Long-Term Variability of the Timing of Freezing and the Duration of Ice Phenomena in the White Sea Based on Satellite and in Situ Observations for 1980–2020

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Abstract

The long-term variability of the ice regime of the White Sea for the period 1980–2020 was studied. The reliability of the satellite data used was also assessed by comparing them with in situ observation data. We use the regular hydrometeorological monitoring data from eight marine observation points, as well as satellite microwave passive sounding data (NSIDC) with a spatial resolution of 25 km and a time step of 1–2 days to form series of the main elements of the sea ice regime (the characteristic dates of ice regime and duration of ice phenomena). The average statistical dates of the start freezing and the ice breakup for the entire water area of the White Sea and its regions were obtained. Regression analysis of the data showed that the start freezing and the ice breakup dates have shifted towards the winter months over the past 40 years. The shifts occurred at average rates of 7.4 days/10 years and 4.7 days/10 years, respectively, according to in situ observations, and 11.9 days/10 years and 4.1 days/10 years, respectively, according to satellite observations. Overall, over the past 40 years, the average duration of ice phenomena in the White Sea has decreased by 47 days according to in situ observations and by 62 days according to satellite observations. Comparative analysis of satellite and in situ data showed significant differences in the average values of absolute deviations (up to 70 days) in determining the characteristic dates of the White Sea ice regime; however, the time series of characteristic dates are in good agreement with each other (pair correlation coefficients of 0.76/0.82 between the time series of dates of start freezing and dates of ice break up). This proves the possibility of using satellite data to calculate the regime indicators of ice phenomena over a long period in order to identify patterns in the development of ice processes, assess their climatic trends and develop methods for forecasting ice conditions.

About the authors

V. N Baklagin

Northern Water Problems Institute of the Karelian Research Centre of the Russian Academy of Sciences

