121 5.8 to 7.9 NOXb 120 to 470 2.1 to 71 590 to 200 9.5 to 7.6 NOXh 415 75 239 8.6 Shipping NOXb 76 to 31 36 to 25 190 to 130 6.1 to 4.2 NOXc 107 73 135 SO2b 150 to 37 43 to 11 44 to 11 6.1 to 1.5 SO2, Arctice 47 13 OC, Arctice 151 43 BC ari, Arctic e 2037 579 BC on snow, Arctice 764 217 Energy Related BC ari + albedog 2,800 +/- 1,800 790 +/- 530 OC Energy relatedg 110 ( 40, 210) 30 ( 12, 60) Industry/Power BC, Asiaf 3,260 910 Household BC, Asiaf 2,680 750 Transport BC, Asiaf 2,640 740 Transport BC, North Americaf 3,900 1,090 Household OC, Asiaf 260 72 Transport OC, Asiaf 180 50 Industry/Power SO2, Asiaf 106 (ari) 30 (ari) Industry/Power SO2, North Americaf 215 (ari) 60 (ari) Coal-fired power, NOXd 20 Coal-fired power, SO2d 189 (ari) 53 (ari) Petroleum Production BC ari, Arctic 2,369 673 BC on snow, Arctice 4,104 1,166 SO2, Arctice 64 18 OC, Arctice 152 43 Open Biomass BC ari + albedog 3,100 +/- 1,300 880 +/- 370 OCg 180 ( 70, 360) 53 ( 20, 100) Notes: a Myhre et al. (2011) b Fuglestvedt et al. (2010) c Collins et al. (2010) d Shindell and Faluvegi (2010) e Odemark et al. (2012) f Shindell et al. (2008) g Bond et al. (2011) h Köhler et al. (2013) 8SM-40 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material 8.SM.18 Further Information on Temperature Impact from Various Sectors to Support Section 8.7.2 Table 8.SM.19 | Information about emissions and metric values used in calculations of temperature impacts of sectors. Species Global Emissions (Gg) AGTP Values Based on GTP20 GTP100 8SM CO2 3.69E+07 Joos et al. (2013) and AR5 1 1 CH4 3.64E+05 Updated by AR5 67 4.3 N2O 1.07E+04 Updated by AR5 277 234 HCFC-141b 7.68E-01 Updated by AR5 1853 111 HCF-142b 6.18E+00 Updated by AR5 4393 356 HFC-23 1.75E+01 Updated by AR5 11524 12709 HFC-32 2.36E+00 Updated by AR5 1362 94 HFC-125 3.00E+01 Updated by AR5 5797 967 HFC-134a 1.63E+02 Updated by AR5 3053 201 HFC-143a 3.25E+01 Updated by AR5 6957 2505 HFC-152a 2.79E+01 Updated by AR5 174 19 HFC-227ea 7.18E+00 Updated by AR5 5283 1500 HFC-236fa 1.59E-01 Updated by AR5 7397 8377 HFC-245fa 4.11E+00 Updated by AR5 1974 121 HFC-365mfc 1.73E+00 Updated by AR5 1893 114 HFC-43-10mee 2.69E-01 Updated by AR5 3718 281 SF6 6.50E+00 Updated by AR5 18904 28215 NF3 1.75E-01 Updated by AR5 13723 18119 PFC-14 1.12E+01 Updated by AR5 5272 8038 PFC-116 2.43E+00 Updated by AR5 8877 13456 PFC-218 4.13E-01 Updated by AR5 7176 10654 PFC-318 2.49E-02 Updated by AR5 7676 11456 PFC-3-1-10 1.96E-02 Updated by AR5 7419 11016 PFC-4-1-12 9.58E-06 Updated by AR5 6856 10284 PFC-5-1-14 3.78E-01 Updated by AR5 6365 9493 Bond et al. (2013) aerosol radiation interaction BC 5.31E+03 703 91 and albedo effect included in metric values OC 1.36E+04 Fuglestvedt et al. (2010) 71 9.1 SO2 1.27E+05 (in SO2) Fuglestvedt et al. (2010) 41 5.3 Contrails and CIC Updated by AR5 0.75 0.10 Stevenson et al. (2004), as given by Fuglestvedt Aircraft NOx 204 6.7 et al. (2010) Fuglestvedt et al. (2008), as given by Fuglestvedt Shipping NOx 162 4.0 et al. (2010) 3.72E+04 (includes shipping and The global run in Wild et al. (2001), as given by Surface NOx 86 2.8 air, in N) Fuglestvedt et al. (2010) Derwent et al. (2001), as given by Fuglestvedt et CO 8.93E+05 3.7 0.27 al. (2010) Collins et al. (2002) , as given Fuglestvedt et al. VOC 1.60E+05 7.4 0.61 (2010) NH3 4.93E+04 Shindell et al. (2009) 23 3.0 ACI ( 0.45)/( 0.4)*SO2, updated by AR5 46 5.9 8SM-41 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing AGTPs for the aerosols OC and SO2 are from Fuglestvedt et al. (2010). For BC, the metric parameterization is based on Bond et al. (2013); the RF of the aerosol radiation interaction (0.71 W m 2) and snow and ice ) albedo effects (0.1 + 0.03 W m 2). ( The parameters for the ozone precursors NOx is from the global run in Wild et al. (2001), CO from Derwent et al. (2001) and VOC from Collins 8SM et al. (2002), as given by Fuglestvedt et al. (2010). For NOx emissions from shipping and aircraft, the parameters are from Fuglestvedt et al. (2008) and Wild et al. (2001), respectively, as given by Fuglestvedt et al. (2010). The parameters for the indirect effect of contrails and contrail induced cirrus (CIC) are updated for AR5. The lifetime is set to 5 hours, as in ( ) Fuglestvedt et al. (2010), while the REs are based on a radiative forc- ing of 10 m W m 2 and 50 m W m 2 for contrails and the sum of con- Figure 8.SM.9 | Temperature responses from the various sectors as function of time for 1-year pulse emissions. trails and CIC, respectively. The calculations are based emissions from aviation of about 776 Tg(CO2), which comes from EDGAR 2008 (http:// edgar.jrc.ec.europa.eu/overview.php?v=42). The aerosol cloud interaction has been calculated with a scaling rela- tive to the direct effect of sulphate. The scaling is 0.45 / 0.4 = 1.125 ( ) and is used across almost all sectors (i.e., no separate scaling used for aerosol cloud interaction for shipping). We do not account for any aerosol cloud interaction from the aviation sector, as we include the impact of contrails and CIC. We have tested the effect of various aerosol cloud interaction values and attributions to components (e.g., attributing aerosol cloud inter- action equally to OC and sulphate, setting the aerosol cloud inter- action at the maximum or minimum of its 90% confidence interval, choosing a larger BC forcing, etc.). The ranking of sectors for global ( ) emissions differs little between the different parameterizations and mostly for the shortest time horizons. Figure 8.SM.10 | Temperature responses from the various sectors as function of time, assuming constant emissions. The calculations presented here do not include the climate carbon feedback for non-CO2 emissions, which can substantially increase those values (Collins et al., 2013). Emissions: Emission Database for Global Atmospheric Research (EDGAR) 2008 (http://edgar.jrc. ec.europa.eu/overview.php?v=42). VOC emissions are converted to carbon mass units based on IPCC (2006). BC and OC emissions for year 2005 are taken from Shindell et al. (2012). Emission data requires fre- quently updates when new information become available (e.g., Lam et al., (2012). BC and OC emission from biomass burning are taken from Lamarque et al. (2010). Figure 8.34 is based on the calculations and data described above. Figure 8.SM.9 shows the net temperature responses as function of time for one year pulse emissions. Figure 8.SM.10 shows the net tem- perature responses as function of time assuming constant emissions from the various sectors. Figures 8.32 and 8.33 are based on the emission data given above for CO2, CH4, N2O, BC, OC, SO2, NOx and CO. The following metric values used are given in Table 8.SM.18. 8SM-42 Anthropogenic and Natural Radiative Forcing Chapter 8 Supplementary Material References Baliunas, S., and R. Jastrow, 1990: Evidence for long-term brightness changes of Fuglestvedt, J., T. Berntsen, G. Myhre, K. Rypdal, and R. Skeie, 2008: Climate forcing solar-type stars. Nature, 348, 520 523. from the transport sectors. Proc. Natl. Acad. Sci. U.S.A., 105, 454 458. Ball, W., Y. Unruh, N. Krivova, S. Solanki, T. Wenzler, D. Mortlock, and A. Jaffe, 2012: Fuglestvedt, J. S., et al., 2010: Transport impacts on atmosphere and climate: Metrics. Reconstruction of total solar irradiance 1974 2009. Astron. Astrophys., 541, Atmos. Environ., 44, 4648 4677. A27. Gao, C., A. Robock, and C. Ammann, 2008: Volcanic forcing of climate over the past Bond, T., C. Zarzycki, M. Flanner, and D. Koch, 2011: Quantifying immediate radiative 1500 years: An improved ice core-based index for climate models. J. Geophys. 8SM forcing by black carbon and organic matter with the Specific Forcing Pulse. Res. Atmos., 113, D23111. Atmos. Chem. Phys., 11, 1505 1525. Gray, L., et al., 2010: Solar influences on climate. Rev. Geophys., 48, RG4001. Bond, T. C., et al., 2013: Bounding the role of black carbon in the climate system: A Haigh, J., A. Winning, R. Toumi, and J. Harder, 2010: An influence of solar spectral scientific assessment. J. Geophys. Res. Atmos., 118, 5380 5552. variations on radiative forcing of climate. Nature, 467, 696 699. Boucher, O., and M. Reddy, 2008: Climate trade-off between black carbon and Hall, J., and G. Lockwood, 2004: The chromospheric activity and variability of cycling carbon dioxide emissions. Energ. Policy, 36, 193 200. and flat activity solar-analog stars. Astrophys. J., 614, 942 946. Boucher, O., P. Friedlingstein, B. Collins, and K. P. Shine, 2009: The indirect global Hansen, J., M. Sato, P. Kharecha, and K. von Schuckmann, 2011: Earth s energy warming potential and global temperature change potential due to methane imbalance and implications. Atmos. Chem. Phys., 11, 13421 13449. oxidation. Environ. Res. Lett., 4, 044007. Harder, J., J. Fontenla, P. Pilewskie, E. Richard, and T. Woods, 2009: Trends in solar Butler, J., B. Johnson, J. Rice, E. Shirley, and R. Barnes, 2008: Sources of differences spectral irradiance variability in the visible and infrared. Geophys. Res. Lett., in on-Orbital total solar irradiance measurements and description of a proposed 36, L07801. laboratory intercomparison. J. Res. Natl. Inst. Stand. Technol., 113, 187 203. Hathaway, D., R. Wilson, and E. Reichmann, 2002: Group sunspot numbers: Sunspot Caldeira, K., and J. Kasting, 1993: Insensitivity of global warming potentials to cycle characteristics. Sol. Phys., 211, 357 370. carbon-dioxide emission scenarios. Nature, 366, 251 253. Holmes, C. D., M. J. Prather, O. A. Svde, and G. Myhre, 2013: Future methane, Clette, F., D. Berghmans, P. Vanlommel, R. Van der Linden, A. Koeckelenbergh, and hydroxyl, and their uncertainties: Key climate and emission parameters for future L. Wauters, 2007: From the Wolf number to the International Sunspot Index: 25 predictions. Atmos. Chem. Phys., 13, 285 302. years of SIDC. Adv. Space Res., 40, 919 928. Hoyt, D., and K. Schatten, 1998: Group Sunspot Numbers: A new solar activity Collins, W., R. Derwent, C. Johnson, and D. Stevenson, 2002: The oxidation of organic reconstruction. Sol. Phys., 179, 189 219. compounds in the troposphere and their global warming potentials. Clim. IPCC, 2006: 2006 IPCC Guidelines for National Greenhouse Gas Inventories. Change, 52, 453 479. Prepared by the National Greenhouse Gas Inventories Programme. Collins, W. J., S. Sitch, and O. Boucher, 2010: How vegetation impacts affect climate Jacquinet-Husson, N., et al., 2011: The 2009 edition of the GEISA spectroscopic metrics for ozone precursors. J. Geophys. Res. Atmos., 115, D23308. database. J. Quant. Spectrosc. Radiat. Transfer, 112, 2395 2445. Collins, W. J., M. M. Fry, H. Yu, J. S. Fuglestvedt, D. T. Shindell, and J. J. West, 2013: Jain, A. K., B. P. Briegleb, K. Minschwaner, and D. J. Wuebbles, 2000: Radiative Global and regional temperature-change potentials for near-term climate forcings and global warming potentials of 39 greenhouse gases. J. Geophys. Res. forcers. Atmos. Chem. Phys., 13, 2471 2485. Atmos., 105, 20773 20790. Crowley, T. J., and M. B. Unterman, 2013: Technical details concerning development Joos, F., M. Bruno, R. Fink, U. Siegenthaler, T. Stocker, and C. LeQuere, 1996: An of a 1200-yr proxy index for global volcanism. Earth Syst. Sci. Data, 5, 187 197. efficient and accurate representation of complex oceanic and biospheric models Daniel, J., E. Fleming, R. Portmann, G. Velders, C. Jackman, and A. Ravishankara, of anthropogenic carbon uptake. Tellus B, 48, 397 417. 2010: Options to accelerate ozone recovery: Ozone and climate benefits. Atmos. Joos, F., et al., 2013: Carbon dioxide and climate impulse response functions for the Chem. Phys., 10, 7697 7707. computation of greenhouse gas metrics: A multi-model analysis. Atmos. Chem. DeLand, M., and R. Cebula, 2012: Solar UV variations during the decline of Cycle 23. Phys., 13, 2793 2825. J. Atmos. Sol. Terres. Phys., 77, 225 234. Judge, P., G. Lockwood, R. Radick, G. Henry, A. Shapiro, W. Schmutz, and C. Lindsey, Derwent, R., W. Collins, C. Johnson, and D. Stevenson, 2001: Transient behaviour of 2012: Confronting a solar irradiance reconstruction with solar and stellar data tropospheric ozone precursors in a global 3-D CTM and their indirect greenhouse (Research Note). Astron. Astrophys., 544, A88. effects. Clim. Change, 49, 463 487. Koehler, M. O., G. Raedel, K. P. Shine, H. L. Rogers, and J. A. Pyle, 2013: Latitudinal Enting, I. G., T. M. L. Wigley, and M. Heimann, 1994: Future Emissions and variation of the effect of aviation NOx emissions on atmospheric ozone and Concentrations of Carbon Dioxide: Key Ocean/Atmosphere/Land Analyses. methane and related climate metrics. Atmos. Environ., 64, 1 9. CSIRO Division of Atmospheric Research Technical Paper no. 31. Krivova, N., L. Vieira, and S. Solanki, 2010: Reconstruction of solar spectral irradiance Feulner, G., 2011: Are the most recent estimates for Maunder Minimum solar since the Maunder minimum. J. Geophys. Res. Space Phys., 115, A12112. irradiance in agreement with temperature reconstructions? Geophys. Res. Lett., Lam, N. L., et al., 2012: Household light makes global heat: High black carbon 38, L16706. emissions from kerosene wick lamps. Environ. Sci. Technol., 46, 13531 13538. Fontenla, J., O. White, P. Fox, E. Avrett, and R. Kurucz, 1999: Calculation of solar Lamarque, J., et al., 2010: Historical (1850 2000) gridded anthropogenic and irradiances. I. Synthesis of the solar spectrum. Astrophys. J., 518, 480 499. biomass burning emissions of reactive gases and aerosols: Methodology and Forster, P., et al., 2005: Resolution of the uncertainties in the radiative forcing of application. Atmos. Chem. Phys., 10, 7017 7039. HFC-134a. J. Quant. Spectrosc. Radiat. Transfer, 93, 447 460. Lean, J., and M. Deland, 2012: How does the sun s spectrum vary? J. Clim., 25, Forster, P., et al., 2007: Changes in atmospheric constituents and in radiative forcing. 2555 2560. In: Climate Change 2007: The Physical Science Basis. Contribution of Working Lee, Y. H., et al., 2013: Evaluation of preindustrial to present-day black carbon and its Group I to the Fourth Assessment Report of the Intergovernmental Panel on albedo forcing from Atmospheric Chemistry and Climate Model Intercomparison Climate Change [Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K. B. Project (ACCMIP). Atmos. Chem. Phys., 13, 2607 2634. Averyt, M. Tignor and H. L. Miller (eds.)] Cambridge University Press, Cambridge, Li, S., and A. Jarvis, 2009: Long run surface temperature dynamics of an A-OGCM: The United Kingdom and New York, NY, USA, 129 234. HadCM3 4xCO2 forcing experiment revisited. Clim. Dyn., 33, 817 825. Foukal, P., A. Ortiz, and R. Schnerr, 2011: Dimming of the 17th century sun. Astrophys. Lockwood, M., and M. Owens, 2011: Centennial changes in the heliospheric J. Letters, 733, L38. magnetic field and open solar flux: The consensus view from geomagnetic data Fox, N., et al., 2011: Accurate radiometry from space: an essential tool for climate and cosmogenic isotopes and its implications. J. Geophys. Res. Space Phys., 116, studies. Philos. Trans. R. Soc. A, 369, 4028 4063. A04109. Freckleton, R., E. Highwood, K. Shine, O. Wild, K. Law, and M. Sanderson, 1998: Morgan, M. G., and M. Henrion, 1990: A Guide to Dealing with Uncertainty in Greenhouse gas radiative forcing: Effects of averaging and inhomogeneities in Quantitative Risk and Policy Analysis. Cambridge University Press, Cambridge, trace gas distribution. Q. J. R. Meteorol. Soc., 124, 2099 2127. United Kingdom, and New York, NY, USA. Frohlich, C., 2009: Evidence of a long-term trend in total solar irradiance. Astron. Myhre, G., and F. Stordal, 1997: Role of spatial and temporal variations in the Astrophys., 501, L27 U508. computation of radiative forcing and GWP. J. Geophys. Res. Atmos., 102, 11181 11200. 8SM-43 Chapter 8 Supplementary Material Anthropogenic and Natural Radiative Forcing Myhre, G., E. J. Highwood, K. P. Shine, and F. Stordal, 1998: New estimates of Shine, K., J. Fuglestvedt, K. Hailemariam, and N. Stuber, 2005: Alternatives to radiative forcing due to well mixed greenhouse gases. Geophys. Res. Lett., 25, the global warming potential for comparing climate impacts of emissions of 2715 2718. greenhouse gases. Clim. Change, 68, 281 302. Myhre, G., F. Stordal, I. Gausemel, C. J. Nielsen, and E. Mahieu, 2006: Line-by-line Sihra, K., M. Hurley, K. Shine, and T. Wallington, 2001: Updated radiative forcing calculations of thermal infrared radiation representative for global condition: estimates of 65 halocarbons and nonmethane hydrocarbons. J. Geophys. Res. CFC-12 as an example. J. Quant. Spectrosc. Radiat. Transfer, 97, 317 331. Atmos., 106, 20493 20505. Myhre, G., et al., 2011: Radiative forcing due to changes in ozone and methane Skeie, R., T. Berntsen, G. Myhre, K. Tanaka, M. Kvalevag, and C. Hoyle, 2011a: caused by the transport sector. Atmos. Environ., 45, 387 394. Anthropogenic radiative forcing time series from pre-industrial times until 2010. 8SM Naik, V., A. K. Jain, K. O. Patten, and D. J. Wuebbles, 2000: Consistent sets of Atmos. Chem. Phys., 11, 11827 11857. atmospheric lifetimes and radiative forcings on climate for CFC replacements: Skeie, R., T. Berntsen, G. Myhre, C. Pedersen, J. Strom, S. Gerland, and J. Ogren, HCFCs and HFCs. J. Geophys. Res. Atmos., 105, 6903 6914. 2011b: Black carbon in the atmosphere and snow, from pre-industrial times Odemark, K., S. B. Dalsoren, B. H. Samset, T. K. Berntsen, J. S. Fuglestvedt, and G. until present. Atmos. Chem. Phys., 11, 6809 6836. Myhre, 2012: Short-lived climate forcers from current shipping and petroleum Svde, O. A., M. Gauss, S. P. Smyshlyaev, and I. S. A. Isaksen, 2008: Evaluation of activities in the Arctic. Atmos. Chem. Phys., 12, 1979 1993. the chemical transport model Oslo CTM2 with focus on arctic winter ozone Olivié, D. J. L., G. Peters, and D. Saint-Martin, 2012: Atmosphere response time scales depletion. J. Geophys. Res. Atmos., 113, D09304. estimated from AOGCM Experiments. J. Clim., 25, 7956 7972. Steinhilber, F., J. Beer, and C. Frohlich, 2009: Total solar irradiance during the Pinnock, S., M. D. Hurley, K. P. Shine, T. J. Wallington, and T. J. Smyth, 1995: Radiative Holocene. Geophys. Res. Lett., 36, L19704. forcing of climate by hydrochlorofluorocarbons and hydrofluorocarbons. J. Stevenson, D., R. Doherty, M. Sanderson, W. Collins, C. Johnson, and R. Derwent, Geophys. Res. Atmos., 100, 23227 23238. 2004: Radiative forcing from aircraft NOx emissions: Mechanisms and seasonal Plattner, G. K., et al., 2008: Long-term climate commitments projected with climate- dependence. J. Geophys. Res. Atmos., 109, D17307. carbon cycle models. J. Clim., 21, 2721 2751. Stevenson, D. S., et al., 2013: Tropospheric ozone changes, radiative forcing and Pongratz, J., T. Raddatz, C. H. Reick, M. Esch, and M. Claussen, 2009: Radiative attribution to emissions in the Atmospheric Chemistry and Climate Model forcing from anthropogenic land cover change since AD 800. Geophys. Res. Lett., Intercomparison Project (ACCMIP). Atmos. Chem. Phys., 13, 3063 3085. 36, L02709. Svalgaard, L., and E. Cliver, 2010: Heliospheric magnetic field 1835 2009. J. Geophys. Prather, M., 1994: Lifetimes and eigenstates in atmospheric chemistry. Geophys. Res. Res. Space Phys., 115, A09111. Lett., 21, 801 804. Svalgaard, L., C. Mandrini, and D. Webb, 2012: How well do we know the sunspot Prather, M., 2007: Lifetimes and time scales in atmospheric chemistry. Philos. Trans. number? Comp. Magnet. Min. Character. Quiet Times Sun Stars, 286, 27 33. R. Soc. London A, 365, 1705 1726. Trenberth, K., and L. Smith, 2005: The mass of the atmosphere: A constraint on global Prather, M. J., C. D. Holmes, and J. Hsu, 2012: Reactive greenhouse gas scenarios: analyses. J. Clim., 18, 864 875. Systematic exploration of uncertainties and the role of atmospheric chemistry. van Vuuren, D., J. Edmonds, M. Kainuma, K. Riahi, and J. Weyant, 2011: A special Geophys. Res. Lett., 39, L09803. issue on the RCPs. Clim. Change, 109, 1 4. Reisinger, A., M. Meinshausen, and M. Manning, 2011: Future changes in global Wang, Y., J. Lean, and N. Sheeley, 2005: Modeling the sun s magnetic field and warming potentials under representative concentration pathways. Environ. Res. irradiance since 1713. Astrophys. J., 625, 522 538. Lett., 6, 024020. Wild, O., M. Prather, and H. Akimoto, 2001: Indirect long-term global radiative Rigby, M., et al., 2013: Re-evaluation of the lifetimes of the major CFCs and CH3CCl3 cooling from NOx emissions. Geophys. Res. Lett., 28, 1719 1722. using atmospheric trends. Atmos. Chem. Phys., 13, 2691 2702. WMO, 1999: Scientific Assessment of Ozone Depletion: 1998, Global Ozone Rothman, L., et al., 2009: The HITRAN 2008 molecular spectroscopic database. J. Research and Monitoring Project. World Meteorological Organisation, Report Quant. Spectrosc. Radiat. Transfer, 110, 533 572. No. 44. World Meterological Organisation, Geneva, Switzerland. Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol WMO, 2011: Scientific Assessment of Ozone Depletion: 2010. Global Ozone optical depths, 1850 1990. J. Geophys. Res. Atmos., 98, 22987 22994. Research and Monitoring Project Report. World Meteorological Organisation, Schmidt, G., et al., 2011: Climate forcing reconstructions for use in PMIP simulations Geneva, Switzerland, 516 pp. of the last millennium (v1.0). Geosci. Model Dev., 4, 33 45. Wright, J., 2004: Do we know of any Maunder minimum stars? Astron. J., 128, Schmidt, G. A., et al., 2012: Climate forcing reconstructions for use in PMIP 1273 1278. simulations of the Last Millennium (v1.1). Geosci. Model Dev., 5, 185 191. Schrijver, C., W. Livingston, T. Woods, and R. Mewaldt, 2011: The minimal solar activity in 2008 2009 and its implications for long-term climate modeling. Geophys. Res. Lett., 38, L06701. Sellevag, S. R., C. J. Nielsen, O. A. Svde, G. Myhre, J. K. Sundet, F. Stordal, and I. S. A. Isaksen, 2004: Atmospheric gas-phase degradation and global warming potentials of 2-fluoro ethanol, 2,2-difluoroethanol, and 2,2,2-trifluoroethanol. Atmos. Environ., 38, 6725 6735. Shapiro, A., W. Schmutz, E. Rozanov, M. Schoell, M. Haberreiter, A. Shapiro, and S. Nyeki, 2011: A new approach to the long-term reconstruction of the solar irradiance leads to large historical solar forcing. Astron. Astrophys., 529, A67. Shindell, D., and G. Faluvegi, 2010: The net climate impact of coal-fired power plant emissions. Atmos. Chem. Phys., 10, 3247 3260. Shindell, D., G. Faluvegi, N. Bell, and G. Schmidt, 2005: An emissions-based view of climate forcing by methane and tropospheric ozone. Geophys. Res. Lett., 32, L04803. Shindell, D., et al., 2008: Climate forcing and air quality change due to regional emissions reductions by economic sector. Atmos. Chem. Phys., 8, 7101 7113. Shindell, D., et al., 2012: Simultaneously mitigating near-term climate change and improving human health and food security. Science, 335, 183 189. Shindell, D. T., G. Faluvegi, D. M. Koch, G. A. Schmidt, N. Unger, and S. E. Bauer, 2009: Improved attribution of climate forcing to emissions. Science, 326, 716 718. Shindell, D. T., et al., 2013: Radiative forcing in the ACCMIP historical and future climate simulations. Atmos. Chem. Phys., 13, 2939 2974. 8SM-44 Detection and Attribution of Climate Change: from Global to Regional 10SM Supplementary Material Coordinating Lead Authors: Nathaniel L. Bindoff (Australia), Peter A. Stott (UK) Lead Authors: Krishna Mirle AchutaRao (India), Myles R. Allen (UK), Nathan Gillett (Canada), David Gutzler (USA), Kabumbwe Hansingo (Zambia), Gabriele Hegerl (UK/Germany), Yongyun Hu (China), Suman Jain (Zambia), Igor I. Mokhov (Russian Federation), James Overland (USA), Judith Perlwitz (USA), Rachid Sebbari (Morocco), Xuebin Zhang (Canada) Contributing Authors: Magne Aldrin (Norway), Beena Balan Sarojini (UK/India), Jürg Beer (Switzerland), Olivier Boucher (France), Pascale Braconnot (France), Oliver Browne (UK), Ping Chang (USA), Nikolaos Christidis (UK), Tim DelSole (USA), Catia M. Domingues (Australia/Brazil), Paul J. Durack (USA/ Australia), Alexey Eliseev (Russian Federation), Kerry Emanuel (USA), Graham Feingold (USA), Chris Forest (USA), Jesus Fidel González Rouco (Spain), Hugues Goosse (Belgium), Lesley Gray (UK), Jonathan Gregory (UK), Isaac Held (USA), Greg Holland (USA), Jara Imbers Quintana (UK), William Ingram (UK), Johann Jungclaus (Germany), Georg Kaser (Austria), Veli-Matti Kerminen (Finland), Thomas Knutson (USA), Reto Knutti (Switzerland), James Kossin (USA), Mike Lockwood (UK), Ulrike Lohmann (Switzerland), Fraser Lott (UK), Jian Lu (USA/Canada), Irina Mahlstein (Switzerland), Valérie Masson-Delmotte (France), Damon Matthews (Canada), Gerald Meehl (USA), Blanca Mendoza (Mexico), Viviane Vasconcellos de Menezes (Australia/ Brazil), Seung-Ki Min (Republic of Korea), Daniel Mitchell (UK), Thomas Mölg (Germany/ Austria), Simone Morak (UK), Timothy Osborn (UK), Alexander Otto (UK), Friederike Otto (UK), David Pierce (USA), Debbie Polson (UK), Aurélien Ribes (France), Joeri Rogelj (Switzerland/ Belgium), Andrew Schurer (UK), Vladimir Semenov (Russian Federation), Drew Shindell (USA), Dmitry Smirnov (Russian Federation), Peter W. Thorne (USA/Norway/UK), Muyin Wang (USA), Martin Wild (Switzerland), Rong Zhang (USA) Review Editors: Judit Bartholy (Hungary), Robert Vautard (France), Tetsuzo Yasunari (Japan) This chapter supplementary material should be cited as: Bindoff, N.L., P.A. Stott, K.M. AchutaRao, M.R. Allen, N. Gillett, D. Gutzler, K. Hansingo, G. Hegerl, Y. Hu, S. Jain, I.I. Mokhov, J. Overland, J. Perlwitz, R. Sebbari and X. Zhang, 2013: Detection and Attribution of Climate Change: from Global to Regional Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www.climatechange2013.org and www.ipcc.ch. 10SM-1 Table of Contents 10.SM.1 Notes and Technical Details on Figures Displayed in Chapter 10.............................. 10SM-3 References .......................................................................... 10SM-15 10SM 10SM-2 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material 10.SM.1 Notes and Technical Details on Figures mond and error bar on this axis indicate best-estimate and uncertainty Displayed in Chapter 10 in attributable anthropogenic warming. Box 10.1, Figure 1 Figure 10.1, Figure 10.2, Figure 10.3 a). Observed global annual mean temperatures 1860 2012 rela- The right panels of Figure 10.1 (Figures 10.1d, e, f) are taken from tive to the 1880 1919 climatology from the Hadley Centre/Climatic Figure 3a of Forster et al. (2013 ), except that data from Fgoals-S2 have Research Unit gridded surface temperature data set 4 (HadCRUT4) been excluded, and that 3-year smoothing to the data has not been data set (coloured dots, with colours also indicating observed tem- applied here. perature) compared with Coupled Model Intercomparison Project Phase 3 (CMIP3)/CMIP5 ensemble mean response to anthropogenic Process and data to create the leftmost panels of Figure 10.1; Figures forcing (orange), natural forcing (blue) and best-fit linear combina- 10.2 and 10.3 are described below. These figures are adapted from tion (black). CMIP series was obtained by a simple average over the Jones et al. (2013 ). models available for each year, with equal weight given to each model. Anthropogenic signal obtained by differencing historical from natural Data simulations. Anthropogenic and natural simulations are masked to cor- All of the data used were provided as monthly Netcdf files, from the respond to observations following Jones et al. (2013 ) and Figure 10.1 CMIP3 and CMIP5 archives, and Daithi Stone (providing data used in and noise-reduced with 5-point running mean. To avoid smoothing out the AR4 figures that were not in the CMIP3 archive). CMIP3 20C3M the volcanic signals, smoothing is not performed over years where the experiments were extended to 2012 by using A1B scenario simula- ensemble mean natural simulations decreases by more than 0.05°C. tions. CMIP5 historical experiments were extended to 2012 by using historicalExt and rcp4.5 experiments. b). Same as panel a), but plotting against CMIP ensemble mean anthropogenic warming instead of time. Note that the only change Regridding from Box 10.1 Figure 1 (a) is the location of points in the horizontal. All data are re-gridded onto the HadCRUT4 spatial grid (5° × 5°) since 10SM HadCRUT4 generally has the most restricted spatial coverage of the c). Same as panel b), but plotting observed annual mean temperatures data sets considered here. There is no infilling into grid boxes with no against CMIP ensemble mean anthropogenic warming in one direction, observations. The re-gridding is done by area averaging any part of the and naturally forced temperature change in the other. Mesh shows old grid that lies within the new grid to produce a new gridpoint value. best-fit plane through the observed points, obtained by an ordinary least-squares fit giving equal weight to all points. Black line shows Masking the best-fit linear combination of model-simulated anthropogenic and The data coverage is limited to where data exists in the equivalent naturally forced temperature change. Length of pins shows residual month/gridpoint of HadCRUT4. climate variability (difference between observations and best-fit). Gra- dients of best-fit surface in anthropogenic and natural directions show Creation of Annual Means best-fit scaling factors on CMIP5 ensemble mean anthropogenic and Anomalies are calculated for each month/gridpoint relative to the natural temperature change. For an animated visualisation of how this 1961 1990 average, where at least 50% of the data in the reference figure is constructed, please see the animation file provided as part of period are needed to calculate the average. Annual means are calculat- the Chapter 10 Supplementary Material. Uncertainty analysis of best- ed from monthly data for each calendar year, where at least 2 months fit gradients in (c) using CMIP5 control variability. are non-missing. d). Best-fit scaling factors on anthropogenic and natural tempera- Global Means ture change, or gradients of the best-fit plane through observations GMST anomalies are calculated by area averaging all available grid- from (c), shown by red diamond. Grey diamonds show corresponding point data for each year. For Figure 10.1 the average of the global gradients obtained applying an identical analysis to 114 non-overlap- mean for the reference period is calculated (1880 1919). The anoma- ping 153-year segments (i.e., 17,442 years in total) of global mean lies are then calculated with respect to the reference period. surface temperature (GMST) from unforced control variability from the CMIP5 ensemble. For this heuristic example, control segments have not Figure 10.1 been masked as in the observations, but residuals are consistent with observed residual variability in both variance and power spectra. Black All model simulations are displayed even if they do not cover the whole ellipse shows two-dimensional 90% confidence interval obtained by period. fitting an F2,114 distribution to the grey diamonds. Red ellipse shows corresponding confidence interval centered on the best-fit gradients Figure 10.2 through the observations. Corresponding one-dimensional confidence intervals on scaling on model anthropogenic and natural warming For each gridpoint a linear regression is applied to the available data shown by the red cross. Upper axis shows corresponding attributable to calculate the trend, requiring no period longer than 5 consecutive anthropogenic warming 1951 2010 obtained from a straight-line fit to years with missing data. the CMIP ensemble mean anthropogenic warming. Location of red dia- 10SM-3 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional Figure 10.3 Figure 10.5 For each latitude (on the HadCRUT4 grid), the average of the trend This figure shows the assessed ranges derived as described in Section across the longitudes is calculated. Any of the observational data sets 10.3.1.1.3. We derive assessed ranges for the attributable contribu- having less than 50% coverage of HadCRUT4 s coverage at a given tion of greenhouse gases (denoted GHG, green), other anthropogenic latitude are not shown on the figure at that latitude. forcings (OA, orange) and natural forcings (NAT, blue) by taking the smallest ranges with a precision of one decimal place that span the 5 Model Spread to 95% ranges of attributable trends for the 1951 2010 period from For Figures 10.2 and 10.3 showing estimates of the spread of models, the Jones et al. (2013 ) weighed multi-model analysis and the Gillett et the 5 to 95% ranges are estimated by ordering the data (after weight- al. (2013 ) multi-model analysis considering observational uncertainty ing each simulation by the inverse of the number of simulations the (Figure 10.4a). The assessed range for the attributable contribution of model it belongs to has and multiplied by the number of models) and combined anthropogenic forcings was derived in the same way from then choosing the central 90% range as limits (see Jones et al., 2013 ). the Gillett et al. (2013 ) multi-model attributable trend shown in Figure 10.4c. The assessment of the internal variability is taken from the Data estimates of the 5th to 95th percentiles of 60-year trends of internal variability estimated by Knutson et al. (2013). We moderate our likeli- Table 10.SM.1 | Observational data sets. hood assessment and report likely ranges rather than very likely ranges Observational Data Set Period Covered directly implied by these studies in order to account for residual sourc- GISTEMP 1880 2012 es of uncertainty (see Section 10.3.1.1.3). Shown on the figure are the HadCRUT4 1850 2012 likely ranges shown as the whiskers with the end of the coloured bars MLOST 1880 2012 being at the mid point of the attributable trend ranges. The midpoint of NAT is zero but the blue NAT bar is widened to make it visible. Table 10.SM.2 | Model Data. Summary of data used. Historical data were extended 10SM into the 21st century either by using any available A1B SRES simulations for CMIP3, and Figure 10.6 RCP4.5 for CMIP5, or RCP8.5 in cases where RCP4.5 was not available. Number of models Total number of This figure is updated from the figure in Imbers et al. (2013) which is Archive used (that cover members (that cover described in detail there. Estimates of contributions to global tempera- 1901 2012 period) 1901 2012 period) ture changes are described in individual contributing papers. Historical CMIP3 13 (9) 63 (35) CMIP5 44 (40) 147 (127) Figure 10.6 is an updated version of an equivalent figure published in historicalNat CMIP3 6 (Hegerl et al., 2010) 30 (Hegerl et al., 2010) Imbers et al. (2013). The four studies represented in Figure 10.6 are CMIP5 17 (10) 52 (38) identical to Figure 1 in Imbers et al. (2013); only the data from the Folland et al. (2013) have been updated. The four studies aims were historicalGHG CMIP3 NA NA slightly different, as well as the signals included into the global mean CMIP5 16 (9) 48 (35) temperature decomposition and length and sampling intervals of their time series. In what follows we briefly describe each of the studies Figure 10.4 represented in Figure 10.6. Scaling factors in (b) shown with a square are reproduced from Ribes The first study shown in Figure 10.6 is from Folland et al. (2013 ). Part and Terray (2013) (Figure 3, top right panel) and those in (d) are repro- of their aim was to forecast annual global mean temperature anom- duced from Ribes and Terray (2013) (Figure 3, top left panel). In cases alies using a statistical model that estimates the contributions of six where Ribes and Terray (2013) show confidence ranges which include physical factors to GMST change and variability. The factors are net both plus and minus infinity, uncertainty bars are shown here as con- forcing from anthropogenic GHGs and aerosols, forcings from volcanic tinuous across the range plotted. Scaling factors shown with a triangle aerosols and changes in solar output, and the influences two internal in (b) are reproduced from Gillett et al. (2013 ) (Figure 4a), and those in modes of variability: El Nino-Southern Oscillation (ENSO) (represented (d) are reproduced from Gillett et al. (2013 ) (Figure S1). Results labelled by the first high-frequency eigenvector of global sea surface tempera- multi correspond to those labelled ObsU in Gillett et al. (2013 ), and tures) and the Atlantic Multi-decadal Oscillation (AMO) (derived from account for observational uncertainty and model uncertainty. Scaling the third low-frequency eigenvector of global sea surface temperatures factors in (b) shown with a diamond are reproduced from Jones et al. of Parker et al. (2007)). (2013 ) (Figure 16a). Results labelled multi correspond to those labelled Weighted avg in Jones et al. (2013 ). Corresponding attributable trends In their predictability analysis, the influence of these factors on observed over the 1951 2010 period are taken directly from Jones et al. (2003) surface temperatures is estimated from cross validated multiple linear (Figure 16b), and are derived from the Ribes and Terray (2013) and Gil- regression using annual surface temperature values from 1891 to 2011 lett et al. (2013 ) regression coefficients by multiplying regression coef- from an average of HadCRUT3, National Climate Data Centre (NCDC) ficients for each forcing by the corresponding least squares trend in and Goddard Institute of Space Studies (GISS). Owing to the cross val- GMST simulated in response to that forcing over the 1951 2010 period. idation method, an ensemble of 121 reconstructions of the observed FGOALS-g2 was excluded from this figure because it did not include the variable is obtained. In our analysis we show the ensemble mean time effects of volcanic aerosol in its historicalNat simulations. 10SM-4 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material series and its 95% confidence range resulting from regression with the output to global mean surface temperature. The statistical model con- HadCRUT3 data set alone to 2012, giving 122 reconstructions. There sists on a multivariate fit to the global monthly mean surface tempera- are two differences between the way the ENSO and volcanic predictors ture anomaly for the period 1953 2007. The signals included in the fit are used here and in Folland et al. (2013). In Figure 10.6 appropriately are the solar, volcanic and anthropogenic components (the latest as a smoothed volcanic and solar data simulated to the end of each year linear trend), and the ENSO3.4 index to represent the effect of El Nino. are used as well as ENSO data simulated from January to September of each year. In Folland et al. (2013) the ENSO data used for the prediction Figure 10.7 of a year were averaged over October and November of the previous year while the volcanic and solar data used were simulated up to the Taken from Figure 7 of Jones et al. (2013 ). end of the previous year. Imbers et al. (2013) used an earlier version of the data of Folland et al. (2013) with the same differences in the Figure 10.8 way ENSO and volcanic predictors are used but using annual surface temperature values from 1891 to 2010 from an average of HadCRUT3 The figure is adapted from Lott et al. (2013). as training data for the statistical model, updated here using annual surface temperature values from 1891 to 2012. Observational Data Sets A number of new radiosonde data sets have been developed since the Lean and Rind s (2009) results are also shown in Figure 10.6. Their studies of a decade ago. Following the review by Thorne et al. (2011) goal was to forecast global and regional climate change in the near and having assessed which sets had coverage for the entire period, future by decomposing the observed record of monthly mean surface four data sets were chosen for analysis. The first of these is Hadley air temperature in terms of its combined linear response to ENSO, solar Centre Atmospheric Temperature data set 2 (HadAT2) (Thorne et al., and volcanic activity and anthropogenic influences (Lean and Rind, 2005). Of the observational data sets, this has the least spatial cover- 2008; see also Kopp and Lean, 2011). They used 1980 2008 monthly age, and thus is used as a common mask for all other data, both obser- time series of mean surface temperature anomalies with respect to vations and models, to allow a like-for-like comparison. 1951 1980 and performed a multivariate linear regression against the 10SM instrumental surface temperature record HadCRUT3v (Brohan et al., The other three observational data sets are from the Radiosonde Inno- 2006) to find the optimal combination of those four signals that better vation Composite Homogenization RAdiosone OBservation COrrection explain that record. Their solar, volcanic, anthropogenic and ENSO sig- using REanalyses (RICH/RAOBCORE) family (Haimberger et al., 2012). nals are lagged by 1, 7, 120 and 4 months respectively with respect to The first of these sets used is RAOBCORE 1.5, which uses the European the temperature observations in order to maximize the proportion of Centre for Medium Range Weather Forecasts (ECMRWF) 40-year rea- global variability that the statistical model captures (76% of the vari- nalysis (ERA-40) (Uppala et al., 2005) and ERA-Interim reanalyses (Dee ance observed since 1980). et al., 2011) to detect and adjust breakpoints. The other two are the ensembles of realizations known as RICH-obs 1.5 and RICH- 1.5. Both The results of the third study considered in Figure 10.6 are from of these generate the ensemble by varying processing decisions (such Kaufmann et al. (2011), who used a statistical model derived to esti- as minimum number of data points or treatment of transitions), with mate the relation between emissions of carbon dioxide (CO2) and meth- breakpoint detection derived from RAOBCORE. However, they differ in ane (CH4), the concentrations of these gases, and global surface tem- the way they handle the adjustments. RICH-obs makes adjustments perature (Kaufmann et al., 2006), to evaluate whether anthropogenic by directly comparing station time series, while RICH- compares the emissions of radiative active gases along with variability can account differences between the time series and the ERA-Interim background. for the 1998 2008 hiatus in warming. The model is estimated with annual data from 1960 to 1998 and used to project 1998 2008 tem- Model Data Sets peratures. The signals included in this model are: GHGs, anthropogenic For the selection of model data sets, the decision was limited by the sulphur emissions, solar insolation, ENSO (represented by the Southern need for that model to have runs with natural forcings (NAT), as well as Oscillation Index (SOI)) and radiative forcing of volcanic sulphates. runs with only GHG forcings and finally with all historical (i.e., anthro- pogenic and natural) forcings (ALL), between 1961 and 2010 available The last study shown in Figure 10.6 is from Lockwood (2008). Lock- on the CMIP5 (Taylor et al., 2012) archive at the time the analysis was wood (2008) intended to analyse the contribution of changes in solar undertaken. This led to the models shown in Table 10.SM.3 being used. Table 10.SM.3 | CMIP5 models used for this study, and the number runs with each forcing. Members Included Modelling Centre (or Group) Model(s) ALL NAT GHG Commonwealth Scientific and Industrial Research Organization in collaboration with Queensland Climate Change Centre of Excellence CSIRO-Mk3.6.0 10 5 5 GISS-E2-R 5 5 5 NASA Goddard Institute for Space Studies GISS-E2-H 5 5 5 Canadian Centre for Climate Modelling and Analysis CanESM2 5 5 5 Met Office Hadley Centre HadGEM2-ES 4 4 4 Beijing Climate Center, China Meteorological Administration BCC-CSM1.1 3 1 1 10SM-5 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional All data sets were adjusted to a common temperature anomaly rela- vidual runs. Red represents all-forcings runs, blue shows natural forc- tive to the 1961 1990 climatology, re-gridded to the HadAT2 grid and ings and green is GHG-forced only. The thick black line is HadAT2, thin masked before zonal averages were taken. The following set of pres- black line is RAOBCORE 1.5, while the dark grey band is the RICH-obs sure levels common to all data sets was used: 850, 700, 500, 300, 200, 1.5 ensemble range and light grey is the RICH- 1.5 ensemble range. 150, 100, 50 and 30 hPa. The three latitude bands analyzed are a tropi- Each band is displayed 25% translucent to better distinguish where cal zone (20°S to 20°N) and north and south extratropical zones (60°S forcings and observations overlap. to 20°S and 20°N to 60°N), along with the average over the whole studied area (i.e., 60°S to 60°N). Trend caluclation shown in Figure 10.8 are for the period 1961 2010. Figure 10.SM.1 shows trend calcuations for the satellite period from Different from Lott et al. (2013) Figures 10.8 and 10.SM.1 do not 1979 to 2010. include the Centre National de Recherches Météorologiques (CNRM- CM5) and Norwegian Earth System Model 1-M (NorESM1-M) models. Figure 10.9 CNRM-CM5 was excluded because of unrealistic stratospheric ozone forcing (Eyring et al., 2013). The NorESMI-M was not included because This figure shows time series of annual mean lower stratosphere the GHG single forcing runs for this model also include ozone forcing. temperatures from three satellite data sets and CMIP5 experiments. It utilizes the same CMIP5 model runs as Figure 10. 8 and individu- Trend Calculations al model runs are shown. Synthetic lower stratosphere temperatures For both the models and observations, the trends at each pressure were calculated using global Microwave Sounding Unit (MSU) verti- level were calculated using a median pairwise algorithm (as this copes cal weighting functions for the lower stratosphere. The three observa- better with outliers than a conventional linear fit) (Lanzante, 1996). tional data sets are used to address observational consistent: Remote These trends were plotted against pressure level, for all models and Sensing System (RSS) Version 3.3, University of Alabama in Huntsville forcings within them. For each forcing ensemble of model runs, the (UAH) version 5.4 and Situation, Task, Action, Result (STAR) version 2.0 shaded region shows the 5 to 95% range determined based on indi- (Santer et al., 2013). 10SM Figure 10.SM.1 | Observed and simulated zonal mean temperatures trends from 1979 to 2010 for CMIP5 simulations containing both anthropogenic and natural forcings (red), natural forcings only (blue) and greenhouse gas forcing only (green) where the 5th to 95th percentile ranges of the ensembles are shown. Three radiosonde observations are shown (thick black line: Hadley Centre Atmospheric Temperature data set 2 (HadAT2), thin black line: RAdiosone OBservation COrrection using REanalyses (RAOBCORE) 1.5, dark grey band: Radiosonde Innovation Composite Homogenization (RICH)-obs 1.5 ensemble and light grey: RICH- 1.5 ensemble. (Adapted from Lott et al. (2013) but for the more recent period from 1979 to 2010.) 10SM-6 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material Synthetic MSU temperature time series from model data were calcu- Masking of Simulated Data onto the Observational Grid lated as follows: First, the land area of the simulated data available in different spatial resolutions is obtained by choosing a grid point as land when its land 1. Select area from 82.5°S to 82.5°N of atmosphere temperature area fraction is greater than or equal to 70%. Second, the simulated fields and time period and calculate area weighted averages. land data are interpolated to the 5°× 5° observational grid using bilin- ear interpolation. Third, the 90% sampling criterion is applied to each 2. Select time series from January 1979 to December 2010 and calcu- regridded model data to obtain the consistent temporal and spatial late annual averages and anomalies relative to the period 1996 data coverage for the simulated and observed data. 2010. Calculation of Spatial and Annual Averages and Anomalies with 3. Select pressure levels (hPa): 1000, 925, 850, 700, 600, 500, 400, regard to the Baseline Climatology 300, 250, 200, 150, 100, 70, 50, 30, 20, 10. For each (regridded and sampled) monthly model data, spatial averag- es are first calculated for the global domain and zonal bands of 60°N 4. Apply vertical weighting function for MSU lower stratosphere tem- to 90°N, 30°N to 60°N and 30°S to 30°N. Annual averages, baseline perature (channel 4) (Mears and Wentz, 2009). climatology (for 1961 1990) and anomalies from the baseline period are then calculated. Figure 10.10 Calculation of Multi-model Means of All and Nat Runs Figure 10.10 is updated from Supplementary Information Figure S1 of Multi-model averages of 30 All runs and 10 Nat runs are calculated. Balan Sarojini et al. (2012 ). The updates include the use of a 11-year smoothing rather than a 5-year smoothing used in Balan Sarojini et Decadal Smoothing for both Observed and Simulated Data al. (2012 ) and simulations from additional models for ALL that have A smoothing of boxcar average with 11-year width (with edges trun- become available since the publication of the paper and that are listed cated) is applied to the resulting time series of annual precipitation below. anomalies. 