One of the most striking manifestations of ongoing climate change is a rapid shrinking of the Arctic sea ice area (SIA). An important feature of the observed SIA loss is a nonlinear rate of a decline with an accelerated decrease in the 2000–2019 period relative to a more gradual decline in 1979–1999. In this study, we perform a quantitative assessment and comparison of the spatial-temporal SIA changes during these two periods. It was found that winter Arctic SIA loss is primarily associated with changes in the Barents Sea, where the SIA decline in 2000–2019 has accelerated more than three-fold in comparison with 1979–1999. In summer and autumn, rates of SIA decline in 2000–2019 increased most strongly in the Kara, Beaufort Seas, the Northwestern Passage, and inner Arctic Ocean. The amplitude of the SIA seasonal cycle has also increased in 2000–2019 in comparison with the earlier period, with the largest changes in the inner Arctic Ocean, the Kara, Laptev, East Siberian and Beaufort Seas in summer and in the Barents Sea in winter. The results may reflect a transition to a new dynamic state in the recent two decades with the triggering of positive feedbacks in the Arctic climate system.

This study is focused on the specific features of ocean–air interaction in the Laptev Sea, in the late summer, on the basis of recurrent measurements during four expeditions in the 2000s and 2010s, atmospheric reanalysis products, and satellite ice concentration data. It was established that in the “icy” years, the accumulation of heat in the upper ocean layer is insignificant for the subsequent ice formation. In the “ice-free” years, the accumulated heat storage in the upper mixed layer depends on the duration of open water and the distance of the point of interest to the nearest ice edge. In a broader context, we considered possible links between the average ice area/extent in the August–September–October (ASO) period, and in the December–January–February (DJF) period, for two representative Arctic regions; that is, the Eurasian segment, defined within the bounds 60–120° E, 65–80° N, and the American segment, defined within the bounds 150° E–150° W, 65–80° N. Significant “seasonal memory”, characterized by the consistent change of the ice cover parameters in sequential seasons, was revealed in the Eurasian segment between 2007 and 2017. No linkage on a seasonal time scale was found in the American segment. A possible explanation for the distinguished contrast between the two geographical regions is proposed.

The Yamal peninsula is a territory of active industrial development as it contains several rich fields of natural condensed gas and oil. The density of the gullies net on the Yamal peninsula is one of the highest in the Russian Arctic. The natural environment or constructions can be potentially damaged by gully erosion and the cost of such damage is high. The models of gully erosion require surface runoff estimates. The hydrological model was developed for surface runoff estimation during the spring snow thaw and summer rains. In the conditions of Arctic climate with deep permafrost, the losses in runoff are limited to evaporation, as soil permeability is negligible. The model was calibrated on the available measurements. The meteorological base for hydrological calculations was ERA5 reanalysis, the fifth generation of European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses, validated on the meteorological data. The deviations of reanalysis data from the measurements cause the errors in the results of surface runoff calculation. The daily surface runoff can vary in the range of 18–30% due to ERA5 errors in air temperature and snow cover depth. As the daily surface runoff is the main input to the models of gully erosion, these errors must be taken into account in the modelling of gully erosion on the Yamal peninsula.

Precipitation extrema over the Barents Sea and the neighbouring locations in Europe were analysed using data obtained from station observations and a highly detailed ERA5 re-analysis dataset. These data did not always spatially coincide (on average, coincidence was ~50%). Daily amounts of precipitation were typically higher in the observation data, although there may be a reverse picture. The analysis revealed that at several stations and in many of the ERA5 grids, the set of precipitation extremes exists as a mixture of two different subsets. The cumulative distribution functions (CDF) of the largest population in the context of both the re-analysis and observational data are well described by Pareto’s law. However, very rare cases exist in which the values deviate and exceed this base distribution value in regions possessing large values. These super-large anomalies do not obey the statistical law common to all other extremes. However, this does not mean that the extremes can be arbitrarily large. They do not exceed the marginal values that are typical for this type of climate and season. The analysis confirms that extreme precipitation in the western sector of the Arctic is caused by the penetration of moist air masses from the Atlantic in the circulation systems of intense cyclones. At certain times, mesoscale convective systems are embedded in atmospheric fronts and can significantly contribute to the formation of precipitation. Intensification of such cyclones corresponding to global warming should lead to a transformation of typical CDF, as modern outliers will become regular components of the Pareto law. This change in the statistics of extreme events reflects the nonstationarity of the climate state. The influence of polar lows on the formation of large daily precipitation amounts is not felt.