Insights from the bias-corrected simulations of CMIP6 in India's future climate

Abstract

In this study, we have used thirteen statistically downscaled bias-corrected General Circulation Models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) under the Shared Socioeconomic Pathways (SSP) scenarios. Based on these models, the projected changes in mean rainfall and daily extremes (number of rainy days and simple daily intensity) during the southwest (SW) and northeast (NE) monsoon seasons in the near (2021–2050) and far future (2071–2100) under the SSP2-4.5 & SSP5-8.5 scenarios relative to the baseline period (1985–2014) are studied. In addition to the rainfall, the maximum, minimum, and mean temperature changes and their daily extremes are also examined. Our results show that the Multi-Model Mean (MMM) slightly underestimated (overestimated) the SW (NE) monsoon rainfall compared to the observed rainfall in the baseline period. A considerable increase in future rainfall during the SW monsoon season is noticed in central India, the Himalayan region, and over the northwestern parts of India. In the NE monsoon season, south peninsular India experiences more rainfall under SSP5-8.5 than SSP2-4.5. In general, both SSPs scenarios shows increase in monsoon rainfall from the mid-century onwards. Interestingly, by the end of the 21st century the number of rainy days are projected to reduce, whereas the intensity of rainfall projected to be drastically increase over many areas of India. Analysis of temperature revealed that there is increase in the projected warming with maximum temperature is around 4.5°C during summertime, with minimum temperatures of about 5°C in the wintertime in northern parts of India by the end of the 21st century. Under the SSP5-8.5 scenario, increase in the highest maximum temperatures is seen in the Himalayan region and elevated minimum temperature pattern over the Indo-Gangetic Basin (IGB) is noted. This increased warming may affect the agriculture, water, health, and power sectors severely.