Snow Redistribution Research Articles

吹雪・吹きだまりを考慮した積雪再配分モデルの開発(II)

吹雪・吹きだまりを考慮した積雪再配分モデルの開発(II)

Recent variability of atmospheric circulation patterns inferred from East Antarctica glaciochemical records

In this study, by using ion chromatography, we analyzed the chemical composition of snow deposited in 1976–2012 at three sites within the Indian Ocean sector of East Antarctica (VK-55, SW-42, and PV-10) to establish the relationships between the formation of the chemical composition of snow and varying mechanisms of atmospheric circulation. Dating of samples was carried out on the basis of sulphate peaks related to the material derived from the Pinatubo volcanic eruption (1991). Chemical compositions of snow showed spatial inhomogeneity even over relatively short distances (of about 40 km) in central East Antarctica. The reasons for the inhomogeneity may have been related to local topography and snow redistribution by wind in areas with extremely low accumulation, as well as to peculiarities of atmospheric circulation processes. According to the principal component analysis (PCA) and 5-day backward trajectories, it was determined that for site PV-10 (400 km from the Indian Ocean coast), zonal circulation in the lower troposphere associated with the cyclonic centers of the Southern Ocean was a leading process while the inland sites (VK-55 and SW-42) were influenced by a combination of processes involving meridional circulation, air mass transport, and transformation in the troposphere over the Antarctic continent.

Testing and application of a model for snow redistribution (Snow_Blow) in the Ellsworth Mountains, Antarctica

AbstractWind-driven snow redistribution can increase the spatial heterogeneity of snow accumulation on ice caps and ice sheets, and may prove crucial for the initiation and survival of glaciers in areas of marginal glaciation. We present a snowdrift model (Snow_Blow), which extends and improves the model of Purves, Mackaness and Sugden (1999,Journal of Quaternary Science14, 313–321). The model calculates spatial variations in relative snow accumulation that result from variations in topography, using a digital elevation model (DEM) and wind direction as inputs. Improvements include snow redistribution using a flux routing algorithm, DEM resolution independence and the addition of a slope curvature component. This paper tests Snow_Blow in Antarctica (a modern environment) and reveals its potential for application in palaeoenvironmental settings, where input meteorological data are unavailable and difficult to estimate. Specifically, Snow_Blow is applied to the Ellsworth Mountains in West Antarctica where ablation is considered to be predominantly related to wind erosion processes. We find that Snow_Blow is able to replicate well the existing distribution of accumulating snow and snow erosion as recorded in and around Blue Ice Areas. Lastly, a variety of model parameters are tested, including depositional distance and erosion vs wind speed, to provide the most likely input parameters for palaeoenvironmental reconstructions.

Physical length scales of wind-blown snow redistribution and accumulation on relatively smooth Arctic first-year sea ice

Snow thickness measurements over relatively smooth Arctic first-year sea ice, obtained near Cambridge Bay in the Canadian Arctic (2014, 2016 and 2017) and near Elson Lagoon in the Alaskan Arctic (2003 and 2006), are analyzed to quantify physical length-scales and their relevant scaling behaviors. We use the multi-fractal temporally weighted detrended fluctuation analysis method to detect two major physical length-scales from the two independent study locations. Our results suggest that physical processes underlying the formation of snow dunes are consistent and that the wind is the main process shaping the snow thickness variability and redistribution. One scale, around 10 m, appears to be related to the formation of the snow ‘dunes’, while the other scale, between 30 and 100 m, is likely associated with the various interactions of the snow dunes such as merging, calving and lateral linking. Results imply that snow on level sea ice shows self-organized characteristics.

