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Semi-volatile organic contaminants (SVOCs) are known for their tendency to evaporate from source regions and undergo atmospheric transport to distant areas. Cold condensation intensifies dry deposition, particle deposition, and scavenging by snow and rain, allowing SVOCs to move from the atmosphere into terrestrial and aquatic ecosystems in alpine and polar regions. However, no standardized methods exist for the sampling, laboratory processing, and instrumental analysis of persistent organic pollutants (POPs) in snow. The lack of reference methods makes these steps highly variable and prone to errors. This study critically reviews the existing literature to highlight the key challenges in the sampling phase, aiming to develop a reliable, consistent, and easily reproducible technique. The goal is to simplify this crucial step of the analysis, allowing data to be shared more effectively through standardized methods, minimizing errors. Additionally, an innovative method for laboratory processing is introduced, which uses activated carbon fibers (ACFs) as adsorbents, streamlining the analysis process. The extraction method is applied to analyze polychlorobiphenyls (PCBs) and chlorinated pesticides (α-HCH, γ-HCH, p,p′-DDE, o,p′-DDT, HCB, and PeCB). The entire procedure, from sampling to instrumental analysis, is subsequently tested on snow samples collected on the Svalbard Islands. To validate the efficiency of the new extraction system, quality control measures based on the EPA methods 1668B and 1699 for aqueous methods are employed. This study presents a new, reliable method that covers both sampling and lab analysis, tailored for detecting POPs in snow. [ABSTRACT FROM AUTHOR]

Semi-volatile organic contaminants (SVOCs) are known for their tendency to evaporate from source regions and undergo atmospheric transport to distant areas. Cold condensation intensifies dry deposition, particle deposition, and scavenging by snow and rain, allowing SVOCs to move from the atmosphere into terrestrial and aquatic ecosystems in alpine and polar regions. However, no standardized methods exist for the sampling, laboratory processing, and instrumental analysis of persistent organic pollutants (POPs) in snow. The lack of reference methods makes these steps highly variable and prone to errors. This study critically reviews the existing literature to highlight the key challenges in the sampling phase, aiming to develop a reliable, consistent, and easily reproducible technique. The goal is to simplify this crucial step of the analysis, allowing data to be shared more effectively through standardized methods, minimizing errors. Additionally, an innovative method for laboratory processing is introduced, which uses activated carbon fibers (ACFs) as adsorbents, streamlining the analysis process. The extraction method is applied to analyze polychlorobiphenyls (PCBs) and chlorinated pesticides (α-HCH, γ-HCH, p,p′-DDE, o,p′-DDT, HCB, and PeCB). The entire procedure, from sampling to instrumental analysis, is subsequently tested on snow samples collected on the Svalbard Islands. To validate the efficiency of the new extraction system, quality control measures based on the EPA methods 1668B and 1699 for aqueous methods are employed. This study presents a new, reliable method that covers both sampling and lab analysis, tailored for detecting POPs in snow.

In high‐latitude and mountain regions, local processes such as redistribution by wind, snow metamorphism, and percolation of water produce a complex spatial distribution of snow depths and snow densities. With its strong control on the ground thermal regime, this snow distribution has pronounced effects on ground temperatures at small spatial scales which are typically not resolved by land surface models (LSMs). This limits our ability to simulate the local impacts of climate change on, for example, vegetation and permafrost. Here, we present a tiling approach combining the CryoGrid permafrost model with snow microphysics parametrizations from the CROCUS snow scheme to account for subgrid lateral exchange of snow and water in a process‐based way. We demonstrate that a simple setup with three coupled tiles, each representing a different snow accumulation class with a specific topographic setting, can reproduce the observed spread of winter‐time ground surface temperatures (GST) and end‐of‐season snow distribution for a high‐Arctic site on Svalbard. For the 3‐year study period, the three‐tile simulations showed substantial improvement compared to traditional single‐tile simulations which naturally cannot account for subgrid variability. Among others, the representation of the warmest and coldest 5% of the observed GST distribution was improved by 1–2°C, while still capturing the average of the distribution. The simulations also reveal positive mean annual GSTs at the locations receiving the greatest snow cover. This could be an indication for the onset of localized permafrost degradation which would be obscured in single‐tile simulations. Key Points: In high‐Arctic areas, wind redistribution of snow leads to a strong variability in snow depths and hence ground surface temperaturesA parametrization for lateral transport of snow between three model tiles is implemented in the CryoGrid 3 permafrost modelThe three‐tile setup reproduces the observed spatial variability of snow depths and ground surface temperatures in a process‐based fashion [ABSTRACT FROM AUTHOR]

Earth system models are valuable tools for understanding how the Arctic snow-ice system and the feedbacks therein may respond to a warming climate. In this analysis, we investigate snow on Arctic sea ice to better understand how snow conditions may change under different forcing scenarios. First, we use in situ, airborne, and satellite observations to assess the realism of the Community Earth System Model (CESM) in simulating snow on Arctic sea ice. CESM versions one and two are evaluated, with V1 being the Large Ensemble experiment (CESM1-LE) and V2 being configured with low- and hightop atmospheric components. The assessment shows CESM2 underestimates snow depth and produces overly uniform snow distributions, whereas CESM1-LE produces a highly variable, excessively-thick snow cover. Observations indicate that snow in CESM2 accumulates too slowly in autumn, too quickly in winter-spring, and melts too soon and rapidly in late spring. The 1950–2050 trends in annual mean snow depths are markedly smaller in CESM2 (−0.8 cm decade−1) than in CESM1-LE (−3.6 cm decade−1) due to CESM2 having less snow overall. A perennial, thick sea-ice cover, cool summers, and excessive summer snowfall facilitate a thicker, longer-lasting snow cover in CESM1-LE. Under the SSP5-8.5 forcing scenario, CESM2 shows that, compared to present-day, snow on Arctic sea ice will: (1) undergo enhanced, earlier spring melt, (2) accumulate less in summer-autumn, (3) sublimate more, and (4) facilitate marginally more snow-ice formation. CESM2 also reveals that summers with snow-free ice can occur ∼30–60 years before an ice-free central Arctic, which may promote faster sea-ice melt. [ABSTRACT FROM AUTHOR]

