Transient climate changes in a perturbed parameter ensemble of emissions-driven earth system model simulations – Supplementary Information

James M. Murphy, Ben B. B. Booth, Chris A. Boulton*, Robin T. Clark, Glen R. Harris, Jason A. Lowe and David M.H. Sexton

Met Office Hadley Centre, FitzRoy Road, Exeter EX1 3PB, UK. *College of Life and Environmental Sciences, Univ. of Exeter, Prince of Wales Road, Exeter EX4 4PS, UK.

james.murphy@metoffice.gov.uk

1. Further information on radiative forcing applied in model simulations

This section provides more information on the specification of radiative forcing agents in the ESPPE and HadGEM2-ES transient climate change simulations, adding to the overview provided in section 2.3 of the main text.

First, we summarise the anthropogenic boundary conditions applied in the ESPPE simulations driven by the A1B scenario (further details in Johns et al., 2003). Historical CO2 emissions are based on estimated contributions from fossil fuel burning and cement production (Marland et al., 1995) and changes in land use (Houghton, 1999). Future values are taken from the IPCC SRES A1B marker dataset (see Appendix II of IPCC, 2001). Past and future changes in other important greenhouse gases (CH4, N2O and a subset of the halocarbons estimated to be dominant for changes over the historic period) are represented as prescribed concentration changes, as are changes in tropospheric and stratospheric ozone. The inputs to the HadCM3C sulphur cycle module (section 2.1, main text) are prescribed time-varying emissions of DMS and low and high (chimney stack) level anthropogenic SO2 emissions. The historical volcanic forcing represents significant injections of aerosol into the stratosphere, and is based on an extended dataset of Sato et al. (1993) (http://www.giss.nasa.gov/data/strataer/). This accounts for observed eruptions up until 1997, after which volcanic aerosol concentrations are relaxed back to a low background level (Stott et al., 2006). Prescribed estimates of observed changes in total solar irradiance (TSI) cover both 11 year solar cycles and longer term changes (Solanki and Krivova, 2003). The longer term variations are represented up until 2003, after which the solar changes simply prescribe a repeating 11 year cycle into the future, assuming no longer term increases or decreases in TSI.

As noted in section 2.3, the RCP-driven ESPPE simulations were branched from the A1B simulations in 1950. They ran from 1951-2100, using emissions of CO2 and sulphate aerosol precursors, and concentrations of other well-mixed greenhouse gases and stratospheric aerosol, specified as in corresponding RCP-driven HadGEM2-ES experiments (see below). Tropospheric ozone concentrations were prescribed as in the A1B scenario.

The specification of radiative forcing agents in HadGEM2-ES uses updated historical and future datasets produced for CMIP5. Historical CO2 emissions are based on more recent estimates of changes due to fossil fuel burning, cement production, gas flaring and land use change, and are harmonised with scenario values to ensure a smooth transition from historical to future values (van Vuuren et al., 2011). Historical and future changes in land use are prescribed by supplying a boundary condition to the dynamic vegetation scheme in the form of a time-varying spatial mask of fractional anthropogenic disturbance representing the sum of crop and pasture coverage. This is imposed by excluding growth of tree and shrub PFTs within the disturbed fraction of land grid boxes (see Jones et al. (2011) for more detail). Forcing due to tropospheric ozone and non-sulphate aerosols is calculated interactively from their respective precursors (see sections 2.2 and 2.3, main

text), in contrast to the ESPPE in which tropospheric ozone is prescribed, and non-sulphate aerosols are not accounted for. As in the ESPPE, solar changes in HadGEM2-ES are specified from annual variations in TSI partitioned across six shortwave spectral bands (details in Stott et al., 2006), although HadGEM2-ES uses an updated TSI dataset (Lean, 2009). Historical changes in stratospheric volcanic aerosol are specified on a monthly time scale for four equal area latitudinal zones, as in the ESPPE. However, in HadGEM2-ES the future volcanic aerosol decays from its 1997 peak to a level for 2000-2020 consistent with the observed background amount reported in Thomason et al. (2008), and then increases during 2020-2040 to a level consistent with the background level found in a HadGEM2-ES control simulation (Jones et al., 2011). This strategy gives higher future levels than specified in the future portion of the ESPPE simulations.

