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An Optimization Strategy for Identifying Parameter Sensitivity in Atmospheric and Oceanic Models

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  • 1 Key Laboratory of Ocean Circulation and Waves, and Institute of Oceanology, Chinese Academy of Sciences, Qingdao, China, and Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, Canada, and Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
  • 2 Department of Environmental Science and Engineering, University of Northern British Columbia, Prince George, British Columbia, Canada, and State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Hangzhou, China
  • 3 Department of Physics and Astronomy, Institute for Marine and Atmospheric Research Utrecht, Utrecht University, Netherlands
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Abstract

A new optimization strategy is proposed to identify the sensitivities of simulations of atmospheric and oceanic models to uncertain parameters. The strategy is based on a nonlinear optimization method that is able to estimate the maximum values of specific parameter sensitivity measures; meanwhile, it takes into account interactions among uncertain parameters. It is tested using the Lorenz’63 model and an intermediate complexity 2.5-layer shallow-water model of the North Pacific Ocean. For the Lorenz’63 model, it is shown that the parameter sensitivities of the model results depend on the initial conditions. For the 2.5-layer shallow-water model used to simulate the Kuroshio large meander (KLM) south of Japan, the optimization strategy reveals that the prediction of the KLM path is insensitive to the uncertainties in the bottom friction coefficient, the interfacial friction coefficient, and the lateral friction coefficient. Rather, the KLM prediction is relatively sensitive to the uncertainties of the reduced gravity representing ocean stratification and the wind stress coefficient.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Qiang Wang, wangqiang@qdio.ac.cn

Abstract

A new optimization strategy is proposed to identify the sensitivities of simulations of atmospheric and oceanic models to uncertain parameters. The strategy is based on a nonlinear optimization method that is able to estimate the maximum values of specific parameter sensitivity measures; meanwhile, it takes into account interactions among uncertain parameters. It is tested using the Lorenz’63 model and an intermediate complexity 2.5-layer shallow-water model of the North Pacific Ocean. For the Lorenz’63 model, it is shown that the parameter sensitivities of the model results depend on the initial conditions. For the 2.5-layer shallow-water model used to simulate the Kuroshio large meander (KLM) south of Japan, the optimization strategy reveals that the prediction of the KLM path is insensitive to the uncertainties in the bottom friction coefficient, the interfacial friction coefficient, and the lateral friction coefficient. Rather, the KLM prediction is relatively sensitive to the uncertainties of the reduced gravity representing ocean stratification and the wind stress coefficient.

Denotes content that is immediately available upon publication as open access.

© 2017 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Qiang Wang, wangqiang@qdio.ac.cn
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