Reanalysis of the US Geological Survey Benchmark Glaciers: long-term insight into climate forcing of glacier mass balance

AbstractMountain glaciers integrate climate processes to provide an unmatched signal of regional climate forcing. However, extracting the climate signal via intercomparison of regional glacier mass-balance records can be problematic when methods for extrapolating and calibrating direct glaciological measurements are mixed or inconsistent. To address this problem, we reanalyzed and compared long-term mass-balance records from the US Geological Survey Benchmark Glaciers. These five glaciers span maritime and continental climate regimes of the western United States and Alaska. Each glacier exhibits cumulative mass loss since the mid-20th century, with average rates ranging from −0.58 to −0.30 m w.e. a−1. We produced a set of solutions using different extrapolation and calibration methods to inform uncertainty estimates, which range from 0.22 to 0.44 m w.e. a−1. Mass losses are primarily driven by increasing summer warming. Continentality exerts a stronger control on mass loss than latitude. Similar to elevation, topographic shading, snow redistribution and glacier surface features often exert important mass-balance controls. The reanalysis underscores the value of geodetic calibration to resolve mass-balance magnitude, as well as the irreplaceable value of direct measurements in contributing to the process-based understanding of glacier mass balance.

Comparison of snow accumulation events on two High-Arctic glaciers to model-derived and observed precipitation

We evaluate how precipitation forcing data used in glacier mass balance models characterize snow accumulation events on synoptic timescales for two glaciers in north-western Svalbard (Kongsvegen and Holtedahlfonna). Using sonic ranger (snow depth) and wind speed data from automatic weather stations located on the glaciers, we distinguish accumulation events occurring under either calm or windy conditions. We show clear differences in the timing and magnitude of snow accumulation events between the two neighbouring glaciers, illustrating the spatial heterogeneity of snow accumulation in this region. The accumulation measurements show that at equivalent elevations, Kongsvegen receives more snowfall than neighbouring Holtedahlfonna, and that Kongsvegen is more affected by wind-driven snow redistribution than Holtedahlfonna. This is consistent with the synoptically-driven precipitation patterns in the region. Accumulation events are then compared to precipitation data from the nearest meteorological station in Ny-Alesund (ca. 30 km distant) and to a downscaled snowfall data product based on the ERA-Interim reanalysis (nearest gridpoint ca. 300 m distant). Evaluation of the synchrony of observed events at the glacier sites and the precipitation products shows that the ERA-Interim precipitation data reproduce more snowfall events than the Ny-Alesund station data, suggesting that the precipitation fields from distributed reanalysis data provide a more reasonable representation of accumulation on the study glaciers, even over short timescales.

Decoupled kinematics of two neighbouring permafrost creeping landforms in the Eastern Italian Alps

AbstractAn overall acceleration of rock glacier displacement rates in the Alps has been observed in recent decades, with several cases of destabilization leading to potential geomorphological hazards. This behaviour has been attributed to the rising permafrost temperature, induced by atmospheric warming and regulated by thermo‐hydrological processes. Landforms derived from the interaction of glacier remnants and permafrost are widespread in mountain areas, but are less studied and monitored than talus rock glaciers. This work presents a comparative study of a talus rock glacier and a glacial‐permafrost composite landform (GPCL) in the Eastern Italian Alps. The two landforms are only 10 km apart, but have rather different elevation ranges and main slope aspects. The kinematics and ground thermal conditions were monitored from 2001 to 2015 along with geomorphological surveys, analyses of historical maps and remote sensing data. The dynamic behaviour of the rock glacier was similar to the majority of monitored rock glaciers in the Alps, with an acceleration after 2008 and a velocity peak in 2015. In contrast, the GPCL had a nearly unchanged displacement rate during the observation period. Statistical analyses of kinematic vs. nivo‐meteorological variables revealed a dynamic decoupling of the two landforms after 2008 that corresponds with increased winter snow accumulation. Although the kinematics of both landforms respond to ground surface temperature variations, the collected evidence suggests a different reaction of ground surface temperature to variations in the precipitation regime. This different reaction is likely due to local topo‐climatic conditions that affect snow redistribution by wind. The different reactions of the two systems to the same climatic forcing is likely a legacy of their different origins. GPCL dynamics result from interaction of permafrost and residual glacial dynamics that are associated with possible peculiarities in the internal/basal meltwater circulation, whose future response is uncertain and requires improved understanding. © 2019 John Wiley & Sons, Ltd. © 2019 John Wiley & Sons, Ltd.

