aariПроблемы Арктики и АнтарктикиArctic and Antarctic Research0555-26482618-6713Государственный научный центр Российской Федерации Арктический и антарктический научно-исследовательский институт10.30758/0555-2648-2019-65-1-5-14aari-130Research ArticleОКЕАНОЛОГИЯOCEANOLOGYСезонный прогноз ледовитости Баренцева моряSeasonal forecast of sea ice extent in the Barents seaГлокН. И.GlokN. I.АлексеевГ. В.AlekseevG. V.alexgv@aari.ruВязиловаА. Е.VyazilovaA. E.ГНЦ РФ Арктический и антарктический научно-исследовательский институтРоссияState Scientific Center of the Russian Federation Arctic and Antarctic Research InstituteRussian Federation201908042019651514Copyright © Глок Н.И., Алексеев Г.В., Вязилова А.Е., 20192019Глок Н.И., Алексеев Г.В., Вязилова А.Е.Glok N.I., Alekseev G.V., Vyazilova A.E.This work is licensed under a Creative Commons Attribution 4.0 License.https://www.aaresearch.science/jour/article/view/130

Использован метод разложения по естественным ортогональным функциям набора значений площади льда с января по июнь за 1979–2014 гг. Главная компонента разложения отражает 83 % межгодовой изменчивости площади льда с января по июнь. Регрессионная прогностическая модель построена на основе связи главной компоненты с аномалиями температуры воды на поверхности океана в низких широтах Северной Атлантики с учетом запаздывания. Сравнение прогностических и фактических значений климатической составляющей для каждого из шести месяцев показало оправдываемость прогнозов с заблаговременностью от 27 до 32 месяцев 83 %, а для прогноза исходных значений площади льда 79 %. Привлечение второго предиктора — аномалий температуры воды в Норвежском море позволило повысить качество прогноза наблюденных значений площади льда. При этом заблаговременность прогноза сократилась до 9–14 месяцев. Коэффициенты корреляции между прогнозными и фактическими значениями площади льда находятся в пределах 0,71–0,77.

Earlier, the authors established a close relationship between the temperature of water coming from the North Atlantic and the sea ice extent (SIE) in the Barents Sea, which accounts for up to 75 % of the inter-annual variability of the monthly SIE from January to June. In turn, temperature variations of the incoming Atlantic water are affected from anomalies of sea surface temperature (SST) in the low latitudes of the North Atlantic. These dependences served as the basis for the development of a forecast method. The empirical orthogonal functions decomposition of the SIE set from January to June for 1979–2014 was used. The main component of decomposition reflects 83 % of the inter-annual variability of SIE from January to June. Regression model of forecast is based on the relation of the main component with SST anomalies taking into account the delay. Comparison of prognostic and actual values of the climatic component for each of the 6 months showed the correctness of forecasts with a lead time of 27 to 32 months is 83 %, and for the prediction of the initial values of SIE 79 %. Appealing to the second predictor — SST anomalies in the Norwegian Sea allowed to improve the quality of the forecast of the observed values of SIE. At the same time, the forecast advance time was reduced to 9–14 months.

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The authors declare that there are no conflicts of interest present.