Selection of multi-model ensemble of GCMs for the simulation of precipitation based on spatial assessment metrics

Abstract

Abstract. The climate modelling community has trialled a large number metrics to evaluate the temporal performance of the Global Circulation Models (GCMs) for the selection of GCMs, while very little attention has been given to spatial performance of GCMs which is equally important. This study evaluated the performance of 20 Coupled Model Intercomparison Project 5 (CMIP5) GCMs pertaining to their skills in simulating mean annual, monsoon and winter precipitation over Pakistan using state-of-the-art spatial metrics; SPAtial EFficiency, Goodman–Kruskal's lambda, Fractions Skill Score, Cramer’s V, Mapcurves, and Kling-Gupta efficiency for the period 1961–2005. The multi-model ensemble (MME) precipitation was generated through intelligent merging of simulated precipitation of selected GCMs employing Random Forest (RF) regression and Simple Mean (SM). The results indicated some differences in the ranks of GCMs for different metrics. The overall ranks indicated NorESM1-M, CESM1-CAM5, GFDL-CM3 and GFDL-ESM2G as the best GCMs in simulating the spatial patterns of mean annual, monsoon and winter precipitation over Pakistan. MME precipitation generated based on the best performing GCMs showed more similarities with observed precipitation compared to precipitation simulated by individual GCMs. The MME developed using RF displayed better performance than the MME-based on SM. Multiple spatial metrics have been used for the first time for selecting GCMs based on their capability to mimic the spatial patterns of annual and seasonal precipitation. The approach suggested in the present study can be extended to any number of GCMs and climate variables and applicable to any region for the suitable selection of an ensemble of GCMs to reduce uncertainties in climate projections.