| DOI: | 10.1175/JCLI-D-13-00063.1
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| Scopus记录号: | 2-s2.0-84892449535
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| 论文题名: | Downscaling of GCM-simulated precipitation using model output statistics |
| 作者: | Eden J.M.; Widmann M.
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| 刊名: | Journal of Climate
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| ISSN: | 8948755
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| 出版年: | 2014
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| 卷: | 27, 期:1 | | 起始页码: | 312
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| 结束页码: | 324
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| 语种: | 英语
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| Scopus关键词: | Climate change simulations
; General circulation model
; Maximum covariance analysis
; Model output statistics
; Principal component regression
; Statistical downscaling
; Statistical relationship
; Statistical techniques
; Climate change
; Climate models
; Precipitation (chemical)
; Principal component analysis
; Statistics
; Computer simulation
; air temperature
; atmospheric circulation
; climate change
; correlation
; downscaling
; general circulation model
; precipitation assessment
; principal component analysis
; statistical analysis
; temporal variation
; Australia
; Europe
; North America
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| 英文摘要: | Producing reliable estimates of changes in precipitation at local and regional scales remains an important challenge in climate science. Statistical downscaling methods are often utilized to bridge the gap between the coarse resolution of general circulation models (GCMs) and the higher resolutions at which information is required by end users. As the skill ofGCMprecipitation, particularly in simulating temporal variability, is not fully understood, statistical downscaling typically adopts a perfect prognosis (PP) approach in which highresolution precipitation projections are based on real-world statistical relationships between large-scale atmospheric predictors and local-scale precipitation. Using a nudged simulation of the ECHAM5 GCM, in which the large-scale weather states are forced toward observations of large-scale circulation and temperature for the period 1958-2001, previous work has shown ECHAM5 skill in simulating temporal variability of precipitation to be high in many parts of the world. Here, the same nudged simulation is used in an alternative downscaling approach, based on model output statistics (MOS), in which statistical corrections are derived for simulated precipitation. Cross-validated MOS corrections based on maximum covariance analysis (MCA) and principal component regression (PCR), in addition to a simple local scaling, are shown to perform strongly throughout much of the extratropics. Correlation between downscaled and observed monthly-mean precipitation is as high as 0.8-0.9 in many parts of Europe, North America, and Australia. For these regions, MOS clearly outperforms PP methods that use temperature and circulation as predictors. The strong performance of MOS makes such an approach to downscaling attractive and potentially applicable to climate change simulations. © 2014 American Meteorological Society. |
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| 资源类型: | 期刊论文
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/51074
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| Appears in Collections: | 气候变化事实与影响
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作者单位: | School of Geography, Earth and Environmental Sciences, University of Birmingham, Birmingham, United Kingdom
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| Recommended Citation: | |
Eden J.M.,Widmann M.. Downscaling of GCM-simulated precipitation using model output statistics[J]. Journal of Climate,2014-01-01,27(1)
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