A multi-model ensemble method that combines imperfect models through learning
- 1Royal Netherlands Meteorological Institute, Wilhelminalaan 10, 3732 GK De Bilt, The Netherlands
- 2Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Geert Grooteplein 21, 6525 EZ Nijmegen, The Netherlands
- 3Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80309, USA
Abstract. 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.