Intense Cyclones in the Black Sea Region: Change, Variability, Predictability and Manifestations in the Storm Activity

Abstract

Cyclonic activity in the midlatitudes is a form of general atmospheric circulation, and the most intense cyclones are the cause of hydrometeorological anomalies that lead to economic damage, casualties and human losses. This paper examines the features of variability of intense cyclonic activity in the Black Sea region and the examples of their regional manifestations in the storm types. Based on 6-hourly NCEP/NCAR reanalysis data on 1000 hPa geopotential height fields with 2° × 2° spatial resolution and using the methodology by M.Yu. Bardin, objective data were obtained for the identification and estimation of the frequency of deep cyclones (reaching 0.75 and 0.95 quantiles by intensity and depth—intense and extreme cyclones, respectively) for the Black Sea region during the period 1951–2017. Additionally, a specific methodology of more precise cyclone identification based on spherical spline interpolation was successfully applied, and then the two methodologies were compared. The key point of the study is the following: In the background of negative significant linear trends and interdecadal variability (period of about 35 years), typical scales of their interannual variability on the periods of about 2.5–3.5 and 6–8 years were identified. These periods coincide with the time scales of the North Atlantic Oscillation and El Nino–Southern Oscillation, providing an outlook for further study of the patterns of their connection. Besides, seasonal forecasts of frequency of intense cyclones in the Black Sea region were successfully modeled using an artificial neural network technique. Finally, the case studies of regional manifestations of deep cyclones in the types of storms in the northern Black Sea coast revealed substantial differences in the location of deep centers of cyclones and storm tracks associated with the large-scale pressure fields.

Temporal Variability and Predictability of Intense Cyclones in the Western and Eastern Mediterranean

“…Previously, the modeling using an artificial neural network approach was successfully applied to forecast the frequency of intense cyclones in the Black Sea region [10] and to predict anomalies in the Nino3.4 region with lead times from 1 to 9 months [67].…”

“…Knowledge about these patterns is necessary for the development of the theory of long-term forecasts of weather and climate anomalies. At the same time, intense cyclones in the Mediterranean region cause dangerous weather phenomena, such as extreme precipitation [8], wind storms [9], extreme waves [10], floods [11], landslides [12], thunderstorms [13], and even extreme dust events [14]. Quite often, they bring negative consequences to regions far from their place of origin, in particular, Central and Eastern Europe, the Middle East, and even Central Asia.…”

“…This made it possible to assess the predictability of intense cyclones over the Western and Eastern Mediterranean for different months. We tested this model previously for the forecast of some hydrological and meteorological parameters in the Black Sea region, and the activity of intense cyclones [10,67].…”

Neural network method for climate forecasting water content of the Chernorechensk reservoir

“…Поэтому в данной работе мы рассмотрим возможность прогноза осадков и, дополнительно, давления с помощью модели на основе искусственных нейронных сетей (НС). Ранее авторы данной работы успешно применили этот метод для прогноза явлений Эль-Ниньо и Ла-Нинья с заблаговременностью до 9 месяцев [6,7], для прогноза повторяемости интенсивных циклонов в Черноморском регионе с заблаговременностью до 6 месяцев [8] и для прогноза стока р. Дунай с заблаговременностью 3 месяца [9]. В качестве входных параметров в НС во всех случаях использовались только атмосферные индексы дальнедействующих связей системы океан -атмосфера.…”

“…Подготовка данных. Ранее в работах [6][7][8][9] моделировались ряды средне-месячных климатических аномалий, нормированные на среднеквадратическое отклонение. При этом не проверялась возможность разложения ряда на ортогональные сигналы с последующим моделированием каждого сигнала по отдельности.…”