Izvestiya, Atmospheric and Oceanic Physics

, Volume 55, Issue 4, pp 365–373 | Cite as

Meridional Mass Transport of Bottom Water in the South Atlantic

  • K. P. Belyaev
  • E. G. MorozovEmail author
  • N. P. Tuchkova


Estimates of the meridional mass transport of Antarctic Bottom Water, calculated using the coupled ocean-atmosphere Earth System Model on the basis of the original data assimilation method are presented. For assimilation, we use data of the latitudinal CTD sections of temperature and salinity of the WOCE international experiment in 1991–1995. Estimates of the current velocities of Antarctic Bottom Water with the assimilation of observational data are given. We used the author’s data-assimilation method, which was previously referred to as the generalized Kalman Filter (GKF) method. In this particular case, it coincides with the classical Kalman method (EnKF). We also present the estimates of mass transport based on a standard geostrophic dynamic scheme. It is shown that model calculations with data assimilation are qualitatively the same and are quantitatively close to the estimates of the geostrophic flow transport based on the dynamic method.


Antarctic Bottom Water CTD casts mass transport GKF data assimilation method Lomonosov-2 supercomputer DKRZ Mistral cluster system MPI-ESM joint model 



This research was performed within the Scientific State Task (theme no. 0149-2019-0004) and supported in part by the Russian Foundation for Basic Research (dynamic calculation) (project no. 17-08-00085) and Russian Science Foundation (analysis of field data) (project no. 16-17-10149). Data assimilation was implemented on the Lomonosov 2 supercomputer at Lomonosov Moscow State University.