10SM Global and zonal average changes in annual mean precipitation (in Plotting mm day 1) for the period 1951 2005, with regard to the baseline The yearly anomalies are plotted with a y-axis range of 1950 2010. period of 1961 1990, are plotted based on Balan Sarojini et al. (2012 ). Multi-model means are in thick solid lines (All in red and Nat in blue) and individual simulations are in thin solid lines. CMIP5 Simulations used are: Historical ( All ): HadGEM2-ES, CSIRO-Mk3-6-0, CNRM-CM5, Statistical Test of Significance for the Changes Between All NorESM1-M, CanESM2, BCC-CSM1-1, INMCM4_ESM, IPSL-CM5A- and Nat Runs LR, GISS-E2-H, GISS-E2-R, MPI-ESM-LR, GFDL-ESM2G, GFDL-ESM2M, Green stars are plotted when the changes are statistically significant CCSM4, MIROC5, MIROC-ESM, MIROC-ESM-CHEM, MRI-CGCM3, at 5% level (p <0.05) between the ensemble of runs with both anthro- IPSL-CM5A-MR, CESM1-BGC, CESM1-CAM5, CESM1-WACCM, pogenic and natural forcings (red lines) and the ensemble of runs with CESM1-FASTCHEM, ACCESS1-0, GFDL-CM3, CMCC-CMS, CMCC- just natural forcings (blue lines) using a two-sample two-tailed t-test CESM, HadGEM2-CC, NorESM1-ME, MPI-ESM-MR. for the last 30 years of the time series. HistoricalNat ( Nat ): HadGEM2-ES, CSIRO-Mk3-6-0, CNRM-CM5, Supplementary Figure to Figure 10.10: Figure 10.SM.2 NorESM1-M, CanESM2, BCC-CSM1-1, MIROC-ESM, MIROC-ESM- CHEM, MRI-CGCM3, GFDL-CM3. Global and zonal average changes in annual mean precipitation (in mm day 1) for the period 1951 2005, with regard to the baseline There are 30 All runs (one each of 30 CMIP5 models forced with both period of 1961 1990, are plotted. anthropogenic and natural forcings) and 10 Nat runs (one each of 10 CMIP5 models forced with natural forcings only) The details of the simulations and procedure for both simulations and observations are same as that for Figure 10.10 except for the observa- Observation used is a gridded observational data set based on station tional data set used and an additional sampling criterion as described data extracted from the Global Historical Climatology Network (updat- below (i.e., Steps 2 and 3). ed from Zhang et al. (2007)). Monthly data for the period 1951 2005, quality controlled and gridded at 5° × 5°, for all land grid squares Observation used is a gridded observational dataset based on station on the globe for which station data are available, are used. In order data extracted from the Climatic Research Unit (updated from CRU to avoid artefacts arising from changes in data coverage, a sampling TS3.1 of Harris et al. (2013) and sampled as in Polson et al. (2013)). criterion of choosing data available for >90% of the analysis period is Monthly data for the period 1951 2005, quality controlled and gridded applied (i.e., each spatial grid point is chosen when data over 90% of at 0.5° × 0.5°, are used. the years (only those years that have data for all months) are present). This data is first interpolated to the common spatial resolution (as to Figure 10.10) of 5°× 5°. In order to avoid artefacts arising from 10SM-7 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional c ­ hanges in data coverage, two sampling criteria are applied: (1) sta- Masking of Simulated Data onto the Observational Grid tion sampling criterion (Polson et al., 2013) of choosing only those 5° First, the land area of the simulated data available in different spatial × 5° grid boxes that have at least one station (in any 0.5° × 0.5° grid resolutions is obtained by choosing a grid point as land when its land box) for the coastal grid boxes and with at least two stations for the area fraction is greater than or equal to 70%. Second, the simulated inland grid boxes. A 5° × 5° grid box is coastal when more than half land data are interpolated to the 5° × 5° observational grid using bilin- of number of the 0.5° × 0.5° boxes is ocean points. (2) A criterion of ear interpolation. Third, the mask of station sampling and the 95% choosing data available for >95% of the analysis period is applied, sampling (described in Step 2) is applied to each regridded model data that is, each spatial grid point is chosen when data over 95% of the to obtain the consistent temporal and spatial data coverage for the years (years that have data available for any number of months) are simulated and observed data. present. Figure 10.11 Land masked by Obs Figure based on Zhang et al. (2007); Min et al. (2008); Min et al. (2011); 0.25 Polson et al. (2013). Left top panel: (a) Global land-annual results from Zhang et al. (2007) 0.00 (first pair of bars) and Polson et al. (2013) (2nd to 5th pair of bars); (b) global land-seasonal results from Polson et al. (2013); (c) Arctic Global -0.25 results from Min et al. (2008) and (d) extreme results from Min et al. 1950 1970 1990 2010 (2011). Right top panel: After Zhang et al. (2007), but updated follow- 0.25 ing Polson et al. (2013): changes expressed in percent climatology and CMIP5 models plotted. Bottom left and right panel: from Polson et al. (2013). 10SM 0.00 Figure 10.12 60N-90N -0.25 December to February mean change of southern border of the Hadley 1950 1970 1990 2010 0.25 circulation. Unit is degree in latitude per decade. Reanalysis data sets are marked with different colours. Trends are all calculated over the period of 1979 2005. According to CMIP5, historicalNAT, historical- 0.00 GHG and historical denote historical simulations with natural forcing, observed increasing GHG forcing and all forcings, respectively. For each 30N-60N reanalysis dataset, the error bars indicate the 95% confidence level of -0.25 the standard t-test. For CMIP5 simulations, trends are first calculated 1950 1970 1990 2010 for each model, and all ensemble members are used. Then, trends are 0.25 averaged for multi-model ensembles. Trend uncertainty is estimated from multi-model ensembles, as twice the standard error. This figure 0.00 is adapted from Hu et al. (2013) with additional trends derived from Climate Forecast System Reanalysis (CSFR) and Modern Era Retrospec- 30S-30N tive-analysis for Research and Applications (MERRA) reanalyses. -0.25 1950 1970 1990 2010 Figure 10.13 Obs Figure 10.13 is adapted from Gillett and Fyfe (2013) (Figure S4), with the Nat following changes. Simulations from the following numbers of models All which cover the 1951 2011 period were used: 106 historical simula- tions from 34 models, 26 historicalGHG simulations from 7 models, 11 aerosols-only simulations from 3 models, 15 ozone-only simulations Figure 10.SM.2 | Global and zonal average changes in annual mean precipitation (mm day 1) over areas of land where there are observations, expressed relative to the from 3 models, and 48 historicalNat simulations from 10 models, and baseline period of 1961 1990, simulated by CMIP5 models forced with both anthropo- control simulations from 43 models. As well as the 5 to 95% range of genic and natural forcings (red lines) and natural forcings only (blue lines) for the global trends simulated in the historical simulations (red boxes), the 5 to 95% mean and for three latitude bands. Multi-model means are shown in thick solid lines. ranges of trends simulated in the control simulations (grey bars) are Observations (gridded values derived from Climatic Research Unit (CRU) station data, also shown. These ranges were derived by weighting each simulation updated from CRU TS3.1 of Harris et al. (2013) and sampled as in Polson et al. (2013) are shown as a black solid line. An 11-year smoothing is applied to both simulations and by the inverse of the product of the number of models and the number observations. Green stars show statistically significant changes at 5% level (p <0.05) of simulations from the model concerned, ranking the trends, deriving between the ensemble of runs with both anthropogenic and natural forcings (red lines) a cumulative distribution function by summing the weights, and then and the ensemble of runs with just natural forcings (blue lines) using a two-sample two- interpolating to find the 5th and 95th percentiles, following Jones et tailed t-test for the last 30 years of the time series. 10SM-8 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material al. (2013 ). Mean responses to each forcing were derived by first aver- Annual means of all model ocean temperature data 700 m of the aging ensemble members for each model, and then averaging across ocean column have been interpolated to the spatial grid and standard models. Uncertainty bars shown for individual forcings are uncertain- vertical depth levels of the observational data (Ishii and Kimoto, 2009). ties in the mean response to each forcing, calculated by dividing the standard deviation across models by the square root of the number of From the CMIP3, control and 20th century (20CEN) runs are consid- models, and multiplying by the Student-t statistic for a cutoff value of ered. The models are further classified as: 0.05 and with the number of degrees of freedom equal to one less than VOL models (those that included volcanic and other natural forc- the number of models. A minor error effecting Gillett and Fyfe (2013) ings) are CCSM3.0, GFDL-CM2.0, GISS-EH, GISS-ER, MIROC-CG- Figure S4 only in which the northern reference latitude for the Southern CM2.3.2, MRI-CGCM2.3.2 Annular Mode (SAM) index was 45°S instead of 40°S following Gong and Wang (1999) was corrected. NoV models are those that did not include natural forcings. These are CCCma-CGCM3.1, CNRM-CM3, CSIRO-Mk3.0, GISS-AOM, Figure 10.14 FGOALS-g1.0 and UKMO-HadCM3. Panel (a) The observed and model simulated historical anomalies are calculated This figure is an update of Figure 2 of Domingues et al. (2008). In this with respect to a 1957 1990 climatology and all control-run anomalies figure: are with respect to the overall time mean of each model s control run. CMIP5 simulations are: Each 20CEN simulation is subsampled in the same manner using the HistoricalNat (can_esm2, ccsm4, cnrm_cm5, csiro_mk3.6, giss_e2_h, 1960 1999 (Ishii, 2009). giss_e2_r, hadgem2-es, miroc_esm, mri_cgcm3, nor_esm1_m) Basin-scale DT changes in the North Atlantic, South Atlantic, North Historical (can_esm2, ccsm4, cnrm_cm5, csiro_mk3.6, giss_e2_h, Pacific, South Pacific, North Indian, and South Indian oceans are com- giss_e2_r, hadgem2-es, miroc5, miroc4h, miroc_esm, mpi_esm_lr, 10SM puted. mri_cgcm3, nor_esm1_m) Residual drift associated with the incomplete spin-up of model control Annual mean ocean heat content (OHC) values are calculated from runs is removed from all DT basin-average time series using a quadrat- models by vertically integrating the annual mean temperature anoma- ic fit. Quadratic fits are computed for the entire control, yielding a drift lies (with respect to a 1960 1980 reference period). Global mean time estimate. This drift is then removed from the original control, yielding series are calculated by integrating over space. an estimate of the true model noise. For each 20CEN simulation, there is a contemporaneous section of the corresponding control and a con- Observed global OHC changes from Domingues et al. (2008); also with temporaneous section of the control-drift estimate. This section of the a reference period of 1960 1980) are smoothed (three-year running control drift is removed from the 20CEN simulation. means) and plotted. The DT anomalies of each ocean basin are then weighted by its volume. Stratospheric Aerosol loading (as global mean AOD) from Sato et al. (1993); data downloaded from the website http://data.giss.nasa.gov/ CMIP3 20CEN runs (1870 1999) are averaged together to produce modelforce/strataer/#References before their December 2012 update) a Multi-Model Response (MMR). If more than one realization of the is plotted. A three-year running mean is also calculated and plotted for 20CEN experiment is available for an individual model, these realiza- comparison against smoothed data. tions are averaged together before averaging across models. Panel (b) The fingerprint is the first Empirical Orthogonal Function (EOF) of the This figure is based on Figure 5(c) of Gleckler et al. (2012). MMR of DT in the six ocean basins, calculated over 1960 1999. Fin- gerprints are computed separately for the simulations that include vol- Anomalies of volume average temperature DT rather than ocean heat canic (V) or exclude volcanic eruptions (NoV) MMRs. content are used. The multimodel noise estimates are based on concatenating all availa- Observed DT estimates are based on globally gridded (1° × 1° latitude/ ble control data from VOL models. longitude) products, not raw measurements. The observed datasets used are: The basin-average upper-ocean temperature changes from observa- Pre-XBT bias correction data: Levitus et al. (2005) and Ishii et al. tions are projected onto the fingerprint yielding the signal projection (2006) time series Z(t). XBT bias corrected data: Levitus et al. (2009), Ishii and Kimoto Trends of increasing length L (least squares fit starting from 1970 and (2009) and Domingues et al. (2008) with an initial L of 10 years) are fit to this time series to yield the signal . 10SM-9 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional Similarly, the DT from the VOL concatenated control runs are projected Figure 10.17 onto the searched-for fingerprint. The resulting projection time series, N(t), provides information about unforced changes in pattern similarity. Figure 10.17: Zwiers et al. (2011). L-year, non-overlapping trends are fitted to N(t), with L varying from Figure 10.17: Detection results for changes in intensity and frequency 10, 11, 12, 39 years. For a given value of L, the noise is the standard of extreme events. Right-hand sides of each panel show scaling fac- deviation of the sampling distribution of the trends. tors and their 90% confidence intervals for changes in the frequency of temperature extremes for winter (October to March for Northern With these, the signal-to-noise (S/N) ratio is calculated as a function of Hemisphere and April to September for Southern Hemisphere), and L. The detection time is defined as the year at which S/N ratio exceeds summer half years. TN10, TX10 are respectively the frequency for daily and remains above a stipulated 5% significance threshold. minimum and daily maximum temperatures falling below their 10th percentiles for the base period 1961 1990. TN90 and TX90 are the Figure 10.15 frequency of the occurrence of daily minimum and daily maximum temperatures above their respective 90th percentiles calculated for the This figure is from three published studies. Panel A is adapted from 1961 1990 base period (Morak et al., 2013), fingerprints are based Figure 3 of Helm et al. (2010). The top and bottom panels of Figure 3 on simulations of Hadley Centre new Global Environmental Model 1 are shown in Panel A of Figure 10.15. Panel B is redrafted and simpli- (HadGEM1) with both anthropogenic and natural forcings). Left side of fied from the original figure, Figure 2A of Durack et al. (2012). Panel C each panel show scaling factors and their 90% confidence intervals for is taken from Figure 11a from Terray et al. (2012). intensity of annual extreme temperatures in response to external forc- ings for the period 1951 2000. TNn and TXn represent annual mini- Figure 10.16 mum daily minimum and maximum temperatures, respectively, while TNx and TXx represent annual maximum daily minimum and maximum Figure 10.16: September sea ice extent for Arctic (top panel) and Ant- temperatures. This is updated from Zwiers et al. (2011) by conducting 10SM arctic (bottom panel) adapted from Wang and Overland (2012). Only exactly the same type of analysis of Zwiers et al. (2011) using spa- CMIP5 models which simulated seasonal mean and magnitude of sea- tial domain defined in Morak et al. (2013), fingerprints are based on sonal cycle in reasonable agreement with observations are included simulations of climate models with both anthropogenic and natural in the plot. forcings). Detection is claimed at the 10% significance level if the 90% confidence interval of a scaling factor is above zero line. The grey lines are the runs from the pre-industrial control simulations, and the red lines are from Historical simulations patched with RCP8.5 Figure 10.18 runs for the period 2005 2012. The black line is based on the sea ice extent data are from National Snow and Ice Data Center (NSIDC). Figure 10.18 combines three figures which are adapted from three dif- ferent papers to provide an overview of different results for attribution There are 24 ensemble members from 11 models for the Arctic and 21 studies using changes in return time as a measure for anthropogenic members from 6 models for the Antarctic plot. influence. The list of simulations that passed the acceptance criteria and plotted Figure 10.18a is directly taken from Pall et al. (2011). The figure is iden- in the figure is: tical to Figure 3d in the paper. Northern Hemisphere: ACCESS1.0, ACCESS1.3, CCSM4, CESM1- CAM5, EC-EARTH, HadGEM2AO, HadGEM2CC, HadGEM2ES, MIROC- Figure 10.18b is adapted from Kay et al. (2011). The first row of Figure ESM, MIROC-ESM-C, MPI-ESM-LR. 5 in the paper shows the return times of 1-day flood peaks in the catch- ment area 27007 (river Ure, UK) for the period October 2000 to March Southern Hemisphere: ACCESS1.3, CMCC-CM, CanESM2, EC-EARTH, 2001 comparing simulations with actual year 2000 climate drivers to MRI-CGCM3, NorESM1-M. four (Figure 5 a d) different sets of counterfactual year 2000 climate drivers. The counterfactual ensembles represent four possible sets of The underlined models are those identified and used by Wang and surface temperatures (SSTs) representative of a world that might have Overland (2012). been without anthropogenic climate forcing. Different SST patterns are obtained from four different models (columns a d) with different The criteria for choosing acceptable simulations models is as follows. scaling factors for the SSTs (colours). We adapted this figure as follows. The simulated mean and seasonal cycle of the sea ice extent is within Instead of calculating the 6-month period October 2000 to March 2001 20% of the observations of the sea ice climatology for the 1981 2005 we considered only the period January 2001 to March 2001 to assess period. The 1981 2005 period was chosen because it overlaps with changes in the return time of 1-day peak floods in spring. In addition, satellite observation period and 2005 is the last year of the historical the catchment used for this study is not the river Ure but the river simulations. The 20% bound chosen here is used in Wang and Over- Don in South Yorkshire, UK. Furthermore we combined the different land (2012), and has also been used by Zhang (2010). A total of 36 SST patterns from all models in one figure. models were evaluated against these selection criteria. 10SM-10 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material Figure 10.18c is directly taken from Otto et al. (2012). The figure is The instrumental data is taken from Morice et al. (2012). identical to Figure 4 in the paper but without displaying temperature equivalents for ERA-interim reanalysis data. All analysis is done on decadally smoothed time-series, using first a 10-year Butterworth filter and then an 11-year box car filter. The analy- Figure 10.19 sis shown in the bottom panel uses the same method and model data as used for the top panel, but is performed on the European domain, All reconstructions used are the same as in Schurer et al. (2013), with following Hegerl et al. (2011). the exception of the Mann et al. (2009) reconstruction, which in the top panel is for 30°N to 90°N land and sea and in the bottom panel is for Figure 10.20a 0° to 60°E 25°N to 65°N land and sea and the Luterbacher et al. (2004) reconstruction which is for the region 25°W to 40°E 35°N to 70°N land The plot contains three different types of reporting on transient climate only (bottom panel). response (TCR) estimation studies: (A) bars indicating estimates of the range of possible TCR values (most, but not all, are 5 to 95% confi- All models used to construct the multi-model ensemble and the control dence interval estimates), (B) these studies are included with both, a simulations used for samples of internal variability are the same as in confidence range represented by a bar and a corresponding probability Schurer et al. (2013) (see Table 10.SM.4). To calculate the multi-model density function (PDF), and (C) some studies from AR4 are included just mean each model set-up contributes equally, that is, the mean of the with their PDFs to show the contrast between AR4 and AR5. five Max Planck Institute Community Earth Systems Models (MPI-COS- MOS) simulations counts as one model whereas the GISS-E2-R simu- Bar-Plot (without Probability Density Functions) lations are treated separately because they contain different forcings. Schwartz (2012) uses a two-time scale formulation of the climate The GISS-E2-R simulations included a significant initial model drift system response (e.g., see Gregory, 2000; Held et al., 2010) to obtain which was removed from the control simulation by fitting a second TCR estimates (more specifically using the notion of transient climate order polynomial to the control simulation. The bold orange line in the sensitivity, more generally defined without reference to a specific rate figure shows the noise reduced multi-model mean multiplied by the of increase in concentration) ranging from 0.9°C to 1.9°C, the lower 10SM best-fit scaling factor. The uncertainty range is calculated by adding in values corresponding to higher values of net forcing over the 20th cen- quadrature the uncertainty in the scaling range to the uncertainty due tury. The range in the figure is generated by multiplying the headline to internal variability. values from the paper (0.23 +/- 0.01 to 0.51 +/- 0.04) K (W m 2) 1, with an assumed forcing for a doubling of CO2 of 3.7 W m 2 (leading to (0.85 The Goosse simulations are taken directly from the simulation described to 1.89 K)). The given range originates from an ensemble of different in Goosse et al. (2012a, 2012b), constrained by the Mann et al. (2009) published forcing estimates, and hence it cannot directly be interpreted reconstruction from 30°N to 90°N. as a 5 to 95% confidence interval. In the top panel the annual mean of the region 30°N to 90°N land and Libardoni and Forest (2011) show that the TCR along with other cli- sea is shown and in the bottom panel the annual mean of the region 0° mate system parameters (see below) can be estimated by comparing to 60°E, 25°N to 65°N. The uncertainty range was estimated from the EMIC simulations with a range of 20th century surface temperature uncertainty given in Goosse et al. (2012a) and Goosse et al. (2012b) atmospheric and ocean temperature data sets. Under a variety of for the annual data-assimilated results. To account for the smoothing assumptions, they obtain 5 to 95% ranges for TCR spanning 0.9 to 2.4 used in the figure these calculated annual standard deviations were K. These values are directly taken from the 2011 paper (0.87 to 2.41 scaled by the ratio between the standard deviation of the smoothed K). Updating this study to include data to 2004 gives results that are and un-smoothed control runs used in Schurer et al. (2013). essentially unchanged. Table 10.SM.4 | Details of the models used. Ensemble Resolution Forcings Model Members Atmosphere Ocean Volcanic Solar Greenhouse Gas Land Use CCSM4* 1 288 × 192 × L26 320 × 384 × L60 GRA VK/WLS SJA PEA/Hur MPI-COSMOS 5 96 × 48 × L19 GR3.0 × L40 CEA JLT Interactive PEA MPI-ESM-P* 1 196 × 98 × L47 256 × 220 × L40 CEA VK/WLS SJA PEA HadCM3 1 96 × 73 × L19 288 × 144 × L20 CEA SBF/WLS SJA PEA GISS-E2-R* 1 144 × 90 × L40 288 × 180 × L32 CEA VK/WLS SJA PEA/Hur GISS-E2-R* 1 144 × 90 × L40 288 × 180 × L32 GRA VK/WLS SJA KK11/Hur Bcc-csm1-1* 1 128 × 64 × L40 360 × 232 × L40 GRA VK/WLS SJA X Notes: Further details can be found in the references for the model and the forcings used; the references for the models are: CCSM4 Landrum et al. (2013); MPI-COSMOS Jungclaus et al. (2010); HadCM3 Schurer et al. (2013); Bcc-csm1-1 Wu (2012). The references for the forcings are: CEA Crowley et al. (2008), GRA Gao et al. (2008), VSK Vieira et al. (2011), SBF Steinhilber et al. (2009) , WLS Wang et al. (2005), SJA Schmidt et al. (2012), PEA Pongratz et al. (2008), Hur- Hurtt et al. (2009), KK11 Kaplan et al. (2009), JLT Jungclaus et al. (2010), MM - MacFarling Meure et al. (2006). An X indicates that the forcing is not included. The models indicated by asterisks have been made available as part of the CMIP5 project. 10SM-11 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional Padilla et al. (2011) use a simple two-time scale model (see the entry deficiencies Sexton et al. (2012). The equilibrium responses are scaled on Schwartz above) to derive an observationally constrained estimate by global temperature changes associated with the sampled model of the TCR of 1.3°C to 2.6°C. The range is directly taken from the head- variants, reweighting the projections based on the likelihood that they line results of the cited paper, with a best estimate of 1.6 K, and includ- correctly replicate observed historical changes in surface temperature, ing an estimate how the 90% confidence range will change in the to predict the TCR distribution. future (reduction of 45% by 2030). Meinshausen et al. (2009) compiled a large set of published marginal Gregory and Forster (2008) estimate real world TCR as 1.3 to 2.3 K (5 PDFs for equilibrium climate sensitivity (ECS) and TCR. In the absence to 95% uncertainty range) from the data of 1970 2006, assuming a of a formal method for combining all of them they chose an illustrative linear relationship between radiative forcing and GMST change and default, choosing a uniform TCR prior PDF from Frame et al. (2006) and disregarding any trend caused by natural forcing. The numbers are constrained the their model parameter with observations. The TCR PDF directly taken from the cited paper (abstract). is reproduced as shown in Figure 1b of the cited paper from supple- mentary data. Stott and Forest (2007) used the observed 20th century temperature change to constrain three models (HadCM3, GFDL-R30 and PCM) and Knutti and Tomassini (2008) compare Earth System Model of Interme- then applied these models to the calculation of TCR for the future. The diate Complexity (EMIC) simulations with 20th century surface and calculated TCR is around 2.1 K and the 5 to 95% probability range is ocean temperatures to derive a probability density function for TCR 1.5 to 2.8 K. The numbers are directly taken from the description of skewed slightly towards lower values with a 5 to 95% percent range Figure 8 of the cited paper. of 1.1°C to 2.3°C. The PDFs for the expert ECS prior and the uniform ECS prior are reproduced as shown in Figure 1b of Meinshausen et al. Gillett et al. (2013 ) base their methodology on Gillett et al. (2012) (2009) from its supplementary data. The 5 to 95% confidence intervals and Stott and Jones (2012), but including a broader range of model are calculated from these numeric PDFs. and observational uncertainties, in particular addressing the efficacy of 10SM non-CO2 gases, and find a TCR range of 0.9°C to 2.3°C. This confidence Dashed Probability Density Functions without Legend Entries range is directly taken from Figure 7a of the cited paper. (AR4 Studies) The TCR PDFs for the GFDL, the HadCM3, and the PCM model as pro- Tung et al. (2008) examined the response to the 11-year solar cycle duced by Stott et al. (2006) and the TCR PDF from Frame et al. (2006) using discriminant analysis, and found a high range for TCR: >2.5°C to are reproduced in Figure 10.19 as shown in Figure 1b of Meinshausen 3.6°C. These numbers are directly taken from Equation 7 of the cited et al. (2009) from its supplementary data. paper. However, this estimate may be affected by different mechanisms by which solar forcing affects climate and possible aliasing with the Figure 10.20b response to other forcing in the 20th century and with internal climate variability, despite attempts to minimize these effects see discussion References for labelled plots: 20th Century: violet: Aldrin et al. in North and Stevens (1998). (2012), solid: uniform prior in ECS, dashed: uniform prior in 1/ECS, and dash-dotted is an update using data to 2010 (see below); gold: Bender Bars and Probability Density Functions et al. (2010); light red: Lewis (2013), dashed: using Forest et al. diagnos- Otto et al. (2013) update the analysis of Gregory et al. (2002) and tic and an objective Bayesian prior, solid using revised diagnostic; cyan: Gregory and Forster (2008) using forcing estimates from Forster et al. Lin et al. (2010); brown: Lindzen and Choi (2011); olive: Murphy et al., (2013) to obtain a 5 to 95% range for TCR of 0.9 to 2.0°C comparing (2009); dark red: Olson et al. (2012); indigo: Otto et al. (2013), solid the decade 2000 2009 with the period 1860 1879. They note, howev- is an estimate using change to 1979 2009, dashed on the change to er, the danger of overinterpreting a single, possibly anomalous, decade, 2000 2009; lime: Schwartz (2012); blue: Tomassini et al. (2007) using and report a larger TCR range of 0.7°C to 2.5°C replacing the 2000s a prior uniform in ECS (solid) and a density ratio prior based on expert with the 40 years 1970 2009. These PDFs are directly taken from Otto elicitations (dashed). Repeated from AR4: green: Frame et al. (2005); et al. (2013), renormalized to a (0.1 to 10) °C support. result using uniform prior in ECS); orange: Gregory et al. (2002); purple: Knutti et al. (2002); Fuchsia: Forster and Gregory (2006) (solid: uniform Rogelj et al. (2012): This PDF is a TCR distribution implied by a 600- prior in feedbacks; dashed transformed to uniform prior in ECS as used member parameter set ensemble drawn from an 82-dimensional in AR4). Palaeoclimate: brown: (Chylek and Lohmann, 2008); orange: parameter space in a way such that the posterior climate sensitivity Hargreaves et al. (2012), solid, dashed showing an update based on distribution matches closely the distribution presented by Rogelj et al. PMIP3 simulations; turquoise: Holden et al. (2010); light red: Koehler et (2012). The methodology for drawing the 600-member parameter sets al. (2010); green: Paleosens Members (2012); purple: Schmittner et al. is described in Meinshausen et al. (2009). (2011), solid is land-and-ocean, dashed land-only, and dash-dotted is ocean-only diagnostic. Repeated from AR4: blue: Schneider von Deim- The PDF for the TCR predicted by the Bayesian methodology of Harris ling et al. (2006). lime: Annan et al. (2005); Combination of evidence: et al. (2013). The distribution is based upon a large sample of emulated violet: Aldrin et al. (2012); turquoise: Libardoni and Forest (2013) with General Circulation Model (GCM) equilibrium responses, constrained dashed being the average value, and solid an update using data to by multiannual mean observations of recent climate and adjusted to 2004; dark red: Olson et al. (2012) and repeated from AR4: lime: Annan account for additional uncertainty associated with model structural and Hargreaves (2006); blue: Hegerl et al. (2006). 10SM-12 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material Processing details: All PDFs were scaled to integrate to 1.0 between 0 samples were drawn from each, merged together, and the resulting and 10.0; information only where further processing is used. histogram used to obtain an estimate of the average posterior; which was then smoothed and plotted. Instrumental Aldrin et al. (2012) Solid: Main result from the paper, that is, with data Additional Information to Version of Figure in Chapter 12 up to 2007 and with radiative forcing (RF)-prior consistent with the IPCC AR4; result from their Figure 6a; dashed Figure 6f; and in bottom Climatological Constraints panel Figure 6k. The dash-dotted is as the first, but updated including Red: Sexton et al. (2012) purple: Knutti et al. (2006) no processing 2010 and with updated RF prior based on Skeie et al. (2011). was required. Gold: Piani et al. (2005). Lewis (2013) two sets of data are used, based on their Figure 3, a Raw Model Range: The bars show the results from five perturbed and b. physics ensembles. Each ensemble provided its histogram, computed Murphy (2009) the 5th, 50th and 95th percentile are shown, based using 0.5° bins. For ease of viewing, the individual bar widths have on published range for feedbacks in paper. been shrunk by 7 (i.e., each bar appears as 0.071° of ECS in width, with a 0.14° gap between bins). The bar height has not been rescaled. Olson et al. (2012) use a uniform and an informed prior. Here we plot The individual dots below the curve represent data from models in the result of using a uniform prior, the informed prior is shown in the the CMIP3 and CMIP5 database. Not all models had completed the combination panel. necessary simulations, so this is a subset of the full models available Otto et al. (2013) Two sets of data are used: in solid is the 1979 based on Table 9.5. 2009 average, and in dashed is the 2000 2009 average. Distributions are shown with percentiles coinciding with corresponding confidence The CMIP5 models shown are: intervals from the likelihood profile reported in the paper. ACCESS1-0, BCC-CSM1-1, BCC-CSM1-1-M, BNU-ESM, CanESM2, CCSM4,CNRM-CM5, CSIRO-Mk3-6-0, GFDL-CM3, GFDL-ESM2G, Schwartz (2012) sampling range from their paper. 10SM GFDL-ESM2M,GISS-E2-H, GISS-E2-R, HadGEM2-ES, INM-CM4, IPSL- Frame et al. (2005) as in AR4. CM5A-LR, IPSL-CM5B-LR,MIROC5, MIROC-ESM, MPI-ESM-LR, MPI- ESM-P, MRI-CGCM3, NorESM1-M. Forster and Gregory (2006) two sets of data are shown: in solid is data produced using a prior that is uniform in feedback parameter Data for the AR4 AOGCMs was provided by Chapter 9, Table 9.5. space, whilst in dashed is a prior that is uniform in ECS space. Data for the dashed curve was based on AR4; for the solid curve based on the Figure 10.21 feedback range given in the paper. Knutti et al. (2002) data was provided as cumulative distribution fre- This material documents the provenance of the data and plotting pro- quency, so binned to get probability distribution, applied a two-stage cedures that were used to create Figure 10.21 in the IPCC WG1 Fifth boxcar average (three-box window followed by two-box window), and Assessment Report. rescaled to ensure integral of PDF equaled 1.0 Continental Temperatures Palaeoclimate Chylek and Lohmann (2008) (note range given is a 95% range). Models and ensemble members used are listed in Table 10.SM.5. Hargreaves et al. (2012) (solid: published estimate, dashed: updated). Data Holden et al. (2010) sampling range from their paper. All of the data used were provided as monthly Netcdf files, from the CMIP3 and CMIP5 archives, and Daithi Stone (providing data used in Schneider von Deimling et al. (2006) sampling range from their paper. the AR4 figures that were not in the CMIP3 archive). CMIP3 20C3M experiments were extended to 2012 by using A1B scenario simula- Combination tions. CMIP5 historical experiments were extended to 2012 by using Aldrin et al. (2012) result from their panel Figure 6k. historicalExt and rcp45 experiments. Libardoni and Forest (2013) (solid: published; dashed update using data to 2004). Observational Data The observed surface temperature data is from HadCRUT4 (Morice et Olson et al. (2012) this is the main result of their paper, using an al., 2012). informed prior in ECS. Regridding The average distribution given for Libardoni and Forest (2013) are cal- All data are re-gridded onto the HadCRUT4 spatial grid (5° × 5°) since culated from an average of the PDFs based on different observational HadCRUT4 generally has the most restricted spatial coverage of the data sets; namely HadCRUT3, NCDC and GISTEMP250. The average datasets considered here. There is no infilling into grid boxes with no distributions were derived by drawing Latin Hypercube samples from observations. The re-gridding is done by area averaging any part of the the posteriors derived using the different data sets. Three 1000-member old grid that lies within the new grid to produce a new gridpoint value. 10SM-13 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional Table 10.SM.5 | Models and ensemble members used for continental temperatures. 20C2M and A1B are the names from CMIP3 for the quasi-equivalent experiments histori- cal and rcp45 in CMIP5. historical historicalExt rcp45 (A1B) Overall period historicalNat Overall period (20C3M) Start Start End Model Realisation Realisation Realisation End Year CMIP3/5 Realisation CMIP3/5 Year Year Year GFDL-CM2.0 r1 r1 1861 2012 3 GFDL-CM2.0 r2 1861 2000 3 GFDL-CM2.0 r3 1861 2000 3 GFDL-CM2.1 r1 r3a 1861 2012 3 GFDL-CM2.1 r2 r1 1861 2012 3 GFDL-CM2.1 r3 r2a 1861 2012 3 GFDL-CM2.1 r4 1861 2000 3 GFDL-CM2.1 r5 1861 2000 3 GISS-E-H r1 r1 1880 2012 3 GISS-E-H r2 r2 1880 2012 3 GISS-E-H r3 r3 1880 2012 3 GISS-E-H r4 1880 1999 3 GISS-E-H r5 1880 1999 3 GISS-E-R r1 1880 2003 3 GISS-E-R r2 1880 2003 3 10SM GISS-E-R r3 r1 1880 2012 3 GISS-E-R r4 1880 2003 3 GISS-E-R r5 1880 2003 3 GISS-E-R r6 r2 1880 2012 3 GISS-E-R r7 r3 1880 2012 3 GISS-E-R r8 r4 1880 2012 3 GISS-E-R r9 r5 1880 2012 3 INM-CM3.0 r1 r1 1871 2012 3 MIROC3.2(hires) r1 r1 1900 2012 3 MIROC3.2(medres) r1 r1 1850 2012 3 r1 1850 2000 3 MIROC3.2(medres) r2 r2 1850 2012 3 r2 1850 2000 3 MIROC3.2(medres) r3 r3 1850 2012 3 r3 1850 2000 3 MIROC3.2(medres) r4 a 1850 2010 3 r4 1850 2000 3 MIROC3.2(medres) r5a 1850 2010 3 r5 1850 2000 3 MIROC3.2(medres) r6a 1850 2010 3 r6 1850 2000 3 MIROC3.2(medres) r7 a 1850 2010 3 r7 1850 2000 3 MIROC3.2(medres) r8a 1850 2010 3 r8 1850 2000 3 MIROC3.2(medres) r9a 1850 2010 3 r9 1850 2000 3 MIROC3.2(medres) r10 a 1850 2010 3 r10 1850 2000 3 MIUB-ECHO-G r1 r1 1860 2012 3 r1 1860 2000 3 MIUB-ECHO-G r2 r2 1860 2012 3 r2 1860 2000 3 MIUB-ECHO-G r3 r3 1860 2012 3 r3 1860 2000 3 MIUB-ECHO-G r4 1860 2000 3 MRI-CGCM2.3.2 r1 1851 2000 3 r1 1850 1999 3 MRI-CGCM2.3.2 r2 1851 2000 3 r2 1850 1999 3 MRI-CGCM2.3.2 r3 1851 2000 3 r3 1850 1999 3 MRI-CGCM2.3.2 r4 1851 2000 3 r4 1850 2000 3 MRI-CGCM2.3.2 r5 1851 2000 3 CCSM3 r1 r1 1870 2012 3 r1 1870 1999 3 CCSM3 r2 r2 1870 2012 3 r2 1870 1999 3 (continued on next page) 10SM-14 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material Table 10.SM.5 (continued) historical historicalExt rcp45 (A1B) Overall period historicalNat Overall period (20C3M) Start Start End Model Realisation Realisation Realisation End Year CMIP3/5 Realisation CMIP3/5 Year Year Year CCSM3 r3 r3 1870 2012 3 r3 1870 1999 3 CCSM3 r4 1870 1999 3 r4 1870 1999 3 CCSM3 r5 r5 1870 2012 3 r5 1870 1999 3 CCSM3 r6 r6 1870 2012 3 CCSM3 r7 r7 1870 2012 3 CCSM3 r8 r8 1870 2011 3 CCSM3 r9 r9 1870 2012 3 PCM r1 1890 1999 3 r1 1890 1999 3 PCM r2 1890 1999 3 r2 1890 1999 3 PCM r3 1890 1999 3 r3 1890 1999 3 PCM r4 1890 1999 3 r4 1890 1999 3 UKMO_HadCM3 r1a 1860 2006 3 r1 1860 1998 3 UKMO_HadCM3 r2 1860 1998 3 UKMO_HadCM3 r3a 1860 2002 3 r3 1860 1998 3 UKMO_HadCM3 r4a 1860 2002 3 r4 1860 1998 3 UKMO_HadGEM1 r1a 1860 2009 3 UKMO_HadGEM1 r2a 1860 2009 3 10SM UKMO_HadGEM1 r3a 1860 2009 3 UKMO_HadGEM1 r4a 1860 2009 3 ACCESS1.0 r1i1p1 r1i1p1 1850 2012 5 ACCESS1.3 r1i1p1 r1i1p1 1850 2012 5 ACCESS1.3 r2i1p1 1850 2005 5 ACCESS1.3 r3i1p1 1850 2005 5 BNU-ESM r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2005 5 CCSM4 r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2005 5 CCSM4 r2i1p1 r2i1p1 1850 2012 5 r2i1p1 1850 2005 5 CCSM4 r3i1p1 r3i1p1 1850 2012 5 CCSM4 r4i1p1 r4i1p1 1850 2012 5 r4i1p1 1850 2005 5 CCSM4 r5i1p1 r5i1p1 1850 2012 5 CCSM4 r6i1p1 r6i1p1 1850 2012 5 r6i1p1 1850 2005 5 CESM1(BGC) r1i1p1 r1i1p1 1850 2012 5 CESM1(CAM5) r1i1p1 r1i1p1 1850 2012 5 CESM1(CAM5) r2i1p1 r2i1p1 1850 2012 5 CESM1(CAM5) r3i1p1 r3i1p1 1850 2012 5 CESM1(FASTCHEM) r1i1p1 1850 2005 5 CESM1(FASTCHEM) r2i1p1 1850 2005 5 CESM1(FASTCHEM) r3i1p1 1850 2005 5 CESM1(WACCM) r1i1p1 1850 2005 5 CMCC-CESM r1i1p1 1850 2005 5 CMCC-CMS r1i1p1 r1i1p1 1850 2012 5 CMCC-CM r1i1p1 r1i1p1 1850 2012 5 CNRM-CM5 r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2012 5 CNRM-CM5 r2i1p1 r2i1p1 1850 2012 5 r2i1p1 1850 2012 5 CNRM-CM5 r3i1p1 r3i1p1 1850 2012 5 r3i1p1 1850 2012 5 CNRM-CM5 r4i1p1 r4i1p1 1850 2012 5 r4i1p1 1850 2012 5 CNRM-CM5 r5i1p1 r5i1p1 1850 2012 5 r5i1p1 1850 2012 5 (continued on next page) 10SM-15 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional Table 10.SM.5 (continued) historical historicalExt rcp45 (A1B) Overall period historicalNat Overall period (20C3M) Start Start End Model Realisation Realisation Realisation End Year CMIP3/5 Realisation CMIP3/5 Year Year Year CNRM-CM5 r6i1p1 r6i1p1 1850 2012 5 CNRM-CM5 r7i1p1 r7i1p1 1850 2012 5 CNRM-CM5 r8i1p1 r8i1p1 1850 2012 5 r8i1p1 1850 2012 5 CNRM-CM5 r9i1p1 r9i1p1 1850 2012 5 CNRM-CM5 r10i1p1 r10i1p1 1850 2012 5 CSIRO-Mk3.6.0 r1i1p1 r1i1p1 1850 2012 2012 r1i1p1 1850 2012 5 CSIRO-Mk3.6.0 r2i1p1 r2i1p1 1850 2012 5 r2i1p1 1850 2012 5 CSIRO-Mk3.6.0 r3i1p1 r3i1p1 1850 2012 5 r3i1p1 1850 2012 5 CSIRO-Mk3.6.0 r4i1p1 r4i1p1 1850 2012 5 r4i1p1 1850 2012 5 CSIRO-Mk3.6.0 r5i1p1 r5i1p1 1850 2012 5 r5i1p1 1850 2012 5 CSIRO-Mk3.6.0 r6i1p1 r6i1p1 1850 2012 5 CSIRO-Mk3.6.0 r7i1p1 r7i1p1 1850 2012 5 CSIRO-Mk3.6.0 r8i1p1 r8i1p1 1850 2012 5 CSIRO-Mk3.6.0 r9i1p1 r9i1p1 1850 2012 5 CSIRO-Mk3.6.0 r10i1p1 r10i1p1 1850 2012 5 CanESM2 r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2012 5 10SM CanESM2 r2i1p1 r2i1p1 1850 2012 5 r2i1p1 1850 2012 5 CanESM2 r3i1p1 r3i1p1 1850 2012 5 r3i1p1 1850 2012 5 CanESM2 r4i1p1 r4i1p1 1850 2012 5 r4i1p1 1850 2012 5 CanESM2 r5i1p1 r5i1p1 1850 2012 5 r5i1p1 1850 2012 5 EC-EARTH r1i1p1 r1i1p1 1850 2012 5 EC-EARTH r2i1p1 r2i1p1 1850 2012 5 EC-EARTH r6i1p1 r6i1p1 1850 2012 5 EC-EARTH r8i1p1 r8i1p1 1850 2012 5 EC-EARTH r9i1p1 r9i1p1 1850 2012 5 EC-EARTH r11i1p1 1850 2012 5 EC-EARTH r12i1p1 r12i1p1 1850 2012 5 FIO-ESM r1i1p1 r1i1p1 1850 2012 5 FIO-ESM r2i1p1 r2i1p1 1850 2012 5 FIO-ESM r3i1p1 r3i1p1 1850 2012 5 GFDL-CM2p1 r1i1p1 1861 2012 5 GFDL-CM2p1 r2i1p1 1861 2012 5 GFDL-CM2p1 r3i1p1 1861 2012 5 GFDL-CM2p1 r4i1p1 1861 2012 5 GFDL-CM2p1 r5i1p1 1861 2012 5 GFDL-CM2p1 r6i1p1 1861 2012 5 GFDL-CM2p1 r7i1p1 1861 2012 5 GFDL-CM2p1 r8i1p1 1861 2012 5 GFDL-CM2p1 r9i1p1 1861 2012 5 GFDL-CM2p1 r10i1p1 1861 2012 5 GFDL-CM3 r1i1p1 r1i1p1 1860 2012 5 r1i1p1 1860 2005 5 GFDL-CM3 r2i1p1 1860 2005 5 GFDL-CM3 r3i1p1 1860 2005 5 r3i1p1 1860 2005 5 GFDL-CM3 r4i1p1 1860 2005 5 GFDL-CM3 r5i1p1 1860 2005 5 r5i1p1 1860 2005 5 GFDL-ESM2G r1i1p1 r1i1p1 1861 2012 5 GFDL-ESM2M r1i1p1 r1i1p1 1861 2012 5 r1i1p1 1861 2005 5 (continued on next page) 10SM-16 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material Table 10.SM.5 (continued) historical historicalExt rcp45 (A1B) Overall period historicalNat Overall period (20C3M) Start Start End Model Realisation Realisation Realisation End Year CMIP3/5 Realisation CMIP3/5 Year Year Year GISS-E2-H-CC r1i1p1 r1i1p1 1850 2012 5 GISS-E2-H r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2012 5 GISS-E2-H r2i1p1 r2i1p1 1850 2012 5 r2i1p1 1850 2012 5 GISS-E2-H r3i1p1 r3i1p1 1850 2012 5 r3i1p1 1850 2012 5 GISS-E2-H r4i1p1 r4i1p1 1850 2012 5 r4i1p1 1850 2012 5 GISS-E2-H r5i1p1 r5i1p1 1850 2012 5 r5i1p1 1850 2012 5 GISS-E2-H r6i1p1 1850 2012 5 GISS-E2-R-CC r1i1p1 r1i1p1 1850 2012 5 GISS-E2-R r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2012 5 GISS-E2-R r2i1p1 r2i1p1 1850 2012 5 r2i1p1 1850 2012 5 GISS-E2-R r3i1p1 r3i1p1 1850 2012 5 r3i1p1 1850 2012 5 GISS-E2-R r4i1p1 r4i1p1 1850 2012 5 r4i1p1 1850 2012 5 GISS-E2-R r5i1p1 r5i1p1 1850 2012 5 r5i1p1 1850 2012 5 GISS-E2-R r6i1p1 r6i1p1 1850 2012 5 HadCM3 r1i1p1 r1i1p1 1860 2012 5 HadCM3 r2i1p1 r2i1p1 1860 2012 5 HadCM3 r3i1p1 r3i1p1 1860 2012 5 10SM HadCM3 r4i1p1 r4i1p1 1860 2012 5 HadCM3 r5i1p1 r5i1p1 1860 2012 5 HadCM3 r6i1p1 r6i1p1 1860 2012 5 HadCM3 r7i1p1 r7i1p1 1860 2012 5 HadCM3 r8i1p1 r8i1p1 1860 2012 5 HadCM3 r9i1p1 r9i1p1 1860 2012 5 HadCM3 r10i1p1 r10i1p1 1860 2012 5 HadGEM2-AO r1i1p1 r1i1p1 1860 2012 5 HadGEM2-CC r1i1p1 r1i1p1 1860 2012 5 HadGEM2-ES r1i1p1 r1i1p1 1860 2012 5 r1i1p1 1860 2012 5 HadGEM2-ES r2i1p1 r2i1p1 1860 2012 5 r2i1p1 1860 2012 5 HadGEM2-ES r3i1p1 r3i1p1 1860 2012 5 r3i1p1 1860 2012 5 HadGEM2-ES r4i1p1 r4i1p1 1860 2012 5 r4i1p1 1860 2012 5 HadGEM2-ES r5i1p1 1860 2005 5 IPSL-CM5A-LR r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2012 5 IPSL-CM5A-LR r2i1p1 r2i1p1 1850 2012 5 r2i1p1 1850 2012 5 IPSL-CM5A-LR r3i1p1 r3i1p1 1850 2012 5 r3i1p1 1850 2012 5 IPSL-CM5A-LR r4i1p1 r4i1p1 1850 2012 5 IPSL-CM5A-LR r5i1p1 1850 2005 5 IPSL-CM5A-LR r6i1p1 1850 2005 5 IPSL-CM5A-MR r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2012 5 IPSL-CM5A-MR r2i1p1 1850 2005 5 r2i1p1 1850 2012 5 IPSL-CM5A-MR r3i1p1 1850 2005 5 r3i1p1 1850 2012 5 IPSL-CM5B-LR r1i1p1 r1i1p1 1850 2012 5 MIROC-ESM-CHEM r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2005 5 MIROC-ESM r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2005 5 MIROC-ESM r2i1p1 1850 2005 5 r2i1p1 1850 2005 5 MIROC-ESM r3i1p1 1850 2005 5 r3i1p1 1850 2005 5 MIROC5 r1i1p1 r1i1p1 1850 2012 5 MIROC5 r2i1p1 r2i1p1 1850 2012 5 (continued on next page) 10SM-17 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional Table 10.SM.5 (continued) historical historicalExt rcp45 (A1B) Overall period historicalNat Overall period (20C3M) Start Start End Model Realisation Realisation Realisation End Year CMIP3/5 Realisation CMIP3/5 Year Year Year MIROC5 r3i1p1 r3i1p1 1850 2012 5 MIROC5 r4i1p1 1850 2012 5 MIROC5 r5i1p1 1850 2012 5 MPI-ESM-LR r1i1p1 r1i1p1 1850 2012 5 MPI-ESM-LR r2i1p1 r2i1p1 1850 2012 5 MPI-ESM-LR r3i1p1 r3i1p1 1850 2012 5 MPI-ESM-MR r1i1p1 r1i1p1 1850 2012 5 MPI-ESM-MR r2i1p1 r2i1p1 1850 2012 5 MPI-ESM-MR r3i1p1 r3i1p1 1850 2012 5 MPI-ESM-P r1i1p1 1850 2005 5 MPI-ESM-P r2i1p1 1850 2005 5 MRI-CGCM3 r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2005 5 MRI-CGCM3 r2i1p1 r2i1p1 1850 2012 5 MRI-CGCM3 r3i1p1 r3i1p1 1850 2012 5 MRI-ESM1 r1i1p1 1851 2005 5 NorESM1-ME r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2012 5 10SM NorESM1-M r1i1p1 r1i1p1 1850 2012 5 NorESM1-M r2i1p1 r2i1p1 1850 2012 5 NorESM1-M r3i1p1 r3i1p1 1850 2012 5 BCC-CSM1.1(m) r1i1p1 r1i1p1 1850 2012 5 BCC-CSM1.1(m) r2i1p1 1850 2012 5 BCC-CSM1.1(m) r3i1p1 1850 2012 5 BCC-CSM1.1 r1i1p1 r1i1p1 1850 2012 5 r1i1p1 1850 2012 5 BCC-CSM1.1 r2i1p1 1850 2012 5 BCC-CSM1.1 r3i1p1 1850 2012 5 INM-CM4 r1i1p1 r1i1p1 1850 2012 5 Notes: a Simulation not in CMIP3 archive. Obtained from model institution or Daithi Stone (as used in figures in IPCC WG1 2007) Masking Global Means The data coverage is limited to where data exists in the equivalent Global and regional mean anomalies are calculated by area averaging month/gridpoint of HadCRUT4. Note that this shortens some model all available gridpoint data for each year. time series (e.g., Antarctica). Regions Multi-Model Mean Continental land areas are based on the SREX defined regions (IPCC, All ensemble members of a specific simulation of a specific model are 2012) shown pictorially in the bottom right most panel of Figure 10.7. averaged into an ensemble mean for a specific simulation and model before the models are averaged into a multi-model mean (details in Precipitation supplementary material to Jones et al., 2013). Therewith, models with more ensemble members are not weighted disproportional to models Models and ensemble members used are listed in Table 10.SM.6. with less ensemble members. Data and Region Creation of Annual Means 50°N 90°N average changes in annual mean precipitation (in mm Anomalies are calculated for each month/gridpoint relative to the day 1) for the period 1951 2005, with regard to the baseline period of 1880 1919 average (except Antarctica where anomalies are relative 1961 1990, are plotted based on Balan Sarojini et al. (2012). to 1950 2010), where at least 50% of the data in the reference period are needed to calculate the average. Annual means are calculated from Observational Data monthly data for each calendar year, where at least 2 months are non- The first observational dataset used (black solid line) is a gridded ob- missing. Shadings are the 5 and 95 percentile among the models. servational dataset based on station data extracted from the Global 10SM-18 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material Table 10.SM.6 | Models and ensemble members used for precipitation. historicalNat Overall period historical Overall period Model Realisation Start Year End Year Realisation Start Year End Year CMIP3/5 ACCESS1.0 r1i1p1 1951 2005 5 BCC-CSM1.1 r1i1p1 1951 2005 r1i1p1 1951 2005 5 CanESM2 r1i1p1 1951 2005 r1i1p1 1951 2005 5 CCSM4 r1i1p1 1951 2005 5 CESM1(BGC) r1i1p1 1951 2005 5 CESM1(CAM5) r1i1p1 1951 2005 5 CESM1 r1i1p1 1951 2005 5 (FASTCHEM) CESM1(WACCM) r1i1p1 1951 2005 5 CMCC-CESM r1i1p1 1951 2005 5 CMCC-CMS r1i1p1 1951 2005 5 CNRM-CM5 r1i1p1 1951 2005 r1i1p1 1951 2005 5 CSIRO-Mk3.6.0 r1i1p1 1951 2005 r1i1p1 1951 2005 5 GFDL-CM3 r1i1p1 1951 2005 r1i1p1 1951 2005 5 GFDL-ESM2G r1i1p1 1951 2005 5 GFDL-ESM2M r1i1p1 1951 2005 5 10SM GISS-E2-H r1i1p1 1951 2005 5 GISS-E2-R r1i1p1 1951 2005 5 HadGEM2-CC r1i1p1 1951 2005 5 HadGEM2-ES r1i1p1 1850 2005 r1i1p1 1850 2005 5 INMCM4_ESM r1i1p1 1850 2005 5 IPSL-CM5A-LR r1i1p1 1850 2005 5 IPSL-CM5A-MR r1i1p1 1951 2005 5 MIROC5 r1i1p1 1951 2005 5 MIROC-ESM r1i1p1 1951 2005 r1i1p1 1951 2005 5 MIROC-ESM- r1i1p1 1951 2005 r1i1p1 1951 2005 5 CHEM MPI-ESM-LR r1i1p1 1951 2005 5 MPI-ESM-MR r1i1p1 1951 2005 5 MRI-CGCM3 r1i1p1 1951 2005 r1i1p1 1951 2005 5 NorESM1-M r1i1p1 1951 2005 r1i1p1 1951 2005 5 NorESM1-ME r1i1p1 1951 2005 5 Historical Climatology Network (updated from Zhang et al., 2007). 