Environmental and Ecological Statistics

, Volume 14, Issue 3, pp 249–266

Multivariate Bayesian analysis of atmosphere–ocean general circulation models

  • Reinhard Furrer
  • Stephan R. Sain
  • Douglas Nychka
  • Gerald A. Meehl
Article

DOI: 10.1007/s10651-007-0018-z

Cite this article as:
Furrer, R., Sain, S.R., Nychka, D. et al. Environ Ecol Stat (2007) 14: 249. doi:10.1007/s10651-007-0018-z

Abstract

Numerical experiments based on atmosphere–ocean general circulation models (AOGCMs) are one of the primary tools in deriving projections for future climate change. Although each AOGCM 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 climate projections. 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 highdimensional hierarchical Bayes model. The challenging aspect is the modeling of the spatial processes on the sphere, the number of parameters and the amount of data involved. We pursue a Bayesian hierarchical model that separates the spatial response into a large scale climate change signal and an isotropic process representing small scale variability among AOGCMs. 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 covering the entire sphere 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 article we consider mean seasonal surface temperature and precipitation as climate variables. Extensions to our model are also discussed.

Keywords

Climate change Spatial process Spherical covariance Hierarchical model Large datasets MCMC 

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  • Reinhard Furrer
    • 1
    • 2
  • Stephan R. Sain
    • 2
    • 3
  • Douglas Nychka
    • 2
  • Gerald A. Meehl
    • 2
  1. 1.Colorado School of MinesGoldenUSA
  2. 2.National Center for Atmospheric ResearchBoulderUSA
  3. 3.University of Colorado at DenverDenverUSA