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Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis

Furrer, R; Knutti, R; Sain, S; Nychka, D; Meehl, G A (2007). Spatial patterns of probabilistic temperature change projections from a multivariate Bayesian analysis. Geophysical Research Letters, 34(06711):1-4.

Abstract

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.

Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:07 Faculty of Science > Institute of Mathematics
Dewey Decimal Classification:510 Mathematics
Scopus Subject Areas:Physical Sciences > Geophysics
Physical Sciences > General Earth and Planetary Sciences
Language:English
Date:2007
Deposited On:10 Dec 2009 18:06
Last Modified:03 Nov 2024 02:36
Publisher:American Geophysical Union
ISSN:0094-8276
OA Status:Hybrid
Free access at:Related URL. An embargo period may apply.
Publisher DOI:https://doi.org/10.1029/2006GL027754
Related URLs:http://www.iac.ethz.ch/people/knuttir/papers
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