2. Ensemble-mean climatological biases

Figure S1 shows the ensemble-mean pattern of land surface air temperature (SAT) from the ESPPE for June to August (JJA). The simulations successfully capture all the major observed sub-continental scale variations in SAT (Fig. S1b). This is based on comparison with the HadCRUT3 gridded observed climatology for 1980-99, derived from station data and blended with marine observations at coastal points (Brohan et al., 2006). Over South America, Africa and Australia, the ESPPE bias pattern (Fig. S1c) is similar to its CMIP3 and CMIP5 counterparts (Fig. S1d, e). In central regions of North America and Eurasia, the warm biases in the ESPPE, while larger than in CMIP3 or CMIP5, are somewhat smaller than in ATMOS, an earlier PPE of sampling only atmosphere parameter perturbations in the AOGCM configuration of HadCM3 (Murphy et al., 2009). In December to February (DJF), the regional biases in the ESPPE, CMIP3 and CMIP5 ensemble mean patterns generally show the same sign, and similar magnitudes (Fig. S2c-e). Cold biases are found in most regions, notable exceptions being over central parts of Canada and the northern USA, and northern Argentina. Each ensemble shows a warm bias over parts of Russia. In CMIP5, this extends westwards across a central strip of the Eurasian land mass between 45 and 55ºN, in contrast to the cold biases found in this region in CMIP3 and the ESPPE.

The ESPPE mean pattern of multiyear average terrestrial precipitation is shown in Fig. S3a for JJA. Fig. S3b shows corresponding observations from the GPCP dataset (Adler et al., 2003), which combines rain gauge data with satellite-based estimates. Values in Figs. S3a,b are given as the natural logarithm of precipitation amounts in mm/day, in order to emphasise major large-scale features. These include the Asian monsoon, and areas of enhanced precipitation over central Africa, Central America and northern parts of South America associated with the seasonal northward migration of the intertropical convergence zone, all of which are successfully captured by the ESPPE. The ESPPE simulates small levels of precipitation over the Middle East in broad accordance with observations, but overestimates observed values over north-western Africa. Differences between the ESPPE and observations are shown in Fig. S3c, in this case as untransformed values in mm/day. The error patterns are qualitatively similar to those of the CMIP3 and CMIP5 ensemble means, particularly over Eurasia, North America and South America (Fig. S3c cf S3d,e). This is also true in DJF (Fig. S4). In the ESPPE, the deficit in precipitation over north-west Europe and northern Russia is probably associated with insufficient north-eastward extension of the north Atlantic storm track into this region (Greeves et al., 2007). Other regions of common bias between the three ensembles are listed in section 3.1, main text. Despite these biases, the ESPPE distribution in DJF successfully replicates the main spatial features of the observed climatological pattern, as in JJA.

