## 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.