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.

Our understanding of the time variability of intense cyclones in the Mediterranean region is still lacking despite its importance for the long-term forecast of climate anomalies. This study examines the month-to-month variability and predictability of cyclones, the intensity of which exceeded the 75th percentile (intense cyclones) and the 95th percentile (extreme cyclones), over the Western and Eastern Mediterranean. The locations of cyclones were obtained by applying the method of M. Yu. Bardin on the 6-hourly 1000 hPa geopotential height data from the NCEP/NCAR reanalysis for the period 1951–2017 (67 years). It was shown that annual frequencies of cyclones were higher in the Western Mediterranean due to the contribution of spring and autumn; monthly averages were higher in the Eastern Mediterranean in December/January–March for intense/extreme cyclones. In the context of global warming, no linear trends significant at the 90% confidence level were found in the variability of intense and extreme cyclones, except for a positive trend in autumn extreme cyclones over the Eastern Mediterranean. The time series of cyclones in both parts of the Mediterranean were characterized by a pronounced interannual variability with a noticeable decadal modulation. According to spectral analysis, these interannual periods were multiples of 2–3 years corresponding to the main global teleconnection patterns. Seasonally, the most energy was concentrated in winter spectra; spring and autumn spectra had lower comparable magnitudes. The correlation analysis between the frequency of cyclones and the indices of the main atmospheric patterns showed that the main synchronous patterns for intense and extreme Mediterranean cyclones in September–April were the Mediterranean Oscillation (with the opposite signs for the Western and Eastern Mediterranean), Scandinavia pattern (positive correlation), and East Atlantic Oscillation (negative correlation). Additional important synchronous teleconnection patterns for some months were the Arctic Oscillation and East Atlantic/West Russia pattern for the Western Mediterranean, and the Polar/Eurasia pattern and Tropical Northern Hemisphere pattern for the Eastern Mediterranean. The outcome of this paper was the use of an artificial neural network model with inputs of global teleconnection indices both in the atmosphere and ocean to describe the temporal variability of the frequency of intense cyclones in the Western and Eastern Mediterranean. The predictability of intense cyclones was shown with the possibility of forecasts with a lead time of 0, 2, 4, and 6 months for the Western Mediterranean in October, January, February, April, and May, and for the Eastern Mediterranean in January, February, March, April, and May. One of the applications of this model may be in forecasting the evolution of the monthly frequency of cyclones with a lead time of 2 to 6 months.