Articles | Volume 19, issue 1
Hydrol. Earth Syst. Sci., 19, 361–377, 2015
Hydrol. Earth Syst. Sci., 19, 361–377, 2015
Research article
21 Jan 2015
Research article | 21 Jan 2015

Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation

T. A. McMahon et al.

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Cited articles

Boer, G. J. and Lambert, S. J.: Second order space–time climate difference statistics, Clim. Dynam., 17, 213–218, 2001.
Bonsal, B. T. and Prowse, T. D.: Regional assessment of GCM-simulated current climate over Northern Canada, Arctic, 59, 115–128, 2006.
Charles, S. P., Bari, M. A., Kitsios, A., and Bates, B. C.: Effect of GCM bias on downscaled precipitation and runoff projections for the Serpentine catchment, Western Australia, Int. J. Climatol., 27, 1673–1690, 2007.
Chervin, R. M.: On the Comparison of Observed and GCM Simulated Climate Ensembles, J. Atmos. Sci., 38, 885–901, 1981.
Chiew, F. H. S. and McMahon, T. A.: Modelling the impacts of climate change on Australian streamflow, Hydrol. Process., 16, 1235–1245, 2002.
Short summary
Here we assess GCM performance from a hydrologic perspective. We identify five better performing CMIP3 GCMs that reproduce grid-scale climatological statistics of observed precipitation and temperature over global land regions for future hydrologic simulation. GCM performance in reproducing observed mean and standard deviation of annual precipitation, mean annual temperature and mean monthly precipitation and temperature was assessed and ranked, and five better performing GCMs were identified.