Influence of Normalization Techniques in CMIP Model Selection Using an MCDM Method MOORA

Global climate models (GCMs) are gaining in importance as scientists can more precisely determine climate characteristics. These characteristics will be of great help in the process of managing water resources plans and strategies. Since studies on GCMs’ performances can model and predict future climate scenarios, conducting such research is essential. The present study, therefore, focuses on ten CMIP6 GCMs using observed gridded precipitation obtained from IMD, to replicate 64 years of historical GCM precipitation. GCM results are compared to observed precipitation using ten performance criteria factors. Taking all characteristics into account and assigning weightage to them using Shannon entropy method, an MCDM algorithm known as Multi-Objective Optimization Ratio Analysis (MOORA) is used to evaluate the ranking based on four normalization techniques. Normalization converts criteria values into a single scale for ranking the options. Due to its high index ranking, the vector normalization technique is found ideally suited for use in MOORA's normalization process. As a consequence of these results, the quality of research on GCM selection has been enhanced through MOORA.