Spatial Variability of Interannual Temperature Oscillations in the Barents Sea and the Kara Sea According to Simulation Results

The following article provides the research on interannual temperature oscillations of the Barents Sea and the Kara Sea based on mode results only without using the reanalysis data in the water temperature distribution during the 1 to 12-year period. Model solution of 1975–2005 received according to the two different spatial resolution model data are used for the following research. The correct representation of the researched area climate by these models is determined. Interannual water temperature oscillations amplitude magnitude may be compared to the ones of the interseasonal oscillations in the researched area. The wavelet analysis method, providing the opportunity for the research of the periodical components despite their frequency changes, is used here to detect the periodicity and possible frequency instability. The analysis results demonstrate that the atmosphere temperature oscillations and ice cover area oscillations show the similar periods. It’s worth mentioning that some of the known oscillations in the Ocean-Atmosphere system match the oscillations intervals received according to model results. In the atmosphere temperature and the water temperature oscillation of the initial data distribution the connection between the oscillations range and the ice edge position can be followed clearly. In the water and air temperature oscillations in the local points the 3 basic supporting frequencies of the presented parameters are distinguished. They correspond to the 1.0–3.6 year, 3.9–5.8 year, 6.3–10.5 year periods as well as to the basic supporting oscillation periods at the “Kola meridian” stations. Ice cover area oscillation ranges are fully contained in the stated ranges of the 3 basic water and air temperature oscillations in the researched local points of the Barents Sea and the Kara Sea. The sea oscillation intensity depends on the ice edge position and is presented mostly in the areas which are covered by the snow during the less period of time. Two-dimensional distribution of the temperature oscillation amplitudes demonstrates that the high-frequent oscillations (periods lasting up to 4.1 year) get the more evident vision on the surface and are poorly presented in the low layers. On the contrary the long period (lasting more than 8 years) oscillations appear nearly everywhere. In the low layers such oscillations can spread especially wide including the distance up to the north end of the researched area.

1. Monin A.S. Theory of climate introduction. Leningrad, Gidrometeoizdat, 1982. 246 p. (in Russian).

2. Gorshkov V.L. The influence of the Earth pole tide on the seismic activity. Izvestia of the St. Petersburg University. 2015, 1, 2 (60), 4, 646–656 p. (in Russian).

3. Serykh I.V., Sonechkin D.M. Chaos and order in the atmosphere dynamics. Part 2. Interannual rhythms of El Nino — South fluctuation. Izvestia Vuzov PND. 2017, 25, 5, 5–25 (in Russian).

4. Nesterov E.S. Northatlantic fluctuation: the atmosphere and the ocean. Moscow, Triada Ltd., 2013. 144 p. (in Russian).

5. Madhusoodanan M.S., Thompson B. Decadal variability of the Arctic Ocean thermal structure. Ocean Dyn. 2011, 61, 7, 873–880.

6. Yndestad H., Turrell W.R., Ozhigin V. Lunar nodal tide effects on variability of sea level, temperature, and salinity in the Faroe-Shetland Channel and the Barents Sea. Deep-Sea Res. Part I: Oceanographic Research Papers. 2008, 55, 10, 1201–1217. doi: 10.1016/j.dsr.2008.06.003

7. Smirnov N.P., Vorobiev V.N., Drozdov V.V. Atmosphere and ocean influence centre in the North Atlantic. Uchenye Zapiski RSHMU. 2010, 15, 117–134 p. (in Russian).

8. Da Costa E., De Verdiere C. The 7.7-year North Atlantic Oscillation. Q.J.R. Meteorol. Soc. 2002, 128, 797–817.

9. Humlum O., Solheim J.-E., Stordahl К. Spectral Analysis of the Svalbard Temperature Record 1912–2010. Adv. Meteor. 2011, Article ID175296, 14 p. doi: 10.1155/2011/175296

10. Julin A.V., Vyazigina E.S. Interannual and seasonal variability of the ice area in the Arctic ocean according to the satellite data. Russian Arctic. 2019, 7, 28–40. doi: 10.24411/2658–4255–2019–10073

11. IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC: Geneva, Switzerland, 2014. 151 p.

12. Jelmert А. et al. Status for miljøet i Barentshavet. Rapport fra Overvåkingsgruppen 2020. Havforskningsinstituttet, 2020. URL: https://www.hi.no/hi/nettrapporter/rapport-fra-havforskningen-2020-13 (date of access: 09.06.2020).

