Self-Organizing Maps of Atmospheric Circulation and Interannual Variability of Hydrometeorological Fields in the Arctic

E. E. Lemeshko

Marine Hydrophysical Institute of RAS, Sevastopol, Russia

e-mail: e.lemeshko@mhi-ras.ru

Abstract

The article suggests the use of a nonlinear method of data analysis based on a neural network – an algorithm of Kohonen self-organizing maps for the task of typing the atmospheric surface circulation in the Arctic. Based on the construction of self-organizing surface pressure maps, the seasonal and interannual variability of atmospheric circulation in the Arctic for the period 1979–2018 is studied. Several modes were distinguished: cyclonic, two anticyclonic, and three mixed types. Indices of seasonal and annual repeatability of self-organizing atmospheric pressure maps are introduced, which allow us to study the temporal variability of atmospheric circulation modes and a composite method is proposed for calculating connected maps of other hydrometeorological parameters. The regimes of variability of the area of sea ice distribution and sea surface temperature depending on the type of atmospheric circulation are highlighted. Depending on the type of wind regime, there is a change in the area of sea ice distribution due to the variability of the flows of warm Atlantic waters into the Arctic Ocean. The characteristic types of sea surface temperature variability in the Barents Sea are identified, which are modulated by cyclonic / anticyclonic regimes of atmospheric circulation in the region and are an indicator of heat advection by the Atlantic waters. The interrelation is established of the repeatability index of self-organizing atmospheric pressure maps characterizing the types of atmospheric circulation with the variability of the Arctic Oscillation Index. The revealed regularities of the change in the types of cyclonic-anticyclonic atmospheric circulation are manifested in the interannual variability of the introduced repeatability index of self-organizing atmospheric pressure maps, which is a development of the Arctic Oscillation Index, improves understanding of the atmospheric climate circulation regimes in the Arctic.

Keywords

Kohonen maps, atmospheric reanalysis, Arctic, atmospheric circulation types, ice area, climate

Acknowledgments

This study was supported by the Russian Federation State Task № 0827-2019-0004.

For citation

Lemeshko, E.E., 2020. Self-Organizing Maps of Atmospheric Circulation and Interannual Variability of Hydrometeorological Fields in the Arctic. Ecological Safety of Coastal and Shelf Zones of Sea, (3), pp. 48–62. doi:10.22449/2413-5577-2020-3-48-62 (in Russian).