  1. 1.
    M. Arhan, H. Mercier, B. Bourles, and Y. Gouriou, “Hydrographic section across the Atlantic at 7°30′ N and 4°30′ S,” Deep-Sea Res. 45, 829–872 (1998).CrossRefGoogle Scholar
  2. 2.
    J. H. Jungclaus, N. Fischer, H. Haak, K. Lohmann, J. Marotzke, D. Matei, U. Mikolajewicz, D. Notz, and J. S. Storch, “Characteristics of the ocean simulations in the Max Planck Institute Ocean Model (MPIOM) the ocean component of the MPI-Earth system model,” J. Adv. Modeling Earth Syst. 5 (2), 422–446 (2013). CrossRefGoogle Scholar
  3. 3.
    F. Lemarié, E. Blayo, and L. Debreu, “Analysis of ocean–atmosphere coupling algorithms: Consistency and stability,” Procedia Comput. Sci. 51, 2066–2075 (2015).CrossRefGoogle Scholar
  4. 4.
    E. M. Volodin, E. V. Mortikov, S. V. Kostrykin, V. Ya. Galin, V. N. Lykosov, A. S. Gritsun, N. A. Diansky, A. V. Gusev, and N. G. Yakovlev, “Simulation of modern climate with the new version of the INM RAS climate model,” Izv., Atmos. Ocean. Phys. 53 (2), 142–155 (2017).CrossRefGoogle Scholar
  5. 5.
    E. M. Volodin, A. V. Gusev, N. A. Diansky, R. A. Ibrayev, and K. V. Ushakov, “Reproduction of World Ocean circulation by the CORE-II scenario with the models INMOM and INMIO,” Izv., Atmos. Ocean. Phys. 54 (1), 86–100 (2018).CrossRefGoogle Scholar
  6. 6.
    K. P. Belyaev, C. A. S. Tanajura, and N. P. Tuchkova, “Comparison of methods for Argo drifters data assimilation into a hydrodynamical model of the ocean,” Oceanology (Engl. Transl.) 52 (5), 593–603 (2012).Google Scholar
  7. 7.
    G. Evensen, Data Assimilation. The Ensemble Kalman Filter (Springer, Berlin, 2009).Google Scholar
  8. 8.
    V. I. Agoshkov, V. M. Ipatova, V. B. Zalesnyi, E. I. Parmuzin, and V. P. Shutyaev, “Problems of variational assimilation of observational data for ocean general circulation models and methods for their solution,” Izv., Atmos. Ocean. Phys. 46 (6), 677–712 (2010).CrossRefGoogle Scholar
  9. 9.
    M. N. Kaurkin, R. A. Ibrayev, and K. P. Belyaev, “Data assimilation in the ocean circulation model of high spatial resolution using the methods of parallel programming,” Russ. Meteorol. Hydrol. 41 (7) 479–486 (2016).CrossRefGoogle Scholar
  10. 10.
    K. Belyaev, A. Kuleshov, C. A. S. Tanajura, and N. Tuchkova, “An optimal data assimilation method and its application to the numerical simulation of the ocean dynamics,” Math. Comput. Modell. Dyn. Syst. 52, 15–25 (2018).Google Scholar
  11. 11.
    K. Belyaev, A. Kuleshov, C. A. S. Tanajura, and N. Tuchkova, “A correction method for dynamic model circulations using observational data and its application in oceanography,” Math. Models Comput. Simul. 8, 391–400 (2016).CrossRefGoogle Scholar
  12. 12.
    Vl. V. Voevodin, S. A. Zhumatii, S. I. Sobolev, A. S. Antonov, P. A. Bryzgalov, D. A. Nikitenko, K. S. Stefanov, and Vad. V. Voevodin, “The practice of the Lomonosov supercomputer,” Otkrytye Sist., No. 7, 36–39 (2012).Google Scholar
  13. 13.
    J. I. Antonov, D. Seidov, T. P. Boyer, R. A. Locarnini, A. V. Mishonov, H. E. Garci, O. K. Baranova, M. M. Zweng, and D. R. Johnson, World Ocean Atlas NOAA, NESDIS V. 2 (69), Ed. by S. Levitus (U.S. Government Printing Office, Washington, D.C., 2010).Google Scholar
  14. 14.
    E. Kalnay, Y. Ota, T. Miyoshi, and J. Liu, “A simpler formulation of forecast sensitivity to observations application to ensemble Kalman filters,” Tellus, Ser. A 64 (1), 18462 (2012). CrossRefGoogle Scholar
  15. 15.
    The International Thermodynamic Equation of Seawater: Manual and Guide (UNESCO, 2010).Google Scholar
  16. 16.
    G. Wüst, Schichtung und Zirkulation des Atlantischen Ozeans, Das Bodenwasser und die Stratosphäre, in Wissenschaftliche Ergebnisse, Deutsche Atlantische Expedition auf dem Forschungs und Vermessungsschiff “Meteor” 1925–1927, Ed. by A. Defant (Walter de Gruyter, Berlin, 1936), Vol. 6, pp. 1–288.Google Scholar
  17. 17.
    W. Zenk and N. G. Hogg, “Warming trend in Antarctic bottom water flowing into the Brazil Basin,” Deep-Sea Res. 43 (9), 1461–1473 (1996).CrossRefGoogle Scholar
  18. 18.
    E. Morozov, A. Demidov, R. Tarakanov, and W. Zenk, Abyssal Channels in the Atlantic Ocean Water Structure and Flows (Springer, Dordrecht, 2010).CrossRefGoogle Scholar
  19. 19.
    E. G. Morozov, A. N. Demidov, and R. Yu. Tarakanov, “Transport of Antarctic waters in the deep channels of the Atlantic Ocean,” Dokl. Earth Sci. 422 (9), 1286–1289 (2008).CrossRefGoogle Scholar
  20. 20.
    F. J. Sandoval and G. L. Weatherly, “Evolution of the deep western boundary current of Antarctic Bottom Water in the Brazil Basin,” J. Phys. Oceanogr. 31 (6), 1440–1460 (2001).CrossRefGoogle Scholar
  21. 21.
    E. G. Morozov and R. Yu. Tarakanov, “The flow of Antarctic Bottom Water from the Vema Channel to the Brazil Basin,” Dokl. Earth Sci. 456 (2), 598–601 (2014).CrossRefGoogle Scholar
  22. 22.
    M. Rhein, L. Stramma, and G. Krahmann, “The spreading of Antarctic Bottom Water in the Tropical Atlantic,” Deep-Sea Res. 45, 507–527 (1998).CrossRefGoogle Scholar
  23. 23.
    C. A. S. Tanajura, L. N. Lima, and K. P. Belyaev, “Assimilation of satellite surface-height anomalies data into a Hybrid Coordinate Ocean Model (HYCOM) over the Atlantic Ocean,” Oceanology (Engl. Transl.) 55 (5), 667–678 (2015).Google Scholar
  24. 24.
    K. P. Koltermann, A. V. Sokov, V. P. Tereschenkov, S. A. Dobroliubov, K. Lorbacher, and A. Sy, “Decadal changes in the thermohaline circulation of the North Atlantic,” Deep-Sea Res. II 46 (1–2), 109–138 (1999).CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  • K. P. Belyaev
    • 1
    • 2
  • E. G. Morozov
    • 1
    Email author
  • N. P. Tuchkova
    • 2
  1. 1.Shirshov Institute of Oceanology, Russian Academy of SciencesMoscowRussia
  2. 2.Dorodnicyn Computing Center FRC CSC, Russian Academy of SciencesMoscowRussia

Personalised recommendations