globalchange  > 气候变化事实与影响
DOI: 10.1175/JCLI-D-13-00099.1
Scopus记录号: 2-s2.0-84897584942
论文题名:
Statistical emulation of climate model projections based on precomputed GCM runs
作者: Castruccio S.; McInerney D.J.; Stein M.L.; Crouch F.L.; Jacob R.L.; Moyer E.J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期:5
起始页码: 1829
结束页码: 1844
语种: 英语
Scopus关键词: Climate anomalies ; Climate impacts ; Climate projection ; Computationally efficient ; Nonlinear evolutions ; Policy analysis ; Spatial patterns ; Statistical modeling ; Carbon dioxide ; Climate models ; air temperature ; climate effect ; climate modeling ; climate prediction ; general circulation model ; geostatistics ; global climate ; nonlinearity ; precipitation (climatology) ; weather forecasting
英文摘要: 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. © 2014 American Meteorological Society.
资助项目: NSF, National Science Foundation ; NSF, National Science Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50924
Appears in Collections:气候变化事实与影响

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作者单位: Department of Statistics, University of Chicago, Chicago, IL, United States; Department of the Geophysical Sciences, University of Chicago, Chicago, IL, United States; Department of Statistics, University of Chicago, Chicago, IL, United States; Mathematics and Computer Science Division, Argonne National Laboratory, Argonne, IL, United States; CEMSE division, King Abdullah University of Science and Technology, Thuwal, Saudi Arabia; Department of Civil, Environmental and Mining Engineering, University of Adelaide, Adelaide, SA, Australia

Recommended Citation:
Castruccio S.,McInerney D.J.,Stein M.L.,et al. Statistical emulation of climate model projections based on precomputed GCM runs[J]. Journal of Climate,2014-01-01,27(5)
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