Multivariate Bayesian analysis of atmosphere–ocean general circulation models |
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Authors: | Reinhard Furrer Stephan R Sain Douglas Nychka Gerald A Meehl |
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Institution: | (1) Colorado School of Mines, Golden, CO 80401-1887, USA;(2) National Center for Atmospheric Research, Boulder, CO 80307-3000, USA;(3) University of Colorado at Denver, Denver, CO 80217-3364, USA |
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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. |
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Keywords: | Climate change Spatial process Spherical covariance Hierarchical model Large datasets MCMC |
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