Email: slava.bakalgin@mail.ru
Petrozavodsk, Russia

References

  1. Баклагин В.Н. Многолетняя изменчивость сплочённости льда Белого моря по спутниковым данным // Лёд и Снег. 2022. Т. 62. № 4. С. 579–590. https://doi.org/10.31857/S2076673422040153
  2. Гидрометеорология и гидрохимия морей СССР. Т. 2. Вып. 1 / Ред. Б.Х. Глуховский. Л.: Гидрометеоиздат, 1991. 241 с.
  3. Думанская И.О. Анализ изменчивости положения кромок дрейфующего льда и максимальной ледовитости Белого моря // Тр. Гидрометцентра России. 2004. Вып. 339. С. 45–54.
  4. Думанская И.О. Ледовые условия морей европейской части России. Обнинск: Изд-во ИГ–СОЦИН, 2014. 608 с.
  5. Заболотских Е.В. Обзор методов восстановления параметров ледяного покрова по данным спутниковых микроволновых радиометров // Изв. РАН. Физика атмосферы и океана. 2019. Т. 55. № 1. C. 128–151. https://doi.org/10.31857/S0002-3515551128-151
  6. Йоханнессен О.М., Александров В.Ю., Фролов И.Е., Сандвен С., Петтерссон Л.Х., Бобылев Л.П., Клостер К., Смирнов В.Г., Миронов Е.У., Бабич Н.Г. Научные исследования в Арктике. Т. 3. Дистанционное зондирование морских льдов на северном морском пути: изучение и применение. СПб.: Наука, 2007. 512 с.
  7. Спутниковые методы определения характеристик ледяного покрова морей / Ред. В.Г. Смирнов. СПб.: ААНИИ, 2011. 240 с.
  8. Наставление гидрометеорологическим станциям и постам РД 52.10.842-2017. Вып. 9. Гидрометеорологические наблюдения на морских станциях и постах. Ч. I. Гидрологические наблюдения на береговых станциях и постах. М.: ООО “Изд-во ИТРК”, 2017. 385 с.
  9. Шалина Е.В. Региональные особенности изменения ледовой обстановки в морях российской Арктики и на трассе Северного морского пути по данным спутниковых наблюдений // Современные проблемы дистанционного зондирования Земли из космоса. 2021. Т. 18. № 5. С. 201–213. https://doi.org/10.21046/2070-7401-2021-18-5-201-213
  10. Шалина Е.В. Сокращение ледяного покрова Арктики по данным спутникового пассивного микроволнового зондирования // Современные проблемы дистанционного зондирования Земли из космоса. 2013. Т. 10. № 1. С. 328–336.
  11. Шалина Е.В., Йоханнессен О.М., Бобылев Л.П. Изменение арктического ледяного покрова по данным спутникового пассивного микроволнового зондирования с 1978 по 2007 год // Современные проблемы дистанционного зондирования Земли из космоса. 2008. Т. 2. Вып. 5. С. 228–223.
  12. Alekseeva T.A., Frolov S.V. Comparative analysis of satellite and shipborne data on ice cover in the Russian Arctic seas // Izvestiya, Atmospheric and Oceanic Physics. 2013. V. 49. P. 879–885. https://doi.org/10.1134/S000143381309017X
  13. Alekseeva T., Tikhonov V., Frolov S., Repina I., Raev M., Sokolova J., Sharkov E., Afanasieva E., Serovetnikov S. Comparison of Arctic Sea Ice Concentrations from the NASA Team, ASI, and VASIA2 Algorithms with Summer and Winter Ship Data // Remote Sensing. 2019. V. 11. 2481. https://doi.org/10.3390/rs11212481
  14. Cavalieri D.J., Parkinson C.L. Arctic sea ice variability and trends, 1979–2010 // The Cryosphere. 2012. № 6. P. 881–889. https://doi.org/10.5194/tc-6-881-2012
  15. Cavalieri D., Parkinson C., DiGirolamo N., Ivanov A. Intersensor calibration between F13 SSM/I and F17 SSMIS for global sea ice data records // IEEE Geoscience and Remote Sensing Letters. 2011. № 9 (2). P. 233–236. https://doi.org/10.1109/LGRS. 2011.2166754
  16. Cavalieri D., Parkinson C., Gloersen P., Comiso J., Zwally H.J. Deriving Long-term Time Series of Sea Ice Cover from Satellite Passive-microwave Multisensor Data Sets // J. Geophys. Res. 1999. № 104 (C7). P. 15803–15814.
  17. Comiso J.C., Parkinson C.L., Gersten R., Stock L. Accelerated decline in the Arctic sea ice cover // Geophys. Research Letters. 2007. V. 34. L01703. https://doi.org/10.1029/2007/GL031972
  18. Filatov N.N., Pozdnyakov D.V., Johannessen O.M., Pettersson L.H. White Sea: Its Marine Environment and Ecosystem Dynamics Influenced by Global Change. Berlin; Heidelberg: Springer, 2005. 463 p. https://doi.org/10.1007/3-540-27695-5
  19. Kern S., Lavergne T., Notz D., Pedersen L.T., Tonboe R. Satellite passive microwave sea-ice concentration data set inter-comparison for Arctic summer conditions // The Cryosphere. 2020. V. 14. P. 2469–2493. https://doi.org/10.5194/tc-14-2469-2020
  20. Kern S., Lavergne T., Notz D., Pedersen L.T., Tonboe R.T., Saldo R., Sørensen A.M. Satellite passive microwave sea-ice concentration data set intercomparison: closed ice and ship-based observations // The Cryosphere. 2019. V. 13. P. 3261–3307. https://doi.org/10.5194/tc-13-3261-2019
  21. Knuth M.A., Ackley S.F. Summer and early-fall Sea-ice concentration in the Ross Sea: Comparison of in Situ ASPeCt observations and satellite passive microwave estimates // Annals of Glaciology. 2006. V. 44. P. 303–309. https://doi.org/10.3189/172756406781811466
  22. Laliberté F., Howell S.E.L., Kushner P.J. Regional variability of a projected sea ice-free Arctic during the summer months // Geophys. Research Letters. 2016. V. 43. P. 256–263. https://doi.org/10.1002/2015GL066855
  23. Maslanik J., Stroeve J., Fowler C., Emery W. Distribution and trends in Arctic sea ice age through spring 2011 // Geophysical Research Letters. 2011. V. 38 (13). L13502. https://doi.org/10.1029/2011GL047735
  24. Smith L.C., Stephenson S.R. New Trans-Arctic shipping routes navigable by midcentury // Proc. Natl. Acad. Sci. U.S.A. 2013. V. 110 (13). P. E1191–E1195. https://doi.org/10.1073/pnas.121421211
  25. Meier W.N. Comparison of passive microwave ice concentration algorithm retrievals with AVHRR imagery, in Arctic peripheral seas // IEEE Transactions on Geoscience and Remote Sensing. 2005. V. 43. P. 1324–1337. https://doi.org/10.1109/TGRS. 2005.846151
  26. Meier W.N., Khalsa S.J.S., Savoie M.H. Intersensor calibration between F-13 SSM/I and F-17 SSMIS nearreal-time Sea Ice estimates // IEEE Transactions on Geoscience and Remote Sensing. 2011. V. 49 (9). P. 3343–3349. https://doi.org/10.1109/TGRS. 2011.2117433
  27. NSIDC // Электронный ресурс. URL: https://nsidc.org/data/g02135/versions/3/ (Дата обращения: 08.05.2025).
  28. Pang X., Pu J., Zhao X., Ji Q., Qu M., Cheng Z. Comparison between AMSR2 Sea Ice Concentration Products and Pseudo-Ship Observations of the Arctic and Antarctic Sea Ice Edge on Cloud-Free Days // Remote Sensing. 2018. V. 10. 317. https://doi.org/10.3390/rs10020317
  29. Spreen G., Kaleschke L., Heygster G. Sea ice remote sensing using AMSR-E 89-GHz channels // Journal of Geophys. Research: Oceans. 2008. V. 113. C02S03. https://doi.org/10.1029/2005JC003384
  30. Stroeve J., Notz D. Changing state of Arctic sea ice across all seasons // Environmental Research Letters. 2018. V. 13. № 10. 103001. https://doi.org/10.1088/1748-9326/aade56
  31. Tikhonov V.V., Raev M.D., Sharkov E.A., Boyarskii D.A., Repina I.A., Komarova N.Yu. Satellite microwave radiometry of sea ice of polar regions: A review // Izvestiya, Atmospheric and Oceanic Physics. 2016. V. 52. P. 1012–1030. https://doi.org/10.1134/S0001433816090267
  32. Tonboe R.T., Eastwood S., Lavergne T., Sørensen A.M., Rathmann N., Dybkjær G., Pedersen L.T., Høyer J.L., Kern S. The EUMETSAT sea ice concentration climate data record // The Cryosphere. 2016. V. 10. P. 2275–2290. https://doi.org/10.15770/EUM_SAF_OSI_0013
  33. Tschudi M.A., Meier W.N., Stewart J.S. An enhancement to sea ice motion and age products at the National Snow and Ice Data Center (NSIDC) // The Cryosphere. 2020. V. 14. P. 1519–1536. https://doi.org/10.5194/tc-14-1519-2020

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