Changes In Snow Depth Research Articles

Effects of snow depth manipulation on the releases of carbon, nitrogen and phosphorus from the foliar litter of two temperate tree species

The effect of snow depth on litter decomposition in cold regions has attracted substantial attention, but the importance of snow depth to the releases of carbon (C), nitrogen (N), and phosphorus (P) and the underlying mechanisms remain unclear. The releases of C, N, and P from the foliar litter of Pinus koraiensis and Quercus mongolica in response to snow depth changes were examined for 12 months in a temperate forest of Northeast China via a snow depth manipulation experiment that included snow-addition (SA), snow-removal (SR), and control (CK) treatments. We found that the SA treatment promoted the releases of C, N, and P from the foliar litter during the snow-covered season but slowed these processes during the following snow-free season; however, the SR treatment produced the opposite results. Compared with the CK treatment, the SA treatment increased the annual releases of C, N, and P by 2.52%, 0.50%, and 4.68%, respectively, whereas the SR treatment decreased the corresponding values. The elemental release during the snow-covered season was associated with the freeze-thaw cycle (FTC) and microbial biomass, whereas that during the snow-free season was mainly controlled by the temperature of the litter layer. Our findings indicated that the snow depth promoted the releases of C, N and P from the foliar litter of the two tree species, especially during the snow-covered season. These results deepen the understanding of the biogeochemical cycling in cold regions under global climate change scenarios.

Changes in winter snow depth affects photosynthesis and physiological characteristics of biological soil crusts in the Tengger Desert

Water availability is a major limiting factor in desert ecosystems. However, a winter snowfall role in the growth of biological soil crusts is still less investigated. Here, four snow treatments were designed to evaluate the effects of snow depth on photosynthesis and physiological characteristics of biological soil crusts. Results showed that snow strongly affected the chlorophyll fluorescence properties. The increased snow depth led to increased contents of photosynthetic pigments and soluble proteins. However, all biological soil crusts also exhibited a decline in malondialdehyde and soluble sugar contents as snow increased. Results demonstrated that different biological soil crusts exhibited different responses to snow depth treatment due to differences in their morphological characteristics and microhabitat. In addition, interspecies differentiation in response to snow depth treatment might affect the survival of some biological soil crusts. Further, this influence might lead to changes in the structural composition and functional communities of biological soil crusts.

The effects of increased snow depth on plant and microbial biomass and community composition along a precipitation gradient in temperate steppes

Shift in precipitation regime could greatly alter plant and microbial activity, and thus the contemporary and future ecosystem dynamics in grasslands. We investigated how changes in snow depth affect plants, microbes and their relationships after 10 consecutive years of snow treatments in different steppes. We selected 8 snow fences along a mean annual precipitation (MAP) gradient from 225 to 375 mm in Inner Mongolia. For each snow fence, study plots were set up at 7 transects with different levels of snow depth. We found that ecosystem properties, including soil moisture, the biomass and nitrogen (N) pools of microbes and plants, the fungi: bacteria ratio and the grass: forb ratio, increased with increasing snow depth at the drier sites with lower MAP, but not at the wetter sites with higher MAP. At any given site, the sensitivity of these ecosystem properties to changes in snow depth was determined by the slopes of these variables against snow depth. The results showed that the sensitivity of these ecosystem properties to changes in snow depth decreased linearly with increase in MAP levels. In addition, we also found that increased snow depth shifted the relationship between microbial and plant biomass from positive to negative. Our work reveals the importance of snow water in regulating plant and microbial processes in temperate steppes, especially under lower MAP conditions. The greater plant and microbial biomass and the shift of community toward greater fungi: bacteria and grass: forb ratio imply that increased snowmelt input alleviated water limitation in temperate steppes and altered plant and microbial communities. Our study helps to better predict that how changes in winter precipitation could affect the biomass and composition of plants and soil microbes in grasslands.

