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The snow data from the Soviet airborne expeditions Sever in the Arctic collected over several decades in March, April and May have been analyzed in this study. The Sever data included more measurements and covered a much wider area, particularly in the Eurasian marginal seas (Kara Sea, Laptev Sea, East Siberian Sea and Chukchi Sea), compared to the Soviet North Pole drifting stations. The latter collected data mainly in the central part of the Arctic Basin. The following snow parameters have been analyzed: average snow depth on the level ice (undisturbed snow) height and area of sastrugi, depth of snow dunes attached to ice ridges and depth of snow on hummocks. In the 1970s-1980s, in the central Arctic, the average depth of undisturbed snow was 21.2 cm, the depth of sastrugi (that occupied about 30% of the ice surface) was 36.2 cm and the average depth of snow near hummocks and ridges was about 65 cm. For the marginal seas, the average depth of undisturbed snow on the level ice varied from 9.8 cm in the Laptev Sea to 15.3 cm in the East Siberian Sea, which had a larger fraction of multiyear ice. In the marginal seas the spatial variability of snow depth was characterized by standard deviation varying between 66 and 100 %. The average height of sastrugi varied from 23 cm to about 32 cm with standard deviation between 50 and 56 %. The average area covered by sastrugi in the marginal seas was estimated to be 36.5% of the total ice area where sastrugi were observed. The main result of the study is a new snow depth climatology for the late winter using data from both the Sever expeditions and the North Pole drifting stations. The snow load on the ice observed by Sever expeditions has been described as a combination of the depth of undisturbed snow on the level ice and snow depth of sastrugi weighted in proportion to the sastrugi area. The height of snow accumulated near the ice ridges was not included in the calculations because there are no estimates of the area covered by those features from the Sever expeditions. The effect of not including that data can lead to some underestimation of the average snow depth. The new climatology refines the description of snow depth in the central Arctic compared to the results by Warren et al. (1999) and provides additional detailed data in the marginal seas. The snow depth climatology is based on 94% Sever data and 6% North Pole data. The new climatology shows lower snow depth in the central Arctic comparing to Warren climatology and more detailed data in the Eurasian seas. [ABSTRACT FROM AUTHOR]

Cold glaciers at the highest locations of the European Alps have been investigated by drilling ice cores to retrieve their stratigraphic climate records. Findings like the Oetztal ice man have demonstrated that small ice bodies at summit locations of comparatively lower altitudes may also contain old ice if locally frozen to the underlying bedrock. In this case, constraining the maximum age of their lower-most ice part may help to identify past periods with minimum ice extent in the Alps. However, with recent warming and consequent glacier mass loss, these sites may not preserve their unique climate information for much longer. Here we utilized an existing ice cave at Chli Titlis (3030 m), central Switzerland, to perform a case study for investigating the maximum age of cold-based summit glaciers in the Alps. The cave offers direct access to the glacier stratigraphy without the logistical effort required in ice core drilling. In addition, a pioneering exploration had already demonstrated stagnant cold ice conditions at Chli Titlis, albeit more than 25 years ago. Our englacial temperature measurements and the analysis of the isotopic and physical properties of ice blocks sampled at three locations within the ice cave show that cold ice still exists fairly unchanged today. State-of-the-art micro-radiocarbon analysis constrains the maximum age of the ice at Chli Titlis to about 5000 years before present. By this means, the approach presented here will contribute to a future systematic investigation of cold-based summit glaciers, also in the Eastern Alps. [ABSTRACT FROM AUTHOR]

