An automatic and effective parameter optimization method for model tuning
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-...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2015-11-01
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| Series: | Geoscientific Model Development |
| Online Access: | http://www.geosci-model-dev.net/8/3579/2015/gmd-8-3579-2015.pdf |
| Summary: | 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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| ISSN: | 1991-959X 1991-9603 |