1951 2005, quality controlled and gridded at 0.5° × 0.5°, are used. Monthly data for the period 1951 2005, quality controlled and gridded This data is first interpolated to the common spatial resolution of 5° × at 5° × 5°, for all land grid squares on the globe for which station data 5°. In order to avoid artefacts arising from changes in data coverage, are available, are used. In order to avoid artefacts arising from changes two sampling criteria are applied: 1) station sampling criterion (Polson in data coverage, a sampling criterion of choosing data available for et al., 2013) of choosing only those 5° × 5° grid boxes that have at >90% of the analysis period is applied (i.e., each spatial grid point is least 1 station (in any 0.5° × 0.5° grid box) for the coastal grid boxes chosen when data over 90% of the years (only those years which have and with at least 2 stations for the inland grid boxes. A 5° × 5° grid data for all months) are present). box is coastal when over half of number of the 0.5° × 0.5° boxes is ocean points. 2) a criterion of choosing data available for >95% of the The second observational dataset (grey solid line) used is a grid- analysis period is applied. i.e., each spatial grid point is chosen when ded observational dataset based on station data extracted from the data over 95% of the years (years which have data available for any C ­ limatic Research Unit, (updated from CRU TS3.1 of Harris et al., 2013) no of months) are present. and sampled as in Polson et al. (2013). Monthly data for the period 10SM-19 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional Masking of Simulated Data onto the Observational Grid Data Treatment First, the land area of the simulated data available in different spatial Before computing the ocean heat content the model output has been resolutions is obtained by choosing a grid point as land when its land- treated as in Pierce et al. (2012), i.e., horizontal regridding to a 10° x area fraction is greater than or equal to 70%. Second, the simulated 10° latitude/longitude grid between 60°S and 60°N over the top 700 land data are interpolated to the 5° × 5° observational grid using bi- m; masking the grid boxes that lack observations; fields are de-drifted linear interpolation. Third, the 90% sampling criterion derived from the using second order polynomials fit to the pre-industrial control runs observations is applied to each regridded model data to obtain the ( piControl ). consistent temporal and spatial data coverage for the simulated and observed data. Annual mean OHC values are calculated from models by vertically inte- grating the annual mean temperature anomalies (with respect to a Calculation of Spatial and Annual Averages and Anomalies with 1960 1980 reference period). Global mean time series are calculated regard to the Baseline Climatology by integrating over space. For each (regridded and sampled) monthly model data, spatial aver- ages are first calculated for the zonal band of 50°N 90°N. Annual av- All OHC time series are relative to the reference period of 1960 1980. erages, baseline climatology (for 1961 1990) and anomalies from the Only Domingues et al. (2008) OHC data are smoothed with a three- baseline period are then calculated. year running means. Plotting Regions The yearly anomalies are plotted with a y-axis range of 1950 2010. Ocean basin definition (Latitudes) are: Multi-model means are in thick solid lines (historical in red and his- Southern Ocean: south of 50°S toricalNat in blue). South Pacific: 50°S to Equator South Atlantic : 50°S to Equator; up to 20°E; The 5-95% confidence interval of the models is in pink shading for Indian Ocean: 50°S to 30°N; 20°E to Australia (Tasmania) 10SM historical runs and in blue shading for historicalNat runs. North Pacific, North Atlantic: Equator to 70°N Ocean Heat Content Sea Ice Models and ensemble members used are listed in Table 10.SM.7. September sea ice extent (concentration >15%) anomalies for the Northern Hemisphere (Arctic) and Southern Hemisphere (Antarctic), Observational Data relative to 1979 1999. Models and ensemble members used for the Three observational data sets are updated from Domingues et al. final figure are listed in Table 10.SM.8. Observational data is from (2008), Levitus et al. (2012) and sourced from http://www.nodc.noaa. NSIDC bootstrap algorithm (SBA; Cavalieri and Parkinson, 2012; Par- gov/OC5/3M_HEAT_CONTENT/index.html and Ishii and Kimoto (2009) kinson and Cavalieri, 2012). and sourced from http://www.data.kishou.go.jp/kaiyou/english/ohc/ ohc_data_en.html (version August 2012). The historical simulations are extended with rcp85 to the year 2012. For both the historicalNat and historical extended with rcp85 the multi- model mean and 5 95% confidence interval for each year are calcu- lated from all models available for that year. Table 10.SM.7 | Models and ensemble members used for ocean heat content. historicalNat Overall period historical Overall period Model Realisation Start Year End Year Realisation Start Year End Year CMIP3/5 CanESM2 r1i1p1 1950 2012 r1i1p1 1950 2005 5 CCSM4 r1i1p1 1950 2005 r1i1p1 1950 2005 5 CNRM-CM5 r1i1p1 1950 2012 r1i1p1 1950 2005 5 CSIRO-MK3.6.0 r1i1p1 1950 2012 r1i1p1 1950 2005 5 GISS-E2-H r1i1p1 1950 2012 r1i1p1 1950 2005 5 GISS-E2-R r1i1p1 1950 2012 r1i1p1 1950 2005 5 HADGEM2-ES r1i1p1 1950 2012 r1i1p1 1950 2003 5 MIROC5 r1i1p1 1950 2005 5 MIROC-ESM r1i1p1 1950 2005 r1i1p1 1950 2005 5 MPI-ESM-LR r1i1p1 1950 2005 5 MRI-CGCM3 r1i1p1 1950 2005 r1i1p1 1950 2005 5 NorESM1-M r1i1p1 1950 2005 r1i1p1 1950 2005 5 10SM-20 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material The simulations have been plotted as anomalies from the mean for the of areas being examined, is above 50% coverage and dashed green reference period (1979 1999) with 5-95% confidence interval of the lines where coverage is below 50%. For example, data coverage of models as shading. The observations are the September sea ice extent Antarctica never goes above 50% of the land area of the continent. anomalies relative to 1979 1999 period mean from the NSIDC sea-ice For ocean heat content and sea-ice panels the solid line is where the data set. coverage of data is good and higher in quality, and the dashed line is where the data coverage is only adequate, based on a qualitative Data Quality expert assessment. See the Table 10.SM.9 for the years of change from adequate to higher quality data. For land and ocean surface temperatures and precipitation panels, solid green lines at bottom of panels indicate where data spatial coverage, ­ Table 10.SM.8 | Models and ensemble members used for sea ice. historicalNat Overall period historical rcp85 Overall period Model Realisation Start Year End Year Realisation Realisation Start Year End Year CMIP3/5 BCC-CSM1.1 r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 BNU-ESM r1i1p1 1950 2005 r1i1p1 r1i1p1 1950 2012 5 CanESM2 r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 CCSM4 r1i1p1 1950 2005 r1i1p1 r1i1p1 1950 2012 5 CNRM-CM5 r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 CSIRO-MK3.6.0 r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 FGOALS-g2 r1i1p1 1950 2009 r1i1p1 r1i1p1 1950 2012 5 10SM GFDL-ESM2M r1i1p1 1950 2005 r1i1p1 r1i1p1 1950 2012 5 GISS-E2-H r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 GISS-E2-R r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 HADGEM2-ES r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 IPSL-CM5A-LR r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 IPSL-CM5A-MR r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 MIROC-ESM r1i1p1 1950 2005 r1i1p1 r1i1p1 1950 2012 5 MIROC-ESM-CHEM r1i1p1 1950 2005 r1i1p1 r1i1p1 1950 2012 5 MRI-CGCM3 r1i1p1 1950 2005 r1i1p1 r1i1p1 1950 2012 5 NorESM1-M r1i1p1 1950 2012 r1i1p1 r1i1p1 1950 2012 5 Table 10.SM.9 | Years of change from adequate to higher quality data, i.e., when dashed lines change to solid lines. Element of climate system Region Year of change from dashed to solid line Continental temperatures Global Land+Ocean 1880 Global Land 1930 Global Ocean 1880 North America 1910 South America 1930 Europe 1860 Africa 1950 Asia 1925 Australia 1910 Antarctica 1944 Ocean Heat Content All basins 1970 Sea Ice Arctic and Antarctica 1979 Precipitation Precipitation 1985 10SM-21 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional FAQ 10.1, Figure 1 This figure is a condensed version of Figures 10.1 and 10.2, so the sup- plemental information for those figures applies to this set of panels too. FAQ 10.2, Figure 1 Data One run each of the historical and RCP8.5 simulations is used from 24 CMIP5 models. The models are ACCESS1.0, CCSM4, CNRM CM5, CSIRO Mk3.6.0, CanESM2, EC EARTH, FGOALS g2, FGOALS s2, GFDL CM3, GFDL ESM2G, GFDL ESM2M, GISS E2 R, HadGEM2 CC, HadGEM2 ES, IPSL CM5A LR, IPSL CM5A MR, MIROC ESM CHEM, MIROC ESM, MIROC5, MPI ESM LR, MRI CGCM3, NorESM1 M, bcc csm1.1, inmcm4. Method The primary test on summer surface temperature is applied using 30- year moving windows at 10-year steps, starting with 1900 1929 as a baseline and ending in 2070 2099 for the RCP8.5 model runs. This procedure is applied to each model and grid cell. The local warming is considered statistically significant when a Kolmogorov Smirnov test rejects with 95% significance that the samples of the two 30-year win- dows are drawn from the same distribution. The last year of the mov- 10SM ing window is taken as the year of emergence in one model. Changes are considered significant in the year when the signal is detected in 80% of the models. This procedure is done for each grid point. The year is then used to estimate the corresponding global temperature change based on the historical and RCP8.5 simulation in each model. 10SM-22 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material References Aldrin, M., M. Holden, P. Guttorp, R. B. Skeie, G. Myhre, and T. K. Berntsen, 2012: Goosse, H., et al., 2012b: The role of forcing and internal dynamics in explaining the Bayesian estimation of climate sensitivity based on a simple climate model Medieval Climate Anomaly . Clim. Dyn., 39, 2847 2866. fitted to observations of hemispheric temperatures and global ocean heat Gregory, J. M., 2000: Vertical heat transports in the ocean and their effect an time- content. Environmetrics, 23, 253 271. dependent climate change. Clim. Dyn., 16, 501 515. Annan, J. D., and J. C. Hargreaves, 2006: Using multiple observationally-based Gregory, J. M., and P. M. Forster, 2008: Transient climate response estimated from constraints to estimate climate sensitivity. Geophys. Res. Lett., 33, L06704. radiative forcing and observed temperature change. J. Geophys. Res. Atmos., Annan, J. D., J. C. Hargreaves, R. Ohgaito, A. Abe-Ouchi, and S. Emori, 2005: Efficiently 113, D23105. constraining climate sensitivity with ensembles of paleoclimate simulations. Sci. Gregory, J. M., R. J. Stouffer, S. C. B. Raper, P. A. Stott, and N. A. Rayner, 2002: An Online Lett. Atmos. (SOLA), 1, 181 184. observationally based estimate of the climate sensitivity. J. Clim., 15, 3117 Balan Sarojini, B., P. Stott, E. Black, and D. Polson, 2012 Fingerprints of changes in 3121. annual and seasonal precipitation from CMIP5 models over land and ocean. Haimberger, L., C. Tavolato, and S. Sperka, 2012: Homogenization of the global Geophys. Res. Lett., 39, L23706. radiosonde temperature dataset through combined comparison with reanalysis Bender, F. A. M., A. M. L. Ekman, and H. Rodhe, 2010: Response to the eruption of background series and neighboring stations. J. Clim., 25, 8108 8131. Mount Pinatubo in relation to climate sensitivity in the CMIP3 models. Clim. Hargreaves, J. C., J. D. Annan, M. Yoshimori, and A. Abe-Ouchi, 2012: Can the Last Dyn., 35, 875 886. Glacial Maximum constrain climate sensitivity? Geophys. Res. Lett., 39, L24702. Brohan, P., J. J. Kennedy, I. Harris, S. F. B. Tett, and P. D. Jones, 2006: Uncertainty Harris, G. R., D. M. H. Sexton, B. B. B. Booth, M. Collins, and J. M. Murphy, 2013: estimates in regional and global observed temperature changes: A new data set Probabilistic projections of transient climate change. Clim. Dyn., doi:10.1007/ from 1850. J. Geophys. Res. Atmos., 111, D12106. s00382-012-1647-y. Cavalieri, D. J., and C. L. Parkinson, 2012: Arctic sea ice variability and trends, 1979 Hegerl, G., J. Luterbacher, F. Gonzalez-Rouco, S. F. B. Tett, T. Crowley, and E. 2010, Cryosphere, 6, 881 889. Xoplaki, 2011: Influence of human and natural forcing on European seasonal Chylek, P., and U. Lohmann, 2008: Aerosol radiative forcing and climate sensitivity temperatures. Nature Geosci., 4, 99 103. deduced from the last glacial maximum to Holocene transition. Geophys. Res. Hegerl, G. C., T. J. Crowley, W. T. Hyde, and D. J. Frame, 2006: Climate sensitivity Lett., 35, L04804. constrained by temperature reconstructions over the past seven centuries. Crowley, T. J., G. Zielinski, B. Vinther, R. Udisti, K. Kreutzs, J. Cole-Dai, and E. Castellano, Nature, 440, 1029 1032. 2008: Volcanism and the Little Ice age. PAGES News, 16, 22 23. Hegerl, G. C., et al., 2010: Good practice guidance paper on detection and Dee, D. P., et al., 2011: The ERA-Interim reanalysis: configuration and performance of attribution related to anthropogenic climate change. In: Meeting Report of the 10SM the data assimilation system. Q. J. R. Meteor. Soc., 137, 553 597. Intergovernmental Panel on Climate Change Expert Meeting on Detection and Domingues, C., J. Church, N. White, P. Gleckler, S. Wijffels, P. Barker, and J. Dunn, 2008: Attribution of Anthropogenic Climate Change [T. F. Stocker, et al. (eds.)]. IPCC Improved estimates of upper-ocean warming and multi-decadal sea-level rise. Working Group I Technical Support Unit, University of Bern, Bern, Switzerland, Nature, 453, 1090 1093. 8 pp. Durack, P., S. Wijffels, and R. Matear, 2012: Ocean salinities reveal strong global Held, I. M., M. Winton, K. Takahashi, T. Delworth, F. R. Zeng, and G. K. Vallis, 2010: water cycle intensification during 1950 to 2000. Science, 336, 455 458. Probing the fast and slow components of global warming by returning abruptly Eyring, V., et al., 2013: Long-term changes in tropospheric and stratospheric ozone to preindustrial forcing. J. Clim., 23, 2418 2427. and associated climate impacts in CMIP5 simulations. J. Geophys. Res. Atmos., Helm, K. P., N. L. Bindoff, and J. A. Church, 2010: Changes in the global hydrological- doi:10.1002/jgrd.50316. cycle inferred from ocean salinity. Geophys. Res. Lett., 37, L18701. Folland, C. K., et al., 2013 High predictive skill of global surface temperature a year Holden, P. B., N. R. Edwards, K. I. C. Oliver, T. M. Lenton, and R. D. Wilkinson, 2010: ahead. Geophys. Res. Lett., 40, 761 767. A probabilistic calibration of climate sensitivity and terrestrial carbon change in Forster, P. M., T. Andrews, P. Good, J. M. Gregory, L. S. Jackson, and M. Zelinka, 2013 GENIE-1. Clim. Dyn., 35, 785 806. Evaluating adjusted forcing and model spread for historical and future scenarios Hu, Y. Y., L. J. Tao, and J. P. Liu, 2013: Poleward expansion of the Hadley circulation in in the CMIP5 generation of climate models. J. Geophys. Res. Atmos., 118, 1139 CMIP5 simulations. Adv. Atmos. Sci., 30, 790 795. 1150. Hurtt, G. C., et al., 2009: Harmonization of global land-use scenarios for the period Forster, P. M. D., and J. M. Gregory, 2006: The climate sensitivity and its components 1500 2100 for IPCC-AR5. Integrat. Land Ecosyst. Atmos. Process. Study (iLEAPS) diagnosed from Earth Radiation Budget data. J. Clim., 19, 39 52. Newslett., 7, 6 8. Frame, D. J., D. A. Stone, P. A. Stott, and M. R. Allen, 2006: Alternatives to stabilization Imbers, J., A. Lopez, C. Huntingford, and M. R. Allen, 2013: Testing the robustness scenarios. Geophys. Res. Lett., 33, L14707. of the anthropogenic climate change detection statements using different Frame, D. J., B. B. B. Booth, J. A. Kettleborough, D. A. Stainforth, J. M. Gregory, M. empirical models. J. Geophys. Res. Atmos., doi:10.1002/jgrd.50296. Collins, and M. R. Allen, 2005: Constraining climate forecasts: The role of prior IPCC, 2012: Managing the Risks of Extreme Events and Disasters to Advance assumptions. Geophys. Res. Lett., 32, L09702. Climate Change Adaptation. A Special Report of Working Groups I and II of the Gao, C., A. Robock, and C. Ammann, 2008: Volcanic forcing of climate over the past Intergovernmental Panel on Climate Change [Field, C.B., et al. (eds.)]. Cambridge 1500 years: An improved ice core-based index for climate models. J. Geophys. University Press, Cambridge, UK, and New York, NY, USA, 582 pp. Res. Atmos., 113, D23111. Ishii, M., and M. Kimoto, 2009: Reevaluation of historical ocean heat content Gillett, N. A., and J. C. Fyfe, 2013: Annular Mode change in the CMIP5 simulations. variations with time-varying XBT and MBT depth bias corrections. J. Oceanogr., Geophys. Res. Lett., 40, 1189 1193. 65, 287 299. Gillett, N. P., V. K. Arora, G. M. Flato, J. F. Scinocca, and K. von Salzen, 2012: Improved Ishii, M., M. Kimoto, K. Sakamoto, and S.-I. Iwasaki, 2006: Steric sea level changes constraints on 21st-century warming derived using 160 years of temperature estimated from historical subsurface temperature and salinity analyses. J. observations. Geophys. Res. Lett., 39, L01704. Oceanogr., 62, 155 170. Gillett, N. P., V. K. Arora, D. Matthews, P. A. Stott, and M. R. Allen, 2013 Constraining Jones, G. S., S. F. B. Tett, and P. A. Stott, 2003: Causes of atmospheric temperature the ratio of global warming to cumulative CO2 emissions using CMIP5 change 1960 2000: A combined attribution analysis. Geophys. Res. Lett., 30, simulations. J. Clim., doi:10.1175/JCLI-D-12-00476.1. 1228. Gleckler, P. J., et al., 2012: Human-induced global ocean warming on multidecadal Jones, G. S., P. A. Stott, and N. Christidis, 2013 Attribution of observed historical timescales. Nature Clim. Change, 2, 524 529. near surface temperature variations to anthropogenic and natural causes using Gong, D., and S. Wang, 1999: Definition of Antarctic oscillation index. Geophys. Res. CMIP5 simulations. J. Geophys. Res. Atmos., doi:10.1002/jgrd.50239. Lett., 26, 459 462. Jungclaus, J. H., et al., 2010: Climate and carbon-cycle variability over the last Goosse, H., J. Guiot, M. E. Mann, S. Dubinkina, and Y. Sallaz-Damaz, 2012a: The millennium. Clim. Past, 6, 723 737. medieval climate anomaly in Europe: Comparison of the summer and annual Kaplan, J. O., K. M. Krumhardt, and N. Zimmermann, 2009: The prehistoric and mean signals in two reconstructions and in simulations with data assimilation. preindustrial deforestation of Europe. Quat. Sci. Rev., 28, 3016 3034. Global Planet. Change, 84 85, 35 47. 10SM-23 Chapter 10 Supplementary Material Detection and Attribution of Climate Change: from Global to Regional Kaufmann, R. K., H. Kauppi, and J. H. Stock, 2006: Emission, concentrations, & Meinshausen, M., et al., 2009: Greenhouse-gas emission targets for limiting global temperature: A time series analysis. Clim. Change, 77, 249 278. warming to 2 degrees C. Nature, 458, 1158 U96. Kaufmann, R. K., H. Kauppi, M. L. Mann, and J. H. Stock, 2011: Reconciling Min, S.-K., X. B. Zhang, and F. Zwiers, 2008: Human-induced arctic moistening. anthropogenic climate change with observed temperature 1998 2008. Proc. Science, 320, 518 520. Natl. Acad. Sci. U.S.A., 108, 11790 11793. Min, S.-K., X. Zhang, F. W. Zwiers, and G. C. Hegerl, 2011: Human contribution to Kay, A. L., S. M. Crooks, P. Pall, and D. A. Stone, 2011: Attribution of Autumn/Winter more intense precipitation extremes. Nature, 470, 378 381. 2000 flood risk in England to anthropogenic climate change: A catchment-based Morak, S., G. C. Hegerl, and N. Christidis, 2013: Detectable changes in the frequency study. J. Hydrol., 406, 97 112. of temperature extremes. J. Clim., 26, 1561 1574. Knutson, T. R., F. Zeng, and A. T. Wittenberg, 2013: Multi-model assessment of Morice, C. P., J. J. Kennedy, N. A. Rayner, and P. D. Jones, 2012: Quantifying regional surface temperature trends. J. Clim., doi:10.1175/JCLI-D-12-00567.1. uncertainties in global and regional temperature change using an ensemble of Knutti, R., and L. Tomassini, 2008: Constraints on the transient climate response observational estimates: The HadCRUT4 data set. J. Geophys. Res. Atmos., 117, from observed global temperature and ocean heat uptake. Geophys. Res. Lett., D08101. 35, L09701. Murphy, D. M., S. Solomon, R. W. Portmann, K. H. Rosenlof, P. M. Forster, and T. Wong, Knutti, R., T. F. Stocker, F. Joos, and G.-K. Plattner, 2002: Constraints on radiative 2009: An observationally based energy balance for the Earth since 1950. J. forcing and future climate change from observations and climate model Geophys. Res. Atmos., 114, D17107. ensembles. Nature, 416, 719 723. North, G. R., and M. J. Stevens, 1998: Detecting climate signals in the surface Knutti, R., G. A. Meehl, M. R. Allen, and D. A. Stainforth, 2006: Constraining climate temperature record. J. Clim., 11, 563 577. sensitivity from the seasonal cycle in surface temperature. J. Clim., 19, 4224 Olson, R., R. Sriver, M. Goes, N. M. Urban, H. D. Matthews, M. Haran, and K. Keller, 4233. 2012: A climate sensitivity estimate using Bayesian fusion of instrumental Koehler, P., R. Bintanja, H. Fischer, F. Joos, R. Knutti, G. Lohmann, and V. Masson- observations and an Earth System model. J. Geophys. Res. Atmos., 117, D04103. Delmotte, 2010: What caused Earth s temperature variations during the last Otto, A., et al., 2013: Energy budget constraints on climate response. Nature Geosci., 800,000 years? Data-based evidence on radiative forcing and constraints on 6, 415 416. climate sensitivity. Quat. Sci. Rev., 29, 129 145. Otto, F. E. L., N. Massey, G. J. van Oldenborgh, R. G. Jones, and M. R. Allen, 2012: Kopp, G., and J. L. Lean, 2011: A new, lower value of total solar irradiance: Evidence Reconciling two approaches to attribution of the 2010 Russian heat wave. and climate significance. Geophys. Res. Lett., 38, L01706. Geophys. Res. Lett., 39, L04702. Lanzante, J. R., 1996: Resistant, robust and non-parametric techniques for the Padilla, L. E., G. K. Vallis, and C. W. Rowley, 2011: Probabilistic estimates of transient analysis of climate data: Theory and examples, including applications to climate sensitivity subject to uncertainty in forcing and natural variability. J. historical radiosonde station data. Int. J. Climatol., 16, 1197 1226. Clim., 24, 5521 5537. 10SM Lean, J. L., and D. H. Rind, 2008: How natural and anthropogenic influences alter Paleosens Members, 2012: Making sense of palaeoclimate sensitivity. Nature, 491, global and regional surface temperatures: 1889 to 2006. Geophys. Res. Lett., 683 691. 35, L18701. Pall, P., et al., 2011: Anthropogenic greenhouse gas contribution to UK autumn flood Lean, J. L., and D. H. Rind, 2009: How will Earth s surface temperature change in risk. Nature, 470, 382 385. future decades? Geophys. Res. Lett., 36, L15708. Parker, D., C. Folland, A. Scaife, J. Knight, A. Colman, P. Baines, and B. Dong, 2007: Levitus, S., J. Antonov, and T. Boyer, 2005: Warming of the world ocean, 1955 2003. Decadal to multidecadal variability and the climate change background. J. Geophys. Res. Lett., 32, L02604. Geophys. Res. Atmos., 112, D18115. Levitus, S., J. I. Antonov, T. P. Boyer, R. A. Locarnini, H. E. Garcia, and A. V. Mishonov, Parkinson, C. L., and D. J. Cavalieri, 2012: Antarctic sea ice variability and trends, 2009: Global ocean heat content 1955 2008 in light of recently revealed 1979 2010, Cryosphere, 6, 871 880. instrumentation problems. Geophys. Res. Lett., 36, L07608. Pierce, D.W., P. J.Gleckler, T. P. Barnett, B. D. Santer, and P. J. Durack, 2012: The Levitus, S., et al., 2012: World ocean heat content and thermosteric sea level change fingerprint of humaninduced changes in the ocean s salinity and temperature (0 2000 m), 1955 2010. Geophys. Res. Lett., 39, L10603. fields. Geophys. Res. Lett., 39, L21704. Lewis, N., 2013: An objective Bayesian, improved approach for applying optimal Piani, C., D. J. Frame, D. A. Stainforth, and M. R. Allen, 2005: Constraints on climate fingerprint techniques to estimate climate sensitivity. J. Clim., doi:10.1175/JCLI- change from a multi-thousand member ensemble of simulations. Geophys. Res. D-12-00473.1. Lett., 32, L23825. Libardoni, A. G., and C. E. Forest, 2011: Sensitivity of distributions of climate system Polson, D., G. C. Hegerl, X. Zhang, and T. J. Osborn, 2013: Causes of robust seasonal properties to the surface temperature dataset. Geophys. Res. Lett., 38, L22705. land precipitation changes. J. Clim., doi:10.1175/JCLI-D-12-00474.1. Libardoni, A. G., and C. E. Forest, 2013: Correction to Sensitivity of distributions of Pongratz, J., C. Reick, T. Raddatz, and M. Claussen, 2008: A reconstruction of global climate system properties to the surface temperature dataset . Geophys. Res. agricultural areas and land cover for the last millennium. Global Biogeochem. Lett., doi:10.1002/grl.50480. Cycles, 22, GB3018. Lin, B., et al., 2010: Estimations of climate sensitivity based on top-of-atmosphere Ribes, A., and L. Terray, 2013: Application of regularised optimal fingerprint analysis radiation imbalance. Atmos. Chem. Phys., 10, 1923 1930. for attribution. Part II: Application to global near-surface temperature Clim. Dyn., Lindzen, R. S., and Y. S. Choi, 2011: On the observational determination of climate doi:10.1007/s00382-013-1736-6. sensitivity and its implications. Asia-Pacif. J. Atmos. Sci., 47, 377 390. Rogelj, J., M. Meinshausen, and R. Knutti, 2012: Global warming under old and new Lockwood, M., 2008: Recent changes in solar outputs and the global mean surface scenarios using IPCC climate sensitivity range estimates. Nature Clim. Change, temperature. III. Analysis of contributions to global mean air surface temperature 2, 248 253. rise. Proc. R. Soc. A, 464, 1387 1404. Santer, B. D., et al., 2013: Identifying human influences on atmospheric temperature. Lott, F. C., et al., 2013: Models versus radiosondes in the free atmosphere: A new Proc. Natl. Acad. Sci. U.S.A., 110, 26 33. detection and attribution analysis of temperature. J. Geophys. Res. Atmos., 118, Sato, M., J. E. Hansen, M. P. McCormick, and J. B. Pollack, 1993: Stratospheric aerosol 2609 2619. optical depth, 1850 1990. J. Geophys. Res. Atmos., 98, 22987 22994. Luterbacher, J., D. Dietrich, E. Xoplaki, M. Grosjean, and H. Wanner, 2004: European Schmidt, G., et al., 2012: Climate forcing reconstructions for use in PMIP simulations seasonal and annual temperature variability, trend, and extremes since 1500. of the last millennium (v1.1). Geoscientif. Model Dev., 5, 185 191. Science, 303, 1499 1503. Schmittner, A., et al., 2011: Climate sensitivity estimated from temperature MacFarling Meure, C., et al., 2006: Law Dome CO(2), CH(4) and N(2)O ice core reconstructions of the last glacial maximum. Science, 334, 1385 1388. records extended to 2000 years BP. Geophys. Res. Lett., 33, L14810. Schneider von Deimling, T., H. Held, A. Ganopolski, and S. Rahmstorf, 2006: Climate Mann, M. E., et al., 2009: Global signatures and dynamical origins of the Little Ice sensitivity estimated from ensemble simulations of glacial climate. Clim. Dyn., age and medieval climate anomaly. Science, 326, 1256 1260. 27, 149 163. Mears, C. A., and F. J. Wentz, 2009: Construction of the remote sensing systems Schurer, A., G. Hegerl, M. E. Mann, S. F. B. Tett, and S. J. Phipps, 2013: Separating V3.2 atmospheric temperature records from the MSU and AMSU microwave forced from chaotic climate variability over the past millennium. J. Clim., sounders. J. Atmos. Ocean. Technol., 26, 1040 1056. doi:10.1175/JCLI-D-12-00826.1. 10SM-24 Detection and Attribution of Climate Change: from Global to Regional Chapter 10 Supplementary Material Schwartz, S. E., 2012: Determination of Earth s transient and equilibrium climate sensitivities from observations over the twentieth century: Strong dependence on assumed forcing. Surv. Geophys., 33 745 777. Sexton, D. M. H., J. M. Murphy, M. Collins, and M. J. Webb, 2012: Multivariate probabilistic projections using imperfect climate models part I: outline of methodology. Clim. Dyn., 38, 2513 2542. Skeie, R. B., T. K. Berntsen, G. Myhre, K. Tanaka, M. M. Kvalevag, and C. R. Hoyle, 2011: Anthropogenic radiative forcing time series from pre-industrial times until 2010. Atmos. Chem. Phys., 11, 11827 11857. Steinhilber, F., J. Beer, and C. Froehlich, 2009: Total solar irradiance during the Holocene. Geophys. Res. Lett., 36, L19704. Stott, P. A., and C. E. Forest, 2007: Ensemble climate predictions using climate models and observational constraints. Philos. Trans. R. Soc. A, 365, 2029 2052. Stott, P. A., and G. S. Jones, 2012: Observed 21st century temperatures further constrain decadal predictions of future warming. Atmos. Sci. Lett., 13, 151 156. Stott, P. A., J. F. B. Mitchell, M. R. Allen, T. L. Delworth, J. M. Gregory, G. A. Meehl, and B. D. Santer, 2006: Observational constraints on past attributable warming and predictions of future global warming. J. Clim., 19, 3055 3069. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc., 93, 485 498. Terray, L., L. Corre, S. Cravatte, T. Delcroix, G. Reverdin, and A. Ribes, 2012: Near- surface salinity as nature s rain gauge to detect human influence on the tropical water cycle. J. Clim., 25, 958 977. Thorne, P. W., J. R. Lanzante, T. C. Peterson, D. J. Seidel, and K. P. Shine, 2011: Tropospheric temperature trends: history of an ongoing controversy. WIREs Clim. Change, 2, 66 88. Thorne, P. W., D. E. Parker, S. F. B. Tett, P. D. Jones, M. McCarthy, H. Coleman, and P. Brohan, 2005: Revisiting radiosonde upper air temperatures from 1958 to 2002. 10SM J. Geophys. Res. Atmos., 110, D18105 Tomassini, L., P. Reichert, R. Knutti, T. F. Stocker, and M. E. Borsuk, 2007: Robust bayesian uncertainty analysis of climate system properties using Markov chain Monte Carlo methods. J. Clim., 20, 1239 1254. Tung, K. K., J. S. Zhou, and C. D. Camp, 2008: Constraining model transient climate response using independent observations of solar-cycle forcing and response. Geophys. Res. Lett., 35 L17707. Uppala, S. M., et al., 2005: The ERA-40 re-analysis. Q. J. R. Meteor. Soc., 131, 2961 3012. Vieira, L. E. A., S. K. Solanki, N. A. Krivova, and I. Usoskin, 2011: Evolution of the solar irradiance during the Holocene. Astron. Astrophys., 531, A6. Wang, M., and J. E. Overland, 2012: A sea ice free summer Arctic within 30 years: An update from CMIP5 models. Geophys. Res. Lett., 39, L18501. Wang, Y. M., J. L. Lean, and N. R. Sheeley, 2005: Modeling the sun s magnetic field and irradiance since 1713. Astrophys. J., 625, 522 538. Zhang, X. B., et al., 2007: Detection of human influence on twentieth-century precipitation trends. Nature, 448, 461 465. Zhang, X. D., 2010: Sensitivity of arctic summer sea ice coverage to global warming forcing: towards reducing uncertainty in arctic climate change projections. Tellus A, 62, 220 227. Zwiers, F. W., X. Zhang, and Y. Feng, 2011: Anthropogenic influence on long return period daily temperature extremes at regional scales. J. Clim., 24, 881 892. 10SM-25 Sea Level Change 13SM Supplementary Material Coordinating Lead Authors: John A. Church (Australia), Peter U. Clark (USA) Lead Authors: Anny Cazenave (France), Jonathan M. Gregory (UK), Svetlana Jevrejeva (UK), Anders Levermann (Germany), Mark A. Merrifield (USA), Glenn A. Milne (Canada), R. Steven Nerem (USA), Patrick D. Nunn (Australia), Antony J. Payne (UK), W. Tad Pfeffer (USA), Detlef Stammer (Germany), Alakkat S. Unnikrishnan (India) Contributing Authors: David Bahr (USA), Jason E. Box (Denmark/USA), David H. Bromwich (USA), Mark Carson (Germany), William Collins (UK), Xavier Fettweis (Belgium), Piers Forster (UK), Alex Gardner (USA), W. Roland Gehrels (UK), Rianne Giesen (Netherlands), Peter J. Gleckler (USA), Peter Good (UK), Rune Grand Graversen (Sweden), Ralf Greve (Japan), Stephen Griffies (USA), Edward Hanna (UK), Mark Hemer (Australia), Regine Hock (USA), Simon J. Holgate (UK), John Hunter (Australia), Philippe Huybrechts (Belgium), Gregory Johnson (USA), Ian Joughin (USA), Georg Kaser (Austria), Caroline Katsman (Netherlands), Leonard Konikow (USA), Gerhard Krinner (France), Anne Le Brocq (UK), Jan Lenaerts (Netherlands), Stefan Ligtenberg (Netherlands), Christopher M. Little (USA), Ben Marzeion (Austria), Kathleen L. McInnes (Australia), Sebastian H. Mernild (USA), Didier Monselesan (Australia), Ruth Mottram (Denmark), Tavi Murray (UK), Gunnar Myhre (Norway), J.P. Nicholas (USA), Faezeh Nick (Norway), Mahé Perrette (Germany), David Pollard (USA), Valentina Radić (Canada), Jamie Rae (UK), Markku Rummukainen (Sweden), Christian Schoof (Canada), Aimée Slangen (Australia/Netherlands), Jan H. van Angelen (Netherlands), Willem Jan van de Berg (Netherlands), Michiel van den Broeke (Netherlands), Miren Vizcaíno (Netherlands), Yoshihide Wada (Netherlands), Neil J. White (Australia), Ricarda Winkelmann (Germany), Jianjun Yin (USA), Masakazu Yoshimori (Japan), Kirsten Zickfeld (Canada) Review Editors: Jean Jouzel (France), Roderik van de Wal (Netherlands), Philip L. Woodworth (UK), Cunde Xiao (China) This chapter supplementary material should be cited as: Church, J.A., P.U. Clark, A. Cazenave, J.M. Gregory, S. Jevrejeva, A. Levermann, M.A. Merrifield, G.A. Milne, R.S. Nerem, P.D. Nunn, A.J. Payne, W.T. Pfeffer, D. Stammer and A.S. Unnikrishnan, 2013: Sea Level Change Supple- mentary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www.climat- echange2013.org and www.ipcc.ch. 13SM-1 Table of Contents 13.SM.1 Methods of Global Mean Sea Level Projections for the 21st Century............... 13SM-3 13.SM.2 Computation of Regional Maps of Sea Level Change from Coupled Model Intercomparison Project Phase 5 Model Output.............................................. 13SM-5 References ........................................................................ 13SM-8 13SM 13SM-2 Sea Level Change Chapter 13 Supplementary Material 13.SM.1 Methods of Global Mean Sea Level statistic is mid for the median, or lower or upper for the limits of the Projections for the 21st Century range. This section summarizes the methods used to produce the projections suffix is txt for plain ASCII text, or nc for netCDF. shown in Section 13.5.1 for the Representative Concentration Pathway (RCP) scenarios and the Special Report on Emission Scenarios (SRES) The text files have two columns, year and sea level change in metres. A1B scenario. The Supplementary Material includes files of the annual The netCDF files describe their contents using the CF convention. time series of median, 5th percentile and 95th percentile for each of the contributions to global mean sea level rise and the sum, corresponding 13.SM.1.1 Derivation of Global Surface Temperature and to the results shown in Table 13.5. The data files are named as follows: Thermal Expansion Time Series from Coupled Model Intercomparison Project Phase 5 scenario _ quantity statistic . suffix Annual time series for change in global mean surface air temperature for instance rcp45_summid.nc. In each name, (SAT) ( tas in the CMIP5 archive) and global-mean sea level (GMSL) rise due to thermal expansion ( zostoga ) in the historical period and scenario is rcp26, rcp45, rcp60 or rcp85, corresponding to the four during the 21st century under RCP scenarios (Section 13.4.1) were representative concentration pathways used in CMIP5, or sresa1b for obtained from a set of 21 CMIP5 AOGCMs (ACCESS1-0, ACCESS1-3, SRES A1B used in CMIP3. CCSM4, CNRM-CM5, CSIRO-Mk3-6-0, CanESM2, GFDL-CM3, GFDL- ES-M2G, GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, IPSL-CM5A-MR, quantity is temperature for global mean surface temperature change, MIROC-ESM, MIROC-ESM-CHEM, MIROC5, MPI-ESM-LR, MPI-ESM- expansion for thermal expansion (sections 13.4.1 and 13.SM.1.2), MR, MRI-CGCM3, NorESM1-M, NorESM1-ME, inmcm4). These were glacier for glaciers (13.4.2 and 13.SM.1.3), greensmb for Greenland all those for which thermal expansion was available, including from a ice-sheet SMB (13.4.3.1 and 13.SM.1.4), antsmb for Antarctic ice- parallel pre-industrial control experiment, which is required to remove sheet SMB (13.4.4.1 and 13.SM.1.5), greendyn for Greenland ice- the thermal expansion due to climate drift in deep-ocean tempera- sheet rapid dynamical change (13.4.3.2 and 13.SM.1.6), antdyn for tures (Gleckler et al., 2012). The drift was removed by subtracting a Antarctic ice-sheet rapid dynamical change (13.4.4.2 and 13.SM.1.6), polynomial fit as a function of time to the control thermal expansion landwater for anthropogenic intervention in water storage on land time series. Where CMIP5 results were not available for a particular (13.4.5 and 13.SM.1.6), greennet for the sum of SMB and rapid Atmosphere Ocean General Circulation Model (AOGCM) and scenario, dynamical contributions from the Greenland ice-sheet, antnet for the they were estimated by the method of Good et al. (2011) and Good sum of SMB and rapid dynamical contributions from the Antarctic ice- et al. (2013) using the response of that AOGCM to an instantaneous sheet, sheetdyn for the sum of the rapid dynamical contributions from quadrupling of carbon dioxide (CO2) concentration. The same method the Greenland and Antarctic ice-sheets, or sum for the sea level projec- was used to estimate the CMIP5 projections for scenario SRES A1B. tion including all contributions. Except for temperature, these are the The method gives estimates of change in global mean surface air quantities shown in Table 13.5. temperature and net radiative flux at the top of the atmosphere. The Table 13.SM.1 | Median values and likely ranges for projections of global-mean sea level rise and its contributions in metres in 2100 relative to 1986 2005 for the four RCP scenarios and SRES A1B. See Section 13.5.1 concerning how the likely range is defined. Because some of the uncertainties in modelling the contributions are treated as uncorrelated, the sum of the lower bound of contributions does not equal the lower bound of the sum, and similarly for the upper bound. Because of imprecision from rounding, the sum of the medians of contributions may not exactly equal the median of the sum. SRES A1B RCP2.6 RCP4.5 RCP6.0 RCP8.5 Thermal expansion 0.24 [0.18 to 0.30] 0.15 [0.11 to 0.20] 0.20 [0.15 to 0.25] 0.22 [0.17 to 0.27] 0.32 [0.25 to 0.39] Glaciers 0.16 [0.09 to 0.23] 0.11 [0.05 to 0.17] 0.13 [0.07 to 0.20] 0.14 [0.07 to 0.20] 0.18 [0.10 to 0.26] Greenland Ice Sheet SMBa 0.07 [0.03 to 0.15] 0.03 [0.01 to 0.08] 0.05 [0.02 to 0.11] 0.05 [0.02 to 0.12] 0.10 [0.04 to 0.22] Antarctic Ice Sheet SMBb 0.04 [ 0.07 to 0.01] 0.02 [ 0.05 to 0.00] 0.03 [ 0.06 to 0.01] 0.03 [ 0.06 to 0.01] 0.05 [ 0.09 to 0.02] Greenland Ice Sheet Rapid Dynamics 0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.04 [0.01 to 0.06] 0.05 [0.02 to 0.09] Antarctic Ice Sheet Rapid Dynamics 0.08 [ 0.02 to 0.19] 0.08 [ 0.02 to 0.19] 0.08 [ 0.02 to 0.19] 0.08 [ 0.02 to 0.19] 0.08 [ 0.02 to 0.19] 13SM Land Water Storage 0.05 [ 0.01 to 0.11] 0.05 [ 0.01 to 0.11] 0.05 [ 0.01 to 0.11] 0.05 [ 0.01 to 0.11] 0.05 [ 0.01 to 0.11] Sea Level Rise 0.60 [0.42 to 0.80] 0.44 [0.28 to 0.61] 0.53 [0.36 to 0.71] 0.55 [0.38 to 0.73] 0.74 [0.52 to 0.98] Greenland Ice Sheet 0.11 [0.07 to 0.19] 0.08 [0.04 to 0.12] 0.09 [0.05 to 0.16] 0.09 [0.06 to 0.16] 0.15 [0.09 to 0.28] Antarctic Ice Sheet 0.05 [ 0.06 to 0.15] 0.06 [ 0.04 to 0.16] 0.05 [ 0.05 to 0.15] 0.05 [ 0.05 to 0.15] 0.04 [ 0.08 to 0.14] Ice-Sheet Rapid Dynamics 0.12 [0.03 to 0.22] 0.12 [0.03 to 0.22] 0.12 [0.03 to 0.22] 0.12 [0.03 to 0.22] 0.14 [0.04 to 0.24] Only the collapse of the marine-based sectors of the Antarctic Ice Sheet could cause GMSL to rise substantially above the likely range during the 21st century. This potential additional contribution cannot be precisely quantified but there is medium confidence that it would not exceed several tenths of a meter of sea level rise. Notes: a Including the height-SMB feedback. b Including the interaction between SMB change and outflow. 13SM-3 Chapter 13 Supplementary Material Sea Level Change latter was integrated in time to obtain the estimated change in heat which this formula is based calculate their results from geographically content of the climate system, and converted to thermal expansion dependent climate change with detailed treatments of glacier surface using the expansion efficiency of heat appropriate to each AOGCM, mass balance (SMB) and the evolution of hypsometry; their complexity as diagnosed from all the available RCPs for that AOGCM. The correla- cannot be accurately reproduced by a simple formula, and the spread tion between heat content change and thermal expansion is very high of their results around the prediction of this formula has a coefficient and the relationship can be accurately treated as linear (Kuhlbrodt and of variation (standard deviation divided by mean) of 20% or less for Gregory, 2012). decadal means for all glacier models and RCPs, except for the early decades of the 21st century under RCP2.6 for the model of Slangen 13.SM.1.2 Interpretation and Combination of Uncertainties and van de Wal (2011) for which there are fractional errors of up to 40%, but the absolute error is small. Therefore we take 20% of the Uncertainties were derived from the CMIP5 ensemble by treating the projection of the formula made using the CMIP5 ensemble mean I(t) as model spread as a normal distribution, and following Section 12.4.1.2 the standard deviation of a normally distributed methodological uncer- it was assumed that the 5 to 95% interval of CMIP5 projections for the tainty in the glacier projection for each global glacier model. In order 21st century for each RCP scenario can be interpreted as a likely range to incorporate this uncertainty into the projections, for each member (Section 13.5.1). The CMIP5 timeseries of thermal expansion X and of the Monte Carlo ensemble of glacier time-series, a normally distrib- global mean surface air temperature T were expressed as anomalies uted random number was chosen, independent of time, as a factor as a function of time t with respect to their time-means for 1986-2005, by which the time-dependent standard deviation should be multiplied, and the timeseries of ensemble means XM(t) and TM(t) and ensemble giving the uncertainty to be added to the glacier time-series. We give standard deviations XS(t) and TS(t) were calculated. As in the AR4, a the four global glacier models equal weight in the projections. Because Monte Carlo was used to generate distributions of timeseries of X and the time integration began in 2006, a constant 9.5 mm was added to T in a perfectly correlated way; for each member of the ensemble, a the projections to account for the glacier contribution from 1996 (the random number r was chosen from a normal distribution, giving X(t) = centre of the reference period for projections) to 2005; this is the mean XM(t) + r XS(t) and T(t) = TM(t) + r TS(t), and T(t) was used to estimate result from the model of Marzeion et al. (2012) using input from CMIP5 land ice contributions to GMSLR, as described in the following sections. AOGCM historical experiments. The formula is not applicable beyond As in the AR4, all the uncertainties described by the land ice methods 2100 because it does not represent the tendency of global glacier mass were assumed to be independent of the climate change uncertainty to reach a new steady value when global climate stabilizes, although represented by the variation of r and of one another, except where the global glacier models on which it is based can predict this as a con- stated, and were combined by Monte Carlo. Because of the use of sequence of the evolution of hypsometry. Glaciers on Antarctica were Monte Carlo, the results for GMSLR have a random uncertainty. For not included in the global glacier projections because they are included different random samples of the sizes used to compute the results in in the projections for the Antarctic ice sheet. Table 13.5, the results vary by up to 0.01 m in GMSLR and its contribu- tions, and 0.1 mm yr-1 in the rate of GMSLR. The projections are shown Table 13.SM.2 | Parameters for the fits to the global glacier models. for 2081-2100 in Table 13.5, and for 2100 in Table 13.SM.1. Global Glacier Model f (mm °C 1 yr 1) p (no unit) 13.SM.1.3 Glaciers Giesen and Oerlemans (2013) 3.02 0.733 Marzeion et al. (2012) 4.96 0.685 Changes in glacier mass in all regions excluding Antarctica from 2006 Radić et al. (2013) 5.45 0.676 onwards were projected using a parameterized scheme which was Slangen and van de Wal (2011) 3.44 0.742 fitted separately to results from each of the global glacier models of Giesen and Oerlemans (2013), Marzeion et al. (2012), Radić et al. (2014) and Slangen and van de Wal (2011). For the model of Giesen and Oerlemans (2013), only the dependence on temperature was con- 13.SM.1.4 Greenland Ice Sheet Surface Mass Balance sidered; the dependences on precipitation and atmospheric transmis- sivity were not included. All of these global glacier models have been The change in Greenland ice sheet SMB Ge(t), excluding changes in ice used to make projections using output from several AOGCMs. Giesen sheet topography, was computed from T(t) using the cubic polynomial and Oerlemans used results from CMIP3 AOGCMs for scenario SRES formula, Equation (2) of Fettweis et al., which predicts the Greenland A1B, and the other authors used results from different sets of CMIP5 SMB anomaly as a function of T, and was obtained by fitting results AOGCMs for RCPs. The RCP results of Slangen and van de Wal (2011) from an RCM using input from several CMIP5 AOGCMs for RCP4.5 and 13SM are not included in their published paper, but use the same glacier RCP8.5. Their Equation (2) Ge = 71.5T 20.4T2 2.8T3 gives Ge in Gt model as in the paper. The parameterized scheme enables estimates to yr 1, which we convert to mm yr 1 SLE. In this formula, T is relative to be made for the glacier contribution to GMSL rise gI as a function of the time mean of 1980 1999, rather than 1986 2005; in the CMIP5 time t for the consistent set of CMIP3 and CMIP5 AOGCMs across all AOGCM results, the former period is cooler by 0.15°C. The results of RCPs and SRES A1B. The scheme gives gI(t) in millimetres with respect this formula were compared with those for the same AOGCMs and RCP to 2006 as fI(t)p, where I(t) is the time integral of T from 2006 to time from Equation (1) of Fettweis et al. (2013), which predicts G(t) from t in degrees Celsius year, and the constants f and p used for each gla- summer (June to August) air temperature at 600 hPa over Greenland. cier model are shown in Table 13.SM.2. The constants were fitted by Equation (1) reproduces the RCM results more accurately but cannot linear regression of log(g) against log(I). The global glacier models on be used for the consistent set of CMIP5 AOGCMs and all RCPs because 13SM-4 Sea Level Change Chapter 13 Supplementary Material their required input data are not available. The results of Equation (2) 1996 2005 for the contribution from Antarctic ice-sheet SMB, because were also compared with those for the same AOGCMs and RCPs with changes during this period are judged to be due solely to dynamical results obtained from the models of Gregory and Huybrechts (2006) change (Section 13.3.3.2). and Yoshimori and Abe-Ouchi (2012), the former being the one used in the AR4. As a result of this comparison of projections (Section 13.4.3.1, 13.SM.1.6 Rapid Ice Sheet Dynamics and Anthropogenic Table 13.4), Ge(t) was estimated as FG2(t), where G2(t) is calculated Change in Land Water Storage from T using Fettweis et al. Equation (2), and F is a factor representing methodological uncertainty. This factor is taken to have a log-normal Following Section 13.3.3.2, the contributions from rapid ice-sheet distribution i.e. one of the form F = eN, where N is a normal distribution dynamics at the start of the projections were taken to be half of the having a mean of zero. A log-normal distribution is used because the observed rate of loss for 2005-2010 from Greenland (half of 0.46-0.80 distributions of Ge(t) from the various Greenland ice sheet SMB models mm yr-1 from Table 4.6) and all of that from Antarctica (0.21-0.61 mm are positively skewed. None of these models simulates the change yr-1 from Table 4.6). The contributions reach 0.020 to 0.085 m at 2100 in SMB caused by the evolution of the ice sheet surface topography, from Greenland for RCP8.5, 0.014 to 0.063 m for the other RCPs and which gives a positive feedback on mass loss (Section 13.4.3.2). To 0.020 to 0.185 m from Antarctica for all RCPs; these are the likely allow for this effect, the Greenland ice sheet SMB change G(t) with ranges from our assessment of existing studies (Sections 13.4.3.2 and respect to 1986 2005 was estimated as EGe(t), where E is a randomly 13.4.4.2). For each ice sheet, a quadratic function of time was fitted varying factor with a uniform probability distribution in the range 1.00 which begins at the minimal initial rate and reaches the minimum final to 1.15. The uncertainties of E and F were assumed not be correlated, amount, and another for the maxima. Time series for the rapid dynamic and independent of time. The ice sheet SMB change G(t) was integrat- contribution lying between these extremes were constructed as com- ed in time to obtain the change in ice sheet mass, starting in 2006. binations of the extreme time series assuming a uniform probability A constant 1.5 mm was added to the projections to account for the density between the extremes. Finally, a constant 1.5 mm was added Greenland SMB contribution from 1996 (the centre of the reference to the contribution from the Greenland ice sheet, and 2.5 mm to the period for projections) to 2005; this is half of the central observational contribution from the Antarctic ice sheet, these being the estimates of estimate of the rate of Greenland ice sheet mass loss during this period those contributions from 1996 to 2005 (using the data presented in (Section 13.3.3.2, using data presented in Figure 4.15). Figures 4.15 and 4.16). 