Modeling Blowing Snow Over the Tibetan Plateau With the Community Land Model: Method and Preliminary Evaluation

AbstractSnow cover in mountainous terrain plays an important role in regional and global water and energy balances, climate change, and ecosystems. Blowing snow is a frequent and important weather phenomenon over the Tibetan Plateau (TP); however, this process is neglected in most current land surface models, despite the consequential role it plays in the land surface and atmospheric water and energy budgets. In this paper, we present a blowing snow model PIEKTUK coupled with the Community Land Model (CLM4.5) to provide a better estimate of the snow dynamics for the consideration of snow redistribution induced by wind. Two simulations with a 0.065° spatial resolution were performed in 2010 over the TP, namely, a sensitivity experiment with the inclusion of blowing snow effects (CLM_BS) and a control run with the original model (CLM). A specific objective of this study was to evaluate the improvements in the simulations of snow dynamics and other key variables in surface energy partitioning provided by the coupled model, such as the surface albedo and land surface temperature (LST). Compared with a variety of remote‐sensing observations, the results show that the surface snow cover, snow depth, and surface albedo can be better reproduced in most of the TP region by CLM_BS than by the original CLM, particularly in the Kunlun Mountains, Hoh Xil area, and the southwestern TP. In areas with reduced bias, variations in the monthly mean snow cover fraction can be reflected particularly well by CLM_BS. For LST, however, a significant decrease in the nighttime LST bias was detected in CLM_BS, while the bias in the daytime LST increases. The results show considerable potential for the inclusion of the blowing snow process to promote the modeling of snow dynamics and land‐atmosphere interactions on the TP.

Characterising spatio-temporal variability in seasonal snow cover at a regional scale from MODIS data: the Clutha Catchment, New Zealand

Abstract. A 16-year series of daily snow-covered area (SCA) for 2000–2016 is derived from MODIS imagery to produce a regional-scale snow cover climatology for New Zealand's largest catchment, the Clutha Catchment. Filling a geographic gap in observations of seasonal snow, this record provides a basis for understanding spatio-temporal variability in seasonal snow cover and, combined with climatic data, provides insight into controls on variability. Seasonal snow cover metrics including daily SCA, mean snow cover duration (SCD), annual SCD anomaly and daily snowline elevation (SLE) were derived and assessed for temporal trends. Modes of spatial variability were characterised, whilst also preserving temporal signals by applying raster principal component analysis (rPCA) to maps of annual SCD anomaly. Sensitivity of SCD to temperature and precipitation variability was assessed in a semi-distributed way for mountain ranges across the catchment. The influence of anomalous winter air flow, as characterised by HYSPLIT back-trajectories, on SCD variability was also assessed. On average, SCA peaks in late June, at around 30 % of the catchment area, with 10 % of the catchment area sustaining snow cover for > 120 d yr−1. A persistent mid-winter reduction in SCA, prior to a second peak in August, is attributed to the prevalence of winter blocking highs in the New Zealand region. In contrast to other regions globally, no significant decrease in SCD was observed, but substantial spatial and temporal variability was present. rPCA identified six distinct modes of spatial variability, characterising 77 % of the observed variability in SCD. This analysis of SCD anomalies revealed strong spatio-temporal variability beyond that associated with topographic controls, which can result in snow cover conditions being out of phase across the catchment. Furthermore, it is demonstrated that the sensitivity of SCD to temperature and precipitation variability varies significantly across the catchment. While two large-scale climate modes, the SOI and SAM, fail to explain observed variability, specific spatial modes of SCD are favoured by anomalous airflow from the NE, E and SE. These findings illustrate the complexity of atmospheric controls on SCD within the catchment and support the need to incorporate atmospheric processes that govern variability of the energy balance, as well as the re-distribution of snow by wind in order to improve the modelling of future changes in seasonal snow.