3. Globally averaged future SAT changes in RCP8.5 simulations

Figure S5 shows simulated future changes in 20-year averages of global mean SAT relative to 1980-99, in ESPPE and CMIP5 earth system model simulations driven by the RCP8.5 scenario. Both ensembles are driven by surface CO2 emissions, hence Figure S5 provides a comparison of the impacts of the different strategies used to sample modelling uncertainties in the two ensembles (parameter perturbations in the ESPPE, cf the multi-model construction of CMIP5). This is in contrast to Figure 8 in the main text, in which differences between the ESPPE and CMIP3 multimodel envelopes (under A1B forcing) arise from a combination of different ensemble construction techniques (as in Fig. S5), plus the use of different model configurations in the two experiments: physical ocean-atmosphere models driven by prescribed CO2 concentrations and vegetation masks in the case of CMIP3, cf the interactive simulation of CO2 and vegetation realised in the ESPPE. The CMIP5 results in Fig. S5 are based on the set of 15 simulations used in Figs. 1,2, 12 and 15 of the main text. The global mean SAT changes in Fig. S5 are used to normalise the regional future changes shown for JJA (Figs. 12 and 15, main text), and DJF (Figs. S7 and S8). The ESPPE simulates a median warming for 2080-99 above the 25-75% range of CMIP5, and a wider 10-90% range of responses than CMIP5, while the latter provides several responses below the smallest warming found in the ESPPE. This illustrates the value of combining results from perturbed parameter and multi-model ensembles to obtain a more complete picture of plausible future changes in global mean SAT, and confirms the conclusions of Booth et al (2013), who compared the ESPPE to ten of the fifteen CMIP5 models considered here. They found that the ESPPE sampled a number of realisations of both carbon cycle feedback and climate sensitivity above the ranges found in the CMIP5 simulations.

4. Country definitions for analysis of regional climate change

Figure S6 shows the 24 countries used in the analysis of future regional changes given in section 5 of the main text. These countries were chosen for a UK Government project to compile information on worldwide climate changes and impacts (see http://www.metoffice.gov.uk/climate-change/policy-relevant/obs-projections-impacts). The spatial definitions were carried out by assigning land points on the 2.5x3.75º latitude-longitude grid of the HadCM3C model forming the basis of the ESPPE.

5. Structural and parametric modelling uncertainties in normalised regional changes

Figures S7 and S8 show uncertainties in 21st century regional changes in the DJF season, for SAT and precipitation respectively. The results are all based on simulated regional changes normalised relative to the corresponding change in global mean SAT, in order to isolate the effects of uncertainties in regional patterns of change. As in the corresponding figures for JJA (Figs. 12 and 15 of the main text), the results provide comparisons between the effects of sampling parametric modelling uncertainties in HadCM3-based ensembles, versus the sampling of structural uncertainties in corresponding multimodel ensembles. Panels (a) in each figure show results from coupled-ocean atmosphere ensembles (ATMOS cf CMIP3), driven by the A1B scenario using prescribed CO2 concentrations; Panels (b) compare the emissions-driven ESPPE and CMIP5 earth system model ensembles, based on the RCP8.5 scenario. For SAT, the median response in the ESPPE is found to be larger than the CMIP5 median at the 5% level in nine of the regions assessed, based on a bootstrap resampling significance test. In one of these (South Korea), the ATMOS median is also significantly larger than that of CMIP3, while it is significantly lower for Kenya and South Africa. The ESPPE gives significantly lower median responses than CMIP5 over Canada and Russia. The interquartile ranges of SAT response are significantly smaller in ATMOS than CMIP3

for Canada and Mexico, though no significant differences are found between the ESPPE and CMIP5 interquartile ranges.

For DJF precipitation, the median responses in both perturbed parameter ensembles significantly exceed their CMIP counterparts over Bangladesh, China, India and USA, but are significantly smaller over Indonesia. The ATMOS median response is also significantly smaller than CMIP3 over South Africa, whilst the ESPPE median is significantly larger than CMIP5 over Japan, South Korea, Spain and Great Britain, in addition to the four countries listed above. Note that use of flux adjustments in the perturbed parameter experiments may be an additional factor driving some of the differences in precipitation responses, notably over SE Asia (see section 5.1, main text).In DJF, the interquartile ranges of response in CMIP3 are significantly larger than in ATMOS over Canada and Mexico for SAT, and over Brazil, Canada and USA for precipitation. The CMIP5 interquartile range significantly exceeds that of the ESPPE in only one case (precipitation over Argentina), while there are no cases where the range in either of the perturbed parameter ensembles significantly exceeds its CMIP counterpart.

