Abstract
Abstract. The objective of this paper is to identify better performing Coupled Model Intercomparison Project phase 3 (CMIP3) global climate models (GCMs) that reproduce grid-scale climatological statistics of observed precipitation and temperature for input to hydrologic simulation over global land regions. Current assessments are aimed mainly at examining the performance of GCMs from a climatology perspective and not from a hydrology standpoint. The performance of each GCM in reproducing the precipitation and temperature statistics was ranked and better performing GCMs identified for later analyses. Observed global land surface precipitation and temperature data were drawn from the Climatic Research Unit (CRU) 3.10 gridded data set and re-sampled to the resolution of each GCM for comparison. Observed and GCM-based estimates of mean and standard deviation of annual precipitation, mean annual temperature, mean monthly precipitation and temperature and Köppen–Geiger climate type were compared. The main metrics for assessing GCM performance were the Nash–Sutcliffe efficiency (NSE) index and root mean square error (RMSE) between modelled and observed long-term statistics. This information combined with a literature review of the performance of the CMIP3 models identified the following better performing GCMs from a hydrologic perspective: HadCM3 (Hadley Centre for Climate Prediction and Research), MIROCm (Model for Interdisciplinary Research on Climate) (Center for Climate System Research (The University of Tokyo), National Institute for Environmental Studies, and Frontier Research Center for Global Change), MIUB (Meteorological Institute of the University of Bonn, Meteorological Research Institute of KMA, and Model and Data group), MPI (Max Planck Institute for Meteorology) and MRI (Japan Meteorological Research Institute). The future response of these GCMs was found to be representative of the 44 GCM ensemble members which confirms that the selected GCMs are reasonably representative of the range of future GCM projections.
Highlights
Our primary objective in this paper is to identify better performing global climate models (GCMs) from a hydrologic perspective
The question of whether GCMs with a finer resolution outperform GCMs with a coarser resolution is addressed in Fig. 6, where GCM performance in reproducing observed terrestrial mean annual precipitation (MAP) and mean annual temperature (MAT), based on the Nash–Sutcliffe efficiency (NSE), is related to GCM resolution, defined as the number of grid cells used in the comparison
Our primary objective in this paper is to identify better performing GCMs from a hydrologic perspective over global land regions
Summary
Our primary objective in this paper is to identify better performing GCMs from a hydrologic perspective. To do this we assess how well 22 global climate models (GCMs) from the World Climate Research Programme’s (WCRP) Coupled Model Intercomparison Project phase 3 (CMIP3) multimodel data set (Meehl et al, 2007) are able to reproduce GCM grid-scale climatological statistics of observed precipitation and temperature over global land regions. We recognise that GCMs model different variables with a range of success and that no single model is best for all variables and/or for all regions (Lambert and Boer, 2001; Gleckler et al, 2008). This review concentrates on GCM variables and statistical techniques that are relevant to engineering hydrologic practice
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