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

Stefano Castruccio, David J. McInerney, Michael L. Stein, Feifei Liu Crouch, Robert L. Jacob, Elisabeth J. Moyer

Research output: Contribution to journalArticlepeer-review

74 Scopus citations


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.
Original languageEnglish (US)
Pages (from-to)1829-1844
Number of pages16
JournalJournal of Climate
Issue number5
StatePublished - Mar 2014

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KAUST Repository Item: Exported on 2020-10-01


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