DOI: 10.1007/s10584-016-1722-1
Scopus记录号: 2-s2.0-84976887018
论文题名: Observation-based blended projections from ensembles of regional climate models
作者: Salazar E. ; Hammerling D. ; Wang X. ; Sansó B. ; Finley A.O. ; Mearns L.O.
刊名: Climatic Change
ISSN: 0165-0009
EISSN: 1573-1480
出版年: 2016
卷: 138, 期: 2018-01-02 起始页码: 55
结束页码: 69
语种: 英语
Scopus关键词: Climate change
; Economic and social effects
; Electric power system interconnection
; Different boundary condition
; Hierarchical bayesian space-time modeling
; Regional climate changes
; Regional climate modeling (RCM)
; Regional climate models
; Spatial variability
; Special report on emissions scenarios
; Statistical framework
; Climate models
; boundary condition
; climate conditions
; climate modeling
; ensemble forecasting
; observational method
; regional climate
; seasonal variation
; spatial variation
; twenty first century
; North America
; United States
英文摘要: We consider the problem of projecting future climate from ensembles of regional climate model (RCM) simulations using results from the North American Regional Climate Change Assessment Program (NARCCAP). To this end, we develop a hierarchical Bayesian space-time model that quantifies the discrepancies between different members of an ensemble of RCMs corresponding to present day conditions, and observational records. Discrepancies are then propagated into the future to obtain high resolution blended projections of 21st century climate. In addition to blended projections, the proposed method provides location-dependent comparisons between the different simulations by estimating the different modes of spatial variability, and using the climate model-specific coefficients of the spatial factors for comparisons. The approach has the flexibility to provide projections at customizable scales of potential interest to stakeholders while accounting for the uncertainties associated with projections at these scales based on a comprehensive statistical framework. We demonstrate the methodology with simulations from the Weather Research & Forecasting regional model (WRF) using three different boundary conditions. We use simulations for two time periods: current climate conditions, covering 1971 to 2000, and future climate conditions under the Special Report on Emissions Scenarios (SRES) A2 emissions scenario, covering 2041 to 2070. We investigate and project yearly mean summer and winter temperatures for a domain in the South West of the United States. © 2016, Springer Science+Business Media Dordrecht. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/84235
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: U.S. Food and Drug Administration, Center for Tobacco Products, Silver Spring, MD, United States; Institute for Mathematics Applied to Geosciences, National Center for Atmospheric Research, Boulder, CO, United States; Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH, United States; Department of Applied Mathematics and Statistics, University of California, Santa Cruz, CA, United States; Departments of Forestry and Geography, Michigan State University, Lansing, MI, United States
Recommended Citation:
Salazar E.,Hammerling D.,Wang X.,et al. Observation-based blended projections from ensembles of regional climate models[J]. Climatic Change,2016-01-01,138(2018-01-02)