Summer storage of snow for tourism has seen an increasing interest in the last years. Covering large snow piles with materials such as sawdust enables more than two-thirds of the initial snow volume to be conserved. We present detailed mass balance measurements of two sawdust-covered snow piles obtained by terrestrial laser scanning during summer 2015. Results indicate that 74 and 63% of the snow volume remained over the summer for piles in Davos, Switzerland and Martell, Italy. If snow mass is considered instead of volume, the values increase to 83 and 72 %. The difference is attributed to settling and densification of the snow. Additionally, we adapted the one-dimensional, physically based snow cover model SNOWPACK to perform simulations of the sawdust-covered snow piles. Model results and measurements agreed extremely well at the point scale. Moreover, we analysed the contribution of the different terms of the surface energy balance to snow ablation for a pile covered with a 40 cm thick sawdust layer and a pile without insulation. Short-wave radiation was the dominant source of energy for both scenarios, but the moist sawdust caused strong cooling by long-wave emission and negative sensible and latent heat fluxes. This cooling effect reduces the energy available for melt by up to a factor of 12. As a result only 9% of the net short-wave energy remained available for melt. Finally, sensitivity studies of the parameters "thickness of the sawdust layer", "air temperature", "precipitation" and "wind speed" were performed. We show that sawdust thickness has a tremendous effect on snow loss. Higher air temperatures and wind speeds increase snow ablation but less significantly. No significant effect of additional precipitation could be found as the sawdust remained wet during the entire summer with the measured quantity of rain. Setting precipitation amounts to zero, however, strongly increased melt. Overall, the 40 cm sawdust provides sufficient protection for mid-elevation (approx. 1500ma.s.l.) Alpine climates and can be managed with reasonable effort. [ABSTRACT FROM AUTHOR]

For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81% in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean. [ABSTRACT FROM AUTHOR]

Observations at the West Antarctic Ice Sheet (WAIS) Divide site show that near-surface snow is strongly altered by weather-related processes such as strong winds and temperature fluctuations, producing features that are recognizable in the deep ice core. Prominent "glazed" surface crusts develop frequently at the site during summer seasons. Surface, snow pit, and ice core observations made in this study during summer field seasons from 2008-2009 to 2012-2013, supplemented by automated weather station (AWS) data with short- and longwave radiation sensors, revealed that such crusts formed during relatively low-wind, low-humidity, clear-sky periods with intense daytime sunshine. After formation, such glazed surfaces typically developed cracks in a polygonal pattern likely from thermal contraction at night. Cracking was commonest when several clear days occurred in succession and was generally followed by surface hoar growth; vapor escaping through the cracks during sunny days may have contributed to the high humidity that favored nighttime formation of surface hoar. Temperature and radiation observations show that daytime solar heating often warmed the near-surface snow above the air temperature, contributing to upward mass transfer, favoring crust formation from below, and then surface hoar formation. A simple surface energy calculation supports this observation. Subsequent examination of the WDC06A deep ice core revealed that crusts are preserved through the bubbly ice, and some occur in snow accumulated during winters, although not as commonly as in summertime deposits. Although no one has been on site to observe crust formation during winter, it may be favored by greater wintertime wind packing from stronger peak winds, high temperatures and steep temperature gradients from rapid midwinter warmings reaching as high as -15 °C, and perhaps longer intervals of surface stability. Time variations in crust occurrence in the core may provide paleoclimatic information, although additional studies are required. Discontinuity and cracking of crusts likely explain why crusts do not produce significant anomalies in other paleoclimatic records. [ABSTRACT FROM AUTHOR]

Two hundred marine-terminating Greenland outlet glaciers deliver more than half of the annually accumulated ice into the ocean and have played an important role in the Greenland ice sheet mass loss observed since the mid-1990s. Submarine melt may play a crucial role in the mass balance and position of the grounding line of these outlet glaciers. As the ocean warms, it is expected that submarine melt will increase, potentially driving outlet glaciers retreat and contributing to sea level rise. Projections of the future contribution of outlet glaciers to sea level rise are hampered by the necessity to use models with extremely high resolution of the order of a few hundred meters. That requirement in not only demanded when modeling outlet glaciers as a stand alone model but also when coupling them with high-resolution 3-D ocean models. In addition, fjord bathymetry data are mostly missing or inaccurate (errors of several hundreds of meters), which questions the benefit of using computationally expensive 3-D models for future predictions. Here we propose an alternative approach built on the use of a computationally efficient simple model of submarine melt based on turbulent plume theory. We show that such a simple model is in reasonable agreement with several available modeling studies. We performed a suite of experiments to analyze sensitivity of these simple models to model parameters and climate characteristics. We found that the computationally cheap plume model demonstrates qualitatively similar behavior as 3-D general circulation models. To match results of the 3-D models in a quantitative manner, a scaling factor of the order of 1 is needed for the plume models. We applied this approach to model submarine melt for six representative Greenland glaciers and found that the application of a line plume can produce submarine melt compatible with observational data. Our results show that the line plume model is more appropriate than the cone plume model for simulating the average submarine melting of real glaciers in Greenland. [ABSTRACT FROM AUTHOR]