The phenology of the subnivium

The subnivium is a seasonal refuge that exists at the interface between the snowpack and the ground, and provides a haven for a diversity of species to survive extreme winter temperatures. Due to the fitness of many plants and animals being strongly influenced by winter conditions, much attention has been given to changes in the timing of snow cover extent and duration in seasonally snow-covered environments; however, these broad-scale characteristics do not capture the finer-scale dynamics of the subnivium. To study the factors associated with subnivium development, we quantified three critical phenophases of the subnivium: establishment, maintenance, and disintegration along a latitudinal and land cover gradient in the Great Lakes Region of North America. We hypothesized that subnivium phenophases would depend primarily on snow depth and air temperature, but that these would be mediated by latitude and land cover. We found that patterns in both establishment and disintegration were affected by latitude more than land cover, but that variability in the timing of early season snowfall events overrode the effects of both factors in subnivium establishment. In contrast, disintegration was predictably later in more northerly sites, regardless of interannual variation in weather patterns. We found that the subnivium was the result of a balance between ambient temperature, snow depth, and snow density, but that ambient temperatures constrained the system by contributing to the frequency of snowfall and inducing changes in snow depth and density. Areas in lake effect zones, characterized by high snow depths and persistent snow cover, may be the last refugia for subnivia-dependent species given the predicted shifting climate regimes of the 21st century.

Wind Tunnel Experiments: Influence of Erosion and Deposition on Wind-Packing of New Snow

Wind sometimes creates a hard, wind-packed layer at the surface of a snowpack. The formation of such wind crusts was observed during wind tunnel experiments with combined SnowMicroPen and Microsoft Kinect sensors. The former provides the hardness of new and wind-packed snow and the latter spatial snow depth data in the test section. Previous experiments showed that saltation is necessary but not sufficient for wind-packing. The combination of hardness and snow depth data now allows to study the case with saltation in more detail. The Kinect data requires complex processing but with the appropriate corrections, snow depth changes can be measured with an accuracy of about 1 mm. The Kinect is therefore well suited to quantify erosion and deposition. We found that no hardening occurred during erosion and that a wind crust may or may not form when snow is deposited. Deposition is more efficient at hardening snow in wind-exposed than in wind-sheltered areas. The snow hardness increased more on the windward side of artificial obstacles placed in the wind tunnel. Similarly, the snow was harder in positions with a low Sx parameter. Sx describes how wind-sheltered (high Sx) or wind-exposed (low Sx) a position is and was calculated based on the Kinect data. The correlation between Sx and snow hardness was -0.63. We also found a negative correlation of -0.4 between the snow hardness and the deposition rate. Slowly deposited snow is harder than a rapidly growing accumulation. Sx and the deposition rate together explain about half of the observed variability of snow hardness.

Spatiotemporal variability of snow depth across the Eurasian continent from 1966 to 2012

Abstract. Snow depth is one of the key physical parameters for understanding land surface energy balance, soil thermal regime, water cycle, and assessing water resources from local community to regional industrial water supply. Previous studies by using in situ data are mostly site specific; data from satellite remote sensing may cover a large area or global scale, but uncertainties remain large. The primary objective of this study is to investigate spatial variability and temporal change in snow depth across the Eurasian continent. Data used include long-term (1966–2012) ground-based measurements from 1814 stations. Spatially, long-term (1971–2000) mean annual snow depths of >20 cm were recorded in northeastern European Russia, the Yenisei River basin, Kamchatka Peninsula, and Sakhalin. Annual mean and maximum snow depth increased by 0.2 and 0.6 cm decade−1 from 1966 through 2012. Seasonally, monthly mean snow depth decreased in autumn and increased in winter and spring over the study period. Regionally, snow depth significantly increased in areas north of 50° N. Compared with air temperature, snowfall had greater influence on snow depth during November through March across the former Soviet Union. This study provides a baseline for snow depth climatology and changes across the Eurasian continent, which would significantly help to better understanding climate system and climate changes on regional, hemispheric, or even global scales.

L-Band Temporal Coherence Assessment and Modeling Using Amplitude and Snow Depth over Interior Alaska

Interferometric synthetic aperture radar (InSAR) provides the capability to detect surface deformation. Numerous processing approaches have been developed to improve InSAR results and overcome its limitations. Regardless of the processing methodology, however, temporal decorrelation is a major obstacle for all InSAR applications, especially over vegetated areas and dynamic environments, such as Interior Alaska. Temporal coherence is usually modeled as a univariate exponential function of temporal baseline. It has been, however, documented that temporal variations in surface backscattering due to the change in surface parameters, i.e., dielectric constant, roughness, and the geometry of scatterers, can result in gradual, seasonal, or sudden decorrelations and loss of InSAR coherence. The coherence models introduced so far have largely neglected the effect of the temporal change in backscattering on InSAR coherence. Here, we introduce a new temporal decorrelation model that considers changes in surface backscattering by utilizing the relative change in SAR intensity between two images as a proxy for the change in surface scattering parameters. The model also takes into account the decorrelation due to the change in snow depth between two images. Using the L-band Advanced Land Observation Satellite (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) data, the model has been assessed over forested and shrub landscapes in Delta Junction, Interior Alaska. The model decreases the RMS error of temporal coherence estimation from 0.18 to 0.09 on average. The improvements made by the model have been statistically proved to be significant at the 99% confidence level. Additionally, the model shows that the coherence of forested areas are more prone to changes in backscattering than shrub landscape. The model is based on L-band data and may not be expanded to C-band or X-band InSAR observations.

Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models

In this study, we investigate the uncertainties associated with land surface processes in an ensemble predication context. Specifically, we compare the uncertainties produced by a coupled atmosphere–land modeling system with two different land surface models, the Noah- MP land surface model (LSM) and the Noah LSM, by using the Maximum Likelihood Ensemble Filter (MLEF) data assimilation system as a platform for ensemble prediction. We carried out 24-hour prediction simulations in Siberia with 32 ensemble members beginning at 00:00 UTC on 5 March 2013. We then compared the model prediction uncertainty of snow depth and solid precipitation with observation-based research products and evaluated the standard deviation of the ensemble spread. The prediction skill and ensemble spread exhibited high positive correlation for both LSMs, indicating a realistic uncertainty estimation. The inclusion of a multiple snowlayer model in the Noah-MP LSM was beneficial for reducing the uncertainties of snow depth and snow depth change compared to the Noah LSM, but the uncertainty in daily solid precipitation showed minimal difference between the two LSMs. The impact of LSM choice in reducing temperature uncertainty was limited to surface layers of the atmosphere. In summary, we found that the more sophisticated Noah-MP LSM reduces uncertainties associated with land surface processes compared to the Noah LSM. Thus, using prediction models with improved skill implies improved predictability and greater certainty of prediction.

MAPPING SNOW DEPTH WITH AUTOMATED TERRESTRIAL LASER SCANNING – INVESTIGATING POTENTIAL APPLICATIONS

Abstract. This contribution presents an automated terrestrial laser scanning (ATLS) setup, which was used during the winter 2016/17 to monitor the snow depth distribution on a NW-facing slope at a high-alpine study site. We collected data at high temporal [(sub-)daily] and spatial resolution (decimetre-range) over 0.8 km² with a Riegl LPM-321, set in a weather-proof glass fibre enclosure. Two potential ATLS-applications are investigated here: monitoring medium-sized snow avalanche events, and tracking snow depth change caused by snow drift. The results show the ATLS data’s high explanatory power and versatility for different snow research questions.

Spatio‐temporal variability of Great Lakes basin snow cover ablation events

AbstractIn the Great Lakes basin of North America, annual run‐off is dominated by snowmelt. This snowmelt‐induced run‐off plays an important role within the hydrologic cycle of the basin, influencing soil moisture availability and driving the seasonal cycle of spring and summer lake levels. Despite this, relatively little is understood about the patterns and trends of snow ablation event frequency and magnitude within the Great Lakes basin. This study uses a gridded dataset of Canadian and United States surface snow depth observations to develop a regional climatology of snow ablation events from 1960 to 2009. An ablation event is defined as an interdiurnal snow depth decrease within an individual grid cell. A clear seasonal cycle in ablation event frequency exists within the basin and peak ablation event probability is latitudinally dependent. Most of the basin experiences peak ablation frequency in March, while the northern and southern regions of the basin experience respective peaks in April and February. An investigation into the interannual frequency of ablation events reveals ablation events significantly decrease within the northeastern and northwestern Lake Superior drainage basins and significantly increase within the eastern Lake Huron and Georgian Bay drainage basins. In the eastern Lake Huron and Georgian Bay drainage basins, larger ablation events are occurring more frequently, and a larger impact to the hydrology can be expected. Trends in ablation events are attributed primarily to changes in snowfall and snow depth across the region.

