Long-Term Variability of Thermohaline Characteristics of the Azov Sea Based on the Numerical Eddy-Resolving Model

A. I. Mizyuk1, ✉, G. K. Korotaev1, A. V. Grigoriev2, O. S. Puzina1, P. N. Lishaev1

1 Marine Hydrophysical Institute, Russian Academy of Sciences, Sevastopol, Russian Federation

2 N. N. Zubov State Oceanographic Institute, Moscow, Russian Federation

✉ e-mail: artem.mizyuk@mhi-ras.ru

Abstract

Purpose. Decline of the river Don runoff to its historic minima, as well as intensive cyclonic activity and abnormal advection of the Black Sea waters led to the fact that in 2014–2016, very high salinity values (up to 12 psu) were observed in the Taganrog Bay. Under certain hydrometeorological conditions, salt water can penetrate deep into the river Don mouth. Therefore, study of changes in the Azov Sea hydrothermodynamics is rather an actual problem, which is proposed to be solved by numerical modeling.

Methods and Results. The paper represents the methodology for carrying out long-term model runs for joint dynamics of the Black, Azov and Marmara seas based on the eddy-resolving configuration of the NEMO modeling framework. A new-generation ERA5 reanalysis with a sufficiently high spatial resolution was used for the first time as a weather forcing for the region. New information on the rivers Don and Kuban’ runoffs were used and adjustment simulations were done to obtain the initial conditions. The results were verified based on the data from coastal hydrometeorological stations in the Sea of Azov. Some results of model simulations for the period from mid-2007 to 2016 are represented. A positive salinity trend in the basin of the Azov Sea is well pronounced. Surface boundary conditions for the heat flux were corrected for the purpose of carrying out simulations without ice modeling and reproducing adequate temperature values of the Azov Sea waters.

Conclusions. The performed numerical experiments showed applicability for the developed model regional configuration to further investigations. However, more detailed analysis of the results obtained for the Black Sea basin is required. Consideration of the basic external conditions in modeling made it possible to reproduce positive tendency of salinity in the Sea of Azov. The temperature simulation results indirectly agree with the sea ice data.

Keywords

numerical ocean modeling, Sea of Azov, ERA5, free-run simulations, verification, Black Sea, Exinus cascade

Acknowledgements

The investigation is carried out at the RFBR financial support (grant No. 18-05-80025\18 “Dangerous phenomena”).

Original russian text

Original Russian Text © The Authors, 2019, published in MORSKOY GIDROFIZICHESKIY ZHURNAL, Vol. 35, Iss. 5, pp. 496–510 (2019)

For citation

Mizyuk, A.I., Korotaev, G.K., Grigoriev, A.V., Puzina, O.S. and Lishaev, P.N., 2019. Long-Term Variability of Thermohaline Characteristics of the Azov Sea Based on the Numerical Eddy-Resolving Model. Physical Oceanography, 26(5), pp. 438-450. doi:10.22449/1573-160X-2019-5-438-450