We proposed new snow grain model and snow impurity mixture models for the purpose of accurate retrievals of snow grain size and concentration of light‐absorbing particles (LAP) in snow from the optical remote sensing data. Two kinds of ice crystal models, irregularly shaped Voronoi columns, and Voronoi aggregates were employed. LAP can be captured by the snow through two processes: dry and wet deposition. Two different snow impurity mixture models were proposed. One is an external mixture model. We employed a coated sphere model in which soot were hydrophilic particles to represent a hygroscopic property of aerosol and assumed the hydrophilic soot particles to be externally mixed with snow particles. The other is an internal mixture model. We employed a dynamic effective medium approximation method in which soot particles were randomly located within snow particle with any size distribution and number concentration. Validation of these models is conducted using a ground‐based spectral radiometer system with in situ measurement data. For snow grain size retrievals, a Voronoi mixture model seamlessly representing a geometrical shape and an optical properties of various snow types can provide accurate retrievals. For the retrieval of LAP concentration in snow, employing different mixing state models depending on season and measurement site gives accurate results. We also discussed the uncertainty of retrieved snow parameters on the surface slope involved in the retrieval accuracy. These models are expected to be useful for advanced airborne/satellite remote sensing and climate studies via radiative transfer modeling in the atmosphere‐snow system. Key Points: New snow grain models for accurate retrievals of snow grain size and concentration of light‐absorbing particles (LAPs) in snow are proposedTwo kinds of ice crystal models, irregularly shaped Voronoi columns and aggregates can be represented from small to large snow grain sizeEmploying different models in LAP mixing state (internal or external mixture model) depending on season and location gives accurate retrievals [ABSTRACT FROM AUTHOR]

The stability of the residual carbon dioxide cap near the south pole of Mars is currently not well understood. The cap's survival depends on its radiation budget, controlled by the visible albedo and infrared emissivity. We investigated the role of CO2 snowfall in altering the albedo and emissivity, leading to the observed asymmetry in the net CO2 accumulation at the two poles. Uncontaminated snowfall increases albedo, and lowers emissivity, due to scattering by optically thick clouds and granular surface deposits. Data from the Mars Climate Sounder (MCS) show that fall and winter snowfall is correlated with higher springtime albedo at both poles. For the seasonal CO2 deposits in each polar region >60° latitude, we find mean albedo values of 0.39 in the north and 0.51 in the south, and winter 32‐μm emissivity values of 0.84 in the north and 0.87 in the south. Using a radiative transfer model and the MCS data, we find that the north polar deposits have ∼10× higher dust content than those in the south, explaining the ∼31% lower albedo of the north seasonal cap during spring. Our model shows that greater amounts of snowfall can explain the ∼4% lower emissivity of the north polar seasonal cap. These findings demonstrate that winter snowfall and dust transport affect the composition of Mars' seasonal ice caps and polar energy balance. Snowfall and dust loading are therefore important in modeling the CO2 cycle on Mars, as well as the planet's long‐term climate variations. Plain Language Summary: The permanent carbon dioxide (CO2, also known as dry ice) deposits on Mars control the planet's global atmospheric pressure. Seasonal fluctuations of the CO2 cycle drive pressure variations measured at the surface anywhere on the planet. These variations are buffered by the stable permanent CO2 deposit at the south pole. The north pole has the more favorable altitude and pressure to be in equilibrium with the atmosphere, but satellite observations showed that the residual cap at the south pole was in equilibrium and not the north. Other studies found evidence of carbon dioxide snowfall in both polar regions. Using data from Mars Climate Sounder on board the Mars Reconnaissance Orbiter, we found that there is an asymmetry between the optical properties of the northern and the southern permanent caps. We attribute this asymmetry to dust and snowfall quantities, which promote more CO2 accumulation in the south, relative to the north. Key Points: Infrared emissivity and visible albedo of Mars' seasonal ice caps are correlatedThe north seasonal cap has greater snowfall, lower emissivity, and lower albedoHigher dust loading in the northern fall and winter seasons causes lower ice cap albedo [ABSTRACT FROM AUTHOR]

Snow failure is the result of gradual damage accumulation culminating in macroscopic cracks. The failure type strongly depends on the rate of the applied load or strain. Our aim was to study the microstructural mechanisms leading to the macroscopic loading rate dependence. We modeled snow failure and the concurrent acoustic emissions for different loading rates with a fiber bundle model and compared the model results to laboratory experiments. The fiber bundle model included two time‐dependent healing mechanisms opposing the loading‐induced damage process: (a) sintering of broken fibers and (b) relaxation of load inhomogeneities due to viscous deformation. The experimental acoustic emissions features could only be reproduced correctly if both healing mechanisms were included in the model. We conclude that both sintering and viscous deformation at a microscopic level essentially contribute to the macroscopic loading‐ and strain‐rate dependent behavior of snow. Plain Language Summary: Snow slab avalanches originate from an initial crack in a weak snow layer followed by subsequent crack propagation within the snowpack. Understanding how this initial crack arises is essential for understanding the formation of spontaneous avalanches and subsequently also for avalanche hazard management. We used a numerical model mimicking the snow microstructure (fiber bundle model) to model snow failure experiments, where the load applied to the snow samples increased at different speeds until the samples failed. The crackling noise (or acoustic emissions) produced by the formation or growth of microscopic cracks within the snow was used to assess the damage process in the snow. We included in the model sintering and viscous deformation. Sintering means that two ice particles bond almost immediately upon contact, allowing the healing of cracks in snow. Viscous deformation means that ice deforms with time also at a constant force and, due to this, concentrations of forces in the snow are smoothed out over time. The model could reproduce the experiments only if both sintering and viscous deformation of ice were considered. We conclude that both mechanisms essentially contribute to snow failure and are of crucial importance for understanding snow failure and thus predicting avalanche release. Key Points: We evaluate a fiber bundle model (FMB) that includes sintering and viscous deformation to model snow failure behaviorThe model can reproduce results of snow failure experiments, in particular the acoustic emission (AE) features and their rate dependenceThough very simple, the model allows representing key mechanical features of snow by including sintering as well as viscous deformation [ABSTRACT FROM AUTHOR]

