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Title: 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 CO 2 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. In conclusion, it may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.
Authors:
 [1] ;  [2] ;  [3] ;  [3] ;  [4] ;  [3]
  1. Univ. of Chicago, Chicago, IL (United States); King Abdullah Univ. of Science and Technology, Thuwal (Saudi Arabia)
  2. Univ. of Chicago, Chicago, IL (United States); Univ. of Adelaide, Adelaide (Australia)
  3. Univ. of Chicago, Chicago, IL (United States)
  4. Argonne National Lab. (ANL), Argonne, IL (United States)
Publication Date:
Grant/Contract Number:
AC02-06CH11357
Type:
Accepted Manuscript
Journal Name:
Journal of Climate
Additional Journal Information:
Journal Volume: 27; Journal Issue: 5; Journal ID: ISSN 0894-8755
Publisher:
American Meteorological Society
Research Org:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; Statistics; General circulation models; Model output statistics
OSTI Identifier:
1395163

Castruccio, Stefano, McInerney, David J., Stein, Michael L., Crouch, Feifei Liu, Jacob, Robert L., and Moyer, Elisabeth J.. Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*. United States: N. p., Web. doi:10.1175/JCLI-D-13-00099.1.
Castruccio, Stefano, McInerney, David J., Stein, Michael L., Crouch, Feifei Liu, Jacob, Robert L., & Moyer, Elisabeth J.. Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*. United States. doi:10.1175/JCLI-D-13-00099.1.
Castruccio, Stefano, McInerney, David J., Stein, Michael L., Crouch, Feifei Liu, Jacob, Robert L., and Moyer, Elisabeth J.. 2014. "Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*". United States. doi:10.1175/JCLI-D-13-00099.1. https://www.osti.gov/servlets/purl/1395163.
@article{osti_1395163,
title = {Statistical Emulation of Climate Model Projections Based on Precomputed GCM Runs*},
author = {Castruccio, Stefano and McInerney, David J. and Stein, Michael L. and Crouch, Feifei Liu and Jacob, Robert L. and Moyer, Elisabeth J.},
abstractNote = {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. In conclusion, it may therefore find wide application in climate impacts assessments and other policy analyses requiring rapid climate projections.},
doi = {10.1175/JCLI-D-13-00099.1},
journal = {Journal of Climate},
number = 5,
volume = 27,
place = {United States},
year = {2014},
month = {2}
}