Advanced Search
Article Contents

Assimilation of Sea Surface Temperature in a Global Hybrid Coordinate Ocean Model


doi: 10.1007/s00376-018-7284-6

  • The Hybrid Coordinate Ocean Model (HYCOM) uses different vertical coordinate choices in different regions. In HYCOM, the prognostic variables include not only the seawater temperature, salinity and current fields, but also the layer thickness. All prognostic variables are usually adjusted in the assimilation when multivariate data assimilation methods are used to assimilate sea surface temperature (SST). This paper investigates the effects of SST assimilation in a global HYCOM model using the Ensemble Optimal Interpolation multivariate assimilation method. Three assimilation experiments are conducted from 2006-08. In the first experiment, all model variables are adjusted during the assimilation process. In the other two experiments, the temperature alone is adjusted in the entire water column and in the mixed layer. For comparison, a control experiment without assimilation is also conducted. The three assimilation experiments yield notable SST improvements over the results of the control experiment. Additionally, the experiments in which all variables are adjusted and the temperature alone in all model layers is adjusted, produce significant negative effects on the subsurface temperature. Also, they yield negative effects on the subsurface salinity because it is associated with temperature and layer thickness. The experiment adjusting the temperature alone in the mixed layer yields positive effects and outperforms the other experiments. The heat content in the upper 300 m and 300-700 m layers further suggests that it yields the best performance among the experiments.
    摘要: 垂直混合坐标模式HYCOM因在不同的海域采用了不同的坐标,而使得模拟的海洋状态比单坐标模式更符合实际。与通常的海洋模式相比,HYCOM除了具有传统的海水温度、盐度和流场外,还具有层厚这个预报量,层厚对于海水温度盐度具有重要的影响:即层厚发生较小的改变,会引发海水温度盐度发生显著的变化,尤其在跃层。多变量同化是一种同化过程同时调整多个变量的方法。考虑到HYCOM具有不同于传统模式的预报量,基于多变量同化方法集合最优插值,利用全球HYCOM,探讨了三种同化方案下,海表温度(SST)同化带来的影响。方案一:同化过程调整所有的模式变量(AssimMultiVar),方案二:同化过程只调整温度(AssimTonly),方案三:采用了垂直局地化函数,将同化的影响局限在混合层,且只调整了温度(AssimTmxl)。利用三种同化方案进行了三种同化试验,同时也开展了控制试验(CNTL,不同化任何观测)。试验结果表明,对于SST,三种同化方案都能比控制试验产生显著的改进。但是对于次表层的海水温度盐度而言,方案AssimMultiVar和AssimTonly带来显著的负面影响,这与SST和其他变量(或次表层变量)之间的不准确相关性有关,而考虑了垂直局地化的方案AssimTmxl可以带来正面的影响。同时海水热容也进一步证实了方案AssimTmxl要显著优于其他两种方案。这表明,当基于集合同化方法同化SST时,需要注意是否采用多变量同化方案。因为集合同化方法样本是取样于模式输出,而模式误差会引发SST与其他变量之间的不准确相关性,从而会引发不准确的变量调整,尤其是当某个变量起主导作用的情况。
  • 加载中
  • Aikman, F. III, G. L. Mellor, T. Ezer, D. Sheinin, P. Chen, L. Breaker, K. Bosley, D. B. Rao, 1996: Towards an operational nowcast/forecast system for the US. East Coast. Vol. 61, Modern Approaches to Data Assimilation in Ocean Modeling, P. Malanotte-Rizzoli, Ed.,. Elsevier, Amsterdam, 347- 376.
    Behringer D. W.,M. Ji, and A. Leetmaa, 1998: An improved coupled model for ENSO prediction and implications for ocean initialization. Part I: The ocean data assimilation system. Mon. Wea. Rev., 126, 1013-1021, https://doi.org/10.1175/1520-0493(1998)126<1013:AICMFE>2.0.CO;2
    Bentsen M.,G. Evensen, H. Drange, and A. D. Jenkins, 1999: Coordinate transformation on a sphere using conformal mapping. Mon. Wea. Rev., 127, 2733-2740, https://doi.org/10.1175/1520-0493(1999)127<2733:CTOASU>2.0.CO;2
    Bertino L.,K. A. Lisæter, and S. Scient, 2008: The TOPAZ monitoring and prediction system for the Atlantic and Arctic Oceans.Journal of Operational Oceanography, 1(2), 15-18. https://doi.org/10.1080/1755876X.2008.11020098.
    Bleck R.,2002: An oceanic general circulation model framed in hybrid isopycnic-Cartesian coordinates.Ocean Modelling, 4, 55-88, https://doi.org/10.1016/S1463-5003(01)00012-9.
    Carton J. A.,G. Chepurin, X. H. Cao, and B. Giese, 2000: A simple ocean data assimilation analysis of the global upper ocean 1950-95. Part I: Methodology. J. Phys. Oceanogr., 30, 294-309, https://doi.org/10.1175/1520-0485(2000)030<0294:ASODAA>2.0.CO;2
    Chassignet E. P.,L. T. Smith, G. R. Halliwell, and R. Bleck, 2003: North Atlantic Simulations with the Hybrid Coordinate Ocean Model (HYCOM): Impact of the vertical coordinate choice, reference pressure, and thermobaricity. J. Phys. Oceanogr., 33, 2504-2526, https://doi.org/10.1175/1520-0485(2003)033<2504:NASWTH>2.0.CO;2
    Dee, D. P.,Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system.Quart. J. Roy. Meteor. Soc., 137 553-597, https://doi.org/10.1002/qj.828
    Durand, F., L. Gourdeau, T. Delcroix, J. Verron, 2003: Can we improve the representation of modeled ocean mixed layer by assimilating surface-only satellite-derived data? A case study for the tropical Pacific during the 1997-1998 El Niño.J. Geophys. Res., 108(C6), 3200, https://doi.org/10.1029/2002JC001603
    Evensen G.,2003: The Ensemble Kalman Filter: Theoretical formulation and practical implementation.Ocean Dynamics, 53 343-367, https://doi.org/10.1007/s10236-003-0036-9
    Ezer T.,G. L. Mellor, 1994: Continuous assimilation of Geosat altimeter data into a three-dimensional primitive equation Gulf Stream model. J. Phys. Oceanogr., 24, 832-847, https://doi.org/10.1175/1520-0485(1994)024<0832:CAOGAD>2.0.CO;2
    Ezer T.,G. L. Mellor, 1997: Data assimilation experiments in the Gulf Stream region: How useful are satellite-derived surface data for nowcasting the subsurface fields? J. Atmos. Ocean. Technol., 14, 1379-1391, https://doi.org/10.1175/1520-0426(1997)014<1379:DAEITG>2.0.CO;2
    Freeman, E., Coauthors, 2017: ICOADS Release 3.0: A major update to the historical marine climate record. Int. J. Climatol., 37 2211-2232, https://doi.org/10.1002/joc.4775
    Fu C. B.,H. F. Diaz, and J. O. Fletcher, 1986: Characteristics of the response of sea surface temperature in the central Pacific associated with warm episodes of the Southern Oscillation. Mon. Wea. Rev., 144, 1716-1739, https://doi.org/10.1175/1520-0493(1986)114<1716:COTROS>2.0.CO;2
    Fujii, Y., Coauthors, 2015: Evaluation of the tropical Pacific observing system from the ocean data assimilation perspective.Quart. J. Roy. Meteor. Soc., 141 2481-2496, https://doi.org/10.1002/qj.2579
    Gaspari G.,S. E. Cohn, 1999: Construction of correlation functions in two and three dimensions.Quart. J. Roy. Meteor. Soc., 125 723-757, https://doi.org/10.1002/qj.49712555417
    Giglio D.,S. T. Gille, A. C. Subramanian, and S. Nugyen, 2017: The role of wind gusts in upper ocean diurnal variability.J. Geophys. Res., 122 7751-7764, https://doi.org/10.1002/2017JC012794
    Gill A. E.,1983: An estimation of sea-level and surface-current anomalies during the 1972 El Niño and consequent thermal effects. J. Phys. Oceanogr., 13, 586-606, https://doi.org/10.1175/1520-0485(1983)013<0586:AEOSLA>2.0.CO;2