In snow‐dominated regions, a key source of uncertainty in hydrologic prediction and forecasting is the magnitude and distribution of snow water equivalent (SWE). With ensemble simulations, this work demonstrates that SWE variability across the mountain ranges of the western United States (represented by 246 Snow Telemetry stations) can largely be captured at the daily time scale by a simple mass and energy‐balance snow model with four physically reasonable parameters—three snow albedo parameters and one snow temperature threshold for precipitation partitioning. The model skill is lower in the maritime Pacific Northwest where SWE variability is more sensitive to errors associated with simulated energy balance (e.g., downward radiation fluxes) and the temperature‐only precipitation partitioning approach. Poor model skill in high‐altitude, windy locations in the Northern Rockies can be attributed to precipitation undercatch and underrepresented wind processes. For the purpose of large‐domain hydrologic applications, regional snow parameters were developed for eight ecoregions characterized by a distinct hydroclimatic regime across the western United States. Results suggest that regionally coherent snow parameterizations are able to capture daily variations in SWE at most Snow Telemetry stations, suggesting that areas with a similar hydroclimate share a similar snow regime. While the three albedo parameters show limited spatial variability across all regions, the regional snow temperature threshold (Ts) shows marked spatial variation correlated with relative humidity; the Ts values increase from 0.2 °C in the higher‐humidity Pacific Northwest to 4.0 °C in the colder, lower‐humidity Rocky Mountains. Plain Language Summary: About 50% of the total runoff in the western United States originates as snowmelt, where snow modeling is a key source of uncertainty in hydrologic prediction for water management. However, appropriate regional parameterization of snow processes is not well addressed in current large‐domain hydrologic modeling that generally focus on matching simulations to observed streamflows. In this context, this work develops regionally coherent snow parameters, for a relatively simple mass and energy‐balance snow model, for eight ecological regions covering the mountain ranges of the western United States. The snow parameters proven to be critical for simulating snow processes consist of three snow albedo parameters and one snow temperature threshold for rain/snow partitioning. Results demonstrate that regional snow parameters can largely capture the snow regime at the daily time scale across the western United States represented by 246 Snow Telemetry stations characterized by diverse hydroclimatic conditions. While the three albedo parameters show limited spatial variability across all regions, the snow temperature threshold shows marked spatial variation negatively correlated with relative humidity. In sum, the regional snow parameters are expected to reduce uncertainties in regional to continental snow and hydrologic modeling. Key Points: Daily SWE variability across the western mountains can largely be captured by a simple snow model with four sensitive parametersRegionally coherent snow parameters developed for eight ecoregions can largely capture snow regimes of the western United StatesThe model skill is lower in the Pacific Northwest where SWE is more sensitive to errors in energy balance and rain/snow partitioning [ABSTRACT FROM AUTHOR]

Several postdepositional processes impact snow nitrate; however, only the isotopic effects of nitrate photolysis have been quantified. Here we discuss results from experiments in field Antarctic snow investigating isotopic fractionation of nitrate due to volatilization. At −35 °C, concentration and isotopic composition of nitrate remained constant during the 16‐day experiment. At −24 °C, 7.5% of nitrate was lost, synchronous with 1.5‰ decrease in δ18O and a constant δ15N. At −4 °C, 38% of nitrate was lost, and δ15N and δ18O decreased by 3.1 and 1.8‰, respectively. Results at −4 °C yield calculated fractionation constants close to theoretical estimates including equilibrium isotopic exchange between nitric acid and nitrate and the desorption of nitric acid from water in quasi‐liquid layers. This suggests that isotopic fractionation associated with nitrate volatilization across most of Antarctica, especially at sites with temperatures <−24 °C, should be minor, but the isotopic effects at warmer sites should be considered in interpreting archived nitrate records. Plain Language Summary: In polar regions, there is great interest in using nitrate archived in snow/ice to reconstruct aspects of atmospheric composition and the influence of natural versus man‐made emissions. However, nitrate can be lost from the snowpack, such that the archived signals do not directly reflect atmospheric loading. Stable isotopes of nitrate in snow/ice allow for tracking and understanding the origins of nitrate and how it might have changed after deposition. While the isotopic fractionation associated with photolytic loss of nitrate has been directly quantified, that of volatilization remains poorly understood. Thus, we completed field experiments in Antarctica to investigate the isotopic effects of nitrate volatilization from snow. Results show that nitrate volatilization loss is significant at −4 °C, resulting in an important change (depletion of heavy isotopes) in both nitrogen and oxygen in remaining snow nitrate. This can be largely explained by a combination of equilibrium isotopic exchange between nitric acid and nitrate and the desorption of nitric acid from water in ice surface layers. At lower temperatures (<−24 °C), nitrate loss is rather minor, and the isotopic fractionation is found to be negligible. The findings suggest that the isotopic effects of NO3− volatilization in snow are closely related to temperature. Key Points: Field experiments in Antarctica (over 16 days) were used to quantify the isotopic effects of NO3− volatilization from snowVolatilization at −4 °C depletes heavy isotopes of NO3− in remaining snow versus relatively constant isotopic ratios observed at −35 °CVolatilization can decrease δ15N in remaining snow NO3− in warm regions by several per mil, contrary to isotopic effects of photolysis [ABSTRACT FROM AUTHOR]

Freezing/thawing indices are useful for assessments of climate change, surface and subsurface hydrology, energy balance, moisture balance, carbon exchange, ecosystem diversity and productivity. Current freezing/thawing indices are inadequate to meet these requirements. We use 16 Coupled Model Intercomparison Project phase 5 (CMIP5) models available for 1850–2005, three representative concentration pathways (RCP2.6, RCP4.5, and RCP8.5) during 2006–2100, and Climatic Research Unit gridded observations for 1901–2014, to assess the performance of freezing/thawing indices derived from CMIP5 models during 1901–2005. We also analyzed past spatial patterns of freezing/thawing indices and projected these over three RCPs. Results show that CMIP5 models can reproduce the spatial pattern of freezing/thawing indices in the Northern Hemisphere but that the thawing index slightly underestimated observations and the freezing index slightly overestimated them. The thawing index agreed slightly better with observations than did the freezing index. There is significant spatial variability in the freezing/thawing indices, ranging from 0° to 10 000°C day. Over the entire Northern Hemisphere, the time series of the area-averaged thawing index derived from CMIP5 output increased significantly at about 1.14°C day yr−1 during 1850–2005, 1.51°C day yr−1 for RCP2.6, 5.32°C day yr−1 for RCP4.5, and 13.85°C day yr−1 for RCP8.5 during 2006–2100. The area-averaged freezing index decreased significantly at −1.39°C day yr−1 during 1850–2004, −1.2°C day yr−1 for RCP2.6, −4.3°C day yr−1 for RCP4.5, and −9.8°C day yr−1 for RCP8.5 during 2006–2100. The greatest decreases in the freezing index are projected to occur at high latitudes and high altitudes, where the magnitude of the decreasing rate of the freezing index is far greater than that of the increasing rate of the thawing index. [ABSTRACT FROM AUTHOR]

