The hotspot patterns for the three future time periods of RCP8.5 and RCP4.5 are shown in Fig. 1. The dominant global hotspot pattern emerges early in the 21st century, with areas of the Amazon, the Sahel and tropical West Africa, Indonesia, and the Tibetan Plateau emerging early in the 21st century and exhibiting relatively high aggregate climate change in all three periods of both forcing pathways. In addition, areas of southern Africa, the Mediterranean, the Arctic, and Central America/western North America emerge at intermediate and/or high levels of forcing, while areas of southern South America, Australia, the Indian Peninsula, and East Asia exhibit relatively small – but increasing – aggregate climate change throughout the 21st century (Fig. 1).
The aggregate hotspot patterns reflect the pattern and magnitude of changes in the mean, variability and extremes of seasonal temperature and precipitation (Figs. 2, 3, S1 and S2). The regions that show the strongest aggregate climate changes exhibit large values of relative change in a number of different climate indicators (Fig. 2 and S2). For example, in the 2080–2099 period, the Amazon exhibits areas of relatively large changes in JJA mean precipitation (Figs. 2 and S2), DJF and SON precipitation variability (Fig. S2), DJF and MAM temperature variability (Fig. S2), and DJF, MAM, JJA and SON extreme dry seasons (Fig. S2). Likewise, northeast Eurasia exhibits areas of relatively large changes in DJF, MAM and SON mean temperature (Figs. 2 and S2), DJF and MAM mean precipitation (Figs. 2 and S2), and DJF, MAM, JJA and SON extreme wet seasons (Fig. S2).
Comparisons of different periods of the two forcing pathways suggest that the pattern of aggregate change is fairly robust to the level of global warming below approximately 2 °C of global warming (relative to the late-20th-century baseline), but not at the higher levels of global warming that occur in the late-21st-century period of the RCP8.5 pathway (Fig. 1). For example, the tropical regions exhibit the greatest relative change throughout the RCP4.5 pathway, with much of central Africa exhibiting increases in aggregate change from approximately 1.2 during the 2016–2035 period of RCP4.5, to approximately 1.9 during the 2046–2065 period of RCP4.5, to approximately 2.1 during the 2080–2099 period of RCP4.5. In contrast, the high latitudes consistently exhibit smaller relative aggregate change than the tropics throughout the RCP4.5 pathway, with broad areas of the Arctic exhibiting increases in aggregate change from approximately 0.7 during the 2016–2035 period of RCP4.5, to approximately 1.3 during the 2046–2065 period of RCP4.5, to approximately 1.6 during the 2080–2099 period of RCP4.5. The pattern of greatest relative aggregate change occurring over tropical regions is also seen during the 2046–2065 period of RCP8.5, when median global warming is larger than in the 2080–2099 period of RCP4.5 (Rogelj et al. 2012)). However, the highest values of relative aggregate change occur much more broadly during the late-21st-century period of RCP8.5, with central Africa and Indonesia both exhibiting lower aggregate values (up to 2.5) than the Arctic (up to 3.0), the Mediterranean (up to 2.9), the Sahel (up to 2.9), the Amazon (up to 2.8), Southern Africa (up to 2.8), and Tibet (up to 2.8).
The apparent acceleration of relative aggregate climate change over areas of the extra-tropics at high levels of global warming (Fig. 1) results in part from the fact that intensification of extreme hot season occurrence emerges most strongly over the tropics in the early- and mid-21st century periods of both forcing pathways, but emerges equally strongly over most areas of the globe by the late 21st century of RCP8.5 (Figs. 3 and S3). For example, the occurrence of extreme hot seasons over tropical Africa, tropical South America, and Indonesia is at least twice as large as the occurrence over most mid- and high-latitude areas in the 2016–2035 period. This regional contrast is almost entirely eliminated in the 2080–2099 period of RCP8.5 as extreme hot seasons become common over all inhabited land areas, meaning that differences in the relative metric are instead shaped by other climate indicators.
In addition to climate change hotspots, our metric also identifies areas that exhibit relatively small aggregate response to global warming. For example, southern South America and the Indian Peninsula consistently exhibit reduced magnitude of change in mean, variability and extremes of temperature and precipitation relative to other areas of the globe (Figs. 3 and S2), suggesting that those regions could face reduced risk of increasing climate-related stresses. However, areas that exhibit relatively low aggregate change could still be vulnerable to climate changes that occur in response to continued global warming. For example, in the 2080–2099 period of RCP8.5, at least 65 % of all seasons are extremely hot over all land areas, and at least 80 % of all seasons are extremely hot over most land areas (Figs. 3 and S2). Frequent extreme heat could have substantial impacts on natural and human systems (e.g., (Ciais et al. 2005; White et al. 2006; Schlenker and Roberts 2009; Toomey et al. 2011; Diffenbaugh et al. 2012)), regardless of the global pattern of relative aggregate climate change.
Although the hotspot patterns appear to be robust to varying levels of greenhouse forcing (Fig. 1), the results are subject to other sources of uncertainty. For example, although the CMIP5 ensemble is unprecedented in its scope, the number of models and of realizations is insufficient to span the full range of uncertainty in global climate sensitivity and regional response to global warming (Taylor et al. 2012). As a result, although we have attempted to give all models equal contribution to the ensemble mean (see SI), the results reported here could be sensitive to the number of models included in the ensemble, and to the number of realizations of each model. In addition, internal climate system variability could overwhelm the identified climate change patterns for time scales that are shorter than the multi-decadal scales explored here, meaning that individual decadal or sub-decadal periods could show different patterns of aggregate climate anomalies.