DOI: 10.1175/JCLI-D-11-00687.1
Scopus记录号: 2-s2.0-84872933880
论文题名: Reassessing statistical downscaling techniques for their robust application under climate change conditions
作者: Gutiérrez J.M. ; San-Martín D. ; Brands S. ; Manzanas R. ; Herrera S.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2013
卷: 26, 期: 1 起始页码: 171
结束页码: 188
语种: 英语
Scopus关键词: Accuracy measures
; Change conditions
; Distributional similarities
; Down-scaling
; Downscaling methods
; Global climate model
; Historical periods
; Lower troposphere
; Max Planck Institute
; Maximum temperature
; Near surface temperature
; Predictor sets
; Regression method
; Robust application
; SD method
; Statistical downscaling
; Temperature anomaly
; Temperature inversions
; Warming climate
; Weather generator
; Atmospheric temperature
; Climate change
; accuracy assessment
; air temperature
; climate change
; climate modeling
; correlation
; downscaling
; regression analysis
; statistical analysis
; twentieth century
英文摘要: The performance of statistical downscaling (SD) techniques is critically reassessed with respect to their robust applicability in climate change studies. To this end, in addition to standard accuracy measures and distributional similarity scores, the authors estimate the robustness of the methods under warming climate conditions working with anomalous warm historical periods. This validation framework is applied to intercompare the performances of 12 different SD methods (from the analog, weather typing, and regression families) for downscaling minimum and maximum temperatures in Spain. First, a calibration of these methods is performed in terms of both geographical domains and predictor sets; the results are highly dependent on the latter, with optimum predictor sets including near-surface temperature data (in particular 2-m temperature), which appropriately discriminate cold episodes related to temperature inversion in the lower troposphere. Although regression methods perform best in terms of correlation, analog and weather generator approaches are more appropriate for reproducing the observed distributions, especially in case of wintertime minimum temperature. However, the latter two families significantly underestimate the temperature anomalies of the warm periods considered in this work. This underestimation is found to be critical when considering the warming signal in the late twenty-first century as given by a global climate model [the ECHAM5-Max Planck Institute (MPI) model]. In this case, the different downscaling methods provide warming values with differences in the range of 1°C, in agreement with the robustness significance values. Therefore, the proposed test is a promising technique for detecting lack of robustness in statistical downscaling methods applied in climate change studies. © 2013 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/52064
Appears in Collections: 气候变化事实与影响
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作者单位: Instituto de Física de Cantabria (UNICAN-CSIC), Santander, Spain
Recommended Citation:
Gutiérrez J.M.,San-Martín D.,Brands S.,et al. Reassessing statistical downscaling techniques for their robust application under climate change conditions[J]. Journal of Climate,2013-01-01,26(1)