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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.

The authors write that "for models to predict future climate conditions reliably, not only must they accurately simulate the current climate system, but they should also skilfully simulate changes in the climate system," noting that "this can involve running models in a hindcast mode to ascertain if they can accurately predict observed regional changes over multi-decadal time scales."

What was done
Working in this mode, Fu et al. indicate that "the comprehensive archive of Australian gridded climate data, the SILO Data Drill [Jeffrey et al., 2001] was used in this study to assess GCM performance across southeastern Australia" for 25 different CMIP3 GCMs, where the study period was 1961-2000 and the 25 GCM runs were "forced by 20th-century emissions scenarios, i.e., IPCC AR4 20th-century experiment scenario 20C3M."

What was learned
The five researchers report that (1) "the mean observed annual rainfall for the study region is 502 mm, whereas the GCM values vary from 195 to 807 mm," that (2) "12 out of 25 GCMs produce a negative correlation coefficient of [the] monthly rainfall annual cycle," that (3) the "GCMs overestimate [the] trend magnitude for temperature," but they (4) "underestimate for rainfall," that (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 that (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."

What it means
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." And there are actually people who call this progress???

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.

Reviewed 5 March 2014