Snow Patterns Research Articles

Snow deposition patterns on southern Spitsbergen glaciers, Svalbard, in relation to recent meteorological conditions and local topography

ABSTRACTWe present a detailed study on snow cover on six different glaciers in southern Spitsbergen, Svalbard: Amundsenisen, Flatbreen, Hansbreen, Nannbreen, Storbreen and Werenskioldbreen. Fieldwork was carried out in April–May 2013, at the end of the accumulation season, to determine large-scale spatial distribution patterns of snow cover on glaciers surrounding the Hornsund Fjord. Snow depth was measured using an 800 MHz ground-penetrating radar (GPR). In addition, the structure of the snowpack was determined by digging snow pits and collecting snow cores from different glacier facies. These samples were subsequently analysed against circulation types and meteorological data from selected sites. In particular, snow patterns were compared against rain-on-snow events. The mean snow depth measures ranged from 1.90 m (Werenskioldbreen) to 3.80 m (Amundsenisen), whereas the accumulation gradient ranged from 15 cm 100 m−1 (Storbreen) to 74 cm 100 m−1 (Nannbreen). These results followed previous observations, suggesting a decrease in snow accumulation from coastal areas towards the island’s interior. The estimated snow water equivalent values were lower than those measured in the 1990s and during 2010 (Hansbreen: c. −48%), with the exception of Amundsenisen (c. +17%). The relatively high densities found in the snowpack did not compensate for the shallow snow depths measured, which can be ascribed to low precipitation totals and an increased frequency of rain events during the winter months.

Early formation of preferential flow in a homogeneous snowpack observed by micro‐CT

AbstractWe performed X‐ray microtomographic observations of wet‐snow metamorphism during controlled continuous melting and melt‐freeze events in the laboratory. Three blocks of snow were sieved into boxes and subjected to cyclic, superficial heating or heating‐cooling to reproduce vertical water infiltration patterns in snow similarly to natural conditions. Periodically, samples were taken at different heights and scanned. Results suggest that wet‐snow metamorphism dynamics are highly heterogeneous even in an initially homogeneous snowpack. Consistent with previous work, we observed an increase with time in the thickness of the ice structure, which is a measure of grain size. However, this was coupled with large temporal scatter between consecutive measurements of the specific surface area and of the statistical moments of grain thickness distributions. Because of marked differences in the right tail, grain thickness distributions did not show shape invariance with time, contrary to previous analyses. In our experiments, wet‐snow metamorphism showed two strikingly different patterns: homogeneous coarsening superimposed by faster heterogeneous coarsening in areas that were affected by preferential percolation of water. Liquid water movement in snow and fast structural evolution may be thus intrinsically coupled by early formation of preferential flow at local scale. These observations suggest that further experiments are highly needed to fully understand wet‐snow metamorphism and infiltration patterns in a natural snowpack.

Subgrid parameterization of snow distribution at a Mediterranean site using terrestrial photography

Abstract. Subgrid variability introduces non-negligible scale effects on the grid-based representation of snow. This heterogeneity is even more evident in semiarid regions, where the high variability of the climate produces various accumulation melting cycles throughout the year and a large spatial heterogeneity of the snow cover. This variability in a watershed can often be represented by snow accumulation–depletion curves (ADCs). In this study, terrestrial photography (TP) of a cell-sized area (30 × 30 m) was used to define local snow ADCs at a Mediterranean site. Snow-cover fraction (SCF) and snow-depth (h) values obtained with this technique constituted the two datasets used to define ADCs. A flexible sigmoid function was selected to parameterize snow behaviour on this subgrid scale. It was then fitted to meet five different snow patterns in the control area: one for the accumulation phase and four for the melting phase in a cycle within the snow season. Each pattern was successfully associated with the snow conditions and previous evolution. The resulting ADCs were associated to certain physical features of the snow, which were used to incorporate them in the point snow model formulated by Herrero et al. (2009) by means of a decision tree. The final performance of this model was tested against field observations recorded over four hydrological years (2009–2013). The calibration and validation of this ADC snow model was found to have a high level of accuracy, with global RMSE values of 105.8 mm for the average snow depth and 0.21 m2 m−2 for the snow-cover fraction in the control area. The use of ADCs on the cell scale proposed in this research provided a sound basis for the extension of point snow models to larger areas by means of a gridded distributed calculation.

