Articles | Volume 17, issue 5
https://doi.org/10.5194/esd-17-1589-2026
https://doi.org/10.5194/esd-17-1589-2026
Research article
 | 
28 Oct 2026
Research article |  | 28 Oct 2026

On combining climate models into weighted ensembles

Britta Grusdt, Mahé Perrette, and Alexander Robinson

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Cited articles

Abramowitz, G., Herger, N., Gutmann, E., Hammerling, D., Knutti, R., Leduc, M., Lorenz, R., Pincus, R., and Schmidt, G. A.: ESD Reviews: Model Dependence in Multi-Model Climate Ensembles: Weighting, Sub-Selection and out-of-Sample Testing, Earth Syst. Dynam., 10, 91–105, https://doi.org/10.5194/esd-10-91-2019, 2019. a, b
Bissiri, P. G., Holmes, C. C., and Walker, S. G.: A General Framework for Updating Belief Distributions, J. Roy. Stat. Soc. B, 78, 1103–1130, https://doi.org/10.1111/rssb.12158, 2016. a
Boé, J.: Interdependency in Multimodel Climate Projections: Component Replication and Result Similarity, Geophys. Res. Lett., 45, 2771–2779, https://doi.org/10.1002/2017GL076829, 2018. a
brittaGrusdt: awi-esc/WeightedEnsembles: code and data for final paper, Version v1.0.0, Zenodo [computer software], https://doi.org/10.5281/zenodo.23334598, 2026. a
Brunner, L., Pendergrass, A. G., Lehner, F., Merrifield, A. L., Lorenz, R., and Knutti, R.: Reduced Global Warming from CMIP6 Projections When Weighting Models by Performance and Independence, Earth Syst. Dynam., 11, 995–1012, https://doi.org/10.5194/esd-11-995-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l
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We provide practical guidelines for producing weighted ensembles of climate models. We distinguish between two approaches, the individual- and the ensemble performance weighting approach, and formulate both in probabilistic terms, making the underlying assumptions transparent. Using constructed examples, we show the advantage of the ensemble weighting approach to account for model co-dependencies and examine how methodological choices affect the climate projections of the weighted ensemble.
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