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In their seminal paper, Schleussner et al. (2016) provide a comparison of projected climate change risk at warming levels of 2 °C and 1.5 °C above pre-industrial levels, using five Global Circulation Models (GCMs) to capture regional uncertainty in climate projection. The present study complements this and the more recent Byers et al. (2018) study well by covering coastal flooding and disease risk, and by applying a different modelling approach to simulate agricultural impacts, water stress and flooding (Supplementary Methods) as well as providing an independent analysis of hotspots. Arnell et al. (2016) use an approach based on damage functions and projections from 25 GCMs to explore heat extremes, water resources, fluvial and coastal flooding, agriculture and energy use rather than detailed model simulations as in this study, while the damage functions applied themselves emerge from different underlying impact models. Byers et al. (2018) provide a risk assessment and hotspot analysis for a similar range of sectors and metrics across a consistent set of climate change scenarios from 5 GCMs, exploring a wider range of future socioeconomic scenarios than was possible in this study but is based on the analysis of existing databases of projected risk metrics such as ISIMIP-Fast Track (Warszawski et al. 2014), whereas in this study, we explicitly create our own risk simulations using sophisticated modelling approaches. Piontek et al. (2014) previously explored hotspots, but unlike Byers et al. (2018) and our study, based the analysis on a comparison of regional changes in hazards emerging from the projections of three GCMs only, without considering population exposure.
Risk analysis carried out in this study using sWBGT finds hundreds of millions of people to be additionally affected by heat stress at each (successively higher) warming level. This result is consistent in magnitude with other recent studies, such as Matthews et al. (2017) who project 350 million more megacity region inhabitants to be exposed to deadly heat by 2050 for an end of century warming level of 1.5 °C. Andrews et al. (2018) also project hundreds of millions of people to be exposed to extreme heat for warming levels of 1.5 °C and above. As has been shown in other multi-sectoral impacts, studies which include humid heat metrics (e.g. Byers et al. 2018) projected heat exposure is most pronounced in the tropics, and as such, we identify benefits of reduced exposure associated with limiting warming in low-latitude regions. Our study focuses on applying targeted climate scenario data to calculate global (combined urban and rural) population heat stress using the sWBGT metric. While sWBGT can produce an overestimate of heat exposure risk during cloudy or windy conditions and vice versa, Willett and Sherwood (2012) argue that changes in solar radiation and wind speed are unlikely to impact significantly on global patterns. Population impacts of exposure to heat stress will depend on the activity of the person concerned and the choices that they make.
The constantly reducing crop yields we obtain under increasing global temperature align well with results in the existing literature using both process-based models and statistical models (see SM), although compared to these studies, our results appear to be conservative. Projections for regional changes in crop yields are consistent with a previous study (Schleussner et al. 2016) identifying Africa, SE Asia, and C&S America as hotspots for projected declines in yield and indeed for projected avoided risks if warming is limited to 1.5 °C rather than 2 °C (Figure 2). Equally, they are well aligned with the hotspots identified by Byers et al. (2018) and Piontek et al. (2014). Similar to Arnell et al. (2016), our results indicate that reductions of maize yield in all regions and soybean yields are projected to potentially increase in Europe, North America and Australasia but to decline in other regions. In case of wheat, we project declines in all regions, while Arnell et al. (2016) obtained mixed results. We suspect that this is due to the different types of wheat that were analysed. For rice, our projections indicate strong losses in Africa and South-East Asia but increasing yields in Europe and Australasia. Limiting global warming to 1.5 °C rather than 2 °C would provide benefits for most regions across the globe, particularly in the Americas, Europe and Africa (Figure 2), which is also in line with the findings of Arnell et al. (2016). Overall, our results suggest an inequality in risk of crop yield loss between the Northern and Southern hemispheres and especially tropical and non-tropical regions. The main limitation of the models used here is that they are based on unevenly spaced national data and that the area harvested was assumed to remain constant so that potential future land use change is not accounted for.
There are some notable differences however between the various studies. Byers et al. (2018) project larger risks in China than in our own study, probably because of the inclusion of a greater number of land-based climate change risks than in our own study. Similarly, Arnell et al. (2016) estimate heat events and hydrological drought irrespective of human exposure, identifying Australasia as an area at particular risk, which does not emerge from our study. This is because our study focused heavily on population exposure to hazards, and hence areas experiencing large increases in drought or water scarcity but with low human population do not emerge strongly in our analysis.
In general, there is good agreement with other studies, although our projections of climate change impacts on crop yields and exposure to heat stress may be relatively conservative. Projected risks to biodiversity which were beyond the scope of this study will also be important and will interact, via loss of ecosystem services, with the projected risks to human systems estimated here (Fischlin et al. 2007; Warren et al. 2013, 2018).
RWarren designed the study, wrote the paper and coordinated the research; OA produced the projections for heat stress exposure and contributed to the paper; SB produced the projections for coastal flooding and contributed to the paper; FC-G produced the projections for dengue and malaria exposure and contributed to the paper; NF produced the projections for crop yield and drought, managed the project database and contributed to the paper; DVV and DG provided the climate change scenarios and contributed to the paper; PG produced the matching sea level rise scenarios and contributed to the paper; IH downscaled the climate data and contributed to the paper; YH and DM produced the projections for water stress and fluvial flooding and contributed to the paper; CH produced the projections for economic damage and contributed to the paper; TO advised on the production of monthly and daily climate change projections and contributed to the paper; JP supervised the work on crop yield and drought and contributed to the paper; RWright drew the figures.
California experienced a particularly drawn-out drought from December 2011 to March 2019, broken in part by the wettest winter in the United States. 2020 saw widespread, prolonged drought that was exacerbated by heat waves in more than a dozen Western and Central states. The intense drought and heat combined to wither vegetation, intensifying Western wildfires that burned record acreage.
Nationwide, conditions reached their peak in December 2020, when the greatest extent of land since 2012 was under extreme drought conditions. In the West, drought has continued and intensified in 2021, and has been exacerbated in the Pacific Northwest by record heat.
Displays flood and flash flood reports as well as intense rainfall observations for user-selectable time ranges and customizable geographic regions. Includes ability to download reports and associated metadata in csv format. GEFS Probabilities Plots of GEFS probabilistic forecast of precipitation, temperature, and sea-level pressure exceeding various thresholds. 2b1af7f3a8