Home – Home – Jornals – Earth’s Cryosphere 2026 number 3

Under the observed climate warming in the highlands of Central Asia, studies of the thermal state and dynamics of permafrost have become particularly relevant. This paper presents the results of rock temperature monitoring at two key sites in the northern Tian Shan: Zhosaly Kezen Pass and a west-facing slope near Bolshoye Almatinskoye Lake. At the high-mountain pass, a statistically significant increase in mean annual permafrost temperatures was observed: from 0.13 °C/decade at the base of the active layer (5 m depth) to 0.06 °C/decade in the lower part of the section (25 m depth). Particularly noticeable changes occurred after 2010, when the active layer thickness consistently exceeded 5 m. The results indicate permafrost degradation through both the thickening of the active layer and the upward shift of permafrost base. These processes occur against the background of increasing mean annual air temperature (0.30 °C/decade). In the intra-annual dynamics, the most intensive rock warming occurs during summer months (up to 0.89 °C/decade) in the upper layers (1-2 m) and correlates with April-July air temperature rise (up to 0.80 °C/decade). Near Bolshoye Almatinskoye Lake, mean annual temperatures in seasonally frozen layers (to a depth of 3 m) remain stable, while at depths of 5.0-9.8 m, they show a warming trend of 0.31-0.35 °C/decade. These trends align with data on temperatures of thawed rocks and permafrost from other areas of Asia’s mountain permafrost.

Geotechnical monitoring of buildings and structures in permafrost regions has become an integral part of regulatory documentation. There are clear requirements for the methodology and frequency of observations. However, with regard to geotechnical forecasting, the regulatory document adopted in 2020 provides only general requirements for predictive thermal and geotechnical calculations. Analysis of monitoring results and comparison with forecast values are left to “specialized organizations”. This paper aims to show that a geotechnical monitoring program (the document on the basis of which the work is carried out) requires more than just making a thermal field forecast and planning observation networks. It also requires developing measures for restoring geocryological conditions in case deviations occur. This allows estimating the time needed to return the temperature field to its design conditions.

The results of drilling a series of boreholes in Central Yakutia are discussed. At a depth of 250-300 m, in the Cambrian carbonate rocks and in the Lower Jurassic terrigenous sediments, a zone of sulfide mineralization with contrasting geochemical anomalies was discovered. The zone of sulfide-saturated rocks is localized in the interval from the permafrost base to the table of subpermafrost groundwater; it extends laterally for 80 km and does not depend on the composition and age of sediments. The development of sulfide mineralization in the Cambrian carbonate rocks and Lower Jurassic terrigenous sediments was due to cryogenic processes during sharp and strong cooling of the climate. In that period, the groundwater of bicarbonate-sodium composition depleted of lithochalcophile elements was formed. This was accompanied by the formation of the zone of sulfide mineralization and contrasting anomalies of Mo, V, Ni, Cu, Li, Sn, Ta, Nb, Ge, and Au at the permafrost base.

The article considers the application of machine learning methods for predicting the retreat rates of the coasts composed of frozen unlithified sediments. To test machine learning technologies, three well-studied coastal sites in the southwestern part of the Kara Sea were selected. These sites have been monitored by the Laboratory of Geoecology of the North at Lomonosov Moscow State University since the 1980s. Data have been collected at 10-meter intervals for each site, including categorical characteristics of the coastal zone (geomorphological level, lithology, dominant permafrost process) and quantitative indicators of coastal retreat rate. A sequential application of correlation analysis using the Random Forest algorithm and the dispersion analysis (Welch’s ANOVA) made it possible to identify features that most significantly affect the retreat rate of the bluff, as well as to determine zones with the highest risk of intensive changes and average values of the coastal retreat rate with a certain combination of categorical features. The study proposes an approach that enables the segmentation of the sea coastline based on its resilience to key natural factors affecting coastal dynamics in the permafrost zone using machine learning technologies.

The results of six years (2018-2024) of geocryological monitoring at the CALM R24A-2 site (Kumzha) in the Pechora River Delta are presented. The study is based on the integrated application of ground-penetrating radar (GPR) with different offsets: zero-offset profiling with a 300 MHz antenna for boundary tracing and multi-offset sounding with a 150 MHz antenna for precise velocity determination. It was found that the average depth of permafrost table increased by (1.3 ± 0.2) m, which corresponds to a trend of 20 cm/year. A set of different offset GPR techniques has demonstrated its effectiveness as a non-destructive method of geocryological monitoring. The obtained data provide the basis for verifying and improving predictive models of permafrost development, which is crucial for planning sustainable nature management and minimizing damage from permafrost degradation.

The review is devoted to the Ilya Arkadyevich Komarov’s monograph, which is a textbook on one of the main areas of geocryology - thermodynamics and thermophysics of frozen soils, published by “Nauchny Mir” (Moscow) in 2025.