Near-term acceleration in the rate of temperature change

Transcription

1 ARY INFORMATION DOI: 0.0/NCLIMATE Near-term acceleration in the rate of temperature change Steven J. Smith, James Edmonds, Corinne A Hartin, Anupriya Mundra, and Katherine Calvin Joint Global Change Research Institute, Pacific Northwest National Laboratory, University Research Court, Suite 00, College Park, MD 00 CONTENTS SI Northern Hemisphere Proxy Temperature Reconstructions... SI Regional rates of change: PAGES K... SI Regional and global rates of change: CMIP... SI. CMIP Regional Rates of Change Compared to PAGES K... SI. Rates of change in CMIP simulations... SI. Rates of change CMIP: region size sensitivity... SI. Rates of change CMIP: hemispheric land/ocean/size sensitivity... SI. Comparison of 0, 0, 0, and 0-year rates of change CMIP... SI Adjusted Observational Estimates of Near-Term Rates of Change... SI Methods: Climate model and aerosol forcing... MAGICC Climate Model... Historical Emissions... Sensitivity Test Assumptions... SI Simple Model Decomposition Analysis and Comparison To CMIP... SI Comparison To Previous Work on Near-term Trends... 0 SI References... 0 SI Northern Hemisphere Proxy Temperature Reconstructions Over millennial time scales, forcing of the Earth s climate system includes changes in solar irradiance, volcanic aerosol injection, and changes in greenhouse gas concentrations. These combine with significant effects from internal modes of variability such as the El Niño-Southern Oscillation (ENSO), the Arctic & North Atlantic Oscillations (AO & NAO), and the Pacific Decadal Oscillation (PDO) (Mann 00). Figure SI- shows northern hemisphere temperature trends from the reconstructions used in the main text. While temperature time series were smoothed for display in Figure SI-, unsmoothed data were used for all other calculations. The majority of these time series were presented in (Jansen et al. 00). We have added two more recent reconstructions from Mann et al. (00) and Christiansen and Ljungqvist (0). Differences between reconstructions are larger in first half of the last millennium as compared to more recent times, that is, there is larger uncertainty in temperature reconstructions further back in time. NATURE CLIMATE CHANGE 0 Macmillan Publishers Limited. All rights reserved.

2 Note that these reconstructions cover different portions of the northern hemisphere (see Table SI-), have different sample densities, and differ in other characteristics. We examine these taken together as a whole to provide an indication of past trends in terms of rates of change and to illustrate that, while there are some similarities between many of the reconstructions, there are also substantial differences between reconstructions that limit firm conclusions on long-term historical rates and trends. As we examine in more detail in following sections (.), CMIP modeling results indicate that differences in spatial coverage in the Northern Hemisphere have only modest impacts on rates of change as compared to the changes expected over the st century..0! "Northerm Hemisphere" Temperature (-year smoothing)! MNN00! MJ00! BOS..00! B000! JBB..! ECS00! RMO..00! 0.! MSH..00! DWJ00! HCA..00! O00! PS00! CL0_g*! HADCRU! Temperature ( C)! 0.0! -0.! -.0! -.! 000! 00! 00! 00! 00! 000! Figure SI-. Northern hemisphere temperature reconstructions using a variety of proxy methods (colored lines). The thick black line shows northern hemisphere temperatures from the HADCRU dataset up to 0 (Brohan et al., 00, downloaded --0). Data sources: MNN00 (), MJ00 (), BOS 00 (), B000 (), JBB.. (), ECS00 (), RMO..00 (), MSH..00 (), DWJ00 (), HCA..00 (0), O00 (), PS00 (), CL0_g (). Data box smoothed over years (for graphing only) centered on the year shown except for HCA 00, O00, PS00, and CL0_g. See end of supplement for data references. Year! Note that the analysis by Christiansen and Ljungqvist (0) uses a methodology that the authors argue better preserves long-term changes as compared to other reconstructions. This reconstruction shows much larger temperature changes over the last couple centuries than other methods. Note that their methodology enhances short-term variability, therefore, they present results smoothed with a 0-year filter. The shorter-term trends we consider here derived from this dataset, need to be interpreted with some caution. However, there is little indication that rates of change are dramatically different in this record as compared to other reconstructions, aside from the higher rate of change in the SI- 0 Macmillan Publishers Limited. All rights reserved.

3 0 th century (Figure main text), which may not be consistent with the measured temperature record. During the latter half of the 0 th century to the present day, anthropogenic influences begin to dominate the climate signal, and the northern hemisphere rate of climate change is now above 0. C/decade (Figure, main text). Warming rates during the early 0 th century neared this value in many proxy reconstructions and also the instrumental record. This early 0 th century warming appears to be due to largely natural forcings with a smaller anthropogenic component (Stott et al. 00, Meehl et al. 00, Ring et al 0). In the main text and in supplementary analysis we use rates from the period -0 as a comparison period in the CMIP analysis. While there may be a small anthropogenic signal in these data, this provide a standard reference period to which we can compare CMIP model runs up through the st century. Source Ref # Season Spatial Domain MNN00 Annual Land + Ocean 0 0 N MJ00 Annual Land + Ocean 0 0 N BOS 00 Summer Land, 0 N 0 N B000 Summer Land, 0 N 0 N JBB.. Summer Land, 0 N 0 N ECS00 Annual Land, 0 N 0 N RMO..00 Annual Land + Ocean 0 0 N MSH..00 Annual Land + Ocean 0 0 N DWJ00 Annual Land, 0 N 0 N HCA Annual Land, 0 N 0 N O00 Summer Global Land PS00 Annual Land, 0 N 0 N CL0_g Annual Land, "Extratropical" Table SI-. Details for northern hemisphere paleo-climate datasets. Note that for all the rate of change calculations presented in this work, the rate of change indicates the trend up to the year shown. (That is, the 0-year trend for 000 is the linear trend over the 0-year period -000.) SI- 0 Macmillan Publishers Limited. All rights reserved.

