Assessment of GCM simulations of annual and seasonal rainfall and daily rainfall distribution across south-east Australia
Version of Record online: 3 DEC 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Volume 25, Issue 9, pages 1486–1497, 30 April 2011
How to Cite
Vaze, J., Teng, J. and Chiew, F. H. S. (2011), Assessment of GCM simulations of annual and seasonal rainfall and daily rainfall distribution across south-east Australia. Hydrol. Process., 25: 1486–1497. doi: 10.1002/hyp.7916
- Issue online: 13 APR 2011
- Version of Record online: 3 DEC 2010
- Manuscript Accepted: 20 SEP 2010
- Manuscript Received: 29 JUN 2010
- climate change;
- global warming;
- GCM assessment
Global warming can potentially lead to changes in future rainfall and runoff and can significantly impact the regional hydrology and future availability of water resources. All the large-scale climate impact studies use the future climate projections from global climate models (GCMs) to estimate the impact on future water availability. This paper presents results from a detailed assessment to investigate the capability of 15 GCMs to reproduce the observed historical annual and seasonal mean rainfalls, the observed annual rainfall series and the observed daily rainfall distribution across south-east Australia. The assessment shows that the GCMs can generally reproduce the spatial patterns of mean seasonal and annual rainfalls. However, there can be considerable differences between the mean rainfalls simulated by the GCMs and the observed rainfall. The results clearly show that none of the GCMs can simulate the actual annual rainfall time series or the trend in the annual rainfall. The GCMs can also generally reproduce the observed daily (ranked) rainfall distribution at the GCM scale. The GCMs are ranked against their abilities to reproduce the observed historical mean annual rainfall and daily rainfall distribution, and, based on the combined score, the better GCMs include MPI-ECHAM5, MIUB, CCCMA_T47, INMCM, CSIRO-MK3·0, CNRM, CCCMA_T63 and GFDL 2·0 and those with poorer performances are MRI, IPSL, GISS-AOM, MIROC-M, NCAR-PCM1, IAP and NCAR-CCSM. However, the reduction in the combined score as we move from the best- to the worst-performing GCMs is gradual, and there is no evident cut-off point or threshold to remove GCMs from climate impact studies. There is some agreement between the results here and many similar studies comparing the performance of GCMs in Australia, but the results are not always consistent and do significantly disagree with several of the studies. Copyright © 2010 John Wiley & Sons, Ltd.