Snow Cover Duration Research Articles

Spatiotemporal patterns of microclimatic buffering in relict alpine communities

AbstractQuestionsIn alpine landscapes, topography creates a mosaic of microclimatic niches that might prevent local extinctions, but the influence of this spatial heterogeneity on plant communities is largely unknown. Here we ask (1) how soil microclimatic variation is comparable at temporal and spatial scales, and (2) how such variation influences species composition and local extinctions in relict alpine communities.LocationPicos de Europa National Park, northern Spain.MethodsWe resurveyed permanent plots in four alpine sites following the recording of soil temperatures (temporal survey) for 10 years. We then sampled the spatial variation in species composition and microclimatic temperatures in 80 plots around the permanent plots (spatial survey). We evaluated the variation of six microclimatic indices between the temporal and the spatial surveys, and calculated the temporal trends observed in species cover. We finally predicted local extinction rates under microclimatic scenarios based on the observed microclimate–community relations.ResultsDespite high interannual variation, we found a 10‐year trend of temperature warming on (microridge) fellfields and (microvalley) snowbeds. Microclimatic variation was larger in space than in time, with little temperature variation in snowbeds and extreme low temperatures recorded in fellfields. Species composition was mainly influenced by growing degree days (GDD) and freezing degree days (FDD), which were both related to snow cover duration. Plant cover of 16 species (out of 36 frequent species) showed significant responses to microclimatic variation. Local extinctions were mainly predicted under relatively hotter and more freezing conditions.ConclusionsOur results support the idea that microclimatic spatial heterogeneity can reduce the negative influence of climate change on alpine plant communities. However, a continuous reduction of snow cover will result in a tipping point beyond which the buffer effect of this spatial heterogeneity will not be effective in protected microsites, leading to community homogenization. This process may have started in relict alpine communities where species from snowy microclimates are being outcompeted by species adapted to below‐zero winter temperatures.

Facultative migration in two thrush species (Fieldfare and Redwing): Rowanberry abundance is more important than winter weather

Facultative migration is thought to be influenced by food and weather, with birds remaining in northern areas if food is abundant and weather conditions are benign, but migrating south when food is scarce and weather is harsh. However, the relative importance of these two factors has rarely been tested with long-term data, and effects of weather are poorly documented. Here, I assess whether winter numbers of the Fieldfare (Turdus pilaris) and the Redwing (T. iliacus) in southern Norway during the period 1980–2020 varied in relation to the abundance of an important source of winter food (rowanberries, Sorbus aucuparia) and indices of winter harshness (North Atlantic Oscillation index, temperature, duration of snow cover). Two regions with contrasting winter harshness (western Norway with a maritime climate, eastern Norway with a continental climate) were compared. Winter numbers of Fieldfare in both regions, and Redwing in eastern Norway, were highest in years with high rowanberry abundance. Weather had mixed influence on thrush winter numbers, but interacted with rowanberry abundance in several cases so that small numbers of thrushes occurred with the combination of low rowanberry abundance and harsh weather. Such interactions occurred both in eastern and western Norway, and for both species, including for the Redwing in western Norway. In conclusion, facultative migration was strongly related to food availability with large numbers wintering in years with large rowanberry crops, and with additional effects of harsh weather working in concert with low rowanberry abundance to reduce wintering numbers to low levels in some years.

Passive microwave remote-sensing-based high-resolution snow depth mapping for Western Himalayan zones using multifactor modeling approach

