Editorial Type:
Article
Article Type:
Research Article

Probabilistic Multimodel Regional Temperature Change Projections

Arthur M. Greene The International Research Institute for Climate and Society, The Earth Institute at Columbia University, Palisades, New York

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Lisa Goddard The International Research Institute for Climate and Society, The Earth Institute at Columbia University, Palisades, New York

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Upmanu Lall The International Research Institute for Climate and Society, The Earth Institute at Columbia University, Palisades, New York

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Print Publication:
01 Sep 2006
DOI:
https://doi.org/10.1175/JCLI3864.1
Page(s):
4326–4343
Restricted access

Abstract

Regional temperature change projections for the twenty-first century are generated using a multimodel ensemble of atmosphere–ocean general circulation models. The models are assigned coefficients jointly, using a Bayesian linear model fitted to regional observations and simulations of the climate of the twentieth century. Probability models with varying degrees of complexity are explored, and a selection is made based on Bayesian deviance statistics, coefficient properties, and a classical cross-validation measure utilizing temporally averaged data. The model selected is shown to be superior in predictive skill to a naïve model consisting of the unweighted mean of the underlying atmosphere–ocean GCM (AOGCM) simulations, although the skill differential varies regionally. Temperature projections for the A2 and B1 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios are presented.

Corresponding author address: Arthur M. Greene, The International Research Institute for Climate and Society, The Earth Institute at Columbia University, 202 Monell Building, 61 Route 9W, Palisades, NY 10964. Email: amg@iri.columbia.edu

Abstract

Regional temperature change projections for the twenty-first century are generated using a multimodel ensemble of atmosphere–ocean general circulation models. The models are assigned coefficients jointly, using a Bayesian linear model fitted to regional observations and simulations of the climate of the twentieth century. Probability models with varying degrees of complexity are explored, and a selection is made based on Bayesian deviance statistics, coefficient properties, and a classical cross-validation measure utilizing temporally averaged data. The model selected is shown to be superior in predictive skill to a naïve model consisting of the unweighted mean of the underlying atmosphere–ocean GCM (AOGCM) simulations, although the skill differential varies regionally. Temperature projections for the A2 and B1 scenarios from the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios are presented.

Corresponding author address: Arthur M. Greene, The International Research Institute for Climate and Society, The Earth Institute at Columbia University, 202 Monell Building, 61 Route 9W, Palisades, NY 10964. Email: amg@iri.columbia.edu

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