The risk of heatwave events and their persistence has intensified in recent past and is expected to increase faster in future. However, the anticipated changes in socioeconomic exposure to heatwaves are still unexplored. Here, we investigate the projected heat stress and associated socioeconomic exposure across South Asia (SA) and its subregions using the newly released ensemble mean of 23 global climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6), population, and Gross Domestic Product (GDP) projections. We used two Shared Socioeconomic Pathways (SSPs), namely SSP2-4.5 and SSP5-8.5, and three-time periods, that is, near-term, midterm, and long-term relative to the base period (1985–2005). We found that SA region has the potential for widespread changes to Wet bulb globe temperature (WBGT) of 6.5°C, which can exceed the theoretical limits of human tolerance by the mid of 21st century. The SA population's exposure significantly increases during midterm and long-term periods by ∼ person-hours under the SSP5-8.5 scenario. The GDP exposure is the greatest for the same period's up to dollar-hours under the SSP2-4.5. Moreover, the foothills Himalayans and northern parts of Pakistan are presently unaffected by WBGT during midterm and long-term periods under both scenarios. Among subregions (hereafter R1, R2, R3, and R4), the frequency of subdaily WBGT is projected to increase in the region R2 and R4 by ∼70% and ∼90% under the SSP2-4.5 and SSP5-8.5 scenarios relative to the base period. The highest upsurge in exposure is anticipated for R2, including southern Pakistan and southwestern India, followed by R1 and R3. Notably, the climate effect is more dominant than the population, whereas changes in GDP effect contribute to the total change in GDP exposure. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1029/2021EF002240

Projected Changes in Socioeconomic Exposure to Heatwaves in South Asia Under Changing Climate

Recent variability of sub-seasonal monsoon precipitation and its potential drivers in Myanmar using in-situ observation during 1981–2020

Drought is one of the most complex natural phenomena affecting the life and livelihood of people, especially in the current time of human-induced climate change. This research employs ground-based observations to assess the recent spatiotemporal characteristics of meteorological drought events over Rwanda. The drought is examined based on the Standardized Precipitation Evapotranspiration Index (SPEI) and Standardized Precipitation Index (SPI) at seasonal and annual time scales from 1981 to 2020. The Man–Kendal test was used to evaluate the trends in rainfall, temperature, and SPEI values at the annual scale and during the March to May (MAM) and October to December (OND) seasons. The analysis revealed nonsignificant trends in annual (8.4 mm/decade), MAM (−3.4 mm/decade), and OND (4.5 mm/decade) rainfall, while an apparent significant increasing trend in surface air temperature was obtained during the MAM (0.19 °C/decade), OND (0.2 °C/decade), and annual (0.23 °C/decade) time slices. Overall, the SPEI characteristics indicated that the country is more prone to moderate drought events than severe and extreme drought events during MAM and OND seasons. However, the intensity, duration, and frequency differ spatially among seasons. The findings of this study inform policy and decision-makers on the past experienced drought behavior, which can serve as a baseline for future drought mitigation and adaptation plans.

https://www.mdpi.com/2071-1050/14/3/1519/pdf?version=1643719547

Observed Changes in Meteorological Drought Events during 1981–2020 over Rwanda, East Africa

The present study aimed to evaluate the performance of 46 global climate models (GCMs) from the newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) in the historical simulation of precipitation and temperature, and select the best performing GCMs for future projection across China and three major river basins. This study uses four shared socioeconomic pathways (SSPs), namely SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 relative to the base period (1961–2014). Initially, 46 models were evaluated across China employing an improved Taylor diagram method. Based on relatively better performance, 10 best‐performing models (TBMs) were selected out of 46 models for further evaluation. The results show that historical temperature was well reproduced by CMIP6 over the study regions with a high correlation coefficient (CC). All the TBMs produced good CC ranging from 0.8 to 0.99 presenting the precipitation and temperature distribution well. Meanwhile, EC‐Earth3 and EC‐Earth3‐Veg well simulated the precipitation and temperature amounts as well as trends over selected three river basins. The multimodel ensemble mean (MME) underestimates temperature over China and selected three basins with bias values of −0.53, −0.21, −0.91, and −0.68°C, respectively. In contrast, MEM overestimated the simulated precipitation with the amount of 27.7, 32.4, 21.0, and 104.6% across China and selected three basins. During future projections, increased precipitation and temperature trends are projected over three selected river basins as well as all across China. The increasing trend of future precipitation over China under SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5 scenarios are 0.65, 0.86, 1.29, and 0.76 mm·a−1, whereas, the increasing trend of temperature is 0.008, 0.028, 0.050, and 0.065°C·a−1, respectively. Comparatively, the greater the radiation force, the higher projected increases in precipitation and temperature across China and three major river basins were observed. The extent of CMIP6 models over the target region and its river basins calls for further deep assessment of the attribution and possible implementation of robust methods that can accurately simulate the observed patterns for future practice.