Seasonal Crustal Seismic Velocity Changes Throughout Japan

AbstractNoise‐based crustal seismic velocity changes are known to be affected by environmental perturbations, such as rainfall, atmospheric pressure loading, and temperature changes. Similar to geodetic observations, these external perturbations can mask the effects of tectonic and volcanic processes. In this study, we benefit from the dense Hi‐net short‐period seismic network that covers the entire Japan to measure continuous changes in seismic velocities over a few years, using noise‐based seismic monitoring. Some strong seasonal seismic velocity changes are observed in both southern Japan (Kyushu Island) and northern Japan (Hokkaido Island). Decreasing of seismic velocities in summer in southern Japan can be clearly explained by a model of increased crustal fluid pore pressure associated with high rainfall. In northern Japan, it is necessary to adopt a more complex model to explain the observed seismic velocity variations, which takes into account precipitation, snow depth, and sea level changes. Moreover, western and eastern Hokkaido Island show very different responses to these different external perturbations. The models developed are used to remove the seasonal components of the seismic velocity changes. The minimum remaining detectable seismic velocity change reduces to 10−5, which allows detection of crustal responses to small earthquakes that are previously hidden in the strong seasonal perturbations.

Variability in snow depth time series in the Adige catchment

Study regionThe Upper and Middle Adige catchment, Trentino-South Tyrol, Italy. Study focusWe provide evidence of changes in mean seasonal snow depth and snow cover duration in the region occurred in the period from 1980 to 2009. New hydrological insights for the regionStations located above and below 1650 m a.s.l. show different dynamics, with the latter experiencing in the last decades a larger reduction of average snow depth and snow cover duration, than the former. Wavelet analyses show that snow dynamics change with elevation and correlate differently with climatic indices at multiple temporal scales. We also observe that starting from the late 1980s snow cover duration and mean seasonal snow depth are below the average in the study area. We also identify an elevation dependent correlation with the temperature. Moreover, correlation with the Mediterranean Oscillation Index and with the North Atlantic Oscillation Index is identified.

Observations of flooding and snow‐ice formation in a thinner Arctic sea‐ice regime during the N‐ICE2015 campaign: Influence of basal ice melt and storms

AbstractSeven ice mass balance instruments deployed near 83°N on different first‐year and second‐year ice floes, representing variable snow and ice conditions, documented the evolution of snow and ice conditions in the Arctic Ocean north of Svalbard in January–March 2015. Frequent profiles of temperature and thermal diffusivity proxy were recorded to distinguish changes in snow depth and ice thickness with 2 cm vertical resolution. Four instruments documented flooding and snow‐ice formation. Flooding was clearly detectable in the simultaneous changes in thermal diffusivity proxy, increased temperature, and heat propagation through the underlying ice. Slush then progressively transformed into snow‐ice. Flooding resulted from two different processes: (i) after storm‐induced breakup of snow‐loaded floes and (ii) after loss of buoyancy due to basal ice melt. In the case of breakup, when the ice was cold and not permeable, rapid flooding, probably due to lateral intrusion of seawater, led to slush and snow‐ice layers at the ocean freezing temperature (−1.88°C). After the storm, the instruments documented basal sea‐ice melt over warm Atlantic waters and ocean‐to‐ice heat flux peaked at up to 400 W m−2. The warm ice was then permeable and flooding was more gradual probably involving vertical intrusion of brines and led to colder slush and snow‐ice (−3°C). The N‐ICE2015 campaign provided the first documentation of significant flooding and snow‐ice formation in the Arctic ice pack as the slush partially refroze. Snow‐ice formation may become a more frequently observed process in a thinner ice Arctic.

Variations and changes in snow depth at meteorological stations Barentsburg and Hornsund (Spitsbergen)

ABSTRACTIn this study, seasonality and interannual variability of snow depth at two stations (Hornsund and Barentsburg) located in western Spitsbergen are investigated. For this purpose, the novel Moving Average over Shifting Horizon method combined with trend estimation was used. The Hornsund and Barentsburg stations exhibit similar snow depth trends during early autumn and late spring when statistically significant decreases were estimated at both stations (for August 1984–July 2016). In the remaining period, there are differences in outcomes between stations. The results for Barentsburg from October till the end of May are characterised by the lack of a trend while at Hornsund decreases of snow depth were estimated. The largest changes occur in May when the snow depth was at its maximum. Differences in the estimated tendencies were explained with the help of a trend analysis for air temperature and precipitation. An analysis of maximum snow depth, snow onset date, snow disappearance date and snow-cover duration is included. The results of the assessment depend on the location, with a lack of statistically significant changes in Barentsburg, and later snow onset date, shorter duration and decrease of maximum depth in Hornsund.

