Snow Research Articles

Study of contaminated snow cover using remote sensing in the Eastern Himalayas of Arunachal Pradesh, India.

Snow is considered contaminated when any foreign materials are deposited/mixed with it, which can accelerate melting and significantly impact the snow cover's radiative balance. Such an enhanced melting rate results in a reduction in freshwater sources at the catchment level. In optical remote sensing, snow contamination is widely studied using a normalizing difference index called the snow contamination index. This is based on the finding that the impact of snow contamination diminishes with wavelength and is most noticeable in the visible spectrum (0.3-0.7μm). However, the study of snow contamination using optical remote sensing is hindered in the Himalayan terrain due to enduring cloud cover in the region. Synthetic Aperture Radar (SAR) data such as Sentinel-1 can be used to ensure all-weather monitoring of such areas. This study focuses on the SAR backscattering behavior at the C-band of clear and contaminated snow for March 2022 in a part of the Eastern Himalayas of Arunachal Pradesh, India. An attempt has been made to utilize Landsat-9 and Sentinel-1 to study the snow contamination. The SAR backscattering for snow conditions (clear/contaminated) is studied using thresholds obtained from the Landsat-9 snow cover map. The SCI and SAR backscattering statistical analysisshows a negative correlation (R2 > 0.6) at a95% confidence level. It is observed that in the microwave region of the C-band, contaminated snow has a comparatively higher backscattering value than clear snow. However, in the visible wavelength, the contaminated snow has a lower reflectance value than clean snow. Such behavior of the snowpack in the microwave region of the C-band is explained using the physical properties of the snowpack and thedominant scattering mechanism over the surface. The key findings of this study suggest that SAR backscattering is affected by snow contamination due to changes in the local incidence angle, snow wetness, and surface roughness. This research provides critical insight into snow contamination using microwave remote sensing, which can be the first step toward developing an index for radar observations.

Microclimatic variation regulates seed germination phenology in alpine plant communities

Abstract For most terrestrial plants, regeneration depends on the ability of seeds to germinate in the most favourable climatic conditions. Understanding seed germination phenology is thus crucial for predicting plant responses to environmental changes. However, a substantial gap persists regarding how microclimatic conditions influence germination in seasonal ecosystems. Here, we investigate the germination phenology of alpine plants in snow‐related microclimates as a tool for predicting the resilience of plant communities to climate change. We conducted a continuous seasonal experiment with fresh seeds to investigate germination phenology in 54 co‐occurring species from temperate and Mediterranean alpine communities. Using long‐term field microclimatic data series, we precisely mimicked two contrasting microclimatic regimes in growth chambers: (1) windy exposed edges with a snow‐free period in winter and warmer temperatures in summer (‘fellfield’) and (2) sheltered areas with lengthy snow cover and cooler temperatures (‘snowbed’). We validated the laboratory results with field sowing experiments to provide a complete picture of germination phenology. The analysis of phenology traits demonstrated that both communities displayed similar responses to microclimatic variation. Small microclimatic differences of 2–3°C a week, accumulated across a whole year in the laboratory, resulted in a quantifiable germination phenology delay in snowbed regime, with an average of 60 and 45 days for temperate and Mediterranean alpine respectively. The results from climatic chambers under realistic microclimatic regimes were consistent with the germination phenology registered from field experiments. We also observed macroclimatic effects manifested as reduced dormancy and increased autumn germination in Mediterranean alpine species. Synthesis. This study combines novel laboratory and field experimentation to tackle the understudied topic of germination phenology in habitats with sharp microclimatic gradients. Specifically, our findings suggest a predictable phenological shift in the germination of alpine plants along microclimatic gradients. In warmer conditions with reduced snow cover, alpine species are expected to advance germination 52 days on average, with potential disrupting effects on cold‐adapted species with strict germination requirements. This highlights the role of germination phenology to determine plant‐environmental relationships in mid‐latitude ecosystems, with strong impact on plant establishment and extinction risks under local microclimatic gradients.