13.SM.1.5 Antarctic Ice Sheet Surface Mass Balance The same method was followed for the anthropogenic land water stor- age contribution (initial rates as for 1993 2010 from Table 13.1 and The change in Antarctic ice sheet SMB A(t) with respect to 1986 2005 amounts for the time-mean of 2081-2100 from Section 13.4.5, with no was assumed to be due solely to an increase in accumulation (thus, A < additional amount for land water storage from 1996 to 2005). These 0 in units of sea level equivalent, because accumulation on the ice sheet contributions are treated as uncorrelated with the magnitude of global removes mass from the ocean), which was estimated using the results climate change and as independent of scenario (except for the higher of Gregory and Huybrechts (2006) from CMIP3 AOGCMs. Accumulation rate of change for Greenland ice sheet outflow under RCP8.5). This was taken to increase at 5.1 +/- 1.5% °C 1 of warming in Antarctica rel- treatment does not imply that the contributions concerned will not ative to 1985-2005, the ratio of warming in Antarctic to T was taken depend on the scenario followed, only that the current state of knowl- to be 1.1 +/- 0.2, and the accumulation for the reference period was edge does not permit a quantitative assessment of the dependence. taken to be 1923 Gt yr-1 (Section 13.3.3.2). Both of these uncertainties (standard deviations) were treated as normally distributed methodo- logical uncertainties in the projections. The resulting spread of projec- 13.SM.2 Computation of Regional Maps of Sea tions is very close to the spread of the results from the high-resolution Level Change from Coupled Model Antarctic SMB models of Krinner et al. (2007), Bengtsson et al. (2011) Intercomparison Project Phase 5 and Ligtenberg et al. (2013) assessed in Section 13.4.4.1. The effect Model Output of increased accumulation on the dynamics of the Antarctic ice sheet (Section 13.4.4.2) was taken into account by adding a rate SA(t) (a Several results and figures in Section 13.6 are based on published positive number in units of sea level equivalent, because the increase methods as referred to in the main text but have not been published in outflow opposes the increase in accumulation and adds mass to the independently. This document details all information that led to num- ocean) to the GMSL projections, where S is a randomly varying factor bers and figures shown in Section 13.6 on regional sea level projec- with a uniform probability distribution in the range 0.00 to 0.35. The tions. Data files for each figure are available. 13SM uncertainties in accumulation sensitivity, Antarctic warming ratio, and the factor S were assumed not to be correlated, but S was perfectly For each figure or each step involved, the underlying technical details correlated with the distribution of Antarctic rapid ice sheet dynamics that were used are described. The Supplementary Material includes (next paragraph), in the sense that when the rapid dynamical increase files containing the data in each case. in outflow is large, the increase in outflow due to the dynamical reac- tion to increased accumulation is also large. The mass balance changes Figures 13.15, 13.16 and 13.24 show maps of regional sea level chang- A and SA were integrated in time to obtain the change in the ice es computed from CMIP5 coupled climate models. The following steps sheet mass, starting from 2006. Unlike for Greenland ice sheet SMB, were pursued in the preparation of those figures. no addition to the projections was made to account for the period 13SM-5 Chapter 13 Supplementary Material Sea Level Change 13.SM.2.1 Sea Surface Height from Coupled Climate 13.SM.2.4 Combining All Sea Level Rise Components Models Figures 13.18, 13.19, 13.22 and 13.23 show projected sea level chang- Sea surface height (SSH) data, labeled the zos variable, from the es as they result after combining various different contributions to sea CMIP5 AOGCM database, are used to show regional changes in SSH level change in addition to those available from CMIP5 models. The over time, and include the regional variability of dynamic topography following steps were necessary to obtain those maps and figures. changes due to water mass advection, thermohaline circulation and to the wind-driven circulation (see Table 13.SM.3). These regional chang- Contributions to regional sea level change due to changes in other es are corrected for regional control drift by removing the linearly fitted components of the climate system were added to the thermosteric/ control run drift from each latitude longitude grid box individually, dynamic SSH from the AOGCMs. These components include surface on a per-model basis. After this correction, the global average of this mass balance and dynamic ice sheet contributions from Greenland and regional SSH field (a function of x, y, t) is forced to be the global ther- Antarctica, a glacier contribution, a land water storage contribution, mal expansion ( zostoga variable) at each time step by first subtract- glacial isostatic adjustment (GIA), and the inverse barometer effect ing the globally averaged regional SSH field at each time step from (IBE). The projections of the various land ice contributions and the land each grid box, and then adding the global thermal expansion time water storage contribution are described elsewhere (Sections 13.4, series to each grid box (the same number at every grid box, for a given 13.5.1 and 13.SM.1 in the Supplementary Material). These global esti- time). The global thermal expansion time series was also corrected for mates were turned into regional maps of sea level response, due to the control drift by removing a quadratic fit to the control run s thermal addition of mass increasing the global ocean volume (the barystatic expansion time series before being added to the regional SSH data. contribution) plus the resultant gravitational and rotational changes, As not all models had multiple ensemble forced runs for the various through application of an iterative sea level equation solver (Slangen RCP scenarios, only one run from each model (in each RCP scenario) et al., 2012). The groundwater storage change contribution to regional was used to compute the multi-model ensemble means (i.e., the results sea level rise was also found similarly by taking estimates of its geo- for each individual model are only a single realization per scenario, as graphical distribution from Wada et al. (2012) and applying the same shown in Figure 13.24). sea level equation solver. The GIA contribution was calculated from the mean of the ICE-5G model (Peltier 2004) and the ANU ice sheet 13.SM.2.2 Interpolation model (Lambeck et al. 1998 and subsequent improvements) with the SELEN code for the sea level equation (Farrell and Clark 1976; Spada All of the steps outlined above were performed on each model s own and Stocchi 2006, 2007), including updates to allow for coastline var- grid, with interpolation to a common 1° × 1° grid only being applied iation through time, near-field meltwater damping and Earth rotation after statistical analyses, to each model s relative sea level changes, in a self-consistent manner (Milne and Mitrovica, 1998; Kendall et al., means and variances. The interpolation procedure involves applying a 2006). The IBE contribution was found by using an ensemble of atmos- nearest-neighbour interpolation and a bilinear interpolation, with the pheric results from the atmospheric component of the same CMIP5 nearest-neighbour interpolation chosen close to the coasts where the models used for the SSH data. All of these components were calculated bilinear interpolation loses grid boxes. offline (i.e., were not part of diagnostic zos and zostoga variables in the models) and then added to the regional sea level rise results 13.SM.2.3 Masking previously derived from CMIP5 zos and zostoga variables. Some of the models, on their original grids, had detached marginal 13.SM.2.5 Uncertainties seas (e.g., the Mediterranean, Hudson Bay, Baltic Sea, etc.), and in most cases, the SSH in the marginal seas behaved differently than Figures 13.19, 13.21 and 13.23 show uncertainty measures for sea in the nearby ocean, with some models having significant numerical level projections. Those uncertainties were computed as follows. instability, and others undergoing a different SSH evolution in these seas. To remove large and obvious errors from the ensemble mean The uncertainties in the results directly from the CMIP5 model data (and other ensemble statistics) and to treat all the models consist- are estimated with the ensemble spread: one standard deviation of ently, marginal seas were masked out from individual models, if they the members means is treated as the standard error for the ensemble were detached from the adjacent ocean basin, on the common 1° × 1° mean. This applies to the dynamic/thermosteric SSH ocean data, and grid. This results in a final ensemble mean product that consists of, for the IBE atmospheric data. The ice sheet, glacier and land water storage example, for the RCP4.5 run, a 21-model mean over most of the ocean, uncertainties are found regionally from the global uncertainties of 13SM but has only as few as 12 ensemble members contributing to the mean the sources using the same iterative sea level equation solver used for some marginal seas (9 is the lowest number of RCP4.5/8.5 mem- to obtain the regional distribution from their means. The one standard bers for which regional data are shown for ensemble statistics). error of the GIA uncertainty is evaluated as the departures of the two different GIA estimates (from ICE-5G and ANU/SELEN models) from their mean value. To combine these uncertainties, for both maps of uncertainty as well as time series of uncertainty at individual stations, it is assumed that contributions that correlate with global air temperature have correlated uncertainties, which are therefore added linearly. This combined uncertainty is then added to the other 13SM-6 Sea Level Change Chapter 13 Supplementary Material components uncertainties in quadrature. The uncertainties in the The 90% confidence limits for the ice components are asymmetric projected ice sheet SMB changes were assumed to be dominated by and were combined with the 90% confidence limit uncertainties of the magnitude of climate change, rather than their methodological the CMIP5 ocean components to find the lower and upper uncertainty uncertainty, while the uncertainty in the projected glacier change limits separately (Figures 13.19 and 13.23), using the given equation. was assumed to be dominated by its methodological uncertainty. The In Figure 13.21, in which a single standard error at each location is formula shown below for the regional error, when applied to the global used, the s used in the equation were standard deviations for all com- contributions, estimates a global uncertainty close to that given in ponents except LW, dyn_a and dyn_g; these latter had uniform PDFs Table 13.5. The estimated squared uncertainty (standard error) at each in the global projections, and the half-range of the distribution was grid box is found as ­ ollows: f used for s. To find the 90% confidence limits of the ocean components, regional uncertainties were multiplied by 1.645, thus treating them as s2 = (s steric/dyn + s smb_a + s smb_g)2 + s2 + s2 + s2 + s2 + s2 + s2 tot glac IBE GIA LW dyn_a dyn_g methodological, normally distributed uncertainties. (13.SM.1) where: steric/dyn = global thermal expansion uncertainty + dynamic SSH (ensemble spread) smb_a = Antarctic ice sheet SMB uncertainty (including interaction of SMB and dynamics) smb_g = Greenland ice sheet SMB uncertainty (including interaction of SMB and dynamics) glac = Glacier uncertainty IBE = inverse barometer effect uncertainty (ensemble spread) GIA = glacial isostatic adjustment uncertainty LW = land water storage uncertainty dyn_a = Antarctica ice sheet rapid dynamics uncertainty dyn_g = Greenland ice sheet rapid dynamics uncertainty Table 13.SM.3 | Availability of zos variable from CMIP5. Model RCP2.6 RCP6.0 RCP4.5 / RCP8.5 ACCESS-1.0 X BCC-CSM1.1 X X X CanESM2 X CNRM-CM5 X CSIRO-MK3.6.0 X X X GFDL-ESM2G X X X GFDL-ESM2M X X X GISS-E2-R X X X HadGEM2-CC X HadGEM2-ES X X INM-CM4 X IPSL-CM5A-LR X X X IPSL-CM5A-MR X X 13SM MIROC5 X X X MIROC-ESM X X X MIROC-ESM-CHEM X X X MPI-ESM-LR X X MPI-ESM-MR X X MRI-CGCM3 X X X NorESM1-M X X X NorESM1-ME X X X 13SM-7 Chapter 13 Supplementary Material Sea Level Change References Bengtsson, L., S. Koumoutsaris, and K. Hodges, 2011: Large-scale surface mass balance of ice sheets from a comprehensive atmosphere model. Surv. Geophys., 32, 459 474. Farrell, W.E., and Clark, J.A., 1976. On postglacial sea-level, Geophys. J. R. Astr. Soc., 46, 647 667. Fettweis, X., B. Franco, M. Tedesco, J. H. van Angelen, J. T. M. Lenaerts, M. R. van den Broeke, and H. Gallee, 2013: Estimating Greenland ice sheet surface mass balance contribution to future sea level rise using the regional atmospheric model MAR. Cryosphere, 7, 469 489. Giesen, R. H., and J. Oerlemans, 2013: Climate-model induced differences in the 21st century global and regional glacier contributions to sea-level rise. Clim. Dyn., 41, 3283 3300. Gleckler, P. J., et al., 2012: Human-induced global ocean warming on multidecadal timescales. Nature Clim. Change, 2, 524 529. Good, P., J. M. Gregory, and J. A. Lowe, 2011: A step-response simple climate model to reconstruct and interpret AOGCM projections. Geophys. Res. Lett., 38, L01703. Good, P., J. M. Gregory, J. A. Lowe, and T. Andrews, 2013: Abrupt CO2 experiments as tools for predicting and understanding CMIP5 representative concentration pathway projections. Clim. Dyn., 40, 1041 1053. Gregory, J. M., and P. Huybrechts, 2006: Ice-sheet contributions to future sea-level change. Philos. Trans. R. Soc. London A, 364, 1709 1731. Kendall, R., Latychev, K., Mitrovica, J.X., Davis, J.E., and Tamisiea, M., 2006. Decontaminating tide gauge records for the influence of Glacial Isostatic Adjustment: the potential impact of 3-D Earth structure, Geophys. Res. Lett., 33, L24318, doi:10.1029/2006GL028448. Krinner, G., O. Magand, I. Simmonds, C. Genthon, and J. L. Dufresne, 2007: Simulated Antarctic precipitation and surface mass balance at the end of the twentieth and twenty-first centuries. Clim. Dyn., 28, 215 230. Kuhlbrodt, T., and J. M. Gregory, 2012: Ocean heat uptake and its consequences for the magnitude of sea level rise and climate change. Geophys. Res. Lett., 39, L18608. Lambeck, K., C. Smither, and P. Johnston, 1998: Sea-level change, glacial rebound and mantle viscosity for northern Europe. Geophys. J. Int., 134, 102 144. Ligtenberg, S. R. M., W. J. van de Berg, M. R. van den Broeke, J. G. L. Rae, and E. van Meijgaard, 2013: Future surface mass balance of the Antarctic ice sheet and its influence on sea level change, simulated by a regional atmospheric climate model. Clim. Dyn., 41, 867 884. Marzeion, B., A. H. Jarosch, and M. Hofer, 2012: Past and future sea-level changes from the surface mass balance of glaciers. Cryosphere, 6, 1295 1322. Milne, G.A., and Mitrovica, J.X., 1998. Postglacial sea-level change on a rotating Earth, Geophys. J. Int., 133, 1 19. Peltier, W. R., 2004: Global glacial isostasy and the surface of the ice-age earth: The ICE-5G (VM2) model and GRACE. Annu. Rev. Earth Planet. Sci., 32, 111 149. Radić, V., A. Bliss, A. D. Beedlow, R. Hock, E. Miles, and J. G. Cogley, 2014: Regional and global projections of twenty-first century glacier mass changes in response to climate scenarios from global climate models. Clim. Dyn., 42, 37 58. Slangen, A. B. A., and R. S. W. van de Wal, 2011: An assessment of uncertainties in using volume-area modelling for computing the twenty-first century glacier contribution to sea-level change. Cryosphere, 5, 673 686. Slangen, A. B. A., C. A. Katsman, R. S. W. van de Wal, L. L. A. Vermeersen, and R. E. M. Riva, 2012: Towards regional projections of twenty-first century sea-level change based on IPCC SRES scenarios. Clim. Dyn., 38, 1191 1209. Spada, G., and Stocchi, P., 2006. The Sea Level Equation, Theory and Numerical Examples, Aracne, Roma, p. 96, ISBN: 88 548 0384 7. Spada, G., and Stocchi, P., 2007. SELEN: a Fortran 90 program for solving the Sea Level 13SM Equation , Comput. Geosci., 33(4), 538 562, doi:10.1016/j.cageo.2006.08.006. Wada, Y., L. P. H. van Beek, F. C. S. Weiland, B. F. Chao, Y. H. Wu, and M. F. P. Bierkens, 2012: Past and future contribution of global groundwater depletion to sea-level rise. Geophys. Res. Lett., 39, L09402. Yoshimori, M., and A. Abe-Ouchi, 2012: Sources of spread in multi-model projections of the Greenland ice-sheet surface mass balance. J. Clim., 25, 1157 1175. 13SM-8 Climate Phenomena and their Relevance for Future Regional Climate Change Supplementary Material 14SM Coordinating Lead Authors: Jens Hesselbjerg Christensen (Denmark), Krishna Kumar Kanikicharla (India) Lead Authors: Edvin Aldrian (Indonesia), Soon-Il An (Republic of Korea), Iracema Fonseca Albuquerque Cavalcanti (Brazil), Manuel de Castro (Spain), Wenjie Dong (China), Prashant Goswami (India), Alex Hall (USA), Joseph Katongo Kanyanga (Zambia), Akio Kitoh (Japan), James Kossin (USA), Ngar-Cheung Lau (USA), James Renwick (New Zealand), David B. Stephenson (UK), Shang-Ping Xie (USA), Tianjun Zhou (China) Contributing Authors: Libu Abraham (Qatar), Tércio Ambrizzi (Brazil), Bruce Anderson (USA), Osamu Arakawa (Japan), Raymond Arritt (USA), Mark Baldwin (UK), Mathew Barlow (USA), David Barriopedro (Spain), Michela Biasutti (USA), Sébastien Biner (Canada), David Bromwich (USA), Josephine Brown (Australia), Wenju Cai (Australia), Leila V. Carvalho (USA/Brazil), Ping Chang (USA), Xiaolong Chen (China), Jung Choi (Republic of Korea), Ole Bssing Christensen (Denmark), Clara Deser (USA), Kerry Emanuel (USA), Hirokazu Endo (Japan), David B. Enfield (USA), Amato Evan (USA), Alessandra Giannini (USA), Nathan Gillett (Canada), Annamalai Hariharasubramanian (USA), Ping Huang (China), Julie Jones (UK), Ashok Karumuri (India), Jack Katzfey (Australia), Erik Kjellström (Sweden), Jeff Knight (UK), Thomas Knutson (USA), Ashwini Kulkarni (India), Koteswara Rao Kundeti (India), William K. Lau (USA), Geert Lenderink (Netherlands), Chris Lennard (South Africa), Lai-yung Ruby Leung (USA), Renping Lin (China), Teresa Losada (Spain), Neil C. Mackellar (South Africa), Victor Magana (Mexico), Gareth Marshall (UK), Linda Mearns (USA), Gerald Meehl (USA), Claudio Menéndez (Argentina), Hiroyuki Murakami (USA/Japan), Mary Jo Nath (USA), J. David Neelin (USA), Geert Jan van Oldenborgh (Netherlands), Martin Olesen (Denmark), Jan Polcher (France), Yun Qian (USA), Suchanda Ray (India), Katharine Davis Reich (USA), Belén Rodriguez de Fonseca (Spain), Paolo Ruti (Italy), James Screen (UK), Jan Sedláček (Switzerland) Silvina Solman (Argentina), Martin Stendel (Denmark), Samantha Stevenson (USA), Izuru Takayabu (Japan), John Turner (UK), Caroline Ummenhofer (USA), Kevin Walsh (Australia), Bin Wang (USA), Chunzai Wang (USA), Ian Watterson (Australia), Matthew Widlansky (USA), Andrew Wittenberg (USA), Tim Woollings (UK), Sang-Wook Yeh (Republic of Korea), Chidong Zhang (USA), Lixia Zhang (China), Xiaotong Zheng (China), Liwei Zou (China) Review Editors: John Fyfe (Canada), Won-Tae Kwon (Republic of Korea), Kevin Trenberth (USA), David Wratt (New Zealand) This chapter supplementary material should be cited as: Christensen, J.H., K. Krishna Kumar, E. Aldrian, S.-I. An, I.F.A. Cavalcanti, M. de Castro, W. Dong, P. Goswami, A. Hall, J.K. Kanyanga, A. Kitoh, J. Kossin, N.-C. Lau, J. Renwick, D.B. Stephenson, S.-P. Xie and T. Zhou, 2013: Cli- mate Phenomena and their Relevance for Future Regional Climate Change Supplementary Material. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Available from www.climatechange2013.org and www.ipcc.ch. 14SM-1 Table of Contents 14.SM.1 Monsoon Systems.......................................... 14SM-3 14.SM.2 El Nino-Southern Oscillation and Its Flavours...................................................... 14SM-6 14.SM.3 Annular and Dipolar Modes....................... 14SM-6 14.SM.4 Large-scale Storm Systems......................... 14SM-7 14.SM.5 Additional Phenomena of Relevance.................................................. 14SM-10 14.SM.6 Future Regional Climate Change............ 14SM-10 References .......................................................................... 14SM-56 14SM 14SM-2 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material 14.SM.1 Monsoon Systems 14.SM.1.3.1 South America Monsoon System 14.SM.1.1 Global Overview Although the changes in wind direction from winter to summer occur only in a small area within South America, there are large differences Monsoons are seasonal phenomena and are responsible for the major- in the atmospheric circulation and in sources of humidity from winter ity of summer rainfall within the tropics. In the classical view, the mon- to summer. These differences are related to the rainy season in central soon is driven by the seasonal cycle of solar heating and difference in and southeastern Brazil, which begins at the middle/end of spring and thermal inertia of land and ocean that establish a land sea tempera- finishes at the middle/end of autumn (Silva and Carvalho, 2007; Raia ture difference. This contrast, with the land being warmer than the sur- and Cavalcanti, 2008). rounding ocean in late spring and summer, gives favourable conditions for the occurrence of convection in the summer hemisphere, allowing The lifecycle of the South America Monsoon System (SAMS) is dis- the monsoon to be viewed as a seasonal migration of the Inter-Tropi- cussed in Raia and Cavalcanti (2008), where the main atmospheric cal Convergence Zone (ITCZ). As the monsoon season matures, latent characteristics in the onset and demise are related to the rainy season. heat released by convection high above the land surface helps to pull The changes in humidity flux linked to the low-level flow changes over in additional moisture from nearby oceans over the land, maintaining the northernmost part of South America and the Amazonia region, the wet season. This thermal forcing depends on large-scale orography eastward shifting of subtropical high, strong northwesterly moisture and controls the regional monsoon domain and intensity. The land sea flux east of tropical Andes, are the main features in the onset. At high temperature difference is projected to become larger in the summer levels, the Bolivian High and the Northeast High Level Cyclonic Vortex season as seen from larger warming over land than ocean (Section are established during this period. The moisture flux from the Atlantic 12.4.3.1 and Annex I Figures AI.4 to AI.5). However, this does not lead Ocean over northern South America, crossing the Amazonia region and to a generally stronger monsoon circulations in the future, as chang- directed to the southeast, increases the humidity over southeastern es in regional monsoon characteristics are rather complex. In broad Brazil, favouring the intensification of convection there. The resulting terms, the precipitation characteristics over Asia-Australia, Americas coupling between Amazonia convection and frontal systems, and the and Africa can be viewed as an integrated global monsoon system, favourable high-level anomalous circulation over the continent, often associated with a global-scale persistent atmospheric overturning cir- associated with the Pacific South American (PSA) wave train, origi- culation (Trenberth et al., 2000). Wang and Ding (2008) demonstrated nate the South Atlantic Convergence Zone (SACZ). The whole cycle of that the global monsoon is the dominant mode of annual variation of SAMS comprises three stages, the rainfall beginning over northwestern the tropical circulation, characterizing the seasonality of the Earth s South America, SACZ establishment and precipitation increase over the climate in tropical latitudes. The monsoon-affected region is, however, mouth of Amazon River (Nieto-Ferreira and Rickenbach, 2010). not uniform in the historical record (Conroy and Overpeck, 2011), and it could vary in the future. In a recent review of SAMS, the main structure and lifecycle; the onset features; and the diurnal, mesoscale, synoptic, intraseasonal, interan- 14.SM.1.2 Definition of Global Monsoon Area, Global nual and inter-decadal variability are discussed, as well as the long- Monsoon Total Precipitation and Global term variability and climate change (Marengo et al., 2010). Monsoon Precipitation Intensity Jones and Carvalho (2013) used the Large scale Index for South Ameri- The global monsoon area (GMA) is defined as where the annual ca Monsoon LISAM index (Silva and Carvalho, 2007), which is obtained range of precipitation exceeds 2.5 mm day 1. Here, the annual range from the combined Empirical Orthogonal Function (EOF) analysis of is defined as the difference between the May to September (MJJAS) low-level (850 hPa) zonal and meridional winds, temperature and spe- mean and the November to March (NDJFM) mean. The global monsoon cific humidity. total precipitation (GMP) is defined as the mean of summer rainfall in the monsoon area. The global monsoon precipitation intensity (GMI) is Seasonal precipitation variability over South America is well represent- defined as GMP divided by GMA. ed by Atmospheric General Circulation Models (AGCMs) and Coupled General Circulation Models (CGCMs), mainly the large differences 14.SM.1.3 Definition of Monsoon Onset, Retreat between summer and winter. However, the intensity or configuration and Duration of rainfall patterns in the summer season is not well represented by some models. Vera et al. (2006), and Vera and Silvestri (2009) analysed Monsoon onset date, retreat date and its duration are determined using seven models of World Climate Research Programme Coupled Model the criteria proposed by Wang and LinHo (2002) utilizing only precipita- Intercomparison Project Phase 3 (WCRP CMIP3) for the 20th century tion data. Based on the regionally averaged relative climatological mean and showed that seasonal precipitation differences are well represent- daily precipitation, which is the difference between the climatological ed. Some models capture the precipitation variability, indicated by the daily precipitation and dry month (January in the Northern Hemisphere standard deviation, and maximum rainfall associated with the SACZ, and July in the Southern Hemisphere ) mean precipitation, the onset in the first three months (January, February and March (JFM)) and the (retreat) date is defined as the date when the relative precipitation first last three months (October, November and December (OND)), but with exceeds (last drops below) 5 mm day 1, and the duration is defined as different intensities compared to the observations. The ensemble mean their difference. The daily climatology of precipitation was defined as precipitation analysis of nine models WRCP-CMIP3, also for the 20th the sum of the first 12 harmonics of daily average precipitation. century, by Seth et al. (2010), indicated reasonable comparisons of 14SM 14SM-3 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change SON and DJF with observations, although specific features as the ITCZ south thermal contrast, the shear vorticity of zonal winds, the south- intensity and position, and extension of SACZ to the ocean, were not westerly monsoon and the South China Sea monsoon. Although the properly represented. existing indices highlight different aspects of the EASM, they agree well in the traditional Chinese meaning of a strong EASM, viz. an Other comparisons of IPCC CMIP3 models with observed precipitation, abnormal northward extension of the southerlies into North China. The in Bombardi and Carvalho (2009), show that some models capture the associated precipitation anomaly appears as excessive rainfall in North main features of SAMS, as the NW SE band from Amazonia to the China along with a deficient Meiyu in the Yangtze River Valley (see southeast, representing SACZ occurrences, and the Atlantic ITCZ. How- Figure 3 of Zhou et al., 2009b for patterns of precipitation over eastern ever, intensities and positions of maximum precipitation are not well China associated with stronger and weaker monsoon circulations). represented. The annual cycle in small areas of South America is not well represented by the majority of models, but has good representa- 14.SM.1.5 Present Understanding of the Weakening tion in southern Amazon and central Brazil. The duration of the rainy Tendency of East Asian Summer Monsoon season is overestimated over west South America and underestimated Circulation Since the End of the 1970s over central Brazil, in CMIP3 models (Bombardi and Carvalho, 2009). Some aspects of the humidity flux over South America are well repre- From 1950 to present, the EASM circulation has experienced an sented by a set of CMIP3 models (Gulizia et al., 2013). inter-decadal scale weakening after the 1970s (Figure 14.SM.1), result- ing in deficient rainfall in North China but excessive rainfall in central The South Indian, Pacific and Atlantic Oceans have a role on SAMS East China along 30°N (Hu, 1997; Wang, 2001; Gong and Ho, 2002; Yu variability (Drumond and Ambrizzi, 2005; Grimm et al., 2007); therefore et al., 2004). The weakening of EASM is associated with weakening of it is expected that projected changes in sea surface temperature (SST) 850 hPa southwesterly wind (Xu et al., 2006), a tropospheric cooling patterns may affect this variability. over East Asia (Yu and Zhou, 2007; Zhou and Zhang, 2009), a westward extension of the western Pacific Subtropical High (Zhou et al., 2009a), a Changes in the annual cycle of the SAMS, from the 20th to the end of zonal expansion of South Asian High (Gong and Ho, 2002; Zhou et al., 21st century, projected by nine models, considering the A2 scenario 2009a) and an enhanced subtropical westerly jet (Zhang et al., 2006; were presented by Seth et al. (2010). The ensemble shows increased Yu and Zhou, 2007). The circulation changes have led to significant precipitation over SESA region (southern sector of southeastern South changes in mean and extreme precipitation (Zhai et al., 2005), frequen- America). cy and intensity of rainfall events (Qian et al., 2009; Yu et al., 2010c; Bennartz et al., 2011; Li et al., 2011a; Liu et al., 2011; Guo et al., 2013). Some CMIP3 models project precipitation increase in austral summer and a decrease in austral spring in the SAMS region (Seth et al., 2011). The weakening of the EASM circulation since the 1970s is dominat- Precipitation increase at the end of the monsoon cycle and reduced ed by natural decadal variability (Lei et al., 2011; Zhu et al., 2012). precipitation in the onset in central monsoon region could indicate a The combination of tropical ocean warming associated with the phase shifting in the lifecycle monsoon period. These changes were related to transition of Pacific Decadal Oscillation (PDO; see Figure 14.SM.1 for less moisture convergence in the austral spring and more convergence the EASM circulation response to PDO-related SST forcing in AGCM during summer. During the dry season, the changes are very small. The experiments, Zhou et al., 2008; Li et al., 2010c; Zhou and Zou, 2010) warmer troposphere and increased stability due to global warming and weakening of atmospheric heating over the Tibetan Plateau leads (Chou and Chen, 2010) act as a remote mechanism to reduced precip- to a reduction of land sea thermal contrast, and thereby a weakened itation of SAMS in the winter. During summer, the local mechanisms, monsoon circulation (Ding et al., 2008, 2009; Duan and Wu, 2008). such as increased evaporation and decreased stability, contribute to The weakening of the Tibetan Plateau heating is caused by increased the increased precipitation. Both mechanisms seem to reduce precip- snow cover and depth in winter associated with North Atlantic Oscilla- itation during spring, when there is not enough soil moisture and still tion (NAO) phase change and North Indian Ocean warming (Zhang et atmospheric stability. al., 2004; Ding and Wang, 2009). The specified aerosol forcing cannot reproduce the observed EASM circulation changes (Figure 14.SM.1). Idealized experiments with a coupled atmospheric ocean model sub- jected to increasing carbon dioxide (CO2) show intensification of the 14.SM.1.6 Details of Precipitation Changes over East precipitation difference between summer and winter in the global China Associated with the Weakening monsoon regions, including the SAMS region (Cherchi et al., 2011). Tendency of East Asian Summer monsoon Circulation Since the End of the 1970s 14.SM.1.4 What Is a Stronger East Asian Summer Monsoon? Precipitation changes due to the weakening tendency of the EASM circulation are evident in both mean and extreme precipitation (Zhai et Unlike the Indian summer monsoon, which can be defined in terms al., 2005). Analysis based on daily data shows that both the frequency of simple scalar indices partly due to its homogeneity in rainfall dis- and amount of light rain have decreased in eastern China during 1956 tribution, it is more complicated to define an index for the East Asian 2005, with high spatial coherency, attributable in part to the warm Summer Monsoon (EASM; Zhou et al., 2009b). Wang et al. (2008) dis- rain suppression by aerosols (Qian et al., 2009; Liu et al., 2011; McKee cussed the meanings of 25 existing EASM indices and classify these et al., 2011). The results of early studies based on daily precipitation 14SM indices into five categories: the east west thermal contrast, north data have been argued by recent studies based on hourly data. Recent 14SM-4 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material 14.SM.1.7 Uncertainties in the Aerosol Effects on the Observed East Asian Summer Monsoon Changes The aerosol effect on EASM circulation and precipitation changes during the past 60 years has large uncertainties. The combined effect of BC and sulphate aerosols is hypothesized to produce a weakened EASM but enhanced precipitation over South China (Liu et al., 2009a). Sulphate aerosol may reduce the surface heating over land and dimin- ish land sea thermal contrast ). The increases of both sulphate and black carbon aerosol since 1950 may have weakened the land-sea temperature contrast and curtailed the monsoon in East Asia by acting to reduce September s rainfall (Guo et al., 2013). However, some aer- osols (e.g., sulphate) could cool the atmosphere and surface but some (e.g., EC and dust) could cool the surface and warm the atmosphere. So the aerosol forcing impacts on land-ocean temperature contrast and hence EASM circulation is not well known yet. GCM experiments have shown that increased aerosol optical depth in China causes a notice- able increase in precipitation in the southern part of China in July, through induced surface cooling in mid-latitude leading to strengthen- ing of the Hadley circulation (Gu et al., 2006). However, the inclusion of black carbon in the simulations does not necessarily produce the observed north drought/south flood precipitation pattern in China during the past 50 years (Wang and Zhou, 2005).Sulphate aerosols have been shown to affect rainfall redistribution over East Asia in late spring and early summer, and weaken monsoon rainfall through direct (Kim et al., 2007; Liu et al., 2009b) or semi-direct (Zhang et al., 2009) effects. However, these results do not explain the observations of the north/dry and south/wet pattern in East Asia in recent decades. Some GCM experiments showed that the aerosol forcing may not be a forc- ing mechanism for the weakening tendency of EASM circulation and Figure 14.SM.1 | Time series of East Asian Summer Monsoon (EASM) indices (bars) and their trend lines (dashed line) from National Centers for Environmental Predic- precipitation (Li et al., 2007, 2010c). tion/National Center for Atmospheric Research (NCEP/NCAR) reanalysis, (b) European Centre for Medium range Weather Forecast (ECMWF) 40-year reanalysis of the global 14.SM.1.8 The Dynamics of the North American atmosphere and surface conditions (ERA-40 reanalysis, (c) Global Ocean Global Atmo- Monsoon System sphere (GOGA) run of Community Atmosphere Model version 3 (CAM3), (d) difference between GOGAI and GOGA run of CAM3. Also shown is the slope of the trend (b, change per 50 years). The EASM index is defined as the normalized zonal wind shear Seasonal mean precipitation in the North American monsoon region between 850 and 200 hPa averaged over 20°N to 40°N and 110°E to 140°E. GOGA (mainly Mexico and the extreme Southwestern USA) is generally con- run is forced by observed monthly SSTs over the global oceans from 1950 to 2000, trolled by the establishment of a continental-scale upper-level anticy- while GOGAI is driven by global sea surface temperature (SST) plus IPCC 20th century clone and a lower-level thermal low (Higgins et al., 1997; Vera et al., atmospheric (primarily greenhouse gases and direct aerosol) forcings (Li et al., 2010b). 2006), it is also under the influence of factors operating at multiple This figure demonstrated that the weakening tendency of EASM circulation was driven by Pacific Decadal Oscillation (PDO). spatial and temporal scales, including propagating waves and troughs in the tropics, synoptic disturbances and fronts entering the domain from the mid-latitudes and land-falling tropical cyclones (Douglas and Englehart, 2007). It is fed by two distinct, relatively narrow low-level analysis of hourly data finds that the rainfall amount and frequency moisture sources the Great Plains Low-level Jet (LLJ) to the east of have significantly increased (decreased) but the rainfall intensity has the Sierra Madres, which is approximately 200 to 400 km in width, and decreased (increased) in the middle to lower reaches of the Yangtze the narrower Gulf of California LLJ to the west of the Sierra Madres, River valley (North China).The wetter South-drier North pattern of which is approximately 100 km in width. Further, the large-scale cir- mean precipitation is mostly attributed to moderate and low inten- culation features including the upper-tropospheric monsoon ridge, sity rainfall ( 10 mm hr 1) rather than the extreme rainfall ( 20 mm/ the North Atlantic subtropical (or Bermuda) high, the ITCZ, and the hour, Yu et al., 2010c), although the frequency of extreme rain events subtropical jet stream in which these phenomena develop are mod- has substantially increased along the Yangtze River (Qian et al., 2007a, ified by slowly evolving coupled climate features associated with the 2007b). The drier North China is dominated by decreased long duration PDO, the Atlantic Multi-decadal Oscillation (AMO) and solar activity (persist longer than 6 hours) rainfall events, especially those occurring (van Loon et al., 2004; Feng and Hu, 2008; Seager et al., 2009; Metcalfe between midnight and morning, while the wetter South China is asso- et al., 2010; Arias et al., 2012). Dust aerosol may also have an impact ciated with both the substantially increased frequency and amount of on the North American monsoonal precipitation (Zhao et al., 2012). long duration precipitation (Li et al., 2011a). 14SM 14SM-5 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change 14.SM.2 El Nino-Southern Oscillation and Its Changes in the impacts from conventional El Nino to CP El Nino are Flavours possibly due to the change in the location of tropical atmospheric heating source (Hoerling et al., 1997; Kug et al., 2010a). For example, The El Nino-Southern Oscillation (ENSO) is a coupled ocean atmos- conventional El Nino leads to the Pacific North American (PNA)-like phere phenomenon naturally occurring at the interannual time scale. El atmospheric pattern along with changes in the Aleutian low strength Nino involves anomalous warming of tropical eastern-to-central Pacific (Müller and Roeckner, 2008), while CP El Nino is more linked to the SST usually peaking at the end of the calendar year, which leads to a atmospheric variability over the North Pacific such as the North Pacific weakening of zonal SST contrast between the tropical western Pacif- Oscillation (NPO), which represents a meridional shift of the Aleutian ic warm pool and the tropical eastern Pacific cold tongue (Figure low pressure centre (Di Lorenzo et al., 2010). 14.12). It is closely linked to its atmospheric counterpart, the Southern Oscillation, which is a surface pressure seesaw between Darwin and Some studies argue that more frequent occurrence of CP El Nino events Tahiti or more comprehensively the equatorial zonal-overturning called during recent decades is related to the changes in the tropical Pacific the Walker Circulation . El Nino and Southern Oscillation are two dif- mean state in response to increased greenhouse gas (GHG) forcing ferent aspects of ENSO and are caused by a positive feedback between (Yeh et al., 2009). In particular, a flattening of thermocline depth in the atmosphere and the tropical Pacific Ocean referred to as Bjerknes the equatorial Pacific and a weakened Walker Circulation under global feedback (Bjerknes, 1966, 1969). The opposite phase to El Nino, when warming modulate the relative importance of feedback processes asso- the eastern equatorial Pacific cools, has been named La Nina. ciated with El Nino dynamics (Yeh et al., 2009). A heat budget analy- sis in the ocean mixed layer reveals that zonal advection is a major Beyond the classical view of the El Nino pattern, another structure of dynamical feedback process in developing of CP El Nino and the anom- anomalous warm SST, that is, the warming in the equatorial central alous surface heat flux in the decaying of CP El Nino (Kug et al., 2010b; Pacific (CP) sandwiched by anomalous cooling to the east and west Yu et al., 2010b). On the other hand, other studies (Lee and McPhaden, (hereafter referred to as CP El Nino; other names are listed in Table 2010; McPhaden et al., 2011) further showed that the future climate 14.SM.3; Trenberth and Tepaniak, 2001; Larkin and Harrison, 2005), condition change associated with the increased occurrence of CP El has been frequently observed in the tropical Pacific since the 1990s Nino is not consistent with the observed climate condition that leads (Ashok et al., 2007; Kao and Yu, 2009; Kug et al., 2009; see also Section to more frequent occurrence of CP El Nino. Thus, whether the mean cli- 2.7.8; Table 14.SM.3; Yeh et al., 2009). CP El Nino shows no basin-wide mate state change leads to more frequent emergence of CP El Nino or features or distinct propagation of SST anomalies and it occurs rather the other way around is not yet known. The increase in the frequency episodically in comparison with the conventional El Nino (Yu et al., of CP El Nino during recent decades may be a manifestation of natural 2010b). Many indices of CP El Nino have been proposed, but no clear climate variability (Na et al., 2011; Yeh et al., 2011). and agreed definition has yet emerged to identify both CP El Nino and conventional El Nino (see Table 14.SM.3). Furthermore, several stud- ies using other classification methods do not find such a distinction 14.SM.3 Annular and Dipolar Modes between CP and conventional El Nino events (Newman et al., 2011; Lian and Chen, 2012), seeing changes in the location of El Nino from 14.SM.3.1 Southern Annular Mode the western to the eastern Pacific as part of a continuous random dis- tribution (Giese and Ray, 2011). Hence, CP El Nino and conventional El The Southern Annular Mode (SAM, also known as Antarctic Oscilla- Nino may not be different phenomena but rather a nonlinear evolution tion (AAO)), is the leading mode of climate variability in the Southern of the ENSO phenomenon (Takahashi et al., 2011). A debate remains Hemisphere extratropics, comprising co-varying sea level pressure or as to whether the CP El Nino is intrinsically different from the conven- geopotential height anomalies of opposite sign in middle and high tional El Nino or if every event is a varying mix of these two patterns. latitudes, extending through the depth of the troposphere, which are related to fluctuations in the latitudinal position and strength of the The global impacts of CP El Nino are different from those of the conven- mid-latitude jet. When pressures/heights are below (or above) average tional El Nino (Ashok et al., 2007; Kao and Yu, 2009; Hu et al., 2012), over Antarctica the SAM is defined as being in its positive (or negative) including monsoonal rainfall over India (Kumar et al., 2006), China, phase and the circumpolar westerly winds are stronger (or weaker) Korea (Feng et al., 2010; Feng and Li, 2011; Kim et al., 2012) and over than average. Associated with this, the storm tracks move poleward Australia (Ashok et al., 2007; Wang and Hendon, 2007; Taschetto and during the positive SAM and equatorward during the negative SAM. England, 2009; Taschetto et al., 2009), USA air temperature and rainfall Although broadly annular in nature, hence its name, the spatial pattern (Mo, 2010), winter temperature over the North Atlantic and Eurasian of the SAM includes a substantial non-annular component in the Pacif- regions (Graf and Zanchettin, 2012), typhoon activity in the western ic sector (Figure 14.27, Kidston et al., 2009; Fogt et al., 2012). SAM var- North Pacific (Guanghua and Chi-Yung, 2010; Hong et al., 2011; Kim et iability has a major influence on the climate of Antarctica, Australasia, al., 2011) and the warming in West Antarctica (Lee et al., 2010b; Ding southern South America and South Africa (Watterson, 2009; Thompson et al., 2011). The influence of CP El Nino on Atlantic hurricanes may et al., 2011 and references therein). also be different from the conventional El Nino (Kim et al., 2009), but it has been shown that the anomalous atmospheric circulation in the The SAM exhibits marked seasonal variability in both its structure and hurricane main development region during CP El Nino is similar to that in its effects on regional climate. For example, correlations between during conventional El Nino (Lee et al., 2010a). the SAM and temperature at some Antarctic Peninsula stations change 14SM sign between seasons (Marshall, 2007) while the effect of the SAM on 14SM-6 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material temperature and rainfall over New Zealand (Kidston et al., 2009) and tions of annual frequency in individual ocean basins, however, can be on regional Australian rainfall (Hendon et al., 2007) changes marked- greater than 40% of the means in those basins, which reduces the sig- ly through the year. Moreover, nonlinearities in the structure of the nal-to-noise ratio and introduces substantial uncertainty into regional positive and negative polarities of the SAM result in polarity-specific tropical cyclone frequency trend detection. changes in surface climate impacts (Fogt et al., 2012). Detection of past trends in various measures of tropical cyclone activity Silvestri and Vera (2009) discussed decadal variability in the effects is constrained by the quality of the historical data records and uncer- of the SAM on regional climate, emphasising broad-scale changes in tain quantification of natural variability in these measures (Knutson et the sign of precipitation relationships over southern South America al., 2010; Lee et al., 2012; Seneviratne et al., 2012; see also Chapters and temperature relationships over Australia during 1958 1979 and 2 and 10). Consideration of global trends as well as trends in specific 1983 2004. Marshall et al. (2011) examined a regional change in the regions is further complicated by substantial regional differences in sign of a SAM temperature relationship in part of East Antarctica and data quality, collection protocols and record length (Knapp and Kruk, demonstrated that changes in the phase and magnitude of the zonal 2010; Song et al., 2010). Attempts to detect trends in even smaller wave-number 3 pattern, superimposed upon the annular structure of intra-basin regions such as those defined by islands or archipelagos are the SAM, were responsible for the reversal. Using ice core data they further constrained by the reduced data sample size associated with also showed that such changes occurred throughout the 20th centu- finely subdividing the global data. Intra-basin regional trend detection ry and hence were likely to reflect internal natural variability rather is also substantially challenged by variability in tropical cyclone tracks than an anthropogenic forcing. Such changes in coastal Antarctica (Kossin and Camargo, 2009). will impact the role of the SAM in driving the formation of Antarctic Bottom Water, a central component of the global thermohaline circu- This variability is driven largely by random fluctuations in atmospheric lation (McKee et al., 2011). Others have shown that the impact of the steering currents, but also is observed across a broad range of time SAM on Antarctic climate also depends on how it interacts with other scales in response to more systematic modes of climate variability modes of circulation variability, such as those related to ENSO (e.g., such as the ENSO, PDO, NAO, Atlantic Meridional Mode (AMM), NPO, Fogt and Bromwich, 2006). and Madden Julian Oscillation (MJO; Ho et al., 2004; Wu et al., 2005; Camargo et al., 2007, 2008; Kossin and Vimont, 2007; Wang et al., The physical mechanisms of the SAM are generally well understood, 2007; Chand and Walsh, 2009; Tu et al., 2009; Kossin et al., 2010; Wang and the SAM is well represented in many climate models, although et al., 2010; Chu et al., 2012), and potentially in response to global the detailed spatial and temporal characteristics vary between models warming (Wang et al., 2011). Even modest tropical cyclone track vari- (Raphael and Holland, 2006). In the past few decades the SAM index ability can lead to large differences in associated impacts at a specific has exhibited a positive trend in austral summer and autumn (Mar- location. For example, a particular group of islands can be affected by shall, 2007; Figure 14.6.1; e.g., Jones et al., 2009), a change attributed multiple tropical cyclones in one season (e.g., the Philippines in 2009) primarily to the effects of ozone depletion and, to a lesser extent, the and then remain largely unaffected for multiple subsequent years increase in GHGs (Thompson et al., 2011, see also Section 10.3.3.5), even while the total number of storms in the larger, but immediate thus demonstrating that ozone depletion has had a direct effect on surrounding region exhibits normal variability. This type of temporal surface climate in the Southern Hemisphere, through its influence on clustering can occur randomly or via systematic modulation by cli- the SAM trend. It is likely that these two factors will continue to be the mate variability, and can also strongly affect the impact of tropical principal drivers into the future, but as the ozone hole recovers they cyclones on ecosystems such as coral reefs (Mumby et al., 2011). The will be competing to push the SAM in opposite directions (Arblaster combination of data issues (quality and sample size), signal-to-noise et al., 2011; Thompson et al., 2011; Bracegirdle et al., 2013), at least issues and the natural variability of tropical cyclone tracks introduce during late austral spring and summer, when ozone depletion has had substantial uncertainties into detection-attribution studies as well as its greatest impact on the SAM. The SAM is also influenced by tele- disaster and mitigation planning aimed at specific intra-basin regions. connections to the tropics, primarily associated with ENSO (Carvalho Furthermore, while theoretical arguments have been put forward link- et al., 2005; L Heureux and Thompson, 2006). Changes to the tropical ing tropical cyclone intensity and genesis with anthropogenic climate circulation, and to such teleconnections, as the climate warms could change (Emanuel, 1987; Rappin et al., 2010), there is little theoretical further affect SAM variability (Karpechko et al., 2010). guidance available to help elucidate the relationships between climate and tropical cyclone track variability. 