Abstract. Spatiotemporal snow depth (SD) mapping in the Indian Western Himalayan (WH) region is essential in many applications pertaining to hydrology, natural disaster management, climate, etc. In situ techniques for SD measurement are not sufficient to represent the high spatiotemporal variability in SD in the WH region. Currently, low-frequency passive microwave (PMW) remote-sensing-based algorithms are extensively used to monitor SD at regional and global scales. However, fewer PMW SD estimation studies have been carried out for the WH region to date, which are mainly confined to small subregions of the WH region. In addition, the majority of the available PMW SD models for WH locations are developed using limited data and fewer parameters and therefore cannot be implemented for the entire region. Further, these models have not taken the auxiliary parameters such as location, topography, and snow cover duration (SCD) into consideration and have poor accuracy (particularly in deep snow) and coarse spatial resolution. Considering the high spatiotemporal variability in snow depth characteristics across the WH region, region-wise multifactor models are developed for the first time to estimate SD at a high spatial resolution of 500 m × 500 m for three different WH zones, i.e., Lower Himalayan Zone (LHZ), Middle Himalayan Zone (MHZ), and Upper Himalayan Zone (UHZ). Multifrequency brightness temperature (TB) observations from Advanced Microwave Scanning Radiometer 2 (AMSR2), SCD data, terrain parameters (i.e., elevation, slope, and ruggedness), and geolocation for the winter period (October to March) during 2012–2013 to 2016–2017 are used for developing the SD models for dry snow conditions. Different regression approaches (i.e., linear, logarithmic, reciprocal, and power) are used to develop snow depth models, which are evaluated further to find if any of these models can address the heterogeneous association between SD observations and PMW TB. From the results, it is observed from the analysis that the power regression SD model has improved accuracy in all WH zones with the low root mean square error (RMSE) in the MHZ (i.e., 27.21 cm) compared to the LHZ (32.87 cm) and the UHZ (42.81 cm). The spatial distribution of model-derived SD is highly affected by SCD, terrain parameters, and geolocation parameters and has better SD estimates compared to regional and global products in all zones. Overall results indicate that the proposed multifactor SD models have achieved higher accuracy in deep snowpack (i.e., SD >25 cm) of the WH region compared to previously developed SD models.

Variability and drivers of winter near-surface temperatures over boreal and tundra landscapes

Abstract. Winter near-surface air temperatures have important implications for ecosystem functioning such as vegetation dynamics and carbon cycling. In cold environments, the persistence of seasonal snow cover can exert a strong control on the near-surface temperatures. However, the lack of in situ measurements of both snow cover duration and surface temperatures over high latitudes has made it difficult to estimate the spatio-temporal variability in this relationship. Here, we quantified the fine-scale variability in winter near-surface air temperatures (+2 cm) and snow cover duration (calculated from temperature time series) using a total of 441 microclimate loggers in seven study areas across boreal and tundra landscapes in Finland during 2019–2021. We further examined the drivers behind this variation using a structural equation model and the extent to which near-surface air temperatures are buffered from free-air temperatures during winter. Our results show that while average winter near-surface temperatures stay close to 0 ∘C across the study domain, there are large differences in their fine-scale variability among the study areas. Areas with large topographical variation, as well as areas with shallow snowpacks, showed the greatest variation in near-surface temperatures and in snow cover duration. In the tundra, for example, differences in minimum near-surface temperatures between study sites were close to 30 ∘C and topography was shown to be an important driver of this variability. In contrast, flat topography and long snow cover duration led to little spatial variation, as well as long periods of decoupling between near-surface and air temperatures. Quantifying and understanding the landscape-wide variation in winter microclimates improves our ability to predict the local effects of climate change in the rapidly warming boreal and tundra regions.

A method for robust estimation of snow seasonality metrics from Landsat and Sentinel-2 time series data over Atlas Mountains scale using Google Earth Engine