    Good S. A.,M. J. Martin, and N. A. Rayner, 2013: EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates.J. Geophys. Res., 118 6704-6716, https://doi.org/10.1002/2013JC009067
    Gouretski V.,F. Reseghetti, 2010: On depth and temperature biases in bathythermograph data: Development of a new correction scheme based on analysis of a global ocean database.Deep Sea Research Part I: Oceanographic Research Papers, 57, 812-833, https://doi.org/10.1016/j.dsr.2010.03.011
    Griffies S. M.,A. J. Adcroft, 2008: Formulating the equations for ocean models. Eddy Resolving Ocean Models, M. Hecht, H. Hasumi, Eds., AGU, 281- 317.
    Griffies, S. M.,Coauthors, 2000: Developments in ocean climate modelling.Ocean Modelling, 2(3-4), 123-192, https://doi.org/10.1016/S1463-5003(00)00014-7
    Han G. J.,H. L. Fu, X. F. Zhang, W. Li, X. R. Wu, X. D. Wang, and L. X. Zhang, 2013: A global ocean reanalysis product in the China Ocean Reanalysis (CORA) project.Adv. Atmos. Sci., 30(6), 1621-1631. https://doi.org/10.1007/s00376-013-2198-9
    Horton C.,M. Clifford, J. Schmitz, and L. H. Kantha, 1997: A real-time oceanographic nowcast/forecast system for the Mediterranean Sea.J. Geophys. Res., 102 25 123-25 156, https://doi.org/10.1029/97JC00533
    Ji Q. Y.,X. M. Zhu, H. Wang, G. M. Liu, S. Gao, X. L. Ji, and Q. Xu, 2015: Assimilating operational SST and sea ice analysis data into an operational circulation model for the coastal seas of China.Acta Oceanologica Sinica, 34(7), 54-64. https://doi.org/10.1007/s13131-015-0691-y
    Kelley J. G. W.,D. W. Behringer, H. J. Thiebaux, and B. Balasubramaniyan, 2002: Assimilation of SST Data into a real-time coastal ocean forecast system for the U.S. East Coast. Wea. Forecasting, 17, 670-690, https://doi.org/10.1175/1520-0434(2002)017<0670:AOSDIA>2.0.CO;2
    Large W. G.,J. M. Caron, 2015: Diurnal cycling of sea surface temperature,salinity, and current in the CESM coupled climate model. J. Geophys. Res., 120, 3711-3729, https://doi.org/10.1002/2014JC010691.
    Large W. G.,J. C. McWilliams, and S. C. Doney, 1994: Oceanic vertical mixing: A review and a model with a nonlocal boundary layer parameterization.Rev. Geophys., 32 363-403, https://doi.org/10.1029/94RG01872
    Large W. G.,G. Danabasoglu, S. C. Doney, and J. C. McWilliams, 1997: Sensitivity to surface forcing and boundary layer mixing in a global ocean model: Annual-mean climatology. J. Phys. Oceanogr., 27, 2418-2447, https://doi.org/10.1175/1520-0485(1997)027<2418:STSFAB>2.0.CO;2
    Martin M. J.,A. Hines, and M. J. Bell, 2007: Data assimilation in the FOAM operational short-range ocean forecasting system: A description of the scheme and its impact.Quart. J. Roy. Meteor. Soc., 133 981-995, https://doi.org/10.1002/qj.74
    Mayer M.,J. T. Fasullo, K. E. Trenberth, and L. Haimberger, 2016: ENSO-driven energy budget perturbations in observations and CMIP models.Climate Dyn., 47 4009-4029, https://doi.org/10.1007/s00382-016-3057-z
    NOAA, 1988: Data announcement 88-MGG-02, digital relief of the surface of the earth. NOAA, National Geophysical Data Center, Boulder, CO, U.S.A.,
    O'Dea, E. J.,Coauthors, 2012: An operational ocean forecast system incorporating NEMO and SST data assimilation for the tidally driven European North-West shelf.Journal of Operational Oceanography, 5(1), 3-17. https://doi.org/10.1080/1755876X.2012.11020128.
    Oke P. R.,G. B. Brassington, D. A. Griffin, and A. Schiller, 2008: The Bluelink ocean data assimilation system (BODAS).Ocean Modelling, 21(1-2), 46-70, https://doi.org/10.1016/j.ocemod.2007.11.002.
    Picaut J.,T. Delcroix, 1995: Equatorial wave sequence associated with warm pool displacements during the 1986-1989 El Niño and La Niña.J. Geophys. Res., 100 18 398-18 408, https://doi.org/10.1029/95JC01358
    Reynolds R. W.,T. M. Smith, C. Y. Liu, D. B. Chelton, K. S. Casey, and M. G. Schlax, 2007: Daily high-resolution-blended analyses for sea surface temperature.J. Climate, 20 5473-5496, https://doi.org/10.1175/2007JCLI1824.1
    Rosati A.,K. Miyakoda, and R. Gudgel, 1997: The impact of ocean initial conditions on ENSO forecasting with a coupled model. Mon. Wea. Rev., 125, 754-772, https://doi.org/10.1175/1520-0493(1997)125<0754:TIOOIC>2.0.CO;2
    Sakov P.,F. Counillon, L. Bertino, K. A. Lisæter, P. R. Oke, and A. Korablev, 2012: TOPAZ4: An ocean-sea ice data assimilation system for the North Atlantic and Arctic.Ocean Science, 8(4), 633-656. https://doi.org/10.5194/os-8-633-2012.
    Seo G.-H.,B.-J. Choi, Y.-K. Cho, Y. H. Kim, and S. Kim, 2010: Assimilation of sea surface temperature in the Northwest Pacific Ocean and its marginal seas using the ensemble Kalman filter.Ocean Science Journal, 45(4), 225-242. https://doi.org/10.1007/s12601-010-0021-4
    Shu Y. Q.,J. Zhu, D. X. Wang, C. X. Yan, and X. J. Xiao, 2009: Performance of four sea surface temperature assimilation schemes in the South China Sea.Cont. Shelf Res., 29(11-12): 1489-1501, https://doi.org/10.1016/j.csr.2009.03.016.
    Steele M.,R. Morley, and W. Ermold:2001: PHC: A global ocean hydrography with a high-quality Arctic Ocean. J. Climate, 14, 2079-2087, https://doi.org/10.1175/1520-0442(2001)014<2079:PAGOHW>2.0.CO;2
    Storkey, D., Coauthors, 2010: Forecasting the ocean state using NEMO: The new FOAM system.Journal of Operational Oceanography, 3(1), 3-15.https://doi.org/10.1080/1755876X.2010.11020109.
    Syu H.-H.,J. D. Neelin, 2000: ENSO in a hybrid coupled model.Part II: Prediction with piggyback data assimilation. Climate Dyn., 16 35-48, https://doi.org/10.1007/s003820050003
    Tang Y. M.,R. Kleeman, 2002: A new strategy for assimilating SST data for ENSO predictions.Geophys. Res. Lett., 29 22-1-22-4, https://doi.org/10.1029/2002GL014860
    Tang Y. M.,W. W. Hsieh, 2003: ENSO simulation and prediction in a hybrid coupled model with data assimilation.J. Meteor. Soc. Japan, 81 1-19, https://doi.org/10.2151/jmsj.81.1
    Tang Y. M.,R. Kleeman, and A. M. Moore, 2004: SST assimilation experiments in a tropical Pacific Ocean model.J. Phys. Oceanogr., 34 623-642, https://doi.org/10.1175/3518.1
    Testut C.-E.,P. Brasseur, J.-M. Brankart, and J. Verron, 2003: Assimilation of sea-surface temperature and altimetric observations during 1992-1993 into an eddy permitting primitive equation model of the North Atlantic Ocean.J. Mar. Syst., 40-41, 291-316, https://doi.org/10.1016/S0924-7963(03)00022-8.
