Extremely Strong Winds and Weather Patterns over Arctic seas

Galina Surkova; Aleksey Krylov

doi:10.24057/2071-9388-2019-22

Extremely Strong Winds and Weather Patterns over Arctic seas

Galina Surkova, Aleksey Krylov

https://doi.org/10.24057/2071-9388-2019-22

Full Text:

PDF (Eng)

Abstract

Strong wind is the main cause of storm sea waves. In order to minimize risks and damages from this phenomenon in the future, precise projections of future climate conditions are necessary. Extremely high wind speed events in the 20th - 21st centuries over Arctic seas were investigated using ERA-Interim reanalysis data (1981-2010) and CMIP5 models ensemble (RCP8.5 scenario, 2005-2100). Two different approaches were applied to investigate extreme wind events. The first one is traditional and involves direct analysis of wind speed data. It was used for the entire area of the Arctic seas. The second approach is based on an assumption that local and mesoscale extreme weather events are connected with large-scale synoptic processes. As it was shown in previous studies for the Black, Caspian and Baltic seas, it is possible to make climate projection of sea storm waves indirectly, studying the heterogeneity of sea level atmospheric pressure (SLP) fields that are the main factors of strong wind speed and wind waves. In this case, it is not necessary to run long-term simulations with a sea wave model to predict storm activity for the future climate. It is possible to analyze projections of storm SLP fields that are predicted by climate models much better than the wind speed required for a wave model. This method was implemented for the high wind speed events over the Barents Sea. Four major types of SLP fields accompanying high wind speed were revealed for the modern climate. It was shown that the frequency of their occurrence is expected to increase by the end of the 21st century.

Keywords

wind speed extremes, global warming, Arctic, weather patterns, oceanatmosphere interaction

About the Authors

Galina Surkova

Lomonosov Moscow State University
Russian Federation
Moscow, Russia

Aleksey Krylov

Lomonosov Moscow State University
Russian Federation
Moscow, Russia

References

1. Arkhipkin V.S., Gippius F.N., Koltermann K.P., Surkova G.V. (2014). Wind waves in the Black Sea: results of a hindcast study. Natural Hazards and Earth System Science, 14 (11), 2883-2897.

2. Brisson E., Demuzere M., Kwakernaak B., Van Lipzig N. P. M. (2010). Relations between atmospheric circulation and precipitation in Belgium. Meteorol Atmos Phys. DOI 10.1007/s00703-010-0103-y.

3. Cannon A.J., Whitfield P.H., Lord E.R. (2002). Synoptic map pattern classification using recursive partitioning and principal component analysis. Monthly Weather Review, 130, 1187–1206.

4. Cassou C. (2010). Euro-Atlantic regimes and their teleconnections. Proceedings: ECMWF Seminar on Predictability in the European and Atlantic regions, 6 – 9 September 2010, 14 pp. https://www.ecmwf.int/en/learning/workshops-and-seminars/past-workshops/2010-annual-seminar (July 11, 2019).

5. Cheng X.H., Wallace J.M. (1993). Cluster analysis of the northern hemisphere winter 500-hPa height field: spatial patterns. J. Atmos. Sci., 50, 2674–2696.

6. Climate of Russia. (2001). Ed.: Kobysheva. 656 pp. (in Russian with English summary).

7. Corte-Real J., Qian B., Xu H. (1998). Regional climate change in Portugal: precipitation variability associated with large-scale atmospheric circulation. International Journal of Climatology, 18, 619–635.

8. Corte-Real J., Qian B. and Xu H. (1999). Circulation patterns, daily precipitation in Portugal and implications for climate change simulated by the second Hadley Centre GCM. Clim. Dyn., 15, 921–935.

9. Dee D.P, Uppala S.M., Simmons A.J., et al. (2011). The ERA-Interim reanalysis: configu-ration and performance of the data assimilation system. Q. J. R. Meteorol. Soc., 137, 553–597.

10. Demuzere M., Kassomenos P., Philipp A. (2011). The COST733 circulation type classification software: an example for surface ozone concentrations in Central Europe. Theor Appl Climatol., 105, 143–166, DOI 10.1007/s00704-010-0378-4.

11. Hydrometeorology and hydrochemistry of the seas of the USSR. Barents Sea. (1990).V.1 (1). Hydrometeorological conditions. Leningrad. Hydrometizdat. 280 p. (in Russian with English summary).

12. Huth R., Beck C., Philipp A., et al. (2008). Classifications of Atmospheric Circulation Patterns Recent Advances and Applications. Trends and Directions in Climate Research. Ann. N.Y. Acad. Sci. 1146, 105–152.

13. IPCC (2013). Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Edited by T. F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S. K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P. M. Midgley. Cambridge University Press. Cambridge, United Kingdom and New York, USA, 2013, 1535 pp.

