An Ensemble Adjustment Kalman Filter for the CCSM4 Ocean Component

被引:52
|
作者
Karspeck, Alicia R. [1 ]
Yeager, Steve [1 ]
Danabasoglu, Gokhan [1 ]
Hoar, Tim [1 ]
Collins, Nancy [1 ]
Raeder, Kevin [1 ]
Anderson, Jeffrey [1 ]
Tribbia, Joseph [1 ]
机构
[1] Natl Ctr Atmospher Res, Boulder, CO 80307 USA
关键词
Kalman filters; Climate prediction; Ensembles; Forecasting; Data assimilation; Ocean models; DATA ASSIMILATION SYSTEM; SEA-SURFACE TEMPERATURE; OVERTURNING CIRCULATION; BIAS CORRECTION; CLIMATE MODELS; PREDICTABILITY; REPRESENTATION; VARIABILITY; ERROR;
D O I
10.1175/JCLI-D-12-00402.1
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
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 ocean-state 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 1000 m 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.
引用
收藏
页码:7392 / 7413
页数:22
相关论文
共 50 条
  • [31] Error covariance modeling in the GMAO ocean ensemble Kalman filter
    Keppenne, Christian L.
    Rienecker, Michele M.
    Jacob, Jossy P.
    Kovach, Robin
    MONTHLY WEATHER REVIEW, 2008, 136 (08) : 2964 - 2982
  • [32] A Characterization of the Present-Day Arctic Atmosphere in CCSM4
    de Boer, Gijs
    Chapman, William
    Kay, Jennifer E.
    Medeiros, Brian
    Shupe, Matthew D.
    Vavrus, Steve
    Walsh, John
    JOURNAL OF CLIMATE, 2012, 25 (08) : 2676 - 2695
  • [33] The relationship between the Arctic Oscillation and ENSO as simulated by CCSM4
    Zhu Yali
    Wang Tao
    ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2016, 9 (03) : 198 - 203
  • [34] Westerly wind bursts simulated in CAM4 and CCSM4
    Tao Lian
    Youmin Tang
    Lei Zhou
    Siraj Ul Islam
    Chan Zhang
    Xiaojing Li
    Zheng Ling
    Climate Dynamics, 2018, 50 : 1353 - 1371
  • [35] Westerly wind bursts simulated in CAM4 and CCSM4
    Lian, Tao
    Tang, Youmin
    Zhou, Lei
    Ul Islam, Siraj
    Zhang, Chan
    Li, Xiaojing
    Ling, Zheng
    CLIMATE DYNAMICS, 2018, 50 (3-4) : 1353 - 1371
  • [36] Ensemble Adjustment Kalman Filter Data Assimilation for a Global Atmospheric Model
    Singh, Tarkeshwar
    Mittal, Rashmi
    Upadhyaya, H. C.
    DYNAMIC DATA-DRIVEN ENVIRONMENTAL SYSTEMS SCIENCE, DYDESS 2014, 2015, 8964 : 284 - 298
  • [37] CGCM and AGCM seasonal climate predictions: A study in CCSM4
    Infanti, Johnna M.
    Kirtman, Ben P.
    JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES, 2017, 122 (14) : 7416 - 7432
  • [38] Impact of Agulhas Leakage on the Atlantic Overturning Circulation in the CCSM4
    Weijer, Wilbert
    van Sebille, Erik
    JOURNAL OF CLIMATE, 2014, 27 (01) : 101 - 110
  • [39] The relationship between the Arctic Oscillation and ENSO as simulated by CCSM4
    ZHU Yali
    WANG Tao
    Atmospheric and Oceanic Science Letters, 2016, 9 (03) : 198 - 203
  • [40] Ensemble Kalman filter
    School of Electrical Engineering and Computer Science, University of Oklahoma, Norman, OK, United States
    不详
    IEEE Control Syst Mag, 2009, 3 (34-46):