Because of its location, Canada is particularly affected by snow processes and their impact on the atmosphere and hydrosphere. Yet, snow mass observations that are ongoing, global, frequent (1–5 days), and at high enough spatial resolution (kilometer scale) for assimilation within operational prediction systems are presently not available. Recently, Environment and Climate Change Canada (ECCC) partnered with the Canadian Space Agency (CSA) to initiate a radar-focused snow mission concept study to define spaceborne technological solutions to this observational gap. In this context, an Observing System Simulation Experiment (OSSE) was performed to determine the impact of sensor configuration, snow water equivalent (SWE) retrieval performance, and snow wet/dry state on snow analyses from the Canadian Land Data Assimilation System (CaLDAS). The synthetic experiment shows that snow analyses are strongly sensitive to revisit frequency since more frequent assimilation leads to a more constrained land surface model. The greatest reduction in spatial (temporal) bias is from a 1-day revisit frequency with a 91% (93%) improvement. Temporal standard deviation of the error (STDE) is mostly reduced by a greater retrieval accuracy with a 65% improvement, while a 1-day revisit reduces the temporal STDE by 66%. The inability to detect SWE under wet snow conditions is particularly impactful during the spring meltdown, with an increase in spatial RMSE of up to 50 mm. Wet snow does not affect the domain-wide annual maximum SWE nor the timing of end-of-season snowmelt timing in this case, indicating that radar measurements, although uncertain during melting events, are very useful in adding skill to snow analyses. [ABSTRACT FROM AUTHOR]

Measurement of lead isotope ratios in snow samples using inductively coupled plasma-dynamic reaction cell-mass spectrometry (ICP-DRC-MS) at a low sample consumption rate was thoroughly studied. Sample volumes were reduced from 20.0 to 0.200 mL by freeze-drying, and the pre-concentrates were efficiently introduced into the plasma source at 20 μL min−1 by using the heated torch-integrated sample introduction system (hTISIS). In addition, the DRC was pressurised with neon to improve the precision by collisional damping. Sensitivity was maximised by optimising the operating parameters of the sample introduction system (nebulizer/sheathing gas flow rate ratio), the reaction cell (rod offset and damping gas flow rate) and the signal measurement conditions. The developed method was then characterised in terms of the analytical working range, accuracy and uncertainty estimation. Under the optimal conditions, isotope ratios did not differ in the 0.1–20 μg L−1 range. Internal precision, expressed as relative standard deviation, varied from 0.12 to 0.18% (c = 10 μg L−1; n = 12), while the external precision was ＜0.1%. The potential influence of the pre-concentration procedure on the analytical data was carefully investigated in terms of blank contribution, recovery and isotopic fractionation. The lowest Pb concentration needed in the samples to limit the influence of the blanks within an experimental precision of 0.5% was 4.4 pg g−1, and the recovery of five standard reference solutions with a Pb concentration of 100 ng L−1 was 90 ± 5%, with no significant isotopic fractionation. The developed ICP-DRC-MS method was finally applied to a number of representative Antarctic snow samples previously characterised by multi-collector ICP-MS, obtaining a satisfactory agreement between the data provided by the two techniques and analytical results consistent with literature data on the glaciochemistry of Antarctic ice cores. [ABSTRACT FROM AUTHOR]

Understanding seasonal snow cover dynamics is critical for management of hydrological regimes, habitat availability for wildlife species, forest fire risk assessment and recreational demands. Although data products provided at 500 m spatial resolution by the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor provide important and readily available information on snow cover, capturing snow dynamics at finer spatial resolutions remains problematic due to the lack of high temporal and spatial resolution data, which limits the number of available observations each year. In this paper we present a new approach to create a daily time-series of 30-m snow observations (called SNOWARP), derived from daily MODIS Normalised Difference Snow Index (NDSI) snow cover data to capture the temporal dynamics of snow cover and Dynamic Time Warping (DTW) to re-order historical Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) observations to account for inter-annual variability. The SNOWARP product was produced for 2000–2018 for an area of Western Alberta (approximately 30,000 km 2 ) and was calibrated against a network of time-lapse cameras and snow pillows. Results indicate the RMSE of the SNOWARP fractional product ranges from 31.3%–68.3%, while F score of the SNOWARP binary product ranges from 87.7% - 98.6% when compared to ground truth data. Capturing fractional snow cover at a fine spatial and temporal scale is important due to the spatial heterogeneity of snow cover, particularly in mountainous regions with implications for biodiversity assessment and monitoring. SNOWARP demonstrates a novel method to increasing the temporal resolution of Landsat-derived snow cover data, providing valuable insights on regional snow cover dynamics for use in a range of applications. [ABSTRACT FROM AUTHOR]

Global monitoring of soil moisture and snow is now available through various satellite observations from optical, microwave, and gravitational sensors. However, very few modeling frameworks exist that conjointly use the above sensors to produce mutually and physically consistent earth system records. To this goal, a prototype of multi-sensor land data assimilation system is developed by linking the Community Land Model version 4 (CLM4) and a series of forward models with the Data Assimilation Research Testbed (DART). The deterministic Ensemble Adjustment Kalman Filter (EAKF) within the DART is utilized to estimate global soil moisture and snow by assimilating brightness temperature, snow cover fraction, and daily total water storage observations from the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E), Moderate Resolution Imaging Spectroradiometer (MODIS), and Gravity Recovery and Climate Experiment (GRACE), respectively. A 40-member of Community Atmosphere Model version 4 (CAM4) reanalysis is adopted to introduce ensemble spread in CLM4 land states and some methods are used to reduce the computational load. Data assimilation with different combinations of sensors is implemented for 2003–2009 to investigate individual contributions from different satellite observations. Evaluation results and cross-comparison of open-loop and data assimilation cases suggest that 1) assimilation of MODIS snow cover fraction slightly improves snow estimation in mid and high latitudes; 2) lower and higher frequencies of AMSR-E brightness temperature play complementary roles in improving global soil moisture and snow estimation; 3) assimilation of GRACE tends to degrade soil moisture estimation but poses potential in improving snow depth estimation in most high-latitude regions. Generally, the combination of MODIS, GRACE, and AMSR-E observations with regard to spatial locations holds promise to provide a robust global soil moisture and snow estimation through the multi-sensor land data assimilation system. [ABSTRACT FROM AUTHOR]