Seasonal snow cover provides the majority of freshwater supplies for human society and natural ecosystems especially in semi-arid regions. For water resource managers, precise data regarding the spatiotemporal variability of snow cover and snow phenology is of paramount importance. Owing to the great spatial and temporal heterogeneity of the snowpack and the inaccessibility of high mountainous areas, gapless satellite remote sensing presents an unprecedented opportunity to monitor snow cover effectively and affordably on a fine scale, from different aspects, and with regular revisit time. This study derives the snow seasonality metrics (first day of snowfall, last day of snow melt; and snow cover duration) over a large semi-arid region in Morocco’s Atlas Mountains. We calculate these metrics by combining over 10,000 images from Landsat 8 and Sentinel 2 satellites for four hydrological years (2016–2021) to create a harmonized product with a time interval of about 3 days using the Google Earth Engine (GEE) platform. This dense and large time series facilitates a gap-filling method to minimize and overcome the effect of cloud cover, and its assessment shows a positive correlation between the masked pixels and the interpolated ones. These methods allowed us to realize a map of the snow cover area and extract a homogeneous Normalized Difference Snow Index (NDSI) profile over the four years whereby we were able to determine the optimal threshold to separate the presence of snow from its absence. The results showed that derived snow cover metrics provide considerable variation in both time and space, where an increase in snowpack measurement values at higher elevations can be noted. Overall, the snow duration ranges between November and April depending on the characteristics of each hydrological year. The retrieved Landsat 8 and Sentinel 2 snow dates had a high level of agreement with in-situ data observations with almost a day-and-a-half delay with an overall accuracy equal to 0.96. The analysis of snow cover dynamics via GEE has offered the ability to calculate the first day of snowfall, last day of snowmelt, and snow cover duration annually at a pixel level, providing the user with the ability to track the seasonal and interannual variability in the timing of snowmelt toward a better understanding of how the hydrological cycles of higher latitude and mountainous regions are responding to climate change.

Changes in snow cover climatology and its elevation dependency over Romania (1961–2020)

Study Region: Romanian territory and the Carpathian Mountains, Romania.Study focus: We provide a consistent picture of long-term changes in relevant snow cover characteristics, including phenology (timing of the snow onset and melting), snow cover duration, and frequency of snow cover and snow-free days in Romania, from 1961 to 2020, that could be relevant for understanding the regional dynamics of terrestrial water resources.New hydrological insights: The trend behaviour shows different dynamics between the elevation bands and Koeppen-Geiger climate regions of Romania, which may play a crucial role in the variability of snow water budgets and the distribution of water resources. We found systematic delays in snow onsets and earlier snow melting throughout the country. These trends are particularly prominent at mid-elevations and in the lowlands, reflecting the contribution of the intensified seasonal warming process in the last decades. We also observed widespread and significant declines in snow cover duration, snow cover day frequency, and increases in snow-free days in most climate regions of the country. The trends in snow cover parameters are elevation-dependent up to 2000 m. Above 2000 m the observed changes are visibly diminished or reversed (e.g., earlier snow onsets in the alpine areas). The long-term snow cover variability shows significant shifts in the mean of the snow cover parameters, generally clustered during the 1990 s or earlier. Relationships between the large-scale atmospheric circulation (herein expressed as NAO) and changes in seasonal temperature and precipitation have been identified.

Quantifying uncertainty: The benefits of removing snow cover from remote sensing time series on the extraction of climate-influenced grassland phenology on the Qinghai–Tibet Plateau

Vegetation phenology is a sensitive indicator of climate change. Remote sensing technology is often utilized to estimate vegetation phenology over expansive regions, but it contains uncertainties stemming from methods of data preprocessing and phenology extraction. Previous studies have primarily focused on individual causes of uncertainty, neglecting potential interactions between various causes and the consequent effects on analyzing vegetation's response to climate change. In this study, the uncertainty introduced by multiple time-series processing procedures in extracting the start of the growing season (SOS) of grassland on the Qinghai–Tibetan Plateau (QTP) was assessed using remote sensing data. This includes data preprocessing (time-series smoothing and snow cover removal) and phenology extraction. How these uncertainties might influence the analysis of SOS response to climate change was further quantified. Our findings demonstrated that the average uncertainty in grassland SOS extraction on the QTP for the period 2001–2015 reached 20.01 d, and snow-induced noise emerged as the principal factor affecting SOS estimates. Temporal trends of SOS revealed a regional average uncertainty of 1.06 d/y, both snow cover removal and the smoothing method were influential, each introducing uncertainty of 1.04 d/y and 1.05 d/y, respectively. After the removal of snow cover, uncertainties were reduced by 35 % for the SOS temporal trend. SOS response to climate change was extracted by various methods, leading to divergent and at times conflicting outcomes. The uncertainty for SOS sensitivity to temperature was averagely 10.50 d/°C on the QTP, while that for SOS sensitivity to precipitation was 1.75 d/mm. As the duration of snow cover lengthened, uncertainty increased in SOS response to temperature, while uncertainty increased in arid regions for SOS response to precipitation. The insights provided here contribute to enhancing phenology extraction accuracy and understanding the interactions between global climate change and vegetation phenology.

