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Volume 25 Issue 4

Jul.  2008

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2008 >  25(4): 680-691

A New Global Four-Dimensional Variational Ocean Data Assimilation System and Its Application

  • 1.

    State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029; Graduate University of Chinese Academy of Sciences, Beijing 100049;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029; Graduate University of Chinese Academy of Sciences, Beijing 100049;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029


doi: 10.1007/s00376-008-0680-6

  • A four-dimensional variational data assimilation (4DVar) system of the LASG/IAP Climate Ocean Model, version 1.0 (LICOM1.0), named LICOM-3DVM, has been developed using the three-dimensional variational data assimilation of mapped observation (3DVM), a 4DVar method newly proposed in the past two years. Two experiments with 12-year model integrations were designed to validate it. One is the assimilation run, called ASSM, which incorporated the analyzed weekly sea surface temperature (SST) fields from Reynolds and Smith (OISST) between 1990 and 2001 once a week by the LICOM-3DVM. The other is the control run without any assimilation, named CTL. ASSM shows that the simulated temperatures of the upper ocean (above 50 meters), especially the SST of equatorial Pacific, coincide with the Tropic Atmosphere Ocean (TAO) mooring data, the World Ocean Atlas 2001 (WOA01) data and the Met Office Hadley Centre's sea ice and sea surface temperature (HadISST) data. It decreased the cold bias existing in CTL in the eastern Pacific and produced a Ni\~no index that agrees with observation well. The validation results suggest that the LICOM-3DVM is able to effectively adjust the model results of the ocean temperature, although it's hard to correct the subsurface results and it even makes them worse in some areas due to the incorporation of only surface data. Future development of the LICOM-3DVM is to include subsurface {\it in situ} observations and satellite observations to further improve model simulations.
  • [1] ZHAO Ying, WANG Bin, 2008: Numerical Experiments for Typhoon Dan Incorporating AMSU-A Retrieved Data with 3DVM, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 692-703.  doi: 10.1007/s00376-008-0692-2
    [2] Xiao DONG, Renping LIN, Jiang ZHU, Zeting LU, 2016: Evaluation of Ocean Data Assimilation in CAS-ESM-C: Constraining the SST Field, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 795-807.  doi: 10.1007/s00376-016-5234-8
    [3] Jincheng WANG, Xingwei JIANG, Xueshun SHEN, Youguang ZHANG, Xiaomin WAN, Wei HAN, Dan WANG, 2023: Assimilation of Ocean Surface Wind Data by the HY-2B Satellite in GRAPES: Impacts on Analyses and Forecasts, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 44-61.  doi: 10.1007/s00376-022-1349-2
    [4] CHU Kekuan, TAN Zhemin, Ming XUE, 2007: Impact of 4DVAR Assimilation of Rainfall Data on the Simulation of Mesoscale Precipitation Systems in a Mei-yu Heavy Rainfall Event, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 281-300.  doi: 10.1007/s00376-007-0281-9
    [5] LI Shuanglin, CHEN Xiaoting, 2014: Quantifying the Response Strength of the Southern Stratospheric Polar Vortex to Indian Ocean Warming in Austral Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 492-503.  doi: 10.1007/s00376-013-2322-x
    [6] Yujie WU, Wansuo DUAN, 2018: Impact of SST Anomaly Events over the Kuroshio-Oyashio Extension on the "Summer Prediction Barrier", ADVANCES IN ATMOSPHERIC SCIENCES, 35, 397-409.  doi: 10.1007/s00376-017-6322-0
    [7] Ben TIAN, Hong-Li REN, 2022: Diagnosing SST Error Growth during ENSO Developing Phase in the BCC_CSM1.1(m) Prediction System, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 427-442.  doi: 10.1007/s00376-021-1189-5
    [8] HU Ruijin, LIU Qinyu, MENG Xiangfeng, J. Stuart GODFREY, 2005: On the Mechanism of the Seasonal Variability of SST in the Tropical Indian Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 451-462.  doi: 10.1007/BF02918758
    [9] Wang Yunfeng, Wu Rongsheng, Wang Yuan, Pan Yinong, 2000: A Simple Method of Calculating the Optimal Step Size in 4DVAR Technique, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 433-444.  doi: 10.1007/s00376-000-0034-5
    [10] YAN Changxiang, ZHU Jiang, XIE Jiping, 2015: An Ocean Data Assimilation System in the Indian Ocean and West Pacific Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1460-1472.  doi: 10.1007/s00376-015-4121-z
    [11] WANG Bin, LIU Juanjuan, WANG Shudong, CHENG Wei, LIU Juan, LIU Chengsi, Qingnong XIAO, Ying-Hwa KUO, 2010: An Economical Approach to Four-dimensional Variational Data Assimilation, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 715-727.  doi: 10.1007/s00376-009-9122-3
    [12] WANG Yunfeng, WANG Bin, HAN Yueqi, ZHU Min, HOU Zhiming, ZHOU Yi, LIU Yudi, KOU Zheng, 2004: Variational Data Assimilation Experiments of Mei-Yu Front Rainstorms in China, ADVANCES IN ATMOSPHERIC SCIENCES, 21, 587-596.  doi: 10.1007/BF02915726
    [13] Xiangjun TIAN, Xiaobing FENG, 2019: An Adjoint-Free CNOP-4DVar Hybrid Method for Identifying Sensitive Areas in Targeted Observations: Method Formulation and Preliminary Evaluation, ADVANCES IN ATMOSPHERIC SCIENCES, , 721-732.  doi: 10.1007/s00376-019-9001-5
    [14] FENG Junqiao, HU Dunxin, YU Lejiang, 2012: Low-Frequency Coupled Atmosphere--Ocean Variability in the Southern Indian Ocean, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 544-560.  doi: 10.1007/s00376-011-1096-2
    [15] YAN Li, WANG Panxing, YU Yongqiang, LI Lijuan, WANG Bin, 2010: Potential Predictability of Sea Surface Temperature in a Coupled Ocean--Atmosphere GCM, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 921-936.  doi: 10.1007/s00376-009-9062-y
    [16] Xiang LI, Tiejun LING, Yunfei ZHANG, Qian ZHOU, 2018: A 31-year Global Diurnal Sea Surface Temperature Dataset Created by an Ocean Mixed-Layer Model, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 1443-1454.  doi: 10.1007/s00376-018-8016-7
    [17] LIN Pengfei, LIU Hailong, ZHANG Xuehong, 2007: Sensitivity of the Upper Ocean Temperature and Circulation in the Equatorial Pacific to Solar Radiation Penetration Due to Phytoplankton, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 765-780.  doi: 10.1007/s00376-007-0765-7
    [18] FAN Lei, Zhengyu LIU, LIU Qinyu, 2011: Robust GEFA Assessment of Climate Feedback to SST EOF Modes, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 907-912.  doi: 10.1007/s00376-010-0081-5
    [19] FU Jianjian, LI Shuanglin, LUO Dehai, 2009: Impact of Global SST on Decadal Shift of East Asian Summer Climate, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 192-201.  doi: 10.1007/s00376-009-0192-z
    [20] Juan AO, Jianqi SUN, 2016: The Impact of Boreal Autumn SST Anomalies over the South Pacific on Boreal Winter Precipitation over East Asia, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 644-655.  doi: 10.1007/s00376-015-5067-x
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    • Manuscript History