14. IPCC 2012 (2012). Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change. Eds.: Field C. B., V. Barros, T. F. Stocker, D. Qin, D. J. Dokken, K. L. Ebi, M. D. Mastrandrea, K. J. Mach, G.-K. Plattner, S. K. Allen, M. Tignor, and P. M. Midgley. Cambridge University Press, Cambridge, UK, and New York, NY, USA, 2012, 582 pp.

15. Johnson N.L., Kotz S., Balakrishnan N. (1995). Continuous Univariate Distributions. Volume 2, 2nd Edition. John Wiley & Sons. 752 pp.

16. Johnson N.C., Feldstein S.B., Tremblay B. (2008). The continuum of Northern Hemisphere teleconnection patterns and a description of the NAO shift with the use of self-organized maps. J. Clim., 21, 6354–6371.

17. Lindsay R., Wensnahan M., Schweiger A., Zhang J. (2014). Evaluation of Seven Different Atmospheric Re-analysis Products in the Arctic, J. Climate, 27, 2588–2606.

18. Michelangeli P., Vautard R., Legras B. (1995). Weather regimes: recurrence and quasi-stationarity. J. Atmos. Sci., 52, 1237–1256.

19. Moss R. H., Babiker M., Brinkman S., et al. (2008). Towards New Scenarios for Analysis of Emissions, Climate Change, Impacts, and Response Strategies. Intergovernmental Panel on Climate Change. Geneva. 132 pp.

20. Myslenkov S.A., Platonov V.S., Toropov P.A., Shestakova A.A. (2015). Numerical modelling of storm waves in the Barents Sea with the SWAN model on the base of wind data from COSMO-CLM and WRF-ARW. Vestnik of the Lomonosov Moscow State University, Seria 5, Geography, No. 6, 65-75.

21. Philipp A., Bartholy J., Beck C., et al. (2010). Cost733cat - a database of weather and circulation type classifications. Phys Chem Earth (Special Issue), 35, 360–373.

22. Santos J.A., Corte-Real J., Leite S.M. (2005). Weather regimes and their connection to the winter rainfall in Portugal. Int. J. Climatol., 25, 33–50.

23. Solman S.A., Menendez C.G. (2003). Weather regimes in the South American sector and neighbouring oceans during winter. Climate Dynamics, 21, 91–104.

24. Stahl K., Moore R.D., McKendry I.G. (2006). The role of synoptic-scale circulation in the linkage between large-scale ocean-atmosphere indices and winter surface climate in British Columbia, Canada. Int. J. Climatol., 26, 541–560.

25. Surkova G.V. (2015). Climate projection of storm weather patterns. Proceedings of the Twelfth International Conference on the Mediterranean Coastal Environment MEDCOAST 15, Özhan, E. (Ed.) 6–10 October, Varna, Bulgaria, 531-540.

26. Surkova G.V., Arkhipkin V.S., Kislov A.V. (2013), Atmospheric circulation and storm events in the Black Sea and Caspian Sea. Central European Journal of Geosciences, 5, № 4, 548-559.

27. Surkova G.V., Krylov A.A. (2016). Synoptic Patterns Of Extreme Wind Speed Over The Barents Sea. Vestnik MGU (Moscow University Bulleten). Series 5.Geography, 6, 18-24 (in Russian).

28. Surkova G.V., Krylov A.A. (2017). Changes of hydro-thermal climate resources of the Arctic due to global warming of 21 century. Arctic and Antarctic, 1, 47-61. DOI: 10.7256/2453-8922.2017.1.22265. http://e-notabene.ru/arctic/article_22265.html (in Russian with English summary).

29. Surkova G.V., Sokolova L.A., Chichev A.R. (2015). Long-term Regime of Daily Wind Speed Maximum over Barents and Cara Seas. Vestnik MGU (Moscow University Bulleten). Series 5.Geography, 5, 54-59 (in Russian with English summary).

30. Taylor K.E. (2001). Summarizing multiple aspects of model performance in a single diagram. Journal of Geophysical Research (April, 16) 106, № D7, 7183-7192.

31. Taylor. K.E., Stouffer R.J., Meehl G.A. (2012). The CMIP5 experiment design, Bull. Amer. Meteor. Soc. 93, 485–498.

32. Volodin. E.M., Diansky N.A., Gusev A.V. (2013). Simulation and prediction of climate changes in the 19th to 21st centuries with the Institute of Numerical Mathematics. Russian Academy of Sciences, model of the Earth’s climate system, Izv. Atmos. Ocean. Phys. 49, 347. https://doi.org/10.1134/S0001433813040105.

33. Wilks. D.S. (1995). Statistical methods in the atmospheric sciences. Academic Press. 476.

34. Yarnal B. (1993). Synoptic climatology in environmental analysis. Belhaven Press, London, 195.

For citation:

Surkova G., Krylov A. Extremely Strong Winds and Weather Patterns over Arctic seas. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2019;12(3):34-42. https://doi.org/10.24057/2071-9388-2019-22