Ice processes on medium‐sized north‐temperate lakes

AbstractLake ice supports a range of socio‐economic and cultural activities including transportation and winter recreational actives. The influence of weather patterns on ice‐cover dynamics of temperate lakes requires further understanding for determining how changes in ice composition will impact ice safety and the range of ecosystem services provided by seasonal ice cover. An investigation of lake ice formation and decay for three lakes in Central Ontario, Canada, took place over the course of two winters, 2015–2016 and 2016–2017, through the use of outdoor digital cameras, a Shallow Water Ice Profiler (upward‐looking sonar), and weekly field measurements. Temperature fluctuations across 0°C promoted substantial early season white ice growth, with lesser amounts of black ice forming later in the season. Ice thickening processes observed were mainly through meltwater, or midwinter rain, refreezing on the ice surface. Snow redistribution was limited, with frequent melt events limiting the duration of fresh snow on the ice, leading to a fairly uniform distribution of white ice across the lakes in 2015–2016 (standard deviations week to week ranging from 3 to 5 cm), but with slightly more variability in 2016–2017 when more snow accumulated over the season (5 to 11 cm). White ice dominated the end‐of‐season ice composition for both seasons representing more than 70% of the total ice thickness, which is a stark contrast to Arctic lake ice that is composed mainly of black ice. This research has provided the first detailed lake ice processes and conditions from medium‐sized north‐temperate lakes and provided important information on temperate region lake ice characteristics that will enhance the understanding of the response of temperate lake ice to climate and provide insight on potential changes to more northern ice regimes under continued climate warming.

Modeling snowdrift on roofs using Immersed Boundary Method and wind tunnel test

Snow drifts on roofs grow with time under wind blowing during snowfalls. Computational Fluid Dynamics (CFD) is recently developed to predict this process. However, generation of body-fitted CFD grids remains challenging nowadays because of the time-changing snowdrift boundaries. In this paper, a Single-Phase Steady-State (SPSS) CFD model augmented with an Immersed Boundary Method (IBM) was proposed to simulate the snow drifts development on large roofs. Flow field of wind was described under the Eulerian framework and modeled with Reynolds-averaged Navier–Stokes (RANS) equations. The movement of snow particles in the air was modeled using the Eulerian method. The snowfall process was divided into n steady stages, and the snow boundary of each stage was updated by moving the coordinates of Immersed Boundary (IB) points. This method can adapt to complex and changeable snow boundary without re-meshing. The friction velocity over snow surface can be calculated accurately using the modified model of wall shear stress proposed in this paper. Numerical simulation results were verified by field observation and wind tunnel test of snow distribution on stepped flat roof models. The method of conducting snow redistribution test on flat roof models in wind tunnel is difficult to be applied on curved roofs. Therefore, a wind tunnel test of snowfall simulation on spherical roofs was conducted by spraying high-density silica sand particles. Good agreement between numerical simulations and experimental results was achieved and the applicability of this method to curved roofs was verified. The effects of different threshold friction velocities and test durations on the snow distribution on spherical shells were also analyzed.

