Open Access
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


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.


Download Citation

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.


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

Vol.5 • No. 1 • March 2011
Back to Top