J. David Neelin, Annalisa Bracco, Hao Luo, James C. McWilliams, and Joyce E. Meyerson, 2010:
Proc. Nat. Acd. Sci., in print Dec. 2010.
·Open Access Paper
·Supporting Information (100 KB)
Abstract. Climate models exhibit high sensitivity in some respects, such as for differences in predicted precipitation changes under global warming. Despite successful large-scale simulations, regional climatology features prove difficult to constrain towards observations, with challenges including high-dimensionality, computationally-expensive simulations, and ambiguity in the choice of objective function. In an atmospheric General Circulation Model forced by observed sea surface temperature or coupled to a mixed-layer ocean, many climatic variables yield root-mean-square error objective functions that vary smoothly through the feasible parameter range. This occurs despite nonlinearity strong enough to reverse the curvature of the objective function in some parameters, and to imply limitations on multi-model ensemble means as an estimator of global warming precipitation changes. Low-order polynomial fits to the model output spatial fields as a function of parameter (quadratic in model field, 4th order in objective function) yield surprisingly successful metamodels for many quantities, and facilitate a multi-objective optimization approach. Trade-offs arise as optima for different variables occur at different parameter values, but with agreement in certain directions. Optima often occur at the limit of the feasible parameter range, identifying key parameterization aspects warranting attention-here the interaction of convection with free tropospheric water vapor. Analytic results for spatial fields of leading contributions to the optimization help to visualize trade-offs at a regional level, e.g., how mismatches between sensitivity and error spatial fields yield regional error under minimization of global objective functions. The approach is sufficiently simple to guide parameter choices and to aid intercomparison of sensitivity properties among climate models.
Citation. Neelin, J. D., A. Bracco, H. Luo, J. C. McWilliams, and J. E. Meyerson, 2010: Considerations for parameter optimization and sensitivity in climate models. Proc. Nat. Acd. Sci.. Submitted.