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Climate models have become an important tool in the study of climate andclimate change, and ensemble experiments consisting of multiple climate-modelruns are used in studying and quantifying the uncertainty in climate-modeloutput. However, there are often only a limited number of model runs availablefor a particular experiment, and one of the statistical challenges is tocharacterize the distribution of the model output. To that end, we havedeveloped a multivariate hierarchical approach, at the heart of which is a newrepresentation of a multivariate Markov random field. This approach allows forflexible modeling of the multivariate spatial dependencies, including thecross-dependencies between variables. We demonstrate this statistical model onan ensemble arising from a regional-climate-model experiment over the westernUnited States, and we focus on the projected change in seasonal temperature andprecipitation over the next 50 years.

A spatial analysis of multivariate output from regional climate models