In many mountainous regions around the world, snow and soil moisture are key components of the hydrologic cycle. Preferential flow paths of snowmelt water through snow have been known to occur for years with few studies observing the effect on soil moisture. In this study, statistical analysis of the topographical and hydrological controls on the spatiotemporal variability of snow water equivalent (SWE) and soil moisture during snowmelt was undertaken at a subalpine forested setting with north, south, and flat aspects as a seasonally persistent snowpack melts. We investigated if evidence of preferential flow paths in snow can be observed and the effect on soil moisture through measurements of snow water equivalent and near-surface soil moisture, observing how SWE and near-surface soil moisture vary on hillslopes relative to the toes of hillslopes and flat areas. We then compared snowmelt infiltration beyond the near-surface soil between flat and sloping terrain during the entire snowmelt season using soil moisture sensor profiles. This study was conducted during varying snowmelt seasons representing above-normal, relatively normal, and below-normal snow seasons in northern Colorado. Evidence is presented of preferential meltwater flow paths at the snow-soil interface on the north-facing slope causing increases in SWE downslope and less infiltration into the soil at 20 cm depth; less association is observed in the near-surface soil moisture (top 7 cm). We present a conceptualization of the meltwater flow paths that develop based on slope aspect and soil properties. The resulting flow paths are shown to divert at least 4% of snowmelt laterally, accumulating along the length of the slope, to increase the snow water equivalent by as much as 170% at the base of a north-facing hillslope. Results from this study show that snow acts as an extension of the vadose zone during spring snowmelt and future hydrologic investigations will benefit from studying the snow and soil together. [ABSTRACT FROM AUTHOR]

