Abstract
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 combinations. In the present study, we propose a method to select the models that yield a multi-model ensemble combination that efficiently reproduces target features of the observations. 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. We can thereby determine an efficient multi-model ensemble. We illustrated the methodology with results focusing on the mean sea surface temperature seasonal cycle in the Senegalo-Mauritanian region. We compared 47 CMIP5 model configurations to available observations. The method allows us to identify a subset of CMIP5 models able to form an efficient multi-model ensemble. The future decrease in the Senegalo-Mauritanian upwelling proposed in recent studies is then revisited using this multi-model selection.
Highlights
In this study, we present a methodology aimed at selecting a coherent sub-ensemble of the models involved in the Climate Model Intercomparison Project Phase 5 (CMIP5) that best represents specific observed characteristics
This paper proposed a novel methodology for selecting efficient climate models in a specific area with respect to observations and according to well-defined statistical criteria
The present study has focused on the ability of climate models to reproduce the ocean sea surface temperature (SST) annual cycle observed in specific sub-regions of the studied domain during the period 1975–2005 as reported in the ERSST_v3b data set
Summary
We present a methodology aimed at selecting a coherent sub-ensemble of the models involved in the Climate Model Intercomparison Project Phase 5 (CMIP5) that best represents specific observed characteristics. Sylla et al (2019) have recently shown that the intensity of the sea surface temperature (SST) seasonal cycle along the coasts of Senegal and Mauritania was a good marker of the upwelling in this specific region in climate models. They have used this index together with other more dynamical indices to predict that the upwelling will decrease by about 10 % of its present-day amplitude by the end of the 21st century.
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