DOI: 10.1175/JCLI-D-14-00331.1
Scopus记录号: 2-s2.0-84980315517
论文题名: Statistical downscaling in the tropics can be sensitive to reanalysis choice: A case study for precipitation in the Philippines
作者: Manzanas R. ; Brands S. ; San-Martín D. ; Lucero A. ; Limbo C. ; Gutiérrez J.M.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2015
卷: 28, 期: 10 起始页码: 4171
结束页码: 4184
语种: 英语
Scopus关键词: Calibration
; Climate models
; Tropics
; Climate change projections
; Generalized linear model
; Model output statistics
; Reanalysis
; Regression coefficient
; Sources of uncertainty
; Statistical downscaling
; Statistical techniques
; Climate change
; climate change
; climate modeling
; climate prediction
; downscaling
; numerical model
; precipitation (climatology)
; statistical analysis
; tropical region
; Philippines
英文摘要: This work shows that local-scale climate projectionsobtainedbymeans of statisticaldownscaling are sensitive to the choice of reanalysis used for calibration. To this aim, a generalized linear model (GLM) approach is applied to downscale daily precipitation in the Philippines. First, theGLMs are trainedandtestedseparatelywithtwodistinct reanalyses (ERA-Interim and JRA-25) using a cross-validation scheme over the period 1981-2000. When the observed and downscaled time series are compared, the attained performance is found to be sensitive to the reanalysis considered if climate change signal-bearing variables (temperature and/or specific humidity) are included in the predictor field. Moreover, performance differences are shown to be in correspondence with the disagreement found between the raw predictors from the two reanalyses. Second, the regression coefficients calibrated either with ERA-Interim or JRA-25 are subsequently applied to the output of a global climate model (MPI-ECHAM5) in order to assess the sensitivity of local-scale climate change projections (up to 2100) to reanalysis choice. In this case, the differences detected in present climate conditions are considerably amplified, leading to ''delta-change'' estimates differing by up to 35%(on average for the entire country) depending on the reanalysis used for calibration. Therefore, reanalysis choice is an important contributor to the uncertainty of localscale climate change projections and, consequently, should be treated with as much care as other better-known sources of uncertainty (e.g., the choice of theGCMand/or downscalingmethod). Implications of the results for the entire tropics, as well as for the model output statistics downscaling approach are also briefly discussed. ©2015 American Meteorological Society. 资助项目: EC, European Commission
; FP7, Seventh Framework Programme
; FP7, Seventh Framework Programme
; FP7, Seventh Framework Programme
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/50400
Appears in Collections: 气候变化事实与影响
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作者单位: Grupo de Meteorología, Dpto. Matemática Aplicada y CC. Computación, Universidad de Cantabria, Santander, Spain; Grupo de Meteorología, Instituto de Física de Cantabria, CSIC-Universidad de Cantabria, Santander, Spain; Predictia Intelligent Data Solutions, Santander, Spain; Philippine Atmospheric, Geophysical and Astronomical Services Administration, Quezon City, Philippines
Recommended Citation:
Manzanas R.,Brands S.,San-Martín D.,et al. Statistical downscaling in the tropics can be sensitive to reanalysis choice: A case study for precipitation in the Philippines[J]. Journal of Climate,2015-01-01,28(10)