With its high albedo, low thermal conductivity and large water storing capacity, snow strongly modulates the surface energy and water balance, which makes it a critical factor in mid- to high-latitude and mountain environments. However, estimating the snow water equivalent (SWE) is challenging in remote-sensing applications already at medium spatial resolutions of 1km. We present an ensemble-based data assimilation framework that estimates the peak subgrid SWE distribution (SSD) at the 1km scale by assimilating fractional snow-covered area (fSCA) satellite retrievals in a simple snow model forced by downscaled reanalysis data. The basic idea is to relate the timing of the snow cover depletion (accessible from satellite products) to the peak SSD. Peak subgrid SWE is assumed to be log-normally distributed, which can be translated to a modeled time series of fSCA through the snow model. Assimilation of satellite-derived fSCA facilitates the estimation of the peak SSD, while taking into account uncertainties in both the model and the assimilated data sets. As an extension to previous studies, our method makes use of the novel (to snow data assimilation) ensemble smoother with multiple data assimilation (ES-MDA) scheme combined with analytical Gaussian anamorphosis to assimilate time series of Moderate Resolution Imaging Spectroradiometer (MODIS) and Sentinel-2 fSCA retrievals. The scheme is applied to Arctic sites near Ny-Ålesund (79° N, Svalbard, Norway) where field measurements of fSCA and SWE distributions are available. The method is able to successfully recover accurate estimates of peak SSD on most of the occasions considered. Through the ES-MDA assimilation, the root-mean-square error (RMSE) for the fSCA, peak mean SWE and peak subgrid coefficient of variation is improved by around 75, 60 and 20 %, respectively, when compared to the prior, yielding RMSEs of 0.01, 0.09m water equivalent (w.e.) and 0.13, respectively. The ES-MDA either outperforms or at least nearly matches the performance of other ensemble-based batch smoother schemes with regards to various evaluation metrics. Given the modularity of the method, it could prove valuable for a range of satellite-era hydrometeorological reanalyses. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Twenty-first century snowfall changes over the European Alps are assessed based on high-resolution regional climate model (RCM) data made available through the EURO-CORDEX initiative. Fourteen different combinations of global and regional climate models with a target resolution of 12 km and two different emission scenarios are considered. As raw snowfall amounts are not provided by all RCMs, a newly developed method to separate snowfall from total precipitation based on near-surface temperature conditions and accounting for subgrid-scale topographic variability is employed. The evaluation of the simulated snowfall amounts against an observation-based reference indicates the ability of RCMs to capture the main characteristics of the snowfall seasonal cycle and its elevation dependency but also reveals considerable positive biases especially at high elevations. These biases can partly be removed by the application of a dedicated RCM bias adjustment that separately considers temperature and precipitation biases. Snowfall projections reveal a robust signal of decreasing snowfall amounts over most parts of the Alps for both emission scenarios. Domain and multi-model mean decreases in mean September-May snowfall by the end of the century amount to -25 and -45% for representative concentration pathway (RCP) scenarios RCP4.5 and RCP8.5, respectively. Snowfall in low-lying areas in the Alpine forelands could be reduced by more than -80 %. These decreases are driven by the projected warming and are strongly connected to an important decrease in snowfall frequency and snowfall fraction and are also apparent for heavy snowfall events. In contrast, high-elevation regions could experience slight snowfall increases in midwinter for both emission scenarios despite the general decrease in the snowfall fraction. These increases in mean and heavy snowfall can be explained by a general increase in winter precipitation and by the fact that, with increasing temperatures, climatologically cold areas are shifted into a temperature interval which favours higher snowfall intensities. In general, percentage changes in snowfall indices are robust with respect to the RCM postprocessing strategy employed: similar results are obtained for raw, separated, and separated-bias-adjusted snowfall amounts. Absolute changes, however, can differ among these three methods. [ABSTRACT FROM AUTHOR]

Density is a fundamental property of porous media such as snow. A wide range of snow properties and physical processes are linked to density, but few studies have addressed the uncertainty in snow density measurements. No study has yet quantitatively considered the recent advances in snow measurement methods such as micro-computed tomography (µCT) in alpine snow. During the MicroSnow Davos 2014 workshop, different approaches to measure snow density were applied in a controlled laboratory environment and in the field. Overall, the agreement between µCT and gravimetric methods (density cutters) was 5 to 9 %, with a bias of -5 to 2 %, expressed as percentage of the mean µCT density. In the field, density cutters overestimate (1 to 6 %) densities below and underestimate (1 to 6 %) densities above a threshold between 296 to 350 kgm-3, dependent on cutter type. Using the mean density per layer of all measurement methods applied in the field (µCT, box, wedge, and cylinder cutters) and ignoring ice layers, the variation between the methods was 2 to 5% with a bias of -1 to 1 %. In general, our result suggests that snow densities measured by different methods agree within 9 %. However, the density profiles resolved by the measurement methods differed considerably. In particular, the millimeter-scale density variations revealed by the high-resolution µCT contrasted the thick layers with sharp boundaries introduced by the observer. In this respect, the unresolved variation, i.e., the density variation within a layer which is lost by lower resolution sampling or layer aggregation, is critical when snow density measurements are used in numerical simulations. [ABSTRACT FROM AUTHOR]

