Ranking of global climate models for India using multicriterion analysis

Abstract

Global Climate Models (GCMs) are used to simulate the climate and are gaining importance due to their ability to project future climate changes and consequently their impacts on hydrologic systems. Nested Bias Correction (NBC) methodology is used to remove the systematic biases in the GCMs simulations. Eleven GCMs - BCCR-BCCM2.0, INGV-ECHAM4, GFDL2.0, GFDL2.1, GISS, IPSL-CM4, MIROC3, MRI-CGCM2, NCAR-PCMI, UKMO-HADCM3 and UKMO-HADGEM1 are evaluated for India (covering 73 grid points of size 2.50 x 2.50) for the climate variable precipitation rate using five performance indicators. Performance indicators used are Correlation Coefficient (CC), Normalised Root Mean Square Error (NRMSE), Absolute Normalised Mean Bias Error (ANMBE), Average Absolute Relative Error (AARE) and Skill Score (SS). Entropy method is employed to obtain weights of the five indicators. Ranks of the eleven GCMs are obtained through a Multicriterion Decision Making (MCDM) outranking method, PROMETHEE-2 (Preference Ranking Organisation METHod of Enrichment Evaluation). Equal weight scenario (assigning 0.2 weight for each indicator) is also employed to rank the GCMs. An effort is also made to rank GCMs for four river basins - Godavari, Krishna, Mahanadi and Cauvery in peninsular India. Upper Malaprabha catchment, Karnataka, India is chosen for demonstrating Entropy and PROMETHEE-2 methods. Spearman rank correlation coefficient is employed to assess the association between the ranking patterns. It is suggested that the ensemble of GFDL2.0, MIROC3, BCCR-BCCM2.0, UKMO-HADCM3, MPI-ECHAM4 and UKMO-HADGEM1 is suitable for India. The methodology proposed can be extended to rank GCMs for any selected region.