Multi-physics ensemble modelling of Arctic tundra snowpack properties

Abstract. Sophisticated snowpack models such as Crocus and SNOWPACK struggle to properly simulate profiles of density and specific surface area (SSA) within Arctic snowpacks due to underestimation of wind-induced compaction, misrepresentation of basal vegetation influencing compaction and metamorphism, and omission of water vapour flux transport. To improve the simulation of profiles of density and SSA, parameterisations of snow physical processes that consider the effect of high wind speeds, the presence of basal vegetation, and alternate thermal conductivity formulations were implemented into an ensemble version of the Soil, Vegetation, and Snow version 2 (SVS2-Crocus) land surface model, creating Arctic SVS2-Crocus. The ensemble versions of the default and Arctic SVS2-Crocus were driven with in situ meteorological data and evaluated using measurements of snowpack properties (snow water equivalent, SWE; depth; density; and SSA) at Trail Valley Creek (TVC), Northwest Territories, Canada, over 32 years (1991–2023). Results show that both the default and Arctic SVS2-Crocus can simulate the correct magnitude of SWE (root-mean-square error, RMSE, for both ensembles – 55 kg m−2) and snow depth (default RMSE – 0.22 m; Arctic RMSE – 0.18 m) at TVC in comparison to measurements. Wind-induced compaction within Arctic SVS2-Crocus effectively compacts the surface layers of the snowpack, increasing the density, and reducing the RMSE by 41 % (176 kg m−3 to 103 kg m−3). Parameterisations of basal vegetation are less effective in reducing compaction of basal snow layers (default RMSE – 67 kg m−3; Arctic RMSE – 65 kg m−3), reaffirming the need to consider water vapour flux transport for simulation of low-density basal layers. The top 100 ensemble members of Arctic SVS2-Crocus produced lower continuous ranked probability scores (CRPS) than the default SVS2-Crocus when simulating snow density profiles. The top-performing members of the Arctic SVS2-Crocus ensemble featured modifications that raise wind speeds to increase compaction in snow surface layers and to prevent snowdrift and increase viscosity in basal layers. Selecting these process representations in Arctic SVS2-Crocus will improve simulation of snow density profiles, which is crucial for many applications.

Climatic Indicators and Their Variation Trends as Conditions for Forest Flammability Hazard in the South of Tyumen Oblast

This study analyzes the forest flammability hazard in the south of Tyumen Oblast (Western Siberia, Russia) and identifies variation patterns in fire areas depending on weather and climate characteristics in 2008–2023. Using correlation analysis, we proved that the area of forest fires is primarily affected by maximum temperature, relative air humidity, and the amount of precipitation, as well as by global climate change associated with an increase in carbon dioxide in the atmosphere and the maximum height of snow cover. As a rule, a year before the period of severe forest fires in the south of Tyumen Oblast, the height of snow cover is insignificant, which leads to insufficient soil moisture in the following spring, less or no time for the vegetation to enter the vegetative phase, and the forest leaf floor remaining dry and easily flammable, which contributes to an increase in the fire area. According to the estimates of the CMIP6 project climate models under the SSP2-4.5 scenario, by the end of the 21st century, a gradual increase in the number of summer temperatures above 35 °C is expected, whereas the extreme SSP5-8.5 scenario forecasts the tripling in the number of such hot days. The forecast shows an increase of fire hazardous conditions in the south of Tyumen Oblast by the late 21st century, which should be taken into account in the territory’s economic development.

Long‐Term Changes in the Relative Humidity in Poland in 1966–2020

ABSTRACTThe main purpose of this paper was to identify significant changes in air moisture conditions in Poland, which accompany climate warming. Meteorological data used in the research included the relative humidity (RH) values from 48 stations obtained from the Institute of Meteorology and Water Management—National Research Institute from 1966 to 2020. The monthly mean, standard deviation (SD) and coefficient of variation of RH from 12 PM for all months (with particular reference to the middle months of seasons—January, April, July and October) were used. Additionally, the dry weather (RH < 30%) frequency in the warm half of the year was observed. Long‐term changes were found by comparing relative humidity values in three 15‐year subperiods (1966–1980, 1986–2000 and 2006–2020) and the statistical significance was estimated using the Mann‐Kendall test. The most considerable long‐term changes were noticed in April and July, especially in the last 15‐year subperiod. The statistical significance was higher, mostly in warmer months. The SD was also higher in April and July than in January and October. Hence the humidity conditions in the warmer half of the year fluctuated more and more widely. A significant decrease in the RH mean values and an increase in SDs in spring and summer impact the increasing frequency of dry weather. Relations between meteorological characteristics suggest the warming climate contributes to drying the near‐surface atmosphere but also impacts intensive precipitation events or snow cover parameters. The decreasing trend of long‐term relative humidity may negatively impact the environment, human health and well‐being and cause serious economic losses.