Camouflage efficiency in a colour-polymorphic predator is dependent on environmental variation and snow presence in the wild.

Colour polymorphism can be maintained by colour morph-specific benefits across environmental conditions. Currently, the amount and the duration of snow cover during winter decrease especially in northern latitudes, which can alter the potential for camouflage of animals with light and dark morphs. Tawny owls, Strix aluco, are colour-polymorphic avian predators with dark (brown) and light (grey) colour morphs, where the grey morph is presumed to enjoy camouflage benefits under snowy conditions. We studied the camouflage potential of morphs in two tawny owls potential using passerines' probability to mob in the wild during winter with and without snow. For comparison with other seasons, we also repeated the experiment during spring and autumn. We found that grey tawny owls have a lower probability of being mobbed than the brown tawny owls only during snowy winters. The two colour morphs therefore experience differential benefits across snow conditions, which may help to maintain colour morphs in the population, although further warming of winter climate will reduce the potential for camouflage for grey tawny owls in northern latitudes.

Distance to a River Modifies Climate Legacy on Vegetation Growth in a Boreal Riparian Forest

Inter-annual variability in growing season temperature and precipitation, together with snow coverage duration, determine vegetation growth in boreal ecosystems. However, little is known about the impact of concurrent and antecedent climate, particularly snow cover duration, on vegetation growth in a boreal riparian forest. Additionally, significant uncertainty exists regarding whether the distance to a river (as a proxy of groundwater availability) further modifies these climatic legacy effects on vegetation growth. To fill this knowledge gap, we quantified the responses of different vegetation types (shrub, deciduous coniferous and broadleaf forests) to concurrent and antecedent climate variables in a boreal riparian forest, and further determined the magnitude and duration of climate legacies in relation to distance to a river, using MODIS-derived NDVI time series with gridded climate data from 2001 to 2020. Results showed that higher temperature and precipitation and longer snow cover duration increased vegetation growth. For deciduous coniferous forests and broadleaf forests, the duration of temperature legacy was about one year, precipitation legacy about two years and snow cover duration legacy was 3 to 4 years. Further, distance to a river modified the concurrent and antecedent temperature and snow cover duration legacy effects on vegetation growth, but not that of precipitation. Specifically, temperature and snow cover duration legacies were shorter at the sites near a river compared to sites at greater distance to a river. Our research highlights the importance of snow cover duration on vegetation growth and that closeness to a river can buffer adverse climate impacts by shortening the strength and duration of climate legacies in a boreal riparian forest.

Variability in lake bacterial growth and primary production under lake ice: Evidence from early winter to spring melt

AbstractClimate change is causing seasonally ice‐covered lakes of the boreal region to undergo changes in their winter regime by altering patterns of precipitation and temperature, often reflected as reduced snow and ice cover duration. The duration, extent and quality of ice, and snow cover have a pivotal role for production and carbon cycling in lakes in winter, with potentially cascading effects for the following open water period. We investigated under‐ice carbon cycling by assessing bacterial growth (including bacterial production, bacterial respiration, and bacterial growth efficiency) and primary production at five water depths during early winter, midwinter, late winter and melting season in a boreal lake, and report significantly different temporal patterns. Bacterial respiration was dominant in early and midwinter, whereas the late winter and melting season were dominated by bacterial production. Multiple linear regression models indicated that high early winter bacterial respiration was associated with senescing phytoplankton, whereas bacterial production was promoted by the onset of spring processes. Collectively, bacterial growth indices were inherently linked with bacterioplankton community composition and specific biomarker taxa. Primary production under ice increased in late winter when light‐blocking snow cover melted, and primary production measured from the lake ice exceeded that of the water column at the melting season. Ice samples hosted diverse eukaryotic communities including photoautotrophs, suggesting that the habitat potential of the understudied lake ice and the role of ice for ecological processes at ice melt should be further explored.

