Will population exposure to heat extremes intensify over Southeast Asia in a warmer world?

Abstract

Temperature extremes have increased during the past several decades and are expected to intensify under current rapid global warming over Southeast Asia (SEA). Exposure to rising temperatures in highly vulnerable regions affects populations, ecosystems, and other elements that may suffer potential losses. Here, we evaluate changes in temperature extremes and future population exposure over SEA at global warming levels (GWLs) of 2.0 °C and 3.0 °C using outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Results indicate that temperature extreme indices are projected to increase over SEA at both GWLs, with more significant magnitudes at 3.0 °C. However, daily temperature ranges (DTR) show a decrease. The substantial increase in total SEA population exposure to heat extremes from 730 million person-days at 2.0 °C GWL to 1,200 million person-days at 3.0 °C GWL is mostly contributed by the climate change component, accounting for 48%. In addition, if the global warming is restricted well below 2.0 °C, the avoided impacts in population exposure are prominent for most regions over SEA with the largest mitigation in the Philippines (PH). Aggregate population exposure to impacts is decreased by approximately 39% at 2.0 °C GWL, while the interaction component effect, which is associated with increased population and climate change, would decrease by 53%. This indicates the serious consequences for growing populations concurrent with global warming impacts if the current fossil-fueled development pathway is adhered to. The present study estimates the risks of increased temperature extremes and population exposure in a warmer future, and further emphasizes the necessity and urgency of implementing climate adaptation and mitigation strategies in SEA.

Increased moist heat stress risk across China under warming climate

“…However, sparse distribution of CMA station observations in western China when compared to the eastern China results in relatively lower quality of CLDAS data in the western than in the eastern China 33 . Besides, we spatially downscaled and fused the CMIP6 data, uncertainty is still unavoidable in the CMIP6 data under different climate models due to different model types, parameterization schemes, and spatial resolutions 14,31 . What's more, the GLASS leaf area index data we used from 1998-2018 are mainly based on the results of satellite inversion, which have relatively higher accuracy and are better than MODIS and AVHRR.…”

“…We integrated the exposure of MHS, population, GDP and LAI under different scenarios to represent the spatiotemporal evolutions of MHSR. Yin et al analyzed the spatiotemporal distribution and risk of heat waves by apparent temperature, population and NDVI data, and the results showed that the eastern part of China deepens economic development while taking heat wave risk into account 31 . Here we better reflected the spatial variations by CEEMDAN method under different scenarios.…”

To what extent horizontal resolution improves the simulation of precipitation in CMIP6 HighResMIP models over Southwest China?

“…Nowadays, benefiting from the Coupled Model Intercomparison Project (CMIP) established and promoted by the World Climate Research Program (WCRP), global climate models (GCMs) have become available tools for understanding current and future climate change variations (Li et al, 2013;Sillmann et al, 2013;Stanfield et al, 2016;Sun et al, 2022). Previous studies have shown that the models commonly overestimate precipitation in mountainous areas relative to observed data, especially in the eastern of the TP (Su et al, 2013;Lin et al, 2018;Luo et al, 2022).…”

“…This indicates that the uncertainty of simulated precipitation is still relatively large over the SWC. In addition, the model resolution is considered as the one of primary elements influencing the performance in simulating precipitation (Sun and Ao, 2013;Kim et al, 2019;Xie and Wang, 2021). The resolution is too coarse to reproduce the important processes and features within the regional scale (Xu et al, 2017;Bonekamp et al, 2018), and precipitation events related to complex terrain could not be captured (Ménégoz et al, 2013;Liu et al, 2018;Schneider et al, 2018;Duan et al, 2019).…”

How Striking Is the Intergenerational Difference in Exposure to Compound Heatwaves Over Southeast Asia?

“…Undergoing rapid economic, environmental and demographic change maybe one of the most important development challenges facing the region (Weiss, 2009). The frequency of weather and climate extremes has shown an upward trend during the last several decades (Ge et al, 2019;Sun et al, 2022). The Asian-Australian monsoon (Chang et al, 2005;Chevuturi et al, 2018;Ge et al, 2021b;Wang et al, 2004) and the El Niño-Southern Oscillation (Ge et al, 2017;Juneng & Tangang, 2005;Lin & Qian, 2019;Thirumalai et al, 2017) significantly modulate the climate conditions over the SEA mainland-for example, precipitation and temperature in Indonesia are mainly associated with the cycle of the El Niño-Southern Oscillation and its signal is especially strong in areas with a monsoonal climate (Irawan, 2002).…”