Airborne light detection and ranging (lidar) measurements carried out in the southern Sierra Nevada in 2010 in the snow-free and peak-snow-accumulation periods were analyzed for topographic and vegetation effects on snow accumulation. Point-cloud data were processed from four primarily mixed-conifer forest sites covering the main snow-accumulation zone, with a total surveyed area of over 106 km². The percentage of pixels with at least one snowdepth measurement was observed to increase from 65-90 to 99% as the sampling resolution of the lidar point cloud was increased from 1 to 5 m. However, a coarser resolution risks undersampling the under-canopy snow relative to snow in open areas and was estimated to result in at least a 10 cm overestimate of snow depth over the main snowaccumulation region between 2000 and 3000m, where 28% of the area had no measurements. Analysis of the 1m gridded data showed consistent patterns across the four sites, dominated by orographic effects on precipitation. Elevation explained 43% of snow-depth variability, with slope, aspect and canopy penetration fraction explaining another 14% over the elevation range of 1500-3300 m. The relative importance of the four variables varied with elevation and canopy cover, but all were statistically significant over the area studied. The difference between mean snow depth in open versus under-canopy areas increased with elevation in the rain-snow transition zone (1500-1800 m) and was about 35±10 cm above 1800 m. Lidar has the potential to transform estimation of snow depth across mountain basins, and including local canopy effects is both feasible and important for accurate assessments. [ABSTRACT FROM AUTHOR]

Snow consists of non-spherical grains of various shapes and sizes. Still, in many radiative transfer applications, single-scattering properties of snow have been based on the assumption of spherical grains. More recently, second-generation Koch fractals have been employed. While they produce a relatively flat phase function typical of deformed non-spherical particles, this is still a rather ad hoc choice. Here, angular scattering measurements for blowing snow conducted during the CLimate IMpacts of Short-Lived pollutants In the Polar region (CLIMSLIP) campaign at Ny Ålesund, Svalbard, are used to construct a reference phase function for snow. Based on this phase function, an optimized habit combination (OHC) consisting of severely rough (SR) droxtals, aggregates of SR plates and strongly distorted Koch fractals is selected. The single-scattering properties of snow are then computed for the OHC as a function of wavelength λ and snow grain volume-to-projected area equivalent radius rvp. Parameterization equations are developed for λ = 0.199-2.7 μm and rvp = 10-2000 μm, which express the single-scattering co-albedo β, the asymmetry parameter g and the phase function P11 as functions of the size parameter and the real and imaginary parts of the refractive index. The parameterizations are analytic and simple to use in radiative transfer models. Compared to the reference values computed for the OHC, the accuracy of the parameterization is very high for β and g. This is also true for the phase function parameterization, except for strongly absorbing cases (β >0.3). Finally, we consider snow albedo and reflected radiances for the suggested snow optics parameterization, making comparisons to spheres and distorted Koch fractals. [ABSTRACT FROM AUTHOR]

In recent years, marked improvements in our knowledge of the statistical properties of the spatial distribution of snow properties have been achieved thanks to improvements in measuring technologies (e.g., LIDAR, terrestrial laser scanning (TLS), and ground-penetrating radar (GPR)). Despite this, objective and quantitative frameworks for the evaluation of errors in snow measurements have been lacking. Here, we present a theoretical framework for quantitative evaluations of the uncertainty in average snow depth derived from point measurements over a profile section or an area. The error is defined as the expected value of the squared difference between the real mean of the profile/field and the sample mean from a limited number of measurements. The model is tested for one- and two-dimensional survey designs that range from a single measurement to an increasing number of regularly spaced measurements. Using high-resolution ( ~1 m) LIDAR snow depths at two locations in Colorado, we show that the sample errors follow the theoretical behavior. Furthermore, we show how the determination of the spatial location of the measurements can be reduced to an optimization problem for the case of the predefined number of measurements, or to the designation of an acceptable uncertainty level to determine the total number of regularly spaced measurements required to achieve such an error. On this basis, a series of figures are presented as an aid for snow survey design under the conditions described, and under the assumption of prior knowledge of the spatial covariance/correlation properties. With this methodology, better objective survey designs can be accomplished that are tailored to the specific applications for which the measurements are going to be used. The theoretical framework can be extended to other spatially distributed snow variables (e.g., SWE - snow water equivalent) whose statistical properties are comparable to those of snow depth. [ABSTRACT FROM AUTHOR]

