Snow Profiles Research Articles

남극 장보고과학기지 인근에서 채취한 눈시료 내의 주요 이온성분들의 고해상도 계절변동성 연구

A continuous series of 60 snow samples was collected at a 2.5-cm interval from a 1.5-m snow pit at a site on the Styx Glacier Plateau in Victoria Land, Antarctica, during the 2011/2012 austral summer season. Various chemical components (δD, δ 18 O, Na + , K + , Mg 2+ , Ca 2+ , Cl ? , SO 4 2? , NO 3 ? , F ? , CH 3 SO 3 ? , CH 3 CO 2 ? and HCO 2 ? ) were determined to understand the highly resolved seasonal variations of these species in the coastal atmosphere near the Antarctic Jang Bogo station. Based on vertical profiles of δ 18 O, NO 3 ? and MSA, which showed prominent seasonal changes in concentrations, the snow samples were dated to cover the time period from 2009 austral winter to 2012 austral summer with a mean accumulation rate of 226 kg H 2 O m ?2 yr ?1 . Our snow profiles show pronounced seasonal variations for all the measured chemical species with a different pattern between different species. The distinctive feature of the occurrence patterns of the seasonal variations is clearly linked to changes in the relative strength of contributions from various natural sources (sea salt spray, volcanoes, crust-derived dust, and marine biogenic activities) during different short-term periods. The results allow us to understand the transport pathways and input mechanisms for each species and provide valuable information that will be useful for investigating long-term (decades to century scale periods) climate and environmental changes that can be deduced from an ice core to be retrieved from the Styx Glacier Plateau in the near future.

Microbial abundance and community structure in a melting alpine snowpack.

Snowmelt is a crucial period for alpine soil ecosystems, as it is related to inputs of nutrients, particulate matter and microorganisms to the underlying soil. Although snow-inhabiting microbial communities represent an important inoculum for soils, they have thus far received little attention. The distribution and structure of these microorganisms in the snowpack may be linked to the physical properties of the snowpack at snowmelt. Snow samples were taken from snow profiles at four sites (1930-2519 m a.s.l.) in the catchment of the Tiefengletscher, Canton Uri, Switzerland. Microbial (Archaea, Bacteria and Fungi) communities were investigated through T-RFLP profiling of the 16S and 18S rRNA genes, respectively. In parallel, we assessed physical and chemical parameters relevant to the understanding of melting processes. Along the snow profiles, density increased with depth due to compaction, while other physico-chemical parameters, such as temperature and concentrations of DOC and soluble ions, remained in the same range (e.g. <2 mg DOC L(-1), 5-30 μg NH4 (+)-N L(-1)) in all samples at all sites. Along the snow profiles, no major change was observed either in cell abundance or in bacterial and fungal diversity. No Archaea could be detected in the snow. Microbial communities, however, differed significantly between sites. Our results show that meltwater rearranges soluble ions and microbial communities in the snowpack.

Density, specific surface area, and correlation length of snow measured by high‐resolution penetrometry

AbstractPrecise measurements of snow structural parameters are crucial to understand the formation of snowpacks by deposition and metamorphism and to characterize the stratigraphy for many applications and remote sensing in particular. The area‐wide acquisition of structural parameters at high spatial resolution from state‐of‐the‐art methods is, however, still cumbersome, since the time required for a single profile is a serious practical limitation. As a remedy we have developed a statistical model to extract three major snow structural parameters: density, correlation length, and specific surface area (SSA) solely from the SnowMicroPen (SMP), a high‐resolution penetrometer, which allows a meter profile to be measured with millimeter resolution in less than 1 min. The model was calibrated by combining SMP data with 3‐D microstructural data from microcomputed tomography which was used to reconstruct full‐depth snow profiles from different snow climates (Alpine, Arctic, and Antarctic). Density, correlation length, and SSA were derived from the SMP with a mean relative error of 10.6%, 16.4%, and 23.1%, respectively. For validation, we compared the density and SSA derived from the SMP to traditional measurements and near‐infrared profiles. We demonstrate the potential of our method by the retrieval of a two‐dimensional stratigraphy at Kohnen Station, Antarctica, from a 46 m long SMP transect. The result clearly reveals past depositional and metamorphic events, and our findings show that the SMP can be used as an objective, high‐resolution tool to retrieve essential snow structural parameters efficiently in the field.

