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

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.

[1]  D. Nychka,et al.  Multivariate Bayesian analysis of atmosphere–ocean general circulation models , 2007, Environmental and Ecological Statistics.

[2]  R. Rosso,et al.  Wind control of storm‐triggered shallow landslides , 2007 .

[3]  C. Tebaldi,et al.  Two Approaches to Quantifying Uncertainty in Global Temperature Changes , 2006 .

[4]  Upmanu Lall,et al.  Probabilistic multimodel regional temperature change projections , 2006 .

[5]  P. Stott,et al.  Uncertainty in continental‐scale temperature predictions , 2006 .

[6]  Reto Knutti,et al.  Probabilistic climate change projections for CO2 stabilization profiles , 2005 .

[7]  Richard L. Smith,et al.  Quantifying Uncertainty in Projections of Regional Climate Change: A Bayesian Approach to the Analysis of Multimodel Ensembles , 2005 .

[8]  Sw. Banerjee,et al.  Hierarchical Modeling and Analysis for Spatial Data , 2003 .

[9]  F. Joos,et al.  Probabilistic climate change projections using neural networks , 2003 .

[10]  F. Giorgi,et al.  Probability of regional climate change based on the Reliability Ensemble Averaging (REA) method , 2003 .

[11]  P. Stott,et al.  Origins and estimates of uncertainty in predictions of twenty-first century temperature rise , 2002, Nature.

[12]  Reto Knutti,et al.  Constraints on radiative forcing and future climate change from observations and climate model ensembles , 2002, Nature.

[13]  Andrei P. Sokolov,et al.  Quantifying Uncertainties in Climate System Properties with the Use of Recent Climate Observations , 2002, Science.

[14]  T. Palmer,et al.  A Probability and Decision-Model Analysis of a Multimodel Ensemble of Climate Change Simulations , 2001 .

[15]  T. Wigley,et al.  Interpretation of High Projections for Global-Mean Warming , 2001, Science.

[16]  Raquel V. Francisco,et al.  Uncertainties in regional climate change prediction: a regional analysis of ensemble simulations with the HADCM2 coupled AOGCM , 2000 .