Multicycle Parameter Estimations in Coupled Earth System Models Based on Multiscale Sensitivity Responses in the Context of Low-Order Models

Haoyu Yang aKey Laboratory of Physical Oceanography, Ministry of Education, Institute for Advanced Ocean Study, Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, China
dInstitute of Offshore Geotechnical Engineering, Ocean University of China, Qingdao, China
cAcademy of the Future Ocean, Ocean University of China, Qingdao, China

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Shaoqing Zhang aKey Laboratory of Physical Oceanography, Ministry of Education, Institute for Advanced Ocean Study, Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, China
bThe College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao, China
cAcademy of the Future Ocean, Ocean University of China, Qingdao, China

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Jinzhuo Cai aKey Laboratory of Physical Oceanography, Ministry of Education, Institute for Advanced Ocean Study, Frontiers Science Center for Deep Ocean Multispheres and Earth System (DOMES), Ocean University of China, Qingdao, China
dInstitute of Offshore Geotechnical Engineering, Ocean University of China, Qingdao, China
cAcademy of the Future Ocean, Ocean University of China, Qingdao, China

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Dong Wang dInstitute of Offshore Geotechnical Engineering, Ocean University of China, Qingdao, China

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Xiong Deng eCollege of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China

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Yang Gao fFrontiers Science Center for Deep Ocean Multi-spheres and Earth System, and Key Laboratory of Marine Environment and Ecology, Ministry of Education, Ocean University of China, Qingdao, China

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Abstract

Climate model simulations tend to drift away from the real world because of model errors induced by an incomplete understanding and implementation of dynamics and physics. Parameter estimation uses data assimilation methods to optimize model parameters, which minimizes model errors by incorporating observations into the model through state-parameter covariance. However, traditional parameter estimation schemes that simultaneously estimate multiple parameters using observations could fail to reduce model errors because of the low signal-to-noise ratio in the covariance. Here, based on the saturation time scales of model sensitivity that depend on different parameters and model components, we design a new multicycle parameter estimation scheme, where each cycle is determined by the saturation time scale of sensitivity of the model state associated with observations in each climate system component. The new scheme is evaluated using two low-order models. The results show that due to high signal-to-noise ratios sustained during the parameter estimation process, the new scheme consistently reduces model errors as the number of estimated parameters increases. The new scheme may improve comprehensive coupled climate models by optimizing multiple parameters with multisource observations, thereby addressing the multiscale nature of component motions in the Earth system.

Significance Statement

Parameter estimation is used to reduce model errors by optimizing the model parameter values with observational information, which is important for improving long-term predictions. In previous parameter estimation methods, multisource observations have not yet been sufficiently used because the quality and dimension size of the optimized parameters are limited. Here, based on the multiscale nature of component motion in the Earth system, we develop a new parameter estimation method that makes full use of multisource observations. The new method processes the parameters being estimated sequentially according to sensitivity magnitudes and saturation time scales so that the parameters can be continuously optimized. This new method has large application potential for weather and climate reanalyses and predictions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shaoqing Zhang, szhang@ouc.edu.cn

Abstract

Climate model simulations tend to drift away from the real world because of model errors induced by an incomplete understanding and implementation of dynamics and physics. Parameter estimation uses data assimilation methods to optimize model parameters, which minimizes model errors by incorporating observations into the model through state-parameter covariance. However, traditional parameter estimation schemes that simultaneously estimate multiple parameters using observations could fail to reduce model errors because of the low signal-to-noise ratio in the covariance. Here, based on the saturation time scales of model sensitivity that depend on different parameters and model components, we design a new multicycle parameter estimation scheme, where each cycle is determined by the saturation time scale of sensitivity of the model state associated with observations in each climate system component. The new scheme is evaluated using two low-order models. The results show that due to high signal-to-noise ratios sustained during the parameter estimation process, the new scheme consistently reduces model errors as the number of estimated parameters increases. The new scheme may improve comprehensive coupled climate models by optimizing multiple parameters with multisource observations, thereby addressing the multiscale nature of component motions in the Earth system.

Significance Statement

Parameter estimation is used to reduce model errors by optimizing the model parameter values with observational information, which is important for improving long-term predictions. In previous parameter estimation methods, multisource observations have not yet been sufficiently used because the quality and dimension size of the optimized parameters are limited. Here, based on the multiscale nature of component motion in the Earth system, we develop a new parameter estimation method that makes full use of multisource observations. The new method processes the parameters being estimated sequentially according to sensitivity magnitudes and saturation time scales so that the parameters can be continuously optimized. This new method has large application potential for weather and climate reanalyses and predictions.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Shaoqing Zhang, szhang@ouc.edu.cn
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