    Tian G.-L.,M. Q. Wang, and L. X. Song, 2014: Variable selection in the high-dimensional continuous generalized linear model with current status data.Journal of Applied Statistics, 41, 467-483,https://doi.org/10.1080/02664763.2013.840271.
    Twigt D. J.,E. D. De Goede, E. J. O. Schrama, and G. Herman, 2007: Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing.Ocean Dynamics, 57 467-484, https://doi.org/10.1007/s10236-007-0123-4
    Vialard J.,P. Delecluse, and C. Menkes, 2002: A modeling study of salinity variability and its effects in the tropical Pacific Ocean during the 1993-1999 period.J. Geophys. Res., 107(C12), SRF 6-1-SRF 6-14, https://doi.org/10.1029/2000JC000758
    Vialard J.,C. Menkes, J.-P. Boulanger, P. Delecluse, E. Guilyardi, M. J. McPhaden, and G. Madec, 2001: A model study of oceanic mechanisms affecting equatorial Pacific sea surface temperature during the 1997-98 El Niño. J. Phys. Oceanogr., 31, 1649-1675, https://doi.org/10.1175/1520-0485(2001)031<1649:AMSOOM>2.0.CO;2
    Wang X. L.,S. L. Zhao, and M. Q. Wang, 2017: Restricted profile estimation for partially linear models with large-dimensional covariates.Statistics & Probability Letters, 128, 71-76, https://doi.org/10.1016/j.spl.2017.04.013.
    Xie J. P.,L. Bertino, F. Counillon, K. A. Lisæter, and P. Sakov, 2017: Quality assessment of the TOPAZ4 reanalysis in the Arctic over the period 1991-2013.Ocean Science, 13, 123-144, https://doi.org/10.5194/os-13-123-2017.
    Yan C. X.,J. Zhu, and J. P. Xie, 2015: An ocean data assimilation system in the Indian ocean and west Pacific ocean.Adv. Atmos. Sci., 32 1460-1472, https://doi.org/10.1007/s00376-015-4121-z
    Zeng X. B.,A. Beljaars, 2005: A prognostic scheme of sea surface skin temperature for modeling and data assimilation.Geophys. Res. Lett., 32(14), L14605, https://doi.org/10.1029/2005GL023030
  • [1] 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
    [2] 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
    [3] HU Dingzhu, TIAN Wenshou, XIE Fei, SHU Jianchuan, and Sandip DHOMSE, , 2014: Effects of Meridional Sea Surface Temperature Changes on Stratospheric Temperature and Circulation, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 888-900.  doi: 10.1007/s00376-013-3152-6
    [4] Bohua Huang, James L. Kinter III, Paul S. Schopf, 2002: Ocean Data Assimilation Using Intermittent Analyses and Continuous Model Error Correction, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 965-992.  doi: 10.1007/s00376-002-0059-z
    [5] LIN Pengfei, LIU Hailong, ZHANG Xuehong, 2008: Effect of Chlorophyll-a Spatial Distribution on Upper Ocean Temperature in the Central and Eastern Equatorial Pacific, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 585-596.  doi: 10.1007/s00376-008-0585-4
    [6] SUN Jianqi, YUAN Wei, 2009: Contribution of the Sea Surface Temperature over the Mediterranean-Black Sea to the Decadal Shift of the Summer North Atlantic Oscillation, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 717-726.  doi: 10.1007/s00376-009-8210-8
    [7] Jiangyu MAO, Ming WANG, 2018: The 30-60-day Intraseasonal Variability of Sea Surface Temperature in the South China Sea during May-September, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 550-566.  doi: 10.1007/s00376-017-7127-x
    [8] Li Wei, Yu Rucong, Zhang Xuehong, 2001: Impacts of Sea Surface Temperature in the Tropical Pacific on Interannual Variability of Madden-Julian Oscillation in Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 429-444.  doi: 10.1007/BF02919322
    [9] Xue Feng, 2001: Interannual to Interdecadal Variation of East Asian Summer Monsoon and its Association with the Global Atmospheric Circulation and Sea Surface Temperature, ADVANCES IN ATMOSPHERIC SCIENCES, 18, 567-575.  doi: 10.1007/s00376-001-0045-x
    [10] Shuai WANG, Ralf TOUMI, 2018: Reduced Sensitivity of Tropical Cyclone Intensity and Size to Sea Surface Temperature in a Radiative-Convective Equilibrium Environment, ADVANCES IN ATMOSPHERIC SCIENCES, 35, 981-993.  doi: 10.1007/s00376-018-7277-5
    [11] Yan XIA, Yongyun HU, Jiankai ZHANG, Fei XIE, Wenshou TIAN, 2021: Record Arctic Ozone Loss in Spring 2020 is Likely Caused by North Pacific Warm Sea Surface Temperature Anomalies, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1723-1736.  doi: 10.1007/s00376-021-0359-9
    [12] Wenjing SHI, Ziniu XIAO, Jianjun XUE, 2016: Teleconnected Influence of the Boreal Winter Antarctic Oscillation on the Somali Jet: Bridging Role of Sea Surface Temperature in Southern High and Middle Latitudes, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 47-57.  doi: 10.1007/s00376-015-5094-7
    [13] ZHAO Xia, LI Jianping, 2009: Possible Causes for the Persistence Barrier of SSTA in the South China Sea and the Vicinity of Indonesia, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 1125-1136.  doi: 10.1007/s00376-009-8165-9
    [14] NIU Ning, LI Jianping, 2008: Interannual Variability of Autumn Precipitation over South China and its Relation to Atmospheric Circulation and SST Anomalies, ADVANCES IN ATMOSPHERIC SCIENCES, 25, 117-125.  doi: 10.1007/s00376-008-0117-2
    [15] HAN Jinping, ZHANG Renhe, 2009: The Dipole Mode of the Summer Rainfall over East China during 1958--2001, ADVANCES IN ATMOSPHERIC SCIENCES, 26, 727-735.  doi: 10.1007/s00376-009-9014-6
    [16] LIU Xiangwen, WU Tongwen, YANG Song, JIE Weihua, NIE Suping, LI Qiaoping, CHENG Yanjie, LIANG Xiaoyun, 2015: Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1156-1172.  doi: 10.1007/s00376-015-4194-8
    [17] FENG Juan, LI Jianping, ZHU Jianlei, LI Fei, SUN Cheng, 2015: Simulation of the Equatorially Asymmetric Mode of the Hadley Circulation in CMIP5 Models, ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1129-1142.  doi: 10.1007/s00376-015-4157-0
    [18] Jiawei YAO, Wansuo DUAN, Xiaohao QIN, 2021: Which Features of the SST Forcing Error Most Likely Disturb the Simulated Intensity of Tropical Cyclones?, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 581-602.  doi: 10.1007/s00376-020-0073-z
    [19] Xinyu LI, Riyu LU, Gen LI, 2021: Different Configurations of Interannual Variability of the Western North Pacific Subtropical High and East Asian Westerly Jet in Summer, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 931-942.  doi: 10.1007/s00376-021-0339-0
    [20] Xiangdong ZHANG, Yunfei FU, Zhe HAN, James E. OVERLAND, Annette RINKE, Han TANG, Timo VIHMA, Muyin WANG, 2022: Extreme Cold Events from East Asia to North America in Winter 2020/21: Comparisons, Causes, and Future Implications, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 553-565.  doi: 10.1007/s00376-021-1229-1

