DOI: 10.1016/j.gloplacha.2012.11.003
论文题名: Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast
作者: Ahmed K.F. ; Wang G. ; Silander J. ; Wilson A.M. ; Allen J.M. ; Horton R. ; Anyah R.
刊名: Global and Planetary Change
ISSN: 0921-8374
出版年: 2013
卷: 100 起始页码: 320
结束页码: 332
语种: 英语
英文关键词: Bias correction
; Climate change impact analysis
; Extreme climate index
; Statistical downscaling
Scopus关键词: Bias correction
; Climate change impact
; Climate index
; Dynamical downscaling
; Future climate
; General circulation model
; Model outputs
; Multiple models
; Precipitation data
; Regional climate models
; Regional impacts
; Spatial patterns
; Spatial resolution
; Spatial scale
; Statistical downscaling
; Climate change
; Statistics
; Climate models
; climate change
; climate modeling
; downscaling
; environmental impact assessment
; error correction
; general circulation model
; precipitation intensity
; regional climate
; statistical analysis
; United States
英文摘要: Statistical downscaling can be used to efficiently downscale a large number of General Circulation Model (GCM) outputs to a fine temporal and spatial scale. To facilitate regional impact assessments, this study statistically downscales (to 1/8° spatial resolution) and corrects the bias of daily maximum and minimum temperature and daily precipitation data from six GCMs and four Regional Climate Models (RCMs) for the northeast United States (US) using the Statistical Downscaling and Bias Correction (SDBC) approach. Based on these downscaled data from multiple models, five extreme indices were analyzed for the future climate to quantify future changes of climate extremes. For a subset of models and indices, results based on raw and bias corrected model outputs for the present-day climate were compared with observations, which demonstrated that bias correction is important not only for GCM outputs, but also for RCM outputs. For future climate, bias correction led to a higher level of agreements among the models in predicting the magnitude and capturing the spatial pattern of the extreme climate indices. We found that the incorporation of dynamical downscaling as an intermediate step does not lead to considerable differences in the results of statistical downscaling for the study domain. © 2012 Elsevier B.V. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84870853962&doi=10.1016%2fj.gloplacha.2012.11.003&partnerID=40&md5=4ba78c94ba62221ba1f8889075853b84
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/11280
Appears in Collections: 全球变化的国际研究计划
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作者单位: Department of Civil and Environmental Engineering, University of Connecticut, United States
Recommended Citation:
Ahmed K.F.,Wang G.,Silander J.,et al. Statistical downscaling and bias correction of climate model outputs for climate change impact assessment in the U.S. northeast[J]. Global and Planetary Change,2013-01-01,100.