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| Titel |
A multi-model ensemble method that combines imperfect models through learning |
| VerfasserIn |
L. A. Berge, F. M. Selten, W. Wiegerinck, G. S. Duane |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2190-4979
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| Digitales Dokument |
URL |
| Erschienen |
In: Earth System Dynamics ; 2, no. 1 ; Nr. 2, no. 1 (2011-06-30), S.161-177 |
| Datensatznummer |
250000466
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| Publikation (Nr.) |
 copernicus.org/esd-2-161-2011.pdf |
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| Zusammenfassung |
In the current multi-model ensemble approach climate model simulations are
combined a posteriori. In the method of this study the models in the ensemble
exchange information during simulations and learn from historical
observations to combine their strengths into a best representation of the
observed climate. The method is developed and tested in the context of small
chaotic dynamical systems, like the Lorenz 63 system. Imperfect models are
created by perturbing the standard parameter values. Three imperfect models
are combined into one super-model, through the introduction of connections
between the model equations. The connection coefficients are learned from
data from the unperturbed model, that is regarded as the truth.
The main result of this study is that after learning the super-model is a
very good approximation to the truth, much better than each imperfect model
separately. These illustrative examples suggest that the super-modeling
approach is a promising strategy to improve weather and climate simulations. |
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