14.SM.4 Large-scale Storm Systems Regional analyses of century-scale variability and trends of vari- ous measures of tropical cyclone activity provide mixed results from 14.SM.4.1 Tropical Cyclones which robust conclusions are difficult to establish (also see Chapter 2). Regional trends in tropical cyclone frequency have been identified 14.SM.4.1.1 Regional Detection of Past Changes in the North Atlantic, with storm frequency increasing sharply over the past 20 to 30 years. Over longer time periods, especially since the Annual mean global tropical cyclone frequency since 1980 (within the late 19th Century, the fidelity of the reported trends is debated (Hol- modern geostationary satellite era) has remained roughly steady at land and Webster, 2007; Landsea, 2007; Mann et al., 2007b). Different about 90 per year, with a standard deviation of about 10% (9 storms), methods for estimating undercounts in the earlier part of the North consistent with the expectations of a Poisson process. Standard devia- Atlantic tropical cyclone record provide mixed conclusions (Chang and 14SM 14SM-7 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Guo, 2007; Mann et al., 2007a; Kunkel and coauthors, 2008; Vecchi i ­dentified in trends in wave power in Atlantic buoy data (Bromirski and and Knutson, 2008, 2011). Trends in cyclone frequency have also been Kossin, 2008), and what part is due to trends in basin-wide frequency identified over the past 50 to 60 years in the North Indian Ocean and or intensity. The difference between Callaghan and Power (2010), who may be due to changes in the strength of the tropical easterly jet (Rao show a long-term decreasing trend in Australian landfall events and et al., 2008; Krishna, 2009) but again uncertainties in the regional trop- Grinsted et al. (2012), suggesting a long-term increasing trend in storm ical cyclone data quality substantially limit reliability, particularly when surge associated with USA landfall events, underscores the challenge attempting to detect Century-scale trends (Mohapatra et al., 2011). of understanding and projecting region-specific changes in tropical Furthermore, metrics based solely on storm frequency can be strongly cyclones. influenced by weak and/or short-lived storms (Landsea et al., 2010), which are arguably of much lesser physical relevance than stronger When data uncertainties due to past changes in observing capabili- and/or longer-lived storms. This limits the usefulness of such metrics ties are taken into account, confidence in the fidelity of any reported that do not take storm intensity or duration into account. basin-wide trends in tropical cyclone activity on time scales longer than about 50 years is compromised. Shorter term increases, such as Regional trends in the frequency of very intense tropical cyclones can observed in the Atlantic since 1970, appear to be robust (Kossin et al., be identified in the historical data over the past 30 to 40 years (Web- 2007), and have been hypothesized to be related, in part, to regional ster et al., 2005), although confidence in the amplitude of these trends external forcing by greenhouse gasses and aerosols (discussed below), is compromised by data homogeneity uncertainties (Landsea et al., but the more steady century-scale trends that may be expected from 2006; Kossin et al., 2007). There has been a sharp increase in annual CO2 forcing alone are much more difficult to assess given the data tropical cyclone power dissipation (which represents an amalgamation uncertainty in the available tropical cyclone records. This presents a of frequency, intensity and storm duration) in the Atlantic since 1970 confounding factor to formal detection of trends that may be attrib- (Emanuel, 2005; Kossin et al., 2007), but longer-term trends are more uted to anthropogenic effects because the expected natural variability uncertain because of data heterogeneities, particularly in the records of on multi-decadal time scales is not yet well quantified in the various storm intensity (Hagen and Landsea, 2012; Hagen et al., 2012; Landsea regions. et al., 2012). Upward regional and global trends in the intensity of the strongest storms have been identified in a more homogeneous data 14.SM.4.1.2 Understanding the Causes of Past and Projected record by Elsner et al. (2008), but their analysis was necessarily limited Regional Changes to the modern geostationary satellite period and spans only about 30 years. Consistently positive trends in the duration of the active part of Although there is evidence that SST in the tropics has increased due to the Atlantic hurricane season over the period 1851 2007 have been increasing GHGs (Karoly and Wu, 2005; Knutson et al., 2006; Santer et identified, but confidence in these trends remains low due to a com- al., 2006; see also Chapter 10 and Section 3.1.1.4; Gillett et al., 2008) bination of marginal statistical significance (p-values near or below and there is a theoretical expectation that increases in potential inten- 0.9), and the potential for data heterogeneity to artificially amplify the sity (PI) will lead to stronger tropical cyclones (Emanuel, 2000; Wing trends (Kossin, 2008). et al., 2007; Elsner et al., 2008), the relationship between SST and PI under CO2 warming has not yet been fully elucidated (see also Chapter Increasing trends in the frequency of land-falling tropical cyclones 10). PI describes the theoretical limit to how strong a tropical cyclone have not been identified in any region (Wang and Lee, 2008; Chan can become based on the three-dimensional thermodynamic environ- and Xu, 2009; Kubota and Chan, 2009; Lee et al., 2012; Weinkle et al., ment that the storm moves through (Emanuel, 1987). Observations 2012) although Callaghan and Power (2010) identified a statistically demonstrate a strong positive correlation between SST and PI, but it is significant downward trend in the number of severe tropical cyclones known that this relationship is not unique. For example, raising SST by making landfall over northeastern Australia since the late 19th centu- reducing surface wind speed produces a much more rapid increase in ry. Measurements of tropical cyclone landfall frequency are generally PI with SST than does raising it by increasing CO2 because other factors considered to be more reliable than those of storms that remain at sea that control PI will vary differently according to each process (Eman- throughout their lifetimes, particularly in the earlier parts of the his- uel et al., 2012). Similarly, vertical wind shear, which affects tropical torical records. But as described above, confining storm counts to any cyclone genesis and intensification, is apparently modulated differently pre-defined region cannot discriminate between basin-wide frequency by internal variability versus external radiative forcing of regional SST variability and track variability, and it remains uncertain whether the (e.g., Zhang and Delworth, 2009). trend reported by Callaghan and Power (2010) is driven by natural processes or whether some part is anthropogenically forced. A signif- Because of the known non-uniqueness of the relationship between icant positive trend has been identified in the frequency of large sea SST and PI, it is generally agreed that regional projections of SST by level anomaly events along the USA East and Gulf Coast in a tide- themselves are not a useful proxy for future PI. For example, the rela- gauge record spanning 1923 2008 and this trend has been argued tionship between SST and PI in CMIP3 projections in the western North to represent a trend in storm surge associated with landfalling hur- Pacific has been shown to be non-stationary because the projected ricanes (Grinsted et al., 2012). The long-term (86-year) and roughly tropical warming anomalies in the SRES A1B scenario are amplified in linear nature of the trend identified by Grinsted et al. (2012) is com- the upper troposphere, which convectively stabilizes the atmosphere pelling and the relevance is high because the trend is argued to relate and suppresses the increase in PI for a given increase in SST (Tsut- to high-impact surge events, but there is still the question of what sui, 2010, 2012). However, there is a growing body of research since 14SM portion of the trend is due to systematic track shifts, as previously the IPCC Fourth Assessment Report suggesting that the ­ ifference d 14SM-8 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material between regional SST and spatially averaged SST in the tropics (typ- the ­ ypothesis that long-term relative SST projections can serve as a h ically referred to as relative SST ) can serve as a useful proxy for useful proxy for future tropical cyclone PI, this remains an active area regional PI (Vecchi and Soden, 2007b; Xie et al., 2010; Ramsay and of research (and debate) without a clear consensus yet. Sobel, 2011; Camargo et al., 2012). The hypothesis is largely phenom- enological and based on observed correlation, but has some physical The distinction between the competing hypotheses described above basis in the theory that upper tropospheric temperatures are sensitive is a critical one because while tropical SST is expected to continue to mean tropical SST (Sobel et al., 2002), while regional lower trop- to increase under global warming, there is much more uncertainty in ospheric temperatures are more sensitive to local SST. This combina- how regional SST is expected to change relative to the tropical mean tion of factors affects regional lapse rates, which in turn affects PI. In (Vecchi et al., 2008; Villarini et al., 2011). In general, future relative SST this case, localized SST changes are hypothesized to be more effective changes forced by increasing WMGHG in the tropics are not expected at altering PI than a more globally uniform tropical SST change (e.g., to be large in regions where storms form and track (Vecchi and Soden, as would be expected from forcing by well-mixed greenhouse gases 2007b) and thus if relative SST is a useful proxy for PI, there would not (WMGHGs)) of the same magnitude. be an expectation for large increases in future tropical cyclone inten- sity (Vecchi et al., 2008). The results of Emanuel (2010) and Emanuel However, it has been argued that the physical link between relative SST et al. (2012) do not provide alternative projections of PI, but only state and PI is only valid on time scales shorter than the ocean mixed-lay- that they are not constrained by any measure of future SST alone. As er equilibration time scale (Emanuel, 2010; Emanuel et al., 2012). On an example of the ramifications of the differences, the present approx- longer time scales of a few years or more, which allow the ocean mixed imately 40-year period of heightened tropical cyclone activity in the layer to equilibrate to surface forcing, Emanuel et al. (2012) argue that North Atlantic, concurrent with comparative recent quiescence in most PI is mostly controlled by local surface radiative balance and ocean other ocean basins (Maue, 2011), is apparently related to differences in heat flux convergence and in general, SST cannot be considered an the rate of SST increases, as global SST has been rising steadily but at external control on PI, but merely a co-factor. By this argument (and a slower rate than the Atlantic (Trenberth and Shea, 2006). The present the assumptions that it is based on), projections of SST by themselves, period of relatively enhanced warming in the tropical North Atlantic whether absolute SST or relative SST, cannot uniquely determine future has been proposed to be due primarily to internal variability (Ting et PI changes, and hence they cannot uniquely determine future tropical al., 2009; Zhang and Delworth, 2009; Camargo et al., 2012), and both cyclone changes. Still, the studies of Camargo et al. (2012), Ramsay direct (dimming) and indirect (cloud albedo) effects of radiative forc- and Sobel (2011), Vecchi and Soden (2007b), Xie et al. (2010), and ing by anthropogenic tropospheric aerosols (Mann and Emanuel, 2006; others have demonstrated that the correlation between relative SST Booth et al., 2012) and mineral (dust) and volcanic aerosols (Evan et and PI is in fact consistently evident on multi-decadal and longer time al., 2009, 2011, 2012). None of these proposed mechanisms provide a scales. Thus, while the presumptive theoretical arguments of Emanuel clear expectation that North Atlantic SST will continue to increase at (2010) and Emanuel et al. (2012) suggest that there is no reason to a greater rate than the tropical mean SST and thus if future PI can be expect such a relationship (and therefore there is no physical justifi- described by relative SST, the present steep upward trend in tropical cation for using 21st century relative SST projections to statistically cyclone intensity in the North Atlantic would be expected to abate. infer future PI), both data and model projections support the existence of a useful relationship between relative SST and PI on decadal and Projected changes in potential intensity calculated from CMIP5 mul- longer time scales. Although the balance of relevant literature supports ti-model ensembles are shown in Figure 14.SM.2. Figure 14.SM.2 | Change in seasonal mean tropical cyclone potential intensity for end of the century RCP8.5 (2081 2100) minus Historical Control (1986 2005) in CMIP5 multi-model ensembles. (Top) August to October, 10°S to 40°N and (bottom) January to March, 40°S to 10°N. Potential intensity computation uses the method of Bister and Emanuel (1998) applied to monthly means fields to compute the potential maximum surface wind speed (m s 1) of tropical cyclones. The seasons for each panel are the historical high frequency periods for tropical cyclones in each hemisphere. The number of models in the ensemble appears in the upper right of each panel. 14SM 14SM-9 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change 14.SM.5 Additional Phenomena of Relevance Izumo et al. (2010) made use of these transition processes in the TBO to document El Nino forecast skill by monitoring the state of the IOD 14.SM.5.1 The Role of the Pacific North American in northern fall. In addition, convective heating anomalies in the Pacific Pattern in Linking El Nino-Southern Oscillation (Wu and Kirtman, 2004), or in the Indian Ocean associated with the IOD and the North Atlantic Oscillation (e.g., Annamalai et al., 2005), or a combination from the southeastern Indian Ocean and western Pacific (Clarke et al., 1998; Li et al., 2001, Recent diagnoses (see review by Bronnimann, 2007) show that ENSO 2006) affect the southeastern Indian Ocean and western north Pacif- may impact European climate through modulation of the NAO, espe- ic anticyclones. The resulting wind stress anomalies in the equatorial cially during late winter and early spring. The observational and model western Pacific contribute to TBO SST transitions in the eastern equa- results reported by Li and Lau (2012b) and Li and Lau (2012a) illus- torial Pacific (Lau and Wu, 2001; Turner et al., 2007). Such consecutive trate that one possible mechanism for this connection is related to annual SST anomaly and anomalous monsoon transitions from one sign the PNA-like teleconnection pattern forced by ENSO events. Specifi- to another characterize the TBO. Thus, the TBO provides the fundamen- cally, this response pattern is accompanied by systematic changes in tal framework for understanding coupled processes across the Indo-Pa- the position and intensity of the storm tracks over the North Pacific cific region involving the Asian-Australian monsoon, the IOD, and ENSO. and North America. The transient disturbances along the storm tracks propagate farther eastward and reach the North Atlantic. The ensuing The processes that produce the TBO are affected by internally gen- dynamical interactions between these stormtrack eddies and the local erated decadal-time scale variability. Just as the Inter-decadal Pacific quasi-stationary circulation lead to changes in the NAO. In addition to Oscillation (IPO) influences the nature of interannual variability in the tropospheric processes, Ineson and Scaife (2009), Bell et al. (2009) and Australia-Pacific region (Power et al., 1999), so does the IPO affect the Cagnazzo and Manzini (2009) have demonstrated a stratospheric link decade-to-decade strength of the TBO (Meehl and Arblaster, 2011). between ENSO and NAO in late winter. During periods of positive IPO (warmer SSTs in the tropical Pacific on the decadal timescale, e.g., from the 1970s to 1990s), the TBO was 14.SM.5.2 Tropospheric Biennial Oscillation weak, and vice versa for negative IPO with a stronger TBO (post-1990s; Meehl and Arblaster, 2012). Thus, prediction of decadal time scale var- It has long been noted that there is a biennial tendency of many phe- iability assessed in Chapter 11 that can be associated, for example, nomena in the Indo-Pacific region that affects droughts and floods with the IPO (e.g., Meehl et al., 2010) can influence the accuracy of over large areas of south Asia and Australia (e.g., Troup, 1965; Tren- shorter term predictions of interannual variability associated with the berth, 1975; Nicholls, 1978; Mooley and Parthasarathy, 1983). Brier TBO across the entire Indo-Pacific region (Turner et al., 2011). This set (1978) suggested a possible central role of air sea coupling, and of regional processes from interannual to decadal is of great relevance Meehl (1987) proposed a mechanism involving large-scale dynamically for decadal climate prediction and the short-term climate change prob- coupled interactions across the Indo-Pacific to account for the biennial lem (Chapter 11). tendency, termed the Tropospheric Biennial Oscillation (TBO, Meehl, 1997). There was also a role for atmospheric circulation anomalies over south Asia and consequent land surface temperature anomalies 14.SM.6 Future Regional Climate Change that contributed to anomalous meridional temperature gradients and biennial monsoon variability (Meehl, 1994a, 1994b), thus giving rise to 14.SM.6.1 Future Regional Climate Change, Overview explanations of the TBO that involved processes in the Indian sector (Chang and Li, 2000; Li et al., 2001). SST anomalies in the equatorial 14.SM.6.1.1 How the Confidence Table Was Constructed eastern Pacific Ocean in the TBO tend to transition from positive to negative (or vice versa) in northern spring, so the seasons leading up The confidence levels in Columns 2, 3, 6 and 7 of the confidence table to those transitions are crucial to the TBO (e.g., Meehl and Arblaster, (Table.14.2) are based on subjectively determined criteria, but the cri- 2002a, 2002b). The fundamental nature of the dynamically coupled teria are applied objectively. processes involved with the TBO have been additionally documented in a number of global coupled climate model simulations (e.g., Meehl, Each regional entry in Column 2 of the table, evaluating confidence in 1997; Ogasawara et al., 1999; Loschnigg et al., 2003; Nanjundiah et al., models ability to simulate present-day temperature, is based on values 2005; Meehl and Arblaster, 2011). shown in Figures 9.39 and 9.40. Regional patterns of SST anomalies in the TBO in the Indian Ocean The following criteria have been applied to determine confidence level during the northern fall season following the south Asian monsoon (see table, next page): subsequently became known as the Indian Ocean Dipole (IOD, e.g., Saji et al., 1999; Webster et al., 1999; Section 14.3.3). Thus, a negative For precipitation (Column 3), replace 2°C in the above table with 20%. IOD in northern fall (negative SST anomalies in the western tropical For both temperature and precipitation, these values are chosen to rep- Indian Ocean, and positive SST anomalies in the eastern tropical Indian resent the accuracy with which the models simulate gross features of Ocean), with negative SST anomalies in the equatorial eastern Pacif- present-day mean climate. ic, transition to basin-wide negative SST anomalies across the Indian Ocean in northern winter, with positive SST anomalies in the eastern For future projections (Columns 6 and 7), confidence levels are based on 14SM equatorial Pacific in the following northern spring and summer in the analyses of how much the model signals rise above natural variability. TBO (Meehl et al., 2003). 14SM-10 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Model Spread (difference between 25th and 75th percentiles) >2°C in both seasons <2°C in only one season <2°C in both seasons <2°C in both seasons Medium (M) High (H) High (H) Bias of Ensemble Mean <2°C in only one season Low (L) Medium (M) High (H) >2°C in both seasons Low (L) Low (L) Medium (M) Both the signals and the natural variability are based on averages over Each assessment of relevance is traceable back to confidence state- the SREX regions. Natural variability is quantified in a similar way as in ments. So for example, if there is high confidence in the projected Annex I standard deviations of model-estimated present-day natural change in a phenomenon (HP) and also high confidence that the phe- variability of 20-year mean differences. The framework for comparing nomenon has an impact on temperature or precipitation of a certain the signal to natural variability is similar to that adopted in Annex I region (HI) it is then assigned high relevance (red). Or, if there is only (see Annex I definition of hatching), except that here we require the low confidence in the projected change in a phenomenon (LP) but signal to be larger than two standard deviations of natural variability there is high confidence that it has a strong impact on a region (HI) rather than one, because averaging over a region gives a much more then the phenomenon is assigned medium relevance (yellow) for the robust signal than for individual grid points used in Annex I. region. Then the following principles were applied: The confidence statements in projections of the phenomena concern whether or not there will be an effect rather than the magnitude of High confidence is assigned when all 3 percentiles of the model the effect. Thus, when a phenomenon has high relevance for a region signal distribution (25%, 50% and 75%) rise above the natural it is meant that there will be a change in the regional climate due to variability. In other words, the great majority of models give sig- the future change in the phenomenon, but it does not imply that the nals that rise above the noise. regional change is necessarily dominated by changes in the phenom- Medium confidence is assigned when 2 out of 3 percentiles rise enon. above the natural variability. This means that a majority of the models give a signal that rises above the noise.  14.SM.6.1.4 Assignment of Confidence in Projections of Major Climate Phenomena Low confidence is assigned when one or none of the 3 percentiles rises above the natural variability. There is no significant fraction The level of confidence in the major phenomena changing due to of the models giving a signal that rises above the noise.  anthropogenic forcing is assigned as follows based on the model pro- In the case of precipitation, if any of the 3 percentiles disagree jections assessed in Sections 14.2 to 14.7. with the others on the sign of the change, the projected change is deemed to be not significantly different from zero. The assigned 14.SM.6.1.5 Assignment of Confidence in Regional Impact of confidence is medium, marked by an asterisk (*), no matter what Major Climate Phenomena confidence level arises from the 3 principles above. In these regions, no change is projected. The confidence in the impact of major phenomena on each region is assessed to be as follows (see table next page): 14.SM.6.1.2 How the Relevance Table Was Constructed Arctic Table 14.3 is a summary of the relevance of anthropogenically forced There is high confidence that both the NAO and extratropical cyclones changes in major climate phenomena for future regional climate. For (ETCs) impact the Arctic climate; high confidence in NAO projections the sake of brevity, we present only the most relevant highlights for the and medium confidence in ETC projected change, resulting in high rel- major phenomena discussed in Sections 14.2 to 14.7. evance for both. 14.SM.6.1.3 Assignment of Relevance Levels North America This climatically diverse continent is influenced to varying degrees by Relevance is based on the confidence that there will be a change in the many of the major phenomena: Monsoons, ITCZ, ENSO, NAO/NAM, phenomenon, and the confidence that the phenomenon has an impact and tropical and ETCs. The high relevance N. American monsoon results on the regional climate. from the high confidence that this phenomenon has an impact on the annual cycle of rainfall in the western sector and the medium con- Four levels of relevance are assigned (high, medium, low, not yet evi- fidence in future changes in the phenomenon, especially the shift in dent) and colour coded as follows: Confidence in Future Projections of the Phenomenon Low (LP) Medium (MP) High (HP) High (HI) Medium relevance High relevance High relevance Confidence in the Regional 14SM Medium (MI) Low relevance Medium relevance High relevance Impact of the Phenomenon Low (LI) Not yet evident Low relevance Medium relevance 14SM-11 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Major Climate Phenomenon Confidence Relevant Section Monsoons Medium 14.2 Tropical Phenomena, Convergence Zones High 14.3.1 Tropical Phenomena, MJO Low 14.3.2 Tropical Phenomena, IOD Medium 14.3.3 Tropical Phenomena, AOM Low 14.3.4 El Nino-Southern Oscillation Low 14.4 Annular and Dipolar Modes High 14.5 Tropical Cyclones Medium 14.6.1 Extratropical cyclones Medium NH/High SH 14.6.2 timing to later in the season. The high confidence in projected ITCZ Europe and Mediterranean shifts combined with the low confidence in impact on regional climate There is high confidence in projections of increasing NAO and also a in North America results in medium relevance. The low confidence in high confidence that this phenomenon has an impact on regional cli- future projections of ENSO and high confidence in impacts lead to an mate which leads to high relevance, especially over NW Europe. The assignment of medium relevance. The high confidence in NAO projec- high impact of ETCs on the regional climate and the medium confi- tions and the medium impact of this phenomenon in the eastern sector dence in projections of this phenomenon give a high level of relevance. of the region lead to high relevance. The high confidence that tropical and ETCs have impact in this region and the medium confidence in Africa their projected change, gives high relevance. There is medium confidence in changes in projections of the West Afri- can monsoon but high confidence in impact leading to high relevance. Central America and Caribbean The high confidence that tropical cyclones have a climate impact and Climate in this region is influenced by Monsoons, ITCZ, ENSO and the medium confidence in the projected change in tropical cyclones tropical cyclones. The high confidence that monsoon has an impact on results in a high level of relevance. The high impact of ETCs on the precipitation in the region and the medium confidence in a projected regional climate and the medium confidence in projections of this change in the phenomenon results in the high relevance. The high con- phenomenon give a high level of relevance. The low confidence in fidence in projected ITCZ shifts combined with the high confidence in ENSO future projections and its high impact lead to an assignment of the regional climate change result in a medium relevance. The low con- medium relevance. The high confidence in projected ITCZ shifts com- fidence in ENSO future projections and its high impact on the regional bined with low confidence in the regional climate signal determines a climate lead to an assignment of medium relevance (yellow shading) medium relevance. There is low confidence in projections of Atlantic of this phenomenon for future regional change. The high confidence Ocean SSTs, but medium confidence in Indian Ocean projections both that tropical cyclones have a climate impact and the medium confi- with a high impact on West, resp. East Africa, all together resulting in dence in the projected change in tropical cyclones result in a high level medium relevance. of relevance (red shading) for those systems in future climate change in the region. Central and North Asia Medium confidence in projections of monsoon change and also South America medium confidence in impact lead to medium relevance. The low con- Climate over this large latitudinal region has impacts from all of the fidence that NAO/NAM has an impact on the regional climate and major phenomena apart from tropical cyclones. The high relevance the high confidence in projections of this phenomenon determines its assigned to the South American Monsoon results from the high confi- medium level of relevance. dence that this phenomenon influences precipitation extremes within the monsoon-affected area and the medium confidence in the phe- East Asia nomenon future change. The high confidence in projected SACZ dis- There is medium confidence in the impact of Monsoon over East Asia placement combined with the high confidence in the southeast sector and there is also a medium confidence in the projected changes in the climate impact gives a high relevance for this phenomenon. The low East Asia Monsoon resulting in the medium level of relevance for Mon- confidence in ENSO future projections and its high impact lead to an soon for East Asia. Although there is a high impact of ENSO on the assignment of medium relevance. The high confidence in SAM projec- region, there is low confidence in the future projections of ENSO lead- tions and the high impact of this phenomenon in the southern sector of ing to a medium level of relevance of ENSO for the East Asia region. the region give it a high relevance. As ETCs have high confidence in a There is a high confidence in the impact of TC on East Asia and also projected poleward movement and medium confidence in their impact given that there is a medium confidence in the future projections of the on the regional climate, they are assigned a high relevance. characteristics of TC, a high level of relevance is assigned for TC for East Asia. There is a medium confidence in the projections of ETCs and also a medium confidence in their impact on the winter precipitation over 14SM East Asia resulting in a medium level of relevance of this phenomenon to East Asia. 14SM-12 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material West Asia Pacific Islands Region The low confidence in the impact of ITCZ over the southern sector of The high confidence in projected changes in the SPCZ, combined with the region and the high confidence in projected changes of this phe- high confidence in impact results in high relevance. The high confidence nomenon result in the assigned medium level of relevance. There is in the impact of ENSO combined with low confidence in projected fu- medium confidence in projections of tropical cyclones change but a ture changes in ENSO gives medium relevance. As tropical cyclones high confidence in its impact on precipitation over the southern sector, have high impact and there is medium confidence in projected changes hence it is assessed a high relevance to this phenomena for regional in tropical cyclone behaviour, the assigned level of relevance is high. climate change. Finally, medium confidence in projected poleward shift of ETCs but a low confidence in their impact on the northern sector Antarctica gives a low level of relevance. The low confidence in ENSO projections and the medium confidence South Asia in its impact on Antarctica climate lead to assess a low relevance. As There is a medium confidence that Indian Monsoon will impact South there is a high confidence in SAM projected changes and also high con- Asia but with a medium confidence in the projections of Indian Mon- fidence in its influence the assigned level of relevance is high. Finally, soon, a medium level of relevance is assigned to this phenomenon for given the medium confidence that ETCs have impact on the regional South Asia. Although tropical phenomena such as ITCZ, MJO and IOD climate and the high confidence in projections, these systems have a can potential impact South Asia, there is low confidence in the projec- high level of relevance. tion of some of these phenomena and also a medium confidence in their impact resulting in a low level of relevance of these phenomena for 14.SM.6.2 South America South Asia. There is medium confidence that ENSO will impact both the precipitation and temperature over South Asia but with low confidence ENSO is the main source of interannual variability over South America. in the projections of ENSO, a medium level of relevance is assigned to There are several regions that are influenced by Pacific SST, such as ENSO for South Asia. There is high confidence that rainfall extremes will Peru, Ecuador (Lagos et al., 2008), Chile (Garreaud and Falvey, 2009), impact South Asia but with a medium confidence in the projections of Bolivia (Ronchail and Gallaire, 2006), Brazil (Grimm and Tedeschi, TC, a high level of relevance is assigned to TC for South Asia. 2009; Tedeschi et al., 2013), Paraguay (Fraisse et al., 2008), Uruguay and Argentina (Barros et al., 2008). The mechanisms of these influ- Southeast Asia ences are changes in the Walker Circulation that affect tropical South There is a medium confidence that Mari-time continent Monsoon will America, and influences of wave trains from tropical Pacific to South impact the precipitation in South East Asia but there is low confidence in America that affect the southern and southeastern continent. A recon- the projections of Maritime Continent Monsoon resulting in a low level struction of ENSO events since 16th century indicated the increase of of relevance of this phenomenon for South East Asia. There is a medium frequency of such events in the 20th century, likely related to anthro- confidence that warming associated with IOD will reduce the rainfall pogenic forcing (Gergis and Fowler, 2009). Atmospheric Global Circu- over Indonesia during July to October period and with high confidence lation Models represent well this influence, in simulations with pre- in the projection of IOD, a high level of relevance is given to this phe- scribed SST (Pezzi and Cavalcanti, 2001). A study with the European nomenon for Southeast Asia. While the impact of ENSO has a high confi- Centre for Medium Range Weather Forecasts (ECMWF) and Hamburg dence, the low confidence in the projection of ENSO results in a medium (ECHAM5-OM) model indicated that the ENSO connection with south- level of relevance of ENSO to Southeast Asia. There is a high confidence eastern South America could weaken in the projected future climate that the extreme precipitation associated with TCs will increase while (Grimm and Natori, 2006; Grimm, 2011). there is a medium confidence in the projection of TC characteristics leading to a high level of relevance of TC to Southeast Asia. Aside from Pacific Ocean influences on South America, tropical Atlantic SST anomalies also affect precipitation over northern and northeastern Australia and New Zealand South America. Northeastern Brazil, a region with high temporal and Climates in this large region are influenced to varying degrees by all spatial variability, is frequently affected by droughts associated with of the major phenomena. The low relevance assigned to monsoon the ITCZ anomalies. Tropical North Atlantic SST anomalies can be relat- results from the low confidence in how this phenomenon influences ed to displacements of NAO centres which changes the atmospheric the climate in northern Australia and the medium confidence in the circulation and affect ITCZ position (Souza and Cavalcanti, 2009). A phenomenon s projected future change. The high confidence in pro- positive trend of tropical Atlantic interhemispheric gradient of SST, jected SPCZ changes combined with the low confidence in the asso- observed from the beginning of 20th century up to 1980, indicated ciated NE Australia climate impact lead to a medium relevance level. strong warming in the south sector compared to the north (Chang et The low confidence in ENSO future projections and its strong impact al., 2011). This trend was associated with the aerosol increase over the on the regional climate lead to medium relevance. The high confidence North Atlantic, implying a southward shift of the ITCZ (Chang et al., in SAM projections and the medium impact of this phenomenon in the 2011). However, the reduction of aerosol in the first decade of the 21st southern sector of the region lead to high relevance. As TCs have high century and continuous increase of the GHGs in the atmosphere pro- impact and there is medium confidence in the projections, the assigned moted a reversal in the SST gradient, with observed increases of North level of relevance is high. Finally, extra-­ropical cyclones have both t Atlantic SST and effects on South America (Cox et al., 2008). high ­confidence in projected change and in their impact on the region- al climate and thus have a high relevance for future climate change. 14SM 14SM-13 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Analysis of north south Atlantic SST gradient in Good et al. (2008) Other region that is influenced by modes of variability is the La Plata during June, July and August (JJA) showed high negative correlation Basin (LPB) region in southeastern South America. This is the second with precipitation over Amazonia, and also over northeast Brazil. Rela- largest basin in South America and has the main hydroelectric power tions between this gradient and precipitation in southern Amazonia plant of this continent. The region has been recognized as sensitive were also obtained in a CGCM under 1% CO2 increase by Good et al. to climate variability and change because of potential consequences (2008), who suggested that uncertainties in projected changes of the for water resources and agriculture activity over the region (Boulanger meridional Atlantic SST gradient would be linked to uncertainties in et al., 2011). LPB receives large portion of humidity from the Amazon southern Amazonia precipitation during the dry season. This SST gra- region through the Low-Level Jet (LLJ), which feeds mesoscale convec- dient also occurs during the rainy season, similar to what occurred in tive systems frequent in the region and several times responsible for 2005 and 2010 associated with the extreme droughts. AGCM experi- flooding. ments in Harris et al. (2008) also indicate the influence of Atlantic SST north south gradient and Pacific SST on Amazonia precipitation. Atmospheric circulation and precipitation changes over southern South America, in future projections of a regional model, were related Amazonia has a large influence on the global climate, as it has large to the shifting of Atlantic and Pacific subtropical highs southward and contribution to the hydrological cycle. It is one of the three regions with increase of the Chaco low, through a decreased sea level pressure (SLP) maximum tropical precipitation, together with Indonesia and Tropical over northern Argentina, an increase in northerly winds over northeast- Africa. The source of humidity to the atmosphere due to evapotranspi- ern Argentina, which causes moisture convergence and precipitation in ration is also large, being responsible for precipitation in other areas of that region (Nunez et al., 2009). The geopotential height increase over South America. Extreme droughts in the first decade of 21st century in southern South America, in projections of JJA, indicates a strengthen- Amazonia (2005 and 2010) were considered the worst droughts since ing of the meridional gradient and stronger westerlies. The changes 1950 (Marengo et al., 2008). These extreme precipitation conditions are consistent with a poleward shifting in the subtropical storm tracks. over Amazonia affected the Amazonas and Solimoes River discharges The changes in circulation induce the projected precipitation chang- in 2005 and 2010 (Espinoza et al., 2011; Marengo et al., 2011; Toma- es: increased precipitation in central Argentina associated with the sella et al., 2011). Studies on the causes of these droughts indicated enhanced cyclonic circulation of the Chaco low, southward shifting the role of North Atlantic warmer than normal SST (Marengo et al., of the Atlantic subtropical high, with humidity advection displaced to 2008, 2011; Yoon and Zeng, 2010; Espinoza et al., 2011; Lewis et al., that area, in the summer. In the winter, there is reduced precipitation 2011). The related atmospheric circulation anomalies were also dis- projection over southeastern South America, due to a poleward shift cussed in Trenberth and Fasullo (2012). This condition enhanced ascent of the stormtracks that reduces the cyclonic activity over the region. motion over North Atlantic and forced subsidence over Amazonia. The The shifting of the subtropical high polewards agrees with results of Lu north south SST gradient was favourable for the ITCZ displacement et al. (2007) on the Hadley Cell expansion under global warming. This northward, and it was consistent with convection shift to the north and expansion changes the region of subsidence and the subtropical high changes in the low-level trade winds, which normally brings humidity pressures moves southwards. to the continent in the beginning of the South America Monsoon. Occurrences of extreme droughts and floods in South America have The deforestation in the region has been reduced in recent years, but contributions from large-scale atmospheric and oceanic features, syn- large areas in the southern sector were already changed to agriculture optic conditions (Cavalcanti, 2012) and also from local conditions. or pastures areas. Changes in the vegetation due to projected warming Local responses resulting from changes in the main regional systems in future climate can contribute to precipitation reduction in Amazonia, and in the large-scale modes of variability can be reinforced through as shown in experiments of Salazar et al. (2007) and Sampaio et al. land feedback to precipitation or temperature (e.g., reduced soil mois- (2007). Replacement of forest by pasture or soybean cropland reduced ture during spring over Amazonia contributes to a delayed onset of the precipitation in the region in model experiments (Costa et al., 2007). monsoon season (Collini et al., 2008). Southeastern South America is a The risk of fires in projected deforested areas of Amazonia (eastern and hotspot of strong coupling between land and both evapotranspiration southern areas) increases under projected changes in CMIP3 models and precipitation during summer (Sörensson and Menendez, 2011). (Golding and Betts, 2008). However, only some local stations show a significant precipitation decrease in the last 80 years (Satyamurty et Precipitation over southeastern South America and southeastern Brazil al., 2010). is influenced by the Southern Annular Mode (SAM; Reboita et al., 2009; Vasconcellos and Cavalcanti, 2010). The mechanisms of these In Central Chile the negative trends in precipitation during the 20th influences are related to changes in storm tracks, jet streams position century were related to a weakening of the Pacific subtropical High in and intensification of PSA anomalous centres by the SAM. The wave the northern sector and to the positive trends of the Southern Annular train over South America intensified by the influence of SAM on PSA, Mode (SAM) in the southern sector (Quintana and Aceituno, 2012). results in a cyclonic/anticylonic pair over the continent and a related precipitation dipole anomaly, responsible for extreme precipitation in In the Andes, warmer and drier conditions in future projections resulted the South Atlantic Convergence Zone (SACZ), as discussed in Vascon- in snow and streamflow reduction (Vicuna et al., 2011). Projections cellos and Cavalcanti (2010). The future projections indicate increase using a tropical glacier climate model indicate Andean glaciers will of SLP at middle latitudes of South Atlantic Ocean (Seth et al., 2010), continue to retreat (Vuille et al., 2008). as the Atlantic Subtropical High is displaced polewards, behaviour that 14SM can be related to the positive trend of the AAO index and poleward 14SM-14 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material shifting of the stormtracks. The southward shift of the South Atlantic Europe/Mediterranean (MED) an opposite behaviour is observed, pos- High and moisture transport from the Atlantic Ocean towards west- sibly with the