DOI: 10.1175/JCLI-D-11-00714.1
Scopus记录号: 2-s2.0-84876094668
论文题名: Impact of enthalpy-based ensemble filtering sea ice data assimilation on decadal predictions: Simulation with a conceptual pycnocline prediction model
作者: Zhang S. ; Winton M. ; Rosati A. ; Delworth T. ; Huang B.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2013
卷: 26, 期: 7 起始页码: 2368
结束页码: 2378
语种: 英语
Scopus关键词: Coupled models
; Data assimilation
; Decadal predictions
; Decadal variability
; Enthalpy space
; Nonlinear functions
; Prediction model
; Sea ice concentration
; Climate models
; Enthalpy
; Mathematical transformations
; Oceanography
; Probability distributions
; Sea ice
; climate modeling
; computer simulation
; data assimilation
; decadal variation
; ensemble forecasting
; enthalpy
; prediction
; probability
; pycnocline
; sea ice
英文摘要: The non-Gaussian probability distribution of sea ice concentration makes it difficult to directly assimilate sea ice observations into a climate model. Because of the strong impact of the atmospheric and oceanic forcing on the sea ice state, any direct assimilation adjustment on sea ice states is easily overridden by model physics. A new approach implements sea ice data assimilation in enthalpy space where a sea ice model represents a nonlinear function that transforms a positive-definite space into the sea ice concentration subspace. Results from observation-assimilation experiments using a conceptual pycnocline prediction model that characterizes the influences of sea ice on the decadal variability of the climate system show that the new scheme efficiently assimilates "sea ice observations" into the model: while improving sea ice variability itself, it consistently improves the estimates of all "climate" components. The resulted coupled initialization that is physically consistent among all coupled components significantly improves decadal-scale predictability of the coupled model. © 2013 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/51971
Appears in Collections: 气候变化事实与影响
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作者单位: NOAA/GFDL, Princeton University, Princeton, NJ, United States; COLA, Calverton, MD, United States; Department of Atmospheric, Oceanic and Earth Sciences, George Mason University, Fairfax, VA, United States
Recommended Citation:
Zhang S.,Winton M.,Rosati A.,et al. Impact of enthalpy-based ensemble filtering sea ice data assimilation on decadal predictions: Simulation with a conceptual pycnocline prediction model[J]. Journal of Climate,2013-01-01,26(7)