DOI: 10.1007/s00382-014-2098-4
Scopus记录号: 2-s2.0-84895775674
论文题名: Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada
作者: Gaitan C.F. ; Hsieh W.W. ; Cannon A.J.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2014
卷: 43, 期: 12 起始页码: 3201
结束页码: 3217
语种: 英语
英文关键词: Artificial neural networks
; Climate extremes
; Future evaluation
; Nonlinear methods
; Precipitation
; Statistical downscaling
英文摘要: Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate. © 2014, Springer-Verlag Berlin Heidelberg. 资助项目: NSERC, Natural Sciences and Engineering Research Council of Canada
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/54478
Appears in Collections: 过去全球变化的重建
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作者单位: Department of Earth, Ocean and Atmospheric Sciences, 2020-2207 Main Mall, University of British Columbia, Vancouver, BC, Canada; Pacific Climate Impacts Consortium, University of Victoria, PO Box 3060, Stn CSC, Victoria, BC, Canada; South Central Climate Science Center, NOAA-GFDL, 201 Forrestal Road, Princeton, NJ, United States
Recommended Citation:
Gaitan C.F.,Hsieh W.W.,Cannon A.J.. Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec, Canada[J]. Climate Dynamics,2014-01-01,43(12)