CryoLand (2011-2015) is a project carried out within the 7th Framework of the European Commission aimed at developing downstream services for monitoring seasonal snow, glaciers and lake/river ice primarily based on satellite remote sensing. The services target private and public users from a wide variety of application areas, and aim to develop sustainable services after the project is completed. The project has performed a thorough user requirement survey in order to derive targeted requirements for the service and provide recommendations for the design and priorities of the service. In this paper we describe the methods used, the major findings in this user survey, and how we used the results to design and specify the CryoLand snow and land ice service. The user requirement analysis shows that a European operational snow and land ice service is required and that there exists developed cryosphere products that can meet the specific needs. The majority of the users were mainly interested not only in the snow services, but also the lake/river ice products and the glacier products were desired. [ABSTRACT FROM AUTHOR]

We have studied the possibility of combining the high-resolution synthetic aperture radar (SAR) segmentation and ice concentration estimated by radiometer brightness temperatures. Here we present an algorithm for mapping a radiometer-based concentration value for each SAR segment. The concentrations are estimated by a multi-layer perceptron (MLP) neural network which has the AMSR-2 (Advanced Microwave Scanning Radiometer 2) polarization ratios and gradient ratios of four radiometer channels as its inputs. The results have been compared numerically to the gridded Finnish Meteorological Institute (FMI) ice chart concentrations and high-resolution AMSR-2 ASI (ARTIST Sea Ice) algorithm concentrations provided by the University of Hamburg and also visually to the AMSR-2 bootstrap algorithm concentrations, which are given in much coarser resolution. The differences when compared to FMI daily ice charts were on average small. When compared to ASI ice concentrations, the differences were a bit larger, but still small on average. According to our comparisons, the largest differences typically occur near the ice edge and sea–land boundary. The main advantage of combining radiometerbased ice concentration estimation and SAR segmentation seems to be a more precise estimation of the boundaries of different ice concentration zones. [ABSTRACT FROM AUTHOR]

Climate models predict Antarctic precipitation to increase during the 21st century, but their present day Antarctic precipitation differs. A model-independent climatology of the Antarctic precipitation characteristics, such as snowfall rates and frequency, is needed to assess the models, but it is not yet available. Satellite observations of precipitation by active sensors has been possible in the polar regions since the launch of CloudSat in 2006. Here, we use two CloudSat products to generate the first multi-year, model-independent climatology of Antarctic precipitation. The first product is used to determine the frequency and the phase of precipitation, while the second product is used to assess the snowfall rate. The mean snowfall rate from August 2006 to April 2011 is 171mmyear-1 over the Antarctic ice sheet, north of 82° S. While uncertainties on individual precipitation retrievals from CloudSat data are potentially large, the mean uncertainty should be much smaller, but cannot be easily estimated. There are no in situ measurements of Antarctic precipitation to directly assess the new climatology. However, distributions of both precipitation occurrences and rates generally agree with the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA-Interim data set, the production of which is constrained by various in situ and satellite observations, but does not use any data from CloudSat. The new data set thus offers unprecedented capability to quantitatively assess Antarctic precipitation statistics and rates in climate models. [ABSTRACT FROM AUTHOR]

The Extended Column Test (ECT) and the Propagation Saw Test (PST) are two commonly used tests to assess the likelihood of crack propagation in a snowpack. Guidelines suggest beams with lengths of around 1 m, yet little is known about how test length affects propagation. Thus, we performed 163 ECTs and PSTs 1.0-10.0m long. On days with full crack propagation in 1.0-1.5m tests, we then made videos of tests 2.0-10.0m long. We inserted markers for particle tracking to measure collapse amplitude, propagation speed, and wavelength. We also used a finite element (FE) model to simulate the strain energy release rate at fixed crack lengths.We find that (1) the proportion of tests with full propagation decreased with test length; (2) collapse was greater at the ends of the beams than in the centers; (3) collapse amplitude was independent of beam length and did not reach a constant value; (4) collapse wavelengths in the longer tests were around 3 m, two times greater than what is predicted by the anticrack model. We also confirmed the prediction that centered PSTs had double the critical length of edge PSTs. Based on our results, we conclude that cracks propagated more frequently in the shorter tests because of increased stress concentration from the far edge. The FE model suggests this edge effect occurs for PSTs of up to 2m long or a crack to beam length ratio ≥0.20. Our results suggest that ECT and PST length guidelines may need to be revisited. [ABSTRACT FROM AUTHOR]