The uncertainty in assessing the numerous atmospheric pollutants transported via wind from arid and semi-arid regions is affecting the glacial ecosystem. In our study area due to the complexity of the system, a prominent seasonal difference noticed among major ions (Ca2+, Mg2+, SO42−, and NO3−). There is a need for understanding the ions cycling as a whole and the directionality of the feedback loops in the system. Therefore, we provide an appraisal of our current hypothesis for seasonal difference in major ion concentration from snow samples for two corresponding years (2013 and 2015) at Dokriani Glacier. A systematic study of chemical compositions in the shallow snow pit from Dokriani Glacier was undertaken for the pre-monsoon season to understand the cycling of major ions from atmosphere to solute acquisition process. The intimating connections of ions cycling in snow and its temporal behavior was observed and analyzed through various statistical tests. Among major ions, the SO42− has the highest concentration among anions on an average considered as 14.21% in 2013 and 29.46% in 2015. On the other side Ca2+ is the dominant cation contributing 28.22% in 2013 and 15.3% in 2015 on average. The average ratio of Na+/Cl− was higher in 2013 whereas lower in 2015. The backward trajectory analysis suggests the possible sources of the ions transported from Central Asia through the Western Disturbance (WD) as a prominent source of winter precipitation mainly in the Central Himalaya. Ionic concentration of Ca2+ in cations was highly dominated while in anion SO42− played the major role. Factor analysis and correlation matrix suggested that, the precipitation chemistry is mostly influenced by anthropogenic, crustal, and sea salt sources over the studied region. The elemental cycling through ocean, atmosphere and biosphere opens up new ways to understand the geochemical processes operating at the glacierized catchments of the Himalaya. Moreover, increasing the field-based studies in the coming decades would also have the certain advantage in overcoming the conceptual and computational geochemical modelling difficulties. [ABSTRACT FROM AUTHOR]

In this paper, we introduce and provide access to a daily (1960-2017) and hourly (1993-2017) dataset of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l, Charteuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to April 2015. A detailed study of the uncertainties originating from both measurements errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse to total shortwave broadband irradiance is affected by an uncertainty of ± 0.21. The estimated root mean squared deviation, that can be mainly attributed to spatial variability, is ± 10 cm for snow depth, ± 25 kg m-2 for snow water and ± 1 K for soil temperature (± 0.4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site with a reduction of the mean snow depth (Dec. 1st to April 30th of 39 cm from 1960-1990 to 1990-2017 and an increase in temperature of + 0.90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) datacenter: https://doi.org/10.17178/CRYOBSCLIM.CDP.2018. [ABSTRACT FROM AUTHOR]

This work presents the Guadalfeo Monitoring Network in Sierra Nevada (Spain), a snow monitoring network in the Guadalfeo Experimental Catchment, a semiarid area in southern Europe representative of snow packs with highly variable dynamics on both the annual and seasonal scales, and significant topographic gradients. The network includes weather stations that cover the high mountain area in the catchment and time-lapse cameras to capture the variability of the ablation phases on different spatial scales. The data sets consist of continuous meteorological high frequency records at five automatic weather stations located at different altitudes ranging from 1300 to 2600 m a.s.l. that include precipitation, air temperature, wind speed, air relative humidity, and the short- and long-wave components of the incoming radiation, dating from 2009 for the oldest station (2530 m a.s.l.) (https://doi.pangaea.de/10.1594/PANGAEA.895236); additionally, daily data sets of fractional snow cover area and snow depth from the imagery from two time-lapse cameras are presented, with different scene area (30x30 m, and 2 km², respectively) and spatial resolution, from 2009 (https://doi.org/10.1594/PANGAEA.871706). Some research applications of these datasets are also included to highlight the value of high resolution data sources to improve the understanding of snow processes and distribution in highly variable environments. The datasets are available from different open source sites and provide both the snow hydrology scientific community and other research fields, such as terrestrial ecology, riverine ecosystems or water quality in high mountains, with a valuable and high-potential information in snow-dominated areas in semiarid regions. [ABSTRACT FROM AUTHOR]

For effective avalanche risk mitigation, numerical models with a correct description of snow rheology are needed. Conventionally, velocity in snow flow experiments is inferred by cross-correlating the voltage signals of paired sensors. The intention of this paper is to reconsider this problem to enhance processing of these data, leading to more effective estimates of fluctuating velocity quantities. The algorithm consists of a wavelet decomposition, a denoising step and a weighting method for the reconstituted signal. The resulting velocity time series are both consistent and informative, providing confidence that one can analyse not only the mean velocity profiles, but also the velocity distribution. Our approach is illustrated using a typical chute experiment undertaken at Col du Lac Blanc in the French Alps. Not only has the mean velocity profile a more complex shape than the bilinear one postulated from the results of the standard cross-correlation processing, but the probability distribution functions of the velocity at different heights is much more continuous and dispersed, revealing interesting new patterns of greater dynamical relevance. [ABSTRACT FROM AUTHOR]

A multiresolution (MR) approach was successfully implemented in the context of a data assimilation (DA) framework to efficiently estimate snow water equivalent (SWE) over a large head water catchment in the Colorado River basin (CRB), while decreasing computational constraints by 60 %. A total of 31 years of fractional snow cover area (fSCA) images derived from Landsat TM, ETMC, and OLI sensor measurements were assimilated to generate two SWE reanalysis datasets, a baseline case at a uniform 90mspatial resolution and another using theMRapproach. A comparison of the two showed negligible differences in terms of snow accumulation, melt, and timing for the posterior estimates (in terms of both ensemble median and coefficient of variation). The MR approach underestimated the baseline peak SWE by less than 2% and underestimated day of peak and duration of the accumulation season by a day on average. The largest differences were, by construct, limited primarily to areas of low complexity, where shallow snowpacks tend to exist. The MR approach should allow for more computationally efficient implementations of snow data assimilation applications over large-scale mountain ranges, with accuracies similar to those that would be obtained using ~100m simulations. Such uniform resolution applications are generally infeasible due to the computationally expensive nature of ensemble-based DA frameworks. [ABSTRACT FROM AUTHOR]