Assessing climate-driven glacial retreat, snow-cover reduction and GLOF risks: implications for water resource management amid rising global temperatures and CO2

Context The consequences of climate change, including alterations in snow and glacier patterns and rising temperatures, pose a risk of glacial lake outburst floods (GLOFs), which can have cross-border impacts, leading to the loss of life and property downstream. Aims This research focused on the assessment of changes in snow cover within Chitral district imposed by temperature increase. Methods ArcGIS and Origin programs were used in this research to study the glacier pattern of Chitral. Results Therer was a significant increase of 266.7% in urban development and an expansion of 135.9 km2 in agricultural areas within this remote mountainous region. The findings of the study show that in the year 2000, >51.6% of Chitral district’s total land was covered by snow during early winter. However, this coverage drastically declined to ~6.8% by 2008. Across the initial two 4-year periods, ~44.7% (5694.6 km2) of the glaciated area transitioned to barren rock, whereas the overall reduction in snow-covered areas accounted for ~43.3% (5514.6 km2). Conclusion Owing to the increasing stress on freshwater resources, it is essential to conduct thorough analyses and monitoring of snow patterns to ensure sustainable freshwater availability and effective environmental risk management. Implications This study emphasises the critical consequences of climate-driven glacial retreat, waning snow cover and increased risks of glacial lake outburst floods (GLOFs) for the management of water resources with the rise in global CO2 and temperatures levels. These changes threaten the timing and availability of water supply, with impacts on agriculture, ecosystem and hydropower. Effective adaptation measures and sustainable management practices are crucial to alleviate these risks and ensure water security in a warming world.

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

Elevation-Dependent Snow Cover Dynamics and Associated Topo-Climate Impacts in Upper Indus River Basin

In the present study, Improved Moderate Resolution Imaging Spectro-radiometer (MODIS) snow cover product (MOYDGL06*) has been used to evaluate the snow cover area (SCA) in Kabul, Jhelum, and Indus river basins for the time period of 2003 – 2020 with available MODIS land surface temperature (LST), and CHIRPS (precipitation) with objectives to evaluate the spatio-temporal SCA, and climate variables with respect to different elevations analyzed from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model Version 3 (GDEM v3) and also to correlate the climatic variables with SCA. The results presented average annual SCA is around 50.7% – 64.7% in sub-basins of UIB, further it has been observed that SCA is decreasing on annual and seasonal timescale in all three basins. Elevation-dependent SCA, temperature, and precipitation presented a mix of trend on annual, seasonal, and monthly timescale at lower and higher altitude in all selected basins. Moreover, it was noticed that topography (slope, & aspect) also influences the SCA in the region. Furthermore, it has been examined that temperature has significant inverse relationship with SCA at middle and higher altitude in Indus, while in Kabul, and Jhelum no significant relationship observed at extreme lower and higher altitudes. It is also evident from relationship between SCA and climate variable that temperature is significantly responsible for decreasing trend of SCA rather than intense precipitation in all three river basins. Thus, all these elevation-dependent changes can improve our hydrological understanding which can have a considerable implication for hydrology, climate science, water resource management and socio-economic activities.

Suspended sediment connectivity analysis: Snowmelt-driven dynamics in an alpine basin

Understanding snowmelt and its related suspended sediment transport in mountain streams is a crucial issue in the world of hydraulic and sediment research. Field-based approaches are still poorly used to investigate the response of mountain basins to different stages of snowmelt. To explain the suspended sediment dynamics of the Rio Cordon basin (Italy, Dolomites), the snowmelt in the year 2021 was analysed. First, a descriptive analysis of temporal trends of meteorological, hydrological, and sedimentological variables was carried out. Second, suspended sediment budgets of the main channel were quantified, considering the contribution of the tributaries at week- and event- scale, and supported by hysteresis analyses. Third, the role of sediment sources and snow cover was examined and integrated to comprehend variations in sediment supply from tributaries. Data were collected using a (i) multiparametric sonde installed at the outlet, (ii) water and sediment samples at significant points along the channel network, (iii) satellite images and sediment sources inventory. The whole snowmelt period 2021 featured 210.8 mm of precipitation and a mean temperature of 4.6°C. The total load of suspended sediment was 100 t. The main period of ablation was May, which showed contrasting suspended sediment dynamics in the four weeks. Sediment availability exceeded transport capacity in the initial two weeks, whereas transport capacity exceeded sediment availability in the subsequent two weeks. The main snowmelt event analysis mirrored the same trend: limited transport during the rising limb and the opposite during the falling limb. Such results were confirmed by the hysteresis analysis, proving to be an effective tool for detecting changes in suspended sediment connectivity. The variations of snow cover and the extent of sediment sources across the sub-basins influence the sediment supply. This study enhances our comprehension of snowmelt-driven hydrological and sedimentological dynamics, providing insights into the resilience of mountains to changing climate conditions.

