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Center for Nonlinear Studies |
Although climate models have steadily improved their ability to reproduce the observed climate, over the years there has been little change to the wide range of sensitivities exhibited by different models to a doubling of atmospheric CO2 concentrations. Stochastic optimization is used to mimic how six independent climate model development efforts might use the same atmospheric general circulation model, set of observational constraints, and model skill criteria to choose different settings for parameters thought to be important sources of uncertainty. As compared to the default model sensitivity of 2.4ºC, each optimized model improved global skill scores by a similar 7% and had nearly identical 3ºC sensitivities, but with different regional responses. The implication is that current generation models are close to a critical level of skill enabling more convergent predictions of change at the largest scales even though regional differences persist.