In summary, the DJF results in Figs. S7 and S8 suggest that consideration of information from two types of ensemble of contrasting design provides a more robust basis for assessment of future climate risks, compared to the use of either multimodel or perturbed parameter results in isolation. In this respect they support the JJA results of Figs. 12 and 15 (section 5.1, main text), although we note that the instances of major differences between the perturbed parameter and multimodel results are somewhat fewer than found in JJA.

6. Regional effects of accounting for uncertainties in different earth system components

Figure S9 compares normalised regional changes in DJF for (a) SAT and (b) precipitation, comparing in this case future changes simulated under A1B forcing by the ATMOS and ESPPE simulations. Corresponding results for JJA are shown in Fig. 12, and discussed in section 5.1 of the main text. Differences between the ESPPE and ATMOS provide an indication of regional effects of accounting for uncertainties in perturbed sulphur cycle, ocean and terrestrial ecosystem processes in the ESPPE, and their interactions with uncertainties in atmospheric processes (sampled in both ensembles). In DJF, statistically significant shifts to larger normalised SAT changes are found in the ESPPE for India, Bangladesh, Mexico, Argentina, Kenya and South Africa, while the ESPPE interquartile ranges do not significantly exceed their ATMOS counterparts in any region. For precipitation, the ESPPE and ATMOS distributions show differences of detail, but none of these are statistically significant. A number of significant differences are found for precipitation in JJA, however (see Fig. 12b, main text, and related discussion).

7. Simulated changes in SAT extremes

Table S1 gives 10th, 50th and 90th percentiles of the ESPPE distributions of future change in hot days and nights, and cold days and nights, for 2080-99 relative to 1980-99 under A1B forcing. These correspond to changes for 20 year mean SAT shown in Table 1, main text. Results for cold (hot) extremes are given for winter (summer) in the relevant hemisphere. Past and future extreme events are defined as the 1st and 99th percentiles of the distributions of daily values of diurnal maximum or minimum temperature in the relevant 20 year period (Tmax1, Tmax99, Tmin1, Tmin99). The results consistently show substantial increases, accompanied by wide uncertainties. There are large increases in Tmax99, the intensity of a typical hottest day of summer, particularly at the upper ends of the ESPPE ranges of change. Large increases are also found in the typical coldest day and night of winter, in mid- and high-latitude northern hemisphere countries affected by future reductions in

snow cover. In Canada, for example, the median ESPPE warming in Tmin1 exceeds 10 ºC, and this is also the case in Germany, Russia and Japan. These results are provided for readers interested in potential impacts suggested by the ESPPE simulations, however section 5.2 (main text) emphasises the importance of further work to understand better the relative plausibility of the different simulated realisations, which is beyond the scope of the present paper.

References

Adler, R.F. et al., 2003: The Version 2 Global Precipitation Climatology Project (GPCP) monthly precipitation analysis (1979 – present). J. Hydrometeorol. 4: 1147–1167.

Booth, B.B.B., D. Bernie, D. McNeall, E. Hawkins, J. Caesar, C. Boulton, P. Friedlingstein and D. Sexton, 2013: Scenario and modelling uncertainty in global mean temperature change derived from emission driven global climate models. Earth. Syst. Dyn. 4:95-108.

Brohan, P., J.J. Kennedy, I. Harris, S.F.B. Tett and P. D. Jones, 2006: Uncertainty estimates in regional and global observed temperature changes: A new dataset from 1850. J. Geophys. Res. 111: D12106.

Greeves, C.Z., V.D. Pope, R.A. Stratton and G.M. Martin, 2007: Representation of northern hemisphere winter storm tracks in climate models. Clim. Dyn. 28: 683-702.

Houghton, R.A., 1999: The annual net flux of carbon to the atmosphere from changes in land use 1850-1990. Tellus B 51: 298-313.

IPCC, 2001: Climate change 2001: the scientific basis. In: Houghton J.T., Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell and C.A. Johnson (eds) Contribution of Working Group I to the 3rd assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, 881 pp.

