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A spatial analysis of multivariate output from regional climate models

Journal Article


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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.

Authors


  •   Sain, Stephan R. (external author)
  •   Furrer, Reinhard (external author)
  •   Cressie, Noel A.

Publication Date


  • 2011

Citation


  • Sain, S., Furrer, R. & Cressie, N. A. (2011). A spatial analysis of multivariate output from regional climate models. Annals Of Applied Statistics, 5 (1), 150-175.

Scopus Eid


  • 2-s2.0-83555179381

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=10124&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2788

Number Of Pages


  • 25

Start Page


  • 150

End Page


  • 175

Volume


  • 5

Issue


  • 1

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.

Authors


  •   Sain, Stephan R. (external author)
  •   Furrer, Reinhard (external author)
  •   Cressie, Noel A.

Publication Date


  • 2011

Citation


  • Sain, S., Furrer, R. & Cressie, N. A. (2011). A spatial analysis of multivariate output from regional climate models. Annals Of Applied Statistics, 5 (1), 150-175.

Scopus Eid


  • 2-s2.0-83555179381

Ro Full-text Url


  • http://ro.uow.edu.au/cgi/viewcontent.cgi?article=10124&context=infopapers

Ro Metadata Url


  • http://ro.uow.edu.au/infopapers/2788

Number Of Pages


  • 25

Start Page


  • 150

End Page


  • 175

Volume


  • 5

Issue


  • 1