Skip to main content
Article thumbnail
Location of Repository

A spatial analysis of multivariate output from regional climate models

By Stephan R. Sain, Reinhard Furrer and Noel Cressie

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.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS369 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Topics: Statistics - Applications
Year: 2011
DOI identifier: 10.1214/10-AOAS369
OAI identifier: oai:arXiv.org:1104.2703
Download PDF:
Sorry, we are unable to provide the full text but you may find it at the following location(s):
  • http://arxiv.org/abs/1104.2703 (external link)
  • http://www.imstat.org) (external link)
  • http://www.imstat.org/aoas/) (external link)
  • http://dx.doi.org/10.1214/10-A... (external link)
  • Suggested articles


    To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.