Quantitative Classification of Spring Discharge Patterns: A Cluster Analysis Approach

ABSTRACTSprings provide critical water resources that are sensitive to changing climate and catchment processes. In many regions, understanding the temporal variability and spatial distribution of spring discharge is therefore crucial for sustainable water management. Knowledge of these discharge characteristics, organised in a coherent framework, is essential for protecting spring water and preventing shortages. To establish such a framework, we conducted a comparative analysis of long‐term discharge records from 96 springs across Austria. Based on discharge seasonality and autocorrelation, we derived a broad‐scale classification through cluster analysis and explored associations between individual clusters. The identified similarities in discharge patterns were grouped into four distinct spring categories, each demonstrating common behaviour. To determine the main factors influencing discharge across these four groups, we compared their spatial and temporal patterns with regional climate and catchment characteristics. They align with physical drivers of spring discharge, including precipitation frequency and intensity, snow cover duration, and dominant aquifer type. As these factors were not included in the classification procedure, their alignment supports the validity of our statistical approach. We conclude that the quantitative information derived from this analysis provides a valuable complement to traditional spring classification schemes, which are often based on qualitative knowledge. Our proposed strategy refines these classification approaches, enhances objectivity and reproducibility, and promotes conformity across hydrological disciplines.

Zero‐Flow Dynamics for Headwater Streams in a Humid Forested Landscape

ABSTRACTMuch of our understanding on temporary headwater streams is from arid and sub‐humid environments. We know less about zero‐flow periods in humid headwater catchments that experience seasonal snow cover. Our study characterised the temporal and spatial patterns of zero‐flow periods for forested headwater streams in a snow‐dominated landscape. We used 36 years of streamflow data from 13 headwater catchments within the Turkey Lakes Watershed located on the Canadian Shield in Ontario, Canada, near the eastern shores of Lake Superior. These headwater catchments differ substantially in their number of May–November zero‐flow days (0–166 days per year) despite being clustered in a small geographical area with similar geology, physiography and vegetation cover. The catchments also experience similar continental climatic conditions with relatively even precipitation inputs throughout the year (mean annual precipitation of 1210 mm/year). Inter‐annual variability in the number of zero‐flow days was primarily associated with May–November precipitation and evapotranspiration. Despite the large seasonal snowpacks that form in this region, the amount of snow did not appear to influence the extent of zero‐flow periods. We found that between‐catchment variability in zero‐flow occurrences was related to differences in catchment area and catchment properties typically associated with greater groundwater influence. Our study suggests that occurrences of zero‐flows in headwater streams can be highly variable even over small geographical regions and that flow permanence may be more sensitive to spring to fall weather conditions than the influence of snow due partly to the shallow soils typically found on the Canadian Shield.

Impact of Climate Change on Spatiotemporal Patterns of Snow Hydrology: Conceptual Frameworks, Machine learning versus Nested Model

Snow accumulation in mountainous watersheds plays a paramount role in the hydrological cycle and environmental stability. In the current research, three different snow modeling approaches including conceptual models, Random Forest (RF) and their nested (Nested-RF) model have been implemented to project climate change. Also, three distinct calibration scenarios employing Snow Cover Area (SCA) and Snow Water Equivalent (SWE) have been used. These scenarios involve using SCA and SWE data individually or combined to calibrate both conceptual snowpack models and machine learning methods. Considering comparative analysis in the mountainous and complex topographic area of Tashk-Bakhtegan watershed as a case study, the Guo model (calibrated using both SCA and SWE data) achieves the highest prediction accuracy in predicting SCA/SWE using conceptual method. Considering various statistics, RF using the same calibration scenario demonstrates high accuracy in estimating SWE and shows promising results in detecting SCA. Considering the feature importance analysis and statistics of the RF and Nested-RF models indicate that the feeding conceptual outputs as extra information into the Nested-RF had minimal influence and even slightly reduced accuracy. Future projections using RF indicate significant reductions in mean annually SCA/SWE, especially under SSP8.5, with a 28.4% decrease in SCA and a reduction in SWE to 13.62 mm from a historical baseline of 19.4 mm. Lower emission scenarios (SSP2.6 and SSP4.5) show less drastic declines. While minor differences were observed between the best conceptual model and the RF approach in capturing spatiotemporal and annual snow patterns, these discrepancies did not reach statistical significance.

