DOI: 10.1007/s10584-016-1598-0
Scopus记录号: 2-s2.0-84961286074
论文题名: Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?
作者: Dixon K.W. ; Lanzante J.R. ; Nath M.J. ; Hayhoe K. ; Stoner A. ; Radhakrishnan A. ; Balaji V. ; Gaitán C.F.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2016
卷: 135, 期: 2018-03-04 起始页码: 395
结束页码: 408
语种: 英语
Scopus关键词: Climate change
; Decision support systems
; Electrostatic devices
; Electrostatic discharge
; Product design
; Regression analysis
; Statistics
; Time series analysis
; Analysis techniques
; Climate projection
; Decision support applications
; Future climate projections
; Future projections
; Global climate model
; Maximum temperature
; Statistical downscaling
; Climate models
; downscaling
; experimental design
; future prospect
; general circulation model
; information
; performance assessment
; spatiotemporal analysis
; statistical analysis
英文摘要: Empirical statistical downscaling (ESD) methods seek to refine global climate model (GCM) outputs via processes that glean information from a combination of observations and GCM simulations. They aim to create value-added climate projections by reducing biases and adding finer spatial detail. Analysis techniques, such as cross-validation, allow assessments of how well ESD methods meet these goals during observational periods. However, the extent to which an ESD method’s skill might differ when applied to future climate projections cannot be assessed readily in the same manner. Here we present a “perfect model” experimental design that quantifies aspects of ESD method performance for both historical and late 21st century time periods. The experimental design tests a key stationarity assumption inherent to ESD methods – namely, that ESD performance when applied to future projections is similar to that during the observational training period. Case study results employing a single ESD method (an Asynchronous Regional Regression Model variant) and climate variable (daily maximum temperature) demonstrate that violations of the stationarity assumption can vary geographically, seasonally, and with the amount of projected climate change. For the ESD method tested, the greatest challenges in downscaling daily maximum temperature projections are revealed to occur along coasts, in summer, and under conditions of greater projected warming. We conclude with a discussion of the potential use and expansion of the perfect model experimental design, both to inform the development of improved ESD methods and to provide guidance on the use of ESD products in climate impacts analyses and decision-support applications. © 2016, The Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84340
Appears in Collections: 气候减缓与适应 气候变化事实与影响
There are no files associated with this item.
作者单位: NOAA Geophysical Fluid Dynamics Laboratory, 201 Forrestal Road, Princeton, NJ, United States; Climate Science Center, Texas Tech University, Lubbock, TX, United States; Engility, Chantilly, VA, United States; Princeton University, Princeton, NJ, United States; University of Oklahoma, Norman, OK, United States
Recommended Citation:
Dixon K.W.,Lanzante J.R.,Nath M.J.,et al. Evaluating the stationarity assumption in statistically downscaled climate projections: is past performance an indicator of future results?[J]. Climatic Change,2016-01-01,135(2018-03-04)