4 0.! 0.! "Northerm Hemisphere" Rate of Temp Change (0-year Trends)! MNN00! MJ00! BOS..00! B000! JBB..! ECS00! RMO..00! MSH..00! DWJ00! HCA..00! O00! PS00! CL0_g! HADCRU! 0.! 0.! 0.0! -0.! -0.! -0.! -0.! 000! 00! 00! 00! 00! 00! 00! 00! 00! 00! 000! Figure SI-. Northern hemisphere rates of temperature change over 0-year periods (colored lines). The thick line shows the corresponding rate of change of northern hemisphere temperatures from the HADCRU dataset (Morice et al., 0). Trends are linear fits ending at the year shown. Year! 0.! 0.! "Northerm Hemisphere" Rate of Temp Change (0-year Trends)! MNN00! MJ00! BOS..00! B000! JBB..! ECS00! RMO..00! MSH..00! DWJ00! HCA..00! O00! PS00! CL0_g! HADCRU! Temperature Trend ( C\decade)! 0.! 0.0! -0.! -0.! -0.! 000! 00! 00! Year! 00! 00! 000! Figure SI-. Northern hemisphere rates of temperature change over 0-year periods (colored lines). The thick line shows the corresponding rate of change of northern hemisphere temperatures from the HADCRU dataset (Morice et al., 0). Trends are linear fits ending at the year shown. Temperature trends over 0-year periods (in contrast to the 0-year periods shown in the main text) are shown in Figure SI-. There is evidence from several reconstructions that SI- 0 Macmillan Publishers Limited. All rights reserved.

5 rates of temperature increase above 0. C/decade have occurred for 0-year periods in the past. Note that, unlike rates of change evaluated over longer periods, the historical rates of change over the last 0-years do not stand out from the longer-term record. Now considering 0-year periods (Figure SI-), there is evidence that rates of temperature change of over 0. C/decade over 0 year periods have occurred a few times in the past (Figure SI-). There is no evidence that rates of change as high as 0. C/decade have occurred for 0-year periods. The 0-year rate of change in the historical record is now (in 00) above 0. C/decade, a value that is seen in only a few proxy reconstructions. Recent rates of change from the instrumental record may be becoming distinguishable from natural variability when considered over 0-year time periods, however it is difficult to draw firm conclusions since change over this length of time might not be faithfully reproduced by the proxy reconstructions. See main text discussion of the PAGES k reconstructions as compared to CMIP climate models. Peak rates of change in the paleo-climate data decline as the length of the trend period is increased. Some proxy reconstructions show peak trends prior to the 0 th century of up to around 0. C/decade when examined over 0-year periods. When examined over 0 year periods the long-term trends are even smaller. Rate of Change ( C/Decade)! 0.! 0.! 0.! 0.! 0! -0.! -0.! -0.! Northern Hemisphere 0-year Rate of Change! -0.! 000! 00! 00! 00! 00! 00! 00! 00! 00! 00! 000! Year! Figure SI-. Northern hemisphere rates of temperature change over 0-year periods for simulations from a coupled carbon-climate model from Jungclaus et al. (00). For comparison with the temperature proxy data, Figure SI- shows 0-year temperature trends from the simulations reported in Jungclaus et al. (00). These simulations were forced with estimates of volcanic, solar, orbital, land-use change, and greenhouse gas forcing. The overall character of the historical rates of change are similar to the temperature reconstructions, with 0-year rates of change of up to 0. C/decade commonly seen. However, the variability in this model is somewhat larger than that seen SI- 0 Macmillan Publishers Limited. All rights reserved.

6 in the reconstructions. Positive excursions reach 0. C/decade, and one cooling event reached -0. C/decade (compare to Figure, main text). SI Regional rates of change: PAGES K Temperature trends in the PAGES k regional temperature reconstructions in Figure (main text) were examined as linear fits over 0-year periods of the annual PAGES k data (Database S - April 0 version) over all 0-year periods up to 00. PAGES k data for North America are not used as these were provided only at 0-year intervals. SI Regional and global rates of change: CMIP CMIP model results were analyzed in an analogous manner to the PAGES k data. In order to obtain a larger sample size, CMIP data were generally analyzed from 0 through 0 as a historical reference period. While there is some anthropogenic influence during this period, the contribution of anthropogenic forcing is still relatively small at this point (Ring et al. 0), particularly relative to the 0-year trends we are estimating. For this analysis we analyze area-averaged annual-mean temperatures for the first ensemble member (rip) for each model that contributed data to the CMIP database for which data was available for both the historical time period and the RCP. scenario, as well as data for grid cell area and land fraction. The models used are listed in Table SI-. We required that data be available for the entire 0-year period considered at any point for a trend to be calculated. Note that for a few models (GFDL and HADCM models) data was not available until about 0. Land-only averages were calculated using a weighted average of land area within each model grid cell. CMIP data analysis was performed using the RCMIP package ( Because these models are not independent and share components, and because models may not correctly represent all relevant physical processes, the statistics presented in this work cannot be interpreted probabilistically. ACCESS-0 bcc-csm- BNU-ESM CCSM CESM-BGC CESM-CAM CMCC-CM CMCC-CMS CNRM-CM FGOALS-g GFDL-CM GFDL-ESMG GFDL-ESMM GISS-E-H GISS-E-R HadGEM-CC HadGEM-ES IPSL-CMA-LR IPSL-CMA-MR IPSL-CMB-LR MIROC-ESM MIROC-ESM- CHEM MIROC MPI-ESM-MR MRI-CGCM Table SI-. CMIP model runs analyzed in this project. SI- 0 Macmillan Publishers Limited. All rights reserved.

