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.
Original language | English |
---|---|
Pages (from-to) | 4803-4824 |
Number of pages | 22 |
Journal | Hydrology and Earth System Sciences |
Volume | 23 |
Issue number | 11 |
DOIs | |
Publication status | Published - 25 Nov 2019 |
Bibliographical note
Publisher Copyright:© 2019 Author(s).
Funding
by the National Foundation Research (grant no. NRF-2016R1D1A1B04931844). This work was supported by the Professional Development Research University (PDRU) grant no. Q.J130000.21A2.04E10 of Universiti Teknologi Malaysia. The fourth author is supported by Turkish Scientific and Technical Research Council (TÜBÍTAK) grant no. 118C020.
Funders | Funder number |
---|---|
Professional Development Research University | |
Turkish Scientific and Technical Research Council | |
TÜBÍTAK | 118C020 |
National Research Foundation of Korea |