Variability of temperature and salinity as well as quantifying global trends are fundamental for understanding changes in the Earth's climate. In current paper, a long-term variability of the hydrological regime of the Sea of Azov for 1913–2018 is studied. On the basis of oceanographic information, the seasonal variability of temperature and salinity by the areas of the Sea of Azov is analysed. Temperature anomalies have been revealed, periods of salinization and desalination of the Sea of Azov have been noted and linear trends of the anomaly have been obtained.

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One of the most demanded directions of oceanographic problems is the study of long-term variability and modeling of future climatic changes and also the possibility of obtaining continuous information on the thermohaline structure of the sea based on the joint use of remote sensing data and the results of assimilation modeling. In current paper, research, of the Sea of Azov for the period 1913–2018 was carried out. We used the data of contact measurements from the oceanographic data base of the SSC RAS, Atlas of climate change in large marine ecosystems of the Northern Hemisphere, as well as data from the oceanographic data bank of the Marine Hydrophysical Institute RAS. Based on the analysis of the information, the calculation of the average monthly temperature and salinity was carried out, the periods of anomalous temperature were revealed, the periods of salinization and desalination of the Azov Sea also were noted. Data analysis made possibility to identify intrasecular climate fluctuations. Abnormally cold water temperature in winter was noted in the periods: 1926–1932, 1951– 1956, 2003–2012. In turn, the abnormally warm water temperature in winter, which was recorded in the periods: 1935–1939, 1958–1972, 1983–1992. by accompanied a cold spring-summer period. During the last five years the spring-summer period is characterized by an increased water temperature of the Azov Sea. The long-term variability of the salinity of the Sea of Azov significantly depends on the inflow of saltier waters of the Black Sea and river runoff. Due to these circumstances, the Sea of Azov is characterized by periods of salinization and desalination: the period of salinization was recorded from 1955 to 1973, a shorter period of desalination falls on 2003–2018. On average, the value of the ratio between the mean monthly salinity values during these periods is 2,2 times.

This study proposes a method for obtaining information on the salinity of the Sea of Azov, based on the use of contact and remote sensing data. The approach to the salinity fields recovery is based on obtaining generalized regression equations relating in situ archival data with regional biooptical products obtained from standard level-2 MODIS products. This analysis showed the possibility of using various approaches to obtain generalized empirical (regression) equations for the spring and summer seasons, the differences in which are ~10 %. The results of the recovered salinity values were verified using in situ data. It was found that the plots of the average values of the recovered salinity are in the region of 95 % of the confidence bands of the modern long-term average trends for 1986–2018 and 2000–2018. The possibility of using the results of the proposed method in the construction of spatial maps of the Azov Sea salinity, synchronized in time with satellite scenes, is shown.

To make a reliable forecast for the level of dust, many external factors such as the wind energy and the soil content in the moisture must be considered. The numerical prediction of the Black sea region’s content of dust is the focus of this study, and for this purpose, the WRF-Chem model is used. The investigation is based on the statistics of the prediction coincidence and the actual result extracted from the data of the backward trajectories of AERONET and aerosol stratification maps in the atmosphere constructed with the help of the CALIPSO satellite. A comprehensive set of data was collected, and a comparative analysis of the results was carried out using machine learning techniques. The investigation identified 89% hits in the prediction of dust events, which is a very satisfactory result.