7 0 0 0 SI. CMIP Regional Rates of Change Compared to PAGES K Data were analyzed over the same regions used for the PAGES K analysis so that regional rates of change from the CMIP trends could be directly compared to the paleoclimate estimates. The regions are given in Table SI-. Table SI- shows: ) the average across the CMIP models of regional rates of change for various years, ) the standard deviation between the rate of change with each modeled value as one data point, and ) the ratio of these two quantities (which provides a measure of the signal to noise ). We note that, as with other collections of paleo-climate reconstructions, the PAGES k data target different combinations of land and ocean. As we demonstrate below ( SI-.,.) the exact size of the region and land/ocean fraction do not have a large impact on rate of change results. The different PAGES k regions also target different seasons. PAGES k regions Europe, Asia, South America, and Australasia target the warm season, while the polar region reconstructions target annual averages. There are differences between seasonal and annual trends, with seasonal trends averaging 0% higher than annual trends for South America and Australasia and little net difference for Europe and Asia. Overall, the range in CMIP trends, when matched by seasonal target, and land/ocean average 0% larger over continental regions and 0% higher over the polar regions, as compared to the PAGES k ranges. A larger range in trends from the climate models as compared to the paleoclimate reconstructions could be due to a combination of several reasons. This could be an indication of inherent smoothing over 0-year periods in the paleoclimate records. There is a larger consistent difference in polar regions. It is possible that proxy data in these regions, for example ice cores, do not resolve trends at this scale. It is also possible that the models exhibit unrealistically large variability due to incomplete representations of dynamic processes relevant for these regions. The difference between CMIP and PAGES k when compared at the seasonal level may be, at least in part, overstated because the CMIP data consist of trends from exactly the months given in Table SI-. The PAGES k regional reconstructions, however, generally contain proxies that correspond to changes outside the target season (see Pages K supplemental material), and even seasonal proxies may have some sensitivity to temperature changes outside the target season. This difference is an interesting scientific question that should be further explored. For purposes of this work, however, we wish to consider the implications of this difference for the outcome shown in Figure (main text) for the rates of change in the near-future as compared to pre-industrial times. Consider two contrasting cases: Case ) The CMIP ensemble is an accurate estimate of variability, while PAGES k underestimates variability on 0-year time scales. SI- 0 Macmillan Publishers Limited. All rights reserved.

8 0 0 0 In this case, Figure (main text) can be interpreted directly as discussed in the main text. Case ) That the CMIP ensemble overestimates the range in temperature trends. In this case, this would imply that the CMIP pre-industrial range (0-0 in Figure, main text) is overestimated, due to too much natural variability in the model responses across the CMIP ensemble. The CMIP future range in trends, however, consists of two components: a natural variability component, and a component due to differing model responses to changes in anthropogenic forcings (e.g., greenhouse gases, aerosols, and land-use change). The change in the range into the future is due to differences in model response to anthropogenic forcings. Even if the models are overestimating natural variability, this does not imply any change in the range in model responses to changing forcing. Case implies, therefore, that the pre-industrial range should decrease relatively more than the future range. Therefore, the difference between preindustrial and future rates of change would be slightly larger than implied by Figure. In either case, our conclusion that near-term rates of change are becoming larger than pre-industrial estimates is not substantially impacted by the differences between CMIP and PAGES k. Region Definition Reconstruction Target Australasia 0-0 S, 0 E-0 E (Sept Feb) Land and ocean Arctic North of 0 N Annual Land & ocean Europe -0 N, 0 W-0 E Summer (JJA) Land only Asia. - N, 0-0 E Summer (JJA) Land only North America 0 - N, -0 W Annual Land and ocean South America 0 - S, 0-0 W Summer (DJF) Land only Antarctica South of 0 Mean annual Land only Table SI-. Regional bounds used for analysis. Regions are from the PAGES K project. Also shown are the reconstruction targets for each regional PAGES K data series. One final point, Figure SI- compares CMIP to PAGES k data over for 0-year time trends (as compared to the 0-year trends shown in the main text, Figure ). Over this period we see that the PAGES k data consistently under-report trends as compared to the CMIP models in most regions. This is not a surprising result, since the proxies used to reconstruct past temperatures are likely to have difficulties representing temperature changes at this scale due to combinations of inherent smoothing (e.g., diffusion with snow layers that ultimately form ice cores) and dating uncertainties. We conclude that, the PAGES k data appears to significantly under-report the range of temperature trends when evaluated over 0-year timescales. While there is still some consistent different SI- 0 Macmillan Publishers Limited. All rights reserved.