DOI

10.22449/2413-5577-2020-3-48-62

References

  1. Armitage, T.W.K., Bacon, S. and Kwok, R., 2018. Arctic Sea Level and Surface Circulation Response to the Arctic Oscillation. Geophysical Research Letters, 45(13), pp. 6576–6584. https://doi.org/10.1029/2018GL078386
  2. Bromwich, D.H. and Wang, S.H., 2008. A Review of the Temporal and Spatial Variability of Arctic and Antarctic Atmospheric Circulation Based upon ERA-40. Dynamics of Atmospheres and Oceans, 44(3–4), pp. 213–243. https://doi.org/10.1016/j.dynatmoce.2007.09.001
  3. Wang, Z., Hamilton, J. and Su, J., 2017. Variations in Freshwater Pathways from the Arctic Ocean into the North Atlantic Ocean. Progress in Oceanography, 155, pp. 54–73. https://doi.org/10.1016/j.pocean.2017.05.012
  4. Ivanov, V., Varentsov, M., Matveeva, T., Repina, I., Artamonov, A. and Khavina, E., 2019. Arctic Sea Ice Decline in the 2010s: The Increasing Role of the Ocean-Air Heat Exchange in the Late Summer. Atmosphere, 10(4), 184. https://doi.org/10.3390/atmos10040184
  5. Timmermans, M.-L. and Marshall, J., 2020. Understanding Arctic Ocean Circulation: a Review of Ocean Dynamics in a Changing Climate. Journal of Geophysical Research: Oceans, 125(4), e2018JC014378. https://doi.org/10.1029/2018JC014378
  6. Zabolotskikh, E.V., Gurvich, I.A. and Chapron, B., 2015. New Areas of Polar Lows Over the Arctic as a Result of Sea Ice Extent Decrease. Issledovanie Zemli iz Kosmosa, (2), pp. 64–77. https://doi.org/10.7868/S0205961415020116 (in Russian).
  7. Johnsson, V., ed., 2016. Applications of Self-Organizing Maps. 298 p. doi:10.5772/3464
  8. Richardson, A.J., Risien, C. and Shillington, F.A., 2003. Using Self-Organizing Maps to Identify Patterns in Satellite Imagery. Progress in Oceanography, 59(2–3), pp. 223–239. https://doi.org/10.1016/j.pocean.2003.07.006
  9. Capet, A., Barth, A., Beckers, J.-M. and Marilaure, G., 2012. Interannual Variability of Black Sea’s Hydrodynamics and Connection to Atmospheric Patterns. Deep-Sea Research Part II: Topical Studies in Oceanography, 77–80, pp. 128–142. http://dx.doi.org/10.1016/j.dsr2.2012.04.010
  10. Sonnewald, M., Wunsch, C. and Heimbach, P., 2019. Unsupervised Learning Reveals Geography of Global Ocean Dynamical Regions. Earth and Space Science, 6(5), pp. 784–794. https://doi.org/10.1029/2018EA000519
  11. Liu, Y., Weisberg, R.H. and Mooers, C.N.K., 2006. Performance Evaluation of the Self-Organizing Map for Feature Extraction. Journal of Geophysical Research: Oceans, 111, C05018. https://doi.org/10.1029/2005JC003117
  12. Lemeshko, E.E., Polozok, A.A. and Lemeshko, E.M., 2016. Analysis of Azov Sea Level Variability by Self Organization Maps on Altimetry Data. Ecological Safety of Coastal and Shelf Zones, (3), pp. 54–60 (in Russian).
  13. Lemeshko, E.M. and Lemeshko, E.E., 2019. Long-Term Variability of Air Temperature in the Arctic Region for the Period 1979–2017. In: O. A. Romanovskii and G. G. Matvienko, eds., 2019. Proceedings of SPIE 11208, 25th International Symposium on Atmospheric and Ocean Optics: Atmospheric Physics, 1–5 July 2019. Novosibirsk, 112089I. https://doi.org/10.1117/12.2540946
  14. Byshev, V.I., Kononova, N.K., Neiman, V.G. and Romanov, Yu.A., 2004. Quantitative Evaluation of Climate Change Development in Ocean–Atmosphere System. Oceanology, 44(3), pp. 341–353 (in Russian).
  15. Hegyi, B.M. and Taylor, P.C., 2017. The Regional Influence of the Arctic Oscillation and Arctic Dipole on the Wintertime Arctic Surface Radiation Budget and Sea Ice Growth. Geophysical Research Letters, 44(9), pp. 4341–4350. https://doi.org/10.1002/2017GL073281
  16. Bushuk, M. and Giannakis, D., 2017. The Seasonality and Interannual Variability of Arctic Sea Ice Reemergence. Journal of Climate, 30(12), pp. 4657–4676. https://doi.org/10.1175/JCLI-D-16-0549.1
  17. Arthun, M., Eldevik, T. and Smedsrud, L.H., 2019. The Role of Atlantic Heat Transport in Future Arctic Winter Sea Ice Loss. Journal of Climate, 32(11), pp. 3327–3341. https://doi.org/10.1175/JCLI-D-18-0750.1
  18. Proshutinsky, A., Dukhovskoy, D., Timmermans, M.-L., Krishfield, R. and Bamber, J.L., 2015. Arctic Circulation Regimes. Philosophical transactions of the Royal Society A, 373(2052), 20140160. https://doi.org/10.1098/rsta.2014.0160
  19. Dukhovskoy, D.S., Yashayaev, I., Proshutinsky, A., Bamber, J.L., Bashmachnikov, I.L., Chassignet, E.P., Lee, C.M. and Tedstone, A.J., 2019. Role of Greenland Freshwater Anomaly in the Recent Freshening of the Subpolar North Atlantic. Journal of Geophysical Research: Oceans, 124(5), pp. 3333–3360. https://doi.org/10.1029/2018JC014686
  20. Kravtsov, S., Tilinina, N., Zyulyaeva, Y. and Gulev, S.K., 2016. Empirical Modeling and Stochastic Simulation of Sea Level Pressure Variability. Journal of Applied Meteorology and Climatology, 55(5), pp. 1197–1219. https://doi.org/10.1175/JAMC-D-15-0186.1
  21. Belokopytov, V.N., 2017. Factors Reducing Efficiency of the Operational Oceanographic Forecast Systems in the Arctic Basin. Physical Oceanography, (2), pp. 19–24. https://doi.org/10.22449/1573-160X-2017-2-19-24

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