DOI: 10.1175/JCLI-D-12-00402.1
Scopus记录号: 2-s2.0-84884968820
论文题名: An ensemble adjustment kalman filter for the CCSM4 ocean component
作者: Karspeck A.R. ; Yeager S. ; Danabasoglu G. ; Hoar T. ; Collins N. ; Raeder K. ; Anderson J. ; Tribbia J.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2013
卷: 26, 期: 19 起始页码: 7392
结束页码: 7413
语种: 英语
Scopus关键词: Climate prediction
; Community climate system model
; Data assimilation
; Eastern boundary current
; Ensemble adjustment Kalman filter
; Ensembles
; Ocean model
; Ocean model simulations
; Climate models
; Climatology
; Forecasting
; Kalman filters
; Oceanography
; climate modeling
; climate prediction
; climate variation
; data assimilation
; ensemble forecasting
; Kalman filter
; salinity
; surface temperature
英文摘要: The authors report on the implementation and evaluation of a 48-member ensemble adjustment Kalman filter (EAKF) for the ocean component of the Community Climate System Model, version 4 (CCSM4). The ocean assimilation system described was developed to support the eventual generation of historical oceanstate estimates and ocean-initialized climate predictions with the CCSM4 and its next generation, the Community Earth System Model (CESM). In this initial configuration of the system, daily subsurface temperature and salinity data from the 2009 World Ocean Database are assimilated into the ocean model from 1 January 1998 to 31 December 2005. Each ensemble member of the ocean is forced by a member of an independently generated CCSM4 atmospheric EAKF analysis, making this a loosely coupled framework. Over most of the globe, the time-mean temperature and salinity fields are improved relative to an identically forced ocean model simulation without assimilation. This improvement is especially notable in strong frontal regions such as the western and eastern boundary currents. The assimilation system is most effective in the upper 1000m of the ocean, where the vast majority of in situ observations are located. Because of the shortness of this experiment, ocean variability is not discussed. Challenges that arise from using an ocean model with strong regional biases, coarse resolution, and low internal variability to assimilate real observations are discussed, and areas of ongoing improvement for the assimilation system are outlined. © 2013 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51632
Appears in Collections: 气候变化事实与影响
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作者单位: National Center for Atmospheric Research, Boulder, CO, United States
Recommended Citation:
Karspeck A.R.,Yeager S.,Danabasoglu G.,et al. An ensemble adjustment kalman filter for the CCSM4 ocean component[J]. Journal of Climate,2013-01-01,26(19)