9 0 when 0-year trends are considered, this difference is much smaller than when 0-year trends are considered..00! 0.0! 0.00! -0.0! -.00! 0% Occurrence Range - 0 year trends! CMIP (0-0) & PAGESk! Europe Asia South America Australasia Arctic Antarctica Figure SI-. As in Figure (main text) comparing PAGES k (solid lines) with CMIP (dashed lines) for 0-year temperature trends. SI. Rates of change in CMIP simulations Figure SI- examines the strength of the CMIP temperature trend relative to the intermodel variation by showing the ratio of the average rate of change across the CMIP archive to the standard deviation between the models of the rate of change (as also given in Table SI-). During much of the historical period the deviation between the models is at least several times larger than the trend averaged across all models. This indicates that in these periods where the models do not agree on the trend, in large part due to internalvariability that is uncorrelated between models or model runs and is large compared to any consistent trends. SI-

10 Temp ROC/STDev!.0!.0!.0!.0!.0!.0! Ratio: Average T Rate of Change / STDev! Antarctica! Arctic! Asia! Australasia! Europe! North America! South America! 0 0.0! 00! 0! 000! 00! 00! Year! Figure SI-. Ratio of the average regional rate of change (absolute value) to the standard deviation between the CMIP models. All data from the CMIP archive for the RCP. future scenario. Only the first ensemble member for each climate model was used. By 000, however, the model average trend rises above the inter-model standard deviation in all regions, indicating that the trend (and increasingly dominated by anthropogenic forcing) is becoming more robust across models. The ratio between trend and inter-model deviation averages around from with the noticeable exception of Antarctica, where the relative agreement between models is much lower. SI-0

11 Average CMIP RCP. 0-year rate of change ( C/decade) Antarctica Arctic Asia Australasia Europe North America South America Global STD Dev CMIP RCP. 0-year rate of change ( C/decade) Antarctica Arctic Asia Australasia Europe North America South America Global Ratio (ROC/STD) Antarctica Arctic Asia Australasia Europe North America South America Global Table SI-. Average regional rates of change over 0-year periods from the CMIP model archive, the standard deviation between models, and their ratio. The charts below (Figure SI-) present the CMIP decadal rates of change as the -% occurrence ranges for 0-year rates of change for two time periods. First, all figures show the range for rates of change for a reference period of -0 where anthropogenic influences are relatively small (dotted lines). These are indicative of rates of change where the primary influences are natural variability and natural forcings (volcanoes and solar forcing). Each figure also shows a contrasting case showing the -% occurrence range over a recent decadal period. As shown in these figures, for 0-year periods ending in -0 rates of change are not substantially different from those a century earlier. Rates of change increase with each succeeding decade until the current time, with periods ending in 0-00, where the smallest rates of change are comparable to the largest values from a century ago. The 0% percentile of the CMIP rates of change over the current decade are larger than the % percentile from a century ago by an average of a factor of in all regions except for Antarctica. The same data is shown in several additional formats in the following sets of figures. SI-

12 0 As shown in Figure SI- below, there is generally little overlap between the rates of change ending in the 0-00 decade as compared to the rates of change in the early anthropogenic period. The % values over 0-00 are comparable to the % values over -0 except for Antarctica (with a slightly larger overlap in South America). This means that there appears to be very little chance that 0-year rates of change in most regions will be within a pre-industrial range (if we take the rates modeled change from the early anthropogenic period to be indicative of pre-industrial rates of change). There is a larger overlap for South America. Antarctica is the exception where, while the trends by 00 have shifted to be predominantly positive, there is a more substantial overlap in the range of modeled trends between these two periods. 0.! 0.! 0.! 0.! 0! -0.! Europe Asia North America South America Australasia Arctic Antarctica Range For 0-year Rates of Change! CMIP (-0 vs -000 )! -0.! Europe Asia North America South America Australasia Arctic Antarctica SI-

13 0.! 0.! 0.! 0.! 0! -0.! -0.! 0.! 0.! 0.! 0.! 0! -0.! -0.!! 0.! 0.! 0.! 0.! 0! -0.! -0.! Europe Asia North America Europe Asia North America Range For 0-year Rates of Change! CMIP (-0 vs -00 )! South America Australasia Arctic Antarctica Range For 0-year Rates of Change! CMIP (-0 vs -00)! South America Australasia Arctic Antarctica Range For 0-year Rates of Change! CMIP (-0 vs -00)! SI-

14 0! 0.! 0.! 0.! 0.! 0! -0.! -0.!.! 0.! 0.! 0! Europe Asia North America Europe Asia North America South America Australasia Arctic Antarctica Range For 0-year Rates of Change! CMIP (-0 vs -00)! South America Australasia Arctic Antarctica Range For 0-year Rates of Change! CMIP (-0 vs 00-00)! -0.! Europe Asia North South Australasia Arctic Antarctica America America Figure SI-. -% occurrence range of the global decadal rate of change over 0-year periods from the CMIP archive over a time with small anthropogenic influences (-0, dotted lines) and various more recent periods. These are annual trends over land+ocean areas for all regions. Because the CMIP archive is a sample of convenience, these percentages should not be interpreted as probabilities. The results are not sensitive to the scenario out to about 0, but after this point modeled ranges are specific to the RCP. scenario. 0.0! Europe - 0% Occurrence Range - CMIP! 0.0! Asia - 0% Occurrence Range - CMIP! 0.0! 0.0! 0.00! -0.0! -0.0! 00! 0! 000! 00! 0.0! 0.0! 0.00! -0.0! -0.0! 00! 0! 000! 00! SI-

15 0.0! North America- 0% Occurrence Range - CMIP! 0.0! South America- 0% Occurrence Range - CMIP! 0.0! 0.0! 0.0! 0.00! -0.0! 0.0! 0.0! 0.00! -0.0! -0.0! 00! 0! 000! 00! -0.0! 00! 0! 000! 00! 0.0! Australasia - 0% Occurrence Range - CMIP! 0.0! Global - 0% Occurrence Range - CMIP! 0.0! 0.0! 0.00! -0.0! 0.0! 0.0! 0.00! -0.0! -0.0! 00! 0! 000! 00! -0.0! 00! 0! 000! 00! Arctic - 0% Occurrence Range - CMIP!.0!.00! 0.0! 0.0! 0.0! 0.0! 0.00! -0.0! -0.0! 00! 0! 000! 00! Antarctica - 0% Occurrence Range - CMIP! 0.0! 0.0! 0.0! 0.00! -0.0! -0.0! 00! 0! 000! 00! Figure SI-. -% ranges for 0-year annual trends (land+ocean areas) ending in decadal periods from for the RCP. scenario in the CMIP archive. (e.g., The range shown for 00 is the range for all 0-year trends that end over the period ) Note that not all y-axes have the same scale. SI-

