DOI: 10.1007/s00382-015-2688-9
Scopus记录号: 2-s2.0-84959110696
论文题名: Credibility of statistical downscaling under nonstationary climate
作者: Salvi K. ; Ghosh S. ; Ganguly A.R.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2016
卷: 46, 期: 2017-05-06 起始页码: 1991
结束页码: 2023
语种: 英语
英文关键词: Climate change
; Stationarity
; Statistical downscaling
英文摘要: Statistical downscaling (SD) establishes empirical relationships between coarse-resolution climate model simulations with higher-resolution climate variables of interest to stakeholders. These statistical relations are estimated based on historical observations at the finer resolutions and used for future projections. The implicit assumption is that the SD relations, extracted from data are stationary or remain unaltered, despite non-stationary change in climate. The validity of this assumption relates directly to the credibility of SD. Falsifiability of climate projections is a challenging proposition. Calibration and verification, while necessary for SD, are unlikely to be able to reproduce the full range of behavior that could manifest at decadal to century scale lead times. We propose a design-of-experiments (DOE) strategy to assess SD performance under nonstationary climate and evaluate the strategy via a transfer-function based SD approach. The strategy relies on selection of calibration and validation periods such that they represent contrasting climatic conditions like hot-versus-cold and ENSO-versus-non-ENSO years. The underlying assumption is that conditions such as warming or predominance of El Niño may be more prevalent under climate change. In addition, two different historical time periods are identified, which resemble pre-industrial and the most severe future emissions scenarios. The ability of the empirical relations to generalize under these proxy conditions is considered an indicator of their performance under future nonstationarity. Case studies over two climatologically disjoint study regions, specifically India and Northeast United States, reveal robustness of DOE in identifying the locations where nonstationarity prevails as well as the role of effective predictor selection under nonstationarity. © 2015, Springer-Verlag Berlin Heidelberg. 资助项目: NSF, National Science Foundation
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/53801
Appears in Collections: 过去全球变化的重建
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作者单位: Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, Mumbai, India; Interdisciplinary Program in Climate Studies, Indian Institute of Technology Bombay, Powai, Mumbai, India; Sustainability and Data Sciences Laboratory, Civil and Environmental Engineering, Northeastern University, Boston, MA, United States
Recommended Citation:
Salvi K.,Ghosh S.,Ganguly A.R.. Credibility of statistical downscaling under nonstationary climate[J]. Climate Dynamics,2016-01-01,46(2017-05-06)