exception of the eastern and southeastern rims of the ern and then eastern Argentina resulted in a significant increase of basin (Feliks et al., 2010). There is evidence that the NAO precipitation annual precipitation during the 20th century over the southern sector teleconnection patterns have changed in the past (Hirschi and Sen- of southeastern South America and a negative trend in the SACZ con- eviratne, 2010) and that the relationships are scenario dependent in tinental area (Barros et al., 2008). climate simulations (Vicente-Serrano and López-Moreno, 2008). The summertime NAO has a more northerly position and a smaller extent Correlations of SAM index with precipitation over South America show and thus a weaker but still perceptible influence on the region. In its a strong influence in OND, with negative correlations over part of La positive (negative) phase higher (lower) than normal summer temper- Plata basin and positive in central-north continent (Vera and Silvestri, atures are experienced all over Europe, except in the eastern MED, and 2009). In AMJ there is also this kind of dipole correlation over South less (more) than normal precipitation in NEU and CEU and the opposite America, but covering a smaller area in southern SA and positive over in eastern MED (Folland et al., 2009; Bladé et al., 2012; Mariotti and northwestern Amazonia. The correlations in JAS are opposite to OND, Dell Aquila, 2012). when positive correlations occur over part of La Plata basin, and neg- ative over extreme northern South America. Seven models analysed by Europe is among the regions with most frequent blocking events in the Vera and Silvestri (2009) did not reproduce such correlations. world (Woollings et al., 2010b). The persistence of this phenomenon leads to strong climate anomalies of different sign depending on the Significant correlations were found between number of cold nights in location of the high-pressure centre that diverts the westerly storms Uruguay and SAM negative phase in the summer period of 1949 1975, around. When it is located over Scandinavia West Russia higher than which were not seen in the period of 1976 2005 (Renom et al., 2011). normal precipitation (dry, cold) prevails over MED (NEU and CEU) in The number of warm nights in the winter had high correlations with the winter half, while the opposite occurs when the blocking forms Tropical Pacific SST in the first period, which weakened in the second over west-central Europe (Barriopedro et al., 2006). In the summer period. Correlations of warm nights with Atlantic SST anomalies were season heat waves mostly occur during blocking situations (Dole et high during the second period. al., 2011). The influence of IOD on South America is view through a wave train Several studies have shown that the NAO and blocking phenomena pattern that extends from the Indian Ocean to South Pacific and South non-locally interact with other phenomena (Küttel and Lutterbacher, Atlantic and over South America (Saji and Yamagata, 2003). Similar to 2011; Pinto and Raible, 2012). Diverse authors showed that winter PSA influence, the centres over the continent can affect precipitation NAO anti-correlates with AMO (e.g., Marullo et al., 2011; Sutton and and temperature. IOD influence on South America temperature was Dong, 2012) and a significant relationship between AMO and summer discussed by Saji et al. (2005). Influences on South America precipita- NAO variations (Folland et al., 2009) or western European and MED tion is presented in Chan et al. (2008). summer heat waves (Della-Marte et al., 2007; Mariotti and Dell Aquila, 2012). Through a complex chain of air sea interactions, Bulic et al. 14.SM.6.3 Europe and Mediterranean (2012) explain the often observed time-lagged anomalies that ENSO events induce in large-scale circulation over the North Atlantic Euro- 14.SM.6.3.1 Phenomena Affecting Regional Climate pean region: a positive (negative) ENSO event in winter leads to posi- tive (negative) spring precipitation anomalies in Europe (Bronnimann, The most relevant phenomena affecting climate variability in diverse 2007; Shaman and Tziperman, 2011). Also Cassou (2008) showed that periods and time scales are those related to the extratropical large- the diverse phases of MJO affects the wintertime daily NAO regimes scale atmospheric circulation: ETCs (see Section 14.6.2), NAO (see with a time lag of few days by an interaction mechanism between Section 14.5.1) and blocking (see Section 14.6.3). Other patterns such tropical forced Rossby waves and mid-latitude transient eddies. A simi- as the East-Atlantic pattern (EAP) are also required to describe the lar mechanism is proposed between a strong Indian summer monsoon strength and position of the North Atlantic jet and storm track (Sei- and above normal rainfall and below normal temperature over CEU erstad et al., 2007; Woollings et al., 2010a). The EAP resembles NAO and the western NEU along with positive temperature anomalies in although displaced and enhanced over MED (Krichak and Alpert, the eastern MED, being the opposite situation during a weak monsoon 2005).These variability modes in turn seem to be modulated by inter- (Lin and Wu, 2012). actions with the North Atlantic AMO pattern (Section 14,7,6) and with lesser intensity diverse tropical phenomena, in particular ENSO, MJO and Indian summer Monsoon (see Sections 14.5 and 14.6). The NAO influence on winter temperature anomalies is very relevant in Northern Europe (NEU) and Central Europe (CEU) due to the rela- tive mild (cold) air westerly (easterly) advections prevailing over these sectors during its positive (negative) phase. The cold season precipita- tion (October to March) interannual variability is controlled mainly by NAO. In the positive (negative) phase higher (lower) than normal pre- cipitation prevails in the NEU and CEU sub-regions while in ­ outhern S 14SM 14SM-15 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1a | Temperature and precipitation projections by the CMIP5 global models. The figures shown are averages over SREX regions (Seneviratne et al., 2012) of the pro- jections by a set of 32 global models for the RCP2.6 scenario. Added to the SREX regions are an additional six regions containing the two polar regions, the Caribbean, Indian Ocean and Pacific Island States (see Annex I for further details). The 26 SREX regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America (WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), South- Eastern South America (SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF), Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeast Asia (SEA), Northern Australia (NAS) and Southern Australia/New Zealand (SAU). The area mean temperature and precipitation responses are first averaged for each model over the 1986 2005 period from the historical simulations and the 2016 2035, 2046 2065 and 2081 2100 periods of the RCP2.6 experiments. Based on the difference between these two periods, the table shows the 25th, 50th and 75th percentiles, and the lowest and highest response among the 32 models, for temperature in degrees Celsius and precipitation as a per cent change. Regions in which the middle half (25 to 75%) of this distribution is all of the same sign in the precipitation response are coloured light brown for decreasing and light green for increasing precipitation. Information is provided for land areas contained in the boxes unless otherwise indicated. The temperature responses are averaged over the boreal winter and summer seasons; December, January and February (DJF) and June, July and August (JJA) respectively. The precipitation responses are averaged over half year periods, boreal winter; October, November, December, January, February and March (ONDJFM) and summer; April, May, June, July, August and September (AMJJAS). RCP2.6     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Arctic (land) DJF 2035 0.5 1.2 1.8 2.1 3.8 1 5 8 12 18   2065 1.2 2.0 2.6 3.2 6.5 4 10 12 18 32   2100 3.9 1.9 2.5 3.3 6.7 11 9 12 18 36   JJA 2035 0.3 0.7 1.0 1.5 2.6 0 3 5 7 21   2065 0.2 0.9 1.2 2.1 4.1 1 5 7 10 31   2100 1.1 0.7 1.0 2.2 4.4 4 4 6 10 33   Annual 2035 0.3 1.1 1.4 1.8 3.4 1 4 6 8 20     2065 1.1 1.7 2.1 2.7 5.5 3 7 9 11 31     2100 2.9 1.4 1.9 2.8 5.6 8 6 9 11 34 (sea) DJF 2035 0.3 1.9 2.5 3.2 5.2 2 6 10 13 25   2065 2.2 3.0 3.9 5.0 9.3 9 11 15 22 31   2100 7.3 2.8 3.6 5.2 10.5 23 9 15 20 37   JJA 2035 0.0 0.4 0.6 0.8 1.5 1 5 6 7 16   2065 0.6 0.7 0.9 1.2 2.4 4 6 9 12 19   2100 1.5 0.5 0.8 1.3 2.7 3 4 8 12 20   Annual 2035 0.3 1.4 1.8 2.5 3.8 1 6 8 9 18     2065 1.5 2.2 2.7 3.6 6.3 7 9 11 15 25     2100 4.6 1.9 2.6 3.6 6.8 15 7 11 16 28 High latitudes Canada/ DJF 2035 0.2 1.2 1.5 1.8 3.3 1 3 5 7 13 Greenland/ 2065 1.1 1.9 2.3 3.0 5.3 3 5 7 12 18 Iceland 2100 3.5 1.6 2.4 3.2 5.0 9 4 9 13 20   JJA 2035 0.3 0.7 1.0 1.3 2.5 1 2 4 5 10     2065 0.4 0.8 1.4 1.7 3.9 0 4 5 8 13     2100 1.2 0.7 1.2 1.9 4.1 1 3 5 7 14   Annual 2035 0.4 1.0 1.2 1.4 2.7 1 2 4 6 10   2065 1.1 1.3 1.8 2.4 4.4 2 5 6 9 14   2100 2.5 1.3 1.7 2.5 4.4 4 4 7 9 16 North Asia DJF 2035 0.1 0.9 1.6 2.1 3.4 2 5 7 10 18   2065 0.4 1.6 2.1 2.4 5.7 0 7 9 13 32   2100 1.9 1.4 2.0 2.7 5.4 1 7 10 13 29   JJA 2035 0.3 0.7 1.1 1.5 2.7 1 2 4 6 12   2065 0.3 0.9 1.4 2.0 3.8 2 4 5 8 22   2100 0.7 0.8 1.3 2.1 3.8 5 4 6 8 21   Annual 2035 0.3 0.9 1.3 1.7 2.9 2 3 5 8 14     2065 0.2 1.4 1.7 2.3 4.4 1 5 7 8 25     2100 1.7 1.2 1.6 2.4 4.3 2 5 7 9 24 14SM (continued on next page) 14SM-16 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1a (continued) RCP2.6     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max North America Alaska/ DJF 2035 0.3 1.1 2.0 2.6 3.5 2 2 7 9 17 NW Canada 2065 0.3 1.8 2.7 3.4 5.6 2 5 10 14 25   2100 0.2 1.6 2.3 3.6 5.8 2 5 8 14 25     JJA 2035 0.3 0.7 0.9 1.5 3.1 1 4 6 7 15     2065 0.4 0.8 1.5 2.1 3.5 1 4 8 10 24   2100 0.3 0.7 1.2 2.2 3.6 7 5 7 11 27   Annual 2035 0.5 1.1 1.4 1.8 2.7 0 4 6 7 14   2065 0.6 1.4 1.9 2.5 4.1 0 6 8 11 23   2100 0.5 1.2 1.9 2.6 4.0 2 5 8 10 25 West North DJF 2035 0.2 0.6 1.0 1.4 2.6 3 1 2 5 9 America 2065 0.3 1.0 1.6 2.1 4.0 3 1 4 6 11     2100 0.3 1.1 1.7 2.1 4.2 1 3 4 6 12   JJA 2035 0.4 0.8 1.1 1.3 2.0 6 2 1 5 11     2065 0.2 1.0 1.4 1.8 3.0 4 0 2 4 13   2100 0.4 0.8 1.3 1.9 3.1 3 1 4 7 13   Annual 2035 0.5 0.8 1.0 1.3 2.0 3 1 2 3 8   2065 0.6 1.1 1.5 1.7 3.0 1 1 3 5 13   2100 0.3 1.0 1.4 1.9 3.2 0 2 3 7 12 Central North DJF 2035 0.2 0.6 1.1 1.4 2.5 7 2 2 4 10 America 2065 0.2 0.9 1.5 1.9 3.3 4 1 2 6 18   2100 0.2 0.8 1.5 2.1 3.4 10 0 2 6 15     JJA 2035 0.4 0.7 1.2 1.4 2.2 8 1 1 4 8     2065 0.5 1.0 1.4 2.0 2.9 8 0 2 4 9   2100 0.1 0.8 1.2 1.7 3.4 6 0 2 6 12   Annual 2035 0.4 0.8 1.0 1.2 2.0 5 1 2 3 7   2065 0.4 0.9 1.4 1.9 2.6 6 0 2 5 11   2100 0.1 0.8 1.4 1.8 2.8 6 1 2 5 10 Eastern North DJF 2035 0.5 0.5 1.0 1.5 2.4 5 0 4 6 9 America 2065 0.1 0.8 1.4 2.1 3.5 4 2 5 6 16   2100 0.3 0.9 1.6 2.3 3.6 4 0 3 7 16     JJA 2035 0.5 0.7 1.0 1.3 2.2 3 0 3 5 6     2065 0.4 1.0 1.3 1.8 3.2 5 2 3 7 12   2100 0.1 0.9 1.2 1.7 3.6 2 1 3 7 15   Annual 2035 0.3 0.8 1.0 1.2 1.9 2 1 3 5 6   2065 0.4 0.9 1.4 1.8 2.9 1 2 4 6 11   2100 0.0 0.9 1.2 1.8 3.2 1 1 3 6 15 Central America Central DJF 2035 0.3 0.6 0.7 0.9 1.2 6 2 0 3 8 America 2065 0.5 0.8 1.0 1.2 1.7 8 2 1 5 12   2100 0.4 0.7 0.9 1.3 2.0 21 2 1 5 14   JJA 2035 0.5 0.7 0.8 0.9 1.4 6 2 1 2 7   2065 0.7 0.9 1.0 1.4 2.0 10 4 0 2 7   2100 0.3 0.7 1.0 1.4 2.2 11 2 1 2 10   Annual 2035 0.5 0.7 0.7 0.8 1.3 6 2 0 2 6     2065 0.6 0.9 1.0 1.3 1.9 9 3 0 3 6     2100 0.4 0.7 1.0 1.3 2.1 15 1 0 2 9 (continued on next page) 14SM 14SM-17 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1a (continued) RCP2.6     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Caribbean DJF 2035 0.3 0.5 0.6 0.7 1.0 12 3 3 5 10 (land and sea) 2065 0.4 0.7 0.8 1.1 1.6 6 1 2 7 13   2100 0.0 0.6 0.8 1.1 1.6 16 3 1 6 15   JJA 2035 0.3 0.5 0.5 0.7 1.2 12 7 4 0 11   2065 0.4 0.7 0.8 1.0 1.6 15 6 3 2 19   2100 0.1 0.6 0.8 1.1 1.7 34 6 0 3 9   Annual 2035 0.4 0.5 0.6 0.7 1.1 11 3 1 0 7     2065 0.4 0.7 0.8 1.0 1.6 9 5 0 1 0     2100 0.1 0.6 0.8 1.1 1.7 25 4 0 0 4 South America Amazon DJF 2035 0.4 0.7 0.8 0.9 1.6 12 3 0 1 5   2065 0.6 0.8 1.0 1.3 2.2 10 3 1 2 6   2100 0.0 0.8 1.0 1.3 2.5 20 4 1 1 6   JJA 2035 0.6 0.7 0.9 1.1 1.9 11 3 0 1 5   2065 0.8 1.0 1.1 1.6 2.9 19 4 0 2 7   2100 0.5 0.9 1.1 1.5 2.8 17 5 2 1 10   Annual 2035 0.5 0.7 0.8 1.0 1.8 12 3 0 1 5     2065 0.7 0.9 1.1 1.4 2.5 14 3 0 1 5     2100 0.3 0.9 1.1 1.4 2.8 19 3 1 0 5 Northeast DJF 2035 0.2 0.6 0.8 0.9 1.4 12 7 1 5 13 Brazil 2065 0.6 0.8 1.0 1.2 1.8 11 6 1 4 16   2100 0.1 0.7 1.0 1.2 2.2 14 4 2 4 18   JJA 2035 0.1 0.7 0.8 0.9 1.5 22 9 3 1 15   2065 0.6 0.9 1.1 1.3 2.4 24 12 6 1 16   2100 0.1 0.8 1.1 1.4 2.0 31 11 4 2 21   Annual 2035 0.4 0.6 0.8 0.9 1.3 12 6 1 4 11     2065 0.6 0.8 1.1 1.3 2.1 15 7 2 1 15     2100 0.3 0.8 1.0 1.3 2.0 19 5 2 3 20 West Coast DJF 2035 0.4 0.6 0.7 0.9 1.1 6 1 1 2 5 South America 2065 0.5 0.8 1.0 1.2 1.6 8 1 1 3 5   2100 0.3 0.7 0.9 1.2 1.9 7 0 2 5 7   JJA 2035 0.3 0.6 0.8 0.8 1.4 10 2 0 2 7   2065 0.6 0.9 1.0 1.3 1.8 8 1 1 2 8   2100 0.4 0.8 0.9 1.3 2.1 11 1 1 4 7   Annual 2035 0.4 0.6 0.7 0.8 1.2 7 1 1 2 5     2065 0.6 0.8 1.0 1.2 1.7 8 0 1 2 5     2100 0.3 0.8 0.9 1.2 2.0 8 0 2 3 6 Southeastern DJF 2035 0.2 0.5 0.7 0.8 1.4 6 1 0 2 8 South America 2065 0.3 0.6 0.9 1.2 1.8 6 1 0 3 11   2100 0.3 0.7 0.8 1.2 2.0 7 2 1 3 9   JJA 2035 0.1 0.3 0.7 0.8 1.1 13 3 2 4 14   2065 0.2 0.5 0.7 1.1 1.6 15 1 1 3 14   2100 0.3 0.5 0.8 1.1 1.7 17 4 0 7 17   Annual 2035 0.3 0.5 0.6 0.7 1.3 7 1 0 2 10     2065 0.4 0.7 0.9 1.0 1.7 7 1 1 2 13     2100 0.4 0.7 0.8 1.1 1.8 9 1 1 3 9 (continued on next page) 14SM 14SM-18 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1a (continued) RCP2.6     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Europe Northern Europe DJF 2035 0.6 0.7 1.4 2.0 3.4 5 1 4 7 15   2065 2.7 1.3 2.1 2.5 4.1 3 2 6 9 17   2100 8.0 1.5 1.8 2.5 3.8 3 3 5 11 16   JJA 2035 0.0 0.6 1.0 1.3 2.7 8 0 3 5 10   2065 1.1 0.9 1.3 2.0 3.6 5 0 4 7 20   2100 2.5 0.7 1.2 1.8 3.6 12 2 4 7 14   Annual 2035 0.1 0.6 1.2 1.5 2.4 6 2 4 5 9     2065 1.9 1.3 1.6 2.0 3.2 3 3 5 7 18     2100 5.2 1.1 1.5 2.1 3.3 4 2 5 8 15 Central Europe DJF 2035 0.2 0.6 1.0 1.5 2.3 4 1 2 5 11   2065 0.9 0.6 1.3 1.9 3.4 4 1 3 7 11   2100 1.9 1.0 1.3 2.4 2.9 1 2 4 6 16   JJA 2035 0.4 0.7 1.1 1.4 2.5 6 1 2 3 6   2065 0.4 0.9 1.4 2.0 3.3 9 0 2 6 8   2100 0.7 0.8 1.3 2.0 3.4 12 1 4 8 14   Annual 2035 0.1 0.7 1.0 1.3 1.9 2 0 1 4 9     2065 0.5 1.0 1.3 1.8 2.9 4 0 3 5 9     2100 1.4 0.9 1.3 1.9 2.8 5 2 3 7 12 Southern Europe/ DJF 2035 0.2 0.7 0.7 1.0 1.4 10 5 1 2 10 Mediterranean 2065 0.1 0.8 1.1 1.5 1.9 12 7 1 4 12   2100 0.9 0.8 1.1 1.3 1.8 23 4 0 4 9   JJA 2035 0.4 1.0 1.1 1.5 2.6 15 7 2 1 9   2065 0.4 1.0 1.6 1.9 3.6 17 7 2 0 12   2100 0.2 1.0 1.4 1.8 3.9 18 4 2 0 18   Annual 2035 0.4 0.8 0.9 1.1 1.7 9 4 2 0 7     2065 0.2 1.0 1.3 1.5 2.6 13 6 2 1 6     2100 0.4 0.9 1.2 1.5 2.7 21 5 1 2 10 Africa Sahara DJF 2035 0.1 0.7 0.9 1.0 1.3 37 6 2 8 77   2065 0.4 1.0 1.3 1.5 1.8 27 11 3 12 74   2100 0.3 0.9 1.2 1.4 1.7 33 8 2 6 90   JJA 2035 0.5 0.9 0.9 1.1 2.0 18 5 3 11 44   2065 0.7 1.0 1.2 1.7 2.9 26 5 6 14 56   2100 0.2 1.0 1.2 1.8 3.0 41 4 4 13 60   Annual 2035 0.6 0.8 0.9 1.1 1.6 17 5 2 9 36     2065 0.5 1.1 1.2 1.5 2.3 26 6 6 13 44     2100 0.1 0.9 1.1 1.5 2.4 36 4 1 11 61 West Africa DJF 2035 0.4 0.6 0.7 0.9 1.4 5 1 1 2 6   2065 0.6 0.9 1.1 1.3 2.0 4 1 1 5 8   2100 0.4 0.9 1.0 1.3 2.2 7 0 1 3 7   JJA 2035 0.4 0.6 0.7 0.9 1.4 4 1 0 2 6   2065 0.5 0.9 1.0 1.3 2.0 7 1 0 1 4   2100 0.2 0.8 1.0 1.2 2.3 8 2 0 1 4   Annual 2035 0.4 0.6 0.7 0.9 1.3 4 1 1 2 5     2065 0.6 0.9 1.0 1.3 1.9 6 1 1 2 4     2100 0.2 0.8 1.0 1.2 2.2 7 1 0 2 4 (continued on next page) 14SM 14SM-19 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1a (continued) RCP2.6     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max East Africa DJF 2035 0.4 0.6 0.7 0.8 1.4 4 1 2 5 8   2065 0.6 0.9 1.0 1.3 1.9 6 1 1 6 13   2100 0.3 0.7 0.9 1.3 2.0 4 2 2 5 16   JJA 2035 0.3 0.6 0.8 0.9 1.3 7 3 0 2 10   2065 0.5 0.9 1.0 1.3 1.8 11 5 2 2 14   2100 0.0 0.8 1.0 1.2 2.1 10 4 1 2 15   Annual 2035 0.4 0.6 0.7 0.9 1.3 5 1 1 3 9     2065 0.6 0.8 1.0 1.2 1.8 8 2 0 4 13     2100 0.2 0.7 0.9 1.3 2.0 7 2 0 2 14 Southern DJF 2035 0.4 0.7 0.8 1.0 1.2 11 4 2 0 9 Africa 2065 0.6 1.0 1.1 1.4 2.0 13 7 3 0 4   2100 0.2 0.8 1.1 1.5 2.1 13 7 3 0 4   JJA 2035 0.5 0.7 0.8 0.9 1.3 24 8 3 0 10   2065 0.7 0.9 1.1 1.3 1.9 30 9 5 2 8   2100 0.4 0.8 1.0 1.3 2.1 32 11 8 1 12   Annual 2035 0.5 0.7 0.8 0.9 1.4 12 4 2 1 9     2065 0.7 1.0 1.1 1.3 2.0 13 6 4 0 4     2100 0.4 0.9 1.1 1.5 2.1 13 8 4 1 3 West Indian DJF 2035 0.3 0.5 0.5 0.7 1.0 4 0 1 3 10 Ocean 2065 0.5 0.6 0.7 1.0 1.4 6 1 2 4 12   2100 0.2 0.6 0.7 1.0 1.6 2 1 3 6 14   JJA 2035 0.3 0.5 0.5 0.7 1.0 6 0 1 4 9   2065 0.4 0.6 0.7 1.0 1.4 3 0 2 6 12   2100 0.1 0.6 0.7 1.0 1.6 3 1 4 7 11   Annual 2035 0.3 0.5 0.5 0.7 1.0 4 0 2 2 9     2065 0.5 0.6 0.7 1.0 1.3 2 1 2 3 12     2100 0.2 0.6 0.7 1.0 1.6 1 2 3 6 11 Asia West Asia DJF 2035 0.3 0.7 0.9 1.2 1.8 6 1 3 5 12   2065 0.1 0.9 1.3 1.9 2.6 13 0 5 9 25   2100 0.9 0.8 1.3 1.7 2.7 13 0 3 8 16   JJA 2035 0.7 0.8 1.1 1.4 2.2 14 3 3 6 42   2065 0.3 1.1 1.4 1.9 3.1 17 4 5 9 38   2100 0.0 0.9 1.3 1.9 3.4 31 1 3 11 67   Annual 2035 0.6 0.8 1.0 1.2 1.8 7 1 2 6 21     2065 0.2 1.0 1.3 1.8 2.7 15 1 6 8 20     2100 0.4 0.9 1.2 1.7 2.7 23 1 3 9 31 Central Asia DJF 2035 0.4 0.8 1.0 1.5 2.2 9 0 4 7 14   2065 0.2 1.0 1.5 2.1 3.6 10 0 4 12 19   2100 1.3 0.7 1.6 2.2 3.3 13 0 5 10 18   JJA 2035 0.3 0.8 1.1 1.4 2.0 14 1 3 7 16   2065 0.1 0.9 1.4 2.0 3.5 9 0 3 8 21   2100 0.5 0.6 1.2 1.7 3.8 19 1 5 10 17   Annual 2035 0.4 0.9 1.1 1.3 1.8 9 0 3 6 14     2065 0.0 1.1 1.4 1.9 3.2 9 1 4 8 18     2100 0.8 1.0 1.4 1.8 3.1 16 0 5 7 17 (continued on next page) 14SM 14SM-20 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1a (continued) RCP2.6     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Eastern Asia DJF 2035 0.3 0.6 1.1 1.4 2.0 6 1 2 4 8   2065 0.2 1.2 1.5 1.9 3.4 6 1 4 10 15   2100 0.5 1.0 1.4 2.0 3.3 5 2 6 11 22   JJA 2035 0.2 0.8 0.9 1.1 1.7 3 0 2 3 7   2065 0.2 0.9 1.3 1.7 2.7 1 3 5 6 17   2100 0.3 0.8 1.2 1.7 2.8 4 2 5 7 20   Annual 2035 0.4 0.8 1.0 1.1 1.7 2 0 2 3 7     2065 0.2 1.0 1.4 1.7 2.7 3 3 5 6 16     2100 0.4 0.9 1.3 1.7 2.7 4 2 5 7 21 Tibetan DJF 2035 0.3 0.8 1.0 1.5 2.1 2 2 4 6 12 Plateau 2065 0.1 1.2 1.6 2.1 3.6 3 4 6 10 17   2100 0.9 0.9 1.5 2.0 3.4 4 4 7 10 22   JJA 2035 0.2 0.8 1.1 1.3 2.4 3 1 4 6 19   2065 0.3 1.1 1.4 1.8 3.8 3 3 5 8 24   2100 0.4 0.9 1.3 1.8 3.9 4 4 6 9 24   Annual 2035 0.4 0.8 1.1 1.4 2.0 2 2 4 5 16     2065 0.2 1.1 1.5 1.9 3.3 2 3 5 9 20     2100 0.6 0.9 1.4 1.9 3.2 4 4 6 9 22 South Asia DJF 2035 0.2 0.7 0.8 1.1 1.7 11 2 0 5 10   2065 0.5 0.9 1.2 1.6 2.3 13 0 3 7 19   2100 0.1 0.8 1.1 1.5 2.4 20 2 5 9 27   JJA 2035 0.2 0.5 0.7 0.9 1.2 3 1 3 5 9   2065 0.5 0.8 0.9 1.3 1.9 7 3 5 7 15   2100 0.1 0.7 0.9 1.1 2.2 10 1 5 7 17   Annual 2035 0.2 0.6 0.8 0.9 1.3 2 0 3 4 8     2065 0.7 0.9 1.0 1.4 2.0 5 2 5 7 14     2100 0.2 0.9 1.0 1.4 2.3 5 1 5 8 15 North Indian DJF 2035 0.3 0.5 0.5 0.7 1.0 9 0 4 10 19 Ocean 2065 0.3 0.7 0.8 1.1 1.4 15 2 5 13 27   2100 0.3 0.5 0.8 1.1 1.7 17 1 8 14 28   JJA 2035 0.3 0.5 0.5 0.7 1.0 6 1 2 6 18   2065 0.5 0.7 0.7 1.0 1.5 8 1 3 8 27   2100 0.2 0.6 0.7 1.0 1.7 16 1 4 7 17   Annual 2035 0.3 0.5 0.5 0.6 1.0 3 1 3 5 18     2065 0.5 0.6 0.8 1.1 1.4 4 2 5 9 23     2100 0.3 0.6 0.7 1.1 1.7 11 1 6 9 23 Southeast DJF 2035 0.3 0.5 0.6 0.7 1.2 5 1 0 2 10 Asia (land) 2065 0.4 0.7 0.9 1.1 1.6 5 1 2 4 9   2100 0.2 0.7 0.8 1.2 2.0 5 0 2 4 9   JJA 2035 0.3 0.5 0.6 0.7 1.2 5 1 1 3 6   2065 0.5 0.7 0.8 1.1 1.7 5 1 1 5 7   2100 0.2 0.6 0.8 1.0 1.8 6 0 1 3 11   Annual 2035 0.3 0.5 0.6 0.7 1.2 5 1 0 2 8     2065 0.5 0.7 0.8 1.1 1.6 4 0 1 4 7     2100 0.2 0.7 0.8 1.2 1.9 5 0 1 4 10 (continued on next page) 14SM 14SM-21 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1a (continued) RCP2.6     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Southeast DJF 2035 0.3 0.5 0.5 0.6 1.1 5 1 0 3 6 Asia (sea) 2065 0.5 0.6 0.7 1.0 1.5 2 0 2 4 6   2100 0.3 0.6 0.7 1.0 1.7 3 0 2 4 7   JJA 2035 0.2 0.5 0.5 0.6 1.0 5 0 1 3 6   2065 0.5 0.6 0.7 0.9 1.5 3 1 2 3 7   2100 0.3 0.5 0.7 1.0 1.7 5 1 2 4 9   Annual 2035 0.3 0.5 0.5 0.6 1.0 4 0 1 2 4     2065 0.5 0.6 0.7 1.0 1.5 2 1 2 4 6     2100 0.3 0.5 0.7 1.0 1.6 2 1 2 4 7 Australia North Australia DJF 2035 0.5 0.6 0.8 1.0 1.7 17 6 0 3 8   2065 0.6 0.9 1.1 1.5 2.4 23 9 2 0 13   2100 0.4 0.9 1.1 1.5 3.2 26 12 7 0 3   JJA 2035 0.5 0.6 0.7 1.1 1.6 41 11 7 1 4   2065 0.6 0.9 1.1 1.3 1.8 47 15 6 1 15   2100 0.5 0.8 1.0 1.2 1.8 38 14 6 1 8   Annual 2035 0.5 0.7 0.8 1.0 1.3 17 6 0 2 8     2065 0.5 0.9 1.1 1.4 1.8 21 10 4 0 10     2100 0.4 0.8 1.1 1.4 2.4 24 11 6 0 4 South Australia/ 2065 0.5 0.8 1.0 1.3 1.8 18 6 3 2 6 New Zealand  2100 0.3 0.8 1.0 1.3 1.9 23 10 3 0 7   JJA 2035 0.3 0.5 0.6 0.7 0.9 18 3 0 1 5   2065 0.4 0.7 0.8 1.0 1.4 22 6 2 3 9   2100 0.3 0.6 0.7 1.0 1.5 16 6 1 1 9   Annual 2035 0.4 0.5 0.7 0.8 0.9 16 4 0 1 4     2065 0.6 0.7 0.9 1.1 1.4 18 5 1 0 4     2100 0.3 0.7 0.9 1.0 1.5 19 6 3 0 8 The Pacific Northern DJF 2035 0.3 0.5 0.6 0.6 1.0 5 0 2 3 7 Tropical Pacific 2065 0.4 0.6 0.8 1.0 1.4 5 0 3 4 13   2100 0.3 0.6 0.8 1.1 1.5 6 1 2 4 14   JJA 2035 0.3 0.4 0.5 0.7 1.1 7 1 1 2 8   2065 0.4 0.6 0.7 1.0 1.5 5 2 0 3 8   2100 0.2 0.6 0.7 1.1 1.7 5 1 1 3 6   Annual 2035 0.3 0.5 0.6 0.6 1.1 3 1 1 2 6   2065 0.5 0.6 0.8 1.0 1.5 5 1 1 3 10     2100 0.3 0.6 0.7 1.1 1.6 4 1 2 3 7 Equatorial Pacific DJF 2035 0.2 0.5 0.6 0.8 1.1 25 3 7 10 78   2065 0.4 0.7 0.8 1.1 1.6 25 3 9 19 112   2100 0.1 0.6 0.9 1.1 2.2 25 5 12 27 230   JJA 2035 0.3 0.5 0.6 0.8 1.2 31 4 10 15 68   2065 0.4 0.7 0.8 1.0 1.8 12 7 12 23 81   2100 0.0 0.6 0.8 1.1 2.0 16 7 15 25 199   Annual 2035 0.3 0.5 0.6 0.8 1.1 14 4 8 11 72     2065 0.4 0.7 0.8 1.0 1.6 18 7 11 20 98     2100 0.0 0.6 0.8 1.1 2.1 21 5 14 25 218 (continued on next page) 14SM 14SM-22 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1a (continued) RCP2.6     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Southern Pacific DJF 2035 0.3 0.4 0.5 0.6 0.9 10 0 1 2 4   2065 0.4 0.5 0.7 0.9 1.1 20 1 2 3 5   2100 0.3 0.5 0.6 0.9 1.3 19 1 2 3 6   JJA 2035 0.1 0.4 0.4 0.6 0.9 11 1 0 3 6   2065 0.3 0.5 0.6 0.8 1.2 15 1 1 3 8   2100 0.3 0.5 0.6 0.8 1.4 19 1 1 2 6   Annual 2035 0.3 0.4 0.5 0.6 0.9 12 0 1 1 4     2065 0.4 0.5 0.6 0.8 1.2 18 1 1 3 7     2100 0.3 0.5 0.6 0.9 1.4 19 0 1 2 6 Antarctica (land) DJF 2035 0.1 0.4 0.6 0.8 1.2 3 1 2 5 7   2065 0.3 0.6 0.9 1.2 1.8 9 1 3 6 11   2100 0.0 0.6 0.8 1.3 2.1 6 1 3 5 12   JJA 2035 0.3 0.4 0.7 0.9 1.9 0 2 3 6 13   2065 0.5 0.6 0.9 1.4 1.9 2 3 5 10 12   2100 0.8 0.7 1.0 1.2 2.3 4 3 6 9 16   Annual 2035 0.0 0.5 0.6 0.9 1.4 1 2 3 6 10     2065 0.3 0.6 0.8 1.3 1.8 5 2 4 8 11     2100 0.3 0.7 0.9 1.3 2.2 5 3 5 6 14 (sea) DJF 2035 0.2 0.1 0.2 0.5 0.7 1 1 2 3 5   2065 0.5 0.3 0.5 0.7 1.0 1 1 3 4 7   2100 0.3 0.3 0.5 0.8 1.2 2 1 2 3 7   JJA 2035 0.6 0.3 0.4 0.9 2.0 0 1 2 3 5   2065 1.1 0.4 0.7 1.3 2.2 1 2 3 5 9   2100 1.2 0.3 0.7 1.6 2.3 1 2 3 5 8   Annual 2035 0.4 0.2 0.3 0.7 1.3 0 1 2 3 5     2065 0.7 0.3 0.5 1.0 1.5 1 2 3 4 8     2100 0.7 0.4 0.6 1.2 1.8 1 1 3 4 7 Notes: a *Precipitation changes cover 6 months; ONDJFM and AMJJAS for winter and summer (northern hemisphere) 14SM 14SM-23 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1b | Temperature and precipitation projections by the CMIP5 global models. The figures shown are averages over SREX regions (Seneviratne et al., 2012) of the pro- jections by a set of 25 global models for the RCP6.0 scenario. Added to the SREX regions are an additional six regions containing the two polar regions, the Caribbean, Indian Ocean and Pacific Island States (see Annex I for further details). The 26 SREX regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America (WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), South- Eastern South America (SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF), Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeast Asia (SEA), Northern Australia (NAS) and Southern Australia/New Zealand (SAU). The area-mean temperature and precipitation responses are first averaged for each model over the 1986 2005 period from the historical simulations and the 2016 2035, 2046 2065 and 2081 2100 periods of the RCP6.0 experiments. Based on the difference between these two periods, the table shows the 25th, 50th and 75th percentiles, and the lowest and highest response among the 25 models, for temperature in degrees Celsius and precipitation as a per cent change. Regions in which the middle half (25 to 75%) of this distribution is all of the same sign in the precipitation response are coloured light brown for decreasing and light green for increasing precipitation. Information is provided for land areas contained in the boxes unless otherwise indicated. The temperature responses are averaged over the boreal winter and summer seasons; December, January and February (DJF) and June, July and August (JJA) respectively. The precipitation responses are averaged over half year periods, boreal winter; October, November, December, January, February and March (ONDJFM) and summer; April, May, June, July, August and September (AMJJAS). RCP6.0     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Arctic (land) DJF 2035 0.1 1.3 1.5 1.9 4.0 4 6 8 11 21   2065 1.0 2.5 3.0 4.0 6.4 3 12 16 19 30   2100 1.1 5.0 5.8 6.8 12.3 8 24 29 35 62   JJA 2035 0.2 0.7 0.8 1.3 2.8 2 3 5 6 20   2065 0.8 1.3 1.7 2.3 4.1 1 6 9 11 29   2100 1.1 2.1 2.8 4.0 6.8 4 11 14 19 42   Annual 2035 0.1 1.2 1.3 1.6 3.6 0 4 6 7 21     2065 0.8 1.9 2.6 3.1 5.5 2 9 11 14 30     2100 1.0 3.7 4.5 5.4 9.1 5 16 20 23 50 (sea) DJF 2035 0.0 1.9 2.4 2.9 5.9 8 6 10 14 22   2065 0.5 3.2 4.3 6.2 9.5 0 11 18 26 37   2100 0.3 7.1 8.0 10.2 17.1 2 26 32 41 54   JJA 2035 0.1 0.4 0.5 0.7 1.6 1 4 6 7 13   2065 0.3 0.7 1.0 1.4 2.4 2 8 10 12 19   2100 0.0 1.4 1.9 2.3 4.8 1 14 17 21 32   Annual 2035 0.1 1.3 1.7 2.2 4.1 3 6 7 10 17     2065 0.5 2.3 3.1 4.1 6.3 1 10 13 19 24     2100 0.5 4.5 5.5 7.0 10.6 1 20 24 27 43 High latitudes Canada/ DJF 2035 0.2 1.1 1.4 1.8 3.4 2 3 5 7 12 Greenland/ 2065 1.1 2.4 3.1 3.8 4.9 1 9 10 14 21 Iceland 2100 1.7 4.4 5.1 6.5 9.9 2 14 19 24 36   JJA 2035 0.3 0.6 0.9 1.2 2.5 2 2 3 4 7   2065 0.8 1.2 1.7 2.2 3.9 1 4 6 9 14   2100 1.0 2.3 3.1 3.6 6.4 4 8 10 14 23   Annual 2035 0.0 1.0 1.1 1.4 2.9 2 3 4 6 8     2065 0.8 1.9 2.3 2.8 4.3 2 6 8 11 15     2100 1.2 3.3 3.9 4.8 7.6 3 11 14 18 25 North Asia DJF 2035 0.5 1.0 1.4 2.1 3.2 1 4 7 9 17   2065 1.3 1.8 3.0 3.3 5.8 1 9 13 15 29   2100 1.8 4.2 4.8 5.6 8.5 6 17 21 28 47   JJA 2035 0.4 0.8 1.0 1.2 2.4 0 2 3 6 14   2065 0.5 1.3 1.9 2.3 3.7 0 3 6 10 19   2100 1.5 2.3 3.1 4.1 6.0 2 7 10 13 28   Annual 2035 0.5 0.9 1.2 1.7 2.7 1 3 5 7 15     2065 1.1 1.7 2.3 2.7 4.3 2 5 7 11 23     2100 1.3 3.1 3.7 4.7 6.6 2 12 14 18 34 14SM (continued on next page) 14SM-24 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1b (continued) RCP6.0     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max North America Alaska/ DJF 2035 0.3 1.1 1.6 2.4 3.5 7 3 6 10 19 NW Canada 2065 1.0 2.5 3.0 4.4 5.9 0 6 10 15 27   2100 3.5 4.6 5.4 6.4 10.4 5 14 18 23 43   JJA 2035 0.3 0.7 1.0 1.3 2.1 1 2 4 5 16   2065 0.6 1.3 1.6 2.3 3.3 0 5 7 9 26   2100 1.4 2.0 3.0 4.1 5.9 1 10 13 18 38   Annual 2035 0.3 0.9 1.2 1.6 2.6 1 3 5 7 14     2065 1.3 1.8 2.2 2.9 4.3 3 6 8 11 24     2100 2.5 3.3 4.0 4.9 7.2 4 13 15 18 37 West North DJF 2035 0.0 0.6 1.0 1.2 2.0 4 0 2 3 7 America 2065 0.7 1.4 2.0 2.4 3.9 1 2 3 6 10   2100 1.5 2.4 3.2 3.9 6.1 1 6 7 10 16   JJA 2035 0.3 0.8 1.0 1.1 1.9 4 0 2 4 8   2065 0.9 1.5 1.8 2.2 3.1 5 2 1 3 9   2100 1.6 2.5 3.2 4.0 5.0 6 0 3 5 10   Annual 2035 0.5 0.7 0.9 1.1 1.5 2 0 1 2 6     2065 1.0 1.4 1.8 2.2 3.2 2 1 2 4 7     2100 1.8 2.4 3.1 3.8 5.0 1 2 6 8 12 Central DJF 2035 0.4 0.6 0.9 1.1 2.1 4 1 2 5 11 North 2065 0.2 1.3 1.8 2.2 3.4 13 3 4 7 13 America 2100 1.3 2.3 3.0 4.4 5.7 9 1 5 12 19   JJA 2035 0.4 0.6 0.9 1.2 1.8 6 0 2 3 9   2065 1.0 1.4 1.9 2.2 2.9 7 2 0 5 10   2100 1.7 2.5 3.1 4.1 5.1 13 2 3 7 17   Annual 2035 0.4 0.6 0.9 1.2 1.6 5 1 1 3 9     2065 1.0 1.4 1.7 2.1 3.0 10 2 2 6 11     2100 1.8 2.5 3.0 3.7 5.0 6 1 3 9 15 Eastern DJF 2035 0.3 0.8 1.0 1.2 2.1 2 0 3 6 13 North 2065 0.7 1.4 1.9 2.3 3.8 0 4 7 11 15 America 2100 1.5 2.5 3.2 4.4 6.1 0 7 12 14 20   JJA 2035 0.6 0.7 0.9 1.0 1.9 7 0 3 4 9   2065 1.1 1.4 1.7 2.1 3.3 4 1 4 6 10   2100 1.8 2.4 3.1 3.8 5.7 4 2 4 7 15   Annual 2035 0.3 0.6 0.9 1.1 2.0 4 1 3 5 7     2065 1.0 1.5 1.7 1.9 3.2 1 2 5 8 11     2100 1.8 2.5 3.2 3.8 5.2 1 5 8 10 13 Central America Central DJF 2035 0.4 0.5 0.6 0.8 1.1 8 2 1 4 9 America 2065 0.9 1.2 1.3 1.6 2.1 17 3 1 4 11   2100 1.5 2.0 2.2 2.8 3.4 21 6 3 3 11   JJA 2035 0.4 0.6 0.7 0.8 1.3 5 2 0 3 6   2065 1.1 1.2 1.4 1.7 2.2 12 5 2 2 7   2100 1.9 2.2 2.3 3.1 3.8 14 6 3 3 5   Annual 2035 0.4 0.6 0.7 0.8 1.2 4 2 0 2 7     2065 1.1 1.2 1.4 1.7 2.1 15 3 1 2 5     2100 1.8 2.1 2.3 2.9 3.5 17 5 3 1 5 (continued on next page) 14SM 14SM-25 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1b (continued) RCP6.0     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Caribbean DJF 2035 0.3 0.5 0.5 0.7 1.0 7 4 1 3 6 (land and sea) 2065 0.8 0.9 1.0 1.2 1.7 9 5 3 3 11 2100 1.0 1.5 1.6 2.2 2.7 23 8 1 5 10   JJA 2035 0.3 0.4 0.5 0.7 1.0 14 7 4 1 9   2065 0.7 0.9 0.9 1.2 1.8 19 9 6 3 6   2100 1.0 1.4 1.7 2.1 2.9 43 21 9 5 10   Annual 2035 0.3 0.5 0.5 0.7 1.0 11 5 2 1 7     2065 0.8 0.9 1.0 1.2 1.7 15 7 2 1 10     2100 1.0 1.5 1.7 2.2 2.9 33 13 7 2 8 South America Amazon DJF 2035 0.5 0.6 0.7 0.9 1.4 7 2 0 2 7   2065 0.9 1.3 1.5 1.7 2.2 9 3 1 2 5   2100 1.9 2.1 2.4 3.0 3.9 14 5 1 2 5   JJA 2035 0.5 0.7 0.8 1.0 1.6 6 1 0 3 7   2065 1.0 1.3 1.6 1.9 2.9 9 5 0 2 11   2100 1.8 2.2 2.8 3.3 4.2 12 5 2 3 12   Annual 2035 0.5 0.7 0.8 0.9 1.7 6 1 1 2 7     2065 1.1 1.3 1.5 1.8 2.8 8 3 0 2 8     2100 1.9 2.2 2.5 3.3 4.4 9 5 0 1 7 Northeast DJF 2035 0.4 0.5 0.7 0.9 1.3 7 2 2 5 14 Brazil 2065 0.8 1.1 1.5 1.7 2.1 13 4 2 4 23   2100 1.5 2.0 2.4 2.9 3.7 13 6 4 7 38   JJA 2035 0.3 0.6 0.8 0.9 1.2 18 8 3 1 15   2065 0.7 1.2 1.5 1.7 2.4 16 10 5 0 21   2100 1.5 2.1 2.5 2.9 3.5 39 14 9 6 21   Annual 2035 0.4 0.6 0.8 0.9 1.2 10 3 0 3 15     2065 1.0 1.2 1.5 1.7 2.2 13 5 2 2 23     2100 1.6 2.1 2.5 3.0 3.6 13 9 5 2 34 West Coast DJF 2035 0.3 0.6 0.7 0.8 1.0 5 1 1 2 3 South 2065 0.9 1.1 1.3 1.6 1.9 6 1 2 4 6 America 2100 1.7 2.0 2.2 2.7 3.2 8 1 3 6 12   JJA 2035 0.5 0.6 0.7 0.9 1.2 8 2 1 0 7   2065 1.0 1.2 1.3 1.6 2.2 10 3 0 3 8   2100 1.8 2.0 2.1 2.8 3.5 15 4 1 5 12   Annual 2035 0.4 0.6 0.7 0.8 1.1 4 1 0 1 3     2065 1.0 1.2 1.4 1.6 2.1 8 1 2 3 4     2100 1.8 2.0 2.2 2.7 3.4 11 1 3 5 10 Southeastern DJF 2035 0.1 0.5 0.6 0.8 1.2 6 1 1 3 6 South 2065 0.9 1.0 1.3 1.4 2.0 4 1 3 6 9 America 2100 1.4 1.9 2.1 2.7 3.5 9 0 4 6 15 JJA 2035 0.1 0.4 0.6 0.8 1.0 12 3 0 5 14   2065 0.6 0.9 1.1 1.3 1.9 16 1 3 6 16   2100 1.3 1.4 1.7 2.6 3.0 24 4 4 14 30   Annual 2035 0.3 0.5 0.6 0.7 1.0 5 1 1 3 8     2065 0.7 1.0 1.1 1.4 1.9 7 0 3 6 11     2100 1.4 1.6 2.0 2.7 3.3 12 1 3 8 16 (continued on next page) 14SM 14SM-26 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1b (continued) RCP6.0     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Europe Northern DJF 2035 0.4 0.6 1.4 2.2 2.8 5 1 5 5 11 Europe 2065 0.6 1.7 2.5 3.5 5.4 2 4 8 10 18   2100 1.4 3.3 4.0 5.2 6.6 5 9 14 17 29   JJA 2035 0.2 0.5 0.8 1.3 2.4 7 0 3 6 8   2065 0.2 1.1 1.7 2.3 3.9 6 1 3 7 16   2100 0.8 2.0 2.8 3.8 5.4 13 2 5 9 21   Annual 2035 0.1 0.7 1.2 1.5 2.3 2 0 4 6 9     2065 0.1 1.5 1.9 2.6 3.9 4 4 6 8 17     2100 1.3 2.7 3.1 4.2 5.3 4 8 8 11 25 Central DJF 2035 0.4 0.6 0.8 1.3 3.2 2 1 2 3 10 Europe 2065 0.7 1.5 1.7 2.6 3.7 1 2 5 7 14   2100 0.6 2.5 3.1 3.8 5.1 0 5 7 11 18   JJA 2035 0.3 0.7 0.9 1.2 2.0 9 3 0 5 10   2065 0.6 1.5 1.9 2.6 3.4 11 3 0 5 9   2100 1.3 2.4 3.2 4.2 5.6 20 7 1 3 10   Annual 2035 0.3 0.7 1.0 1.2 2.0 4 1 1 3 9     2065 0.8 1.4 1.9 2.2 3.1 4 0 1 5 10     2100 0.8 2.4 3.1 3.6 4.7 5 1 2 6 11 Southern Europe/ DJF 2035 0.3 0.5 0.7 0.8 1.3 11 4 3 2 4 Mediterranean 2065 0.4 1.2 1.5 1.7 2.1 15 8 2 0 8 2100 0.5 1.9 2.3 2.9 3.3 23 14 5 2 11 JJA 2035 0.4 0.8 1.1 1.3 2.3 12 6 4 2 7   2065 1.0 1.6 2.0 2.2 3.6 14 10 6 3 9   2100 2.0 2.8 3.5 4.0 6.2 30 19 15 7 11   Annual 2035 0.3 0.7 0.9 1.0 1.6 8 5 2 0 5     2065 0.8 1.5 1.6 1.9 2.6 13 9 4 0 4     2100 1.3 2.4 2.8 3.2 4.6 21 14 9 6 5 Africa Sahara DJF 2035 0.2 0.6 0.8 1.0 1.6 36 6 0 18 38   2065 1.0 1.3 1.6 1.9 2.3 39 11 0 12 26   2100 1.4 2.3 2.9 3.2 3.7 22 11 1 15 45   JJA 2035 0.3 0.9 1.0 1.1 1.9 13 8 2 11 31   2065 1.3 1.7 1.9 2.0 3.1 15 9 1 14 64   2100 2.1 2.8 3.1 3.7 5.1 23 11 2 16 101   Annual 2035 0.6 0.7 0.9 1.1 1.5 13 5 0 9 24     2065 1.3 1.5 1.7 2.0 2.5 20 7 0 12 54     2100 1.8 2.6 3.0 3.5 4.4 19 9 2 17 86 West Africa DJF 2035 0.4 0.6 0.8 0.9 1.2 5 0 2 3 6   2065 1.1 1.3 1.5 1.7 2.3 7 1 3 4 12   2100 1.7 2.2 2.5 3.0 4.1 8 3 6 7 19   JJA 2035 0.5 0.6 0.7 0.9 1.3 6 0 2 3 5   2065 1.0 1.2 1.4 1.6 2.4 5 0 2 4 6   2100 1.9 2.1 2.3 3.0 3.9 7 1 2 5 13   Annual 2035 0.5 0.7 0.7 0.9 1.2 6 0 2 3 4     2065 1.1 1.3 1.4 1.6 2.4 5 0 2 4 7     2100 1.8 2.2 2.3 2.9 4.0 5 2 4 5 12 (continued on next page) 14SM 14SM-27 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1b (continued) RCP6.0     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max East Africa DJF 2035 0.4 0.6 0.7 0.8 1.2 3 0 2 5 10   2065 0.9 1.2 1.4 1.7 2.2 4 0 3 7 12   2100 1.6 2.0 2.3 2.8 3.9 5 2 9 14 23   JJA 2035 0.5 0.7 0.8 0.9 1.1 8 3 0 2 5   2065 1.0 1.3 1.5 1.8 2.4 8 5 2 3 14   2100 1.8 2.2 2.6 2.9 3.8 9 6 1 7 24   Annual 2035 0.5 0.6 0.8 0.9 1.1 4 1 0 3 8     2065 1.0 1.2 1.5 1.8 2.3 5 2 1 4 13     2100 1.7 2.1 2.4 2.9 3.9 6 1 4 10 21 Southern DJF 2035 0.5 0.7 0.8 1.0 1.5 12 4 1 2 8 Africa 2065 1.0 1.3 1.6 2.0 2.2 13 7 2 1 5   2100 1.9 2.2 2.8 3.3 4.0 20 9 2 0 5   JJA 2035 0.4 0.7 0.8 0.9 1.3 17 4 1 2 11   2065 1.1 1.4 1.6 1.8 2.3 15 10 6 1 2   2100 2.0 2.3 2.7 3.2 3.9 33 15 8 3 5   Annual 2035 0.5 0.7 0.8 0.9 1.3 10 3 1 1 8     2065 1.2 1.4 1.7 1.9 2.3 10 7 2 1 4     2100 2.0 2.4 2.8 3.3 4.0 18 10 3 1 5 West DJF 2035 0.3 0.5 0.5 0.7 0.9 2 0 2 4 7 Indian 2065 0.8 0.9 1.0 1.3 1.6 5 1 3 6 14 Ocean 2100 1.4 1.5 1.6 2.3 2.7 11 0 3 8 18   JJA 2035 0.3 0.5 0.5 0.7 1.0 4 0 4 5 9   2065 0.7 0.9 1.0 1.2 1.7 5 0 3 7 14   2100 1.4 1.5 1.7 2.2 2.8 7 0 4 9 17   Annual 2035 0.3 0.5 0.5 0.7 1.0 2 0 2 3 7     2065 0.8 0.9 1.0 1.2 1.6 2 1 3 5 14     2100 1.4 1.5 1.6 2.2 2.7 4 0 3 8 18 Asia West Asia DJF 2035 0.5 0.7 1.0 1.2 1.6 7 2 3 6 13   2065 0.9 1.4 1.7 2.0 2.9 9 1 4 7 10   2100 1.2 2.5 2.9 3.4 4.9 17 3 4 9 19   JJA 2035 0.6 0.8 1.0 1.2 2.2 16 4 2 5 22   2065 1.3 1.5 2.0 2.2 3.4 16 4 2 9 11   2100 2.1 2.6 3.2 3.9 5.6 22 11 0 10 22   Annual 2035 0.7 0.7 0.9 1.2 1.6 7 2 2 5 12     2065 1.1 1.5 1.8 2.1 2.7 10 2 3 7 9     2100 1.5 2.5 3.0 3.7 4.7 18 5 4 6 15 Central Asia DJF 2035 0.3 0.9 1.1 1.5 2.1 9 2 4 11 14   2065 0.9 1.6 2.0 2.5 4.0 14 0 4 9 18   2100 1.1 2.8 3.3 4.0 5.8 15 5 6 12 24   JJA 2035 0.3 0.7 1.0 1.2 2.2 12 2 3 7 14   2065 0.8 1.4 1.8 2.3 3.7 16 0 4 9 18   2100 1.6 2.5 3.3 4.2 6.1 29 2 2 11 16   Annual 2035 0.6 0.9 1.0 1.2 1.9 7 0 5 7 13     2065 0.9 1.6 1.9 2.2 3.0 10 1 5 8 18     2100 1.2 2.7 3.3 3.9 5.3 19 2 3 8 22 (continued on next page) 14SM 14SM-28 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1b (continued) RCP6.0     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Eastern Asia DJF 2035 0.0 0.8 1.0 1.3 2.0 4 0 2 4 11   2065 0.5 1.5 1.9 2.3 3.1 12 1 3 5 13   2100 1.2 2.7 3.1 3.8 5.0 7 4 8 12 19   JJA 2035 0.4 0.6 0.7 0.9 1.5 3 1 2 4 6   2065 0.9 1.3 1.6 1.8 2.7 3 1 2 5 7   2100 1.4 2.2 2.8 3.6 4.5 2 5 7 10 21   Annual 2035 0.4 0.7 0.9 1.1 1.4 2 1 2 3 6     2065 0.9 1.5 1.6 1.9 2.7 3 1 3 4 6     2100 1.2 2.3 2.9 3.5 4.5 0 5 8 10 18 Tibetan DJF 2035 0.3 0.8 1.1 1.5 2.1 0 3 6 8 13 Plateau 2065 0.9 1.7 2.0 2.6 3.5 1 6 8 12 17   2100 1.7 2.9 3.4 4.3 5.8 1 7 12 18 30   JJA 2035 0.4 0.8 0.9 1.1 2.3 3 1 3 6 15   2065 1.1 1.6 1.8 2.1 4.0 4 3 6 9 23   2100 1.7 2.7 3.2 3.9 6.2 4 5 9 14 40   Annual 2035 0.6 0.7 1.1 1.2 1.8 1 2 4 6 13     2065 1.2 1.7 2.0 2.2 3.2 2 5 6 10 20     2100 1.5 2.7 3.3 3.9 5.4 1 7 12 14 35 South Asia DJF 2035 0.3 0.6 0.9 1.1 1.3 7 1 5 8 14   2065 0.8 1.3 1.5 1.8 2.4 9 1 7 10 22   2100 1.9 2.3 2.7 3.3 3.9 12 5 10 13 51   JJA 2035 0.2 0.5 0.7 0.8 1.1 3 1 3 4 7   2065 0.7 1.0 1.3 1.5 2.2 5 3 5 6 15   2100 1.6 1.9 2.2 2.8 3.8 8 8 11 14 25   Annual 2035 0.3 0.6 0.8 0.9 1.2 2 1 4 5 7     2065 1.1 1.2 1.3 1.7 2.0 5 2 5 6 16     2100 1.9 2.2 2.4 3.3 3.8 5 9 11 14 24 North DJF 2035 0.3 0.5 0.6 0.7 1.0 7 1 6 10 16 Indian 2065 0.7 0.9 1.0 1.3 1.6 8 3 8 19 25 Ocean 2100 1.3 1.6 1.7 2.3 2.8 7 8 14 27 57   JJA 2035 0.3 0.5 0.6 0.7 1.0 6 2 1 5 10   2065 0.8 0.9 1.1 1.3 1.7 6 0 4 8 21   2100 1.5 1.6 1.8 2.3 2.9 13 3 6 13 36   Annual 2035 0.3 0.5 0.6 0.7 1.0 3 1 3 6 12     2065 0.8 0.9 1.0 1.3 1.6 4 2 6 9 23     2100 1.5 1.6 1.7 2.4 2.8 7 3 12 15 47 Southeast DJF 2035 0.3 0.5 0.6 0.7 1.0 8 1 0 1 4 Asia (land) 2065 0.7 0.9 1.1 1.4 1.8 6 2 1 4 8   2100 1.3 1.7 1.8 2.4 3.2 9 0 4 8 14   JJA 2035 0.4 0.5 0.6 0.7 1.1 5 2 0 2 5   2065 0.7 1.0 1.2 1.4 2.0 5 2 0 4 6   2100 1.5 1.8 1.9 2.4 3.4 6 1 4 8 13   Annual 2035 0.4 0.5 0.6 0.7 1.0 6 1 0 2 4     2065 0.8 1.0 1.1 1.4 1.8 5 1 1 4 7     2100 1.5 1.7 1.9 2.5 3.2 4 0 3 8 14 (continued on next page) 14SM 14SM-29 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1b (continued) RCP6.0     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Southeast DJF 2035 0.3 0.5 0.5 0.6 1.1 3 1 0 1 5 Asia (sea) 2065 0.7 0.8 1.0 1.2 1.7 5 1 2 4 7   2100 1.4 1.5 1.5 2.1 2.9 5 0 3 6 14   JJA 2035 0.2 0.5 0.5 0.6 1.0 4 0 1 2 5   2065 0.8 0.8 1.0 1.2 1.7 4 0 2 4 9   2100 1.4 1.5 1.6 2.1 2.9 4 2 3 5 13   Annual 2035 0.2 0.5 0.5 0.6 1.0 3 0 1 2 3     2065 0.7 0.9 0.9 1.2 1.7 2 1 2 4 7     2100 1.4 1.5 1.6 2.1 2.8 2 1 3 5 13 Australia North DJF 2035 0.3 0.6 0.7 1.0 1.9 17 5 3 1 9 Australia 2065 0.9 1.4 1.5 1.9 2.7 16 4 1 3 10   2100 1.3 2.2 2.6 3.0 4.0 30 8 1 6 18   JJA 2035 0.1 0.6 0.8 1.1 1.3 36 12 5 2 17   2065 0.9 1.4 1.5 1.9 2.1 46 12 3 2 14   2100 1.8 2.1 2.8 3.1 3.5 57 18 4 5 15   Annual 2035 0.4 0.6 0.8 1.0 1.4 14 8 3 1 9     2065 1.0 1.3 1.6 1.8 2.2 18 5 1 3 9     2100 1.5 2.3 2.7 3.1 3.6 29 9 0 3 15 South Australia/ DJF 2035 0.5 0.6 0.7 0.9 1.4 22 5 1 1 6 New Zealand 2065 0.8 1.3 1.4 1.6 2.1 24 6 1 3 10 2100 1.6 1.9 2.3 2.9 3.7 19 5 1 2 11 JJA 2035 0.4 0.5 0.7 0.7 0.9 16 4 1 1 4   2065 0.6 1.0 1.2 1.4 1.6 27 6 1 2 5   2100 1.3 1.7 2.1 2.3 2.8 28 9 2 3 10   Annual 2035 0.5 0.6 0.7 0.8 1.0 17 3 1 0 5     2065 0.9 1.1 1.4 1.5 1.7 24 4 1 3 4     2100 1.5 1.9 2.3 2.5 2.9 23 5 1 2 9 The Pacific Northern DJF 2035 0.3 0.4 0.5 0.6 0.9 4 1 0 2 7 Tropical 2065 0.7 0.8 1.0 1.2 1.6 5 1 2 4 7 Pacific 2100 1.2 1.5 1.7 2.2 2.8 9 1 2 6 13   JJA 2035 0.3 0.4 0.5 0.6 1.0 7 2 1 3 6   2065 0.7 0.9 0.9 1.2 1.8 5 1 1 4 6   2100 1.3 1.4 1.6 2.3 3.0 10 2 2 6 12   Annual 2035 0.3 0.5 0.5 0.6 0.9 4 2 0 2 6     2065 0.7 0.9 1.0 1.2 1.7 4 1 1 3 6     2100 1.3 1.4 1.7 2.3 2.9 9 0 2 5 9 Equatorial DJF 2035 0.4 0.5 0.6 0.8 1.0 2 1 6 14 41 Pacific 2065 0.5 1.0 1.1 1.4 1.9 6 9 14 20 133   2100 1.0 1.6 1.8 2.4 3.3 11 7 18 32 250   JJA 2035 0.4 0.5 0.6 0.7 1.1 1 5 9 13 21   2065 0.7 0.9 1.1 1.3 2.1 15 11 16 25 87   2100 1.2 1.5 1.8 2.3 3.4 3 21 29 42 164   Annual 2035 0.4 0.5 0.6 0.7 0.9 1 5 7 12 33     2065 0.6 1.0 1.1 1.3 1.9 1 9 14 21 113     2100 1.2 1.6 1.8 2.3 3.1 5 16 22 31 215 14SM (continued on next page) 14SM-30 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1b (continued) RCP6.0     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Southern DJF 2035 0.2 0.4 0.5 0.6 0.8 7 1 1 2 4 Pacific 2065 0.6 0.8 0.9 1.1 1.5 18 2 2 3 5   2100 1.0 1.3 1.5 1.8 2.4 32 4 3 5 8   JJA 2035 0.2 0.4 0.5 0.6 0.9 7 1 1 3 7   2065 0.6 0.8 0.9 1.0 1.5 15 2 1 4 6   2100 1.1 1.3 1.5 1.8 2.5 24 2 0 4 9   Annual 2035 0.2 0.4 0.5 0.5 0.8 8 0 1 2 4     2065 0.7 0.8 0.8 1.1 1.5 18 1 2 3 5     2100 1.1 1.3 1.5 1.8 2.4 29 2 2 4 9 Antarctica (land) DJF 2035 0.1 0.3 0.5 0.7 1.2 6 1 2 4 7   2065 0.1 1.0 1.3 1.5 2.1 6 2 5 7 13   2100 0.7 1.7 2.2 2.8 3.7 6 5 6 13 22   JJA 2035 0.4 0.2 0.6 0.8 1.7 2 1 3 5 10   2065 0.1 1.1 1.4 1.7 2.2 0 5 8 11 15   2100 0.0 1.9 2.1 3.0 3.9 2 10 12 20 29   Annual 2035 0.3 0.4 0.5 0.8 1.2 3 1 3 5 9     2065 0.0 1.1 1.2 1.6 2.2 3 3 7 9 14     2100 0.5 1.7 2.0 2.9 3.8 2 7 10 17 25 (sea) DJF 2035 0.5 0.1 0.3 0.4 0.8 0 1 3 3 4   2065 0.4 0.4 0.6 0.8 1.2 1 2 4 5 7   2100 0.3 0.7 1.0 1.4 2.2 2 4 7 10 12   JJA 2035 1.0 0.2 0.5 0.8 1.8 0 2 2 4 6   2065 0.8 0.5 1.0 1.6 2.2 1 3 5 7 9   2100 0.4 1.1 1.6 2.8 3.8 4 6 7 12 16   Annual 2035 0.7 0.1 0.4 0.6 1.2 0 1 2 3 5     2065 0.6 0.5 0.7 1.2 1.8 1 2 4 6 8     2100 0.3 1.0 1.3 2.1 3.0 4 5 7 11 13 Notes: a *Precipitation changes cover 6 months; ONDJFM and AMJJAS for winter and summer (northern hemisphere) 14SM 14SM-31 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1c | Temperature and precipitation projections by the CMIP5 global models. The figures shown are averages over SREX regions (Seneviratne et al., 2012) of the projections by a set of 39 global models for the RCP8.5 scenario. Added to the SREX regions are an additional six regions containing the two polar regions, the Caribbean, Indian Ocean and Pacific Island States (see Annex I for further details). The 26 SREX regions are: Alaska/NW Canada (ALA), Eastern Canada/Greenland/Iceland (CGI), Western North America (WNA), Central North America (CNA), Eastern North America (ENA), Central America/Mexico (CAM), Amazon (AMZ), NE Brazil (NEB), West Coast South America (WSA), South-Eastern South America (SSA), Northern Europe (NEU), Central Europe (CEU), Southern Europe/the Mediterranean (MED), Sahara (SAH), Western Africa (WAF), Eastern Africa (EAF), Southern Africa (SAF), Northern Asia (NAS), Western Asia (WAS), Central Asia (CAS), Tibetan Plateau (TIB), Eastern Asia (EAS), Southern Asia (SAS), Southeast Asia (SEA), Northern Australia (NAS) and Southern Australia/New Zealand (SAU). The area mean temperature and precipitation responses are first averaged for each model over the 1986 2005 period from the historical simulations and the 2016 2035, 2046 2065 and 2081 2100 periods of the RCP8.5 experiments. Based on the difference between these two periods, the table shows the 25th, 50th and 75th percentiles, and the lowest and highest response among the 39 models, for temperature in degrees Celsius and precipitation as a per cent change. Regions in which the middle half (25 to 75%) of this distribution is all of the same sign in the precipitation response are colored light brown for decreasing and light green for increasing precipitation. Information is provided for land areas contained in the boxes unless otherwise indicated. The temperature responses are averaged over the boreal winter and summer seasons; December, January and February (DJF) and June, July and August (JJA) respectively. The precipitation responses are averaged over half year periods, boreal winter; October, November, December, January, February and March (ONDJFM) and summer; April, May, June, July, August and September (AMJJAS). RCP8.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Arctic (land) DJF 2035 0.7 1.7 2.1 2.5 4.3 0 8 10 14 19   2065 2.4 4.5 5.3 6.2 10.1 11 21 23 29 51   2100 5.3 8.6 9.6 12.4 16.8 27 40 47 64 93   JJA 2035 0.4 0.8 1.1 1.4 2.8 0 4 6 8 23   2065 1.1 2.0 2.6 3.3 5.9 3 11 13 18 41   2100 2.6 4.0 4.7 6.1 9.2 9 20 25 32 61   Annual 2035 0.5 1.4 1.7 2.0 3.7 2 6 7 10 22     2065 1.7 3.7 4.1 4.6 7.8 6 15 17 21 45     2100 4.4 6.3 7.5 8.6 12.4 17 30 34 40 74 (sea) DJF 2035 0.7 2.4 3.1 3.5 6.3 2 7 11 15 23   2065 2.0 6.3 7.4 8.7 14.2 2 21 27 32 47   2100 7.8 12.2 13.5 17.4 23.3 19 44 53 61 83   JJA 2035 0.2 0.5 0.7 0.9 1.7 2 5 6 8 15   2065 0.4 1.4 1.6 2.1 3.8 2 12 16 18 30   2100 1.3 2.6 3.3 4.8 8.1 0 23 27 34 45   Annual 2035 0.6 1.9 2.3 2.5 4.4 1 6 9 11 19     2065 1.4 4.5 5.1 6.3 9.0 0 16 20 24 39     2100 5.2 7.7 9.2 11.5 14.8 11 32 38 46 63 High latitudes Canada/ DJF 2035 0.6 1.5 2.0 2.2 3.4 1 4 6 8 13 Greenland/ 2065 2.6 4.2 4.8 5.9 7.9 3 12 15 21 29 Iceland 2100 4.6 7.2 8.7 10.8 13.3 13 25 29 41 55   JJA 2035 0.5 0.9 1.1 1.3 2.7 0 3 4 6 11   2065 1.2 2.3 2.6 3.1 5.6 5 7 9 12 21   2100 2.2 4.1 4.6 5.9 9.0 7 14 16 21 35   Annual 2035 0.6 1.3 1.5 1.7 2.9 1 4 5 6 10     2065 1.8 3.2 3.6 4.2 6.3 4 10 12 16 22     2100 4.2 5.6 6.4 7.9 10.5 11 19 22 28 40 North Asia DJF 2035 0.6 1.3 1.9 2.2 4.0 1 6 8 11 23   2065 2.4 3.5 4.1 5.2 7.9 8 14 19 24 45   2100 4.7 6.9 7.9 9.6 12.4 18 28 33 46 74   JJA 2035 0.4 0.9 1.2 1.5 2.9 1 3 5 6 14   2065 1.3 2.0 2.9 3.8 5.3 1 6 9 12 26   2100 2.6 4.1 5.1 7.0 8.3 0 9 14 19 37   Annual 2035 0.6 1.2 1.5 2.0 3.3 1 5 5 8 17     2065 1.9 2.8 3.6 4.2 6.1 6 9 12 15 32     2100 3.9 5.2 6.5 7.6 9.8 11 19 22 26 50 14SM (continued on next page) 14SM-32 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1c (continued) RCP8.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max North America Alaska/ DJF 2035 0.8 1.6 2.1 3.2 4.8 5 4 6 11 17 NW Canada 2065 2.9 4.5 5.1 6.3 8.8 2 10 17 21 37   2100 5.3 7.7 9.3 11.8 14.8 12 22 33 43 73   JJA 2035 0.2 0.8 1.2 1.5 2.6 1 3 6 7 16   2065 0.9 2.0 2.7 3.4 5.4 4 10 13 14 32   2100 2.3 3.8 4.9 6.0 8.3 10 17 23 27 47   Annual 2035 0.4 1.3 1.7 2.3 3.0 2 4 5 8 16     2065 2.3 3.3 3.7 4.6 6.1 7 10 14 18 32     2100 4.1 5.6 6.9 8.5 10.5 11 23 26 32 51 West North DJF 2035 0.2 1.0 1.3 1.7 2.5 4 1 3 4 8 America 2065 1.0 2.5 3.0 3.4 5.0 2 3 6 8 20   2100 2.7 4.1 5.0 6.1 8.7 3 8 12 13 27   JJA 2035 0.6 1.0 1.3 1.5 2.2 4 0 2 3 8   2065 1.6 2.3 2.9 3.7 4.4 7 1 2 4 15   2100 3.5 4.7 5.2 6.5 7.8 16 4 0 5 20   Annual 2035 0.5 1.0 1.2 1.5 2.0 2 1 2 4 7     2065 1.7 2.4 2.8 3.4 4.2 2 1 4 6 18     2100 3.1 4.2 5.0 6.1 7.7 2 3 6 8 25 Central DJF 2035 0.0 1.0 1.2 1.7 2.3 6 2 2 6 11 North 2065 1.1 2.2 2.7 3.6 4.8 12 1 7 10 17 America 2100 2.9 4.1 5.0 5.9 7.7 18 4 11 17 28   JJA 2035 0.5 1.1 1.2 1.4 2.3 9 2 1 3 9   2065 1.7 2.3 2.9 3.4 4.6 19 1 3 6 13   2100 3.6 4.6 5.4 6.2 8.0 19 4 3 7 16   Annual 2035 0.6 1.0 1.2 1.5 2.0 5 1 2 4 10     2065 1.7 2.4 2.8 3.3 4.2 10 1 4 7 15     2100 3.4 4.4 5.1 5.7 7.4 14 0 7 9 16 Eastern DJF 2035 0.0 0.9 1.4 1.6 2.7 3 1 4 6 11 North 2065 1.8 2.4 3.0 3.7 5.1 1 6 9 15 20 America 2100 3.2 4.6 5.5 6.4 8.6 2 12 17 24 32   JJA 2035 0.4 0.9 1.1 1.4 2.3 7 1 3 5 10   2065 1.4 2.4 2.8 3.3 4.8 8 2 5 7 12   2100 2.8 4.2 5.1 5.9 8.3 11 1 7 9 25   Annual 2035 0.4 0.9 1.2 1.4 2.1 3 1 4 5 7     2065 1.7 2.5 2.8 3.3 4.5 1 4 7 10 14     2100 3.5 4.3 5.1 6.0 7.6 0 6 11 15 22 Central America Central DJF 2035 0.5 0.7 0.9 1.0 1.3 12 4 0 3 13 America 2065 1.4 1.9 2.1 2.4 2.9 17 6 2 1 12   2100 2.3 3.3 3.9 4.6 5.3 27 11 5 4 13   JJA 2035 0.6 0.8 0.9 1.1 1.5 14 4 1 3 9   2065 1.6 2.0 2.3 2.6 3.2 14 7 5 0 11   2100 2.9 3.6 4.2 5.1 6.0 27 14 11 1 16   Annual 2035 0.5 0.8 0.9 1.1 1.4 11 3 1 3 6     2065 1.5 1.9 2.1 2.6 3.0 14 7 5 1 7     2100 2.9 3.5 3.9 4.9 5.5 26 12 8 0 11 (continued on next page) 14SM 14SM-33 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1c (continued) RCP8.