Get Citation+

Export:  

Share Article

Manuscript History

Manuscript received: 09 November 2017
Manuscript revised: 09 March 2018
Manuscript accepted: 21 March 2018
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

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索

Assimilation of Sea Surface Temperature in a Global Hybrid Coordinate Ocean Model

  • 1. International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
  • 2. University of Chinese Academy of Sciences, Beijing 100049, China
  • 3. State Key Laboratory of Acoustics, Institute of Acoustics, Chinese Academy of Sciences, Beijing 100190, China

Abstract: The Hybrid Coordinate Ocean Model (HYCOM) uses different vertical coordinate choices in different regions. In HYCOM, the prognostic variables include not only the seawater temperature, salinity and current fields, but also the layer thickness. All prognostic variables are usually adjusted in the assimilation when multivariate data assimilation methods are used to assimilate sea surface temperature (SST). This paper investigates the effects of SST assimilation in a global HYCOM model using the Ensemble Optimal Interpolation multivariate assimilation method. Three assimilation experiments are conducted from 2006-08. In the first experiment, all model variables are adjusted during the assimilation process. In the other two experiments, the temperature alone is adjusted in the entire water column and in the mixed layer. For comparison, a control experiment without assimilation is also conducted. The three assimilation experiments yield notable SST improvements over the results of the control experiment. Additionally, the experiments in which all variables are adjusted and the temperature alone in all model layers is adjusted, produce significant negative effects on the subsurface temperature. Also, they yield negative effects on the subsurface salinity because it is associated with temperature and layer thickness. The experiment adjusting the temperature alone in the mixed layer yields positive effects and outperforms the other experiments. The heat content in the upper 300 m and 300-700 m layers further suggests that it yields the best performance among the experiments.

摘要: 垂直混合坐标模式HYCOM因在不同的海域采用了不同的坐标,而使得模拟的海洋状态比单坐标模式更符合实际。与通常的海洋模式相比,HYCOM除了具有传统的海水温度、盐度和流场外,还具有层厚这个预报量,层厚对于海水温度盐度具有重要的影响:即层厚发生较小的改变,会引发海水温度盐度发生显著的变化,尤其在跃层。多变量同化是一种同化过程同时调整多个变量的方法。考虑到HYCOM具有不同于传统模式的预报量,基于多变量同化方法集合最优插值,利用全球HYCOM,探讨了三种同化方案下,海表温度(SST)同化带来的影响。方案一:同化过程调整所有的模式变量(AssimMultiVar),方案二:同化过程只调整温度(AssimTonly),方案三:采用了垂直局地化函数,将同化的影响局限在混合层,且只调整了温度(AssimTmxl)。利用三种同化方案进行了三种同化试验,同时也开展了控制试验(CNTL,不同化任何观测)。试验结果表明,对于SST,三种同化方案都能比控制试验产生显著的改进。但是对于次表层的海水温度盐度而言,方案AssimMultiVar和AssimTonly带来显著的负面影响,这与SST和其他变量(或次表层变量)之间的不准确相关性有关,而考虑了垂直局地化的方案AssimTmxl可以带来正面的影响。同时海水热容也进一步证实了方案AssimTmxl要显著优于其他两种方案。这表明,当基于集合同化方法同化SST时,需要注意是否采用多变量同化方案。因为集合同化方法样本是取样于模式输出,而模式误差会引发SST与其他变量之间的不准确相关性,从而会引发不准确的变量调整,尤其是当某个变量起主导作用的情况。