16 SI Cumulative Occurence (%) Rate of Change ( C/decade) End Years 00 Post 00 Pre 00 Europe LandOcean annual Cumulative Occurence (%) Rate of Change ( C/decade) End Years 00 Post 00 Pre 00 Asia LandOcean annual Cumulative Occurence (%) Rate of Change ( C/decade) End Years 00 Post 00 Pre 00 North America LandOcean annual Cumulative Occurence (%) Rate of Change ( C/decade) End Years 00 Post 00 Pre 00 South America LandOcean annual

17 SI Cumulative Occurence (%) Rate of Change ( C/decade) End Years 00 Post 00 Pre 00 Australasia LandOcean annual Cumulative Occurence (%) Rate of Change ( C/decade) End Years 00 Post 00 Pre 00 Arctic LandOcean annual Cumulative Occurence (%) Rate of Change ( C/decade) End Years 00 Post 00 Pre 00 Antarctica LandOcean annual Cumulative Occurence (%) Rate of Change ( C/decade) End Years 00 Post 00 Pre 00 Global LandOcean annual Figure SI-. Cumulative occurrence graphs for CMIP model runs. The graphs show the distribution of 0-year annual trends (land+ocean areas) that end in decadal periods from - 00 through Intervals before are shown in grey, the interval is in red, and latter intervals are in blue. Note that the y-axes have different scales. SI. Rates of change CMIP: region size sensitivity The regions used for the above analysis vary in size and location. We performed a test to 0 examine how the size of the analysis region impacts the results for rates of change. We examined trends for two of the regions defined in Table SI-, which were designed to test if doubling the regional area, or shifting a region to include more ocean area (to approximately half of the regional area) consistently impacted the distribution of trends.

18 Region North America Large-Land Smaller-Land Large-LandOcean Smaller-LandOcean Definition 0- N, 0-0 W -0 N, - W 0- N, - W -0 N, 0-0 W 0 E Asia Large-Land -0 N, 0-0 E Smaller-Land 0-0 N, 0- W Large-LandOcean -0 N, 00-0 W Smaller-LandOcean 0-0 N, 0- W Table SI-. Regional bounds used for the sensitivity analysis. The trends for these test regions are shown in Figure SI-0. Overall, the impact of doubling the size of a region or shifting a region to include more ocean area (Figure SI- 0) is much smaller than the differences in the distribution of trends across regions. There is a slightly larger impact for the changing the size of the North American region (average % larger trends for the smaller region) as compared to East Asia (% larger trends). The impact of shifting about half the region to ocean is smaller (% in N America and -% in E Asia). This means that, while the differences in size between the PAGES k regions may have some impact on differences in temperature trends between regions, more fundamental regional differences in climate patterns are generally a much larger determinant of differences between regions. Additional differences in the case of the PAGES k data can arise due to a different mix of proxies between regions (given that different types of proxies respond differently to temperature) and spatial and temporal sampling differences. The CMIP data represent uniform, consistent sampling, albeit subject to whatever biases may be present in the models. 0.0! 0.0! 0.0! 0.00! -0.0! 0% Occurrence Range - 0 year trends! CMIP (0-0) & PAGESk! N America-S-Land! N America-S-LandOcean! N America-L-Land! N America-L-LandOcean! East Asia-S-Land! East Asia-S-LandOcean! East Asia-L-Land! -0.0! East Asia-L-LandOcean! -0.0! Figure SI-0. Sensitivity of rates of change to region size and land/ocean fraction. SI-

19 0 0 0 A similar relatively small difference is seen for most of the PAGES k regions if land only trends are considered as compared to land-ocean. Land-only trends average ~0% larger for Europe, Asia, and South America, and % smaller for the polar regions. An exception is Australasia where land-only trends are 0% larger than land+ocean trends. SI. Rates of change CMIP: hemispheric land/ocean/size sensitivity We also examined how rates of change vary at hemispheric scales. This comparison is particularly relevant when comparing hemispheric-scale paleoclimate reconstructions (e.g. Figure main text, Figure SI- through Figure SI-). Figure SI- shows the average rate of change from the CMIP models over several hemispheric-scale areas: northern hemisphere land, northern hemisphere land between 0-0 N, northern hemisphere land+ocean, southern hemisphere, and the globe. Figure SI- shows the standard deviation between the CMIP model runs for the rates of change. This shows the relative size of modeled excursions from the average value. Figure SI- shows the - 0% occurrence intervals for the CMIP models over these different regions. Overall, the differences are as expected, with slightly more rapid changes over land areas as compared to land + ocean, and the smaller 0-0 N land area showing a slightly larger rate of change compared to the entire northern hemisphere. The amount of variability between models (which represents unforced internal variability) also shows the same pattern. Considering the range in rates of change in the CMIP models over the early part of the industrial era where anthropogenic influence is modest (Figure SI-), rates of change over the Northern Hemisphere land and ocean range roughly ±0. C/decade, with rates of change increasing to ±0. C/decade for 0-0 land areas, and a further, somewhat larger range, ±0. C/decade, for the North American land area defined in Table SI- (which is smaller than the total North American land area). Over periods of weak forcing trends (e.g., little anthropogenic forcing), rates of change are driven by a combination of internal variability and changes in forcing, particularly volcanic events. We conclude that, under conditions of weak external forcing, rates of change are surprisingly similar over hemispheric-scale regions, with the rate of change increasing from ±0. to only ±0. C/decade as one moves from northern hemisphere land+ocean to 0-0 northern hemisphere land-only. In all cases, rates of change are much larger in the st century as compared to the historical period no matter what the comparison region, where the average NH Land+Ocean rate of change reaches 0. C/decade and the rate of change over the NH land averages around 0. C/decade. The absolute differences in the historical period between the various northern hemisphere regions considered (e.g. Figure SI-) is far SI-