To influence the weather is a long-treasured dream of mankind. Current cloud seeding technology mostly relies on chemicals such as silver iodide and hygroscopic salts, which may have negative impacts on environment and human health. Here, we present a new method for triggering macroscopic water precipitation in air. Experiments demonstrated that high-voltage generated corona discharge was able to induce rain and snow formation in atmospheric pressure air. The effect was confirmed by precipitation experiments performed in a 1- and 15 000-m3 cloud chamber, where the absolute water content was close to the actual values in natural cumulonimbus clouds. With the presence of electrical charges, the collision efficiency between the water droplets was increased by over one order of magnitude, accelerating the coalescence process and possibly leading to the rain and snow formation otherwise impossible to form. The electric-based technology presented in this paper provides an effective, inexpensive, and environmental friendly tool for triggering water precipitation, and may open up new opportunities for producing water precipitation in large open space. [ABSTRACT FROM AUTHOR]

The Antarctic Plateau snowpack is an important environment for the mercury geochemical cycle. We have extensively characterized and compared the changes in surface snow and atmospheric mercury concentrations that occur at Dome C. Three summer sampling campaigns were conducted between 2013 and 2016. The three campaigns had different meteorological conditions that significantly affected mercury deposition processes and its abundance in surface snow. In the absence of snow deposition events, the surface mercury concentration remained stable with narrow oscillations, while an increase in precipitation results in a higher mercury variability. The Hg concentrations detected confirm that snowfall can act as a mercury atmospheric scavenger. A high temporal resolution sampling experiment showed that surface concentration changes are connected with the diurnal solar radiation cycle. Mercury in surface snow is highly dynamic and it could decrease by up to 90% within 4/6 h. A negative relationship between surface snow mercury and atmospheric concentrations has been detected suggesting a mutual dynamic exchange between these two environments. Mercury concentrations were also compared with the Br concentrations in surface and deeper snow, results suggest that Br could have an active role in Hg deposition, particularly when air masses are from coastal areas. This research presents new information on the presence of Hg in surface and deeper snow layers, improving our understanding of atmospheric Hg deposition to the snow surface and the possible role of re-emission on the atmospheric Hg concentration. [ABSTRACT FROM AUTHOR]

The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1.0 includes the Dense Media Radiative Transfer theory (DMRT), the Improved Born Approximation (IBA) and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between IBA and DMRT theories has been shown here. Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in-situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e.g. sea-ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models. [ABSTRACT FROM AUTHOR]

In the present study, a numerical model is developed for wheel-snow interaction using finite element method based software. For this aim, the model of tire is designed using SolidWorks and ANSYS Design modeler. The analyses of the prepared models are performed using ANSYS Explicit Dynamics considering Mooney-Rivilin tire model. Frictional relationship between wheel and snow ground is established considering snow erosion as linear, in the analyses. Six different mesh sizes are considered, the effect of mesh size and number on the accuracy of the obtained results and solution time is discussed. Finally, it is concluded that models with 0.025 m and 0.02 m mesh sizes give more accurate results than the others and a strong linear proportion exists between the number of iteration and the mesh size. [ABSTRACT FROM AUTHOR]

A case study in terms of variations in differential reflectivity ZDR observed at X band and snow crystal riming is presented for a light-snow event that occurred near Greeley, Colorado, on 26–27 November 2015. In the early portion of the event, ZDR values at near-surface levels were low (0–0.25 dB). During a second time period approximately 8 h later, ZDR values became distinctly positive (+2–3 dB). Digital photographs of the snow particles were obtained by a Multi-Angle Snowflake Camera (MASC) installed at a range of 13 km from the radar. Image-processing and machine-learning techniques applied to the MASC data showed that the snow particles were more heavily rimed during the low-ZDR time period. The aerodynamic effects of these rime deposits promoted a wider distribution of hydrometeor canting angles. The shift toward more random particle orientations underlies the observed reduction in ZDR during the period when more heavily rimed particles were observed in the MASC data. [ABSTRACT FROM AUTHOR]

A meteorological and blowing snow dataset issued from the high-altitude experimental site of Col du Lac Blanc (2720m altitude, Grandes Rousses mountain range, France) is presented and detailed in this paper. Emphasis is placed on data relevant to the observations and modelling of wind-induced snow transport in alpine terrain. This process strongly influences the spatial distribution of snow cover in mountainous terrain with consequences for snowpack, hydrological and avalanche hazard forecasting. In-situ data consist of wind (speed and direction), snow depth, and air temperature measurements (recorded at four automatic weather stations), a database of blowing snow occurrence and measurements of blowing snow fluxes obtained from a vertical profile of Snow Particle Counters. Observations data span the period from December 1st to March 31st for each winter season from 2000-2001 until 2015-2016. The time resolution varies from 15min. at the beginning of the period to 10min. for the last years. Atmospheric data from a local meteorological reanalysis (SAFRAN) are also provided from 1 August 2000 to 1 August 2016. A Digital Elevation Model (DEM) of the study area (1,5km2) at 20-cm resolution is also provided in RGF 93 Lambert 93 coordinates This dataset has been used in the past to develop and evaluate physical parameterizations and numerical models of blowing and drifting snow in alpine terrain. Col du Lac Blanc is also a target site to evaluate meteorological and climate models in alpine terrain. It belongs to the Cryobs-Clim observatory (the CRYosphere, an OBServatory of the CLIMate) which is a part of the national research infrastructure OZCAR (Critical Zone Observatories - Application and Research) (Gaillardet et al., 2018). [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]

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]

Detailed hydrometeorological data from the rain-to-snow transition zone in mountain regions are limited. As the climate warms, the transition from rain to snow is moving to higher elevations, and these changes are altering the timing of downslope water delivery. To understand how these changes impact hydrological and biological processes in this climatologically sensitive region, detailed observations from the rain-to-snow transition zone are required. We present a complete hydrometeorological dataset for water years 2004 through 2014 for a watershed that spans the rain-to-snow transition zone (doi:10.15482/USDA.ADC/1402076). The Johnston Draw watershed (1.8 km2), ranging from 1497-1869 m in elevation, is a sub-watershed of the Reynolds Creek Experimental Watershed (RCEW) in southwestern Idaho, USA. The dataset includes continuous hourly hydrometeorological variables across a 372 m elevation gradient, on north- and south- facing slopes, including air temperature, relative humidity, and snow depth from 11 sites in the watershed. Hourly measurements of incoming shortwave radiation, precipitation, wind speed and direction, and soil moisture and temperature are available at selected stations. The dataset includes hourly stream discharge measured at the watershed outlet. These data provide the scientific community with a unique dataset useful for forcing and validating models and will allow for better representation and understanding of the complex processes that occur in the rain-to-snow transition zone. [ABSTRACT FROM AUTHOR]

