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March 2011 A spatial analysis of multivariate output from regional climate models
Stephan R. Sain, Reinhard Furrer, Noel Cressie
Ann. Appl. Stat. 5(1): 150-175 (March 2011). DOI: 10.1214/10-AOAS369

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.

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Stephan R. Sain. Reinhard Furrer. Noel Cressie. "A spatial analysis of multivariate output from regional climate models." Ann. Appl. Stat. 5 (1) 150 - 175, March 2011. https://doi.org/10.1214/10-AOAS369

Information

Published: March 2011
First available in Project Euclid: 21 March 2011

zbMATH: 1220.62152
MathSciNet: MR2810393
Digital Object Identifier: 10.1214/10-AOAS369

Keywords: Bayesian hierarchical model , Climate change , conditional autoregressive (CAR) model , Lattice data , Markov random field (MRF)

Rights: Copyright © 2011 Institute of Mathematical Statistics

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