13. Bashmachnikov I.L., Yurova A.Y., Bobylev L.P. et al. Seasonal and Interannual Variations of Heat Fluxes in the Barents Sea Region. Izv. Atmos. Ocean. Phys. 2018, 54, 213–222. doi: https://doi.org/10.1134/S0001433818020032

14. Astaf’eva N.M. Wavelet analysis: basic theory and some applications. Phys. Usp. 1996, 39, 1085–1108. doi: 10.1070/PU1996v039n11ABEH000177

15. Sein D.V., Mikolajewicz U., Groger M., Fast I., Cabos W., Pinto J.G., Hagemann S., Semmler T., Izquierdo A., Jacob D. Regionally coupled atmosphere-ocean-sea ice-marine biogeochemistry model ROM: 1. Description and validation. J. Adv. Model. Earth Syst. 2015, 7, 268–304.

16. Marsland S.J., Haak H., Jungclaus J.H., Latif M., Roske F. The Max-Planck-Institute global ocean/sea ice model with orthogonal curvilinear coordinates. Ocean Model. 2003, 5, 91–127.

17. Jacob D. A note to the simulation of the annual and inter-annual variability of the water budget over the Baltic Sea drainage basin. Meteorol. Atmos. Phys. 2001, 77, 61–73. https://doi.org/10.1007/s007030170017

18. Jacob D., Van den Hurk B., Andræ U. et al. A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period. Meteorol. Atmos. Phys. 2001, 77, 19–43. doi: 10.1007/s007030170015

19. Kistler R. et al. The NCEP/NCAR50 year reanalysis: Monthly-means CD-ROM and documentation. Bull. Am. Meteorol. Soc. 2001, 82, 247–267.

20. Marshall J., Adcroft A., Hill C., Perelman L., Heisey C. A finite-volume, incompressible navier-stokes model for studies of the ocean on parallel computers. J. Geophys. Res. 1997, 102(C3), 5753–5766.

21. Hibler III W.D. A dynamic thermodynamic sea ice model. J. Phys. Oceanogr. 1979, 9, 815–846.

22. Hibler III W.D. Modeling a variable thickness sea ice cover. Mon. Wea. Rev. 1980, 1, 1943–1973.

23. Zhang J., Hibler III W.D. On an efficient numerical method for modeling sea ice dynamics. J. Geophys. Res. 1997, 102(C4), 8691–8702.

24. Zhang J.L., Rothrock D.A. Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Mon. Wea. Rev. 2003, 131, 845–861.

25. Schweiger A., Lindsay R., Zhang J., Steele M., Stern H. Uncertainty in modeled arctic sea ice volume. J. Geophys. Res. 2011, 116, C00D06. doi: 10.1029/2011JC007084

26. Martyanov S.D., Dvornikov A.Y., Ryabchenko V.A., Sein D.V., Gordeeva S.M. The research on the initial products and sea ice connection in arctic seas: valuation based on low-order model of the sea ecosystem model. Fundam. Prikl. Gidrofiz. 2018, 11, 2. 108–117 p. doi: 10.7868/S2073667318020107 (in Russian).

27. Mahoney A.R., Barry R.G., Smolyanitsky V., Fetterer F. Observed sea ice extent in the Russian Arctic, 1933–2006. J. Geophys. Res. 2008, 113, C11005. doi: 10.1029/2008JC004830

28. Karsakov A.L. Oceanographic research on the Kola meridian in the Barents Sea during years 1900–2008. Murmansk, PINRO, 2009. 139 p. (in Russian).

29. Smirnov N.P., Vainovskiy P.A., Titov Y.E. Statistical diagnosis and prognosis of the oceanological process. St. Petersburg, Gidrometeoizdat, 1992. 198 p. (in Russian).

30. Sea Ice Edge Location and Extent in the Russian Arctic, 1933–2006, Version 1. URL: https://nsidc.org/data/G02182/versions/1 (date of access: 18.04.2020).