We present snow observations and a validated daily gridded snowpack dataset that was simulated from downscaled reanalysis of data for the Iberian Peninsula. The Iberian Peninsula has long-lasting seasonal snowpacks in its different mountain ranges, and winter snowfalls occur in most of its area. However, there are only limited direct observations of snow depth (SD) and snow water equivalent (SWE), making it difficult to analyze snow dynamics and the spatiotemporal patterns of snowfall. We used meteorological data from downscaled reanalyses as input of a physically based snow energy balance model to simulate SWE and SD over the Iberian Peninsula from 1980 to 2014. More specifically, the ERA-Interim reanalysis was downscaled to 10 × 10 km resolution using the Weather Research and Forecasting (WRF) model. The WRF outputs were used directly, or as input to other submodels, to obtain data needed to drive the Factorial Snow Model (FSM). We used lapse-rate coefficients and hygrobarometric adjustments to simulate snow series at 100 m elevations bands for each 10 × 10 km grid cell in the Iberian Peninsula. The snow series were validated using data from MODIS satellite sensor and ground observations. The overall simulated snow series accurately reproduced the interannual variability of snowpack and the spatial variability of snow accumulation and melting, even in very complex topographic terrains. Thus, the presented dataset may be useful for many applications, including land management, hydrometeorological studies, phenology of flora and fauna, winter tourism and risk management. The data presented here are available for free download from Zenodo (DOI: https://doi.org/10.5281/zenodo.854618).This paper fully describes the work flow, data validation, uncertainty assessment and possible applications and limitations of the database. [ABSTRACT FROM AUTHOR]

A hand-driven friction-sleeve-equipped minipenetrometer was assessed for its efficacy in determining snow physical properties. Numerous factors were investigated including the effect of penetration rate, penetrometer effective diameter, compaction ahead of the penetrometer, distance for representative resistance to be mobilized, and the effect of snow density on penetrometer tip resistance and sleeve friction. Analysis suggests that consideration of additional friction-sleeve data may provide greater insight into snow physical properties compared to consideration of tip-resistance data alone. A friction-sleeve-equipped penetrometer provides valuable data in snow and firn beyond that obtainable through the use of a device measuring only penetrative resistance at the tip. [ABSTRACT FROM AUTHOR]

In this paper we studied some heavy metals (Cu, Zn, Cd, Pb, As, U) probably associated to high molecular weight organic compounds present in the Antarctic snow. Snow-pit samples were collected and analysed for high molecular weight fraction and heavy metals bound to them by means of ultrafiltration treatment. High molecular weight dissolved organic matter (HMW-DOM) recovered by ultrafiltration showed a dissolved organic carbon concentration (HMW-DOC) of about 18–83% of the total dissolved organic carbon measured in Antarctic snow. The characterisation of HMW-DOM fraction evidenced an ageing of organic compounds going from surface layers to the deepest ones with a shift from aliphatic compounds and proteins/amino sugars to more high unsaturated character and less nitrogen content. The heavy metals associated to HMW-DOM fraction follows the order: Zn > Cu > Pb >> Cd ∼ As ∼ U. The percentage fraction of metals bound to HMW-DOM respect to total metal content follows the order: Cu >> Pb > Zn, Cd in agreement with humic substance binding ability (Irwing-William series). Going down to depth of trench, all metals except arsenic, showed a high concentration peak corresponding to 2.0–2.5 m layer. This result was attributed to particular structural characteristic of organic matter able to form different type of complexes (1:1, 1:2, 1:n) with metals. [ABSTRACT FROM AUTHOR]

The date of the definitive start to the Anthropocene is still under debate, and although a lot of progress has been made, currently available data is not precise enough to define the start of the human-dominated geological epoch. We know that during and after the industrial revolution, humans started having a much greater impact on the Earth's environment. Increases in population have led to increases in resource and fossil fuel use, leaving a marked impact on our planet. This impact, in the Northern hemisphere, is effectively recorded in the snow and ice of the Arctic. Human activity has changed various biogeochemical cycles to such an extent, that the climate has started to change. This has disturbed the biosphere, pushing it to adapt in response to these changes through evolutionary pressure. The Arctic is a particularly vulnerable environment and mankind is having a profound impact on its fragile equilibrium. Higher concentrations of heavy metals, organic compounds and radionuclides have been detected in ice cores as well as snow. Although climatic changes are evident on a global scale, in the Arctic these changes have been amplified. Advances in laboratory analysis methods have been applied to ice cores and surface snow samples to help us understand the mechanisms governing this fragile environment and to evaluate the impact and amplitude of human activity. Despite these advances, the fluxes and distributions over time of anthropogenic organic compounds is largely unknown. Hopefully advances in analytical methods will mean that this is not the case in the future. [ABSTRACT FROM AUTHOR]

This paper presents the results of field experiments done for the measurement of attenuation constant and speed of sound in the snow medium. The field experiments were conducted at two locations corresponding to relatively dry and wet snow. The main objective of our method is to overcome the potential limitations of the other methods. There are two major concerns: one is sound signal leakage and the other is the measurement need to be done within a same layer of snowpack. The ref lections from the boundaries will affect the measurements. In our experiments the transducers are kept far from the snowpack boundaries, so that there will be no likelihood of strong ref lected signals from the boundaries. These issues have not been addressed by the other researchers. This work adds to the measurement results of the attenuation constant and speed of sound in snow that are available in the research literature. It is found that sound signal attenuation greatly depends on the frequency of sound signal and wetness of snow. [ABSTRACT FROM AUTHOR]