Transient Modelling of Permafrost Distribution in Iceland

Warming and degradation of permafrost during the ongoing climate change is of growing concern. Recently, permafrost thawing has been recognized as a new factor triggering landslides in Iceland. Therefore, there is an increased need for a more thorough understanding of permafrost distribution and the temporal evolution of the ground thermal regime in this region. This study focuses on regional modelling of ground temperature evolution in Iceland for the last six decades (1960–2016) by employing the transient permafrost model CryoGrid 2 at 1-km spatial resolution. To account for the strong wind redistribution of snow in Iceland, we ran three realizations of the model, by forcing the embodied snow water equivalent model with 50 %, 100 % and 150 % of gridded precipitation. The modelled permafrost extent strongly depends on snow depth, with around three to fifteen times more cells indicating permafrost in the halved-precipitation run in comparison to the other two precipitation runs. A three- to four-decade-long warming trend has led to warming or degradation of permafrost in some areas of Iceland. We roughly estimate that ~11 % and ~7 % of the land area of Iceland was underlain by permafrost during the periods 1980–1989 and 2010–2016, respectively. Model validation with ground temperature measurements and the distribution of permafrost-related landforms, such as active rock glaciers and stable ice-cored moraines, together with palsas and peat plateaus, shows good agreement. The simulation results may be further used as a baseline for modelling of future permafrost evolution at a regional scale or for identification of landslide-susceptible areas in Iceland.

Modeling Subgrid Variability of Snow Depth Using the Fokker‐Planck Equation Approach

AbstractA physically based subgrid variability model for snow process using the Fokker‐Planck equation (FPE) approach was proposed. This FPE can express the evolution of the probability density function (PDF) of snow depth within a finite area, possibly a grid cell of distributed models or a small basin, whose shape can be irregular. The main advantage of this approach is that it does not rely on a given PDF but dynamically computes the PDF through an advection‐diffusion‐type equation, the FPE, which was derived from point‐scale process‐based governing equations. Snow depth was treated as a random variable, while the snow redistribution and snowmelt rate were treated as the sources of stochasticity. The main challenge in solving this FPE is evaluating the time‐space covariances appearing in the diffusion coefficient. In this study, approximations to evaluate the covariance terms, accounting for snowmelt and snow redistribution, were proposed. The simulated results of the FPE model were validated by the measured time series of snow depth at one site and the spatial distributions of snow depth measured by ground penetrating radar and airborne light detection and ranging (Lidar). It was shown that the point‐observed snow depth fell within the simulated range during most of the 2‐year study period. The simulated PDFs of snow depth within the study area were similar to the observed PDFs of snow depth by ground penetrating radar and Lidar. In summary, these results demonstrate the efficacy of the proposed FPE model representing the subgrid variability of snow depth.

An investigation of snow drifting on flat roofs: Wind tunnel tests and numerical simulations

In snowy areas, the accurate prediction of snow distributions on roofs plays an important role in designing structures. The process of snow accumulations on roofs is complicated due to the action of wind, which is related to the wind field conditions (e.g., wind velocity, turbulence intensity, and wind direction), the geometry of roofs, temperature, humidity, and the property of the snow. To systematically study the effect of the length-height ratio of the flat roof and approach flow velocity on the uneven accumulation of snow loads on 3D flat roofs, wind tunnel tests of snow drifting are carried out using silica sand to simulate snow particles. Four models are designed with length-height ratios of 1, 2, 3, and 4, and then four nominal wind velocities are taken into consideration, namely 5.0 m/s, 5.5 m/s, 6.0 m/s, and 6.5 m/s. Two newly added parameters, the snow residual rate η and the erosion rate λ, are set up to illustrate the snow redistribution on 3D flat roofs. Then the effects of wind velocity or roof length on the average transport rate and the mass flux of flat roofs are systematically analyzed. Furthermore, the flow characteristics around the flat roof are obtained by means of numerical simulation, and the relationships between the flow characteristics (vorticity magnitude and friction velocity) near the flat roof and the snow redistribution pattern are carefully discussed. The snow redistribution on flat roofs is obviously uneven due to erosion mainly on both sides where the vorticity magnitude and friction velocity are also larger than in other places. The quantitative analysis indicates that the transport rate of snowdrift on the whole flat roof is proportional to the 4.45 power of the approaching wind velocity, which is slightly larger than the 3.8 derived from the empirical formula from field measurements.