1. Introduction
2. Model and assimilation
  • Version 2.2 of HYCOM, which was developed at the University of Miami (Bleck, 2002; Chassignet et al., 2003) and upgraded by the Nansen Environmental and Remote Sensing Center in Norway (Bertino et al., 2008; Sakov et al., 2012), is used in this study. The model uses hybrid vertical coordinates; thus, it can smoothly transition among three types of vertical coordinates in different regions. It is isopycnal in the open, stratified ocean, smoothly reverts to a terrain-following coordinate in shallow coastal regions, and transitions to a z-level coordinate in the mixed layer or unstratified ocean. The hybrid coordinates are helpful to properly retain water mass characteristics and represent ocean thermodynamic processes and ocean flow.

    The model has a global coverage (see Fig. 1), with the horizontal model grid created by a conformal mapping in which the North Pole is moved into Eurasia (Bentsen et al., 1999). The grid has 1800× 1200 horizontal grid points with approximately 17-25 km grid spacing. The model has 30 hybrid layers, with reference potential densities of 10.1, 10.2, 10.3, 10.4, 10.5, 30.83, 31.11, 31.73, 32.19, 32.68, 33.36, 33.87, 34.22, 34.66, 35.07, 35.50, 35.83, 36.07, 36.25, 36.38, 36.47, 36.65, 36.82, 36.93, 37.01, 37.08, 37.15, 37.19, 37.21 and 37.24. The top five layers are low to remain z-level coordinates with a minimum thickness of 3 m. The model bathymetry is interpolated from ETOPO5 gridded data from the National Geophysical Data Center database (NOAA, 1988). The mixed layer scheme uses the K-Profile Parameter (Large et al., 1994, 1997).

    The model is initialized using version 3.0 of the Polar Science Center Hydrographic Climatology (Steele et al., 2001) and driven by the monthly climatology forcing fields for a 100-yr spin-up. Then, it is forced by the 6-h air temperature, winds, mean sea level pressure, dewpoint temperature, total cloud cover, and precipitation from ERA-Interim (Dee et al., 2011) from 1981 to 2006. The model fields are relaxed toward the same monthly climatology with an e-folding time of 30 days at the sea surface and lateral boundary.

    Figure 1.  Grid layout of the model. The meshes are drawn every 20 grids for the model domain.

  • The SST data from OISSTv2 (Reynolds et al., 2007) are used for assimilation since it is often not practical to use actual observations in as raw a form as possible. The analysis is generated using optimum interpolation. The AVHRR infrared satellite and AMSR satellite data, as well as in-situ observations from ships and buoys obtained from ICOADS, are used in the analysis. Moreover, the analysis also includes a large-scale adjustment of satellite biases with respect to in-situ observations and fills in the missing satellite data gaps with different smoothing. It is available from 1981 to present at a horizontal resolution of 0.25° and a temporal resolution of one day. The observational errors are taken from the dataset provider.

  • An ensemble-based method, EnOI (Evensen, 2003), is used in this paper. The analysis equation is given as \begin{equation} {x}_{\rm a}={x}_{\rm f}+\alpha(L\circ {P}){H}^{\rm T}({H}(L\circ {P}){H}^{\rm T}+{R})^{-1}({y}-{H}{x}_{\rm f}) \ \ (1)\end{equation} where x denotes the model state vector; the subscripts a, f and superscript T represent the analysis, forecast and matrix transposition, respectively; H is the observation operator, which relates the model states to the observations; P is the background error covariance matrix; L is the correlation function used for localization; ° is the Schur product; y represents the observations; R is the observation error covariance matrix; and α is a parameter that determines different weights on the ensemble versus observations. Here, α is set as 0.6 according to (Yan et al., 2015).

    The background error covariance matrix P is defined as \begin{equation} {P}={X}'{X}'^{\rm T}/(N-1) ,\ \ (2) \end{equation} where \(X'=X-\overline{X}\) is the perturbation matrix relative to the ensemble mean, X is an M× N ensemble matrix composed of the model state vectors, \(\overline{X}\) is the ensemble mean, M is the size of the model state vector, and N is the number of ensemble members. For the EnOI, the ensemble is usually static and keeps constant throughout the assimilation experiment. In practice, this assumption is not reasonable because the error structure changes with time. Thus, a static ensemble does not properly represent time variations of the background error covariance. In this paper, the ensemble changes seasonally and the ensemble size is 120. In spring, the ensemble consists of model states sampled over the spring seasons of 1993-2014. For other seasons, it is similar. That means four background error covariance matrixes are used.

    To reduce the sampling error and avoid false long-distance correlations, the background error covariance matrix P is localized by the correlation function L. Every element of L is defined by the quasi-Gaussian function of (Gaspari and Cohn, 1999). The horizontal localization length scale is uniformly 400 km.

3. Results
  • EnOI is a multivariate assimilation method. That means all model variables may be adjusted by the assimilation of the observations of one variable. In HYCOM, the model variables contain not only the traditional temperature, salinity and current velocities, but also layer thicknesses. In this section, we investigate the impact of SST assimilation in HYCOM via EnOI.

  • To examine the impacts of SST assimilation, we carry out four experiments for the period 2006-08.

    Similar to previous studies using the multivariate method (Oke et al., 2008; Sakov et al., 2012; Yan et al., 2015; Seo et al., 2010; Ji et al., 2015), the first experiment modifies all model variables in the assimilation (hereafter called AssimMultiVar). That means that the analysis update is carried out for all variables. The second experiment modifies only the three-dimensional temperature in the entire water column (hereafter called AssimTonly). The analysis update is done for the temperature alone in the entire water column. The third experiment modifies only the temperature in the mixed layer (hereafter called AssimTmxl). In AssimTmxl, a function of depth L v is used and is expressed as \begin{equation} L_{\rm v}(z)=\left\{ \begin{array}{l@{\quad}l} 1 & z<\dfrac{h_{\rm mxl}}{2}\\[3mm] \exp\left(1-\left(\dfrac{4z-2h_{\rm mxl}}{h_{\rm mxl}}\right)^2\right) & \dfrac{h_{\rm mxl}}{2}\leq z\leq h_{\rm mxl}\\[3mm] 0 & z>h_{\rm mxl} \end{array} \right. ,\ \ (3) \end{equation} where h mxl denotes the mixed layer depth (MLD) and z denotes the depth. In fact, the function L v is equivalent to a vertical localization. At z=h mxl, L v drops to a minimum (non-zero value), which indicates L v is discontinuous. Since the correlation between SST and subsurface temperature estimated by the ensemble gradually decreases with depth in the mixed layer, it is relatively low at z=h mxl. Both the low correlation and L v minimum make the analysis increment so small that the effect due to the discontinuity may be neglected. The corresponding analysis equation is given by \begin{equation} {x}_{\rm a}={x}_{\rm f}+\alpha(L_{\rm v}\circ L\circ {P}){H}^{\rm T}({H}(L_{\rm v}\circ L\circ {P}){H}^{\rm T}+{R})^{-1}({y}-{H}{x}_{\rm f}) . \ \ (4)\end{equation} In the three assimilation experiments, only the OISSTv2 data are assimilated, with a seven-day assimilation cycle.

