Author(s): Abdul Rahman; Sreeja Pekkat

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Keywords: Climate change Climate Models CMIP6 Multicriteria decision-making Precipitation

Abstract: Identifying the best Global climate model (GCM) in a pool reduces climate model data uncertainty. This makes hydro-climatological research involving climate change's societal implications more dependable and robust. Two sets of daily gridded data, one as observed and the other as simulated were collected from the Indian Meteorological Department (IMD) and Coupled Model Intercomparison Project phase 6 (CMIP6), respectively from 1965-2014. These data were collected for precipitation from 30 GCMs. Evaluation criteria namely correlation coefficient, skill score, root mean square deviation, and absolute normalized mean bias deviation were adopted. A multicriteria decision approach was employed to compute individual ranks at 28 locations of the Brahmaputra basin in India. The differential weighting method was adopted to allocate weights to each criterion and to get an overall rank for the entire study area. Sensitivity analysis was also performed using equal weights assignment and results revealed no significant change in the ranking pattern for precipitation. The study ranked the GCMs based on their performance in the Brahmaputra basin and suggests top six GCMs for the ensemble for precipitation variable as MPI-ESM-1-1-HAM, AWI-ESM-1-1-LR, NorESM2-LM, MPI-ESM-1-1-LR, MPI-ESM-1-1-HR, and MRI-ESM-2-0.

DOI:

Year: 2025