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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 11
Geosci. Model Dev., 8, 3579-3591, 2015
https://doi.org/10.5194/gmd-8-3579-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Community software to support the delivery of CMIP5

Geosci. Model Dev., 8, 3579-3591, 2015
https://doi.org/10.5194/gmd-8-3579-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 06 Nov 2015

Development and technical paper | 06 Nov 2015

An automatic and effective parameter optimization method for model tuning

T. Zhang1,2, L. Li3, Y. Lin2, W. Xue1,2, F. Xie3, H. Xu1, and X. Huang1,2 T. Zhang et al.
  • 1Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  • 2Center for Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing 100084, China
  • 3State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China

Abstract. Physical parameterizations in general circulation models (GCMs), having various uncertain parameters, greatly impact model performance and model climate sensitivity. Traditional manual and empirical tuning of these parameters is time-consuming and ineffective. In this study, a "three-step" methodology is proposed to automatically and effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. Different from the traditional optimization methods, two extra steps, one determining the model's sensitivity to the parameters and the other choosing the optimum initial value for those sensitive parameters, are introduced before the downhill simplex method. This new method reduces the number of parameters to be tuned and accelerates the convergence of the downhill simplex method. Atmospheric GCM simulation results show that the optimum combination of these parameters determined using this method is able to improve the model's overall performance by 9 %. The proposed methodology and software framework can be easily applied to other GCMs to speed up the model development process, especially regarding unavoidable comprehensive parameter tuning during the model development stage.

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A “three-step” methodology is proposed to effectively obtain the optimum combination of some key parameters in cloud and convective parameterizations according to a comprehensive objective evaluation metrics. The optimal results improve the metrics performance by 9%. A software framework can automatically execute any part of the “three-step” calibration strategy. The proposed methodology and framework can easily be applied to other GCMs to speed up the model development process.
A “three-step” methodology is proposed to effectively obtain the optimum combination of some...
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