Articles | Volume 10, issue 4
Earth Syst. Dynam., 10, 789–807, 2019
Earth Syst. Dynam., 10, 789–807, 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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Cited articles

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Bauer, P., Thorpe, A., and Brunet, G.: The quiet revolution of numerical weather prediction, Nature, 525, 47–55,, 2015. a
Carrassi, A., Bocquet, M., Bertino, L., and Evensen, G.: Data assimilation in the geosciences: An overview of methods, issues, and perspectives, Wiley Interdisciplin. Rev.: Clim. Change, 9, e535,, 2018. a, b
Challinor, A. and Wheeler, T.: Crop yield reduction in the tropics under climate change: processes and uncertainties, Agr. Forest Meteorol., 148, 343–356, 2008. a
Collins, M. and Allen, M. R.: Assessing the Relative Roles of Initial and Boundary Conditions in Interannual to Decadal Climate Predictability, J. Climate, 15, 3104–3109,<3104:ATRROI>2.0.CO;2, 2002. a
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.
Final-revised paper