Above-treeline ecosystems facing drought: lessons from the 2022 European summer heat wave

Abstract. In 2022, a large part of Europe experienced an extremely dry and hot summer. In the Alps, this episode occurred after an unusually low-snowfall winter, which aggravated the dryness of soils. This study examines the impact of this particular year on the canopy greenness of above-treeline ecosystems by comparison with previous heat waves that hit the Alps during the last 2 decades. Normalized difference vegetation index (NDVI) time series derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) satellite were processed to extract the temporal variability in yearly maximum NDVI (NDVImax). The responsiveness of NDVImax to snow cover duration and growing-season weather conditions was evaluated in contrasting hydroclimate regions of the Alps using linear mixed-effect models. The year 2022 was unique in that the summer heat wave led to a widespread negative anomaly of NDVImax. The magnitude of this anomaly was unprecedented in the southwestern, driest part of the Alps, where vegetation activity was found to be particularly responsive to snow cover duration and early summer precipitation. In the colder and wetter regions, all warm to very warm summers before 2022 had led to increased canopy greenness, but the combination of a reduced snow cover and low early summer precipitation counteracted this expected beneficial effect in 2022. This study provides evidence that the control of canopy greenness by temperature and water balance differs markedly across regions of the Alps and that the year 2022 bears witness to a shift toward an increasing importance of moisture availability for regulating plant growth at high elevation. This is viewed as a warning sign of what could become the new norm in the years ahead in the context of increasing frequency and intensity of extreme droughts throughout temperate mountain ecosystems.

Early-melting snowpatch plant communities are transitioning into novel states

Snowpatch plant community distribution and composition are strongly tied to the duration of long-lasting snow cover in alpine areas; they are vulnerable to global climatic changes that result in warmer temperatures and longer growing seasons. We used a revisitation study to quantify early-melting snowpatch floristic and functional diversity change in southern Australia, and document shrub size-class distributions over time to detect evidence for their encroachment into snowpatches, a key prediction with climatic change. Early-melting snowpatch vegetation has declined in areal extent, changed in species composition, and shrub and tussock grass cover has increased, but changes in functional trait diversity were less clear. Species gains, particularly by non-clonal species, accounted for most of the floristic change observed. Shrub recruitment was ongoing by most shrub species. Biotic differentiation is occurring; many early-melting snowpatches are transitioning to a novel state with changed composition and taller vegetation structure, but there is little evidence for species loss having occurred. Given enough time, however, the long-term loss of species is likely (i.e. biotic homogenisation) if taller shrubs outcompete short-statured snowpatch species. Our results provide evidence that this alpine ecosystem is forming a novel community with an uncertain future.

The change and variability of snow cover in Kraków in a 100-year observation series

This article presents the results of research on the changes and variability of snow cover in Kraków in the 100-year period 1921/22–2020/21 and in its two sub-periods covering the years of the slow and rapid territorial, urban and industrial development of Kraków (respectively, 1921/22–1960/61 and 1961/62–2020/21). The long-term variability of the number of days with snow cover, the maximum depth of the snow layer, the dates of the beginning and end of snow cover duration in the winter season, the potential snow cover duration and the index of snow cover stability were analysed. The directions of changes in the snow cover in the last 100 winter seasons in Kraków correspond to the global changes in air temperature presented in the latest IPCC reports: until the end of the 1950s there were no significant trends, or only small trends were observed, whereas from the beginning of the 1960s faster changes in the snow cover duration and maximum seasonal snow depth have been visible. In the last 60 years (1961/62–2020/21), the impact of global changes in Kraków has been joined by the impact of territorial, demographic and industrial development of the city, causing significant negative trends in snow cover with relative values of less than −9% ∙ 10 years−1, both in the case of snow cover duration and its maximum depth in the winter season; these changes are statistically significant. Throughout the whole 100-year period (1921/22–2020/21) and in its second part (1961/62–2020/21), a decrease in snow cover stability has also been observed.