Johns, T.C., et al., 2003: Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Clim. Dyn. 20: 583-612.

Jones, C.D., et al., 2011: The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model. Dev. 4: 543-570.

Lean, J.L., 2009: Available at http://www.geo.fu-berlin.de/en/met/ag/strat/forschung/SOLARIS/Input_data/CMIP5_solar_irradiance.html

Marland, G., T.A. Boden and R.J. Andres, 1995: Global, regional and national annual CO2 emission estimates from fossil-fuel burning, hydraulic-cement production and gas flaring: 1950-1992. CDIAC Commun. Fall, 20-21.

Murphy, J.M., D.M.H. Sexton, G.J. Jenkins, P.M. Boorman, B.B.B. Booth, C.C. Brown, R.T. Clark, M. Collins, G.R. Harris, E.J. Kendon, R.A. Betts, S.J. Brown, T.P. Howard, K.A. Humphrey, M.P. McCarthy, R.E. McDonald, A. Stephens, C. Wallace, R. Warren, R. Wilby and R.A. Wood, 2009: UK Climate Projections Science Report: Climate change projections. Met Office Hadley Centre, Exeter

Sato, M., J.E. Hansen, M.P. McCormick and J.B. Pollack, 1993: Stratospheric aerosol optical depths (1850-1990). J. Geophys. Res. 98: 22987-22994.

Solanki, S.K., and N.A. Krivova, 2003: Can solar variability explain global warming since 1970 ? J. Geophys. Res. 108(A5): 1200.

Stott, P.A., G.S. Jones, J.A, Lowe, P. Thorne, C. Durman, T.C. Johns and J.-C. Thelen, 2006: Transient climate simulations with the HadGEM1 climate model: Causes of past warming and future climate change. J. Clim. 19: 2763-2782.

Thomason, L.W., S.P. Burton, B.-P. Luo and T. Peter, 2008: SAGE II measurements of stratospheric aerosol properties at non-volcanic levels. Atmos. Chem. Phys. 8: 983–995, doi:10.5194/acp-8-983-2008.

Van Vuuren, D.P., J. Edmonds, M. Kainuma, K. Riahi, A. Thomson, K. Hibbard, G.C. Hurtt, T. Kram, V. Krey, J.-F. Lamarque, T. Masui, M. Meinshausen, N. Nakicenovic, S.J. Smith and S.K. Rose, 2011: The representative concentration pathways: an overview. Clim. Change 109: 5-31.

