DOI: 10.1175/JCLI-D-17-0765.1
Scopus记录号: 2-s2.0-85048056701
论文题名: Climate model biases and modification of the climate change signal by intensity-dependent bias correction
作者: Ivanov M.A. ; Luterbacher J. ; Kotlarski S.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2018
卷: 31, 期: 16 起始页码: 6591
结束页码: 6610
语种: 英语
英文关键词: Atmosphere
; Bias
; Climate change
; Climatology
; Policy
; Statistics
Scopus关键词: Climate models
; Climatology
; Earth atmosphere
; Public policy
; Risk assessment
; Statistics
; Wind
; Analytical description
; Bias
; Bias-correction methods
; Climate change adaptation
; Climate change impact
; Daily precipitations
; Intensity-dependent
; Regional climate models
; Climate change
; climate change
; climate modeling
; climatology
; correction
; downscaling
; precipitation (climatology)
; regional climate
英文摘要: Climate change impact research and risk assessment require accurate estimates of the climate change signal (CCS). Raw climate model data include systematic biases that affect the CCS of high-impact variables such as daily precipitation and wind speed. This paper presents a novel, general, and extensible analytical theory of the effect of these biases on the CCS of the distribution mean and quantiles. The theory reveals that misrepresented model intensities and probability of nonzero (positive) events have the potential to distort raw model CCS estimates. We test the analytical description in a challenging application of bias correction and downscaling to daily precipitation over alpine terrain, where the output of 15 regional climate models (RCMs) is reduced to local weather stations. The theoretically predicted CCS modification well approximates the modification by the bias correction method, even for the station-RCM combinations with the largest absolute modifications. These results demonstrate that the CCS modification by bias correction is a direct consequence of removing model biases. Therefore, provided that application of intensity-dependent bias correction is scientifically appropriate, the CCS modification should be a desirable effect. The analytical theory can be used as a tool to 1) detect model biases with high potential to distort the CCS and 2) efficiently generate novel, improved CCS datasets. The latter are highly relevant for the development of appropriate climate change adaptation, mitigation, and resilience strategies. Future research needs to focus on developing process-based bias corrections that depend on simulated intensities rather than preserving the raw model CCS. © 2018 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111439
Appears in Collections: 气候减缓与适应
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作者单位: Department of Geography, Climatology, Climate Dynamics, and Climate Change, Justus-Liebig University of Giessen, Giessen, Germany; Centre for International Development and Environmental Research, Justus-Liebig University of Giessen, Giessen, Germany; Swiss Federal Office of Meteorology and Climatology, Zurich, Switzerland
Recommended Citation:
Ivanov M.A.,Luterbacher J.,Kotlarski S.. Climate model biases and modification of the climate change signal by intensity-dependent bias correction[J]. Journal of Climate,2018-01-01,31(16)