Variability and changes of the height and duration of snow cover on the Gąsienicowa Glade (Tatras)

AbstractThe oldest meteorological station in the Polish Tatra Mountains is that on the Gąsienicowa Glade (1520 m). In this study, two series of observations for the years 1927–1938 and 1947–2020 are presented for the number of days with snow cover higher than 1 cm (SCD—from 142 to 228 days), maximum snow depth (HSmax—from 43 to 271 cm) and average snow depth (HSmean—from 10 to 93 cm). For the remaining meteorological parameters discussed in this article, the observation series is slightly shorter. From 1927 to 2020, the number of days with snow cover show downward trends (SCD—from nearly 200 days in 1927/1928 to about 175 days in 2019/2020). The decrease in the values of SCD discussed is due to an increase in air temperature and an upward trend in the percentage of the number days with rain over the number of days with snowfall. The increase in mean air temperature started in the 1980s. The strongest temperature trends were recorded in spring (TG in June—from 8.2°C in 1927 to 10.0°C in 2020) and in summer (TG in August—from 9.7°C in 1927 to 12.0°C in 2020). The last two decades, in particular, are exceptional for the Tatra Mountains, both in terms of air temperature, days with snow cover, maximum and mean snow depth. Since the beginning of observations in the Gąsienicowa Glade, no such strong decade‐long increases in temperature and decreases in the number of days with snow cover and in maximum and mean snow depth have been recorded. The observed trends in days with snow cover and maximum and mean snow depth in the Tatras are similar to those observed in studies conducted in the Alps.

Snow cover duration in northern Finland and the influence of key variables through a conceptual framework based on observed variations in snow depth

Seasonal snow cover duration is the net result from many processes acting on snow fallen on the Earth's surface. Several of these processes feed back into the atmosphere-cryosphere system causing non-linear interactions. The timing of snow retreat is of essential importance, but the duration of snow cover has large spatiotemporal variabilities. However, from a large data set of observed snow depth changes in northern Finland, systematic similar evolutions are identified that allow for a considerable simplification and reduction of the complexity in snow depth changes. Here, a novel conceptual framework is designed based on dividing the season into two main periods (dark and bright period, based on solar irradiance), for which snow depth decrease is parameterized based on three variables, average temperature, incoming shortwave radiation, and light-absorbing particles (LAP) in the snow. The processes are simplified into two linear relations, and a new formulation for concentration enhancement of LAP, which is dependent on snow depth decrease, is given. The results show that the seasonal snow cover duration is shifted by about one day for every 10 mm snow water equivalent of precipitation. This effect is comparable in scale to that of doubling of the amount of LAP concentration in snow. We also found that the combined shift in snow cover duration from interannual variability in ambient temperature and shortwave radiation (warm and bright vs. cold and dark season) is large enough to explain the variability of a couple of weeks for a given precipitation amount in Northern Finland.

Multi-decadal analysis of past winter temperature, precipitation and snow cover data in the European Alps from reanalyses, climate models and observational datasets

