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Activity Number: 59
Type: Topic Contributed
Date/Time: Sunday, August 2, 2009 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics and the Environment
Abstract - #304296
Title: Hierarchical Spatial Random Effects Model for High-Resolution Data from Regional Climate Models
Author(s): Emily Lei Kang*+ and Noel A. Cressie and Stephan R. Sain
Companies: The Ohio State University and The Ohio State University and National Center for Atmospheric Research
Address: 1958 Neil Avenue, Columbus, OH, 43210,
Keywords: Bayesian prediction ; climate models ; MCMC ; posterior distribution ; spatial random effects model
Abstract:

Driven by atmosphere-ocean general circulation models (AOGCMs), regional climate models (RCMs) address the climate system over a smaller spatial area and, thanks to higher resolution, they capture the local features better. In this talk, we propose a hierarchical spatial random effects model to study the NCEP-driven RCM outputs in the North American Regional Climate Change Assessment Program (NARCCAP). Spatial random effects terms are included to characterize the results from different RCMs, and the reduction in dimension makes the computations efficient. Our analysis allows us to combine different RCMs and to study the internal variability for each RCM. In this talk, the mean seasonal surface temperature is the climate variable of interest.

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