A snow climatology of the Andes Mountains from MODIS snow cover data

ABSTRACTThis study develops a method for characterizing snow climatology in the Andes Mountains using the 8‐day maximum binary snow cover product from the Moderate Resolution Imaging Spectroradiometer sensor. The objectives are to: (1) identify regions with similar snow patterns and (2) identify snow persistence zones within these regions. Within a study area between 8° and 39°S, snow regions are defined using the (1) minimum elevation of snow cover, (2) rate of change of snow persistence with elevation, and (3) timing of the minimum elevation snow cover. In tropical latitudes (8°–23°S), snow cover is constrained to high elevations (>5000 m), and these areas have steep changes in snow persistence with elevation. Minimal differences in the elevation of snow on the east and west sides of the range suggest that temperature is a primary control on snow presence. Snow cover has minimal seasonal variability in the Tropics between 8° and 14°S, but it peaks in austral fall (March) after the wet season from 14° to 23°S. In mid‐latitudes (south of 23°S) snowline decreases in elevation with increasing latitude, and snow persistence changes with elevation are more gradual than in tropical regions. Snow cover peaks in the austral winter throughout the mid‐latitudes. Differences in elevations of snow accumulation between the east and west sides of the Andes are greatest between 28° and 37°S, where high mountain peaks produce a strong orographic effect and precipitation shadow. Within the snow regions, four snow zones are defined based on the average fraction of the year that snow persists: (1) little or no snow, (2) intermittent, (3) seasonal, and (4) permanent snow zones. Tropical latitudes have snow cover only on the highest peaks. Areas of seasonal and permanent snow zones are greatest between latitudes 28° and 37°S as a result of higher precipitation than mountains further north and higher elevations than mountains further south.

Firn Stratigraphic Genesis in Early Spring: Evidence From Airborne Radar

The mapping of internal annual layers in polar firn with ultra-wideband (UWB) surface-based and airborne radars enables the assessment of past snow accumulation amounts and patterns. We used a UWB microwave radar (snow radar) to image and track internal layers in firn over hundreds of kilometers in West Antarctica (WA). We used dated ice-core records to determine that stratigraphic internal layers (SILs) are mainly formed in spring. The analysis uncertainty is generally small, with the highest values associated with regions and/or years with low accumulation. In survey regions with multiple ice-core records, the choice of ice-core record used for the radar analysis was found to have a very small contribution to the overall analysis uncertainty.

Dynamic patterns of compaction in brittle porous media

When compacting a brittle porous medium—think stepping on fresh snow—patterns develop. Simulations and densification experiments with cereals now provide an understanding of compaction patterns in terms of a lattice model with breakable springs.

Impact of windflow calculations on simulations of alpine snow accumulation, redistribution and ablation

AbstractWind redistribution, radiation and turbulent heat fluxes determine seasonal snow accumulation and melt patterns in alpine environments. Mathematical representations of windflow vary in complexity and introduce uncertainty to snow modelling. To characterize this uncertainty, a spatially distributed snow model that considers the physics of blowing snow transport and sublimation and the energy fluxes contributing to snowpack ablation were evaluated for its ability to simulate seasonal snow patterns around a windy alpine ridge in the Canadian Rockies. The model was forced with output from three windflow models of varying computational complexity and physical realism: (i) a terrain‐based empirical interpolation of station observations, (ii) a simple turbulence model and (iii) a computational fluid dynamics model. Compared with wind measurements, the windflow simulations produced similar and relatively accurate (biases lower than ±1.1 m s−1) wind speed estimates. However, the snow mass budget simulated by the snow model was highly sensitive to the windflow simulation used. Compared with measurements, distributed snow model depth and water equivalent errors were smallest using either of the two turbulence models, with the best representation of downwind drifts by the computational fluid dynamics model. Sublimation was an important mass loss from the ridge, and windflow model choice resulted in cumulative seasonal sublimation differences ranging from 10.5% to 19.0% of seasonal snowfall. When aggregated to larger scales, differences in cumulative snowmelt and snow transport were negligible, but persistent differences in sublimation and snow‐covered area suggest that windflow model choice can have significant implications at multiple scales. Uncertainty can be reduced by using physically based windflow models to drive distributed snow models. Copyright © 2015 John Wiley & Sons, Ltd.

