Editorial Type:
Article
Article Type:
Research Article

Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs

Stefano Castruccio Department of Statistics, University of Chicago, Chicago, Illinois

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David J. McInerney Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois

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Michael L. Stein Department of Statistics, University of Chicago, Chicago, Illinois

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Feifei Liu Crouch Department of Statistics, University of Chicago, Chicago, Illinois

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Robert L. Jacob Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinois

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Elisabeth J. Moyer Department of the Geophysical Sciences, University of Chicago, Chicago, Illinois

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Print Publication:
01 Mar 2014
DOI:
https://doi.org/10.1175/JCLI-D-13-00099.1
Page(s):
1829–1844
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Abstract

The authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-13-00099.s1.

Current affiliation: CEMSE division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Current affiliation: Department of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia.

Corresponding author address: Elisabeth Moyer, Department of the Geophysical Sciences, University of Chicago, 5734 S. Ellis Ave., Chicago, IL 60637. E-mail: moyer@uchicago.edu

Abstract

The authors describe a new approach for emulating the output of a fully coupled climate model under arbitrary forcing scenarios that is based on a small set of precomputed runs from the model. Temperature and precipitation are expressed as simple functions of the past trajectory of atmospheric CO2 concentrations, and a statistical model is fit using a limited set of training runs. The approach is demonstrated to be a useful and computationally efficient alternative to pattern scaling and captures the nonlinear evolution of spatial patterns of climate anomalies inherent in transient climates. The approach does as well as pattern scaling in all circumstances and substantially better in many; it is not computationally demanding; and, once the statistical model is fit, it produces emulated climate output effectively instantaneously. It may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JCLI-D-13-00099.s1.

Current affiliation: CEMSE division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia.

Current affiliation: Department of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia.

Corresponding author address: Elisabeth Moyer, Department of the Geophysical Sciences, University of Chicago, 5734 S. Ellis Ave., Chicago, IL 60637. E-mail: moyer@uchicago.edu

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