DOI: 10.1175/JCLI-D-13-00486.1
Scopus记录号: 2-s2.0-84900408826
论文题名: Global sea surface temperature forecasts using an improved multimodel approach
作者: Khan M.Z.K. ; Mehrotra R. ; Sharma A. ; Sankarasubramanian A.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2014
卷: 27, 期: 10 起始页码: 3505
结束页码: 3515
语种: 英语
Scopus关键词: Atmospheric temperature
; Climate models
; Oceanography
; Attractive strategies
; Combining forecasts
; Coupled climate model
; Multi-model ensemble
; Multimodel approach
; Real-time forecasts
; Sea surface temperature (SST)
; Sea surface temperature anomalies
; Forecasting
; accuracy assessment
; algorithm
; climate modeling
; ensemble forecasting
; global climate
; hindcasting
; sea surface temperature
; seasonality
; spatiotemporal analysis
英文摘要: With the availability of hindcasts or real-time forecasts from a number of coupled climate models, multimodel ensemble forecasting systems have gained popularity in recent years. However, many models share similar physics or modeling processes, which may lead to similar (or strongly correlated) forecasts. Assigning equal weights to each model in space and time may result in a biased forecast with narrower confidence limits than is appropriate. Although methods for combining forecasts that take into consideration differences in model accuracy over space and time exist, they suffer from a lack of consideration of the intermodel dependence that may exist. This study proposes an approach that considers the dependence among models while combining multimodel ensemble forecast. The approach is evaluated by combining sea surface temperature (SST) forecasts from five climate models for the period 1960-2005. The variable of interest, the monthly global sea surface temperature anomalies (SSTA) at a 5° × 5° latitude-longitude grid, is predicted three months in advance using the proposed algorithm. Results indicate that the proposed approach offers consistent and significant improvements for all the seasons over the majority of grid points compared to the case in which the dependence among the models is ignored. Consequently, the proposed approach of combining multiple models, taking into account the interdependence that exists, provides an attractive strategy to develop improved SST forecasts. © 2014 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51091
Appears in Collections: 气候变化事实与影响
There are no files associated with this item.
作者单位: School of Civil and Environmental Engineering, The University of New South Wales, Sydney, Australia; Department of Civil, Construction and Environmental Engineering, North Carolina State University, Raleigh, NC, United States
Recommended Citation:
Khan M.Z.K.,Mehrotra R.,Sharma A.,et al. Global sea surface temperature forecasts using an improved multimodel approach[J]. Journal of Climate,2014-01-01,27(10)