Articles | Volume 19, issue 17
https://doi.org/10.5194/gmd-19-8167-2026
https://doi.org/10.5194/gmd-19-8167-2026
Model evaluation paper
 | 
03 Sep 2026
Model evaluation paper |  | 03 Sep 2026

A barycenter-based approach for the multi-model ensembling of subseasonal forecasts

Camille Le Coz, Alexis Tantet, Rémi Flamary, and Riwal Plougonven

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Cited articles

Agueh, M. and Carlier, G.: Barycenters in the Wasserstein Space, SIAM J. Math. Anal., 43, 904–924, https://doi.org/10.1137/100805741, 2011. a, b
Alessandri, A., Borrelli, A., Navarra, A., Arribas, A., Déqué, M., Rogel, P., and Weisheimer, A.: Evaluation of Probabilistic Quality and Value of the ENSEMBLES Multimodel Seasonal Forecasts: Comparison with DEMETER, Mon. Weather Rev., 139, 581–607, https://doi.org/10.1175/2010MWR3417.1, 2011. a
Backhoff-Veraguas, J., Fontbona, J., Rios, G., and Tobar, F.: Bayesian learning with Wasserstein barycenters*, ESAIM: PS, 26, 436–472, https://doi.org/10.1051/ps/2022015, 2022. a
Becker, E., van den Dool, H., and Zhang, Q.: Predictability and Forecast Skill in NMME, J. Climate, 27, 5891–5906, https://doi.org/10.1175/JCLI-D-13-00597.1, 2014. a
Bertino, L., Evensen, G., and Wackernagel, H.: Sequential Data Assimilation Techniques in Oceanography, Int. Stat. Rev., 71, 223–241, https://doi.org/10.1111/j.1751-5823.2003.tb00194.x, 2003. a
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Short summary
We explore an alternative framework for constructing multi-model ensembles by formulating ensemble combination as a barycenter problem. We compare the L2 barycenter (equivalent to pooling) with the Wasserstein barycenter (more precisely its Gaussian approximation). Both have the same ensemble mean but differ in how they represent forecasts uncertainty. In terms of Continuous Ranked Probability Score, the Wasserstein barycenter outperforms more often while performing similarly on average.
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