    As a comparison, a control experiment without any assimilation is also performed (hereafter called CNTL). Table 1 details the design of the experiment. Since the sea-ice model is being tuned, we focus on the region between 60°S and 60°N in this paper.

    Figure 2.  SST RMSEs (units: °C) from different experiments: (a) CNTL; (b) AssimMultiVar; (c) AssimTonly; (d) AssimTmxl.

  • ICOADS (Freeman et al., 2017) contains surface marine observations from ships, buoys and other platform types. It is used to validate the impacts of the assimilation on the SST. Although ICODAS is not independent, it is a set of direct measurements rather than an analysis, and thus has gone through less processing. Moreover, it is only a small fraction of the data used to generate the OISSTv2 analysis. To assess the assimilation performance, the root-mean-square error (RMSE) is used as an evaluation metric. The RMSE is defined as \begin{equation} {\rm RMSE}(e,o)=\dfrac{1}{n}\sqrt{\sum_{i=1}^n(e-o)^2}\;\;, \ \ (5)\end{equation} where e denotes the simulated results from different experiments, o denotes the observations, and n is the number of values in the data. In addition, we also define the impact of assimilation (IOA) as \begin{equation} {\rm IOA}=\dfrac{{\rm RMSE}_{\rm CNTL}-{\rm RMSE}_{\rm Assim}}{{\rm RMSE}_{\rm CNTL}}\times 100 ,\ \ (6) \end{equation} where RMSE Assim and RMSE CNTL represent the RMSEs of the assimilation experiments and the control experiment relative to observations, respectively. The IOA is the percentage reduction in RMSE. When the IOA is positive, the RMSE from the assimilation experiments is reduced, and the assimilation provides a better ocean state than the control experiment. The larger the positive IOA, the greater the improvement due to assimilation.

    Figure 3.  Vertical distribution of the RMSEs of global (a) temperature (units: °C) and (b) salinity (units: psu) for different experiments over the period 2006-08.

    Figure 2 shows the spatial distribution of SST RMSEs against ICOADS from the different experiments. The experiment without any assimilation exhibits large RMSEs in coastal regions and regions with abundant eddies (e.g., the Kuroshio Extension, Gulf Stream system, Agulhas Current, Brazil and Malvinas Confluence region in the southwestern Atlantic, and Antarctic Circumpolar Current). It is evident that the RMSEs of the three assimilation experiments are greatly reduced in the global ocean. Visually, the experiment AssimMultiVar, which adjusts all variables, exhibits large RMSEs in the Kuroshio Extension and Labrador Sea north of Newfoundland compared with the RMSEs of AssimTonly and AssimTmxl, which adjust only temperature.

    To further verify the performance of the assimilation, the global ocean is divided into the Pacific, Atlantic and Indian oceans. Moreover, the IOA of SST is calculated for different regions (Table 2). It is clear that the IOA of the three assimilation experiments is greater than 25% in the global ocean, which indicates that the SST assimilation has a prominent impact (approximately 25% reduction in RMSE) on the SST. The IOA in AssimMultiVar is basically smallest, as shown in Fig. 2. This result implies that AssimMultiVar, in which all variables are adjusted, produces the smallest improvement among the assimilation experiments. In the global ocean, the IOA of AssimTonly is slightly larger than that of AssimTmxl. The difference is observed in the Indian Ocean, where the IOA is 22.57 for AssimTonly and 21.35 for AssimTmxl. In the Agulhas Current and Antarctic Circumpolar Current regions with numerous eddies, the reduction in RMSE produced by AssimTmxl is less than done by AssimTonly (Fig. 2). Of the different ocean basins, the reduction in RMSE is most remarkable in the Atlantic Ocean (greater than 30%), while it is approximately 21% and 18% in the Indian Ocean and Pacific Ocean respectively. In the Atlantic Ocean, the results of AssimTonly are slightly better than those of AssimTmxl. In the Gulf Stream and Brazil and Malvinas Confluence regions, AssimTonly produces lower RMSE than AssimTmxl (Fig. 2). In the Pacific, AssimTmxl slightly outperforms AssimTonly.

    In Fig. 3, the time series of SST RMSEs are shown for various experiments and regions. For the global ocean, the RMSEs of the control experiment are approximately 1.3°C during the entire simulation period, and the RMSEs of the three assimilation experiments are reduced to less than 1°C (Fig. 3a). AssimMultiVar exhibits a larger RMSE than AssimTonly and AssimTmxl in the second halves of 2007 and 2008 (Fig. 3a). The relatively large RMSE of AssimMultiVar is mainly in the Atlantic Ocean (Fig. 3d). Also, the RMSE of SST varies in different regions. Specifically, the highest RMSE can be observed in the Atlantic Ocean, and the RMSE in the Pacific Ocean is slightly lower than that in the Indian Ocean. Moreover, the three assimilation experiments exhibit different improvements in different regions. In the Pacific Ocean, the three assimilation experiments exhibit similar improvements, except AssimMultiVar at the end of 2008. In the Indian Ocean, AssimTmxl exhibits a slightly larger RMSE than both AssimMultiVar and AssimTonly, as shown in Table 2. In the Atlantic Ocean, AssimMultiVar displays higher RMSEs in the second half of both 2007 and 2008 compared to those of AssimTonly and AssimTmxl. Overall, the three assimilation experiments yield pronounced SST improvements over the control experiment in different regions, and both AssimTonly and AssimTmxl outperform AssimMultiVar.

