Assessing the Performance of CMIP3 GCMs in Southeast Australia
Fu, G., Liu, Z., Charles, S.P., Xu, Z. and Yao, Z. 2013. A score-based method for assessing the performance of GCMs: A case study of southeastern Australia. Journal of Geophysical Research: Atmospheres 118: 4154-4167.
Results of the analysis indicate (1) "the mean observed annual rainfall for the study region is 502 mm, whereas the GCM values vary from 195 to 807 mm," (2) "12 out of 25 GCMs produce a negative correlation coefficient of [the] monthly rainfall annual cycle," (3) the "GCMs overestimate [the] trend magnitude for temperature," but they (4) "underestimate for rainfall," (5) "the observed annual temperature trend is +0.007°C/year, while both the median and mean GCM values are +0.013°C/year, which is almost double the observed magnitude," and (6) "the observed annual rainfall trend is +0.62mm/year, while the median and mean values of 25 GCMs are 0.21 and 0.36 mm/year, respectively."
In light of these several findings - and more - Fu et al. conclude that "GCMs currently do not provide reliable rainfall information on regional scales as required by many climate change impacts studies," while adding to emphasize this fact that "the 'best' GCM is a CMIP3 GCM and [the] four 'worst' GCMs are CMIP5 models." Thus, it would appear that the older models perform better than the newer ones, which does not seem to be a step in the right direction.
Jeffrey, S.J., Carter, J.O., Moodie, K.B. and Beswick, A.R. 2001. Using spatial interpolation to construct a comprehensive archive of Australian climate data. Enviromental Modelling and Software 16: 309-330.