Response of seasonal soil freeze depth to climate change across China

Abstract. The response of seasonal soil freeze depth to climate change has repercussions for the surface energy and water balance, ecosystems, the carbon cycle, and soil nutrient exchange. Despite its importance, the response of soil freeze depth to climate change is largely unknown. This study employs the Stefan solution and observations from 845 meteorological stations to investigate the response of variations in soil freeze depth to climate change across China. Observations include daily air temperatures, daily soil temperatures at various depths, mean monthly gridded air temperatures, and the normalized difference vegetation index. Results show that soil freeze depth decreased significantly at a rate of −0.18 ± 0.03 cm yr−1, resulting in a net decrease of 8.05 ± 1.5 cm over 1967–2012 across China. On the regional scale, soil freeze depth decreases varied between 0.0 and 0.4 cm yr−1 in most parts of China during 1950–2009. By investigating potential climatic and environmental driving factors of soil freeze depth variability, we find that mean annual air temperature and ground surface temperature, air thawing index, ground surface thawing index, and vegetation growth are all negatively associated with soil freeze depth. Changes in snow depth are not correlated with soil freeze depth. Air and ground surface freezing indices are positively correlated with soil freeze depth. Comparing these potential driving factors of soil freeze depth, we find that freezing index and vegetation growth are more strongly correlated with soil freeze depth, while snow depth is not significant. We conclude that air temperature increases are responsible for the decrease in seasonal freeze depth. These results are important for understanding the soil freeze–thaw dynamics and the impacts of soil freeze depth on ecosystem and hydrological process.

Spatial–Temporal Variability of Snow Cover and Depth in the Qinghai–Tibetan Plateau

AbstractChanges in snow cover over the Qinghai–Tibetan Plateau have attracted much attention in recent years owing to climate change. Because of the limitations of in situ observations, only a few studies have analyzed the dynamics of snow cover. Using observations from 103 meteorological stations across the Qinghai–Tibetan Plateau, this study investigated the spatial and temporal variability of snow depth and the number of snow-cover days. The results show a very weak negative trend for the snow depth and the number of snow-cover days in spring and winter from 1961 to 2010, but two different trends were found: an initial increase followed by a decrease. In summer and autumn, snow depth and the number of snow-cover days show a significant decreasing trend for most sites. The duration of snow cover exhibits a significant decreasing trend (−3.5 ± 1.2 days decade−1), which was jointly controlled by a later snow starting time (1.6 ± 0.8 days decade−1) and an earlier snow ending time (−1.9 ± 0.8 days decade−1) consistent with a response to climate change. This study highlights the competing effects of rising temperatures and changing precipitation, which remain an important challenge in understanding and interpreting the observed changes in snow depth and the number of snow-cover days for the Qinghai–Tibetan Plateau.

The intraannual variability of land‐atmosphere coupling over North America in the Canadian Regional Climate Model (CRCM5)

AbstractThis study investigates the intraannual variability of soil moisture‐temperature coupling over North America. To this effect, coupled and uncoupled simulations are performed with the fifth‐generation Canadian Regional Climate Model (CRCM5), driven by ERA‐Interim. In coupled simulations, land and atmosphere interact freely; in uncoupled simulations, the interannual variability of soil moisture is suppressed by prescribing climatological values for soil liquid and frozen water contents. The study also explores projected changes to coupling by comparing coupled and uncoupled CRCM5 simulations for current (1981–2010) and future (2071–2100) periods, driven by the Canadian Earth System Model. Coupling differs for the northern and southern parts of North America. Over the southern half, it is persistent throughout the year while for the northern half, strongly coupled regions generally follow the freezing line during the cold months. Detailed analysis of the southern Canadian Prairies reveals seasonal differences in the underlying coupling mechanism. During spring and fall, as opposed to summer, the interactive soil moisture phase impacts the snow depth and surface albedo, which further impacts the surface energy budget and thus the surface air temperature; the air temperature then influences the snow depth in a feedback loop. Projected changes to coupling are also season specific: relatively drier soil conditions strengthen coupling during summer, while changes in soil moisture phase, snow depth, and cloud cover impact coupling during colder months. Furthermore, results demonstrate that soil moisture variability amplifies the frequency of temperature extremes over regions of strong coupling in current and future climates.