Understanding subgrid variability of snow depth at 1‐km scale using Lidar measurements

AbstractIt is well known that snow plays an important role in land surface energy balance; however, modelling the subgrid variability of snow is still a challenge in large‐scale hydrological and land surface models. High‐resolution snow depth data and statistical methods can reveal some characteristics of the subgrid variability of snow depth, which can be useful in developing models for representing such subgrid variability. In this study, snow depth was measured by airborne Lidar at 0.5‐m resolution over two mountainous areas in south‐western Wyoming, Snowy Range and Laramie Range. To characterize subgrid snow depth spatial distribution, measured snow depth data of these two areas were meshed into 284 grids of 1‐km × 1‐km. Also, nine representative grids of 1‐km × 1‐km were selected for detailed analyses on the geostatistical structure and probability density function of snow depth. It was verified that land cover is one of the important factors controlling spatial variability of snow depth at the 1‐km scale. Probability density functions of snow depth tend to be Gaussian distributions in the forest areas. However, they are eventually skewed as non‐Gaussian distribution, largely due to the no‐snow areas effect, mainly caused by snow redistribution and snow melt. Our findings show the characteristics of subgrid variability of snow depth and clarify the potential factors that need to be considered in modelling subgrid variability of snow depth.

Thaw processes in ice-rich permafrost landscapes represented with laterally coupled tiles in a land surface model

Abstract. Earth system models (ESMs) are our primary tool for projecting future climate change, but their ability to represent small-scale land surface processes is currently limited. This is especially true for permafrost landscapes in which melting of excess ground ice and subsequent subsidence affect lateral processes which can substantially alter soil conditions and fluxes of heat, water, and carbon to the atmosphere. Here we demonstrate that dynamically changing microtopography and related lateral fluxes of snow, water, and heat can be represented through a tiling approach suitable for implementation in large-scale models, and we investigate which of these lateral processes are important to reproduce observed landscape evolution. Combining existing methods for representing excess ground ice, snow redistribution, and lateral water and energy fluxes in two coupled tiles, we show that the model approach can simulate observed degradation processes in two very different permafrost landscapes. We are able to simulate the transition from low-centered to high-centered polygons, when applied to polygonal tundra in the cold, continuous permafrost zone, which results in (i) a more realistic representation of soil conditions through drying of elevated features and wetting of lowered features with related changes in energy fluxes, (ii) up to 2 ∘C reduced average permafrost temperatures in the current (2000–2009) climate, (iii) delayed permafrost degradation in the future RCP4.5 scenario by several decades, and (iv) more rapid degradation through snow and soil water feedback mechanisms once subsidence starts. Applied to peat plateaus in the sporadic permafrost zone, the same two-tile system can represent an elevated peat plateau underlain by permafrost in a surrounding permafrost-free fen and its degradation in the future following a moderate warming scenario. These results demonstrate the importance of representing lateral fluxes to realistically simulate both the current permafrost state and its degradation trajectories as the climate continues to warm. Implementing laterally coupled tiles in ESMs could improve the representation of a range of permafrost processes, which is likely to impact the simulated magnitude and timing of the permafrost–carbon feedback.

Impact of Future Climate and Vegetation on the Hydrology of an Arctic Headwater Basin at the Tundra–Taiga Transition

Abstract The rapidly warming Arctic is experiencing permafrost degradation and shrub expansion. Future climate projections show a clear increase in mean annual temperature and increasing precipitation in the Arctic; however, the impact of these changes on hydrological cycling in Arctic headwater basins is poorly understood. This study investigates the impact of climate change, as represented by simulations using a high-resolution atmospheric model under a pseudo-global-warming configuration, and projected changes in vegetation, using a spatially distributed and physically based Arctic hydrological model, on a small headwater basin at the tundra–taiga transition in northwestern Canada. Climate projections under the RCP8.5 emission scenario show a 6.1°C warming, a 38% increase in annual precipitation, and a 19 W m−2 increase in all-wave annual irradiance over the twenty-first century. Hydrological modeling results suggest a shift in hydrological processes with maximum peak snow accumulation increasing by 70%, snow-cover duration shortening by 26 days, active layer deepening by 0.25 m, evapotranspiration increasing by 18%, and sublimation decreasing by 9%. This results in an intensification of the hydrological regime by doubling discharge volume, a 130% increase in spring runoff, and earlier and larger peak streamflow. Most hydrological changes were found to be driven by climate change; however, increasing vegetation cover and density reduced blowing snow redistribution and sublimation, and increased evaporation from intercepted rainfall. This study provides the first detailed investigation of projected changes in climate and vegetation on the hydrology of an Arctic headwater basin, and so it is expected to help inform larger-scale climate impact studies in the Arctic.

