DOI: 10.1175/JCLI-D-17-0904.1
Scopus记录号: 2-s2.0-85049736488
论文题名: Insights on Sea Ice data assimilation from perfect model observing system simulation experiments
作者: Zhang Y.-F. ; Bitz C.M. ; Anderson J.L. ; Collins N. ; Hendricks J. ; Hoar T. ; Raeder K. ; Massonnet F.
刊名: Journal of Climate
ISSN: 8948755
出版年: 2018
卷: 31, 期: 15 起始页码: 5911
结束页码: 5926
语种: 英语
英文关键词: Arctic
; Data assimilation
; Ensembles
; Sea ice
Scopus关键词: Errors
; Forecasting
; Snow
; Snow melting systems
; Arctic
; Data assimilation
; Ensemble Kalman Filter
; Ensembles
; Initial conditions
; Observing system simulation experiments
; Sea ice concentration
; Stakeholder values
; Sea ice
; algorithm
; arctic environment
; computer simulation
; data assimilation
; ensemble forecasting
; ice thickness
; numerical model
; performance assessment
; sea ice
; weather forecasting
; Arctic
英文摘要: Simulating Arctic sea ice conditions up to the present and predicting them several months in advance has high stakeholder value, yet remains challenging. Advanced data assimilation (DA) methods combine real observations with model forecasts to produce sea ice reanalyses and accurate initial conditions for sea ice prediction. This study introduces a sea ice DA framework for a sea ice model with a parameterization of the ice thickness distribution by resolving multiple thickness categories. Specifically, the Los Alamos Sea Ice Model, version 5 (CICE5), is integrated with the Data Assimilation Research Testbed (DART). A series of perfect model observing system simulation experiments (OSSEs) are designed to explore DA algorithms within the ensemble Kalman filter (EnKF) and the relative importance of different observation types. This study demonstrates that assimilating sea ice concentration (SIC) observations can effectively remove SIC errors, with the error of total Arctic sea ice area reduced by about 60% annually. When the impact of SIC observations is strongly localized in space, the error of total volume is also modestly improved. The largest simulation improvements are produced when sea ice thickness (SIT) and SIC are jointly assimilated, with the error of total volume decreased by more than 70% annually. Assimilating multiyear sea ice concentration (MYI) can reduce error in total volume by more than 50%. Assimilating MYI produces modest improvements in snow depth (errors are reduced by around 16%), while assimilating SIC and SIT has no obvious influence on snow depth. This study also suggests that different observation types may need different localization distances to optimize DA performance. © 2018 American Meteorological Society. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/111428
Appears in Collections: 气候减缓与适应
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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; Georges Lemaître Centre for Earth and Climate Research, Earth and Life Institute, Université Catholique de Louvain, Louvain-la-Neuve, Belgium; Earth Science Department, Barcelona Supercomputing Center, Barcelona, Spain
Recommended Citation:
Zhang Y.-F.,Bitz C.M.,Anderson J.L.,et al. Insights on Sea Ice data assimilation from perfect model observing system simulation experiments[J]. Journal of Climate,2018-01-01,31(15)