Laboratory study of nitrate photolysis in Antarctic snow. II. Isotopic effects and wavelength dependence.

Atmospheric nitrate is preserved in Antarctic snow firn and ice. However, at low snow accumulation sites, post-depositional processes induced by sunlight obscure its interpretation. The goal of these studies (see also Paper I by Meusinger et al. ["Laboratory study of nitrate photolysis in Antarctic snow. I. Observed quantum yield, domain of photolysis, and secondary chemistry," J. Chem. Phys. 140, 244305 (2014)]) is to characterize nitrate photochemistry and improve the interpretation of the nitrate ice core record. Naturally occurring stable isotopes in nitrate ((15)N, (17)O, and (18)O) provide additional information concerning post-depositional processes. Here, we present results from studies of the wavelength-dependent isotope effects from photolysis of nitrate in a matrix of natural snow. Snow from Dome C, Antarctica was irradiated in selected wavelength regions using a Xe UV lamp and filters. The irradiated snow was sampled and analyzed for nitrate concentration and isotopic composition (δ(15)N, δ(18)O, and Δ(17)O). From these measurements an average photolytic isotopic fractionation of (15)ɛ = (-15 ± 1.2)‰ was found for broadband Xe lamp photolysis. These results are due in part to excitation of the intense absorption band of nitrate around 200 nm in addition to the weaker band centered at 305 nm followed by photodissociation. An experiment with a filter blocking wavelengths shorter than 320 nm, approximating the actinic flux spectrum at Dome C, yielded a photolytic isotopic fractionation of (15)ɛ = (-47.9 ± 6.8)‰, in good agreement with fractionations determined by previous studies for the East Antarctic Plateau which range from -40 to -74.3‰. We describe a new semi-empirical zero point energy shift model used to derive the absorption cross sections of (14)NO3 (-) and (15)NO3 (-) in snow at a chosen temperature. The nitrogen isotopic fractionations obtained by applying this model under the experimental temperature as well as considering the shift in width and center well reproduced the values obtained in the laboratory study. These cross sections can be used in isotopic models to reproduce the stable isotopic composition of nitrate found in Antarctic snow profiles.

A practical formulation of snow surface diffusion by wind for watershed‐scale applications

AbstractPrediction of snow drift is of importance for structure design and traffic management on snowy and windy prairie landscapes. The snow redistribution by wind is also regarded as one of the largest sources of error in hydrologic snowmelt models. In this study, a snow movement equation was generalized and customized for horizontal two‐dimensional watershed‐scale applications by incorporating snow transport, wind snow diffusion, and snow gravitational movement. Then, the snow surface diffusion process by wind turbulence was formulated in terms of the autocorrelation functions of the measurable wind velocity field using G.I. Taylor's theorem. However, analysis of the example wind data suggested a delta correlation in wind turbulent component that resulted from subtracting their moving average values from the original wind speed data. The dynamic model based on the proposed formulation was able to effectively reproduce the observed equilibrium snow profiles affected by wind drifting. A two‐dimensional model simulation using a 10 m digital elevation model in Muddy Gap, Wyoming was also presented for qualitative validation of the model in the watershed‐scale applications. Additionally, the theoretical extension for preferential snow accumulation process was presented in Appendix . These modeling results together with the observations on the prairie suggested the importance of the snow surface diffusion process in addition to the snow transport.