Snowmelt infiltration and storage within a karstic environment, Vers Chez le Brandt, Switzerland

Summary Even though karstic aquifers are important freshwater resources and frequently occur in mountainous areas, recharge processes related to snowmelt have received little attention thus far. Given the context of climate change, where alterations to seasonal snow patterns are anticipated, and the often-strong coupling between recharge and discharge in karst aquifers, this research area is of great importance. Therefore, we investigated how snowmelt water transits through the vadose and phreatic zone of a karst aquifer. This was accomplished by evaluating the relationships between meteorological data, soil–water content, vadose zone flow in a cave 53 m below ground and aquifer discharge. Time series data indicate that the quantity and duration of meltwater input at the soil surface influences flow and storage within the soil and epikarst. Prolonged periods of snowmelt promote perched storage in surficial soils and encourage surficial, lateral flow to preferential flow paths. Thus, in karstic watersheds overlain by crystalline loess, a typical pedologic and lithologic pairing in central Europe and parts of North America, soils can serve as the dominant mechanism impeding infiltration and promoting shallow lateral flow. Further, hydrograph analysis of vadose zone flow and aquifer discharge, suggests that storage associated with shallow soils is the dominant source of discharge at time scales of up to several weeks after melt events, while phreatic storage becomes import during prolonged periods without input. Soils can moderate karst aquifer dynamics and play a more governing role on karst aquifer storage and discharge than previously credited. Overall, this signifies that a fundamental understanding of soil structure and distribution is critical when assessing recharge to karstic aquifers, particularly in cold regions.

A snow cover climatology for the Pyrenees from MODIS snow products

Abstract. The seasonal snow in the Pyrenees is critical for hydropower production, crop irrigation and tourism in France, Spain and Andorra. Complementary to in situ observations, satellite remote sensing is useful to monitor the effect of climate on the snow dynamics. The MODIS daily snow products (Terra/MOD10A1 and Aqua/MYD10A1) are widely used to generate snow cover climatologies, yet it is preferable to assess their accuracies prior to their use. Here, we use both in situ snow observations and remote sensing data to evaluate the MODIS snow products in the Pyrenees. First, we compare the MODIS products to in situ snow depth (SD) and snow water equivalent (SWE) measurements. We estimate the values of the SWE and SD best detection thresholds to 40 mm water equivalent (w.e.) and 150 mm, respectively, for both MOD10A1 and MYD10A1. κ coefficients are within 0.74 and 0.92 depending on the product and the variable for these thresholds. However, we also find a seasonal trend in the optimal SWE and SD thresholds, reflecting the hysteresis in the relationship between the depth of the snowpack (or SWE) and its extent within a MODIS pixel. Then, a set of Landsat images is used to validate MOD10A1 and MYD10A1 for 157 dates between 2002 and 2010. The resulting accuracies are 97% (κ = 0.85) for MOD10A1 and 96% (κ = 0.81) for MYD10A1, which indicates a good agreement between both data sets. The effect of vegetation on the results is analyzed by filtering the forested areas using a land cover map. As expected, the accuracies decrease over the forests but the agreement remains acceptable (MOD10A1: 96%, κ = 0.77; MYD10A1: 95%, κ = 0.67). We conclude that MODIS snow products have a sufficient accuracy for hydroclimate studies at the scale of the Pyrenees range. Using a gap-filling algorithm we generate a consistent snow cover climatology, which allows us to compute the mean monthly snow cover duration per elevation band and aspect classes. There is snow on the ground at least 50% of the time above 1600 m between December and April. We finally analyze the snow patterns for the atypical winter 2011–2012. Snow cover duration anomalies reveal a deficient snowpack on the Spanish side of the Pyrenees, which seems to have caused a drop in the national hydropower production.

Landsat-based snow persistence map for northwest Alaska

Landsat imagery for northwest Alaska from 1 February to 31 August, 1985–2011 was used to map snow persistence at high spatial resolution. We analyzed 11,645 scenes covering 505,800km2, including five Arctic National Park units and the range of the Western Arctic caribou herd (85 Landsat path/rows). A cloud mask was created using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). Terrain shadows were calculated from ASTER G-DEM2 and solar incidence angle. The presence of snow cover was determined using separate Snowmap algorithms for non-shadowed and shadowed pixels. Resulting snow cover data were reformatted into 562 30×30km tiles, with an average sample size per pixel of 216 cloud-free observations. A binary classification tree was used to successfully determine the day of the year that best marked the change from snow to snow-free conditions for 99.8% of the study area. An internal consistency check evaluating the occurrence of snow-free data earlier than that day or snow data later than that day, showed that 98.7% of the land pixels were consistently classified ≥90% of the time. Comparison with MODIS end of snow season data showed an average difference of 4.2days. The snow persistence map was strongly correlated with the few SNOTEL stations in the study area (r2=0.856). Broadly, most snowmelt over the study area occurs from late April through early June, with timing delayed farther north and at higher elevations. Many local-scale snow patterns are evident in the detailed, 30-m product. The snow persistence map was co-registered to Landsat land cover mapping, creating a powerful, publicly available resource for ecosystem and land use analyses (https://irma.nps.gov/App/Reference/Profile/2203863).