Abstract. Assessing past distributions, variability and trends in the mountain snow cover and its first-order drivers, temperature and precipitation, is key for a wide range of studies and applications. In this study, we compare the results of various modeling systems (global and regional reanalyses ERA5, ERA5-Land, ERA5-Crocus, CERRA-Land, UERRA MESCAN-SURFEX and MTMSI and regional climate model simulations CNRM-ALADIN and CNRM-AROME driven by the global reanalysis ERA-Interim) against observational references (in situ, gridded observational datasets and satellite observations) across the European Alps from 1950 to 2020. The comparisons are performed in terms of monthly and seasonal snow cover variables (snow depth and snow cover duration) and their main atmospherical drivers (near-surface temperature and precipitation). We assess multi-annual averages of regional and subregional mean values, their interannual variations, and trends over various timescales, mainly for the winter period (from November through April). ERA5, ERA5-Crocus, MESCAN-SURFEX, CERRA-Land and MTMSI offer a satisfying description of the monthly snow evolution. However, a spatial comparison against satellite observation indicates that all datasets overestimate the snow cover duration, especially the melt-out date. CNRM-AROME and CNRM-ALADIN simulations and ERA5-Land exhibit an overestimation of the snow accumulation during winter, increasing with elevations. The analysis of the interannual variability and trends indicates that modeling snow cover dynamics remains complex across multiple scales and that none of the models evaluated here fully succeed to reproduce this compared to observational reference datasets. Indeed, while most of the evaluated model outputs perform well at representing the interannual to multi-decadal winter temperature and precipitation variability, they often fail to address the variability in the snow depth and snow cover duration. We discuss several artifacts potentially responsible for incorrect long-term climate trends in several reanalysis products (ERA5 and MESCAN-SURFEX), which we attribute primarily to the heterogeneities of the observation datasets assimilated. Nevertheless, many of the considered datasets in this study exhibit past trends in line with the current state of knowledge. Based on these datasets, over the last 50 years (1968–2017) at a regional scale, the European Alps have experienced a winter warming of 0.3 to 0.4 ∘C per decade, stronger at lower elevations, and a small reduction in winter precipitation, homogeneous with elevation. The decline in the winter snow depth and snow cover duration ranges from −7 % to −15 % per decade and from −5 to −7 d per decade, respectively, both showing a larger decrease at low and intermediate elevations. Overall, we show that no modeling strategy outperforms all others within our sample and that upstream choices (horizontal resolution, heterogeneity of the observations used for data assimilation in reanalyses, coupling between surface and atmosphere, level of complexity, configuration of the snow scheme, etc.) have great consequences on the quality of the datasets and their potential use. Despite their limitations, in many cases they can be used to characterize the main features of the mountain snow cover for a range of applications.

Snow depth and the associated offset in ground temperatures in a landscape manipulated with snow-fences

Seasonal snow cover has an important impact on the difference between soil- and air temperature because of the insulation effect, and is therefore a key parameter in ecosystem models. However, it is still uncertain how specific variations in soil moisture, vegetation composition, and surface air warming, combined with snow dynamics such as compaction affect the difference between soil- and air temperature. Here, we present an analysis of 8 years (2012–2020) of snow dynamics in an Arctic ecosystem manipulation experiment (using snow fences) on Disko Island, West Greenland. We explore the snow insulation effect under different treatments (mesic tundra heath as a dry site and fen area as a wet site, snow addition from snow fences, warming using open top chambers, and shrub removal) on a plot-level scale. The snow fences significantly changed the inter-annual variation in snow depths and -phenology. The maximum annual mean snow depths were 90 cm on the control side and 122 cm on the snow addition side during all study years. Annual mean snow cover duration across 8 years was 234 days on the control side and 239 days on the snow addition side. The difference between soil- and air temperature was significantly higher on the snow addition side than on the control side of the snow fences. Based on a linear mixed-effects model, we conclude that the snow depth was the decisive factor affecting the difference between soil- and air temperature in the snow cover season (p < 0.0001). The change rate of the difference between soil- and air temperature, as a function of snow depth, was slower during the period before maximum snow depth than during the period between the day with maximum snow depth until snow ending day. During the snow-free season, the effects of the open top chambers were stronger than the effects of the shrub removal, and the combination of both contributed to the highest soil temperature in the dry site, but the warming effect of open top chambers was limited and shrub removal warmed soil temperature in the wet site. The warming effects of open top chambers and shrub removal were weakened on the snow addition side, which indicates a lagged effect of snow on soil temperature. This study quantifies important dynamics in soil-air temperature offsets linked to both snow and ecosystem changes mimicking climate change and provides a reference for future surface process simulations.