Supplementary Table

COLD EXTREMES, WINTER HOT EXTREMES, SUMMERTMIN1 TMAX1 TMIN99 TMAX99

P10 P50 P90 P10 P50 P90 P10 P50 P90 P10 P50 P90ARG 2.1 4.0 6.4 1.7 3.2 5.1 3.1 5.2 8.1 3.4 6.0 8.8AUS 2.6 3.8 5.6 2.8 4.0 6.6 3.7 5.0 8.3 3.5 4.7 8.8BGD 4.3 7.1 9.0 3.5 5.6 7.3 2.8 4.5 7.0 2.7 3.8 7.4BRA 2.9 4.7 8.1 2.6 4.3 7.6 3.7 6.8 9.4 3.8 8.0 12.1CAN 8.8 13.7 20.0 8.5 13.3 18.4 4.0 6.8 11.0 4.5 10.2 15.0CHN 3.2 6.5 10.3 2.5 5.2 8.3 4.5 6.6 9.3 4.7 7.5 11.0EGY 3.2 4.6 7.3 2.8 4.6 7.2 4.2 6.1 11.4 4.5 6.8 10.6FRA 4.6 6.0 9.2 3.3 4.8 7.3 5.4 7.8 10.5 7.3 9.1 14.0DEU 6.7 10.1 13.9 5.7 8.0 11.7 4.2 7.7 11.4 5.9 9.1 16.2IND 4.3 6.2 9.8 3.2 4.8 7.4 3.6 4.8 7.4 2.8 4.0 7.0IDN 3.2 4.4 6.9 2.3 3.3 5.4 2.9 4.8 7.3 3.2 6.1 8.6ITA 5.8 8.8 13.8 3.9 5.7 10.1 6.1 8.8 12.5 8.2 10.1 15.0JPN 7.4 10.1 15.2 3.7 5.4 8.1 4.2 6.2 9.7 6.5 8.4 11.0KEN 3.7 4.8 7.6 2.0 3.2 6.5 3.7 5.2 7.9 2.9 4.1 8.1MEX 2.6 4.8 7.2 3.1 5.1 8.1 4.5 6.0 9.9 5.2 7.1 11.7PER 3.1 4.7 8.6 3.0 4.5 7.6 3.5 5.5 8.1 3.4 6.3 10.1RUS 6.8 10.0 14.7 6.7 9.9 14.3 4.6 7.6 12.3 5.1 10.6 16.0SAU 2.8 4.5 7.8 2.5 4.3 7.2 5.1 6.6 9.5 5.2 6.5 9.3ZAF 1.7 2.8 5.2 2.1 3.2 5.3 4.1 5.7 8.7 4.7 7.5 11.3KOR 7.0 7.8 17.8 3.5 5.9 10.4 4.2 5.9 9.5 5.2 8.3 10.0ESP 1.7 3.1 4.9 2.3 4.2 6.3 5.1 7.3 9.8 6.6 8.2 11.4TUR 5.5 8.4 11.8 3.4 4.7 7.2 5.5 8.0 11.1 6.8 9.0 13.7GBR 5.6 7.1 9.3 4.2 5.5 7.4 3.3 5.2 8.2 4.8 7.3 12.1USA 5.8 9.4 13.8 4.9 7.7 11.8 5.5 7.8 11.3 7.0 9.3 13.1Table S1. Simulated changes in surface air temperature extremes for 2080-99 relative to 1980-99 under A1B emissions, for the 24 countries of Figure S6. The left panels give results for the winter season in the relevant country (DJF/JJA for northern/southern hemisphere countries respectively), for the 1st percentile of daily minimum and maximum temperature (Tmin1 and Tmax1). The right panels give results for the summer season (JJA/DJF for northern/southern hemisphere countries respectively), for the 99 th percentile of daily minimum and maximum temperature (Tmin99 and Tmax99). P10,50,90 denote the 10 th, 50th and 90th

percentiles of the ESPPE distributions of simulated changes for each of the temperature extreme metrics.

Supplementary Figures

Figure S1. Ensemble mean pattern of land surface air temperature for June-August (JJA) for 1980-99 from the ESPPE simulations, (a), compared against observations from the HADCRUT3 dataset, (b). Differences between the ESPPE mean and observations are shown in (c). These are compared against corresponding differences for the ensemble means of 22 simulations from CMIP3 coupled-ocean atmosphere models, (d), and 15 simulations from CMIP5 earth system models, (e). During 1980-99, time-dependent CO2

concentrations were prescribed from observations in the CMIP3 simulations, and calculated from prescribed emissions in the ESPPE and CMIP5 simulations. Grid squares for which no observations are available are left blank in (b)-(e).

Figure S2. As Figure S1, for December-February (DJF).

Figure S3. Ensemble mean pattern of land precipitation for June-August (JJA) for 1980-99 from the ESPPE simulations, (a), compared against observations from the GPCP dataset, (b). Differences between the ESPPE mean and observations are shown in (c). These are compared against corresponding differences for the ensemble means of 22 simulations from CMIP3 coupled-ocean atmosphere models, (d), and 15 simulations from CMIP5 earth system models (e). Panels (c), (d) and (e) show differences of untransformed values in mm/day, whereas (a) and (b) show the natural logarithm of precipitation, to highlight large scale spatial variations.

Figure S4. As Figure S3, for DJF.