  • SST observations provide sea surface information. Therefore, it is natural that the assimilation process improves the modeled SST. Whether or not the subsurface thermohaline structure is improved is investigated in this section. As independent observations, the monthly temperature and salinity objective analyses from EN4.2.0 (Good et al., 2013) are used to validate the impacts of SST assimilation on the subsurface temperature and salinity. The bias corrections from (Gouretski and Reseghetti, 2010) are applied in the objective analyses. The World Ocean Database 2013, Global Temperature and Salinity Profile Project, Array for real-time geostrophic oceanography, and Arctic Synoptic Basin-wide Observations project datasets are used to create EN4.2.0.

    Figure 4.  Time series of SST RMSEs (units: °C) in different regions and for different experiments: (a) Global Ocean; (b) Pacific Ocean; (c) Indian Ocean; (d) Atlantic Ocean.

    Figure 4 shows the vertical distribution of the RMSEs of global temperature and salinity. For global temperature, AssimMultiVar, which adjusts all model variables, produces an improvement over the control experiment in the upper 50 m and a significant decline below 50 m. This result is potentially associated with an improper correlation between the layer thickness and SST. A small change in the layer thickness can induce a large change in the temperature, especially in the thermocline region. AssimTonly, which adjusts only the temperature in the entire water column, yields an improvement in the upper 170 m and a decline below 170 m. The improvement of AssimTonly reaches a greater depth than that of AssimMultiVar. Additionally, the decline is likely associated with an improper correlation between the subsurface temperature and the SST described by the ensemble members. AssimTmxl, which adjusts only temperature in the mixed layer, exhibits the best performance. This result implies that the SST assimilation affects not only the SST but also the subsurface temperature in the upper 400 m. For global salinity, AssimMultiVar exhibits the worst performance and the largest RMSE. Additionally, AssimTonly has a negative impact, and the results of AssimTmxl are similar to those of the control experiment. The salinity is associated with the layer thickness and temperature in the model. The negative impact is partly attributable to the layer thickness and partly to the temperature.

    In different ocean basins, the vertical distribution of the RMSE of temperature and salinity is different (Fig. 5). AssimMultiVar performs worst in the three ocean basins, with a temperature improvement in only the upper 50 m and a salinity deterioration in the entire water column in the three ocean basins. Notably, AssimMultiVar yields the largest RMSEs in the Atlantic Ocean, especially below 500 m, which contributes considerably to the global RMSE. For temperature, AssimTonly produces an improvement above the thermocline and a decline below the thermocline in the three ocean basins. This result indicates that the correlation between the subsurface temperature below the thermocline and the SST is possibly poor. Additionally, AssimTonly has negative impacts on salinity in the three ocean basins. AssimTmxl exhibits the best performance in the three ocean basins. In the Indian Ocean, it has a slightly larger temperature RMSE compared with that of the control experiment below 250 m. This difference is mainly from the zonal band of 60°S-50°S in the Indian Ocean (Fig. 6c). For temperature, the RMSE of AssimTmxl is similar to that of CNTL in the Indian ocean north of 50°S (Fig. 6a), while it is notably larger than that of CNTL south of 50°S (Fig. 6c). The large difference is probably associated with the temperature inversion (Fig. 6d) and an improper MLD simulated by the model (Fig. 6e). Since AssimTmxl adjusts only the temperature in the mixed layer, a deep MLD leads to the propagation of SST observation information to a greater depth in the assimilation process, resulting in a large RMSE.

    Figure 5.  Vertical distribution of the RMSEs of (a, c, e) temperature (units: °C) and (b, d, f) salinity (units: psu) for different ocean basins and different experiments: (a, b) Pacific Ocean, (c, d) Atlantic Ocean; (e, f) Indian Ocean.

    Figure 6.  (a, c) RMSEs of temperature (units: °C) and (b, d) area-averaged temperature profiles (units: °C) for different experiments and different regions: (a, b) Indian Ocean north of 50°S; (c, d) Indian Ocean south of 50°S. (e) Difference in MLD between CNTL and EN4.2.0 in the Indian Ocean.

  • The heat content represents the heat stored in the ocean and is an important factor determining subsurface temperature changes. The impacts of three different assimilation experiments on the heat content are investigated in this section.

    Figure 7.  RMSEs of HC300 (units: °C) in different regions and for different experiments: (a) Global Ocean; (b) Pacific Ocean; (c) Indian Ocean; (d) Atlantic Ocean.

    Figure 8.  RMSEs of HC300 (units: °C) for different experiments over 2006-08: (a) CNTL; (b) AssimMultiVar; (c) AssimTonly; (d) AssimTmxl.

    Generally, the ocean heat content in the upper 300 m (called HC300) is defined as the temperature averaged over 0-300 m. Similarly, HC300-700 represents the temperature averaged over 300-700 m. Figure 7 shows the time series of HC300 RMSEs relative to the EN4.2.0 analyses for different ocean basins. The RMSE over the global ocean produced by AssimMultiVar is smaller than that of CNTL before 2007 and increases with time after 2007 (Fig. 7a). Similarly, the RMSE produced by AssimTonly also exhibits an increasing trend, although it is lower than that of CNTL from 2006-08. In fact, there is not an increasing trend in the RMSE of CNTL. Thus, the error induced by adjusting all model variables or only temperature in the entire water column in the assimilation is gradually amplified with time. The RMSE of AssimTmxl is the lowest, and its evolution with time is the same as that of CNTL.

    In the three ocean basins, both AssimMultiVar and AssimTonly exhibit an increasing RMSE with time, which leads to a rising trend in the RMSE over the global ocean. Additionally, AssimMultiVar produces the largest RMSE compared with those of other experiments in the three ocean basins. In the Indian and Atlantic oceans (Figs. 7c and d), the RMSE of AssimMultiVar is much greater than that of CNTL. Additionally, the RMSE of AssimTonly gradually approaches that of CNTL with time, although it is lower in the Pacific and Atlantic oceans (Figs. 7b and d). In the Indian Ocean, the RMSE of AssimTonly is slightly higher compared with that of CNTL in some periods (Fig. 7c). AssimTmxl performs best, with the lowest RMSEs in the three ocean basins.

    The spatial distribution of the RMSE of HC300 (Fig. 8a) is similar to the RMSE of SST for CNTL. Large RMSEs mainly occur in regions with abundant eddies or strong western boundary currents or Antarctic Circumpolar Current. Additionally, the large RMSEs can be observed in the equatorial Indian and Pacific oceans. Compared with CNTL, AssimMultiVar significantly increases the RMSE, especially in the equatorial Indian and Atlantic oceans (Fig. 8b). This result implies that the large RMSE of HC300 in the model simulation contributes to an improper relationship between the variables and the SST, which leads to the poor performance of AssimMultiVar. AssimTonly reduces the RMSE in the global ocean, except in the northern Atlantic south of 30°N (Fig. 8c). AssimTmxl decreases the RMSE in the global ocean (Fig. 8d).

