Articles | Volume 10, issue 4
https://doi.org/10.5194/esd-10-789-2019
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, Frank Selten, Alberto Carrassi, and Noel Keenlyside

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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
AR by Francine Schevenhoven on behalf of the Authors (25 Oct 2019)  Manuscript 
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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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