Snow-cover reconstruction methodology for mountainous regions based on historic in situ observations and recent remote sensing data

Abstract. Spatially distributed snow-cover extent can be derived from remote sensing data with good accuracy. However, such data are available for recent decades only, after satellite missions with proper snow detection capabilities were launched. Yet, longer time series of snow-cover area are usually required, e.g., for hydrological model calibration or water availability assessment in the past. We present a methodology to reconstruct historical snow coverage using recently available remote sensing data and long-term point observations of snow depth from existing meteorological stations. The methodology is mainly based on correlations between station records and spatial snow-cover patterns. Additionally, topography and temporal persistence of snow patterns are taken into account. The methodology was applied to the Zerafshan River basin in Central Asia – a very data-sparse region. Reconstructed snow cover was cross validated against independent remote sensing data and shows an accuracy of about 85%. The methodology can be used in mountainous regions to overcome the data gap for earlier decades when the availability of remote sensing snow-cover data was strongly limited.

Validation of and comparison between a semidistributed rainfall–runoff hydrological model (PREVAH) and a spatially distributed snow‐evolution model (SnowModel) for snow cover prediction in mountain ecosystems

AbstractSnow cover is an important control in mountain environments and a shift of the snow‐free period triggered by climate warming can strongly impact ecosystem dynamics. Changing snow patterns can have severe effects on alpine plant distribution and diversity. It thus becomes urgent to provide spatially explicit assessments of snow cover changes that can be incorporated into correlative or empirical species distribution models (SDMs).Here, we provide for the first time a with a lower overestimation comparison of two physically based snow distribution models (PREVAH and SnowModel) to produce snow cover maps (SCMs) at a fine spatial resolution in a mountain landscape in Austria. SCMs have been evaluated with SPOT‐HRVIR images and predictions of snow water equivalent from the two models with ground measurements. Finally, SCMs of the two models have been compared under a climate warming scenario for the end of the century.The predictive performances of PREVAH and SnowModel were similar when validated with the SPOT images. However, the tendency to overestimate snow cover was slightly lower with SnowModel during the accumulation period, whereas it was lower with PREVAH during the melting period. The rate of true positives during the melting period was two times higher on average with SnowModel with a lower overestimation of snow water equivalent.Our results allow for recommending the use of SnowModel in SDMs because it better captures persisting snow patches at the end of the snow season, which is important when modelling the response of species to long‐lasting snow cover and evaluating whether they might survive under climate change. Copyright © 2014 John Wiley & Sons, Ltd.

Arctic snow distribution patterns at the watershed scale

Watershed-scale hydrologic models require good estimates of spatially distributed snow water equivalent (SWE) at winter's end. Snow on the ground in arctic environments is susceptible to significant wind redistribution, which results in heterogeneous snowpacks. The scarcity and quality of data collected by snow gauges provides a poor indicator of actual snowpack distribution. Snow distribution patterns are similar from year to year because they are largely controlled by the interaction of topography, vegetation, and consistent weather patterns. Consequently, shallow and deep areas of snow tend to be spatially predetermined, resulting in depth (or SWE) differences that may vary as a whole, but not relative to each other. Our aim was to identify snowpack distribution patterns and establish their stability in time and space at a watershed scale. Snow patterns were established by: (1) using numerous field surveys from end-of-winter field campaigns; and (2) differentiating snowpacks that characterize small-scale anomalies (local scale) from snowpacks that represent a large-scale area (regional scale). We concluded that basic snow survey site descriptions could be used to separate survey locations into regional and local-scale representative sites. Removing local-scale influences provides a more accurate representation of the regional snowpack, which will aid in forecasting snowmelt runoff events.

