Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis
Article first published online: 31 MAR 2007
Copyright 2007 by the American Geophysical Union.
Geophysical Research Letters
Volume 34, Issue 6, March 2007
How to Cite
2007), Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis, Geophys. Res. Lett., 34, L06711, doi:10.1029/2006GL027754., , , , and (
- Issue published online: 31 MAR 2007
- Article first published online: 31 MAR 2007
- Manuscript Accepted: 4 FEB 2007
- Manuscript Revised: 18 JAN 2007
- Manuscript Received: 2 AUG 2006
- hierarchical model;
- spatial process;
 We present probabilistic projections for spatial patterns of future temperature change using a multivariate Bayesian analysis. The methodology is applied to the output from 21 global coupled climate models used for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. The statistical technique is based on the assumption that spatial patterns of climate change can be separated into a large scale signal related to the true forced climate change and a small scale signal due to model bias and variability. The different scales are represented via dimension reduction techniques in a hierarchical Bayesian model. Posterior probabilities are obtained with a Markov chain Monte Carlo simulation. We show that with 66% (90%) probability 79% (48%) of the land areas warm by more than 2°C by the end of the century for the SRES A1B scenario.