Interannual responses of Arctic temperatures to Eurasian snow cover variations in early spring

Interannual responses of Arctic temperatures to Eurasian snow cover variations in early spring

Snow Observation from Space: An approach to improving snow cover detection using four decades of Landsat and Sentinel-2 imageries across Switzerland

Snow Observation from Space: An approach to improving snow cover detection using four decades of Landsat and Sentinel-2 imageries across Switzerland

Observed determinants of urban outdoor thermal exposure during hot summer and snowy winter periods in a humid continental climate

Many cities in the midlatitudes experience both extreme heat and cold, and pedestrians are exposed to thermal extremes that cause bodily stress. With growing urban populations, city design that contributes to mitigating summer heat while reducing winter cold exposure is increasingly important. Pedestrian thermal exposure depends on several microclimatic factors, including shortwave and longwave radiation absorption, which can be quantified by the mean radiant temperature (Tmrt). Limited research has been conducted on the radiative components of thermal exposure in hot, humid summers and cold, snowy winters. We gathered micrometeorological data from diverse urban sites and in multiple seasons in Guelph, Canada, using a mobile human-biometeorological weather station (MaRTy cart) that applies the six-directional method to determine Tmrt. Seasonal datasets were analysed and compared to examine the drivers of thermal exposure and recommend strategies for mitigating heat and cold stress. In summer, shade is the primary factor that reduces daytime heat exposure and it slightly increases nighttime heat exposure. Enhanced pervious ground cover is a secondary factor day and night. In winter, reduced shade alleviated daytime cold exposure, while snow cover provided daytime benefits from increased solar reflections and post-sunset penalties associated with reduced longwave radiation from low snow surface temperatures.

Research on controlling measures of snowdrifts around Arctic ground-based buildings through shape optimization

Elevated structures in the Arctic region have proven effective in mitigating surrounding snowdrifts by enhancing the airflow beneath, and serve as a preferred architectural form. However, ground-based buildings still occupy the dominant position due to the constraints posed by construction costs and foundation conditions. One of the effective measures to reduce and prevent snowdrifts around such buildings is to modify their aerodynamic shapes. Therefore, this research endeavors to explore measures for controlling snowdrifts around ground-based buildings through shape optimization. Initially, the predictive accuracy of a refined Mixture model for simulating snowdrifts around ground-based structures was checked against wind tunnel test results. Based on the validated numerical model and representative Arctic meteorological conditions, the snowdrift controlling effects by changing the overall and local building shapes were investigated separately. Overall, the snow reduction and prevention effects can be effectively achieved for ground-based buildings with smoother sidewalls, such as those featuring a circular plane. Conversely, for traditional rectangular ground-based buildings, a larger downwind aspect ratio and a more inclined windward surface can significantly diminish peak snow depth. Additionally, the measures by adding windward side chamfering can effectively manage the locations of snow erosion areas, thereby enabling flexible placement of entrances and exits.

Snow and nitrogen manipulation do not alter the dominant role of fungi in the N2O production of biocrusts in a temperate desert

The impact of global climate change and human-induced nitrogen (N) deposition on winter weather patterns will have consequences for soil N cycling and greenhouse gas emissions in temperate deserts. Biological soil crusts (referred to as biocrusts) are crucial communities in soil and significant sources of nitrous oxide (N2O) emission in desert ecosystems and are sensitive to environmental changes. The contribution of bacteria and fungi to N2O production in drylands has been acknowledged. However, the effect of changes in snow cover and N deposition on the N2O production of different microbial groups of microorganisms is not yet clear. In this study, we examine the responses of fungi and bacteria mediated pathways involved in soil N2O production from biocrusts to long-term snow cover manipulation and N addition experiments in the Gurbantunggut Desert. These soils were incubated and subjected to biocide treatments (such as cycloheximide and streptomycin, and fungal and bacterial inhibitors), after which rates of potential nitrification and N2O production were measured. Compared with controls, snow removal treatments from bare sand, lichen crust and moss crust reduced background rates of N2O production by 29.41 %, 26.21 % and 20.49 %, respectively; N2O production rates were 1.53-fold higher in bare sand, 1.38-fold higher in lichen crust, and 1.56-fold higher in moss crust after N addition. The addition of streptomycin significantly reduced the potential nitrification rates of bare sand and biocrusts, indicating that bacteria may be important sources of NO3− production in biocrusts rather than fungi. Conversely, fungi were main sources of N2O production in biocrusts. Additionally, fungi also played a major role in N2O production in biocrusts after snow cover manipulation and N addition. Both snow cover manipulation and N addition treatment indirectly affected the N2O production in biocrusts by considerably affecting the content of substrate N and the abundance of microbial groups. Our research suggests that fungi are main contributors for denitrification in biocrusts, and that snow cover changes (removal snow and double snow) and N addition alter the contribution of biotic pathways responsible for N cycling.

