Performance assessment of general circulation model in simulating daily precipitation and temperature using multiple gridded datasets

Abstract

The performance of general circulation models (GCMs) in a region are generally assessed according to their capability to simulate historical temperature and precipitation of the region. The performance of 31 GCMs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) is evaluated in this study to identify a suitable ensemble for daily maximum, minimum temperature and precipitation for Pakistan using multiple sets of gridded data, namely: Asian Precipitation- Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE), Berkeley Earth Surface Temperature (BEST), Princeton Global Meteorological Forcing (PGF) and Climate Prediction Centre (CPC) data. An entropy-based robust feature selection approach known as symmetrical uncertainty (SU) is used for the ranking of GCM. It is known from the results of this study that the spatial distribution of best-ranked GCMs varies for different sets of gridded data. The performance of GCMs is also found to vary for both temperatures and precipitation. The Commonwealth Scientific and Industrial Research Organization, Australia (CSIRO)-Mk3-6-0 and Max Planck Institute (MPI)-ESM-LR perform well for temperature while EC-Earth and MIROC5 perform well for precipitation. A trade-off is formulated to select the common GCMs for different climatic variables and gridded data sets, which identify six GCMs, namely: ACCESS1-3, CESM1-BGC, CMCC-CM, HadGEM2-CC, HadGEM2-ES and MIROC5 for the reliable projection of temperature and precipitation of Pakistan.