Evaluating observed and future spatiotemporal changes in precipitation and temperature across China based on CMIP6‐GCMs

Understanding the prevailing changes in temperature and relative humidity (RH) is of crucial importance for climate risk reduction and management. Despite their importance, trends and temperature variability associated with other climate variables over the Southeast Asian nation of Myanmar are not fully understood. This study investigates the annual and seasonal variations in air temperature and RH, as well as dew point temperature and their relationships, based on 47 meteorological stations located around Myanmar from 2001 to 2019. The results indicate that an increasing trend in air temperature was observed in the central, western, deltaic and southern regions of Myanmar. In contrast, air temperatures trended downward in the eastern (southern Shan state), northern (Hkakabo Razi Mountain) and western (Chin state) parts of the country. RH exhibited a significant increase in the northern region and a decrease in the central dry zone. A lower RH always accompanied high temperatures. Dew points increased in the deltaic and southern parts of Myanmar, as opposed to in the eastern (south Shan state) and western (Chin state) parts of the country. Moreover, in comparison to the daily RH variability, the observed daily temperature variability had a relatively stronger influence on Myanmar’s climate, whereas dew points typically remained stationary for a day. The associated linkage between the RH and the dew point temperature was significantly linear, with a correlation coefficient (R2) of 0.65. The annual (seasonal) correlation of air temperature and dew point was highly correlated in the winter, where R2 was measured at 0.71 (0.75). During the rainy season, however, the annual (seasonal) R2 was measured at only 0.30 (0.04). However, the air temperature and RH showed a weak positive correlation of 0.20 (0.26) in summer (winter) and a weak positive correlation in the rainy season (0.01). This study’s findings are important for enhancing seasonal forecasts of extreme heat and can aid policy-makers in formulating better climate change adaptation plans.

Observed spatiotemporal changes in air temperature, dew point temperature and relative humidity over Myanmar during 2001–2019

The substantial reliance of South Asia (SA) to rain-based agriculture makes the region susceptible to food scarcity due to droughts. Previously, most research on SA has emphasized the meteorological aspects with little consideration of agrarian drought impressions. The insufficient amount of in situ precipitation data across SA has also hindered thorough investigation in the agriculture sector. In recent times, models, satellite remote sensing, and reanalysis products have increased the amount of data. Hence, soil moisture, precipitation, terrestrial water storage (TWS), and vegetation condition index (VCI) products have been employed to illustrate SA droughts from 1982 to 2019 using a standardized index/anomaly approach. Besides, the relationships of these products towards crop production are evaluated using the annual national production of barley, maize, rice, and wheat by computing the yield anomaly index (YAI). Our findings indicate that MERRA-2, CPC, FLDAS (soil moisture), GPCC, and CHIRPS (precipitation) are alike and constant over the entire four regions of South Asia (northwest, southwest, northeast, and southeast). On the other hand, GLDAS and ERA5 remain poor when compared to other soil moisture products and identified drought conditions in regions one (northwest) and three (northeast). Likewise, TWS products such as MERRA-2 TWS and GRACE TWS (2002–2014) followed the patterns of ERA5 and GLDAS and presented divergent and inconsistent drought patterns. Furthermore, the vegetation condition index (VCI) remained less responsive in regions three (northeast) and four (southeast) only. Based on annual crop production data, MERRA-2, CPC, FLDAS, GPCC, and CHIRPS performed fairly well and indicated stronger and more significant associations (0.80 to 0.96) when compared to others. Thus, the current outcomes are imperative for gauging the deficient amount of data in the SA region, as they provide substitutes for agricultural drought monitoring.

Shazia Nisar

Papers

Comparison of Multi-Year Reanalysis, Models, and Satellite Remote Sensing Products for Agricultural Drought Monitoring over South Asian Countries