Unravelling the influence of subjectivity on ranking of CMIP6 based climate models: A case study

Abstract

AbstractThe skill of General Circulation Models (GCMs) in mimicking the observed climate is assessed through various procedures and are ranked. The performance of a GCM is site‐specific and the ranking pattern varies spatially. In general, a set of best performing GCMs is extracted to study the impact of climate change. As, there is no universally accepted ranking procedure, the ranking of GCMs is prone to subjectivity. In this study, it is aimed to address the effect of this subjectivity on the GCM rankings. The past performance of GCMs from Coupled Model Intercomparison Project phase 6 (CMIP6) in simulating the maximum and minimum temperature across India are evaluated and ranked by different ranking procedures. These ranking procedures involve combinations of various components present in the ranking procedure such as model evaluation criteria, criteria weightage allocation methods, Multicriteria Decision Making methods (MCDM) and reference gridded datasets. Different criteria and methods involved in the ranking procedures are carefully selected to address the subjectivity involved in ranking of GCMs. The effect of each individual component on the ranking pattern is systematically analysed and the spatial distribution of grids with same ranking patterns across all the combinations are considered as grids with same ranking. The performance of best performing GCM is attributed to homogenous climatic zones and its corresponding topological features. An ensemble of frequently performing top five GCMs among 16 different ranking procedures are extracted for each climate zone as the most suitable set of GCMs.