Validation of ERA5-Land-based reconstructed air temperature and near-surface ground temperature on James Ross Island

ABSTRACT This study evaluates the accuracy of ERA5-Land reanalysis products by comparing them with continuous data from James Ross Island, Antarctic Peninsula. ERA5-Land shows lower variability in air and ground temperatures than measurements from three automatic weather stations (AWSs) on James Ross Island: Johann Gregor Mendel (AWS-JGM), Abernethy Flats (AWS-AF), and Johnson Mesa (AWS-JM). Differences in mean annual air temperature (MAAT) and mean annual ground temperature at 5 cm depth (MAGT5) are noted between ERA5-Land and AWSs for the periods 2007/08-2020/21 and 2011/12-2020/21. Strong correlations (0.88–0.92) exist between ERA5-Land and AWSs, with the highest coefficients and lowest RMSE values for AWS-JM (2.7 °C for AT and 4.9 °C for GT5). Validation statistics reveal seasonal variations in ERA5-Land temperature bias with larger discrepancies in summer and low differences in winter. ERA5-Land’s snow depth estimation indicates unrealistic permanent snow cover between 3–24 meters, affecting ground temperature bias and underestimation. Despite its benefits, ERA5-Land’s inaccuracies in ground temperature data limit its direct use for permafrost research. Corrections were applied to enhance the use of ERA5-Land data for detailed permafrost studies in the JRI area.

Attribution of the Record‐Breaking Extreme Cold Event Over Northern East Asia in December 2023

AbstractAgainst the backdrop of global warming, Northern East Asia experienced a record‐breaking extreme cold event during December 13 to 19 in 2023. The mechanisms behind this extreme cold event remain unclear. This study uses the circulation projection method to detect and quantify the contributions of various factors to this extreme cold event. The findings indicate that large‐scale atmospheric circulation anomalies are the predominant factors triggering this cold wave. The Polar‐Eurasian (POL)‐like teleconnection pattern is identified as a key driver for the cold anomalies, contributing approximately 85% of total cold anomalies in Northern East Asia in this event. Although the POL‐like teleconnection is largely internally generated, the preceding low sea ice levels in the Barents and Kara Seas can strengthen and maintain POL teleconnection. Additionally, preceding November increased snow cover in Northern East Asia can further amplify this cold event by enhancing local surface albedo and reducing incoming solar shortwave radiation.

Atmospheric air pollution from the Ermakovskoe fluorite-beryllium deposit development waste

Relevance. Negative impact of waste from mining enterprises on the ecological state of the surrounding areas. Aim. To determine the migration ability of toxic chemical elements from waste storage sites of the Ermakovskoe beryllium deposit in the air. Object. Ermakovskoe fluorite-bertrandite-phenacite deposit and the surrounding area. Methods. Inductively coupled plasma mass spectrometry, laser diffraction, inductively coupled plasma atomic emission spectrometry. Results and conclusions. The paper introduces the experimental studies of surface atmospheric pollution by mining waste from the Ermakovskoe fluorite-bertrandite-phenakite deposit are presented using an installation for collecting aerosols above the sand surface. It was established that toxic components formed during the decomposition of residual sulfide mineralization and products of the interaction of acidic waters with rocks move from the sand thickness to the surface along with water vapor. The moisture condensed over the sand contains high contents of aluminum, iron, manganese, zinc, and phosphorus. These elements form a halo of air pollution over mining waste and are then dispersed by air currents into the surrounding area. In winter, due to wind dispersion of aerosols, the snow cover becomes contaminated over a vast area. Among the toxic elements found were beryllium, lead, cadmium, and molybdenum, which belong to the second hazard class. The solid residue of the snow cover contains a fine fraction of dust, the size of which is less than 10 microns. The halo of snow contamination with toxic chemical elements and dust extends several kilometers away from the disturbed lands.