Towards an objective assessment of climate multi-model ensembles. A case study in the Senegalo-Mauritanian upwelling region

Abstract. Climate simulations require very complex numerical models. Unfortunately, they typically present biases due to parameterizations, choices of numerical schemes, and the complexity of many physical processes. Beyond improving the models themselves, a way to improve the performance of the modeled climate is to consider multi-model averages. Here, we propose an objective method to select the models that yield an efficient multi-model ensemble average. We used a neural classifier (Self-Organizing Maps), associated with a multi-correspondence analysis to identify the models that best represent some target climate property. One can then determine an efficient multi-model ensemble. We illustrate the methodology with results focusing on the mean sea surface temperature seasonal cycle over the Senegalo-Mauritanian region. We compare 47 CMIP5 model configurations to available observations. The method allowed us to identify a performing multi-model ensemble by averaging 12 climate models only. Future behavior of the Senegalo-Mauritanian upwelling was then assessed using this multi-model ensemble.

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