The impact of strategic consistency on market share and ROA

Purpose– The purpose of this paper is to take a contingency theory approach to examine how performance is affected by the relationships between the Miles &amp; Snow strategic groupings and a variety of marketing strategy concepts, including a firm's service focus, service growth, market coverage, marketing initiative, market growth, Porter strategy, and market orientation.Design/methodology/approach– Data for the study were gathered from a statewide survey among 125 chief executives of credit unions belonging to the Florida Credit Union League (FCUL). ROA figures were derived from government-mandated accounting reports in the state of Florida. ANOVA and correlation analysis were employed to analyze data.Findings– This study shows that firms that match an aggressive Miles and Snow profile with a more aggressive approach to seven other strategy dimensions often enjoy higher market share relative to credit unions characterized by a different alignment of the various aspects of marketing strategy. The results also suggest that achieving such a fit is not relevant to maximizing a firm's ROA.Research limitations/implications– The research sample was biased toward medium to larger firms that may possess strategic resources superior to those of the smaller firms in the industry. Also, credit unions may tend to have somewhat less aggressive profit objectives compared to other institutions in the banking industry.Practical implications– The findings outline to financial services executives the benefits of considering all dimensions of corporate strategy simultaneously, rather than one at a time.Originality/value– The paper illustrates how aligning certain aspects of marketing strategy can boost particular performance indicators and provides insight as to what the most appropriate alignments are depending on the circumstances.

Automatic classification of manual snow profiles by snow structure

Abstract. Manual snowpack observations are an important component of avalanche hazard assessment for the Swiss avalanche forecasting service. Approximately 900 snow profiles are observed each winter, in flat study plots or on representative slopes. So far, these profiles have been manually classified combining both information on snow stability (e.g. Rutschblock test) and snowpack structure (e.g. layering, hardness). To separate the classification of snowpack stability and structure, and also to reduce inconsistencies in ratings between forecasters, we developed and tested an automatic approach to classify profiles by snowpack structure during two winters. The automatic classification is based on a calculated index, which consists of three components: properties of (1) the slab (thickness), (2) weakest layer interface and (3) the percentage of the snowpack which is soft, coarse-grained and consists of persistent grain types. The latter two indices are strongly based on criteria described in the threshold sum approach. The new snowpack structure index allows a consistent comparison of snowpack structure to detect regional patterns, seasonal or inter-annual differences but may also supplement snow-climate classifications.

Hardness estimation and weak layer detection in simulated snow stratigraphy

Numerical modeling of snow cover stratigraphy with, for example, the 1-D snow cover model SNOWPACK has the potential to increase the spatial and temporal resolutions of snow stratigraphy information — data very much needed for avalanche forecasting. One of the key properties for interpreting snow stratigraphy in regard to stability is snow hardness. In manually observed snow profiles, differences in snow hardness between layers were found to be indicative of instability. We improved the hardness parameterization implemented in the snow cover model SNOWPACK. Hardness is estimated from the simulated snow density and grain type. Density thresholds for primary grain types and all hardness steps were calculated using ordinal logistic regression (on a data set of 14,522 manually observed layers). We thus implemented snow hardness as a discrete parameter in SNOWPACK. The new hardness parameterization observed agreed well with the simulated snow hardness. The structural stability index (SSI), and the threshold sum approach (TSA) were then used to detect potential weak layers in the simulated stratigraphy. We evaluated whether failure layers detected with compression tests (CT) in manually observed snow profiles corresponded to the potential weak layers found by either the SSI or TSA in the simulated stratigraphy. CT failure layers corresponded in about half of the cases to the potential weak layers detected with either the SSI or the TSA in the simulated stratigraphy. The agreement improved if only sudden collapse fractures were considered. These findings suggest that stability information can be derived from simulated snow stratigraphy in particular if the method for detecting weak layers is further improved.

