DOI: 10.1175/JCLI-D-14-00754.1
Scopus记录号: 2-s2.0-84945958945
论文题名: Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?
作者: Cannon A.J. ; Sobie S.R. ; Murdock T.Q.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期: 17 起始页码: 6938
结束页码: 6959
语种: 英语
Scopus关键词: Algorithms
; Conformal mapping
; Mapping
; Precipitation (chemical)
; Rain
; Bias
; Coupled Model Intercomparison Project
; Daily precipitations
; Extreme events
; Generalized extreme value
; Precipitation extremes
; Statistical techniques
; Trends
; Climate models
; algorithm
; climate modeling
; error correction
; extreme event
; general circulation model
; precipitation intensity
; sampling bias
; weather forecasting
; Canada
英文摘要: Quantile mapping bias correction algorithms are commonly used to correct systematic distributional biases in precipitation outputs from climate models. Although they are effective at removing historical biases relative to observations, it has been found that quantile mapping can artificially corrupt future model-projected trends. Previous studies on the modification of precipitation trends by quantile mapping have focused on mean quantities, with less attention paid to extremes. This article investigates the extent to which quantile mapping algorithms modify global climate model (GCM) trends in mean precipitation and precipitation extremes indices. First, a bias correction algorithm, quantile delta mapping (QDM), that explicitly preserves relative changes in precipitation quantiles is presented. QDM is compared on synthetic data with detrended quantile mapping (DQM), which is designed to preserve trends in the mean, and with standard quantile mapping (QM). Next, methods are applied to phase 5 of the Coupled Model Intercomparison Project (CMIP5) daily precipitation projections over Canada. Performance is assessed based on precipitation extremes indices and results from a generalized extreme value analysis applied to annual precipitation maxima. QM can inflate the magnitude of relative trends in precipitation extremes with respect to the raw GCM, often substantially, as compared to DQM and especially QDM. The degree of corruption in the GCM trends by QM is particularly large for changes in long period return values. By the 2080s, relative changes in excess of +500% with respect to historical conditions are noted at some locations for 20-yr return values, with maximum changes by DQM and QDM nearing +240% and +140%, respectively, whereas raw GCM changes are never projected to exceed +120%. © 2015 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50426
Appears in Collections: 气候变化事实与影响
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作者单位: Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, Canada; Climate Data and Analysis Section, Climate Research Division, Environment Canada, Canada
Recommended Citation:
Cannon A.J.,Sobie S.R.,Murdock T.Q.. Bias correction of GCM precipitation by quantile mapping: How well do methods preserve changes in quantiles and extremes?[J]. Journal of Climate,2015-01-01,28(17)