20 0 smaller than the increase in rates of change that occur by 00 to the middle of the st century. We note that the variance between models has a sharp increase starting about 0-00, which shows a maximum about 0. This is particularly prominent for Northern Hemisphere land areas. It is perhaps not surprising that models exhibit a wide range of behaviors under the strong increases in forcing over this time period. This does indicate, however, that our knowledge of rates of change in the near-term is particularly uncertain. While the near-term behavior is not sensitive to the scenario, the peak in inter-model variance at 0 is likely due to radiative forcing starting to stabilize in the RCP. scenario. The northern hemisphere land+ocean rates of change average about 0% larger than the global rates of change in the CMIP dataset over the early anthropogenic period (- 0). 0.! SDev Average NH Land And Ocean! 0.! NH_Land! NH0-0_Land! 0.! Global! 0.! SH Land and Ocean! 0.! 0.0! -0.! 0! 00! 0! 000! 00! 00! Year! Figure SI-. Sensitivity of rates of change over various hemisphere-scale areas for the CMIP ensemble. Graph shows rate of change over a 0-year period ending in the year shown. SI-0

21 0.! NH Land And Ocean! NH0-0_Land! NH_Land! Global! SDev - Rate of Change! 0.! S Hemisphere! 0.0! 0! 00! 0! 000! 00! 00! Year! Figure SI-. Graph shows the standard deviation between CMIP models for the 0-year rate of change. 0.! 0.! 0.! 0.0! 0! -0.0! -0.! -0.! -0.! 0% Occurrence Range! CMIP (0-0)! NH Land & Ocean! 0-0 N Land! NH Land! N America Land! Figure SI-. Graph shows the -% range for rates of change for CMIP models over several Northern Hemisphere areas, plus the North America region as defined above (Table SI-). SI-

22 0 SI. Comparison of 0, 0, 0, and 0-year rates of change CMIP 0-year rates of change were selected for analysis in this work because it is over this timescale that rates have change are not substantially different from historical values. Figure SI- shows the % ranges for 0-year trends, 0-year trends, and 0-year trends for Europe. While 0-year rates of change are shifted above historical values, these are more strongly impacted by natural variability and the overall shift is not as strong and 0-year cooling periods still occur in the CMIP archive even in 00. The change for 0-year rates is more evident, with periods of cooling occurring less than % of the time in the CMIP model runs by 00. By the decade ending in 00, 0-year rates of change only slightly overlap with rates from the early anthropogenic period (Figure main text). The difference is stronger still for 0-year trends. Note that rates in the figure below show the range for all trends ending over a 0-year span (for each bar in the graph), while Figure in the main text shows a summary of rates over all 0-year periods from -0 in order to show statistics over a wider period to reduce noise (and also to be more comparable to the longer PAGES k data). Europe - 0% CMIP Range - 0 year trends! 0.! 0.! 0.! 0.! -0.! -0.! Europe - 0% CMIP Range - 0-years! 0.! 0.! 0.! 0.! 0.! -0.! -0.! -0.! 00! 0! 000! 00! -0.! 00! 0! 000! 00! Europe - 0% CMIP Range - 0-year trends! Europe - 0% CMIP Range - 0-years trends! 0.! 0.! 0.! -0.! -0.! 0.! 0.! 0.! -0.! -0.! -0.! 00! 0! 000! 00! -0.! 00! 0! 000! 00! 0 Figure SI-. Graph shows the -0% range for rates of change for CMIP models over Europe (as defined in Table SI-). Note the different scales for some time periods. SI Adjusted Observational Estimates of Near-Term Rates of Change Figure SI- shows the 0-year average rate of change for the recent historical data using the adjusted global temperature time series from Rahmstorf et al. (0), who use SI-

23 correlation analysis to remove the influence of volcanic eruptions, solar irradiance changes and ENSO. With these influences removed, the rate of change shows much less variability than in the raw record, although there are still noticeable differences in the average rate of change over this period between these datasets. The adjusted HADCRU dataset shows a consistent slow increase over this period, while the UAH dataset shows an acceleration, and the NCDC data showing a fairly steady rate of change. These differences could be intrinsic to each dataset or an artifact of the statistical adjustment procedure. 0.! Rate of Temperature Change (0-year average)! 0.! 0.! 0.! CMIP Ave! giss! ncdc! cru! rss! 0 0 uah! Cent Proj! Proj - Min! Proj - Max! 0.0!!!! 00! 00! 00! 00! 00! 0! 0! 0! Year! Figure SI-. Rate of global temperature change for historical observations with the influence of volcanoes, solar irradiance changes, and ENSO removed (Rahmstorf et al 0). Each of the lines extending from through 0 represents a different historical dataset from Rahmstorf et al. For dataset key see Rahmstorf et al (0). The central, high, and low estimates from the GCAM RCP. scenario over 0-year periods are also shown as thick black lines. Also shown, as an X, is the average rate of change over -00 from models in the CMIP archive from Cohen et al. (0). In examining these data, we note that rates of change over 0-year periods do not show a strong anthropogenic signature (e.g., Figure SI-, Figure SI-). So it is perhaps not surprising that model results do not agree with observational estimates over this period. The estimates from Rahmstorf et al (0) attempt to remove known components of natural variability, but trends over this time could still be influenced by factors not accounted for in their analysis. While the rate of change for the adjusted HADCRU data is within the sensitivity range from the simple model (Figure, main text), the recent rates of change from some of the other historical temperature datasets are, at times, slightly lower than our lower bound. Figure SI- highlights the difference in warming rates between observations and even SI-

