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- W4300483642 abstract "It has been argued persuasively that, in order to evaluate climate models, the probability distributions of model output need to be compared to the corresponding empirical distributions of observed data. Distance measures between probability distributions, also called divergence functions, can be used for this purpose. We contend that divergence functions ought to be proper, in the sense that acting on modelers' true beliefs is an optimal strategy. Score divergences that derive from proper scoring rules are proper, with the integrated quadratic distance and the Kullback-Leibler divergence being particularly attractive choices. Other commonly used divergences fail to be proper. In an illustration, we evaluate and rank simulations from fifteen climate models for temperature extremes in a comparison to re-analysis data." @default.
- W4300483642 created "2022-10-03" @default.
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- W4300483642 date "2013-01-24" @default.
- W4300483642 modified "2023-09-24" @default.
- W4300483642 title "Using proper divergence functions to evaluate climate models" @default.
- W4300483642 doi "https://doi.org/10.48550/arxiv.1301.5927" @default.
- W4300483642 hasPublicationYear "2013" @default.
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