Multivariate Bayesian Analysis of Atmosphere-Ocean General Circulation Models

Reinhard Furrer, GSP post doc

Numerical experiments based on atmospheric-ocean general circulation models (AOGCMs) are one of the primary tools in deriving projections for future climate change. The development of such models requires the collaboration of scientists in various domains and are examples of longterm interdisciplinary work. Given the huge size of the problem, the models can only be run on super computers available at only a few research centers. Although each model has the same underlying partial differential equations, modeling large scale effects, they have different small scale parameterizations and different discretizations to solve the equations, resulting in different projections. Each model has its strengths and weaknesses within local and global scales. This motivates climate projections synthesized from results of several AOGCMs' output. We combine present day observations, present day and future climate projections in a single hierarchical Bayes model. The challenging aspect is the modeling of the spatial processes on the sphere and the amount of data involved. Samples from 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 hierarchical framework. The primary data source is provided by the Coupled Model Intercomparison Project (CMIP) and consists of 9 AOGCMs on a 2.8 by 2.8 degree grid under several different emission scenarios. In this talk we consider mean seasonal surface temperature as a climate variable.