DOI: 10.1175/JCLI-D-15-0679.1
Scopus记录号: 2-s2.0-84991258405
论文题名: Multivariate bias correction of climate model output: Matching marginal distributions and intervariable dependence structure
作者: Cannon A.J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2016
卷: 29, 期: 19 起始页码: 7045
结束页码: 7064
语种: 英语
Scopus关键词: Atmospheric humidity
; Atmospheric movements
; Correlation methods
; Hydrology
; Mapping
; Statistics
; Bias
; Dependence structures
; Integrated water vapors
; Marginal distribution
; Measure of performance
; Model output statistics
; Spearman rank correlation
; Statistical techniques
; Climate models
英文摘要: Univariate bias correction algorithms, such as quantile mapping, are used to address systematic biases in climate model output. Intervariable dependence structure (e.g., between different quantities like temperature and precipitation or between sites) is typically ignored, which can have an impact on subsequent calculations that depend on multiple climate variables. A novel multivariate bias correction (MBC) algorithm is introduced as a multidimensional analog of univariate quantile mapping. Two variants are presented. MBCp and MBCr respectively correct Pearson correlation and Spearman rank correlation dependence structure, with marginal distributions in both constrained to match observed distributions via quantile mapping. MBC is demonstrated on two case studies: 1) bivariate bias correction of monthly temperature and precipitation output from a large ensemble of climate models and 2) multivariate correction of vertical humidity and wind profiles, including subsequent calculation of vertically integrated water vapor transport and detection of atmospheric rivers. The energy distance is recommended as an omnibus measure of performance for model selection. As expected, substantial improvements in performance relative to quantile mapping are found in each case. For reference, characteristics of the MBC algorithm are compared against existing bivariate and multivariate bias correction techniques. MBC performs competitively and fills a role as a flexible, general purpose multivariate bias correction algorithm. © 2016 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50127
Appears in Collections: 气候变化事实与影响
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作者单位: Climate Research Division, Science and Technology Branch, Environment and Climate Change Canada, Canadian Centre for Climate Modelling and Analysis, P.O. Box 1700 STN CSC, Victoria, BC, Canada
Recommended Citation:
Cannon A.J.. Multivariate bias correction of climate model output: Matching marginal distributions and intervariable dependence structure[J]. Journal of Climate,2016-01-01,29(19)