Journal of Geophysical Research: Atmospheres

A score-based method for assessing the performance of GCMs: A case study of southeastern Australia

Authors

  • Guobin Fu,

    Corresponding author
    1. CSIRO Land and Water, Wembley, Western Australia, Australia
    • Corresponding author: G. Fu, CSIRO Land and Water, Private Bag 5, Wembley, WA 6913, Australia. (Guobin.Fu@csiro.au); Z. Liu, Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing 100101, China. (zfliu@igsnrr.ac.cn)

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  • Zhaofei Liu,

    1. Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
    2. Key Laboratory of Water and Sediment Sciences, Ministry of Education, College of Water Sciences, Beijing Normal University, Beijing, China
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  • Stephen P Charles,

    1. CSIRO Land and Water, Wembley, Western Australia, Australia
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  • Zongxue Xu,

    1. Key Laboratory of Water and Sediment Sciences, Ministry of Education, College of Water Sciences, Beijing Normal University, Beijing, China
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  • Zhijun Yao

    1. Institute of Geographical Sciences and Natural Resource Research, Chinese Academy of Sciences, Beijing, China
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Abstract

[1] A multi-criteria score-based method is developed to assess General Circulation Model (GCM) performance at the regional scale. Application of the method assessing 25 GCM simulations of monthly mean sea level pressure (MSLP) and air temperature, and monthly and annual rainfall over the southeastern Australia region for 1960/1961–1999/2000 indicate that GCMs usually simulate monthly temperature better than monthly rainfall and mean sea level pressure. For example, the mean observed annual temperature for the study region is 16.7°C while the median and mean values of 25 GCMs are 16.8 and 16.9°C, respectively, and 24 GCMs (except BCC:CM1) can reproduce the annual cycle of temperature accurately, with a minimum correlation coefficient of 0.99. In contrast, the mean observed annual rainfall for the study region is 502 mm, whereas the GCM values vary from 195 to 807 mm, and 12 out of 25 GCMs produce a negative correlation coefficient of monthly rainfall annual cycle. However, GCMs overestimate trend magnitude for temperature, but underestimate for rainfall. The observed annual temperature trend is +0.007°C/yr, while both the median and mean GCM values are +0.013°C/yr, which is almost double the observed magnitude. The observed annual rainfall trend is +0.62mm/yr, while the median and mean values of 25 GCMs are 0.21and 0.36mm/yr, respectively. This demonstrates the advantages of using multi-criteria to assess GCMs performance. The method developed in this study can easily be extended to different study regions and results can be used for better informed regional climate change impact analysis.

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