Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs

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 c...

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Bibliographic Details
Published in:Journal of climate 2014-03, Vol.27 (5), p.1829-1844
Main Authors: Castruccio, Stefano, McInerney, David J., Stein, Michael L., Crouch, Feifei Liu, Jacob, Robert L., Moyer, Elisabeth J.
Format: Article
Language:English
Subjects:
Anomalies
Carbon dioxide
Carbon dioxide concentration
Climate
Climate change
Climate change policy
Climate models
Climatic analysis
Climatic zones
Climatology. Bioclimatology. Climate change
Colleges & universities
Earth, ocean, space
ENVIRONMENTAL SCIENCES
Exact sciences and technology
External geophysics
General circulation models
Geophysics. Techniques, methods, instrumentation and models
Global climate models
Global warming
Mathematical models
Meteorology
Model output statistics
Modelling
Oceans
Precipitation
Scaling
Statistical models
Statistics
Trajectories
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Summary: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.
ISSN:0894-8755
1520-0442
DOI:10.1175/jcli-d-13-00099.1