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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Preprints
https://doi.org/10.5194/gmd-2019-194
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2019-194
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 28 Oct 2019

Submitted as: methods for assessment of models | 28 Oct 2019

Review status
A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

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

Juliette Mignot1, Carlos Mejia1, Charles Sorror1, Adama Sylla1,2, Michel Crépon1, and Sylvie Thiria1,3 Juliette Mignot et al.
  • 1IPSL-LOCEAN, SU/IRS/CNRS/MNHN, Paris, France
  • 2LPAO-SF, ESP, UCAD, Dakar, Sénégal
  • 3UVSQ, F-78035, Versailles, France

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.

Juliette Mignot et al.

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AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Juliette Mignot et al.

Model code and software

ClimModEns v1.0 M. Carlos and S. Charles https://doi.org/10.5281/zenodo.3476724

Juliette Mignot et al.

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Latest update: 01 Jun 2020
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Short summary
The most robust representation of cliamte is susally obtaine by averaging a large number of simulations, thereby cancelling individual models errors. Here, we propose an objective way of selecting the least biased models over a certain region, based on physical parameter. This statistical method based on a neural classifier and multi-correspondence analysis is illustrated here for the senegalo-mauritanian region but it could potentially be developped for any other physical process.
The most robust representation of cliamte is susally obtaine by averaging a large number of...
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