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Caribbean DJF 2035 0.3 0.6 0.7 0.9 1.1 11 3 0 5 7 (land and sea) 2065 1.0 1.4 1.6 1.9 2.4 13 5 1 3 10   2100 2.0 2.5 3.0 3.6 4.0 39 13 5 3 19   JJA 2035 0.3 0.6 0.7 0.8 1.2 17 9 6 2 16   2065 1.2 1.4 1.6 1.9 2.5 34 24 12 5 13   2100 2.2 2.4 2.9 3.4 4.2 60 39 24 17 2   Annual 2035 0.4 0.6 0.7 0.9 1.1 14 5 2 1 11     2065 1.1 1.4 1.6 1.9 2.5 19 14 8 2 10     2100 2.1 2.5 3.0 3.6 4.1 50 23 16 7 9 South America Amazon DJF 2035 0.5 0.8 0.9 1.1 1.8 12 4 1 1 5   2065 1.3 1.9 2.3 2.8 3.9 20 6 1 2 7   2100 1.9 3.5 4.3 5.3 6.3 26 11 3 3 16   JJA 2035 0.5 0.8 1.0 1.2 2.1 17 3 1 1 6   2065 1.5 2.1 2.6 2.9 4.2 28 6 2 2 13   2100 3.0 3.8 4.7 5.7 7.6 44 11 5 1 12   Annual 2035 0.5 0.8 1.1 1.2 1.9 12 3 1 1 4     2065 1.4 2.0 2.5 2.8 4.1 23 5 1 2 8     2100 2.4 3.7 4.3 5.7 7.0 33 11 2 1 14 Northeast DJF 2035 0.5 0.7 0.9 1.1 1.5 13 6 1 3 9 Brazil 2065 1.2 1.8 2.1 2.4 3.2 16 9 0 4 39   2100 2.1 3.4 3.8 4.6 5.6 29 11 4 5 48   JJA 2035 0.4 0.7 0.9 1.1 1.3 22 10 4 1 17   2065 1.2 1.8 2.2 2.5 3.1 24 13 8 3 32   2100 2.6 3.6 4.1 4.8 5.7 41 25 18 5 37   Annual 2035 0.5 0.8 1.0 1.1 1.5 14 6 0 2 7     2065 1.3 1.9 2.2 2.6 3.1 16 10 2 1 38     2100 2.5 3.5 4.1 4.8 5.6 31 14 6 6 45 West Coast DJF 2035 0.4 0.7 0.8 1.0 1.3 5 0 1 3 6 South America 2065 1.6 1.8 2.1 2.4 2.9 8 1 2 4 10   2100 2.6 3.2 3.8 4.7 5.2 11 1 3 7 14   JJA 2035 0.5 0.8 0.9 1.0 1.4 9 2 0 2 6   2065 1.5 1.9 2.2 2.5 3.0 13 3 1 3 8   2100 2.9 3.3 3.8 4.8 5.3 20 6 1 3 12   Annual 2035 0.5 0.7 0.9 1.0 1.4 6 1 1 2 5     2065 1.5 1.8 2.1 2.4 2.9 9 1 1 3 8     2100 2.8 3.3 3.8 4.8 5.1 14 1 1 6 11 Southeastern DJF 2035 0.1 0.6 0.8 1.0 1.5 4 1 2 4 13 South America 2065 1.0 1.7 1.9 2.2 3.5 7 1 3 7 14   2100 1.9 3.0 3.8 4.4 6.2 10 1 6 11 24   JJA 2035 0.2 0.5 0.7 0.9 1.2 12 1 1 4 22   2065 0.9 1.5 1.8 2.1 2.7 21 2 4 8 27   2100 1.9 2.8 3.4 3.9 4.6 24 3 7 21 41   Annual 2035 0.2 0.6 0.8 0.9 1.4 6 0 1 3 14     2065 1.1 1.7 1.9 2.2 3.1 11 1 3 8 18     2100 1.9 3.0 3.6 4.2 5.3 11 1 7 14 27 (continued on next page) 14SM 14SM-34 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1c (continued) RCP8.5     Temperature (°C) Precipitation (%) REGION MONTH a Year min 25% 50% 75% max min 25% 50% 75% max Europe Northern DJF 2035 0.1 1.1 1.5 2.4 3.5 3 3 5 7 16 Europe 2065 0.6 2.9 3.4 4.7 6.8 2 8 11 15 26   2100 2.6 5.3 6.1 7.5 10.4 8 15 20 29 42   JJA 2035 0.3 0.8 1.1 1.5 2.8 4 1 4 7 9   2065 0.1 1.9 2.5 3.2 4.9 8 1 4 10 19   2100 2.1 3.5 4.5 5.8 7.6 17 2 8 12 26   Annual 2035 0.3 0.9 1.2 1.8 2.7 3 3 4 6 12     2065 0.4 2.4 2.9 3.5 4.7 2 6 8 11 23     2100 1.9 4.3 5.0 6.3 7.7 2 12 14 18 34 Central Europe DJF 2035 0.3 0.6 1.0 1.8 3.3 7 1 4 6 15   2065 1.2 2.1 2.6 3.8 5.8 1 4 7 11 19   2100 3.4 4.2 4.9 5.9 8.2 4 7 11 18 29   JJA 2035 0.4 1.0 1.3 1.6 2.8 8 2 1 5 8   2065 1.3 2.3 2.7 3.6 5.5 18 6 2 4 10   2100 2.8 4.3 5.3 6.6 8.5 28 16 8 2 11   Annual 2035 0.5 0.9 1.1 1.6 2.5 4 0 3 5 11     2065 1.0 2.2 2.7 3.3 4.6 5 0 3 6 10     2100 3.1 4.0 4.8 5.8 7.1 8 3 0 7 14 Southern Europe/ DJF 2035 0.0 0.6 0.9 1.1 1.7 10 4 1 1 8 Mediterranean 2065 0.7 1.8 2.2 2.7 3.1 24 9 4 2 6 2100 2.4 3.3 3.8 4.6 5.7 35 18 12 7 0   JJA 2035 0.6 1.1 1.4 1.6 2.7 15 7 3 1 8   2065 2.1 2.6 3.3 3.7 5.6 31 18 12 7 9   2100 3.9 4.9 6.0 6.8 9.3 58 35 24 17 4   Annual 2035 0.4 1.0 1.1 1.3 2.0 8 4 2 0 5     2065 1.6 2.3 2.5 3.0 4.1 23 11 7 5 1     2100 3.3 4.1 4.5 5.6 6.9 35 23 19 13 2 Africa Sahara DJF 2035 0.2 0.9 1.1 1.2 1.6 35 13 1 8 67   2065 1.3 2.2 2.5 2.9 3.2 35 15 2 15 128   2100 3.2 3.8 4.4 5.3 6.4 49 26 10 19 319   JJA 2035 0.4 1.0 1.2 1.4 2.0 24 3 6 19 34   2065 1.9 2.5 2.9 3.3 4.5 23 7 5 16 98   2100 3.4 4.6 5.0 6.5 7.8 41 14 9 25 147   Annual 2035 0.5 1.0 1.1 1.3 1.7 25 3 5 13 28     2065 1.8 2.4 2.7 3.2 3.7 18 8 4 13 84     2100 3.8 4.3 4.6 6.1 6.5 42 15 2 18 155 West Africa DJF 2035 0.5 0.8 1.0 1.1 1.6 6 0 2 4 8   2065 1.5 2.0 2.3 2.7 3.4 2 2 4 9 13   2100 3.1 3.7 4.0 4.9 6.1 8 3 7 13 23   JJA 2035 0.6 0.8 0.8 1.0 1.5 6 1 1 2 8   2065 1.6 1.9 2.0 2.8 3.3 10 1 1 4 9   2100 2.2 3.5 3.9 5.3 5.9 13 2 2 6 13   Annual 2035 0.7 0.8 0.9 1.1 1.5 4 0 1 3 8     2065 1.6 1.9 2.1 2.7 3.3 8 1 2 6 8     2100 2.6 3.6 4.0 5.1 5.9 10 0 5 8 16 (continued on next page) 14SM 14SM-35 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1c (continued) RCP8.5     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max East Africa DJF 2035 0.6 0.8 0.9 1.1 1.6 5 1 2 6 10   2065 1.3 1.8 2.2 2.5 3.2 8 1 6 12 23   2100 2.8 3.4 3.9 4.6 5.6 11 6 15 22 35   JJA 2035 0.6 0.8 0.9 1.2 1.5 8 4 1 3 11   2065 1.3 2.0 2.1 2.8 3.2 12 6 0 5 21   2100 1.8 3.5 4.1 5.2 5.6 15 5 4 13 33   Annual 2035 0.6 0.7 0.9 1.1 1.5 6 2 0 3 9     2065 1.6 1.9 2.2 2.7 3.2 9 1 4 7 20     2100 2.4 3.5 4.0 5.0 5.6 11 0 11 16 34 Southern DJF 2035 0.5 0.8 1.0 1.2 1.5 10 4 2 0 5 Africa 2065 1.5 2.0 2.5 2.8 3.3 19 8 4 1 5   2100 3.1 3.8 4.4 5.2 6.2 26 12 5 3 2   JJA 2035 0.5 0.8 1.0 1.1 1.6 19 9 4 1 5   2065 1.9 2.1 2.4 2.7 3.3 31 18 11 6 6   2100 3.3 4.0 4.5 5.2 6.1 48 27 18 13 1   Annual 2035 0.6 0.9 1.1 1.2 1.6 11 4 2 0 2     2065 1.7 2.2 2.5 2.9 3.4 19 9 5 2 5     2100 3.3 4.1 4.5 5.5 6.3 24 14 9 5 3 West Indian DJF 2035 0.3 0.6 0.7 0.8 1.1 4 1 0 3 10 Ocean 2065 1.1 1.4 1.6 1.9 2.2 6 2 2 5 17   2100 2.1 2.6 2.9 3.5 4.1 10 1 4 11 25   JJA 2035 0.3 0.6 0.7 0.8 1.2 8 2 2 5 9   2065 1.0 1.4 1.6 1.8 2.2 13 1 2 5 17   2100 1.7 2.5 2.9 3.5 4.3 10 1 4 7 24   Annual 2035 0.3 0.6 0.7 0.7 1.1 3 0 1 3 9     2065 1.1 1.4 1.5 1.8 2.2 7 1 1 5 16     2100 2.0 2.6 2.9 3.5 4.2 6 2 4 9 24 Asia West Asia DJF 2035 0.5 0.8 1.2 1.4 1.9 10 2 4 6 19   2065 1.1 2.2 2.6 3.2 4.1 14 0 3 6 36   2100 3.1 4.0 4.6 5.6 6.8 17 2 4 9 45   JJA 2035 0.5 1.0 1.2 1.4 2.4 10 4 1 7 24   2065 1.8 2.6 3.1 3.6 4.8 23 7 4 5 52   2100 3.8 4.6 5.3 6.6 8.2 38 15 8 11 84   Annual 2035 0.5 1.0 1.1 1.4 2.0 8 2 3 7 22     2065 1.6 2.5 2.8 3.4 4.1 14 5 0 6 24     2100 3.7 4.5 5.0 6.2 6.9 21 7 1 8 40 Central Asia DJF 2035 0.4 0.9 1.3 1.6 2.6 11 0 3 7 17   2065 0.8 2.4 3.0 3.6 5.6 13 3 3 13 31   2100 3.5 4.3 5.3 6.3 8.0 20 5 7 14 41   JJA 2035 0.5 1.0 1.3 1.6 2.4 15 1 4 9 24   2065 1.9 2.4 3.0 3.7 5.1 27 5 1 8 18   2100 3.2 4.5 5.7 6.5 8.6 30 7 1 6 26   Annual 2035 0.4 1.0 1.3 1.5 2.1 9 2 3 9 16     2065 1.7 2.5 3.1 3.4 4.4 14 3 4 8 21     2100 3.7 4.6 5.3 6.3 7.6 20 6 2 11 35 (continued on next page) 14SM 14SM-36 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1c (continued) RCP8.5     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Eastern Asia DJF 2035 0.1 0.9 1.2 1.5 2.4 7 1 1 3 14   2065 1.3 2.3 2.9 3.3 4.7 10 3 6 9 23   2100 3.5 4.2 5.4 6.1 7.5 15 9 13 19 32   JJA 2035 0.5 0.9 1.0 1.2 1.9 4 0 1 3 5   2065 1.4 2.1 2.6 3.1 3.9 3 3 6 9 15   2100 2.8 3.8 4.8 5.7 6.8 1 7 9 15 28   Annual 2035 0.5 0.9 1.1 1.3 1.9 2 0 1 3 5     2065 1.6 2.2 2.7 3.1 4.2 1 4 6 9 14     2100 3.3 4.0 4.9 5.6 7.2 3 7 10 16 22 Tibetan DJF 2035 0.2 1.1 1.3 1.7 2.4 2 2 5 7 12 Plateau 2065 1.6 2.6 3.2 3.8 5.3 1 8 13 16 26   2100 3.9 4.8 5.8 7.0 9.1 0 15 20 28 46   JJA 2035 0.6 1.0 1.2 1.4 2.5 3 2 4 6 14   2065 1.8 2.4 2.9 3.3 5.3 2 5 8 11 32   2100 3.6 4.6 5.3 6.0 8.8 3 8 13 18 55   Annual 2035 0.6 1.0 1.3 1.5 2.0 2 3 4 5 13     2065 1.9 2.5 3.0 3.5 4.8 2 6 9 12 28     2100 3.9 4.7 5.5 6.3 8.4 2 10 16 21 47 South Asia DJF 2035 0.3 0.8 1.0 1.2 1.6 13 2 1 6 20   2065 1.5 2.2 2.6 3.0 3.7 16 4 4 10 23   2100 3.5 4.1 4.6 5.7 7.1 17 1 12 21 42   JJA 2035 0.3 0.6 0.8 1.0 1.3 3 1 3 5 16   2065 1.2 1.7 2.0 2.3 3.3 1 7 10 13 27   2100 1.3 3.0 3.7 4.6 5.6 9 13 17 23 57   Annual 2035 0.4 0.8 0.9 1.0 1.4 2 1 3 5 11     2065 1.5 2.0 2.2 2.6 3.1 0 6 8 11 17     2100 3.1 3.7 4.1 5.2 6.0 7 11 18 21 45 North Indian DJF 2035 0.3 0.6 0.7 0.8 1.0 10 0 5 8 22 Ocean 2065 0.9 1.5 1.6 2.0 2.5 18 3 13 21 44   2100 2.1 2.7 3.0 3.9 4.5 7 9 19 34 65   JJA 2035 0.3 0.6 0.7 0.8 1.1 10 0 3 5 10   2065 1.0 1.5 1.6 2.0 2.4 7 3 6 11 29   2100 1.9 2.7 3.0 3.6 4.3 14 7 14 20 52   Annual 2035 0.3 0.6 0.7 0.8 1.0 4 2 3 5 16     2065 1.0 1.5 1.6 2.0 2.4 5 3 7 14 29     2100 2.1 2.7 3.0 3.8 4.3 9 8 18 23 56 Southeast DJF 2035 0.3 0.6 0.7 0.9 1.2 6 0 2 4 10 Asia (land) 2065 1.1 1.5 1.8 2.2 2.7 3 1 5 10 19   2100 2.1 2.9 3.2 4.2 4.9 8 2 8 19 31   JJA 2035 0.4 0.6 0.7 0.9 1.3 4 1 0 2 7   2065 1.1 1.6 1.8 2.1 2.8 5 0 5 9 17   2100 2.1 2.9 3.3 4.2 5.1 8 1 7 16 30   Annual 2035 0.3 0.6 0.8 0.9 1.2 4 0 1 3 8     2065 1.1 1.6 1.7 2.2 2.7 3 0 5 10 17     2100 2.1 3.0 3.2 4.4 4.9 7 0 8 19 29 (continued on next page) 14SM 14SM-37 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.1c (continued) RCP8.5     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Southeast DJF 2035 0.2 0.6 0.6 0.7 1.1 5 1 1 3 7 Asia (sea) 2065 1.0 1.4 1.6 1.9 2.5 4 0 3 5 12   2100 2.1 2.5 2.7 3.4 4.3 12 1 6 11 21   JJA 2035 0.3 0.6 0.6 0.7 1.1 4 0 1 3 5   2065 1.1 1.4 1.5 1.8 2.4 3 2 4 7 12   2100 2.1 2.6 2.8 3.4 4.2 6 2 6 10 22   Annual 2035 0.3 0.6 0.7 0.7 1.1 1 0 1 3 5     2065 1.0 1.4 1.5 1.9 2.4 1 1 4 6 10     2100 2.1 2.6 2.8 3.5 4.2 3 2 6 9 20 Australia North Australia DJF 2035 0.3 0.7 0.9 1.2 1.8 20 7 1 3 14   2065 1.4 1.8 2.2 2.8 3.6 27 8 2 7 15   2100 1.9 3.3 3.9 4.9 5.9 50 13 2 8 33   JJA 2035 0.5 0.8 1.0 1.1 1.5 43 13 4 1 23   2065 1.6 1.9 2.2 2.5 3.0 46 19 6 2 16   2100 2.5 3.6 4.4 4.8 5.5 66 27 15 1 48   Annual 2035 0.5 0.8 1.0 1.1 1.6 17 6 1 2 8     2065 1.6 1.8 2.2 2.7 3.4 26 11 3 4 12     2100 2.4 3.6 4.3 5.1 5.8 51 14 4 5 33 South Australia/ DJF 2035 0.2 0.7 0.9 1.1 1.5 17 5 1 3 7 New Zealand  2065 1.3 1.7 2.1 2.3 3.0 24 5 0 4 8 2100 2.6 3.0 3.8 4.3 5.9 30 8 1 3 21   JJA 2035 0.3 0.6 0.7 0.9 1.1 19 4 2 1 4   2065 1.2 1.5 1.7 1.9 2.1 25 9 2 2 8   2100 2.2 2.8 3.4 3.8 4.3 39 18 7 4 10   Annual 2035 0.4 0.7 0.8 0.9 1.1 17 5 1 1 6     2065 1.4 1.6 1.9 2.2 2.5 22 6 1 2 6     2100 2.6 3.0 3.9 4.1 5.0 33 11 3 2 15 The Pacific Northern DJF 2035 0.4 0.5 0.6 0.8 1.0 6 1 1 3 18 Tropical Pacific 2065 1.1 1.4 1.6 1.9 2.3 6 1 2 7 21   2100 2.1 2.5 2.9 3.6 4.2 8 1 3 11 31   JJA 2035 0.4 0.5 0.6 0.8 1.1 6 1 1 4 12   2065 1.0 1.4 1.6 2.0 2.5 9 1 3 5 17   2100 2.0 2.5 2.8 3.8 4.3 16 0 6 11 26   Annual 2035 0.3 0.6 0.7 0.8 1.1 4 0 1 3 14     2065 1.0 1.4 1.6 2.0 2.4 6 0 2 6 19     2100 2.1 2.5 2.8 3.6 4.2 11 1 5 10 29 Equatorial DJF 2035 0.4 0.6 0.7 0.9 1.1 12 1 9 16 77 Pacific 2065 1.0 1.4 1.7 2.0 2.9 11 4 15 21 257   2100 1.0 2.6 3.1 3.7 5.6 43 10 28 38 635   JJA 2035 0.4 0.6 0.7 0.9 1.3 2 7 12 21 45   2065 0.9 1.5 1.6 2.0 2.9 5 17 23 39 102   2100 1.1 2.5 3.1 3.7 5.0 39 28 48 63 407   Annual 2035 0.4 0.5 0.7 0.8 1.1 5 5 9 15 62     2065 1.0 1.4 1.6 2.0 2.6 8 14 19 30 184     2100 1.0 2.6 3.1 3.7 5.1 42 24 33 54 537 14SM (continued on next page) 14SM-38 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.1c (continued) RCP8.5     Temperature (°C) Precipitation (%) REGION MONTHa Year min 25% 50% 75% max min 25% 50% 75% max Southern Pacific DJF 2035 0.3 0.5 0.6 0.7 0.9 10 1 1 3 8   2065 1.0 1.2 1.4 1.6 2.1 27 1 3 5 11   2100 1.8 2.2 2.6 3.2 3.9 30 0 5 8 17   JJA 2035 0.3 0.5 0.6 0.6 0.9 10 1 1 2 5   2065 1.1 1.2 1.4 1.6 2.1 19 2 0 5 8   2100 2.0 2.2 2.6 3.0 4.0 21 3 2 5 16   Annual 2035 0.3 0.5 0.6 0.7 0.9 10 0 1 2 5     2065 1.0 1.2 1.4 1.6 2.1 24 0 1 4 9     2100 2.0 2.3 2.6 3.1 4.0 24 0 3 6 15 Antarctica (land) DJF 2035 0.1 0.6 0.8 0.9 1.4 5 0 3 5 10   2065 0.4 1.7 1.9 2.3 3.1 6 4 8 12 17   2100 1.8 3.2 3.5 4.4 5.3 2 10 18 24 41   JJA 2035 0.1 0.5 0.8 1.1 1.8 1 2 5 7 11   2065 0.2 1.7 2.2 2.6 3.3 1 8 12 17 24   2100 1.4 3.4 4.0 4.9 6.0 7 17 27 36 44   Annual 2035 0.0 0.6 0.7 1.0 1.4 3 1 5 7 9     2065 0.3 1.7 2.0 2.5 3.1 2 7 10 15 18     2100 1.6 3.2 3.8 4.9 5.5 2 14 23 31 40 (sea) DJF 2035 0.3 0.2 0.3 0.6 0.8 1 2 3 3 5   2065 0.4 0.6 0.9 1.2 1.8 1 4 6 8 11   2100 0.2 1.2 1.7 2.2 3.4 5 8 12 16 21   JJA 2035 0.6 0.4 0.6 1.1 2.2 1 2 3 4 6   2065 0.5 1.1 1.6 2.4 4.3 4 5 7 10 13   2100 0.6 2.3 3.6 4.4 7.2 6 10 15 19 27   Annual 2035 0.4 0.3 0.5 0.8 1.5 1 2 3 4 6     2065 0.4 0.9 1.2 1.8 3.0 3 4 7 9 12     2100 0.4 1.8 2.7 3.3 5.1 6 10 14 18 23 Notes: a *Precipitation changes cover 6 months; ONDJFM and AMJJAS for winter and summer (northern hemisphere) 14SM 14SM-39 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.2a | Projected changes for the future (2080 2099) relative to the present-day (1986 2005) at the 10th, 25th, 50th, 75th and 90th percentile values of global monsoon area (GMA), global monsoon precipitation intensity (GMI), global monsoon total precipitation (GMP), standard deviation of interannual variability in seasonal average precipitation (Psd), simple daily precipitation intensity index (SDII), seasonal maximum 5-day precipitation total (R5d), seasonal maximum consecutive dry days (CDD) and monsoon season duration (DUR) in global monsoon domain for RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenario. Percentage of number of models with positive changes is also shown. Index Scenario 10 25 50 75 90 Ratio GLB RCP2.6 1.5 1.9 3.6 5.5 7.1 100.0 RCP4.5 1.6 3.2 4.3 7.3 8.7 95.8 GMA RCP6.0 2.2 4.3 6.4 8.5 9.8 92.9 RCP8.5 5.2 7.6 9.4 12.0 14.5 96.2 RCP2.6 0.4 1.2 1.7 2.3 3.2 88.9 RCP4.5 0.4 1.4 3.6 4.7 5.0 87.5 GMI RCP6.0 0.7 1.7 3.3 4.5 5.1 78.6 RCP8.5 0.4 3.5 5.2 7.6 8.3 88.5 RCP2.6 3.0 3.5 5.0 7.6 9.3 100.0 RCP4.5 2.5 4.1 8.6 11.8 13.2 95.8 GMP RCP6.0 1.8 6.9 10.0 12.1 14.7 92.9 RCP8.5 4.9 9.5 16.6 19.8 22.5 100.0 RCP2.6 4.0 0.0 3.5 5.8 11.2 77.8 RCP4.5 1.5 3.0 8.7 11.0 16.6 87.5 Psd RCP6.0 4.8 2.9 7.8 12.3 14.1 85.7 RCP8.5 3.0 5.1 10.9 20.0 25.4 84.6 RCP2.6 1.0 2.1 2.4 3.0 3.8 100.0 RCP4.5 1.7 2.2 4.0 5.8 7.1 100.0 SDII RCP6.0 1.9 3.2 4.7 6.0 6.7 100.0 RCP8.5 2.5 4.7 7.0 10.3 16.2 100.0 RCP2.6 0.7 3.6 4.1 5.7 7.1 100.0 RCP4.5 2.1 4.3 7.4 9.0 12.1 95.8 R5d RCP6.0 2.0 7.4 8.8 12.1 16.1 100.0 RCP8.5 1.5 8.9 16.0 20.7 26.2 96.2 RCP2.6 1.6 0.7 3.9 7.5 8.5 77.8 RCP4.5 0.5 1.9 5.1 8.3 12.0 87.5 CDD RCP6.0 0.8 3.0 6.6 8.2 13.2 85.7 RCP8.5 1.6 7.9 12.9 18.4 28.2 96.2 RCP2.6 2.2 0.2 2.4 4.0 9.3 77.8 RCP4.5 0.5 3.3 5.4 7.8 9.0 87.5 DUR RCP6.0 0.7 4.0 4.9 8.5 12.6 92.9 RCP8.5 3.4 4.2 8.5 13.9 16.1 88.5 14SM 14SM-40 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.2b | Projected changes for the future (2080 2099) relative to the present day (1986 2005) at the 10th, 25th, 50th, 75th and 90th percentile values of global mon- soon area (GMA), global monsoon precipitation intensity (GMI), global monsoon total precipitation (GMP), standard deviation of interannual variability in seasonal average precipi- tation (Psd), simple daily precipitation intensity index (SDII), seasonal maximum 5-day precipitation total (R5d), seasonal maximum consecutive dry days (CDD) and monsoon season duration (DUR) in each regional land monsoon domain for RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenario. Percentage of number of models with positive changes is also shown. Index Scenario 10 25 50 75 90 Ratio EAS RCP2.6 0.5 0.8 1.6 5.0 8.4 88.9 RCP4.5 1.3 2.7 5.7 9.0 9.8 91.7 Pav RCP6.0 1.3 2.5 5.3 8.6 12.1 100.0 RCP8.5 2.6 4.1 7.8 12.5 17.0 92.3 RCP2.6 10.6 2.3 6.5 10.3 18.2 72.2 RCP4.5 3.2 6.2 12.1 14.2 25.4 91.7 Psd RCP6.0 2.4 8.7 9.9 23.2 29.0 100.0 RCP8.5 10.9 17.7 25.4 30.2 32.8 96.2 RCP2.6 1.9 3.0 4.0 4.8 6.4 94.4 RCP4.5 2.9 6.0 7.8 9.1 12.3 95.8 SDII RCP6.0 5.0 6.7 8.4 10.6 11.0 100.0 RCP8.5 8.3 12.6 14.2 17.1 20.8 96.2 RCP2.6 1.0 2.3 4.0 8.3 10.5 88.9 RCP4.5 4.1 5.6 11.0 13.4 18.8 95.8 R5d RCP6.0 7.5 9.6 10.4 16.9 20.9 100.0 RCP8.5 9.0 15.4 19.5 26.0 32.7 96.2 RCP2.6 5.2 3.3 1.1 6.8 14.5 61.1 RCP4.5 6.8 3.0 2.8 9.3 13.5 58.3 CDD RCP6.0 3.4 1.9 0.1 13.0 15.9 42.9 RCP8.5 4.8 0.0 6.0 18.6 23.0 73.1 RCP2.6 18 10 5 2 3 33.3 RCP4.5 22 15 8 2 0 8.3 ONS RCP6.0 17 12 6 3 8 28.6 RCP8.5 30 21 11 5 4 15.4 RCP2.6 22 0 3 13 20 66.7 RCP4.5 3 2 8 12 38 79.2 RET RCP6.0 0 3 5 11 41 78.6 RCP8.5 1 4 10 15 41 84.6 RCP2.6 14 0 7 18 37 72.2 RCP4.5 3 8 15 31 47 83.3 DUR RCP6.0 12 1 6 39 53 78.6 RCP8.5 4 7 20 42 54 92.3 SAS RCP2.6 0.4 1.9 4.5 6.7 7.8 88.9 RCP4.5 4.8 6.5 7.5 10.6 12.4 100.0 Pav RCP6.0 4.3 6.2 8.2 10.1 10.8 100.0 RCP8.5 6.6 10.2 13.0 16.3 17.7 100.0 RCP2.6 5.2 4.4 6.2 11.8 16.3 88.9 RCP4.5 1.5 6.9 13.9 20.3 25.4 95.8 Psd RCP6.0 6.4 16.9 17.9 20.4 22.6 100.0 RCP8.5 7.8 10.9 25.5 32.0 49.1 100.0 RCP2.6 2.1 2.8 3.8 5.2 9.1 100.0 RCP4.5 5.2 6.2 7.2 10.1 13.5 100.0 SDII RCP6.0 6.1 7.2 7.8 9.7 11.9 100.0 RCP8.5 8.8 10.8 15.1 17.5 23.1 100.0 14SM (continued on next page) 14SM-41 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.2b (continued) Index Scenario 10 25 50 75 90 Ratio RCP2.6 2.2 5.3 5.8 9.3 15.3 100.0 RCP4.5 5.1 9.8 12.4 16.2 21.2 100.0 R5d RCP6.0 10.1 11.1 15.4 20.2 24.2 100.0 RCP8.5 11.7 18.3 22.4 38.0 47.5 96.2 RCP2.6 7.2 1.2 1.9 3.2 6.8 72.2 RCP4.5 7.9 5.0 0.4 5.5 8.9 50.0 CDD RCP6.0 7.9 5.4 1.3 9.4 16.7 50.0 RCP8.5 11.6 4.9 6.7 11.7 17.1 69.2 RCP2.6 7 2 1 2 3 33.3 RCP4.5 6 5 3 0 1 16.7 ONS RCP6.0 7 5 2 2 5 42.9 RCP8.5 11 9 5 1 1 11.5 RCP2.6 2 1 2 6 10 77.8 RCP4.5 1 3 5 8 9 91.7 RET RCP6.0 2 3 4 10 13 92.9 RCP8.5 2 6 8 12 17 96.2 RCP2.6 6 2 4 7 15 72.2 RCP4.5 2 4 7 13 15 91.7 DUR RCP6.0 2 4 7 11 19 78.6 RCP8.5 4 8 13 19 24 96.2 AUSMC RCP2.6 8.1 3.6 1.5 2.1 4.0 44.4 RCP4.5 8.7 0.3 3.2 7.1 8.7 83.3 Pav RCP6.0 4.2 0.3 3.6 10.2 10.5 64.3 RCP8.5 14.4 0.1 7.3 14.9 19.1 76.9 RCP2.6 10.1 0.1 3.2 11.1 20.9 72.2 RCP4.5 1.2 1.0 6.8 12.6 28.2 83.3 Psd RCP6.0 2.6 1.7 7.4 17.3 26.6 71.4 RCP8.5 12.5 5.0 11.0 20.9 40.7 80.8 RCP2.6 2.6 0.1 1.4 3.9 5.6 77.8 RCP4.5 1.5 1.1 4.3 8.3 11.5 83.3 SDII RCP6.0 0.7 2.2 4.7 6.8 12.5 85.7 RCP8.5 4.1 2.5 7.2 13.9 24.6 84.6 RCP2.6 2.8 1.5 4.6 7.2 9.2 77.8 RCP4.5 0.9 2.1 8.0 12.5 17.2 87.5 R5d RCP6.0 1.2 1.9 9.8 15.7 27.7 92.9 RCP8.5 3.8 3.5 15.0 26.7 36.5 88.5 RCP2.6 1.7 0.2 6.3 12.4 13.6 72.2 RCP4.5 4.1 0.6 4.1 12.5 21.5 75.0 CDD RCP6.0 8.0 4.4 3.2 14.6 17.7 71.4 RCP8.5 10.7 2.4 6.8 25.8 36.8 69.2 RCP2.6 19 6 2 6 21 38.9 RCP4.5 12 6 5 4 11 37.5 ONS RCP6.0 13 7 4 5 9 28.6 RCP8.5 18 12 6 8 13 36.0 RCP2.6 12 2 5 10 15 55.6 RCP4.5 5 2 4 15 20 70.8 RET RCP6.0 2 3 6 11 17 92.9 14SM RCP8.5 8 1 8 13 27 76.0 (continued on next page) 14SM-42 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.2b (continued) Index Scenario 10 25 50 75 90 Ratio RCP2.6 16 13 4 15 25 66.7 RCP4.5 6 2 7 14 21 75.0 DUR RCP6.0 4 1 5 18 26 78.6 RCP8.5 21 3 16 24 38 72.0 NAMS RCP2.6 5.3 3.2 1.9 2.6 5.5 44.4 RCP4.5 12.9 5.8 2.9 0.8 5.0 33.3 Pav RCP6.0 6.0 3.4 2.0 6.7 7.0 42.9 RCP8.5 25.4 11.8 6.5 3.5 6.9 26.9 RCP2.6 7.8 0.1 2.9 9.9 13.3 72.2 RCP4.5 15.5 5.5 2.5 13.5 18.4 62.5 Psd RCP6.0 7.3 3.4 0.4 15.2 21.6 57.1 RCP8.5 14.8 9.1 1.5 17.1 37.2 61.5 RCP2.6 2.6 0.5 1.4 3.2 4.7 72.2 RCP4.5 8.2 3.0 0.1 3.9 10.9 54.2 SDII RCP6.0 2.7 0.3 1.3 7.6 10.8 85.7 RCP8.5 16.5 7.2 0.1 13.1 19.1 50.0 RCP2.6 1.6 0.2 3.6 4.6 11.8 77.8 RCP4.5 6.5 1.5 2.5 9.9 15.1 70.8 R5d RCP6.0 2.0 1.9 6.2 15.8 21.1 78.6 RCP8.5 14.6 3.0 6.7 21.0 38.5 69.2 RCP2.6 3.2 0.9 3.9 10.2 16.6 77.8 RCP4.5 5.6 7.6 10.9 18.3 26.5 95.8 CDD RCP6.0 3.0 4.8 9.4 14.0 15.9 92.9 RCP8.5 12.0 17.4 23.1 37.1 70.9 100.0 RCP2.6 6 3 1 3 7 58.8 RCP4.5 7 4 1 5 8 54.5 ONS RCP6.0 11 4 3 0 2 23.1 RCP8.5 9 4 1 8 13 52.2 RCP2.6 27 8 2 8 12 52.9 RCP4.5 7 3 5 10 11 63.6 RET RCP6.0 14 8 1 10 18 53.8 RCP8.5 47 20 1 20 23 47.8 RCP2.6 29 2 0 12 17 47.1 RCP4.5 13 3 2 10 16 54.5 DUR RCP6.0 10 3 5 13 18 69.2 RCP8.5 54 29 8 18 22 52.2 SAMS RCP2.6 4.1 3.2 0.3 2.2 4.2 44.4 RCP4.5 3.3 1.1 1.2 4.2 6.5 66.7 Pav RCP6.0 2.4 1.4 1.7 4.2 7.4 64.3 RCP8.5 7.7 2.6 2.4 6.2 11.2 69.2 RCP2.6 4.1 2.4 3.6 12.5 27.7 61.1 RCP4.5 5.6 0.6 9.5 19.5 23.9 79.2 Psd RCP6.0 8.8 4.1 3.9 10.7 25.7 71.4 RCP8.5 9.2 2.5 16.8 27.1 46.0 84.6 (continued on next page) 14SM 14SM-43 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.2b (continued) Index Scenario 10 25 50 75 90 Ratio RCP2.6 1.9 0.7 1.6 2.6 4.8 83.3 RCP4.5 0.6 1.1 3.1 6.0 11.1 83.3 SDII RCP6.0 0.0 1.8 4.1 5.7 7.8 85.7 RCP8.5 0.9 4.4 7.2 10.9 18.9 84.6 RCP2.6 2.5 2.3 3.6 4.9 6.6 83.3 RCP4.5 0.7 3.7 7.9 10.4 13.6 83.3 R5d RCP6.0 0.2 5.4 7.2 11.5 18.9 85.7 RCP8.5 2.8 7.6 17.5 20.4 30.7 84.6 RCP2.6 3.5 3.0 5.9 13.7 29.2 88.9 RCP4.5 1.9 3.0 9.0 14.1 19.3 95.8 CDD RCP6.0 1.2 4.3 6.2 13.0 18.3 92.9 RCP8.5 7.7 15.5 19.3 38.8 48.4 96.2 RCP2.6 6 4 1 5 8 55.6 RCP4.5 7 4 1 5 7 54.2 ONS RCP6.0 6 2 3 5 12 64.3 RCP8.5 7 5 0 9 14 50.0 RCP2.6 4 2 0 0 2 22.2 RCP4.5 4 2 0 2 5 50.0 RET RCP6.0 4 2 0 2 3 42.9 RCP8.5 8 2 1 5 6 57.7 RCP2.6 8 5 2 2 5 38.9 RCP4.5 9 7 1 5 8 41.7 DUR RCP6.0 12 8 3 4 7 35.7 RCP8.5 17 10 0 8 11 50.0 NAF RCP2.6 3.7 2.1 0.4 2.3 3.5 50.0 RCP4.5 4.4 1.4 2.2 3.3 7.9 66.7 Pav RCP6.0 3.4 1.6 1.0 6.4 13.7 64.3 RCP8.5 6.7 4.3 3.0 7.9 11.0 61.5 RCP2.6 2.6 1.4 2.2 6.5 16.7 72.2 RCP4.5 4.6 2.8 3.2 8.4 20.4 66.7 Psd RCP6.0 5.1 2.3 5.1 15.6 28.0 71.4 RCP8.5 0.8 4.2 9.9 20.7 44.0 92.3 RCP2.6 1.6 0.0 1.5 2.9 6.3 72.2 RCP4.5 1.6 0.5 2.9 6.4 9.3 75.0 SDII RCP6.0 1.7 0.9 1.8 7.3 8.4 85.7 RCP8.5 0.6 1.4 6.8 9.9 23.1 84.6 RCP2.6 0.6 0.6 1.8 5.3 15.8 88.9 RCP4.5 2.9 1.1 6.6 9.5 16.9 79.2 R5d RCP6.0 1.8 4.9 7.3 9.0 11.8 92.9 RCP8.5 1.9 8.8 13.2 22.4 40.3 96.2 RCP2.6 5.7 2.1 3.4 9.3 13.1 88.9 RCP4.5 4.9 2.7 1.9 13.7 24.4 58.3 CDD RCP6.0 7.9 4.1 4.2 7.9 11.1 57.1 RCP8.5 0.2 5.2 10.9 39.9 49.2 88.5 RCP2.6 8 1 4 8 19 58.8 RCP4.5 16 1 2 9 14 73.9 ONS RCP6.0 11 3 3 6 14 69.2 14SM RCP8.5 10 8 1 6 16 48.0 (continued on next page) 14SM-44 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Table 14.SM.2b (continued) Index Scenario 10 25 50 75 90 Ratio RCP2.6 4 1 2 4 8 64.7 RCP4.5 4 1 4 6 8 78.3 RET RCP6.0 4 4 6 13 14 92.3 RCP8.5 4 5 9 15 20 88.0 RCP2.6 23 7 4 4 14 35.3 RCP4.5 15 8 1 8 20 43.5 DUR RCP6.0 4 2 4 11 24 61.5 RCP8.5 8 0 6 19 25 72.0 SAF RCP2.6 6.0 3.4 0.9 1.7 2.9 38.9 RCP4.5 4.3 1.6 2.1 3.4 4.1 62.5 Pav RCP6.0 4.2 2.2 1.9 3.5 5.2 64.3 RCP8.5 5.0 1.2 2.6 5.4 7.0 69.2 RCP2.6 6.9 4.6 1.7 5.7 10.8 66.7 RCP4.5 6.8 1.2 6.6 12.3 21.7 79.2 Psd RCP6.0 9.7 0.7 0.6 16.0 24.7 57.1 RCP8.5 1.9 6.0 14.3 24.7 33.0 84.6 RCP2.6 1.9 0.9 1.7 2.5 3.6 83.3 RCP4.5 0.7 2.4 3.5 6.8 9.2 91.7 SDII RCP6.0 3.4 3.3 4.1 6.0 8.1 85.7 RCP8.5 4.1 5.5 7.5 12.2 21.3 92.3 RCP2.6 3.8 0.6 2.4 4.6 6.8 83.3 RCP4.5 2.3 3.3 6.6 9.8 12.2 91.7 R5d RCP6.0 6.7 8.1 9.2 10.6 16.9 85.7 RCP8.5 5.7 10.2 16.7 19.8 24.0 92.3 RCP2.6 3.6 0.5 6.6 13.7 16.0 77.8 RCP4.5 1.5 2.5 9.2 15.8 18.4 87.5 CDD RCP6.0 0.1 1.3 9.2 19.3 22.1 92.9 RCP8.5 7.0 12.8 20.3 32.0 38.7 96.2 RCP2.6 11 1 2 5 8 66.7 RCP4.5 3 1 2 4 5 70.8 ONS RCP6.0 2 1 2 7 10 78.6 RCP8.5 2 1 5 10 14 80.8 RCP2.6 6 3 1 3 5 33.3 RCP4.5 4 2 0 4 9 50.0 RET RCP6.0 5 1 1 9 12 78.6 RCP8.5 2 3 6 9 14 80.8 RCP2.6 14 6 3 2 8 33.3 RCP4.5 7 6 1 6 9 45.8 DUR RCP6.0 14 5 0 6 8 50.0 RCP8.5 12 5 1 8 18 50.0 14SM 14SM-45 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.3 | Lists of years for Eastern Pacific and Central Pacific El Nino events. A: Types based on Empirical Orthogonal Function (EOF) by (Ashok et al., 2007), B: Types based on the relative amplitude between NINO3 and NINO4 sea surface temperature (SST) index by (Yeh et al., 2009), C: similar to Yeh et al. (2009) but 1982 2010 climatology by Lee and McPhaden (2010), D: As in C but 1948-2006 climatology by Li et al. (2011b), E: similar to B but including Mixed type, M by Kug et al. (2009), F: Types based on subsurface temperature by Yu et al. (2011), G: Types based on sea surface salinity for 1977 2008 by Singh et al. (2011), H: Modified Ashok et al. (2007) by Li et al. (2010a), I: Similar to Yeh et al. (2009) but 1950 2008 climatology by Hu et al. (2012). CP indicates Central Pacific El Nino , date line El Nino , or El Nino-Modoki events; EP indicates Eastern Pacific El Nino or conventional El Nino ; M indicates the mixed type that belongs to neither EP nor CP type. Each paper uses different terminology but here EP, CP and M instead of various names. El Nino A B C D E F G H I 1950 1951 EP 1957 1958 EP EP EP 1963 1964 CP EP EP CP 1965 1966 EP EP M EP 1968 1969 CP CP CP CP CP 1969 1970 EP EP CP EP 1972 1973 EP EP EP M EP 1976 1977 EP EP EP M 1977 1978 CP CP CP CP CP CP 1979 1980 CP EP EP 1982 1983 EP EP EP EP M EP EP EP 1986 1987 EP EP M M CP EP EP 1987 1988 CP EP EP EP M M CP 1990 1991 CP CP CP CP CP CP 1991 1992 CP EP CP EP M M EP EP EP 1992 1993 CP CP CP 1994 1995 CP CP CP CP CP M CP CP CP 1997 1998 EP EP EP EP EP EP EP EP 2001 2002 CP 2002 2003 CP EP CP CP CP CP CP CP 2003 2004 EP 2004 2005 CP CP CP CP CP CP CP 2006 2007 EP EP EP CP CP 2009 2010 CP CP CP 14SM 14SM-46 Table 14.SM.4a | Projected change in frequency of tropical storms in warm climate runs relative to control run in percent. Red and blue numbers/text denote projected increases and decreases, respectively. Bold text denotes where a statisti- cal significance test was reported that showed significance. The frequency projections from Emanuel et al. (2008) have been computed slightly differently from those shown in Figure 8 of the original paper in order to facilitate intercomparison with projection results from other studies. Tropical Storm Frequency Projections (%) Resolution: Reference Model/type Experiment Basin high to low Global NH SH N Atl. NW Pac. NE Pac. N Ind. S. Ind. SW Pac. (Sugi et al., 2002) JMA time slice T106 L21 10 yr 34 28 39 +61 66 67 +9 57 31 (~120 km) 1 × CO2, 2 × CO2 (McDonald et al., 2005) HadAM3 time slice N144 L30 15 yr IS95a 6 3 10 30 30 +80 +42 +10 18 (~100 km) 1979 1994 2082 2097 (Hasegawa and Emori, 2005) CCSR/NIES/ T106 L56 5 × 20 yr at 1 × CO2     4         FRCGC time slice (~120 km) 7 × 20 yr at 2 × CO2 (Yoshimura et al., 2006) JMA time slice T106 L21 10 yr 1 × CO2, 2 × CO2 15             (~120 km) (Oouchi et al., 2006) MRI/JMA time slice TL959 L60 10 yr A1B 30 28 32 +34 38 34 52 28 43 (~20 km) 1982 1993 2080 2099 (Chauvin et al., 2006) ARPEGE Climat ~50 km Downscale CNRM B2 +18 time slice Downscale Hadley A2 25 (Stowasser et al., 2007) IPRC Regional Downscale NCAR +19 CCSM2, 6 × CO2 (Bengtsson et al., 2007) ECHAM5 time slice T213 2071 2100, A1B 13 8 20 +4 26 Climate Phenomena and their Relevance for Future Regional Climate Change (~60 km) (Bengtsson et al., 2007) ECHAM5 time slice T319 2071 2100, A1B 19 13 28 +7 51 (~40 km) (Emanuel et al., 2008) Statistical- --- Downscale 7 CMIP3 7 +2 13 +4 +6 5 7 12 15 deterministic mods.: A1B, 2180 2200 Average over seven models (Knutson et al., 2008) GFDL Zetac regional 18 km Downscale CMIP3 ens. 27 A1B, 2080 2100 (Knutson et al., 2013) GFDL Zetac regional 18 km Downscale (yr 2081 2100) CMIP3 ens. A1B 27 CMIP5 ens Rcp45 23 Gfdl CM2.1 A1B 9 MPI A1B 38 HadCM3 A1B 52 MRI A1B 25 Gfdl CM2.0 A1B +8 HadGEM1 A1B 62 MIROC hi A1B 33 CCMS3 A1B 28 INGV A1B 22 MIROC med A1B 43 (Leslie et al., 2007) OU-CGCM with Up to 50 km 2000 to 2050 control ~0 high-res. window and IS92a (6 members) (continued on next page) Chapter 14 Supplementary Material 14SM-47 14SM 14SM Table 14.SM.4a (continued) Tropical Storm Frequency Projections (%) 14SM-48 Resolution: Reference Model/type Experiment Basin high to low Global NH SH N Atl. NW Pac. NE Pac. N Ind. S. Ind. SW Pac. (Gualdi et al., 2008) SINTEX-G T106 30 yr 1 × CO2, 2 × CO2, 16 (2×) 14 20 3 13 14 22 coupled model (~120 km) 4 × CO2 44 (4×) (Semmler et al., 2008) Rossby Centre 28 km 16 yr control and 13 regional model A2, 2085 2100 (Zhao et al., 2009) GFDL HIRAM 50 km Downscale A1B: time slice CMIP3 n =18 ens. 20 14 32 39 29 +15 2 30 32 Chapter 14 Supplementary Material GFDL CM2.1 20 14 33 5 5 23 43 33 31 HadCM3 11 +5 42 62 12 +61 2 41 42 ECHAM5 20 17 27 1 52 +35 25 13 48 (Sugi et al., 2009) JMA/MRI global Downscale A1B: AGCM time slice 20 km MRI CGCM2.3 29 31 27 +22 36 39 39 28 22 20 km MRI CGCM2.3 25 25 25 +23 29 30 29 25 27 20 km MIROC-H 27 15 42 18 +28 50 +32 24 90 20 km CMIP3 n = 18 ens. 20 21 19 +5 26 25 15 5 42 60 km MRI CGCM2.3 20 21 17 +58 36 31 12 22 8 60 km MIROC-H 6 0 16 +6 +64 42 +79 +10 69 60 km CMIP3 n = 18 ens. 21 19 25 +4 14 33 +33 18 36 60 km CSIRO 22 29 11 37 +13 49 7 22 +10 (Murakami et al., 2012) JMA/MRI global V3.1 20 km Downscale CMIP3 multi- 23 20 25 +8 27 24 14 10 45 AGCM time slice V3.2 20 km model ens. A1B change 15 14 18 29 23 +1 2 23 15 V3.1 60 km (2075 2099 minus control) 23 23 24 2 20 32 +21 15 39 V3.2 60 km 24 23 25 39 28 10 14 24 27 (Murakami et al., 2011) JMA/MRI global V3.2 60 km Downscale A1B: AGCM time slice YS, CMIP3 ens. 27 27 27 44 33 11 16 29 31 YS, Cluster 1 25 25 27 24 32 30 +19 24 37 YS, Cluster 2 28 30 26 23 42 9 21 20 42 YS, Cluster 3 14 3 35 31 2 +6 +1 46 25 KF, CMIP3 ens. 20 24 16 39 28 3 42 24 11 KF, Cluster 1 20 27 10 40 33 15 28 20 6 KF, Cluster 2 21 28 12 21 44 +5 50 10 24 KF, Cluster 3 14 12 15 53 8 +17 48 26 6 AS, CMIP3 ens. 20 11 33 +1 19 22 +1 31 43 AS, Cluster 1 22 22 24 27 19 42 20 25 27 AS, Cluster 2 13 11 17 +28 32 +24 5 2 44 AS, Cluster 3 14 0 32 24 +8 +15 15 48 11 (Villarini et al., 2011) Statistical --- 24 CMIP3 model mean and Basin: downscale of +/-1 range; A1B scenario, 10 +/- 29% CMIP3 models 21st century trend US land: 3 +/- 26 (Emanuel et al., 2010) Statistical- -- Time slice using CMIP3 +45 (global deterministic model mean SST change, but June to 1990 2090, NICAM model October only) 14 km (Yamada et al., 2010) NICAM 14 km Time slice using CMIP3 35 (global 80 0 0 77 model mean SST but June to change, 1990 2090 October only) (continued on next page) Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.4a (continued) Tropical Storm Frequency Projections (%) Resolution: Reference Model/type Experiment Basin high to low Global NH SH N Atl. NW Pac. NE Pac. N Ind. S. Ind. SW Pac. (Lavender and Walsh, 2011) CSIRO CCAM 15 km A2 1990, 2090 regional model GFDL CM2.1 38 nested in a suite MPI ECHAM5 33 of GCMs CSIRO Mk3.5 27 (Li et al., 2010d) ECHAM5 40 km A1B change 31 +65 time slice (2080 2009) (Yokoi and Takayabu, 2009) CMIP3 ensemble Various A1B (2081 2100) 1 (Murakami et al., 2013) JMA/MRI global V3.2 60 km As in Murakami et al. 2 AGCM time slice (2011), but using different criteria for TC detection (Villarini and Vecchi, 2012) Statistical -- 17 CMIP5 models downscale of Mean and (min/max range) CMIP5 models RCP2.6 4 ( 17,32) RCP4.5 4 ( 30,57) RCP8.5 2 (late 21st century) ( 49,45) Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material 14SM-49 14SM 14SM Table 14.SM.4b | Projected change in frequency of intense tropical cyclones (i.e., more intense than tropical storms) in warm climate runs relative to control run in percent. The rows of reported results are ordered from top to bottom generally in order of decreasing model horizontal resolution. Red and blue numbers/text denote projected increases and decreases, respectively. Bold text denotes where a statistical significance test was reported that showed significance. 14SM-50 Intense Tropical Cyclone Frequency Projections (%) Resolution: Reference Model/type Experiment Basin high to low Global NH SH N Atl. NW Pac. NE Pac. N Ind. S. Ind. SW Pac. (Bender et al., 2010) GFDL Hurricane model, 9 km Downscale TCs from ref 22 cat 4 5 with ocean coupling 18-mod ensemble: freq: (range over 4 indiv. models) +100% ( 66 to +138%) (Knutson et al., 2013) GFDL Hurricane model, 9 km Downscale TCs cat 4 5 Chapter 14 Supplementary Material with ocean coupling (2081 2100) freq: CMIP3 ens. A1B +87% CMIP5 ens RCP45 +39% Gfdl CM2.1 A1B +116% MPI A1B +21% HadCM3 A1B 53% MRI A1B +110% Gfdl CM2.0 A1B +211% HadGEM1 A1B 100% MIROC hi A1B 42% CCMS3 A1B +26% INGV A1B +47% MIROC med A1B 32% (Knutson et al., 2008)  GFDL Zetac regional 18 km Downscale CMIP3 ens. +140% (12 vs 5) # A1B, 2080 2100 w/Vsfc > 45 m s 1 (Murakami et al., 2013) JMA/MRI global V3.2 20 km Downscale CMIP3 multi- # Cat 4 5: AGCM time slice model ens. A1B change +4% +9% 7% +15% 4% +179% +35% +45% 54% (2075 2099 minus control) # Cat. 5: +56% +60% +43% +287% +45% Increase from 0 +100% +261% 61% (Oouchi et al., 2006) MRI/JMA TL959 L60 10 yr A1B Signif. time slice (~20 km) 1982 1993 Increase, # V850 2080 2099 of 55 60 m s 1 (Walsh et al., 2004) CSIRO DARLAM 30 km 3 × CO2; 2061 2090 +26% P regional model minus 1961 1990 < 970 mb (Bengtsson et al., 2007) ECHAM5 time slice T319 (~40 km) 2071 2100, A1B +42%, # > 50 m s 1 (Zhao and Held, 2010) GFDL HIRAM time 50 km Downscale A1B: cat 3 5 hurr % slice with statistical CMIP3 n = 7 ens. 13 refinement of intensity GFDL CM2.0 +9 GFDL CM2.1 +5 HadCM3 28 HadGem1 53 ECHAM5 +24 MRI_CGCM2.3 0 MIROC High 27 (continued on next page) Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.4b (continued) Intense Tropical Cyclone Frequency Projections (%) Resolution: Reference Model/type Experiment Basin high to low Global NH SH N Atl. NW Pac. NE Pac. N Ind. S. Ind. SW Pac. (Zhao and Held, 2012) GFDL HIRAM time slice 50 km Downscale A1B: # > 33 m s 1, % # > 33 # > 33 # > 33 m s 1 # > 33 m s 1 # > 33 # > 33 # > 33 # > 33 CMIP3 n = 8 ens. 15 16 13 20 30 +14 +6 11 14 GFDL CM2.0 6 1 21 +16 19 +30 +20 14 30 GFDL CM2.1 11 5 26 4 +9 34 31 30 19 HadCM3 +6 +17 26 51 11 +121 +39 20 35 HadGem1 11 3 31 84 29 +115 35 46 9 ECHAM5 14 13 16 +25 49 +58 21 +9 56 CCCMA 22 24 16 42 37 +17 21 2 37 MRI_CGCM2.3 16 18 10 +20 33 3 12 12 7 MIROC High 5 6 4 31 17 +44 40 +16 34 (Leslie et al., 2007) OU-CGCM with Up to 50 km 2000 to 2050 control and +100% # high-res. window IS92a (6 members) > 30 m s 1 by 2050 (Bengtsson et al., 2007) ECHAM5 time slice T213 (~60 km) 2071 2100, A1B +32%, # > 50 m s 1 (McDonald et al., 2005) HadAM3 N144 L30 15 yr IS95a Increase time slice (~100 km) 1979 1994 In # strong TCs 2082 2097 (vort > 24 30 × 10 5 s 1) (Sugi et al., 2002) JMA T106 L21 10 yr ~0 time slice (~120km) 1 × CO2, 2 × CO2 # >40 m s 1 Climate Phenomena and their Relevance for Future Regional Climate Change (Gualdi et al., 2008) SINTEX-G coupled T106 (~120 km) 30 yr 1 × CO2, 2 × CO2, 4 × CO2 ~0 model (Hasegawa and CCSR/NIES/ T106 L56 20 yr control Rel. freq. of Emori, 2007) Coupled model (~120 km) Vs +1% yr 1 CO2 Pc <985 mb (yr 61 80) +21 couple +59 uncoup (Yoshimura et al., 2006) JMA T106 L21 10 yr Mixed changes: time slice (~120 km) 1 × CO2, 2 × CO2 # >25 m s 1 Chapter 14 Supplementary Material 14SM-51 14SM 14SM Table 14.SM.4c | Tropical cyclone intensity change projections (percent change in maximum wind speed or central pressure fall, except as noted in the table). The dynamical model projections are ordered from top to bottom in order of decreasing model horizontal resolution. Red and blue colours denote increases and decreases, respectively. Pairs of numbers in parentheses denote ranges obtained using different models as input to a downscaling model or theory. The potential intensity change projections from Emanuel et al. (2008), Knutson and Tuleya (2004) and Vecchi and Soden (2007a) in the table include some unpublished supplemental results (personal communication from the authors) such as 14SM-52 results for individual basins, ranges of results across models and results for additional or modified calculations that are adapted from the original papers but have been modified in order to facilitate intercomparison of methods and projection results from different studies. In some cases, Accumulated Cyclone Energy (ACE) or Power Dissipation Index (PDI) changes are reported, which depend on intensity, frequency and lifetime. Tropical Cyclone Intensity Projections (%) N Atl, Metric/ Resolution/ N S. Technique/ Model Climate Change Scenario Global NH SH NW Pac, N Atl. NW Pac. NE Pac. SW Pac. Reference Metric Type Ind. Ind. NE Pac Dynamical or Stat/Dyn. Avg Model Projections (Max (low, high) Chapter 14 Supplementary Material wind speed % change) (Emanuel et al., 2008) Stat./Dyn. Model Max Wind speed (%) CMIP3 7-model 1.7 3.1 0.2 3.3 2.0 4.1 0.1 0.2 0.5 0.8 A1B (2181 2200 minus 1981 2000) (Bender et al., 2010) GFDL Hurricane model 9 km; Downscale TCs from ref 22 +0.7 (trop. Max Wind speed (%) 18-mod ensemble: storms) CMIP3 A1B; yrs +6 (hurricanes) 2081 2100 minus 2001 2020 (Knutson et al., 2013) GFDL Hurricane model 9 km; Downscale TCs With ocean coupling; (2081 2100) Max Wind speed CMIP3 ens. A1B +6.1 change (%) CMIP5 ens RCP45 +4.0 of hurricanes Gfdl CM2.1 A1B +8.6 MPI A1B +4.2 HadCM3 A1B +2.0 MRI A1B +9.2 Gfdl CM2.0 A1B +11 HadGEM1 A1B 2.7 MIROC hi A1B +2.9 CCMS3 A1B +5.3 INGV A1B +5.9 MIROC med A1B +2.9 (Knutson and Tuleya, 2004) GFDL Hurricane Model 9 km grid inner nest; CMIP2+ 5.9 5.5 5.4 6.6 Max Wind speed (%) +1% yr 1 CO2 (1.5, 8.1) (3.3, (1.1, 10.1) 80-year trend 6.7) (Knutson and Tuleya, 2004) GFDL Hurricane Model 9 km grid inner nest; CMIP2+ 13.8 13.0 13.6 14.8 Pressure fall (%) +1%/yr CO2 (3.2, 21.6) (8.0, 16.5) (3.6, 25.0) 80-year trend (Lavender and Walsh, 2011) CCAM regional model 15 km A2 1990, 2090 +5 to nested in a suite of GCMs Max winds +10% (Knutson et al., 2001) GFDL Hurricane Model 18 km grid w./ GFDL R30 downscale, +1% 6 ocean coupling; yr 1 CO2 yr 71 120 avg Max Wind speed (%) (Knutson et al., 2008) GFDL Zetac regional 18 km; Downscale CMIP3 ens. A1B, 2080 2100 +2.9 Max Wind speed (%) (continued on next page) Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.4c (continued) Tropical Cyclone Intensity Projections (%) N Atl, Metric/ Resolution/ N S. Technique/ Model Climate Change Scenario Global NH SH NW Pac, N Atl. NW Pac. NE Pac. SW Pac. Reference Metric Type Ind. Ind. NE Pac (Knutson et al., 2013) GFDL Zetac regional 18 km; Downscale TCs Max Wind speed (%) (2081 2100) of hurricanes CMIP3 ens. A1B +2.0 CMIP5 ens RCP45 +2.2 Gfdl CM2.1 A1B +2.8 MPI A1B +3.6 HadCM3 A1B +0.9 MRI A1B +4.0 Gfdl CM2.0 A1B +3.6 HadGEM1 A1B +1.5 MIROC hi A1B +2.3 CCMS3 A1B +3.8 INGV A1B +2.0 MIROC med A1B +2.1 (Murakami et al., 2013) JMA/MRI global V3.1 20 km Downscale CMIP3 multi-model ens. A1B 11 12 10 5 18 12 5 10 8 AGCM time slice V3.2 20 km; change (2075 2099 minus control) 4 6 0 10 7 6 7 7 10 Avg. max winds over lifetime of all TCs (Oouchi et al., 2006) MRI/JMA TL959 L60 (~20 10 yr A1B 10.7 8.5 14.1 11.2 4.2 0.6 12.8 17.3 2.0 Time slice km) Avg. lifetime 1982 1993 max windspeed 2080 2099 (Oouchi et al., 2006) MRI/JMA TL959 L60 (~20 10 yr A1B 13.7 15.5 6.9 20.1 2.0 5.0 16.7 8.2 22.5 Climate Phenomena and their Relevance for Future Regional Climate Change time slice km) Avg. annual 1982 1993 max winds 2080 2099 (Semmler et al., 2008) Rossby Centre 28 km; 16-year control and A2, 2085 2100 +4 regional model Max winds (Chauvin et al., 2006) ARPEGE Climat ~50 km Downscale time slice Max winds - CNRM B2 ~0 - Hadley A2 ~0 (Sugi et al., 2002) JMA T106 L21 10 yr ~0 time slice (~120 km) 1 × CO2, 2 × CO2 Max winds (Gualdi et al., 2008) SINTEX-G coupled model T106 (~120 km); 30 yr 1 × CO2, 2 × CO2, 4 × CO2 ~0 Max winds (Hasegawa and Emori, 2005) CCSR/NIES/FRCGC T106 L56 5 × 20 yr at 1 × CO2 Decrease time slice (~120 km) 7 × 20 yr at 2 × CO2 Max winds (Yoshimura et al., 2006)  JMA T106 L21 10 yr ~0 time slice (~120 km) 1 × CO2, 2 × CO2 Max winds (Hasegawa and Emori, 2007) CCSR/NICS/FRC T106 L56 20-year control ~0 for Coupled GCM (~120 km) Vs +1% yr 1 CO2 Pc < Max winds (yr 61 80) 985 mb (continued on next page) Chapter 14 Supplementary Material 14SM-53 14SM 14SM Table 14.SM.4c (continued) Tropical Cyclone Intensity Projections (%) 14SM-54 N Atl, Metric/ Resolution/ N S. SW Technique/ Model Climate Change Scenario Global NH SH NW Pac, N Atl. NW Pac. NE Pac. Reference Metric Type Ind. Ind. Pac. NE Pac Potential Intensity Avg Theory Projections of (low, high) Intensity (% Change) (Vecchi and Soden, 2007b) Emanuel PI, reversible Max Wind CMIP3 18-model A1B 2.6 2.7 2.4 2.1 0.05 2.9 3.5 4.4 3.7 0.99 w/ diss. heating speed (%) (100-year trend) ( 8.0, 4.6) ( 3.1, 12.6) ( 6.4 16.1) ( 3.3, ( 7.6, ( 8.6, 16.0) 17.1) 8.6) Chapter 14 Supplementary Material (Knutson and Tuleya, 2004) Potential Intensity Pressure fall CMIP2+ 5.0 2.6 7.0 5.4 Emanuel, reversible (%) +1% yr 1 CO2 ( 5.6, 12.6) ( 1.0, 19.6) ( 5.0, 80-year trend 21.9) (Knutson and Tuleya, 2004) Potential Intensity, Pressure fall CMIP2+ 7.6 6.0 8.5 8.2 Emanuel, pseudoadiabatic (%) +1% yr 1 CO2 (1.6, 13.2) (2.8, 25.2) ( 3.3, 80-year trend 28.0) (Knutson and Tuleya, 2004) Potential Intensity, Holland Pressure fall CMIP2+ 15.2 12.4 17.3 15.8 (%) +1% yr 1 CO2 ( 4.0, 28.9) (9.4, 30.6) (3.4, 42.5) 80-year trend (Yu et al., 2010a) Emanuel PI modified by Max Wind CMIP3 18 model ensemble 0.1 to 2.3 2.3 2.4 3.3 3.4 1.0 vertical wind shear speed (%) 1% yr 1 CO2, 70-year trend ACE or PDI (% change) using Dynamical or Stat/Dyn. Models (Emanuel et al., 2010) Stat./Dyn. Model Power Dissipa- Time slice using CMIP3 ens. mean +65% in PDI,; tion Index (%) SST change, 1990 2090, and (global but June NICAM model 14 km fields to October only) (Yamada et al., 2010) NICAM GCM 14 km Time slice using CMIP3 model 14% 88% +17% +65% 86% 14% Metric: ACE (Accum. mean SST change, 1990 2090 (ACE) (ACE) (ACE) (ACE) ACE ACE Cyclone Energy) (global but June to October only) (Stowasser et al., 2007) IPRC Regional ~50 km Downscale NCAR CCSM2, 6 × CO2 +50% in PDI,; model PDI incr. intensity (Villarini and Vecchi, 2012) Statistical downscale 17 CMIP5 models of CMIP5 models Mean and (min/max range) PDI: RCP2.6 34 ( 1,126) RCP4.5 57 ( 21,270) RCP8.5 110 (late 21st century) ( 23,320) Climate Phenomena and their Relevance for Future Regional Climate Change Table 14.SM.4d | Tropical cyclone-related precipitation projected changes (%) for the late 21st century (relative to present day). Results from Gualdi et al. (2008) are from original paper and personal communication with the authors (2009, 2010). Tropical Cyclone Precipitation Projections Reference Model/Type Resolution/ Experiment Basins Radius Around Storm Center Percent Change (Hasegawa and CCSR/NIES/FRCGC time slice T106 L56 5 × 20 yr at 1 × CO2 NW Pacific 1000 km +8.4 (all TC periods) Emori, 2005) (~120 km) 7 × 20 yr at 2 × CO2 (Yoshimura et al., 2006) JMA GSM8911 T106 L21 10 yr Global 300 km +10 (all TC periods) Arakawa-Schubert time slice (~120 km) 1 × CO2, 2 × CO2 +15 (all TC periods) Kuo (Chauvin et al., 2006) ARPEGE Climat ~50 km Downscale CNRM B2 Atlantic n/a Substantial increase time slice Downscale Hadley A2 (Bengtsson et al., 2007) ECHAM5 time slice T213 2071 2100, A1B Northern Hemisphere 550 km +21 (all TCs) (~60 km) Accum. along path +30 (TC > 33 m s 1 intensity) (Knutson et al., 2008) GFDL Zetac regional 18 km Downscale CMIP3 ens. A1B, 2080 2100 Atlantic 50 km +37 (all hurricane periods) 100 km +23 400 km +10 (Knutson et al., 2013) GFDL Zetac regional/ 18 km/9 km Downscale TCs Atlantic Zetac/Hurr. Model GFDL hurricane model (2081 2100) CMIP3 ens. A1B 100 km +19/+22 (all TC periods) CMIP5 ens: RCP 4.5 100 km +13/+19 GFDL CM2.1 A1B 100 km +22/+28 MPI A1B 100 km +24/+33 HadCM3 A1B 100 km +12/+8.2 MRI A1B 100 km +28/+24 GFDL CM2.0 A1B 100 km +26/+34 HadGEM1 A1B 100 km +11/-4.3 MIROC hi A1B 100 km +22/+14 NCAR CCMS3 A1B 100 km +23/+29 Climate Phenomena and their Relevance for Future Regional Climate Change INGV A1B 100 km +19/+26 MIROC med A1B 100 km +22/+12 (Knutson and Tuleya, 2004) GFDL Hurricane 9 km inner nest CMIP2+ Atlantic, NE Pacific, ~100 km +22 (at time of max hurricane intensity) Model (idealized) +1% yr 1 CO2 NW Pacific 80-year trend (Gualdi et al., 2008) SINTEX-G coupled model T106 (~120 km) 30 yr 1 × CO2, 2 × CO2 Global 100 km +6.1 (all TC periods) 400 km +2.8 (all TC periods) 100 km +11 (at time of max winds) 400 km +4.9 (at time of max winds) Chapter 14 Supplementary Material 14SM-55 14SM Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change References Annamalai, H., S. Xie, J. McCreary, and R. Murtugudde, 2005: Impact of Indian Ocean Camargo, S. J., A. W. Robertson, S. J. Gaffney, P. Smyth, and M. Ghil, 2007: Cluster sea surface temperature on developing El Nino. J. Clim., 18, 302 319. analysis of typhoon tracks. Part I: General properties. J. Clim., 20, 3635 3653. Arblaster, J. M., G. A. Meehl, and D. J. Karoly, 2011: Future climate change in the Carvalho, L. M. V., C. Jones, and T. Ambrizzi, 2005: Opposite phases of the antarctic Southern Hemisphere: Competing effects of ozone and greenhouse gases. oscillation and relationships with intraseasonal to interannual activity in the Geophys. Res. Lett., 38, L02701. tropics during the austral summer. J. Clim., 18, 702 718. Arias, P. A., R. Fu, and C. M. Kingtse, 2012: Decadal variation of rainfall seasonality Cassou, C., 2008: Intraseasonal interaction between the Madden-Julian Oscillation in the North American monsoon region and its potential causes. J. Clim., 25, and the North Atlantic Oscillation. Nature, 455, 523 527. 4258 4274. Cavalcanti, I. F. A., 2012: Large scale and synoptic features associated with extreme Ashok, K., S. K. Behera, S. A. Rao, H. Y. Weng, and T. Yamagata, 2007: El Nino Modoki precipitation over South America: A review and case studies for the first decade and its possible teleconnection. J. Geophys. Res. Oceans, 112, C11007. of the 21st century. Atmos. Res., 118, 27 40. Barriopedro, D., R. Garcia-Herrera, A. R. Lupo, and E. Hernandez, 2006: A climatology Chan, J. C. L., and M. Xu, 2009: Inter-annual and inter-decadal variations of of Northern Hemisphere blocking. J. Clim., 19, 1042 1063. landfalling tropical cyclones in East Asia. Part I: Time series analysis. Int. J. Barros, V. R., M. Doyle, and I. Camilloni, 2008: Precipitation trends in southeastern Climatol., 29, 1285 1293. South America: Relationship with ENSO phases and the low-level circulation. Chan, S. C., S. K. Behera, and T. Yamagata, 2008: Indian Ocean Dipole influence on Theor. Appl. Climatol., 93, 19 33. South American rainfall. Geophys. Res. Lett., 35, L14S12. Bell, C. J., L. J. Gray, A. J. Charlton-Perez, M. M. Joshi, and A. A. Scaife, 2009: Chand, S. S., and K. J. E. Walsh, 2009: Tropical cyclone activity in the Fiji region: Spatial Stratospheric communication of El Nino teleconnections to European winter. J. patterns and relationship to large-scale circulation. J. Clim., 22, 3877 3893. Clim., 22, 4083 4096. Chang, C., and T. Li, 2000: A theory for the tropical tropospheric biennial oscillation. Bender, M. A., T. R. Knutson, R. E. Tuleya, J. J. Sirutis, G. A. Vecchi, S. T. Garner, and I. J. Atmos. Sci., 57, 2209 2224. M. Held, 2010: Modeled impact of anthropogenic warming on the frequency of Chang, C., J. Chiang, M. Wehner, A. Friedman, and R. Ruedy, 2011: Sulfate aerosol intense Atlantic hurricanes. Science, 327, 454 458. control of tropical Atlantic climate over the twentieth century. J. Clim., 24, Bengtsson, L., K. I. Hodges, M. Esch, N. Keenlyside, L. Kornblueh, J.-J. Luo, and T. 2540 2555. Yamagata, 2007: How may tropical cyclones change in a warmer climate? Tellus Chang, E. K. M., and Y. Guo, 2007: Is the number of North Atlantic tropical cyclones A, 59, 539 561. significantly underestimated prior to the availability of satellite observations? Bennartz, R., J. Fan, J. Rausch, L. Y. R. Leung, and A. K. Heidinger, 2011: Pollution from Geophys. Res. Lett., 34, L14801. China increases cloud droplet number, suppresses rain over the East China Sea. Chauvin, F., J.-F. Royer, and M. Déqué, 2006: Response of hurricane-type vortices to Geophys. Res. Lett., 38, doi: 10.1029/2011GL047235. global warming as simulated by ARPEGE-Climat at high resolution. Clim. Dyn., Bister, M., and K. A. Emanuel, 1998: Dissipative heating and hurricane intensity. 27, 377 399. Meteorol. Atmos. Phys., 65, 233 240. Chen, G., and C.-Y. Tam, 2010: Different impacts of two kinds of Pacific Ocean Bjerknes, J., 1966: A possible response of atmospheric Hadley circulation to warming on tropical cyclone frequency over the western North Pacific. equatorial anomalies of ocean temperature. Tellus, 18, 820 829. Geophysical Research Letters, 37, doi: 10.1029/2009gl041708. Bjerknes, J, 1969: Atmospheric teleconnections from the Equatorial Pacific. Mon. Cherchi, A., A. Alessandri, S. Masina, and A. Navarra, 2011: Effects of increased CO2 Weather Rev., 97, 163 172. levels on monsoons. Clim. Dyn., 37, 83 101. Bladé, I., B. Liebmann, D. Fortuny, and G. Oldenborgh, 2012: Observed and simulated Chou, C., and C.-A. Chen, 2010: Depth of convection and the weakening of tropical impacts of the summer NAO in Europe: Implications for projected drying in the circulation in global warming. J. Clim., 23, 3019 3030. Mediterranean region. Clim. Dyn., 39, 709 727. Chu, P., J. Kim, and Y. Chen, 2012: Have steering flows in the western North Pacific Bombardi, R. J., and L. M. V. Carvalho, 2009: IPCC global coupled model simulations and the South China Sea changed over the last 50 years? Geophys. Res. Lett., 39. of the South America monsoon system. Clim. Dyn., 33, 893 916. Clarke, A., X. Liu, and S. Van Gorder, 1998: Dynamics of the biennial oscillation in the Booth, B. B. B., N. J. Dunstone, P. R. Halloran, T. Andrews, and N. Bellouin, 2012: equatorial Indian and far western Pacific Oceans. J. Clim., 11, 987 1001. Aerosols implicated as a prime driver of twentieth-century North Atlantic Collini, E. A., E. H. Berbery, V. R. Barros, and M. E. Pyle, 2008: How does soil moisture climate variability. Nature, 484, 228 232. influence the early stages of the South American monsoon? J. Clim., 21, 195 Boulanger, J., S. Schlindwein, and E. Gentile, 2011: CLARIS LPB WP1: Metamorphosis 213. of the CLARIS LPB European project: From a mechanistic to a systemic approach. Conroy, J., and J. Overpeck, 2011: Regionalization of present-day precipitation in the CLIVAR Exchanges no. 57 (World Climate Research Programme), 16, 7 10. greater monsoon region of Asia. J. Clim., 24, 4073 4095. Bracegirdle, T. J., et al., 2013: Assessment of surface winds over the Atlantic, Indian, Costa, M. H., S. N. M. Yanagi, P. J. O. P. Souza, A. Ribeiro, and E. J. P. Rocha, 2007: and Pacific Ocean sectors of the Southern Ocean in CMIP5 models: Historical Climate change in Amazonia caused by soybean cropland expansion, as bias, forcing response, and state dependence. J. Geophys. Res. Atmos., 118, compared to caused by pastureland expansion. Geophys. Res. Lett., 34, L07706. 547 562. Cox, P. M., et al., 2008: Increasing risk of Amazonian drought due to decreasing Brier, G. W., 1978: The Quasi-Biennial Oscillation and feedback processes in the aerosol pollution. Nature, 453, 212 215. atmosphere-ocean-earth system. Mon. Weather Rev., 106, 938 946. Della-Marte, F., J. Lutterbacher, H. von Weissenfluh, E. Xoplaki, M. Brunet, and H. Bromirski, P. D., and J. P. Kossin, 2008: Increasing hurricane wave power along the Wanner, 2007: Summer heat waves over western Europe 1880 2003, their U.S. Atlantic and Gulf coasts. J. Geophys. Res. Oceans, 113, C07012. relationship to large-scale forcings and predictability, Clim. Dyn. 29, 251-275. Bronnimann, S., 2007: Impact of El Nino Southern Oscillation on European climate. Di Lorenzo, E., et al., 2010: Central Pacific El Nino and decadal climate change in the Rev. Geophys., 45, doi: 10.1029/2006RG000199. North Pacific Ocean. Nature Geosci., 3, 762 765. Bulic, I., C. Brankovic, and F. Kucharski, 2012: Winter ENSO teleconnections in a Ding, Q., E. Steig, D. Battisti, and M. Kuttel, 2011: Winter warming in West Antarctica warmer climate. Clim. Dyn., 38, 1593 1613. caused by central tropical Pacific warming. Nature Geosci., 4, 398 403. Cagnazzo, C., and E. Manzini, 2009: Impact of the stratosphere on the winter Ding, Q. H., and B. Wang, 2009: Predicting extreme phases of the Indian summer tropospheric teleconnections between ENSO and the North Atlantic and monsoon. J. Clim., 22, 346 363. European region. J. Clim., 22, 1223 1238. Ding, Y., Z. Wang, and Y. Sun, 2008: Inter-decadal variation of the summer Callaghan, J., and S. Power, 2010: A reduction in the frequency of severe land- precipitation in East China and its association with decreasing Asian summer falling tropical cyclones over eastern Australia in recent decades. Clim. Dyn., monsoon. Part I: Observed evidences. Int. J. Climatol., 28, 1139 1161. doi:10.1007/s00382-010-0883-2. Ding, Y., Y. Sun, Z. Wang, Y. Zhu, and Y. Song, 2009: Inter decadal variation of the Camargo, S., M. Ting, and Y. Kushnir, 2012: Influence of local and remote SST on summer precipitation in China and its association with decreasing Asian summer North Atlantic tropical cyclone potential intensity Clim. Dyn., 40, 1515 1529. monsoon Part II: Possible causes. Int. J. Climatol., 29, 1926 1944. Camargo, S. J., A. W. Robertson, A. G. Barnston, and M. Ghil, 2008: Clustering of Dole, R., M. Hoerling, J. Perlwitz, J. Eischeid, and P. Pegion, 2011: Was there a basis 14SM eastern North Pacific tropical cyclone tracks: ENSO and MJO effects. Geochem. for anticipating the 2010 Russian heat wave? doi 10.1029/2010GL046582. Geophys. Geosyst., 9, doi: 10.1029/2007GC001861. 