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• W3105287796 abstract "Climate models have become an important tool in the study of climate and climate change, and ensemble experiments consisting of multiple climate-model runs are used in studying and quantifying the uncertainty in climate-model output. However, there are often only a limited number of model runs available for a particular experiment, and one of the statistical challenges is to characterize the distribution of the model output. To that end, we have developed a multivariate hierarchical approach, at the heart of which is a new representation of a multivariate Markov random field. This approach allows for flexible modeling of the multivariate spatial dependencies, including the cross-dependencies between variables. We demonstrate this statistical model on an ensemble arising from a regional-climate-model experiment over the western United States, and we focus on the projected change in seasonal temperature and precipitation over the next 50 years." @default.
• W3105287796 created "2020-11-23" @default.
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• W3105287796 date "2011-03-01" @default.
• W3105287796 modified "2023-10-13" @default.
• W3105287796 title "A spatial analysis of multivariate output from regional climate models" @default.
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• W3105287796 doi "https://doi.org/10.1214/10-aoas369" @default.
• W3105287796 hasPublicationYear "2011" @default.
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