Projected changes in temperature and precipitation over mainland Southeast Asia by CMIP6 models

Abstract

Five mainland SEA countries (Cambodia, Laos, Myanmar, Vietnam, and Thailand) are threatened by climate change. Here, the latest 18 Coupled Model Intercomparison Project Phase 6 (CMIP6) is employed to examine future climate change in this region under two SSP-RCP (shared socioeconomic pathway-representative concentration pathway) scenarios (SSP2-4.5 and SSP5-8.5). The bias-corrected multi-model ensemble (MME) projects a warming (wetting) over Cambodia, Laos, Myanmar, Vietnam, and Thailand by 1.88–3.89, 2.04–4.22, 1.88–4.09, 2.03–4.25, and 1.90–3.96 °C (8.76–20.47, 12.69–21.10, 9.54–21.10, 13.47–22.12, and 7.03–15.17%) in the 21st century with larger values found under SSP5-8.5 than SSP2-4.5. The MME model displays approximately triple the current rainfall during the boreal summer. Overall, there are robust increases in rainfall during the Southwest Monsoon (3.41–3.44, 8.44–9.53, and 10.89–17.59%) and the Northeast Monsoon (−2.58 to 0.78, −0.43 to 2.81, and 2.32 to 5.45%). The effectiveness of anticipated climate change mitigation and adaptation strategies under SSP2-4.5 results in slowing down the warming trends and decreasing precipitation trends after 2050. All these findings imply that member countries of mainland SEA need to prepare for appropriate adaptation measures in response to the changing climate.

Future Characteristics of Extreme Precipitation Indicate the Dominance of Frequency Over Intensity: A Multi‐Model Assessment From CMIP6 Across India

“…In general, CMIP provides a fundamental basis for coordinated climate research on the international level, as contributed from several climate modeling centers (Eyring et al., 2016). The present study considers the most recent version of CMIP, that is, CMIP6, which undergoes substantial improvements over its earlier versions (such as CMIP3, CMIP5) in multiple aspects, such as finer horizontal resolution, improved parameterizations of cloud microphysics, better representation of the synoptic processes, and better agreement with the global energy balance (Supharatid et al., 2021). Thus, more reasonable and reliable projections can be obtained from CMIP6 outputs as compared to its previous versions (C. A. Chen et al., 2021; Di Luca et al., 2020; Li et al., 2021; Wang et al., 2021) at global, as well as regional scale—which the present study explores.…”

Differential Impacts of Climatic and Land Use Changes on Habitat Suitability and Protected Area Adequacy Across the Asian Elephant’s Range

“…We used the Core Mapper tool from the Gnarly Landscape Utilities to identify core habitats throughout the study area [83]. We identified highly suitable habitat patches in the study area by using a moving window with a 9.4 km radius [23]. In addition, cells within a certain effective distance were included in the targeted core habitats.…”

“…Early studies focused on the analyses of habitat status or suitability by using field survey data [9,11,[16][17][18], while recent studies began to model habitat suitability or change under climate change and human disturbance [6,14,15,[19][20][21]. South and Southeast Asia are the most vulnerable regions to climate change [22,23]. It was predicted that an increase in temperature would be spatially homogenous, while an increase in precipitation would show great spatial variability [23,24].…”

“…South and Southeast Asia are the most vulnerable regions to climate change [22,23]. It was predicted that an increase in temperature would be spatially homogenous, while an increase in precipitation would show great spatial variability [23,24]. Climate change has been one of the most important drivers for the historical shift in the range of distribution of Asian elephants [3].…”

Projected drought conditions by CMIP6 multimodel ensemble over Southeast Asia

“…Many bias correction methods have been employed in previous studies (Teutschbein & Seibert 2012;Supharatid 2016;Araghi et al 2019;Homsi et al 2020) with a critical review by Maraun (2016). In this study, we employed a 'variance scaling' method (similar to Supharatid et al 2021) to correct the historical and projected temperature over SEA from CMIP6 models. This approach can guarantee that the adjusted model simulation in the reference period has the same mean and standard deviation (SD) as the observations.…”