Assessment of CMIP5 and CORDEX-SA experiments in representing multiscale temperature climatology over central India

Abstract

The central India region has been seriously affected by repeated droughts in recent decades due to climate change, which is the main reason for conducting this research. It is still uncertain how the numerous climate models could precisely estimate the future climate for central India. The study mainly focuses on the forcing global climate models (GCMs) and the regional climate models (RCMs). The models have been checked using the coefficient of correlation (r2), Nash Sutcliffe efficiency (NSE) and an improved method, Skill score (SS). The performance is also spatially checked on ArcGIS using the kriging interpolation. The bias-corrected GCMs performed more authentic than the CORDEX RCMs in signifying maximum and minimum temperatures for the Bundelkhand region in central India. Bias-corrected GCMs, EC-EARTH, CCSM4 and GFDL-ESM-2M affirmed the best models on multiple time scales for maximum and minimum temperature in the study region. Maximum NSE and r2 have been observed for seasonal minimum temperature. GCM-EC-EARTH has shown 97% to 98% accuracy, while GCM-GFDL-ESM-2M has demonstrated 84% to 97% accuracy among other selected models. The research outcomes will also assist policymakers in developing strategies and policies for the future climate of central India with the help of more precise projected climatic data.

A novel approach for evaluation of CMIP6 GCMs in simulating temperature and precipitation extremes of Pakistan

“…Several reports looked at how well CMIP6 models predicted temperatures and precipitation in Pakistan (Abbas et al, 2022; Ahmed et al, 2019a; Amin et al, 2018; Iqbal et al, 2020; Karim et al, 2020; Khan et al, 2020a). Most of these studies conducted GCM selection based on the skills of GCMs in simulating precipitation and temperature using different performance indices such as Taylor skills score and interannual variability score (Abbas et al, 2022; Hamed et al, 2022b; Sharma and Kale, 2022; Vishwakarma et al, 2022), spatial metrics and Cramer's V (Raju & Kumar, 2020), map curves, Goodman–Kruskal's lambda, fractions skill score, Kling–Gupta efficiency (Ahmed et al, 2019a) and correlation coefficient (Karim et al, 2020). However, studies specifically focused on assessing the capabilities of CMIP6 models in reproducing temperature and precipitation extreme indices in Pakistan are lacking.…”