Abstract
Abstract. The climate modelling community has trialled a large number of metrics for evaluating the temporal performance of general circulation models (GCMs), while very little attention has been given to the assessment of their spatial performance, which is equally important. This study evaluated the performance of 36 Coupled Model Intercomparison Project 5 (CMIP5) GCMs in relation to their skills in simulating mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan using state-of-the-art spatial metrics, SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency, for the period 1961–2005. The multi-model ensemble (MME) precipitation and maximum and minimum temperature data were generated through the intelligent merging of simulated precipitation and maximum and minimum temperature of selected GCMs employing random forest (RF) regression and simple mean (SM) techniques. The results indicated some differences in the ranks of GCMs for different spatial metrics. The overall ranks indicated NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 as the best GCMs in simulating the spatial patterns of mean annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature over Pakistan. MME precipitation and maximum and minimum temperature generated based on the best-performing GCMs showed more similarities with observed precipitation and maximum and minimum temperature compared to precipitation and maximum and minimum temperature simulated by individual GCMs. The MMEs developed using RF displayed better performance than the MMEs 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 and maximum and minimum temperature. The approach proposed 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.
Highlights
Climate change is a complex, multidimensional phenomenon that has been critically studied over the last few decades (Byg and Salick, 2009; Cameron, 2011)
A comprehensive rating metric was used to derive the overall ranks of general circulation models (GCMs) based on their ranks pertaining to annual, monsoon, winter, pre-monsoon, and post-monsoon precipitation and maximum and minimum temperature
The low normalized root mean square error (NRMSE) and high modified index of agreement confirmed the close agreement of monthly Global Precipitation Climatology Center (GPCC) precipitation and Climatic Research Unit (CRU) temperature with the observed precipitation and temperature extracted from 17 stations located in different climate zones in Pakistan
Summary
Climate change is a complex, multidimensional phenomenon that has been critically studied over the last few decades (Byg and Salick, 2009; Cameron, 2011). The changes in climate are mostly observed by studying the variations in precipitation and temperature regimes (Sheffield and Wood, 2008). K. Ahmed et al.: Selection of multi-model ensemble of GCMs quency of droughts (Ahmed et al, 2019a), floods (Wu et al, 2014), and heatwaves (Perkins-Kirkpatrick and Gibson, 2017) and decreases in the severity and frequency of cold snaps (Wang et al, 2016) in recent years, which are indicative of abrupt variations in the precipitation and temperature regimes. According to the Intergovernmental Panel on Climate Change (IPCC) 5th Assessment Report (AR5), the average global land and ocean surface air temperature has risen by around 0.72 ◦C (0.49–0.89 ◦C) during 1951–2012. It is important to study the variations in spatio-temporal patterns of climate variables such as precipitation and temperature (Akhter et al, 2017)
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