    For the ocean heat content in the upper 300-700 m of the global ocean (Fig. 9a), both AssimMultiVar and AssimTonly yield much larger RMSEs than CNTL. Similar to HC300, the RMSE of HC300-700 also increases with time. However, the growth is much faster. This result indicates that the relationship between the SST and the variables of the subsurface estimated by the ensemble is not reasonable. AssimTmxl exhibits a slight improvement over CNTL, potentially because the temperature below 300 m is adjusted by the model dynamics and thermodynamics rather than by the assimilation. In the Pacific and Atlantic oceans, the evolution of the RMSE (Figs. 9b and d) is similar to that of the global ocean for all experiments. However, it is different in the Indian Ocean. AssimTonly, instead of AssimMultiVar, exhibits the largest RMSE in the Indian Ocean, as shown in Fig. 5e. Moreover, the RMSE of AssimTmxl is slightly greater than that of CNTL (Fig. 9c). As noted above, this difference is probably induced by an inaccurately modeled deep mixed layer and a temperature inversion.

    Figure 9.  RMSEs of HC300-700 (units: °C) in different oceans and for different experiments: (a) Global Ocean; (b) Pacific Ocean; (c) Indian Ocean; (d) Atlantic Ocean.

    Figure 10.  RMSEs of HC300-700 (units: °C) for different experiments over 2006-08: (a) CNTL; (b) AssimMultiVar; (c) AssimTonly; (d) AssimTmxl.

    The distribution of the subsurface temperature error below 300 m (see Fig. 10) is different from that in the upper 300 m (Fig. 8). For CNTL, the simulated large RMSE of HC300-700 in the Gulf Stream region extends southward, and the RMSEs in the tropical Indian and Pacific oceans are lower than those of HC300, although the large RMSEs associated with eddies are still present (Fig. 10a). For AssimMultiVar, the large RMSE of HC300-700 almost spreads throughout the entire Atlantic Ocean. Moreover, the RMSE is further enhanced compared with that of CNTL in the Indian and Pacific oceans. The RMSE of AssimTonly exhibits a pattern similar to that of CNTL, but is much higher in the large-RMSE regions. This result implies that the large errors simulated by the model may lead to an improper correlation between the SST and the subsurface temperature. Compared with AssimTonly, the large RMSE of AssimMultiVar in the tropical Atlantic Ocean (40°S-20°N) is probably induced by the unreasonable, or potentially non-existent, correlation between the SST and other variables. The RMSE of HC300-700 for AssimTmxl is similar to that for CNTL because the temperature below the mixed layer is not modified by the assimilation.

4. Conclusions
  • This study addresses the impact of SST assimilation on the global temperature and salinity simulations of HYCOM with a hybrid vertical coordinate based on the EnOI multivariate assimilation method. Four different runs are carried out for the period 2006-08, of which three are assimilation runs (AssimMultiVar, AssimTonly and AssimTmxl). In AssimMultiVar, all model variables are adjusted. In AssimTonly and AssimTmxl, only the temperature in the entire water column or in the mixed layer is adjusted. A control experiment without any assimilation is also performed for comparison.

    The three assimilation runs lead to improved simulations of SST in comparison with the control run without any assimilation, as expected. Notably, a reduction (greater than 25%) in RMSE is achieved in the global ocean. The runs that adjust only the temperature alone produce better simulation results than the run that adjusts all model variables. Specifically, in the Atlantic Ocean, a reduction of approximately 35% in RMSE is observed for the runs in which only temperature is modified, and a reduction of approximately 33% is observed for the run in which all variables are modified.

    A more stringent validation of the impact of the SST assimilation is carried out by comparing the simulations from the model runs with EN4.2.0 temperature and salinity analyses generated by observations. The vertical distributions of the RMSEs of temperature and salinity show that there is a distinct improvement in temperature, as well as almost no improvement in salinity, as a result of adjusting the temperature alone in the mixed layer in the assimilation. However, there is a negative impact on the temperature at the subsurface and on the salinity throughout the modeled layers when only the temperature in the entire water column or all model variables are adjusted.

    The ocean heat content in the upper 300 m of the global ocean is improved by adjusting the temperature in the mixed layer via assimilation. A rising trend in the RMSE of HC300 is observed over time when the temperature in the entire water column is modified or all variables are modified, and the latter yields a much greater RMSE than that of the CNTL experiment without any assimilation. The spatial distribution of the RMSE of HC300 shows that the assimilation in which all variables are adjusted results in large errors in the tropical Atlantic Ocean (15°S-15°N) and further enhances the inherent model errors in the tropical Pacific and Indian oceans. The ocean heat content from 300 m to 700 m reveals the changes in the subsurface temperature induced by the assimilation. Much larger RMSEs than those of the run without assimilation are found when the temperature in all layers is modified or all variables are modified by the assimilation. In addition, the RMSE exhibits an obvious upward trend with time. This result indicates that model performance deteriorates the longer the model integration continues. When the temperature in the mixed layer is modified, the heat content from 300 m to 700 m changes slightly in comparison with that of CNTL. This change is associated with the adjustment of the temperature below the mixed layer by the model dynamics instead of assimilation. The spatial distribution of the ocean heat content from 300 m to 700 m demonstrates that large errors are present almost everywhere in the Atlantic Ocean when all variables are modified in the assimilation. When the temperature alone in the entire water column is modified, the inherent model errors are amplified or extended in the Atlantic and Indian oceans. This result is probably associated with poor correlation between the SST and the subsurface variables due to model errors or spurious correlations. When the temperature alone in the mixed layer is modified, the spatial pattern is similar to that of CNTL.

    The results of this study suggest that SST assimilation with only temperature modifications in the mixed layer performs best. AssimTmxl is an experiment in which a special vertical localization technique is used. The technique localizes the influence of SST observations to the mixed layer. For the ensemble-based methods, the ensemble with a finite size approximation to the covariance matrix indicates that the correlations are not real. The spurious correlations can induce variable updates in regions that should not be updated. To eliminate the spurious covariances with distant grid points, the horizontal localization (e.g., the correlation function L used in this paper) is usually adopted. The performance of AssimTmxl implies that vertical localization is very important, especially for SST assimilation. Moreover, attention should be paid to SST assimilation by ensemble-based multivariate assimilation methods. The ensemble used in the assimilation is usually taken from model outputs. However, model errors can give rise to improper correlation between the SST and other variables, which can lead to the improper adjustment of variables in the assimilation. In particular, when a specific variable is dominant (e.g., the layer thickness variable in HYCOM), its incorrect adjustment can generate an extensive negative impact on the other variables.

Reference

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return