Sequence-based mapping approach to spatio-temporal snow patterns from MODIS time-series applied to Scotland

Snow cover and its monitoring are important because of the impact on important environmental variables, hydrological circulation and ecosystem services. For regional snow cover mapping and monitoring, the MODIS satellite sensors are particularly appealing. However cloud presence is an important limiting factor. This study addressed the problem of cloud cover for time-series in a boreal-Atlantic region where melting and re-covering of snow often do not follow the usual alpine-like patterns. A key requirement in this context was to apply improved methods to deal with the high cloud cover and the irregular spatio-temporal snow occurrence, through exploitation of space-time correlation of pixel values. The information contained in snow presence sequences was then used to derive summary indices to describe the time series patterns. Finally it was tested whether the derived indices can be considered an accurate summary of the snow presence data by establishing and evaluating their statistical relations with morphology and the landscape. The proposed cloud filling method had a good agreement (between 80 and 99%) with validation data even with a large number of pixels missing. The sequence analysis algorithm proposed takes into account the position of the states to fully consider the temporal dimension, i.e. the order in which a certain state appears in an image sequence compared to its neighbourhoods. The indices that were derived from the sequence of snow presence proved useful for describing the general spatio-temporal patterns of snow in Scotland as they were well related (more than 60% of explained deviance) with environmental information such as morphology supporting their use as a summary of snow patterns over time. The use of the derived indices is an advantage because of data reduction, easier interpretability and capture of sequence position-wise information (e.g. importance of short term fall/melt cycles). The derived seven clusters took into account the temporal patterns of the snow presence and they were well separated both spatially and according to the snow patterns and the environmental information. In conclusion, the use of sequences proved useful for analysing different spatio-temporal patterns of snow that could be related to other environmental information to characterize snow regimes regions in Scotland and to be integrated with ground measures for further hydrological and climatological analysis as baseline data for climate change models.

Remote sensing for snow hydrology in China: challenges and perspectives

Snow is one of the most important components of the cryosphere. Remote sensing of snow focuses on the retrieval of snow parameters and monitoring of variations in snow using satellite data. These parameters are key inputs for hydrological and atmospheric models. Over the past 30 years, the field of snow remote sensing has grown dramatically in China. The 30-year achievements of research in different aspects of snow remote sensing in China, especially in (1) methods of retrieving snow cover, snow depth/snow water equivalent, and grain size and (2) applications to snowmelt runoff modeling, snow response on climate change, and remote sensing monitoring of snow-caused disasters are reviewed/summarized. The importance of the first remote sensing experiment on snow parameters at the upper reaches of the Heihe River Basin, in 2008, is also highlighted. A series of experiments, referred to as the Cooperative Observation Series for Snow (COSS), focus on some key topics on remote sensing of snow. COSS has been implemented for 3 years and will continue in different snow pattern regions of China. The snow assimilation system has been established in some regions using advanced ensemble Kalman filters. Finally, an outlook for the future of remote sensing of snow in China is given.

Shifting mountain snow patterns in a changing climate from remote sensing retrieval

Observed climate change has already led to a wide range of impacts on environmental systems and society. In this context, many mountain regions seem to be particularly sensitive to a changing climate, through increases in temperature coupled with changes in precipitation regimes that are often larger than the global average (EEA, 2012). In mid-latitude mountains, these driving factors strongly influence the variability of the mountain snow-pack, through a decrease in seasonal reserves and earlier melting of the snow pack. These in turn impact on hydrological systems in different watersheds and, ultimately, have consequences for water management. Snow monitoring from remote sensing provides a unique opportunity to address the question of snow cover regime changes at the regional scale. This study outlines the results retrieved from the MODIS satellite images over a time period of 10 hydrological years (2000–2010) and applied to two case studies of the EU FP7 ACQWA project, namely the upper Rhone and Po in Europe and the headwaters of the Syr Darya in Kyrgyzstan (Central Asia). The satellite data were provided by the MODIS Terra MOD-09 reflectance images (NASA) and MOD-10 snow products (NSIDC). Daily snow maps were retrieved over that decade and the results presented here focus on the temporal and spatial changes in snow cover. This paper highlights the statistical bias observed in some specific regions, expressed by the standard deviation values (STD) of annual snow duration. This bias is linked to the response of snow cover to changes in elevation and can be used as a signal of strong instability in regions sensitive to climate change: with alternations of heavy snowfalls and rapid snow melting processes. The interest of the study is to compare the methodology between the medium scales (Europe) and the large scales (Central Asia) in order to overcome the limits of the applied methodologies and to improve their performances. Results show that the yearly snow cover duration increases by 4–5days per 100m elevation during the accumulation period, depending of the watershed, while during the melting season the snow depletion rate is 0.3% per day of surface loss for the upper Rhone catchment, 0.4%/day for the Syr Darya headwater basins, and 0.6%/day for the upper Po, respectively. Then, the annual STD maps of snow cover indicate higher values (more than 45days difference compared to the mean values) for (i) the Po foothill region at medium elevation (SE orientation) and (ii) the Kyrgyzstan high plateaux (permafrost areas). These observations cover only a time-period of 10years, but exhibit a signal under current climate that is already consistent with the expected decline in snow in these regions in the course of the 21st century.