Effects of land use changes on winter-active Collembola in Sanjiang Plain of China

Sanjiang Plain is the largest concentrated area of freshwater wetlands in China, however nearly 80% of these freshwater wetlands were drained or reclaimed in the past 50 years. It is important to know whether wetlands reclamation would affect soil invertebrates, especially the winter-active invertebrates. During November 2011 to April 2012, we used pitfall traps and in-field direct observation methods to study the activity of collembolans in wetland, and a reclaimed forest plantation and soybean field. In total, 3465 collembolans were captured and identified to 8 species from 6 families. Desoria sp. 1, Desoria sp. 2 and Desoria sp. 3 were the three dominant species. Collembolan abundance and assemblages were significantly affected by the land use changes. The results showed that (1) Collembola captured by pitfall traps showed a highest abundance in wetland, with ∼50% decreased abundance in soybean field, and ∼75% decreased in the forest plantation. (2) Collembola activity changed during the winter season, their activity significantly increased from early winter to late winter; with a peak in March in all three land use types. (3) Collembola assemblages were affected by land use changes with a significant decrease of frequency of Desoria sp. 3 and a significant increase of frequency of Desoria sp. 2 in soybean field and forest plantation. (4) Collembolan densities on the snow surface usually peaked between 1400h and 1500h, and decreased quickly when the temperature dropped below freezing. Mean abundance reached 119individualsm−2 on the snow surface in wetland, 152individualsm−2 in soybean field, and 64individualsm−2 in forest plantation. All collembolans moved up and down through the snow profile depending on temperature, no collembolans were found on the snow surface in the evening. Our study indicated that the reclamation of wetland resulted in a significant decrease of abundance and a different assemblage of winter active Collembola in Northeast China, but land use changes did not change their pattern of activity: Collembolans were rarely active in early and middle winter and mostly active in late winter. Their daily densities on the snow surface fluctuated according to both air temperatures and land use types.

N2O emissions and source processes in snow-covered soils in the Swiss Alps

Nitrous oxide (N2O) emissions from snow-covered soils represent a significant fraction of the annual flux from alpine, subalpine or cold-temperate regions. In winter 2010–2011, we investigated the temporal variability of N2O emissions and source processes from a subalpine valley in the Swiss Alps. The study included regular measurements of N2O snow profiles at a fixed location and an intensive sampling campaign along a transversal cut through the valley with grassland at the bottom and coniferous forest at the slopes. During the intensive campaign, recently developed laser spectroscopy was employed for high-precision N2O isotopomer analysis. Maximum N2O fluxes (0.77±0.64 nmol m−2 h−1) were found for periods with elevated air temperature and, in contrast to our expectations, were higher from forest than from grassland in mid-February. At maximum snow height (63 cm) the main N2O source processes were heterotrophic denitrification and nitrifier denitrification. The reduction of N2O by heterotrophic denitrifiers was much more pronounced for the grassland compared with the forest soil, as indicated by the 15N site preferences of 16.4±11.5 ‰ (grassland) and−1.6±2.1 ‰ (forest). This illustrates the potential of laser spectroscopic N2O isotopomer analysis for the identification of source processes even at low emission rates in nutrient poor ecosystems.

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as snow water equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions, but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment and the Helsinki University of Technology microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 and 37 GHz vertically polarised microwaves are consistent with advanced microwave scanning radiometer-earth observing system and special sensor microwave imager retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10-cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method, then it is equivalent to ±13 mm SWE (7 % of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.

Forecasting the formation of critical snow layers using a coupled snow cover and weather model

