DOI: 10.1175/JCLI-D-16-0366.1
Scopus记录号: 2-s2.0-85008224700
论文题名: Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods
作者: San-Martín D. ; Manzanas R. ; Brands S. ; Herrera S. ; M. Gutiérrez J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2017
卷: 30, 期: 1 起始页码: 203
结束页码: 223
语种: 英语
Scopus关键词: Climate models
; Electric power system interconnection
; Space division multiple access
; Statistical methods
; Uncertainty analysis
; Climate prediction
; Distributional similarities
; Ensembles
; Future climate projections
; Regional climate models
; Statistical downscaling
; Statistical forecasting
; Statistical techniques
; Climate change
英文摘要: This is the second in a pair of papers in which the performance of statistical downscaling methods (SDMs) is critically reassessed with respect to their robust applicability in climate change studies. Whereas the companion paper focused on temperatures, the present manuscript deals with precipitation and considers an ensemble of 12 SDMs from the analog, weather typing, and regression families. First, the performance of the methods is cross-validated considering reanalysis predictors, screening different geographical domains and predictor sets. Standard accuracy and distributional similarity scores and a test for extrapolation capability are considered. The results are highly dependent on the predictor sets, with optimum configurations including information from midtropospheric humidity. Second, a reduced ensemble of well-performing SDMs is applied to four GCMs to properly assess the uncertainty of downscaled future climate projections. The results are compared with an ensemble of regional climate models (RCMs) produced in the ENSEMBLES project. Generally, the mean signal is similar with both methodologies (with the exception of summer, which is drier for the RCMs) but the uncertainty (spread) is larger for the SDM ensemble. Finally, the spread contribution of the GCM- and SDM-derived components is assessed using a simple analysis of variance previously applied to the RCMs, obtaining larger interaction terms. Results show that the main contributor to the spread is the choice of the GCM, although the SDM dominates the uncertainty in some cases during autumn and summer due to the diverging projections from different families. © 2017 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/49798
Appears in Collections: 气候变化事实与影响
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作者单位: Grupo de Meteorología, Instituto de Física de Cantabria, Consejo Superior de Investigaciones Científicas-Universidad de Cantabria, Santander, Spain; Predictia Intelligent Data Solutions SL, Santander, Spain; Grupo de Meteorología, Deptartamento de Matemática Aplicada y Ciencias de la Computación, Universidad de Cantabria, Santander, Spain
Recommended Citation:
San-Martín D.,Manzanas R.,Brands S.,et al. Reassessing model uncertainty for regional projections of precipitation with an ensemble of statistical downscaling methods[J]. Journal of Climate,2017-01-01,30(1)