DOI: 10.5194/tc-15-1277-2021
论文题名: Estimating parameters in a sea ice model using an ensemble Kalman filter
作者: Zhang Y.-F. ; Bitz C.M. ; Anderson J.L. ; Collins N.S. ; Hoar T.J. ; Raeder K.D. ; Blanchard-Wrigglesworth E.
刊名: Cryosphere
ISSN: 19940416
出版年: 2021
卷: 15, 期: 3 起始页码: 1277
结束页码: 1284
语种: 英语
英文关键词: climate change
; data assimilation
; Kalman filter
; observational method
; parameter estimation
; sea ice
; snow cover
; Arctic
; Los Alamos
; New Mexico
; United States
英文摘要: Uncertain or inaccurate parameters in sea ice models influence seasonal predictions and climate change projections in terms of both mean and trend. We explore the feasibility and benefits of applying an ensemble Kalman filter (EnKF) to estimate parameters in the Los Alamos sea ice model (CICE). Parameter estimation (PE) is applied to the highly influential dry snow grain radius and combined with state estimation in a series of perfect model observing system simulation experiments (OSSEs). Allowing the parameter to vary in space improves performance along the sea ice edge but degrades in the central Arctic compared to requiring the parameter to be uniform everywhere, suggesting that spatially varying parameters will likely improve PE performance at local scales and should be considered with caution. We compare experiments with both PE and state estimation to experiments with only the latter and have found that the benefits of PE mostly occur after the data assimilation period, when no observations are available to assimilate (i.e., the forecast period), which suggests PE's relevance for improving seasonal predictions of Arctic sea ice. © 2021 BMJ Publishing Group. All rights reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164836
Appears in Collections: 气候变化与战略
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作者单位: Department of Atmospheric Sciences, University of Washington, Seattle, WA, United States; IMAGe, CISL, National Center for Atmospheric Research, Boulder, CO, United States; Atmospheric and Oceanic Sciences, Princeton University, Princeton, NJ, United States
Recommended Citation:
Zhang Y.-F.,Bitz C.M.,Anderson J.L.,et al. Estimating parameters in a sea ice model using an ensemble Kalman filter[J]. Cryosphere,2021-01-01,15(3)