Investigating changes in Himalayan glacier in warming environment: a case study of Kolahoi glacier

There are strong evidences of melting in Himalayan glaciers at unprecedented rate in the past few decades. In the present study, the changes in Kolahoi glaciers in Himalaya are evaluated. Possible reasons behind these changes are also investigated. For this purpose, the changes in black carbon concentration, precipitation temperature and snow depth are investigated. A case study was carried out for the Kolahoi glacier situated at an altitude of 5245 m above sea level. The glacier area and snout change from 1857 to 2015 are investigated. It is reported in this study that the glacier has shrunk from 35 to 09.88 Sq Km. The rate of recession measured from 1922 to 2015 is reported to be 73.26 m per year. Furthermore, the rate of recession of snout is found to be 16.41 m per year from 1857 to 2015. The shirking of glacier area is linked to reduction in snow depth which in turn is affected by the increase in black carbon concentration, temperature and reduction in precipitation. Reanalysis data show that there is decrease of about 1.08 ± 0.65 cm per decade in snow depth over Kolahoi glacier during 1979 to 2013. There are decadal increasing trends of about 76 nanogram/m2 (statistically significant) and 0.39 °C (insignificant) in black carbon concentration and temperature, respectively, over Kolahoi. A decreasing trend of about 2.9 mm/month per decade in precipitation over the study area is also reported. Impact of increase in regional temperature on changes in snow depth is also quantified using inter-annual difference technique. It is reported that there is decrease of about 71 ± 24% in snow depth for each degree increase in temperature over Kolahoi. Reduction in snow depth as a result of increase in black carbon concentration, temperature and reduction in precipitation might have resulted in the shrinking of the Kolahoi glacier.

Accuracy of snow depth estimation in mountain and prairie environments by an unmanned aerial vehicle

Abstract. Quantifying the spatial distribution of snow is crucial to predict and assess its water resource potential and understand land–atmosphere interactions. High-resolution remote sensing of snow depth has been limited to terrestrial and airborne laser scanning and more recently with application of structure from motion (SfM) techniques to airborne (manned and unmanned) imagery. In this study, photography from a small unmanned aerial vehicle (UAV) was used to generate digital surface models (DSMs) and orthomosaics for snow cover at a cultivated agricultural Canadian prairie and a sparsely vegetated Rocky Mountain alpine ridgetop site using SfM. The accuracy and repeatability of this method to quantify snow depth, changes in depth and its spatial variability was assessed for different terrain types over time. Root mean square errors in snow depth estimation from differencing snow-covered and non-snow-covered DSMs were 8.8 cm for a short prairie grain stubble surface, 13.7 cm for a tall prairie grain stubble surface and 8.5 cm for an alpine mountain surface. This technique provided useful information on maximum snow accumulation and snow-covered area depletion at all sites, while temporal changes in snow depth could also be quantified at the alpine site due to the deeper snowpack and consequent higher signal-to-noise ratio. The application of SfM to UAV photographs returns meaningful information in areas with mean snow depth > 30 cm, but the direct observation of snow depth depletion of shallow snowpacks with this method is not feasible. Accuracy varied with surface characteristics, sunlight and wind speed during the flight, with the most consistent performance found for wind speeds < 10 m s−1, clear skies, high sun angles and surfaces with negligible vegetation cover.

Changes in snow distribution and surface topography following a snowstorm on Antarctic sea ice

AbstractSnow distribution over sea ice is an important control on sea ice physical and biological processes. We combine measurements of the atmospheric boundary layer and blowing snow on an Antarctic sea ice floe with terrestrial laser scanning to characterize a typical storm and its influence on the spatial patterns of snow distribution at resolutions of 1–10 cm over an area of 100 m × 100 m. The pre‐storm surface exhibits multidirectional elongated snow dunes formed behind aerodynamic obstacles. Newly deposited dunes are elongated parallel to the predominant wind direction during the storm. Snow erosion and deposition occur over 62% and 38% of the area, respectively. Snow deposition volume is more than twice that of erosion (351 m3versus 158 m3), resulting in a modest increase of 2 ± 1 cm in mean snow depth, indicating a small net mass gain despite large mass relocation. Despite significant local snow depth changes due to deposition and erosion, the statistical distributions of elevation and the two‐dimensional correlation functions remain similar to those of the pre‐storm surface. Pre‐storm and post‐storm surfaces also exhibit spectral power law relationships with little change in spectral exponents. These observations suggest that for sea ice floes with mature snow cover features under conditions similar to those observed in this study, spatial statistics and scaling properties of snow surface morphology may be relatively invariant. Such an observation, if confirmed for other ice types and conditions, may be a useful tool for model parameterizations of the subgrid variability of sea ice surfaces.