A novel framework for selecting general circulation models based on the spatial patterns of climate

Abstract

General circulation models (GCMs), used for climate change projections, should be able to simulate both the temporal variability and spatial patterns of the observed climate. However, the selection of GCMs in most previous studies was either based on temporal variability or mean spatial pattern of past climate. In this study, a framework is proposed for the selection of GCMs based on their ability to reproduce the spatial patterns for different climate variables. The Kling-Gupta efficiency (KGE) was used to assess GCMs ability to simulate the annual spatial patterns of maximum and minimum temperatures (Tmx and Tmn, respectively) and rainfall depth. The mean and standard deviation of KGEs were used as performance indicators to present the GCMs' overall skill. Finally, the global performance indicator was used as a multi-criteria decision-making approach to integrate the results of different climate variables and seasons in order to rank the GCMs. Egypt was considered as a case study. The results revealed the better performance, in order, of the MRI-CGCM3, followed by FGOALS-g2, GFDL-ESM2G, GFDL-CM3 and lastly MPI-ESM-MR over Egypt. The final set of GCMs showed a similar spatial pattern for the projected change in temperature over Egypt. For different scenarios, Tmx was projected to increase in the range of 1.63–4.2°C while the increase in Tmn ranged between 1.28 and 4.43°C. A projected increase in temperature in winter is likely greater than in summer. The selected models also projected a 62% decrease in rainfall depth over the northern coastline where rain is currently most abundant while an increase in the dry southern zones. The rise in temperature and decrease in rainfall depth could have severe implications for a country with dwindling water resources.