The snow cover model SNOWPACK simulates snow cover formation and evolution based on meteorological data. In the past, these data were measured by automated weather stations. Recently, SNOWPACK was also forced with data from numerical weather prediction models (NWP). In this study we assess the capability of such a model chain to simulate two types of critical layers, i.e. surface hoar and melt–freeze crusts, for virtual north and south-facing slopes as well as a flat study plot. Meteorological key parameters for snow cover formation and evolution, e.g. precipitation, radiation, air temperature and relative humidity, were measured and compared to the forecasted data to evaluate the performance of the NWP model. Systematic errors of the NWP model were corrected and the adjusted data were used to force SNOWPACK. The formation of 80 critical layers – 35 surface hoar layers and 45 melt–freeze crusts – from seven winters between 2005 and 2012 observed in the Columbia Mountains of British Columbia, Canada were investigated. To assess the performance of SNOWPACK at simulating the presence and absence of critical layers on different aspects, monthly manual snow profiles systematically observed on north, south and flat terrains between January and March during the winter of 2010–2011 were compared to the corresponding snow cover simulations. The overall accuracy to predict the formation of melt–freeze crusts was found to be between 65% and 77% depending on the aspect. Surface hoar layers were modeled with accuracy between 89% and 100%. The presence and absence of critical layers within the snow cover – surface hoar and melt–freeze crusts – were investigated during the winter of 2010–2011. Ten out of eleven observed critical layers within the snow cover were simulated, resulting in an accuracy of 91%. However, some surface hoar layers and melt–freeze crusts were simulated but not observed resulting in a low accuracy for the absence of critical layers. The simulated snow height tended to be under-estimated during the early winter season and over-estimated during the late winter season for both slopes and the flat site, especially in March on the south-facing aspect. Nevertheless, the model chain is promising considering the source of the input data. This study showed that such a model chain could become a useful tool for avalanche warning services in the future, especially for data sparse areas.

Event-driven deposition of snow on the Antarctic Plateau: analyzing field measurements with SNOWPACK

Abstract. Antarctic surface snow has been studied by means of continuous measurements and observations over a period of 3 yr at Dome C. Snow observations include solid deposits in form of precipitation, diamond dust, or hoar, snow temperatures at several depths, records of deposition and erosion on the surface, and snow profiles. Together with meteorological data from automatic weather stations, this forms a unique dataset of snow conditions on the Antarctic Plateau. Large differences in snow amounts and density exist between solid deposits measured 1 m above the surface and deposition at the surface. We used the snow-cover model SNOWPACK to simulate the snow-cover evolution for different deposition parameterizations. The main adaptation of the model described here is a new event-driven deposition scheme. The scheme assumes that snow is added to the snow cover permanently only during periods of strong winds. This assumption followed from the comparison between observations of solid deposits and daily records of changes in snow height: solid deposits could be observed on tables 1 m above the surface on 94 out of 235 days (40%) while deposition at the surface occurred on 59 days (25%) during the same period, but both happened concurrently on 33 days (14%) only. This confirms that precipitation is not necessarily the driving force behind non-temporary snow height changes. A comparison of simulated snow height to stake farm measurements over 3 yr showed that we underestimate the total accumulation by at least 33%, when the total snow deposition is constrained by the measurements of solid deposits on tables 1 m above the surface. During shorter time periods, however, we may miss over 50% of the deposited mass. This suggests that the solid deposits measured above the surface and used to drive the model, even though comparable to ECMWF forecasts in its total magnitude, should be seen as a lower boundary. As a result of the new deposition mechanism, we found a good agreement between model results and measurements of snow temperatures and recorded snow profiles. In spite of the underestimated deposition, the results thus suggest that we can obtain quite realistic simulations of the Antarctic snow cover by the introduction of event-driven snow deposition.

Bacterial and extracellular polysaccharide content of brine‐wetted snow over Arctic winter first‐year sea ice

