Spatial Hierarchical Bayes Model for AOGCM Climate Projections

• Activity Number: 398
• Type: Topic Contributed
• Date/Time: Thursday, August 12, 2004 : 8:30 AM to 10:20 AM
• Sponsor: Section on Statistics and the Environment
• Author(s): Reinhard Furrer*+ and Stephan R. Sain and Tom M.L. Wigley and Doug Nychka
• Companies: University Corporation for Atmospheric Research and University of Colorado, Denver and University Corporation for Atmospheric Research and National Center for Atmospheric Research
• Keywords: hierarchical Bayes ; climate change ; Gibbs sampler ; spatial processes ; general circulation models

Abstract

Numerical experiments based on atmospheric-ocean general circulation models (AOGCMs) are one of the primary tools in deriving projections for future climate change. However, each model has its strengths and weaknesses within local and global scales. This motivates climate projections synthesized from results of several AOGCMs' output weighted according to model bias and convergence. We combine present day observations, present day, and future climate projections in a single hierarchical Bayes model. The challenging aspect is the modeling of a meaningful covariance structure of the spatial processes. We propose several approaches. The posterior distributions are obtained with computer-intensive MCMC simulations. The novelty of our approach is that we use gridded, high-resolution data within a spatial framework. The primary data source is provided by the MAGICC/SCENGEN program and consists of 17 AOGCMs on a 5x5 degree grid under several different emission scenarios. We consider variables such as the precipitation, temperature, and min/max thereof. Extensions such as a multivariate approach and heavy-tailed error distributions are discussed.