Selection of global climate models for India using cluster analysis

Global climate models (GCMs) are gaining importance due to their capability to ascertain climate variables that will be useful to develop long, medium and short term water resources planning strategies. The applicability of K-Means cluster analysis is explored for grouping 36 GCMs from Coupled Model Intercomparison Project 5 for maximum temperature (MAXT), minimum temperature (MINT) and a combination of maximum and minimum temperature (COMBT) over India. Cluster validation methods, namely the Davies–Bouldin Index (DBI) and F-statistic, are used to obtain an optimal number of clusters of GCMs for India. The indicator chosen for evaluation of GCMs is the probability density function based skill score. It is noticed that the optimal number of clusters for MAXT, MINT and COMBT scenarios are 3, 2 and 2, respectively. Accordingly, suitable ensembles of GCMs are suggested for India for MAXT, MINT and COMBT individually. The suggested methodology can be extended to any number of GCMs and indicators, with minor modifications.