Snowmelt runoff and soil moisture dynamics on steep subalpine hillslopes

AbstractSnowmelt water supplies streamflow and growing season soil moisture in mountain regions, yet pathways of snowmelt water and their effects on moisture patterns are still largely unknown. This study examined how flow processes during snowmelt runoff affected spatial patterns of soil moisture on two steep sub‐alpine hillslope transects in Rocky Mountain National Park, CO, USA. The transects have northeast‐facing and east‐facing aspects, and both extend from high‐elevation bedrock outcrops down to streams in valley bottoms. Spatial patterns of both snow depth and near‐surface soil moisture were surveyed along these transects in the snowmelt and summer seasons of 2008–2010. To link these patterns to flow processes, soil moisture was measured continuously on both transects and compared with the timing of discharge in nearby streams. Results indicate that both slopes generated shallow lateral subsurface flow during snowmelt through near‐surface soil, colluvium and bedrock fractures. On the northeast‐facing transect, this shallow subsurface flow emerged through mid‐slope seepage zones, in some cases producing saturation overland flow, whereas the east‐facing slope had no seepage zones or overland flow. At the hillslope scale, earlier snowmelt timing on the east‐facing slope led to drier average soil moisture conditions than on the northeast‐facing slope, but within hillslopes, snow patterns had little relation to soil moisture patterns except in areas with persistent snow drifts. Results suggest that lateral flow and exfiltration processes are key controls on soil moisture spatial patterns in this steep sub‐alpine location. Copyright © 2014 John Wiley & Sons, Ltd.

Yukimarimo, balls, rollers, strings, and swirls: Natural patterns in snow, and meteorological conditions for formations of “snow beauties”

Yukimarimo, balls, rollers, strings, and swirls: Natural patterns in snow, and meteorological conditions for formations of “snow beauties”

Impact of dust radiative forcing in snow on accuracy of operational runoff prediction in the Upper Colorado River Basin

Accurate prediction of snowmelt runoff is critical in the US Intermountain West, where water demand is increasing and snow patterns are shifting. Here, we show that errors in the National Weather Service Colorado Basin River Forecast Center's operational streamflow predictions are correlated with the interannual variability of dust radiative forcing in snow. With data from 2000–2010, we show that errors in snowmelt period streamflow prediction for the southern Colorado Rockies are linearly related to melt period dust radiative forcing in snow as inferred from NASA Moderate Resolution Imaging Spectroradiometer data, which ranged interannually from 20 to 80 W m−2. Each 10 W m−2 change of melt period dust forcing resulted in a corresponding change in runoff prediction bias of 10.0% ± 1.5% and a 1.5 ± 0.6 day shift in runoff center of mass. Accounting for bias introduced by dust forcing could improve streamflow prediction in regions prone to dust deposition in the snowpack.

Ecosystem Response to Climatic Change: The Importance of the Cold Season

Winter climate and snow cover are the important drivers of plant community development in polar regions. However, the impacts of changing winter climate and associated changes in snow regime have received much less attention than changes during summer. Here, we synthesize the results from studies on the impacts of extreme winter weather events on polar heathland and lichen communities. Dwarf shrubs, mosses and soil arthropods were negatively impacted by extreme warming events while lichens showed variable responses to changes in extreme winter weather events. Snow mould formation underneath the snow may contribute to spatial heterogeneity in plant growth, arthropod communities and carbon cycling. Winter snow cover and depth will drive the reported impacts of winter climate change and add to spatial patterns in vegetation heterogeneity. The challenges ahead lie in obtaining better predictions on the snow patterns across the landscape and how these will be altered due to winter climate change.