DOI

10.22449/1573-160X-2019-5-438-450

References

  1. Matishov, G.G. and Grigorenko, K.S., 2017. Causes of Salinization of the Gulf of Taganrog. Doklady Earth Sciences, [e-journal] 477(1), pp. 1311-1315. https://doi.org/10.1134/ S1028334X17110034
  2. Korotaev, G.K., Oguz, T., Dorofeyev, V.L., Demyshev, S.G., Kubryakov, A.I. and Ratner, Yu.B., 2011. Development of Black Sea Nowcasting and Forecasting System. Ocean Science, [e-journal] 7(5), pp. 629-649. https://doi.org/10.5194/os-7-629-2011
  3. Mizyuk, A.I., Senderov, M.V., Korotaev, G.K. and Sarkysyan, A.S., 2016. Features of the Horizontal Variability of the Sea Surface Temperature in the Western Black Sea from High Resolution Modeling. Izvestiya, Atmospheric and Oceanic Physics, [e-journal] 52(5), pp. 570-578. https://doi.org/10.1134/S0001433816050108
  4. Madec, G., and the NEMO team, 2008. NEMO Ocean Engine. Note du Pôle de modélisation. Technical Report. [e-book] France: Institut Pierre-Simon Laplace. No. 27. Available at: https:// www.nemo-ocean.eu/doc/node1.html [Accessed: 5 November 2016].
  5. Popov, S.K. and Lobov, A.L, 2016. Diagnosis and Forecasts of Flood in Taganrog with the Help of an Operational Hydrodynamic Model. In: Hydrometcentre of Russia, 2016. Proceedings of Hydrometcentre of Russia. Moscow: TRIADA LTD. Iss. 362, pp. 92-108.
  6. Fomin, V.V. and Diansky, N.A., 2018. Simulation of Extreme Surges in the Taganrog Bay with Atmosphere and Ocean Circulation Models. Russian Meteorology and Hydrology, [e- journal] 43(12), pp. 843-851. https://doi.org/10.3103/S1068373918120051
  7. Zalesny, V.B., Diansky, N.A., Fomin, V.V., Moshonkin, S.N. and Demyshev, S.G., 2012. Numerical Model of the Circulation of the Black Sea and the Sea of Azov. Russian Journal of Numerical Analysis and Mathematical Modelling, [e-journal] 27(1), pp. 95-112. https:// doi.org/10.1515/rnam-2012-0006
  8. Stanev, E.V., Grashorn, S. and Zhang, Y.J., 2017. Cascading Ocean Basins: Numerical Simulations of the Circulation and Interbasin Exchange in the Azov-Black-Marmara- Mediterranean Seas System. Ocean Dynamics, [e-journal] 67(8), pp. 1003-1025. https:// doi.org/10.1007/s10236-017-1071-2
  9. Rodi, W., 1987. Examples of Calculation Methods for Flow and Mixing in Stratified Fluids. Journal of Geophysical Research: Oceans, [e-journal] 92(C5), pp. 5305-5328. https:// doi.org/10.1029/JC092iC05p05305
  10. Canuto, V.M., Howard, A., Cheng, Y. and Dubovikov, M.S., 2001. Ocean Turbulence. Part I: One-Point Closure Model – Momentum and Heat Vertical Diffusivities. Journal of Physical Oceanography, [e-journal] 31(6), pp. 1413-1426. doi:10.1175/1520- 0485(2001)031<1413:OTPIOP>2.0.CO;2
  11. Fofonoff, N.P. and Millard, R.C., 1983. Algorithms for Computation of Fundamental Properties of Seawater. UNESCO, 53 p. Available at: https://unesdoc.unesco.org/ark:/48223/pf0000059832. [Accessed: 10 November 2019].
  12. Mesinger, F. and Arakawa, A., 1976. Numerical Methods Used in Atmospheric Models, vol. 1, Geneva: WMO-ICSU Joint Organizing Committee, 76 p.
  13. Zalesak, S.T., 1979. Fully Multidimensional Flux-Corrected Transport Algorithms for Fluids. Journal of Computational Physics, [e-journal] 31(3), pp. 335-362. https:// doi.org/10.1016/0021-9991(79)90051-2
  14. Roullet, G. and Madec, G., 2000. Salt Conservation, Free Surface, and Varying Levels: A New Formulation for Ocean General Circulation Models. Journal of Geophysical Research: Oceans, [e-journal] 105(C10), pp. 23927-23942. https://doi.org/10.1029/2000JC900089
  15. Leclair, M. and Madec, G., 2009. A Conservative Leapfrog Time Stepping Method. Ocean Modelling, [e-journal] 30(2-3), pp. 88-94. https://doi.org/10.1016/j.ocemod.2009.06.006
  16. Large, W.G. and Yeager, S.G., 2004. Diurnal to Decadal Global Forcing for Ocean and Sea- Ice Models: The Data Sets and Flux Climatologies. Boulder: CGD Division of the National Center for Atmospheric Research. doi:10.5065/D6KK98Q6
  17. Kubryakov, A.I., 2004. Application of Nested Grid Technology When Creating Hidrophysical Field Monitoring System in the Black Sea Coastal Areas. In: MHI, 2004. Ecological Safety of Coastal and Shelf Zones and Comprehensive Use of Shelf Resources. Sevastopol: ECOSI- Gidrofizika, (11), pp. 31-50 (in Russian).
  18. National Ice Center, 2008, updated daily. IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions, Version 1. [4 km]. Boulder: NSIDC. https:// doi.org/10.7265/N52R3PMC [Accessed: 10 November 2019].
  19. Donlon, C.J., Martin, M., Stark, J., Roberts-Jones, J., Fiedler, E., Wimmer, W., 2012. The Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) System. Remote Sensing of Environment, [e-journal] 116, pp. 140-158. https://doi.org/10.1016/j.rse.2010.10.017

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