The specific surface area (SSA) of snow can be used as an objective measurement of grain size and is therefore a central variable to describe snow physical properties such as albedo. Snow SSA can now be easily measured in the field using optical methods based on infrared reflectance. However, existing optical methods have only been validated for dry snow. Here we test the possibility to use the DUFISSS instrument, based on the measurement of the 1310 nm reflectance of snow with an integrating sphere, to measure the SSA of wet snow. We perform cold room experiments where we measure the SSA of a wet snow sample, freeze it and measure it again, to quantify the difference in reflectance between frozen and wet snow. We study snow samples in the SSA range 12-37m2 kg-1 and in the mass liquid water content (LWC) range 5-32 %. We conclude that the SSA of wet snow can be obtained from the measurement of its 1310 nm reflectance using three simple steps. In most cases, the SSA thus obtained is less than 10% different from the value that would have been obtained if the sample had been considered dry, so that the three simple steps constitute a minor correction. We also run two optical models to interpret the results, but no model reproduces correctly the water-ice distribution in wet snow, so that their predictions of wet snow reflectance are imperfect. The correction on the determination of wet snow SSA using the DUFISSS instrument gives an overall uncertainty better than 11 %, even if the LWC is unknown. If SSA is expressed as a surface to volume ratio (e.g., in mm-1), the uncertainty is then 13% because of additional uncertainties in the determination of the volume of ice and water when the LWC is unknown. [ABSTRACT FROM AUTHOR]

The article presents a study which evaluates the microstructural changes in snow during temperature gradient metamorphism (TGM) for avalanche risk. The study uses in situ time-lapse X-ray micro-tomography to measure the changes. Results show close relationship between physical properties of snow and its microstructure.

The article presents a study which compares the performance of refreezing parameterizations on the ice sheet in Greenland. The study uses snow models, estimate of the depth of the thermally active layer, and capillary water content to measure the process of meltwater percolation, retention and freezing of the snow. Results show that water can percolate to any depth based on the vertical temperature and density distribution in the snow.

The article presents a study which examines the contribution of very small glaciers to the total mass of ice around the world. The study uses number-size scaling and volume-area scaling to estimate the total ice volume, and calculate the potential error in total volume in relation with any lower-bound threshold in glacier size. Results show that one percent and sometimes 10% errors in the total ice volume are small ice masses.

The article presents a study which examines the vertical profile of the snow's specific surface area (SSA) and density at Dome C and Dumont D'Urville in Antarctica. The study assesses the infrared (IR) reflectance of snow at 1310 nanometers using the dual-frequency integrating sphere for snow (DUFISS) system. It is found out that the vertical profile of SSA and density can be used in calculating the spectral albedo of the snow for several illumination conditions.

The article presents a study which examines the impact of snow depth variability at the plot scale in Rio Ésera Valley in Pyrenees mountains in Spain on the representativeness of depth samples. The study analyzes the spatial distribution of snow by conducting a snow depth sampling between January and April 2009 in 15 plots. It is found out that increasing the number of the snow depth measurements can improve the representativeness of snow depth sampling in a given plot.

The article presents the estimates of the accumulation rate in the dry snow zone of the Greenland Ice Sheet (GrIS) established from radar altimetry. It shows the probability of obtaining precise and spatially extensive accumulation rates from radar altimeters in regions of ice sheets. It demonstrates the uncertainty in the mean annual mass gain and evaluates the accuracy of mass balance estimates of the GrIS.