AbstractDuring freeze‐up and consolidation, sea ice rejects to its surface brine of marine origin that is incorporated into overlying snow. To evaluate the transport of biological components in brines from ice to snow, vertical profiles of temperature, salinity, bacterial abundance, and extracellular polysaccharide substances (EPS) were obtained through snow and first‐year sea ice (Barrow, AK) in consecutive winters (2010, 2011). Snow profiles showed strong interannual variation, with 2010 presenting higher values and wider ranges in salinity (0.3–30.9, practical salinity), bacterial abundance (2.8 × 102–1.5 × 104 cells mL− 1), and particulate EPS (pEPS, 0.04–0.23 glucose equivalents (glu‐eq) mg L− 1) than 2011 (0–11.9, 2.7 × 103–4.2 × 103 cells mL− 1 and 0.04–0.09 glu‐eq mg L− 1, respectively). Surface ice also differed interannually, with 2010 presenting again higher salinity (19.4, n = 1), bacterial abundance (5.4 × 104–9.6 × 104 cells mL− 1) and pEPS (0.13–0.51 glu‐eq mg L− 1) than 2011 (7.7–11.9, 1.7 × 104–2.2 × 104 cells mL− 1, and 0.01–0.09 glu‐eq mg L− 1, respectively). Transport of bacteria and pEPS from sea‐ice brines into snow was evident in 2010 but not 2011, a year with more extreme winter conditions of colder temperature, thinner snow, and stronger wind. By size fraction, the smallest EPS (&lt; 0.1 µm) dominated (&gt; 80%) total EPS in both ice and snow; the &gt; 3 µm fraction of EPS in snow appeared to have an atmospheric source. Evaluation of membrane integrity by Live/Dead stain revealed a high percentage (85%) of live bacteria in saline snow, identifying this vast environment as a previously unrecognized microbial habitat.

Comparison of the multi-layer HUT snow emission model with observations of wet snowpacks

Information on snow properties plays an important role in hydrological, meteorological and climatological applications. Passive microwave remote sensing is an effective method to retrieve snowpack parameters; however, the observations can be obscured if there is wet snow in the satellite footprint. To study the emission properties of wet snow and check its response to snow wetness, this paper applies the multi-layer Helsinki University of Technology (HUT) snow emission model coupled with the Advanced Integral Equation Model to simulate the low-wetness snowpack observed at Luancheng in November 2009, and the high wetness snowpack observed at Weissfluhjoch in June 1995. Input parameters are acquired by the in-situ snow pit measurements, while the snow grain size is fitted by comparing model predictions with the observed passive microwave signals at a range of observing angles. Results show that the application of a multi-layer model is capable to consider the distribution pattern of the snow wetness along the snow profile and the refrozen ice crust of the snow surface. The multi-layer HUT model is able to reproduce the wet snow emission properties, with an rms error of 4.4 K (at Luancheng) and 5.7 K (at Weissfluhjoch) at vertical polarization, and an rms error of 7.9 K (at Luancheng) and 11.4 K (at Weissfluhjoch) at horizontal polarization. Copyright © 2012 John Wiley & Sons, Ltd.

Snowpack concentrations and estimated fluxes of volatile organic compounds in a boreal forest

Abstract. Soil provides an important source of volatile organic compounds (VOCs) to atmosphere, but in boreal forests these fluxes and their seasonal variations have not been characterized in detail. Especially wintertime fluxes are almost completely unstudied. In this study, we measured the VOC concentrations inside the snowpack in a boreal Scots pine (Pinus sylvestris L.) forest in southern Finland, using adsorbent tubes and air samplers installed permanently in the snow profile. Based on the VOC concentrations at three heights inside the snowpack, we estimated the fluxes of these gases. We measured 20 VOCs from the snowpack, monoterpenes being the most abundant group with concentrations varying from 0.11 to 16 μg m−3. Sesquiterpenes and oxygen-containing monoterpenes were also detected. Inside the pristine snowpack, the concentrations of terpenoids decreased from soil surface towards the surface of the snow, suggesting soil as the source for terpenoids. Forest damages (i.e. broken treetops and branches, fallen trees) resulting from heavy snow loading during the measurement period increased the terpenoid concentrations dramatically, especially in the upper part of the snowpack. The results show that soil processes are active and efficient VOC sources also during winter, and that natural or human disturbance can increase forest floor VOC concentrations substantially. Our results stress the importance of soil as a source of VOCs during the season when other biological sources, such as plants, have lower activity.