    Manuscript received: 10 July 2008
    Manuscript revised: 10 July 2008
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    A New Global Four-Dimensional Variational Ocean Data Assimilation System and Its Application

    • 1. State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029; Graduate University of Chinese Academy of Sciences, Beijing 100049;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029; Graduate University of Chinese Academy of Sciences, Beijing 100049;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029
    • Manuscript received: 2008-07-10
    • Manuscript revised: 2008-07-10

    Abstract: A four-dimensional variational data assimilation (4DVar) system of the LASG/IAP Climate Ocean Model, version 1.0 (LICOM1.0), named LICOM-3DVM, has been developed using the three-dimensional variational data assimilation of mapped observation (3DVM), a 4DVar method newly proposed in the past two years. Two experiments with 12-year model integrations were designed to validate it. One is the assimilation run, called ASSM, which incorporated the analyzed weekly sea surface temperature (SST) fields from Reynolds and Smith (OISST) between 1990 and 2001 once a week by the LICOM-3DVM. The other is the control run without any assimilation, named CTL. ASSM shows that the simulated temperatures of the upper ocean (above 50 meters), especially the SST of equatorial Pacific, coincide with the Tropic Atmosphere Ocean (TAO) mooring data, the World Ocean Atlas 2001 (WOA01) data and the Met Office Hadley Centre's sea ice and sea surface temperature (HadISST) data. It decreased the cold bias existing in CTL in the eastern Pacific and produced a Ni\~no index that agrees with observation well. The validation results suggest that the LICOM-3DVM is able to effectively adjust the model results of the ocean temperature, although it's hard to correct the subsurface results and it even makes them worse in some areas due to the incorporation of only surface data. Future development of the LICOM-3DVM is to include subsurface {\it in situ} observations and satellite observations to further improve model simulations.

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