CFD simulation of snow redistribution on gable roofs: Impact of roof slope

A series of 2D steady RANS CFD simulations are conducted in this study to investigate the impact of roof slope on the snow redistribution on gable roofs. A small value of turbulent Schmidt number, which is calibrated by trapping efficiency from experiments, is adopted to enhance the snow particles’ turbulent diffusion in the CFD simulations for snow phase. And a scaled model test is conducted in the wind tunnel to validate the accuracy of numerical simulation, in which the silica sand was used to model snow particles. To uncover the mechanism of wind effects on the snow redistribution on gable roofs, the simulation results of wind-flow patterns, wall friction velocity of the roof surface, snow concentration and snow distributions are analyzed in detail for various roof slopes (5°–60°). Based on the features of wind-flow patterns and distribution patterns of snow, the gable roofs are classified into two categories: low slope where flow separates strongly at the leading edge and high slope where flow separates strongly at the roof ridge. The critical roof slope is between 20° and 25°.

Near‐Surface Environmentally Forced Changes in the Ross Ice Shelf Observed With Ambient Seismic Noise

AbstractContinuous seismic observations across the Ross Ice Shelf reveal ubiquitous ambient resonances at frequencies >5 Hz. These firn‐trapped surface wave signals arise through wind and snow bedform interactions coupled with very low velocity structures. Progressive and long‐term spectral changes are associated with surface snow redistribution by wind and with a January 2016 regional melt event. Modeling demonstrates high spectral sensitivity to near‐surface (top several meters) elastic parameters. We propose that spectral peak changes arise from surface snow redistribution in wind events and to velocity drops reflecting snow lattice weakening near 0°C for the melt event. Percolation‐related refrozen layers and layer thinning may also contribute to long‐term spectral changes after the melt event. Single‐station observations are inverted for elastic structure for multiple stations across the ice shelf. High‐frequency ambient noise seismology presents opportunities for continuous assessment of near‐surface ice shelf or other firn environments.

Snow Depth Structure, Fractal Behavior, and Interannual Consistency Over Haut Glacier d'Arolla, Switzerland

AbstractSnow depth patterns over glaciers are controlled by precipitation, snow redistribution due to wind and avalanches, and the exchange of energy with the atmosphere that determines snow ablation. While many studies have advanced the understanding of ablation processes, less is known about winter snow patterns and their variability over glaciers. We analyze snow depth on Haut Glacier d'Arolla, Switzerland, in the two winter seasons 2006–2007 and 2010–2011 to (1) understand whether snow depth over an alpine glacier at the end of the accumulation season exhibits a behavior similar to the one observed on single slopes and vegetated areas; and (2) investigate the snow pattern consistency over the two accumulation seasons. We perform this analysis on a data set of high‐resolution lidar‐derived snow depth using variograms and fractal parameters. Our first main result is that snow depth patterns on the glacier exhibit a multiscale behavior, with a scale break around 20 m after which the fractal dimension increases, indicating more autocorrelated structure before the scale break than after. Second, this behavior is consistent over the two years, with fractal parameters and their spatial variability almost constant in the two seasons. We also show that snow depth patterns exhibit a distinct behavior in the glacier tongue and the upper catchment, with longer correlation distances on the tongue in the direction of the main winds, suggesting spatial distinctions that are likely induced by different processes and that should be taken into account when extrapolating snow depth from limited samples.