Snow Cover on the Tibetan Plateau and Topographic Controls

Snow cover plays a critical role in global energy and water cycles. Snow cover on the Tibetan Plateau (TP) provides vital water sources in western China and Himalayan regions, in addition to its weather and climate significance. The massive high mountain topography of the TP is the main condition for the presence and persistence of snow cover on the plateau at the mid-low latitudes of the Northern Hemisphere (NH). However, how the mountain topography controls snow-cover distribution on the TP remains largely unclear, and the relationship is not well quantified. Here, the spatial distribution and the topographic controls of snow cover on the TP are examined based on snow cover frequency (SCF) derived from MODIS snow cover product (MOD10A2 v005) and digital elevation model (DEM) data. The results show that snow cover on the TP is spatially unevenly distributed, and that it is characterized by rich snow and high SCF on the interior and the surrounding high mountain ranges, with less snow and low SCF in inland basins and river valleys. Snow cover on the TP presents elevation dependence: the higher the altitude, the higher the SCF, the longer the snow cover duration, and the more stable the intra-annual variation. The annual mean SCF below 3000 m above sea level (m a.s.l) is less than 4%, and it reaches 77% above 6000 m a.s.l. The intra-annual snow cover variation below 4000 m a.s.l features a unimodal distribution, while above 4000 m a.s.l it presents a bimodal distribution. The mean minimum SCF below 6000 m a.s.l occurs in summer, while above 6000 m a.s.l it occurs in winter. Due to differences in solar radiation and moisture condition caused by the mountain slope and aspect, the mean SCF generally increases with mountain slopes, and it is the highest on the north-facing aspect and the lowest on the south-facing aspect.

Studies by Vitaliy Grachev focused on maximum vehicle passability achievement

Introduction (problem statement and relevance). The territory of our country is located in the area with periodic snow cover. There is a great variance in duration of snow cover, its nature and properties among different regions of the country. The Far North snow cover lasts for 10–11 months. To develop the territory of Siberia, Altai, the Far East and the Far North, as well as to solve the issues of energy, technological and economic security, there is a need for vehicles having high passability or off-road performance in a wide range of driving conditions. Despite intensive road construction and growth of the road network, provision of transport accessibility for the far regions of our country continues to be a relevant and prospective task for the automotive industry. The article considers scientific and practical approaches and results of the studies conducted by the Special Design Bureau of the Automotive Plant n.a. Ivan Likhachev (hereinafter – SDB ZIL) led by Vitaliy Grachev and focused on creation of ultrahigh-passability vehicles.The purpose of the study is to review and to analyze the high-passability and ultrahigh-passability land vehicle designs made based on the studies conducted in the SDB ZIL for severe driving conditions (deep snow, water, swamp, sand, engineering obstacles).Methodology and research methods. The article presents the analysis of the results of scientific and practical experiments in driving vehicles with both conventional – wheels and tracks – and non-conventional – rotary screw, pneumatic roller and other – running gears in different environments, which were obtained using the methods proposed by Vitaliy Grachev.Scientific novelty and results. The article offers solutions, methods and recommendations for selection of optimal types and kinds of running gears for operation under specific off-road conditions, as well as reliable design and engineering solutions proven by practical implementation of successful projects.Practical significance. Restoring the belief in capabilities and potential of the Russian scientific and engineering school by informing the new generation of engineers about the achievements and successful projects by the SDB ZIL led by Vitaliy Grachev related to creation of wheeled vehicles and vehicles with non-conventional running gear types (rotary screw and pneumatic roller tracked running gears) in different environments, as well as about the scientific and practical methods used in their implementation.

Estimation of snow bulk density and snow water equivalent on the Tibetan Plateau using snow cover duration and snow depth

Study regionTibetan Plateau (TP), China. Study focusSnow water equivalent (SWE) measurements is few compared with snow depth (SD) but combining with snow bulk density (SBD) can convert SD to SWE. No robust model has been established to estimate SBD and SWE for the TP. In this study, we aims to develop a model capable of obtaining SBD and SWE on the TP based on a significant dataset of simultaneously measured SD and SWE over nearly four decades. New hydrological insights for the regionWe found unique seasonal characteristics and elevation differences in the SBD on the TP. The newly introduced factor snow cover duration (SCD) can reflect the beginning and status of each snowfall-snowmelt cycle in this type of discontinuous snow region. The developed artificial neural network (ANN) model based on two-factor of SCD and SD can capture these unique characteristics, with minimal errors and the best performance in simulating SBD and then estimating SWE on the TP. Our method improves the underestimation of the widely used regional average density during the heavy snow period, enables a reasonable conversion of the large amount of historical and growing SDs into critical SWEs, and thus improves our ability to assess snow water resources on the TP.