Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*

Handle URI:
http://hdl.handle.net/10754/552718
Title:
Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*
Authors:
Castruccio, Stefano; McInerney, David J.; Stein, Michael L.; Liu Crouch, Feifei; Jacob, Robert L.; Moyer, Elisabeth J.
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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
Citation:
Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs* 2014, 27 (5):1829 Journal of Climate
Publisher:
American Meteorological Society
Journal:
Journal of Climate
Issue Date:
Mar-2014
DOI:
10.1175/JCLI-D-13-00099.1
Type:
Article
ISSN:
0894-8755; 1520-0442
Additional Links:
http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00099.1
Appears in Collections:
Articles; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorCastruccio, Stefanoen
dc.contributor.authorMcInerney, David J.en
dc.contributor.authorStein, Michael L.en
dc.contributor.authorLiu Crouch, Feifeien
dc.contributor.authorJacob, Robert L.en
dc.contributor.authorMoyer, Elisabeth J.en
dc.date.accessioned2015-05-14T06:16:22Zen
dc.date.available2015-05-14T06:16:22Zen
dc.date.issued2014-03en
dc.identifier.citationStatistical Emulation of Climate Model Projections Based on Precomputed GCM Runs* 2014, 27 (5):1829 Journal of Climateen
dc.identifier.issn0894-8755en
dc.identifier.issn1520-0442en
dc.identifier.doi10.1175/JCLI-D-13-00099.1en
dc.identifier.urihttp://hdl.handle.net/10754/552718en
dc.description.abstractThe 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.en
dc.publisherAmerican Meteorological Societyen
dc.relation.urlhttp://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-13-00099.1en
dc.rights© Copyright 2014 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act September 2010 Page 2 or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyrights@ametsoc.org.en
dc.subjectStatisticsen
dc.subjectGeneral circulation modelsen
dc.subjectModel output statisticsen
dc.titleStatistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*en
dc.typeArticleen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.identifier.journalJournal of Climateen
dc.eprint.versionPublisher's Version/PDFen
dc.contributor.institutionDepartment of Statistics, University of Chicago, Chicago, Illinoisen
dc.contributor.institutionDepartment of the Geophysical Sciences, University of Chicago, Chicago, Illinoisen
dc.contributor.institutionDepartment of Statistics, University of Chicago, Chicago, Illinoisen
dc.contributor.institutionMathematics and Computer Science Division, Argonne National Laboratory, Argonne, Illinoisen
dc.contributor.institutionDepartment of the Geophysical Sciences, University of Chicago, Chicago, Illinoisen
dc.contributor.institutionDepartment of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, South Australia, Australia.en
kaust.authorCastruccio, Stefanoen
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