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Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis
Author(s) -
Furrer R.,
Knutti R.,
Sain S. R.,
Nychka D. W.,
Meehl G. A.
Publication year - 2007
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1029/2006gl027754
Subject(s) - markov chain monte carlo , bayesian probability , climate change , multivariate statistics , probabilistic logic , scale (ratio) , markov chain , spatial ecology , environmental science , bayesian inference , climate model , monte carlo method , statistics , computer science , climatology , mathematics , geography , geology , cartography , ecology , oceanography , biology
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.