A spatial analysis of multivariate output from regional climate models

Um dos papers do seminário desta sexta-feira:

Sain, Furrerr, Cressie (2011) Annals of Applied Statistics Volume 5, Number 1 (2011), 150-175.

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A spatial analysis of multivariate output from regional climate models
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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.

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