A SPATIAL ANALYSIS OF MULTIVARIATE OUTPUT FROM REGIONAL CLIMATE MODELS

• 成果类型: Article
• 署名作者: Sain, Stephan R.; Furrer, Reinhard; Cressie, Noel
• 署名单位: National Center Atmospheric Research (NCAR) - USA; University of Zurich; University System of Ohio; Ohio State University
• 刊物名称: ANNALS OF APPLIED STATISTICS
• ISSN/ISSBN: 1932-6157
• DOI: 10.1214/10-AOAS369
• 发表日期: 2011
• 页码: 150-175
• 关键词: bayesian-approach; Gaussian process; random-fields; precipitation; uncertainty; projections

摘要

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.