Assessment of Annual and Seasonal Surface Air Temperature Simulations in CMIP6 Models over India

Surface air temperature (SAT) affects both natural systems and human activities, impacting health, agriculture, energy demand, and so on. To investigate and analyze SAT over the region of interest, it is crucial to choose suitable climate models. The study commenced with the evaluation of 42 Coupled Model Intercomparison Project phase 6 (CMIP6) models’ simulations of SAT over India for annual and all four seasons (summer, southwest monsoon, northeast monsoon, and winter) during the historical period 1985 to 2014 with respect to the gridded SAT datasets obtained from the India Meteorological Department (IMD). Multi Model Mean (MMM) of 42 models was included in the evaluation. The evaluation was performed with various statistical metrics such as root mean squared error (RMSE), mean bias error (MBE), correlation coefficient (R), mean squared error (MAE), Taylor skill score (TSS), Brier skill score (BSS), and Interannual variability skill score (IVSS). By the method of estimating Comprehensive Rating Index (CRI), the top-ranking models were identified to be CMCC-CM2-SR5 for the annual and summer season, MIROC6 for the winter season, ACCESS-ESM-1-5 for the southwest monsoon, and NorESM2-LM for northeast monsoon. The novelty of this study lies in the approach of identifying the best ensemble. For each season, statistical metric-wise top-ranked models were picked to develop the best ensemble. Again, the overall ranking of the models along with the best ensemble for each season is determined by estimating CRI. It was observed that for all seasons, the best ensemble falls within the top 3 models’ category. Future projections of SAT under four shared socio-economic pathways (SSP-2.6, 4.5, 7.0, and 8.5) were also analyzed with the best ensemble obtained for each season. The results convey that, the country will witness, especially during the summer season, there will be a 1.160 °C, 1.288 °C and 2.368 °C rise in the mean SAT between historical (1985–2014) and near future (2021–2040), near and mid future (2041–2060), mid and far future (2081–2100) if the pathway, SSP5-8.5 is followed.