24 0 0 0 complex models, such as the differences between observations and the CMIP estimates as noted by Cohen et al. (0). Possible reasons for these differences include changes in stratospheric ozone (Solomon et al 00) and increased heat uptake in the upper (Guemas et al. 0) and deep (Balmaseda et al. 0) ocean. We note also that our simple model sensitivity study did not consider different assumptions for the (long-term) rate of ocean heat uptake (Ross et al. 0), which would widen our range. It is difficult to draw firm conclusions about the implications of these comparisons given our lack of understanding of the drivers of these decadal trends. This is an active area of research. Resolving these differences is critical for future work to better project near-term rates of climate change. SI Methods: Climate model and aerosol forcing MAGICC Climate Model * Greenhouse gas concentrations, radiative forcing, and global climate changes for the diagnostic simple climate model simulations in this paper (Figure, main text) are modeled with the GCAM. version of the MAGICC. model. MAGICC is a simple mechanistic model that includes the ocean and terrestrial carbon-cycle (including CO and temperature feedbacks), parameterized representations of atmospheric chemistry (tropospheric ozone and methane oxidation), a comprehensive suite of radiative forcing agents, differential land-ocean climate sensitivity, and an upwelling-diffusion representation of ocean heat transport (Wigley and Raper, Wigley and Raper 00, Raper and Cubasch ). Using input data on anthropogenic emissions (starting in 0) and historical concentrations and aerosol emissions, MAGICC estimates changes in top of the atmosphere radiative forcing (starting in ), and the subsequent changes in global temperature change in four boxes (land/ocean north and southern hemisphere). The assumed historical values for these forcings are shown in Table SI-. The GCAM model, including the MAGICC component, is described on-line, and also is available as open source software at: The version of the MAGICC model used here is version., re-coded in C++. The major change in this version is, unlike the original version of MAGICC., black carbon (BC) and organic carbon (OC) forcing are explicitly represented. These forcings are assumed to be proportional to anthropogenic emissions of BC and OC and are calibrated to central values of forcing per unit emission from Bond et al. (0). Base year radiative forcing assumptions are shown in Table SI-. Note that future forcing for land albedo, mineral * Portions of this material also appear in the supplementary material for Smith et al. (0b). SI-

25 dust, and nitrate aerosols are assumed constant since reduced form representations of these forcing agents have not been incorporated into this version of MAGICC. Forcing Agent Carbon Dioxide (CO).. Methane (CH) Nitrous Oxide (NO) Fluorinated Gases Trop. Ozone (inc. CH component) Component from CH: 0. Montreal Protocol gases Direct Sulfate Cloud indirect - Sulfate Organic Carbon (OC, including indirect) Black Carbon (BC, inc. on Snow and indirect) Stratospheric Ozone Recovery Stratospheric HO forcing f/ CH oxidization Total Forcing RCP Categories.0. Land albedo Mineral dust and Nitrates Total Anthropogenic Forcing Table SI-. Year 000 and 00 anthropogenic forcing in GCAM. (W/m ). Radiative forcing as used in the scenario definition nomenclature (e.g.,. Stabilization and. overshoot) follows the definition used in the RCP scenarios and includes: all well-mixed greenhouse gases, tropospheric and stratospheric ozone changes, stratospheric water vapor feedback, sulfate aerosols, cloud indirect effect, black carbon (BC), and organic carbon (OC). Land albedo, mineral and nitrate aerosols were not included in this total. While the MAGICC model used here includes these components, they are assumed to be constant over the st century at -0. W/m. Note that equilibrium temperature change would, therefore, be proportional to the climate sensitivity times the RCP forcing minus 0. W/m. The model results used in this work do not include estimates of solar and volcanic forcings, which have a negligible impact on future results if the magnitude of these forcings in the future are similar to historical estimates. Historical Emissions Historical emissions for all substances use estimates from the GCAM. model input database up to 00. These estimates are derived from country level inventory data (UNFCCC emissions reports, UK DEFRA, Environment Canada, Eyring et al. (00), US EPA), with values from the EDGAR. database where country-level inventories SI-

