Ensemble-Based Parameter Estimation in a Coupled GCM Using the Adaptive Spatial Average Method

Y. Liu Center for Climate Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin

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Z. Liu Laboratory for Ocean–Atmosphere Studies, Peking University, Beijing, China, and Center for Climate Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin

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S. Zhang GFDL/NOAA, Princeton University, Princeton, New Jersey

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X. Rong Chinese Academy of Meteorological Sciences, Beijing, China

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R. Jacob ** Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, Illinois

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S. Wu Center for Climate Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin

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F. Lu Center for Climate Research and Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, Wisconsin

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Abstract

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, an adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.

Center for Climate Research Contribution Number 1168.

Corresponding author address: Yun Liu, Center for Climate Research and Dept. Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, WI 53706. E-mail: liu6@wisc.edu

Abstract

Ensemble-based parameter estimation for a climate model is emerging as an important topic in climate research. For a complex system such as a coupled ocean–atmosphere general circulation model, the sensitivity and response of a model variable to a model parameter could vary spatially and temporally. Here, an adaptive spatial average (ASA) algorithm is proposed to increase the efficiency of parameter estimation. Refined from a previous spatial average method, the ASA uses the ensemble spread as the criterion for selecting “good” values from the spatially varying posterior estimated parameter values; these good values are then averaged to give the final global uniform posterior parameter. In comparison with existing methods, the ASA parameter estimation has a superior performance: faster convergence and enhanced signal-to-noise ratio.

Center for Climate Research Contribution Number 1168.

Corresponding author address: Yun Liu, Center for Climate Research and Dept. Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, WI 53706. E-mail: liu6@wisc.edu
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