14SM-56 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Douglas, A. V., and P. J. Englehart, 2007: A climatological perspective of transient Gillett, N. P., P. A. Stott, and B. D. Santer, 2008: Attribution of cyclogenesis region synoptic features during NAME 2004. J. Clim., 20, 1947 1954. sea surface temperature change to anthropogenic influence. Geophys. Res. Lett., Drumond, A. R. M., and T. Ambrizzi, 2005: The role of SST on the South American 35, L09707. atmospheric circulation during January, February and March 2001. Clim. Dyn., Golding, N., and R. Betts, 2008: Fire risk in Amazonia due to climate change in 24, 781 791. the HadCM3 climate model: Potential interactions with deforestation. Global Duan, A., and G. Wu, 2008: Weakening trend in the atmospheric heat source over Biogeochem. Cycles, 22, GB4007. the Tibetan Plateau during recent decades. Part I: Observations. J. Clim., 21, Gong, D. Y., and C. H. Ho, 2002: The Siberian High and climate change over middle to 3149 3164. high latitude Asia. Theor. Appl. Climatol., 72, 1 9. Elsner, J. B., J. P. Kossin, and T. H. Jagger, 2008: The increasing intensity of the Good, P., J. A. Lowe, M. Collins, and W. Moufouma-Okia, 2008: An objective tropical strongest tropical cyclones. Nature, 455, 92 95. Atlantic sea surface temperature gradient index for studies of south Amazon dry- Emanuel, K., 2005: Increasing destructiveness of tropical cyclones over the past 30 season climate variability and change. Philos. Trans. R. Soc. B, 363, 1761 1766. years. Nature, 436, 686 688. Graf, H., and D. Zanchettin, 2012: Central Pacific El Nino, the subtropical bridge, Emanuel, K., 2010: Tropical cyclone activity downscaled from NOAA-CIRES reanalysis, and Eurasian climate. J. Geophys. Res. Atmos., 117, doi: 10.1029/2011JD016493. 1908 1958. J. Adv. Model. Earth Syst., 2, 12. Grimm, A. M., 2011: Interannual climate variability in South America: Impacts Emanuel, K., R. Sundararajan, and J. Williams, 2008: Hurricanes and global warming: on seasonal precipitation, extreme events and possible effects of climate Results from downscaling IPCC AR4 simulations. Bull. Am. Meteorol. Soc., 89, change. Stochast. Environ. Res. Risk Assess., 25, 537 554. 347 367. Grimm, A. M., and A. A. Natori, 2006: Climate change and interannual variability of Emanuel, K., K. Oouchi, M. Satoh, H. Tomita, and Y. Yamada, 2010: Comparison of precipitation in South America. Geophys. Res. Lett., 33, L19706. explicitly simulated and downscaled tropical cyclone activity in a high-resolution Grimm, A. M., and R. G. Tedeschi, 2009: ENSO and extreme rainfall events in South global climate model. J. Adv. Model. Earth Syst., 2, doi: 10.3894/JAMES.2010.2.9. America. J. Clim., 22, 1589 1609. Emanuel, K., S. Solomon, D. Folini, S. Davis, and C. Cagnazzo, 2012: Influence of Grimm, A. M., J. S. Pal, and F. Giorgi, 2007: Connection between spring conditions tropical tropopause layer cooling on Atlantic hurricane activity. J. Clim., 26, and peak summer monsoon rainfall in South America: Role of soil moisture, 2288 2301. surface temperature, and topography in eastern Brazil. J. Clim., 20, 5929 5945. Emanuel, K. A., 1987: Dependence of hurricane intensity on climate. Nature, 326, Grinsted, A., J. C. Moore, and S. Jevrejeva, 2012: Homogeneous record of Atlantic 483 485. hurricane surge threat since 1923. Proc. Natl. Acad. Sci. U.S.A., 109, 19601 Emanuel, K. A., 2000: A statistical analysis of tropical cyclone intensity. Mon. 19605. Weather Rev., 128, 1139 1152. Gu, Y., K. Liou, Y. Xue, C. Mechoso, W. Li, and Y. Luo, 2006: Climatic effects of different Espinoza, J. C., et al., 2011: Climate variability and extreme drought in the upper aerosol types in China simulated by the UCLA general circulation model. J. Solimoes River (western Amazon Basin): Understanding the exceptional 2010 Geophys. Res., 111, D15201. drought. Geophys. Res. Lett., 38, L13406. Gualdi, S., E. Scoccimarro, and A. Navarra, 2008: Changes in tropical cyclone Evan, A., G. Foltz, and D. Zhang, 2012: Physical response of the tropical- activity due to global warming: Results from a high-resolution coupled general subtropical  North Atlantic Ocean to decadal-multidecadal forcing by African circulation model. J. Clim., 21, 5204 5228. dust. J. Clim., 25, 5817 5829. Guanghua, C., and T. Chi-Yung, 2010: Different impacts of two kinds of Pacific Ocean Evan, A., G. Foltz, D. Zhang, and D. Vimont, 2011: Influence of African dust on ocean- warming on tropical cyclone frequency over the western North Pacific. Geophys. atmosphere variability in the tropical Atlantic. Nature Geosci., 4, 762 765. Res. Lett., 37, doi: 10.1029/2009gl041708. Evan, A. T., D. J. Vimont, A. K. Heidinger, J. P. Kossin, and R. Bennartz, 2009: The Gulizia, C., I. Camilloni, and M. Doyle, 2013: Identification of the principal patterns of Role of Aerosols in the Evolution of Tropical North Atlantic Ocean Temperature summer moisture transport in South America and their representation by WCRP/ Anomalies. Science, 324, 778 781. CMIP3 global climate models. Theor. Appl. Climatol., 112, 227 241. Feliks, Y., M. Ghil, and A. W. Robertson, 2010: Oscillatory climate modes in the eastern Guo, L., E. J. Highwood, L. C. Shaffrey, and A. G. Turner, 2013: The effect of regional Mediterranean and their synchronization with the North Atlantic Oscillation. J. changes in anthropogenic aerosols on rainfall of the East Asian Summer Clim., 23, 4060 4079. Monsoon. Atmos. Chem. Phys., 13, 1521 1534. Feng, J., and J. P. Li, 2011: Influence of El Nino Modoki on spring rainfall over south Hagen, A. B., and C. W. Landsea, 2012: On the classification of extreme Atlantic China. J. Geophys. Res. Atmos., 116, doi: 10.1029/2010jd015160. hurricanes utilizing mid-twentieth-century monitoring capabilities. J. Clim., 25, Feng, J., L. Wang, W. Chen, S. Fong, and K. Leong, 2010: Different impacts of two 4461 4475. types of Pacific Ocean warming on Southeast Asian rainfall during boreal winter. Hagen, A. B., D. Strahan-Sakoskie, and C. Luckett, 2012: A reanalysis of the 1944 53 J. Geophys. Res. Atmos., 115, doi: 10.1029/2010JD014761. Atlantic hurricane seasons The first decade of aircraft reconnaissance. J. Feng, S., and Q. Hu, 2008: How the North Atlantic Multidecadal Oscillation may Clim., 25, 4441 4460. have influenced the Indian summer monsoon during the past two millennia? Harris, P. P., C. Huntingford, and P. M. Cox, 2008: Amazon Basin climate under global Geophys. Res. Lett., 35, doi:10.1029/2007GL032484. warming: The role of the sea surface temperature. Philos. Trans. R. Soc. B, 363, Fogt, R. L., and D. H. Bromwich, 2006: Decadal variability of the ENSO teleconnection 1753 1759. to the high-latitude South Pacific governed by coupling with the Southern Hasegawa, A., and S. Emori, 2005: Tropical cyclones and associated precipitation Annular Mode. J. Clim., 19, 979 997. over the western north Pacific: T106 atmospheric GCM simulation for present- Fogt, R. L., J. M. Jones, and J. A. Renwick, 2012: Seasonal zonal asymmetries in the day and doubled CO2 climates. Sola, 1, 145 148. Southern Annular Mode and their impact on regional temperature anomalies. J. Hasegawa, A., and S. Emori, 2007: Effect of air-sea coupling in the assessment of Clim., 25, 6253-6270. CO2 induced intensification of tropical cyclone activity. Geophys. Res. Lett., 34, Folland, C. K., J. Knight, H. W. Linderholm, D. Fereday, S. Ineson, and J. W. Hurrell, doi: 10.1029/2006GL028275. 2009: The summer North Atlantic Oscillation: Past, present, and future. J. Clim., Hendon, H. H., D. W. J. Thompson, and M. C. Wheeler, 2007: Australian rainfall 22, 1082 1103. and surface temperature variations associated with the Southern Hemisphere Fraisse, C. W., V. E. Cabrera, N. E. Breuer, J. Baez, J. Quispe, and E. Matos, 2008: El annular mode. J. Clim., 20, 2452 2467. Nino Southern Oscillation influences on soybean yields in eastern Paraguay. Higgins, R. W., Y. Yao, and X. L. Wang, 1997: Influence of the North American Int. J. Climatol., 28, 1399 1407. monsoon system on the U.S. summer Precipitation. J. Clim., 10, 2600-2622. Garreaud, R. D., and M. Falvey, 2009: The coastal winds off western subtropical Hirschi, M., and S. I. Seneviratne, 2010: Intra-annual link of spring and autumn South America in future climate scenarios. Int. J. Climatol., 29, 543 554. precipitation over France. Clim. Dyn., 35, 1207 1218. Gergis, J., and A. Fowler, 2009: A history of ENSO events since A.D. 1525: Implications Ho, C., J. Baik, J. Kim, D. Gong, and C. Sui, 2004: Interdecadal changes in summertime for future climate change. Clim. Change, 92, 343 387. typhoon tracks. J. Clim., 17, 1767 1776. Giese, B., and S. Ray, 2011: El Nino variability in simple ocean data assimilation Hoerling, M. P., A. Kumar, and M. Zhong, 1997: El Nino, La Nina, and the nonlinearity (SODA), 1871 2008. J. Geophys. Res. Oceans, 116. of their teleconnections. J. Clim., 10, 1769 1786. 14SM 14SM-57 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Holland, G. J., and P. J. Webster, 2007: Heightened tropical cyclone activity in the Kossin, J. P., and S. J. Camargo, 2009: Hurricane track variability and secular potential North Atlantic: Natural variability or climate trend? Philos. Trans. R. Soc. A, 365, intensity trends. Clim. Change, 97, 329 337. 2695 2716. Kossin, J. P., S. J. Camargo, and M. Sitkowski, 2010: Climate modulation of North Hong, C.-C., Y.-H. Li, T. Li, and M.-Y. Lee, 2011: Impacts of central Pacific and eastern Atlantic hurricane tracks. J. Clim., 23, 3057 3076. Pacific El Ninos on tropical cyclone tracks over the western North Pacific. Krichak, S. O., and P. Alpert, 2005: Signatures of the NAO in the atmospheric Geophys. Res. Lett., 38, doi: 10.1029/2011gl048821. circulation during wet winter months over the Mediterranean region. Theor. Hu, Z., A. Kumar, B. Jha, W. Wang, B. Huang, and B. Huang, 2012: An analysis of warm Appl. Climatol., 82, 27 39. pool and cold tongue El Ninos: Air-sea coupling processes, global influences, Krishna, K. M., 2009: Intensifying tropical cyclones over the North Indian Ocean and recent trends. Clim. Dyn., 38, 2017 2035. during summer monsoon Global warming. Global Planet. Change, 65, 12 16. Hu, Z. Z., 1997: Interdecadal variability of summer climate over East Asia and its Kubota, H., and J. C. L. Chan, 2009: Interdecadal variability of tropical cyclone association with 500 hPa height and global sea surface temperature. J. Geophys. landfall in the Philippines from 1902 to 2005. Geophys. Res. Lett., 36, doi: Res. Atmos., 102, 19403 19412. 10.1029/2009GL038108. Ineson, S., and A. Scaife, 2009: The role of the stratosphere in the European climate Kug, J.-S., F.-F. Jin, and S.-I. An, 2009: Two types of El Nino events: Cold tongue El response to El Nino. Nature Geosci., doi:DOI 10.1038/NGEO381, 32 36. Nino and warm pool El Nino. J. Clim., 22, 1499 1515. Izumo, T., et al., 2010: Influence of the state of the Indian Ocean Dipole on the Kug, J. S., S. I. An, Y. G. Ham, and I. S. Kang, 2010a: Changes in El Nino and La Nina following year s El Nino. Nature Geosci., 3, 168 172. teleconnections over North Pacific-America in the global warming simulations. Jones, C., and L. M. V. Carvalho, 2013: Climate change in the South American Theor. Appl. Climatol., 100, 275 282. Monsoon System:present climate and CMIP5 projections. J. Clim., doi:10.1175/ Kug, J. S., J. Choi, S. I. An, F. F. Jin, and A. T. Wittenberg, 2010b: Warm pool and cold JCLI-D-12 00412.1. tongue El Nino events as simulated by the GFDL 2.1 coupled GCM. J. Clim., 23, Jones, J. M., R. L. Fogt, M. Widmann, G. J. Marshall, P. D. Jones, and M. Visbeck, 1226 1239. 2009: Historical SAM variability. Part I: Century-length seasonal reconstructions. Kumar, V., R. Deo, and V. Ramachandran, 2006: Total rain accumulation and rain-rate J. Clim., 22, 5319 5345. analysis for small tropical Pacific islands: A case study of Suva, Fiji. Atmos. Sci. Kao, H. Y., and J. Y. Yu, 2009: Contrasting Eastern-Pacific and Central-Pacific types of Lett., 7, 53 58. ENSO. J. Clim., 22, 615 632. Kunkel, K. E., et al., 2008: Observed changes in weather and climate extremes. In: Karoly, D. J., and Q. G. Wu, 2005: Detection of regional surface temperature trends. Weather and Climate Extremes in a Changing Climate. Regions of Focus: North J. Clim., 18, 4337 4343. America, Hawaii, Caribbean, and U.S. Pacific Islands [T. R. Karl et al. (eds.)]. U.S. Karpechko, A. Y., N. P. Gillett, L. J. Gray, and M. Dall Amico, 2010: Influence of ozone Climate Change Science Program and the Subcommittee on Global Change recovery and greenhouse gas increases on Southern Hemisphere circulation. J. Research, Washington DC, USA, pp. 35 80. Geophys. Res., 115, D22117. Küttel, M., J. Luterbacher, and H. Wanner, 2011: Multidecadal changes in winter Kidston, J., J. A. Renwick, and J. McGregor, 2009: Hemispheric-scale seasonality of circulation-climate relationship in Europe: frequency variations, within-type the Southern Annular Mode and impacts on the climate of New Zealand. J. Clim., modifications, and long-term trends. Climate Dynamics, 36, 957-972. 22, 4759 4770. L Heureux, M. L., and D. W. J. Thompson, 2006: Observed relationships between the Kim, D., K. Choi, and H. Byun, 2012: Effects of El Nino Modoki on winter precipitation El Nino Southern Oscillation and the extratropical zonal-mean circulation. J. in Korea. Clim. Dyn., 38, 1313 1324. Clim., 19, 276 287. Kim, H. M., P. J. Webster, and J. A. Curry, 2009: Impact of shifting patterns of Pacific Lagos, P., Y. Silva, E. Nickl, and K. Mosquera, 2008: El Nino-related precipitation Ocean warming on north Atlantic tropical cyclones. Science, 325, 77 80. variability in Perú. Adv. Geosci., 14, 231 237. Kim, H. M., P. J. Webster, and J. A. Curry, 2011: Modulation of North Pacific tropical Landsea, C., G. Vecchi, L. Bengtsson, and T. Knutson, 2010: Impact of duration cyclone activity by three phases of ENSO. J. Clim., 24, 1839 1849. thresholds on Atlantic tropical cyclone counts. J. Clim., 23, 2508 2519. Kim, M. K., W. K. M. Lau, K. M. Kim, and W. S. Lee, 2007: A GCM study of effects of Landsea, C., et al., 2012: A reanalysis of the 1921 30 Atlantic Hurricane Database. radiative forcing of sulfate aerosol on large scale circulation and rainfall in East J. Clim., 25, 865 885. Asia during boreal spring. Geophys. Res. Lett., 34, L24701. Landsea, C. W., 2007: Counting Atlantic tropical cyclones back to 1900. Eos Trans. Knapp, K. R., and M. C. Kruk, 2010: Quantifying inter-agency differences in tropical (AGU), 88, 197 202. cyclone best track wind speed estimates. Mon. Weather Rev., 138, 1459 1473. Landsea, C. W., B. A. Harper, K. Hoarau, and J. A. Knaff, 2006: Can we detect trends Knutson, T., R. Tuleya, W. Shen, and I. Ginis, 2001: Impact of CO2 induced warming in extreme tropical cyclones? Science, 313, 452 454. on hurricane intensities as simulated in a hurricane model with ocean coupling. Larkin, N. K., and D. E. Harrison, 2005: On the definition of El Nino and associated J. Clim., 14, 2458 2468. seasonal average US weather anomalies. Geophys. Res. Lett., 32, doi: Knutson, T. R., and R. E. Tuleya, 2004: Impact of CO2 induced warming on simulated 10.1029/2005gl022738. hurricane intensity and precipitation: Sensitivity to the choice of climate model Lau, K., and H. Wu, 2001: Principal modes of rainfall-SST variability of the Asian and convective parameterization. J. Clim., 17, 3477 3495. summer monsoon: A reassessment of the monsoon-ENSO relationship. J. Clim., Knutson, T. R., J. J. Sirutis, S. T. Garner, G. A. Vecchi, and I. M. Held, 2008: Simulated 14, 2880 2895. reduction in Atlantic hurricane frequency under twenty-first-century warming Lavender, S., and K. Walsh, 2011: Dynamically downscaled simulations of Australian conditions. Nature Geosci., 1, 479. region tropical cyclones in current and future climates. Geophys. Res. Lett., 38, Knutson, T. R., et al., 2006: Assessment of twentieth-century regional surface doi: 10.1029/2011GL047499. temperature trends using the GFDL CM2 coupled models. J. Clim., 19, 1624 Lee, S.-K., C. Wang, and D. B. Enfield, 2010a: On the impact of central Pacific 1651. warming events on Atlantic tropical storm activity. Geophys. Res. Lett., 37, doi: Knutson, T. R., et al., 2010: Tropical cyclones and climate change. Nature Geosci., 3, 10.1029/2010gl044459. 157 163. Lee, T.-C., T. R. Knutson, H. Kamahori, and M. Ying, 2012: Impacts of climate change Knutson, T. R., et al., 2013: Dynamical downscaling projections of 21st century on tropical cyclones in the western North Pacific basin. Part I: Past observations. Atlantic hurricane activity: CMIP3 and CMIP5 model-based scenarios. J. Trop. Cyclone Res. Rev., 1, 213 230. Clim.,26,  6591 6617. Lee, T., and M. J. McPhaden, 2010: Increasing intensity of El Nino in the central- Kossin, J., K. Knapp, D. Vimont, R. Murnane, and B. Harper, 2007: A globally consistent equatorial Pacific. Geophys. Res. Lett., 37, doi: 10.1029/2010gl044007. reanalysis of hurricane variability and trends. Geophys. Res. Lett., 34, doi: Lee, T., W. Hobbs, J. Willis, D. Halkides, I. Fukumori, E. Armstrong, A. Hayashi, W. 10.1029/2006GL028836. Liu, W. Patzert, and O. Wang, 2010: Record warming in the South Pacific and Kossin, J. P., 2008: Is the North Atlantic hurricane season getting longer? Geophys. western Antarctica associated with the strong central-Pacific El Nino in 2009-10. Res. Lett., 35, L23705. Geophys. Res. Lett., 37, doi: 10.1029/2010GL044865. Kossin, J. P., and D. J. Vimont, 2007: A more general framework for understanding Lei, Y., B. Hoskins, and J. Slingo, 2011: Exploring the interplay between natural Atlantic hurricane variability and trends. Bull. Am. Meteorol. Soc., 88, 1767 decadal variability and anthropogenic climate change in summer rainfall over 14SM 1781. China. Part I: Observational evidence. J. Clim., 24, 4584 4599. 14SM-58 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Leslie, L., D. Karoly, M. Leplastrier, and B. Buckley, 2007: Variability of tropical Marshall, G. J., S. di Battista, S. S. Naik, and M. Thamban, 2011: Analysis of a regional cyclones over the southwest Pacific Ocean using a high-resolution climate change in the sign of the SAM-temperature relationship in Antarctica. Clim. model. Meteorol. Atmos. Phys., 97, 171 180. Dyn., 36, 277 287. Lewis, S. L., P. M. Brando, O. L. Phillips, G. M. F. van der Heijden, and D. Nepstad, 2011: Marullo, S., V. Artale, and R. Santoleri, 2011: The SST multidecadal variability in the The 2010 Amazon drought. Science, 331, 554 554. Atlantic Mediterranean region and its relation to AMO. J. Clim., 24, 4385 4401. Li, G., B. H. Ren, C. Y. Yang, and J. Q. Zheng, 2010a: Indices of El Nino and El Nino Maue, R., 2011: Recent historically low global tropical cyclone activity. Geophys. Res. Modoki: An improved El Nino Modoki index. Adv. Atmos. Sci., 27, 1210 1220. Lett., 38, doi: 10.1029/2011GL047711. Li, H., T. Zhou, and C. Li, 2010b: Decreasing trend in global land monsoon precipitation McDonald, R., D. Bleaken, D. Cresswell, V. Pope, and C. Senior, 2005: Tropical storms: over the past 50 years simulated by a coupled climate model. Adv. Atmos. Sci., Representation and diagnosis in climate models and the impacts of climate 27, 285 292. change. Clim. Dyn., 25, 19 36. Li, H., A. Dai, T. Zhou, and J. Lu, 2010c: Responses of East Asian summer monsoon McKee, D. C., X. Yuan, A. L. Gordon, B. A. Huber, and Z. Dong, 2011: Climate impact to historical SST and atmospheric forcing during 1950 2000. Clim. Dyn., 34, on interannual variability of Weddell Sea Bottom Water. J. Geophys. Res., 116, 501 514. doi: 10.1029/2010jc006484. Li, J., R. Yu, W. Yuan, and H. Chen, 2011a: Changes in duration-related characteristics McPhaden, M. J., T. Lee, and D. McClurg, 2011: El Nino and its relationship to of late-summer precipitation over eastern China in the past 40 years. J. Clim. changing background conditions in the tropical Pacific Ocean. Geophys. Res. 24, 5683 5690. Lett., 38, doi: 10.1029/2011gl048275. Li, L., B. Wang, and T. Zhou, 2007: Contributions of natural and anthropogenic Meehl, G., and J. Arblaster, 2002a: The tropospheric biennial oscillation and Asian- forcings to the summer cooling over eastern China: An AGCM study. Geophys. Australian monsoon rainfall. J. Clim., 15, 722 744. Res. Lett., 34, doi: 10.1029/2007gl030541. Meehl, G., and J. Arblaster, 2002b: Indian monsoon GCM sensitivity experiments Li, T., C. Tham, and C. Chang, 2001: A coupled air-sea-monsoon oscillator for the testing tropospheric biennial oscillation transition conditions. J. Clim., 15, 923 tropospheric biennial oscillation. J. Clim., 14, 752 764. 944. Li, T., P. Liu, X. Fu, B. Wang, and G. Meehl, 2006: Spatiotemporal structures and Meehl, G., and J. Arblaster, 2011: Decadal Variability of Asian-Australian Monsoon- mechanisms of the tropospheric biennial oscillation in the Indo-Pacific warm ENSO-TBO Relationships. J. Clim., 24, 4925 4940. ocean regions. J. Clim., 19, 3070 3087. Meehl, G., J. Arblaster, and J. Loschnigg, 2003: Coupled ocean-atmosphere dynamical Li, T., M. Kwon, M. Zhao, J. Kug, J. Luo, and W. Yu, 2010d: Global warming shifts Pacific processes in the tropical Indian and Pacific Oceans and the TBO. J. Clim., 16, tropical cyclone location. Geophys. Res. Lett., 37, doi: 10.1029/2010GL045124. 2138 2158. Li, W., P. Zhang, J. Ye, L. Li, and P. Baker, 2011b: Impact of two different types of El Meehl, G., A. Hu, and C. Tebaldi, 2010: Decadal prediction in the Pacific Region. J. Nino events on the Amazon climate and ecosystem productivity. J. Plant Ecol., Clim., 23, 2959 2973. 4, 91 99. Meehl, G. A., 1987: The annual cycle and interannual variability in the tropical Pacific Li, Y., and N.-C. Lau, 2012a: Contributions of downstream eddy development to and Indian-Ocean regions. Mon. Weather Rev., 115, 27 50. the teleconnection between ENSO and atmospheric circulation over the North Meehl, G. A., 1994a: Coupled land-ocean-atmosphere processes and south Asian Atlantic. J. Clim., 25, 4993 5010. monsoon variability. Science, 266, 263 267. Li, Y., and N. Lau, 2012b: Impact of ENSO on the atmospheric variability over the Meehl, G. A., 1994b: Influence of the land-surface in the Asian summer monsoon - north Atlantic in late winter-Role of transient eddies. J. Clim., 25, 320 342. external conditions versus internal feedbacks. J. Clim., 7, 1033 1049. Lian, T., and D. Chen, 2012: An evaluation of rotated EOF analysis and its application Meehl, G. A., 1997: The south Asian monsoon and the tropospheric biennial to tropical Pacific SST variability. J. Clim., 25, 5361 5373. oscillation. J. Clim., 10, 1921 1943. Lin, H., and Z. Wu, 2012: Indian summer monsoon influence on the climate in the Meehl, G. A., and J. M. Arblaster, 2012: Relating the strength of the tropospheric North Atlantic European region. Clim. Dyn., 39, 303 311. biennial oscillation (TBO) to the phase of the Interdecadal Pacific Oscillation Liu, B., M. Xu, and M. Henderson, 2011: Where have all the showers gone? Regional (IPO). Geophys. Res. Lett., 39, L20716. declines in light precipitation events in China, 1960 2000. Int. J. Climatol., 31, Metcalfe, S. E., M. D. Jones, S. J. Davies, A. Noren, and A. MacKenzie, 2010: Climate 1177 1191. variability over the last two millennia in the North American Monsoon, recorded Liu, J., B. Wang, Q. H. Ding, X. Y. Kuang, W. L. Soon, and E. Zorita, 2009a: Centennial in laminated lake sediments from Laguna de Juanacatlan, Mexico. Holocene, variations of the global monsoon precipitation in the last millennium: Results 20, 1195 1206. from ECHO-G model. J. Clim., 22, 2356 2371. Mo, K. C., 2010: Interdecadal Modulation of the Impact of ENSO on Precipitation and Liu, Y., J. Sun, and B. Yang, 2009b: The effects of black carbon and sulfate aerosols in Temperature over the United States. J. Clim., 23, 3639 3656. China regions on East Asian monsoon. Tellus B, 61, 642 656. Mohapatra, M., B. K. Bandyopadhyay, and A. Tyagi, 2011: Best track parameters Loschnigg, J., G. Meehl, P. Webster, J. Arblaster, and G. Compo, 2003: The Asian of tropical cyclones over the  North Indian Ocean: A review. Nat. Hazards, monsoon, the tropospheric biennial oscillation, and the Indian Ocean zonal doi:10.1007/s11069 011 9935 0. mode in the NCAR CSM. J. Clim., 16, 1617 1642. Mooley, D. A., and B. Parthasarathy, 1983: Variability of the Indian-summer monsoon Lu, J., G. A. Vecchi, and T. Reichler, 2007: Expansion of the Hadley cell under global and tropical circulation features. Mon. Weather Rev., 111, 967 978. warming. Geophys. Res. Lett., 34, doi: 10.1029/2006gl028443. Müller, W. A., and E. Roeckner, 2008: ENSO teleconnections in projections of future Mann, M. E., and K. A. Emanuel, 2006: Atlantic hurricane trends linked to climate climate in ECHAM5/MPI-OM. Clim. Dyn., 31, 533 549. change. Eos Trans. (AGU), 87, 233 241. Mumby, P., R. Vitolo, and D. Stephenson, 2011: Temporal clustering of tropical Mann, M. E., T. A. Sabbatelli, and U. Neu, 2007a: Evidence for a modest undercount cyclones and its ecosystem impacts. Proc. Natl. Acad. Sci. U.S.A., 108, 17626 bias in early historical Atlantic tropical cyclone counts. Geophys. Res. Lett., 34, 17630. L22707. Murakami, H., R. Mizuta, and E. Shindo, 2011: Future changes in tropical cyclone Mann, M. E., K. A. Emanuel, G. J. Holland, and P. J. Webster, 2007b: Atlantic tropical activity projected by multi-physics and multi-SST ensemble experiments using cyclones revisited. Eos Trans. (AGU), 88, 349 350. the 60-km-mesh MRI-AGCM. Clim. Dyn., doi:10.1007/s00382 011 1223 x. Marengo, J., et al., 2010: Recent developments on the South American Monsoon Murakami, H., M. Sugi, and A. Kitoh, 2013: Future changes in tropical cyclone activity system. Int. J. Climatol., 32, 1 21. in the North Indian Ocean projected by high-resolution MRI-AGCMs. Clim. Dyn., Marengo, J. A., J. Tomasella, L. M. Alves, W. R. Soares, and D. A. Rodriguez, 2011: 40, 1949 1968. The drought of 2010 in the context of historical droughts in the Amazon region. Murakami, H., et al., 2012: Future changes in tropical cyclone activity projected by Geophys. Res. Lett., 38, L12703. the new high-resolution MRI-AGCM. J. Clim., 25, 3237 3260. Marengo, J. A., et al., 2008: The drought of Amazonia in 2005. J. Clim., 21, 495 516. Na, H., B.-G. Jang, W.-M. Choi, and K.-Y. Kim, 2011: Statistical simulations of the Mariotti, A., and A. Dell Aquila, 2012: Decadal climate variability in the Mediterranean future 50 year statistics of cold-tongue El Nino and warm-pool El Nino. Asia- region: Roles of large-scale forcings and regional processes. Clim. Dyn., 38, Pacif. J. Atmos. Sci., 47, 223 233. 1129 1145. Nanjundiah, R., V. Vidyunmala, and J. Srinivasan, 2005: The impact of increase in CO2 Marshall, G. J., 2007: Half-century seasonal relationships between the Southern on the simulation of tropical biennial oscillations (TBO) in 12 coupled general 14SM Annular Mode and Antarctic temperatures. Int. J. Climatol., 27, 373 383. circulation models. Atmos. Sci. Lett., 6, 183 191. 14SM-59 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Newman, M., S. Shin, and M. Alexander, 2011: Natural variation in ENSO flavors. Sampaio, G., C. Nobre, M. H. Costa, P. Satyamurty, B. S. Soares, and M. Cardoso, 2007: Geophys. Res. Lett., 38. Regional climate change over eastern Amazonia caused by pasture and soybean Nicholls, N., 1978: Air-sea interaction and Quasi-Biennial Oscillation. Mon. Weather cropland expansion. Geophys. Res. Lett., 34, doi: 10.1029/2007gl030612. Rev., 106, 1505 1508. Santer, B., et al., 2006: Forced and unforced ocean temperature changes in Atlantic Nieto-Ferreira, R., and T. Rickenbach, 2010: Regionality of monsoon onset in South and Pacific tropical cyclogenesis regions. Proc. Natl. Acad. Sci. U.S.A., 103, America: A three-stage conceptual model. Int. J. Climatol., 31, 1309 1321. 13905 13910. Nunez, M. N., S. A. Solman, and M. F. Cabre, 2009: Regional climate change Satyamurty, P., A. A. de Castro, J. Tota, L. E. D. Gularte, and A. O. Manzi, 2010: Rainfall experiments over southern South America. II: Climate change scenarios in the trends in the Brazilian Amazon Basin in the past eight decades. Theor. Appl. late twenty-first century. Clim. Dyn., 32, 1081 1095. Climatol., 99, 139 148. Ogasawara, N., A. Kitoh, T. Yasunari, and A. Noda, 1999: Tropospheric biennial Seager, R., et al., 2009: Mexican drought: An observational modeling and tree ring oscillation of ENSO-monsoon system in the MRI coupled GCM. J. Meteorol. Soc. study of variability and climate change. Atmosfera, 22, 1 31. Jpn., 77, 1247 1270. Seierstad, I., D. Stephenson, and N. Kvamsto, 2007: How useful are teleconnection Oouchi, K., J. Yoshimura, H. Yoshimura, R. Mizuta, S. Kusunoki, and A. Noda, 2006: patterns for explaining variability in extratropical storminess ? Tellus A, 59, Tropical cyclone climatology in a global-warming climate as simulated in a 20 170 181. km-mesh global atmospheric model: Frequency and wind intensity analyses. J. Semmler, T., S. Varghese, R. McGrath, P. Nolan, S. Wang, P. Lynch, and C. O Dowd, Meteorol. Soc. Jpn., 84, 259 276. 2008: Regional climate model simulations of North Atlantic cyclones: Frequency Pezzi, L. P., and I. F. A. Cavalcanti, 2001: The relative importance of ENSO and tropical and intensity changes. Clim. Res., 36, 1 16. Atlantic sea surface temperature anomalies for seasonal precipitation over Seneviratne, S. I., et al., 2012: Changes in climate extremes and their impacts on the South America: A numerical study. Clim. Dyn., 17, 205 212. natural physical environment. In:  Managing the Risks of Extreme Events and Pinto, J., and C. Raible, 2012: Past and recent changes in the North Atlantic Disasters to Advance Climate Change Adaptation. A Special Report of Working oscillation. Clim. Change, 3, 79 90. Groups I and II of the Intergovernmental Panel on Climate Change (IPCC) [C. B. Power, S., T. Casey, C. Folland, A. Colman, and V. Mehta, 1999: Inter-decadal Field, V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. modulation of the impact of ENSO on Australia. Clim. Dyn., 15, 319 324. Mach, G. -K. Plattner, S. K. Allen, M. Tignor, and P. M. Midgley (eds.)]. Cambridge Qian, W., J. Fu, and Z. Yan, 2007a: Decrease of light rain events in summer associated University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 109 with a warming environment in China during 1961 2005. Geophys. Res. Lett., 230. 34, L11705. Seth, A., M. Rojas, and S. A. Rauscher, 2010: CMIP3 projected changes in the annual Qian, Y., D. P. Kaiser, L. R. Leung, and M. Xu, 2006: More frequent cloud-free sky and cycle of the South American Monsoon. Clim. Change, 98, 331 357. less surface solar radiation in China from 1955 to 2000. Geophys. Res. Lett., 33, Seth, A., S. A. Rauscher, M. Rojas, A. Giannini, and S. J. Camargo, 2011: Enhanced doi: 10.1029/2005GL024586. spring convective barrier for monsoons in a warmer world? Clim. Change, 104, Qian, Y., W. G. Wang, L. R. Leung, and D. P. Kaiser, 2007b: Variability of solar radiation 403 414. under cloud-free skies in China: The role of aerosols. Geophys. Res. Lett., 34, doi: Shaman, J., and E. Tziperman, 2011: An atmospheric teleconnection linking ENSO 10.1029/2006GL028800. and Southwestern European precipitation. J. Clim., 24, 124 139. Qian, Y., D. Gong, J. Fan, L. R. Leung, R. Bennartz, D. Chen, and W. Wang, 2009: Heavy Silva, A. E., and L. M. V. Carvalho, 2007: Large-scale index for South America pollution suppresses light rain in China: Observations and modeling. J. Geophys. Monsoon (LISAM). Atmos. Sci. Lett., 8, 51 57. Res, 114, D00K02. Silvestri, G., and C. Vera, 2009: Nonstationary Impacts of the Southern Annular Mode Quintana, J. M., and P. Aceituno, 2012: Changes in the rainfall regime along the on Southern Hemisphere Climate. J. Clim., 22, 6142 6148. extratropical west coast of South America (Chile): 30 43°S. Atmosfera, 25, Singh, A., T. Delcroix, and S. Cravatte, 2011: Contrasting the flavors of El Nino- 1 22. Southern Oscillation using sea surface salinity observations. J. Geophys. Res. Raia, A., and I. F. A. Cavalcanti, 2008: The life cycle of the South American Monsoon Oceans, 116, doi: 10.1029/2010JC006862. System. J. Clim., 21, 6227 6246. Sobel, A. H., I. M. Held, and C. S. Bretherton, 2002: The ENSO Signal in Tropical Ramsay, H. A., and A. H. Sobel, 2011: The effects of relative and absolute sea surface Tropospheric Temperature. J. Clim., 15, 2702 2706. temperature on tropical cyclone potential intensity using a single column model. Song, J., Y. Wang, and L. Wu, 2010: Trend discrepancies among three best track data J. Clim., 24, 183 193. sets of western North Pacific tropical cyclones. J. Geophys. Res. Atmos., 115, doi: Rao, V. B., C. C. Ferreira, S. H. Franchito, and S. S. V. S. Ramakrishna, 2008: In a 10.1029/2009JD013058. changing climate weakening tropical easterly jet induces more violent tropical Sörensson, A. A., and C. G. Menendez, 2011: Summer soil precipitation coupling in storms over the north Indian Ocean. Geophys. Res. Lett., 35, L15710. South America. Tellus A, 63, 56 68. Raphael, M. N., and M. M. Holland, 2006: Twentieth century simulation of the Souza, P., and I. F. A. Cavalcanti, 2009: Atmospheric centres of action associated with southern hemisphere climate in coupled models. Part 1: Large scale circulation the Atlantic ITCZ position. Int. J. Climatol., 29, 2091 2105. variability. Clim. Dyn., 26, 217 228. Stowasser, M., Y. Wang, and K. Hamilton, 2007: Tropical cyclone changes in the Rappin, E. D., D. S. Nolan, and K. A. Emanuel, 2010: Thermodynamic control of western North Pacific in a global warming scenario. J. Clim., 20, 2378 2396. tropical cyclogenesis in environments of radiative-convective equilibrium with Sugi, M., A. Noda, and N. Sato, 2002: Influence of the global warming on tropical shear. Q. J. R. Meteorol. Soc., 136, 1954 1971. cyclone climatology: An experiment with the JMA global model. J. Meteorol. Soc. Reboita, M. S., T. Ambrizzi, and R. P. da Rocha, 2009: Relationship between the Jpn., 80, 249 272. southern annular mode and southern hemisphere atmospheric systems. Rev. Sugi, M., H. Murakami, and J. Yoshimura, 2009: A reduction in global tropical cyclone Brasil. Meteorol., 24, doi: 10.1590/S0102-77862009000100005. frequency due to global warming. Sola, 5, 164 167. Renom, M., M. Rusticucci, and M. Barreiro, 2011: Multidecadal changes in the Sutton, R. T., and B. Dong, 2012: Atlantic Ocean influence on a shift in European relationship between extreme temperature events in Uruguay and the general climate in the 1990s. Nature Geosci., 5, 788 792. atmospheric circulation. Clim. Dyn., doi:10.1007/s00382-010-0986-9. Takahashi, K., A. Montecinos, K. Goubanova, and B. Dewitte, 2011: ENSO regimes: Ronchail, J., and R. Gallaire, 2006: ENSO and rainfall along the Zongo valley (Bolivia) Reinterpreting the canonical and Modoki El Nino. Geophys. Res. Lett., 38, doi: from the Altiplano to the Amazon basin. Int. J. Climatol., 26, 1223 1236. 10.1029/2011gl047364. Saji, N. H., and T. Yamagata, 2003: Possible impacts of Indian Ocean Dipole mode Taschetto, A. S., and M. H. England, 2009: El Nino Modoki Impacts on Australian events on global climate. Clim. Res., 25, 151 169. Rainfall. J. Clim., 22, 3167 3174. Saji, N. H., T. Ambrizzi, and S. E. T. Ferraz, 2005: Indian Ocean Dipole mode events Taschetto, A. S., C. C. Ummenhofer, A. Sen Gupta, and M. H. England, 2009: Effect of and austral surface air temperature anomalies. Dyn. Atmos. Oceans, 39, 87 101. anomalous warming in the central Pacific on the Australian monsoon. Geophys. Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata, 1999: A dipole Res. Lett., 36, doi: 10.1029/2009gl038416. mode in the tropical Indian Ocean. Nature, 401, 360 363. Tedeschi, R. G., I. F. A. Cavalcanti, and A. M. Grimm, 2013: Influences of two types of Salazar, L. F., C. A. Nobre, and M. D. Oyama, 2007: Climate change consequences on ENSO on South American precipitation. Int. J. Climatol., 33, 1382 1400. 14SM the biome distribution in tropical South America. Geophys. Res. Lett., 34, doi: 10.1029/2007gl029695. 14SM-60 Climate Phenomena and their Relevance for Future Regional Climate Change Chapter 14 Supplementary Material Thompson, D. W. J., S. Solomon, P. J. Kushner, M. H. England, K. M. Grise, and D. J. Villarini, G., G. Vecchi, T. Knutson, M. Zhao, and J. Smith, 2011: North Atlantic tropical Karoly, 2011: Signatures of the Antarctic ozone hole in Southern Hemisphere storm frequency response to anthropogenic forcing: Projections and sources of surface climate change. Nature Geosci., 4, 741 749. uncertainty. J. Clim., 24, 3224 3238. Ting, M., Y. Kushnir, R. Seager, and C. Li, 2009: Forced and internal twentieth-century Vuille, M., B. Francou, P. Wagnon, I. Juen, G. Kaser, B. G. Mark, and R. S. Bradley, 2008: SST trends in the north Atlantic. J. Clim., 22, 1469 1481. Climate change and tropical Andean glaciers: Past, present and future. Earth Sci. Tomasella, J., L. S. Borma, J. A. Marengo, D. A. Rodriguez, L. A. Cuartas, C. A. Nobre, Rev., 89, 79 96. and M. C. R. Prado, 2011: The droughts of 1996 1997 and 2004 2005 in Walsh, K., K. Nguyen, and J. McGregor, 2004: Fine-resolution regional climate model Amazonia: Hydrological response in the river main-stem. Hydrological Processes, simulations of the impact of climate change on tropical cyclones near Australia. 25, 1228 1242. Clim. Dyn., 22, 47 56. Trenberth, K. E., 1975: A quasi-biennial standing wave in the Southern Hemisphere Wang, B., and LinHo, 2002: Rainy season of the Asian-Pacific summer monsoon. J. and interrelations with sea surface temperature. Q. J. R. Meteorol. Soc., 101, Clim., 15, 386 398. 55 74. Wang, B., and Q. Ding, 2008: Global monsoon: Dominant mode of annual variation Trenberth, K. E., and D. P. Tepaniak, 2001: Indices of El Nino evolution. J. Clim., 14, in the tropics. Dyn. Atmos. Oceans, 44, 165 183. 1697 1701. Wang, B., Y. Yang, Q. Ding, H. Murakami, and F. Huang, 2010: Climate control of Trenberth, K. E., and D. J. Shea, 2006: Atlantic hurricanes and natural variability in the global tropical storm days (1965 2008). Geophys. Res. Lett., 37, doi: 2005. Geophys. Res. Lett., 33, L12704. 10.1029/2010GL042487. Trenberth, K. E., and J. T. Fasullo, 2012: Climate extremes and climate change: The Wang, B., Z. Wu, J. Li, J. Liu, C.-P. Chang, Y. Ding, and G. Wu, 2008: How to measure Russian heat wave and other climate extremes of 2010. J. Geophys. Res. Atmos., the strength of the East Asian summer monsoon. J. Clim., 21, 4449 4463. 117, doi: 10.1029/2012jd018020. Wang, C., and S. Lee, 2008: Global warming and United States landfalling hurricanes. Trenberth, K. E., D. P. Stepaniak, and J. M. Caron, 2000: The global monsoon as seen Geophys. Res. Lett., 35, doi: 10.1029/2007GL032396. through the divergent atmospheric circulation. J. Clim., 13, 3969 3993. Wang, G., and H. H. Hendon, 2007: Sensitivity of Australian rainfall to inter-El Nino Troup, A., 1965: Southern Oscillation. Q. J. R. Meteorol. Soc., 91, 490 &. variations. J. Clim., 20, 4211 4226. Tsutsui, J., 2010: Changes in potential intensity of tropical cyclones approaching Wang, H., 2001: The weakening of the Asian monsoon circulation after the end of Japan due to anthropogenic warming in sea surface and upper-air temperatures. 1970 s. Adv. Atmos. Sci., 18, 376 386. J. Meteorol. Soc. Jpn. II, 88, 263 284. Wang, H., J. Sun, and K. Fan, 2007: Relationships between the North Pacific Tsutsui, J., 2012: Estimation of changes in tropical cyclone intensities and associated Oscillation and the typhoon/hurricane frequencies. Sci. China D Earth Sci., 50, precipitation extremes due to anthropogenic climate change. In: Cyclones: 1409 1416. Formation, Triggers and Control [K. Oouchi and H. Fudeyasu (eds.)]. Nova Wang, R., L. Wu, and C. Wang, 2011: Typhoon track changes associated with global Science Publishers, Hauppauge, NY, USA, pp. 125 143. warming. J. Clim., 24, 3748 3752. Tu, J., C. Chou, and P. Chu, 2009: The Abrupt Shift of Typhoon Activity in the Vicinity Wang, Y., and L. Zhou, 2005: Observed trends in extreme precipitation events in of Taiwan and Its Association with Western North Pacific-East Asian Climate China during 1961 2001 and the associated changes in large-scale circulation. Change. J. Clim., 22, 3617 3628. Geophys. Res. Lett., 32, L09707. Turner, A., K. Sperber, J. Slingo, G. A. Meehl, C. R. Mechoso, M. Kimoto, and A. Watterson, I. G., 2009: Components of precipitation and temperature anomalies and Giannini, 2011: Modelling monsoons: Understanding and predicting current and change associated with modes of the Southern Hemisphere. Int. J. Climatol., 29, future behaviour. World Scientific Series on Asia-Pacific Weather and Climate, 809 826. Vol. 5. The Global Monsoon System: Research and Forecast ,2nd ed. [C. P. Chang, Webster, P., G. Holland, J. Curry, and H. Chang, 2005: Changes in tropical cyclone Y. Ding, N.-C. Lau, R. H. Johnson, B. Wang and T. Yasunari (eds.)]. World Scientific, number, duration, and intensity in a warming environment. Science, 309, 1844 Singapore, p 421-454. 1846. Turner, A. G., P. M. Inness, and J. M. Slingo, 2007: The effect of doubled CO2 and Webster, P. J., A. M. Moore, J. P. Loschnigg, and R. R. Leben, 1999: Coupled ocean- model basic state biases on the monsoon-ENSO system. I: Mean response and atmosphere dynamics in the Indian Ocean during 1997 98. Nature, 401, 356 interannual variability. Q. J. R. Meteorol. Soc., 133, 1143 1157. 360. van Loon, H., G. A. Meehl, and J. M. Arblaster, 2004: A decadal solar effect in the Weinkle, J., R. Maue, and R. Pielke, 2012: Historical global tropical cyclone landfalls. tropics in July-August. J. Atmos. Solar-Terres. Phys., 66, 1767 1178. J. Clim., 25, 4729 4735. Vasconcellos, F. C., and I. F. A. Cavalcanti, 2010: Extreme precipitation over Wing, A. A., A. H. Sobel, and S. J. Camargo, 2007: Relationship between the potential Southeastern Brazil in the austral summer and relations with the Southern and actual intensities of tropical cyclones on interannual time scales. Geophys. Hemisphere annular mode. Atmos. Sci. Lett., 11, 21 26. Res. Lett., 34, L08810. Vecchi, G., and T. Knutson, 2011: Estimating annual numbers of Atlantic hurricanes Woollings, T., A. Hannachi, B. Hoskins, and A. Turner, 2010a: A regime view of the missing from the HURDAT database (1878 1965) using ship track density. J. North Atlantic Oscillation and Its response to anthropogenic forcing. J. Clim., Clim., 24, 1736 1746. 23, 1291 1307. Vecchi, G. A., and B. J. Soden, 2007a: Increased tropical Atlantic wind shear in model Woollings, T., A. Charlton-Perez, S. Ineson, A. G. Marshall, and G. Masato, 2010b: projections of global warming. Geophys. Res. Lett., 34, L08702. Associations between stratospheric variability and tropospheric blocking. J. Vecchi, G. A., and B. J. Soden, 2007b: Effect of remote sea surface temperature Geophys. Res. Atmos., 115, D06108. change on tropical cyclone potential intensity. Nature, 450, 1066 1069. Wu, L., B. Wang, and S. Geng, 2005: Growing typhoon influence on east Asia. Vecchi, G. A., and T. R. Knutson, 2008: On estimates of historical North Atlantic Geophys. Res. Lett., 32, doi: 10.1029/2005GL022937. tropical cyclone activity. J. Clim., 21, 3580 3600. Wu, R., and B. Kirtman, 2004: Impacts of the Indian Ocean on the Indian summer Vecchi, G. A., K. L. Swanson, and B. J. Soden, 2008: Whither hurricane activity. monsoon-ENSO relationship. J. Clim., 17, 3037 3054. Science, 322, 687-689. Xie, S. P. D., C. Deser, G. A. Vecchi, J. Ma, H. Teng, and A. T. Wittenberg, 2010: Global Vera, C., and G. Silvestri, 2009: Precipitation interannual variability in South America warming pattern formation: Sea surface temperature and rainfall. J. Clim., 23, from the WCRP-CMIP3 multi-model dataset. Clim. Dyn., 32, 1003 1014. 966 986. Vera, C., et al., 2006: Toward a unified view of the American Monsoon Systems. J. Xu, M., C. Chang, C. Fu, Y. Qi, A. Robock, D. Robinson, and H. Zhang, 2006: Clim., 19, 4977 5000. Steady decline of east Asian monsoon winds, 1969 2000: Evidence from Vicente-Serrano, S., and J. López-Moreno, 2008: Nonstationary influence of the direct ground measurements of wind speed. J. Geophys. Res. Atmos., North Atlantic Oscillation on European precipitation. J. Geophys. Res., 113, doi: doi:10.1029/2006JD007337. 10.1029/2008JD010382. Yamada, Y., K. Oouchi, M. Satoh, H. Tomita, and W. Yanase, 2010: Projection of changes Vicuna, S., R. Garreaud, and J. McPhee, 2011: Climate change impacts on the in tropical cyclone activity and cloud height due to greenhouse warming: Global hydrology of a snowmelt driven basin in semiarid Chile. Clim. Change, 105, cloud-system-resolving approach. Geophys. Res. Lett., 37, L07709. 469 488. Yeh, S.-W., B. P. Kirtman, J.-S. Kug, W. Park, and M. Latif, 2011: Natural variability of Villarini, G., and G. A. Vecchi, 2012: Twenty-first-century projections of North Atlantic the central Pacific El Nino event on multi-centennial timescales. Geophys. Res. 14SM tropical storms from CMIP5 models. Nature Clim. Change, 2, 604 607. Lett., 38, L02704. 14SM-61 Chapter 14 Supplementary Material Climate Phenomena and their Relevance for Future Regional Climate Change Yeh, S. W., J. S. Kug, B. Dewitte, M. H. Kwon, B. P. Kirtman, and F. F. Jin, 2009: El Nino in a changing climate. Nature, 461, 511 515. Yokoi, S., and Y. Takayabu, 2009: Multi-model projection of global warming impact on tropical cyclone genesis frequency over the western north Pacific. J. Meteorol. Soc. Jpn., 87, 525 538. Yoon, J. H., and N. Zeng, 2010: An Atlantic influence on Amazon rainfall. Clim. Dyn., 34, 249 264. Yoshimura, J., M. Sugi, and A. Noda, 2006: Influence of greenhouse warming on tropical cyclone frequency. J. Meteorol. Soc. Jpn., 84, 405 428. Yu, J., Y. Wang, and K. Hamilton, 2010a: Response of tropical cyclone potential intensity to a global warming scenario in the IPCC AR4 CGCMs. J. Clim., 23, 1354 1373. Yu, J. Y., H. Y. Kao, and T. Lee, 2010b: Subtropics-related interannual sea surface temperature variability in the central equatorial Pacific. J. Clim., 23, 2869 2884. Yu, J. Y., H. Y. Kao, T. Lee, and S. T. Kim, 2011: Subsurface ocean temperature indices for Central-Pacific and Eastern-Pacific types of El Nio and La Nia events. Theor. Appl. Climatol., 103, 337 344. Yu, R., J. Li, W. Yuan, and H. Chen, 2010c: Changes in characteristics of late-summer precipitation over eastern China in the past 40 years revealed by hourly precipitation data. J. Clim., 23, 3390 3396. Yu, R. C., and T. J. Zhou, 2007: Seasonality and three-dimensional structure of interdecadal change in the East Asian monsoon. J. Clim., 20, 5344 5355. Yu, R. C., B. Wang, and T. J. Zhou, 2004: Tropospheric cooling and summer monsoon weakening trend over East Asia. Geophys. Res. Lett., 31, L22212. Zhai, P., X. Zhang, H. Wan, and X. Pan, 2005: Trends in total precipitation and frequency of daily precipitation extremes over China. J. Clim., 18, 1096 1108. Zhang, H., Z. Wang, and P. Guo, 2009: A modeling study of the effects of direct radiative forcing due to carbonaceous aerosol on the climate in East Asia. Adv. Atmos. Sci., 26, 57 66. Zhang, R., and T. L. Delworth, 2009: A new method for attributing climate variations over the Atlantic Hurricane Basin s main development region. Geophys. Res. Lett., 36, L06701. Zhang, Y., T. Li, and B. Wang, 2004: Decadal change of the spring snow depth over the Tibetan Plateau: The associated circulation and influence on the East Asian summer monsoon. J. Clim., 17, 2780 2793. Zhang, Y. C., X. Y. Kuang, W. D. Guo, and T. J. Zhou, 2006: Seasonal evolution of the upper-tropospheric westerly jet core over East Asia. Geophys. Res. Lett., 33, L11708. Zhao, C., X. Liu, and L. R. Leung, 2012: Impact of desert dust on the summer monsoon system over Southwestern North America. Atmos. Chem. Phys., 12, 3717 3731. Zhao, M., and I. M. Held, 2010: An analysis of the effect of global warming on the intensity of Atlantic hurricanes using a GCM with statistical refinement. J. Clim., 23, 6382 6393. Zhao, M., and I. Held, 2012: TC-permitting GCM simulations of hurricane frequency response to sea surface temperature anomalies projected for the late twenty- first century. J. Clim., 25, 2995 3009. Zhao, M., I. M. Held, S. J. Lin, and G. A. Vecchi, 2009: Simulations of global hurricane climatology, interannual variability, and response to global warming using a 50-km resolution GCM. J. Clim., 22, 6653 6678. Zhou, T., and J. Zhang, 2009: Harmonious inter-decadal changes of July-August upper tropospheric temperature across the north Atlantic, Eurasian continent, and north Pacific. Adv. Atmos. Sci., 26, 656 665. Zhou, T., R. Yu, H. Li, and B. Wang, 2008: Ocean forcing to changes in global monsoon precipitation over the recent half-century. J. Clim., 21, 3833 3852. Zhou, T., et al., 2009a: Why the western Pacific subtropical high has extended westward since the late 1970s. J. Clim., 22, 2199 2215. Zhou, T. J., and L. W. Zou, 2010: Understanding the predictability of East Asian summer monsoon from the reproduction of land-sea thermal contrast change in AMIP-type simulation. J. Clim., 23, 6009 6026. Zhou, T. J., D. Y. Gong, J. Li, and B. Li, 2009b: Detecting and understanding the multi- decadal variability of the East Asian Summer Monsoon - Recent progress and state of affairs. Meteorol. Z., 18, 455 467. Zhu, C., B. Wang, W. Qian, and B. Zhang, 2012: Recent weakening of northern East Asian summer monsoon: A possible response to global warming. Geophys. Res. Lett., 39, doi: 10.1029/2012GL051155. 14SM 14SM-62