26 0 0 0 were not available, Meinshausen et al. (0) for fluorinated gases, and Smith et al. (0a) for SO (and extended beyond 00 as described below). Emissions are identical for all scenarios until 0. For comparability with the CMIP GCM results, the MAGICC simple climate model results shown in the text also include the impact of historical changes in solar and volcanic forcings from Lean et al. () and Ammann et al. (00) implemented as annual external forcing inputs for the MAGICC model following Wigley (00). Sensitivity Test Assumptions Our sensitivity test was designed to examine the rate of future climate change under a range of climate model assumptions. We have varied two parameters, the climate sensitivity and the current strength of aerosol forcing. The climate sensitivity is varied over a range from. to. C per CO doubling in 0. increments. This was identified as the likey range by Collins et al. (0), where likely is defined as a % probability of the actual value lying within this range. Aerosol forcing is particularly uncertain and here we use constraints based on observational data to bound total aerosol forcing. A formal assessment of the probable range of aerosol forcing using observational constraints has not been done, so we draw on results from studies using a variety of methods that use observational data to constrain climate parameters, as summarized in Table SI- and further discussed in Smith and Bond (0). In order to explore a plausible range of results, we define three sets of aerosol forcing parameters: Strong, Central, and Weak. Based on the results in Table SI-, we use, as sensitivity ranges, a Weak value for aerosol forcing of -0. W/m and a high estimate of -. W/m (the median from the values quoted above). Similar to the range of climate sensitivity used, values outside of this range cannot be excluded, but these studies indicate that agreement with observations is more difficult to achieve with aerosol forcing outside of this range. While there are multiple ways in which specific aerosol components can be combined to achieve any given total aerosol forcing value (Smith and Bond 0), the values given in Table SI- are used for the diagnostic studies in this work. Note that nitrate and mineral dust forcing is constant over the st century in the version of the MAGICC model used here. Black and organic carbon forcing values are derived from Bond et al. (0), with BC and OC forcing proportional to total emissions minus the assumed pre-industrial background biomass burning emissions (taken to the 00 value from Lamarque et al. 00). We note that higher central values for black and organic carbon forcing are suggested by a recent assessment (Bond et al. 0), with the higher forcing values (as compared to Bond et al 0) stemming largely from a finding SI-

27 0 that primary emissions may be larger than indicated in the inventories. The associated uncertainty bounds have also increased substantially. In order to stay within the observational bounds for total aerosol forcing, increasing black carbon forcing would require increasing forcing for some other aerosol component. Observational attribution studies and inventory estimates that reflect these higher black carbon forcing values have yet to be completed. Total Aerosol Forcing Source Strong Central Weak Murphy et al. (00) - σ Andronova and Schlesinger (00) Stott et al (00) Forest et al (00) Shindell & Faluvegi Table SI-. Total historical aerosol forcing ranges estimated from several studies. Ranges represent a -% range (W/m ). The periods covered by each study vary, but generally represent anthropogenic forcing up to around the year 000. See discussion in Smith and Bond (0). Year 000 Aerosol Forcing Aerosol Component Strong Central Weak Sulfate Direct Sulfate Cloud Indirect Black Carbon (direct + indirect) Organic Carbon (direct + indirect) Nitrate and Mineral Dust Total Aerosol Table SI-. Specific assumptions used for aerosol sensitivity cases used in this work (W/m ). All combinations of climate sensitivity and strong, central, and weak aerosol forcing were examined. We note that there is likely to be some correlation between climate sensitivity and aerosol forcing. Because we find that uncertainty in aerosol forcing has a relatively small impact on future rates of change, which are the focus of this work, we did not attempt to incorporate correlations between climate sensitivity and aerosol forcing assumptions as these would not alter the conclusions of this analysis. SI-

28 0 SI Simple Model Decomposition Analysis and Comparison To CMIP Figure SI- shows the MAGICC decomposition analysis for the RCP. scenario (Van Vuuren et al. 0). This analysis was performed in the same manner as described in the main text for the RCP. scenario and illustrates the relative importance of assumptions for climate sensitivity and aerosol forcing. This result can be directly compared with Figure in the main text. Only a stronger climate policy, such as this. W/m peak and decline scenario, can begin to reduce global-mean temperatures by the end of the century. Even under such strong climate mitigation, rates of climate change are above historical values until mid-century, by which point rates of change decline and are negative by 00 as global temperatures begin to decrease. Note that under strong mitigation, the range in rates of change in the nd half of the century are roughly equally impacted by climate sensitivity (whose impact is substantially decreased from the range seen at this point in RCP. and higher forcing scenarios) and aerosol forcing uncertainty (whose range is similar to that in the RCP. scenario). The near-term rates of change are not very sensitive to the emissions scenario chosen. 0 year rate of change RCP. Aerosol sensitivity Climate sensitivity Rate of Change ( C/decade) Central Case Maximum Minimum Observed Year Figure SI-. As in Figure (main text) for the RCP. scenario. Global rates of temperature change over 0-year periods from the MAGICC model using: (top) the RCP. scenario and (bottom) the GCAM. W/m climate stabilization scenario. Results are shown for: central climate assumptions (thick solid line), range due to uncertainty in aerosol forcing (grey shading), and range due to uncertainty in climate SI-

29 0 sensitivity (blue shading). The outer bounding cases are shown as dotted lines. The thin solid black line shows the historical rate of change using the HADCRU observational data. The vertical dashed line indicates 0 Figure SI- shows the MAGICC decomposition analysis for the RCP. scenario, as in Figure of the main text, with results from individual CMIP models overlaid. We see that the CMIP model range matches well the sensitivity range from the MAGICC simple model. There are a few CMIP models that show a very small temperature increase over the st century that are outside of the MAGICC sensitivity range. It is not clear if these models show a lower rate of climate change due to different dynamics, or due to different forcing representation (e.g., models that lack aerosol indirect forcings, might show a lower rate of temperature increase over this period of rapid SO emissions decreases in the RCP. scenario). 0 year rate of change RCP. Rate of Change ( C/decade) Aerosol sensitivity Climate sensitivity Central Case Maximum Minimum Observed Year Figure SI-. Rate of global temperature change for HADCRU historical observations (thin black line), range from simple climate model (blue and grey shaded areas, as in Figure, main text), and individual CMIP global climate model simulations (green lines), showing first ensemble member only for each model.! We note that a number of CMIP models do not reproduce the observed historical cooling trend that peaked over about. The simple model brackets this trend well. SI-