Spatial and temporal distribution of snow water content and response to air temperature in seasonal snow in the western Tianshan Mountains, China

The snow water content was measured with a portable instrument (Snow Fork) in the Tianshan Station for Snow-cover and Avalanche, Chinese Academy of Sciences (TS) during the snow period in the winter of 2009–2010. The results indicated that the variation of snow water content increases exponentially over time. In the accumulation-stable period, the migration of liquid water is from the bottom to the surface of the snow profile. In the snowmelt period, the migration direction is reversed. In the transition period, both directions of transfer exist simultaneously. There are three different types of response to air temperature: (a) when the average temperature is below −7 °C, the snow water content has a best fit with the accumulated air temperature with a simple linear function; (b) when the average temperature is between −7 and −1 °C, the snow water content has a best fit with the average air temperature, with an exponential function; and (c) when the average air temperature is higher than −1 °C, the snow water content has a best fit with the maximum air temperature, with an exponential function.

A new method for visualizing snow stability profiles

Abstract Snow stability assessment by interpreting snow profiles is a time consuming and fairly subjective process, especially when snow stratigraphy was recorded without performing a stability test at the same time. Snow stratigraphy is clearly related to snow stability, as had been shown by various studies that linked specific snowpack properties such as grain size and type to instability. We suggest a new method to visualize snow stratigraphy in regard to stability based on six structural variables (also known as the threshold sum approach). Each snow layer is represented by the number of variables that are not in the corresponding critical range. This approach has not only been implemented for manually recorded snow profiles but also – after adapting the threshold values – for simulated snow stratigraphy provided by the numerical snow cover model SNOWPACK. The new visualization method, applied both to the manually observed and simulated profiles, was tested by analyzing the most critical avalanche situations of the winter 2008–2009 in the Dolomites (north-eastern Italian Alps). Results indicate that the new visualization method is well suited to quickly and intuitively derive snow stability, in particular from simulated snow stratigraphy. Stability information derived from simulated profiles was clearly related to the independently estimated degree of avalanche danger. Supplementing the snow cover model SNOWPACK with the adjusted threshold sum approach increases its usefulness for avalanche forecasting purposes.

A Poisson shot noise model for micro-penetration of snow

As a common starting point for the interpretation of SnowMicroPenetrometer (SMP) data the penetration force is interpreted as a superposition of spatially uncorrelated ruptures of structural elements which follow an ideal elastic–brittle response. We re-state this idea and describe the fluctuating penetration force as a Poisson shot noise process. This allows us to derive simple analytical expressions for the cumulants and the covariance of the penetration force in terms of the micromechanical, force-displacement parameters of individual elements. Vice versa, the micromechanical parameters can be estimated from the statistics of the penetration force. We test our method with simulated shot noise processes and real snow profiles and reveal potential limitations of the underlying assumptions. Our model unifies different previous approaches to snow classification which are based on snow penetration resistance. ► Re-interpretation of a common penetration model as shot noise. ► Analytical solution for force statistics and parameter estimation. ► Unification of previous, yet unrelated approaches.

Multi-Decadal Changes in Snow Characteristics in Sub-Arctic Sweden

A unique long term, 49-year record (divided into three time periods 1961-1976, 1977-1992, and 1993-2009) of snow profile stratigraphy from the Swedish sub Arctic, was analyzed with a focus on changes in snow characteristics. The data set contained grain size, snow layer hardness, grain compactness, and snow layer dryness, observed every second week during the winter season. The results showed an increase in very hard snow layers, with harder snow in early winter and more moist snow during spring. There was a striking increase in the number of observations with very hard snow at ground level over time. More than twice as many occasions with hard snow at ground level were observed between 1993 and 2009 compared to previous years, which may have a significant effect on plants and animals. The changes in snow characteristics are most likely a result of the increasing temperatures during the start and the end of the snow season.