Climate model evaluation

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Climate models can be extremely complex, and involve interactions and feedbacks among different components of the climate system. The resulting climate predictions are often made using the outputs of 20+ different climate models, which leads to a wide spread of future climate projections. However, since some components are shared among some climate models, the multi-model mean response is not truly independent[1]. ML can help identify and leverage relationships between variables within climate models[2][3], which, together with the observed climate changes (i.e., observational constraint) could narrow down the spread in the future climate projections[4].

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  1. Knutti, Reto (2010-10-01). "The end of model democracy?". Climatic Change. 102 (3): 395–404. doi:10.1007/s10584-010-9800-2. ISSN 1573-1480.
  2. Nowack, Peer; Runge, Jakob; Eyring, Veronika; Haigh, Joanna D. (2020-03-16). "Causal networks for climate model evaluation and constrained projections". Nature Communications. 11 (1): 1415. doi:10.1038/s41467-020-15195-y. ISSN 2041-1723.
  3. Schlund, Manuel; Lauer, Axel; Gentine, Pierre; Sherwood, Steven C.; Eyring, Veronika (2020-12-21). "Emergent constraints on equilibrium climate sensitivity in CMIP5: do they hold for CMIP6?". Earth System Dynamics. 11 (4): 1233–1258. doi:10.5194/esd-11-1233-2020. ISSN 2190-4979.
  4. Schlund, Manuel; Eyring, Veronika; Camps‐Valls, Gustau; Friedlingstein, Pierre; Gentine, Pierre; Reichstein, Markus (2020). "Constraining Uncertainty in Projected Gross Primary Production With Machine Learning". Journal of Geophysical Research: Biogeosciences. 125 (11): e2019JG005619. doi:10.1029/2019JG005619. ISSN 2169-8961.