Articles | Volume 10, issue 4
Earth Syst. Dynam., 10, 789–807, 2019
https://doi.org/10.5194/esd-10-789-2019
Earth Syst. Dynam., 10, 789–807, 2019
https://doi.org/10.5194/esd-10-789-2019

Research article 28 Nov 2019

Research article | 28 Nov 2019

Improving weather and climate predictions by training of supermodels

Francine Schevenhoven et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (03 Oct 2019) by Andrey Gritsun
AR by Francine Schevenhoven on behalf of the Authors (11 Oct 2019)  Author's response    Manuscript
ED: Publish as is (18 Oct 2019) by Andrey Gritsun
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Short summary
Weather and climate predictions potentially improve by dynamically combining different models into a supermodel. A crucial step is to train the supermodel on the basis of observations. Here, we apply two different training methods to the global atmosphere–ocean–land model SPEEDO. We demonstrate that both training methods yield climate and weather predictions of superior quality compared to the individual models. Supermodel predictions can also outperform the commonly used multi-model mean.
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