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Biases in Climate Model-Derived Surface Air Temperature Trends

Paper Reviewed
Davy, R. and Esau, I. 2014. Global climate models' bias in surface temperature trends and variability. Environmental Research Letters 9: 10.1088/1748-9326/9/11/114024.

Introducing their recent contribution to the field of climate modelling, Davy and Esau (2014) write that surface air temperature "is a very important parameter in the anthroposphere -- the part of the environment that is inhabited and adapted by humans" -- because "a proper understanding of how the surface air temperature varies is essential to our understanding of Earth's climate and how it responds to forcing." And they thus go on to describe how they used "a statistical measure of model fidelity (Gleckler et al., 2008) to determine model departure from reanalysis [data] for the historical simulations of the CMIP5 program, over the period 1979-2005."

This analysis revealed that the largest biases are seen in shallow, stably-stratified atmospheric boundary layers or ABLs, which exhibit "mean-model biases in excess of 10 K in very shallow layers" In addition, the two researchers report that the climate models they studied "have trouble representing the ABL depth under strongly convective conditions, as many physical processes that occur in these conditions (e.g. self-organization of turbulent structures) are not accounted for in their parameterization schemes."

Nearing the end of their report, Davy and Esau thus state that their "assessment of the CMIP5 model biases and error, with respect to observations and reanalysis, has highlighted the relatively poor performance of these models in stably-stratified conditions." And they state that "it is in these conditions that the models show the greatest departure from observations, and there is the greatest difference between the models in their representation of surface conditions."

And so we note yet another way in which the most advanced state-of-the-art climate models still fall short of where we all hope they would have been by now, in light of the vast amount of money and the large number of entire scientific careers that have been expended in developing climate models that are still plagued by numerous short-comings.

Gleckler, P.J., Taylor, K.E. and Doutriaux, C. 2008. Performance metrics for climate models. Journal of Geophysical Research: Atmospheres 113: D06104.

Posted 16 April 2015