Figure S5. Simulated future changes in 20 year mean annual global surface air temperature (SAT), relative to 1980-99. Orange and blue curves show changes in 57 ESPPE and 15 CMIP5 earth system ensemble members in response to forcing from the RCP8.5 scenario, with CO2 changes prescribed as surface emissions in both ensembles. The box-whisker plots show the 10th, 25th, 50th, 75th and 90th percentiles of the distributions of change for 2080-2099.

Figure S6. Spatial definitions of countries on the 2.5x3.75º latitude-longitude grid of the HadCM3C model, for which information on simulated future climate changes is provided in section 5 of the main text. CAN = Canada; USA = United States of America; MEX = Mexico, PER = Peru; BRA = Brazil; ARG = Argentina; GBR = Great Britain; FRA = France; ESP = Spain; DEU = Germany; ITA = Italy; TUR = Turkey; EGY = Egypt; KEN = Kenya; ZAF = South Africa; SAU = Saudi Arabia; RUS = Russia; IND = India; CHN = China; BGD = Bangladesh; IDN = Indonesia; KOR = South Korea; JPN = Japan; AUS = Australia.

Figure S7. Uncertainties in future regional surface air temperature (SAT) responses in DJF to (a) A1B forcing, and (b) RCP8.5 forcing, normalised in each case by future global mean SAT changes, for the countries of Fig. S6. LND represents the global mean response over land, normalised by the global mean over land and sea combined. Panel (a) shows results for 17 variants of the coupled ocean-atmosphere configuration of HadCM3 (ATMOS, shown in purple), compared against results from 22 corresponding simulations from CMIP3 coupled ocean-atmosphere models (blue). Both of these experiments use CO2

changes prescribed as atmospheric concentrations. Panel (b) shows results from earth system model experiments driven by surface CO2 emissions, comparing results from 57 ESPPE members (orange) against those from 15 CMIP5 models (green). The ATMOS simulations in (a) used the same set of perturbations to physical atmospheric and sea ice processes applied in the ESPPE, but used prescribed vegetation cover and standard unperturbed values for ocean and sulphur cycle parameters, in contrast to the ESPPE. The whiskers, boxes and horizontal bars show the 5th, 25th, 50th, 75th and 95th percentiles of the relevant ensemble distribution of changes. Normalised future changes are estimated by applying linear regression to annual anomalies for the period 1990-2100, calculated relative to a 1980-1999 baseline.

Figure S8. As Figure S7, for normalised precipitation changes in DJF. Changes are in units of percentage future change per degree of global SAT change. However values are omitted for Egypt and Saudi Arabia because baseline precipitation values simulated for 1980-99 are very small, leading to noisy distributions of percentage future change.

Figure S9. Uncertainties in future regional responses to A1B forcing, normalised by future global surface air temperature (SAT) changes, for the countries of Fig. S6. LND represents the spatial mean response over the entire global landmass, normalised by the global mean response over land and sea combined. Panels (a) and (b) show normalised regional SAT and precipitation changes in DJF. Results are shown for ESPPE members (orange) driven by CO2 emissions and simulating interactive vegetation, and for 17 variants of the coupled ocean-atmosphere configuration of HadCM3 (ATMOS, shown in purple), in which the same set of perturbations to physical atmospheric and sea ice processes was applied, using prescribed vegetation cover and CO2 concentrations and standard unperturbed values for ocean and sulphur cycle parameters. The whiskers, boxes and horizontal bars show the 5th, 25th, 50th, 75th and 95th percentiles of the relevant ensemble distribution of changes. Normalised future changes are estimated by applying linear regression to annual anomalies for the period 1990-2100 calculated with respect to the 1980-1999 baseline. Precipitation changes are in units of percentage future change per degree of global SAT change. However values are omitted for Egypt and Saudi Arabia because baseline precipitation values simulated for 1980-99 are very small, leading to noisy distributions of percentage future change.