Abstract: The observation of snow cover by the staff of the Geographical Faculty of Moscow State University of the meteorological observatory has long been researched. This article describes the snow accumulation features and the snow cover's stratigraphy. The third cyclone arrived in Moscow on the night of December 14. There had been a large number of snowdrifts since the beginning of the snow accumulation, and the 49 cm mark was recorded at the MSU weather station. The difficulties of classifying layers in the snow column have been investigated by many glaciologists, something that is also considered in this paper. Machine learning methods were used to classify stratigraphic layers in the snow column according to measurements from the snow micro pen device. The ice crystal shapes within the snow column, resulting from metamorphism (rounded, faceted, thawed), exhibit variations in both density and parameters derived from the snow micro pen device data processing. Specifically, MPF(N) represents the average resistance force, SD(N) denotes its standard deviation, and cv signifies its covariance. This diversity allows for the categorization of processed device data and the incorporation of new measurement data without relying on direct manual drilling results.
The obtained device data underwent thorough processing. Through comparison with data from direct snow stratigraphy surveys, the stratigraphic layers of the snow column were classified. Subsequently, utilizing the classified data of the device's stratigraphic layers, K-nearest neighbors clustering enabled the classification of new data obtained from the device without the need for additional manual surveys in the future.

Abstract: This paper presents the results of field studies conducted at the MSU meteorological site for the winter period of 2022/2023. The purpose of the observations was to study the development of the snow column and its spatial variability in one winter season. Field research consisted of analyzing stratigraphic layers of snow and measuring their density. The data obtained made it possible to characterize and evaluate changes in snow layers, structure, and density in spatiotemporal terms. The results of the work are displayed on the graphs of the spatial and temporal variability of the snow cover for 2022/2023. The evolution of the snow column over the winter period is analyzed. The analysis of observations reflects a high spatial and temporal variability of snow cover in winter, which allows not only to evaluate and compare the data obtained with past studies but also to supplement and improve the already available information on the heterogeneity of snow cover.

Abstract: This paper considers the problem of monitoring seasonal changes in soil temperature in northern and mountainous areas in light of ongoing climate change. To study seasonal changes in soil temperature, the Moscow State University Meteorological Observatory was used as a model site with the ability to monitor air temperature, snow cover thickness, and ground freezing temperature and depth, which was a prototype of a system for monitoring the state of permafrost soils used in the Arctic and mountain territories. The paper presents the results of monitoring seasonal changes in soil temperature based on numerical modeling of the penetration of seasonal fluctuations in soil temperature in 2014–2017 in the MATLAB environment at the MSU Meteorological Observatory model site. The results of the numerical simulation of the penetration of seasonal temperature fluctuations in the ground at the MSU meteorological site in 2014–2017 in the MATLAB environment are in agreement with the thermometry data, and, therefore, the developed calculation scheme shows fairly good simulation results. This makes it possible to use the calculation scheme to assess the thermal state of frozen soils and assess the stability of foundations and buildings and linear structures located on them in the conditions of the north and mountainous territories. Therefore, the presented methodology can serve as a suitable method for monitoring and preventing the destruction of the studied structures in the conditions of climate warming.

Abstract: This article examines the reliability of ice data presented in the HadISST1 database (Hadley Centre Sea Ice and Sea Surface Temperature data set) of Hadley Meteorological Center (Hadley Centre for Climate Prediction and Research, Met Office, UK). The criterion for the reliability of HadISST1 database on the Northern hemisphere serves the average multiyear annual course of insolation in the Northern Hemisphere and the average multiyear annual course of insolation in 5-degree latitudinal zones at the upper boundary of the atmosphere, phase-shifted by two and three months to the past. It is revealed that the representative criterion for assessing the reliability of the business database is the average multiyear course of insolation in 5-degree latitudinal zones shifted by two months to the past. Evaluation of the data array on the Northern Hemisphere on the basis of the selected criterion demonstrates the overall high reliability of ice data reflected therein. However, there are two areas that differ in the degree of reliability of the presented data: 1) pack, drift ice with high negative values of the correlation coefficient; 2) coastal zone, in which the correlation is virtually absent (shore ice and flaw leads). The weak (or absent) correlation is observed over a significant length of the coastal zone in the Arctic Ocean, as well as in the straits and bays of the Canadian Arctic Archipelago. It is established hat the correlation coefficient of the annual course of the ice flow in the cells of the array of size 1 with the annual course of insolation of the Northern Hemisphere and the corresponding 5-degree latitudinal zones increases from the past to the present. This indicates the need for the correction of ice data in the early time range of the array (1901–1978), which precedes the time of the beginning of satellite observations.

Abstract: The subject of this research is the correlation analysis of changes in the area of sea ice in separate regions of the Arctic, and levels of internal regional correlations between multiyear monthly changes in the area of sea ice of different seas and the entire Arctic Ocean. The author also examines peculiarities in the annual amplitude course of interannual variability of monthly indices of the area of sea ice for separate districts of the Arctic, interregional links in the annual course of this amplitude of interannual variability, and determination of correlation between the annual indices of the area of sea ice with annual insolation contrast for various Arctic regions. The research method is the correlation data analysis on the area of distribution of sea ice in different districts of the Arctic and insolation contrast. The author builds an algorithm of the value forecast in the changes of sea ice area. Based on the analysis of internal correlations between multiyear and annual changes in the sea ice area in the Arctic regions, and connection with the insolation and insolation contrast, an algorithm is proposed for the value forecast of changes in the sea ice area in separate districts of the Arctic and Northern Hemisphere overall. For long-term forecast of annual values of the changes in sea ice area, the promising districts are Baffin Bay, Kara Sea, Barents Sea, Greenland Sea and Northern Hemisphere as a whole.

Abstract: On the basis of theoretical calculations of insolation and Earth remote sensing data on the dynamics of the sea ice area in the Arctic Ocean, a close relationship between long-term changes in the sea ice area and annual insolation contrast in the Northern hemisphere was determined. The change in insolation contrast was generalized (in terms of the source and sink of heat) reflects the change in the meridional insolation gradient that regulates the meridional heat transfer in the ocean - atmosphere system. The regression model was used to make an estimated forecast of changes in the area of sea ice in the Arctic Ocean. According to our estimates, the reduction of the average annual sea ice extent in the Arctic Ocean in 2050 will be 18.3% relative to 2018. The Maximum area (March) will be reduced by 10.1%, and the Minimum area (September) by 60.3%. The decrease in the area of sea ice is associated with an increase in the meridional gradient of insolation and meridional heat transfer resulting from a decrease in the inclination of the Earth's rotation axis in the present epoch.