Cosmogenic 35S is useful in understanding a wide variety of chemical and physical processes in the atmosphere, the hydrosphere, and the cryosphere. The 87.4-day half-life and the ubiquity of sulfur in natural environments renders it an ideal tracer of many phenomena. Measurements of 35S in snow and water samples are scarce as existing analytical methods require a large volume of sample (>20 L) due to their high analytical activity background and low counting efficiency. Here, we present a new set of snow/water sample collecting and handling procedures for high-sensitivity determination of cosmogenic 35S using a low-level liquid scintillation spectrometer. Laboratory experiments using diluted 35S standards (with activities of <5 disintegrations per minute) showed a 35S recovery percentage of 95%, demonstrating a relatively small deviation from the true value. Using this method, we successfully measured 35S in 1 L of fresh snow sample collected from a glacier on the Tibetan Plateau to be 47 ± 7 mBq/L. On the basis of 35S activities in 9 natural samples measured in this study, a first proof-of-concept approximation for age determinations and source attributions was presented. This new method will provide a powerful tool in studying 35S in small volumes of snow and water samples, especially those from remote but climatically important regions such as the polar regions and the Tibetan Plateau and Himalayas. The measurements are particularly important as the radioactive sulfur provides an actual clock of glacial melting processes. With the growing rate of glacial loss, the need for measurements from remote locations becomes all the more important. [ABSTRACT FROM AUTHOR]

Contents of 11 most prevalent polycyclic aromatic hydrocarbons (PAHs) in snow and soils of arable, fallow, and forest areas significantly remote from impact technogenic sources of polyarenes have been examined in the Torzhok district of Tver oblast. From the analysis of snow samples, the volumes and composition of PAHs coming from the atmosphere onto the areas of different land use have been determined. Light hydrocarbons prevail in PAHs. They make up 65-70% of total PAHs; their share in soils reaches almost 95%. An increase in the content of PAHs is revealed in fallow soils compared to arable and afforested areas. A direct relationship is revealed between the lateral distribution of total PAHs and the content of organic carbon. The distribution of total PAHs is surface-accumulative in forest soils, mainly uniform in arable soils, and deepaccumulative in fallow soils. PAH groups characterized by similar radial distributions and ratios between their reserves in snow and soils are distinguished: (1) fluorene and phenanthrene, (2) biphenyl and naphthalene, (3) benzo(a)anthracene, chrysene, perylene, and benzo[ a]pyrene, and (4) anthracene and benzo[ ghi]pyrene. [ABSTRACT FROM AUTHOR]

Diagnosing the source of errors in snow models requires intensive observations, a flexible model framework to test competing hypotheses, and a methodology to systematically test the dominant snow processes. We present a novel process-based approach to diagnose model errors through an example that focuses on snow accumulation processes (precipitation partitioning, new snow density, and snow compaction). Twelve years of meteorological and snow board measurements were used to identify the main source of model error on each snow accumulation day. Results show that modeled values of new snow density were outside observational uncertainties in 52% of days available for evaluation, while precipitation partitioning and compaction were in error 45% and 16% of the time, respectively. Precipitation partitioning errors mattered more for total winter accumulation during the anomalously warm winter of 2014-2015, when a higher fraction of precipitation fell within the temperature range where partition methods had the largest error. These results demonstrate how isolating individual model processes can identify the primary source(s) of model error, which helps prioritize future research. [ABSTRACT FROM AUTHOR]

This study investigates the sensitivity of multispectral reflectivity to changing snow correlation lengths. Mätzler's ice-lamellae radiative transfer model was implemented and tested to evaluate the reflectivity of snow correlation lengths at multiple frequencies from the ultraviolet (UV) to the microwave bands. The model reveals that, in the UV to infrared (IR) frequency range, the reflectivity and correlation length are inversely related, whereas reflectivity increases with snow correlation length in the microwave frequency range. The model further shows that the reflectivity behavior can be mainly attributed to scattering rather than absorption for shallow snowpacks. The largest scattering coefficients and reflectivity occur at very small correlation lengths (∼10 −5 m) for frequencies higher than the IR band. In the microwave range, the largest scattering coefficients are found at millimeter wavelengths. For validation purposes, the ice-lamella model is coupled with a multilayer snow physics model to characterize the reflectivity response of realistic snow hydrological processes. The evolution of the coupled model simulated reflectivities in both the visible and the microwave bands is consistent with satellite-based reflectivity observations in the same frequencies. The model results are also compared with colocated in situ snow correlation length measurements (Cold Land Processes Field Experiment 2002–2003). The analysis and evaluation of model results indicate that the coupled multifrequency radiative transfer and snow hydrology modeling system can be used as a forward operator in a data-assimilation framework to predict the status of snow physical properties, including snow correlation length. [ABSTRACT FROM PUBLISHER]

Snow samples were collected during winter 2011/2012 in three posts in the Western Sudety Mountains (Poland) in 3 consecutive phases of snow cover development, i.e. stabilisation (Feb 1st), growth (Mar 15th) and its ablation (Mar 27th). To maintain a fixed number of samples, each snow profile has been divided into six layers, but hydrochemical indications were made for each 10 cm section of core. The complete data set was subjected in the first run of chemometric data interpretation to Cluster Analysis as well as Principal Components Analysis. Further, Self-Organizing Maps, type of neutral network described by Kohonen were used for visualization and interpretation of large high-dimensional data sets. For each site the hierarchical Ward's method of linkage, squared Euclidean distance as similarity measure, standardized raw data, cluster significance test according to Sneath's criterion clustering of the chemical variables was done. Afterwards this grouping of the chemical variables was confirmed by the results from Principal Components Analysis. The major conclusion is that the whole system of three sampling sites four patterns of variable groupings are observed: the first one is related to the mineral salt impact; the second one - with the impact of secondary emissions and organic pollutants; next one - with dissolved matter effect and the last one - with oxidative influence, again with relation to anthropogenic activities like smog, coal burning, traffic etc. It might be also concluded that specificity of the samples is determined by the factors responsible for the data set structure and not by particular individual or time factors. [ABSTRACT FROM AUTHOR]

Regional-scale estimates of snow water equivalent (SWE) are challenging in alpine regions, particularly in areas of high accumulation and dense forest cover, suggesting efforts to improve these estimates may benefit from an evaluation of existing gridded products. Gridded SWE products comprising four reanalysis datasets (ERA-Interim, ERA-Interim/Land, MERRA and MERRA-Land), two land data assimilation system datasets (GLDAS1 and GLDAS2) and two observation-based products (CMC and GlobSnow) have been compared with in-situ measurements over five physiographic regions in British Columbia (BC), Canada. Time series were generated for each survey month (January through June), and median correlation, bias and mean absolute error (MAE) values were found for each product and physiographic region. The best performance in correlation and magnitude of bias and MAE was seen in areas of lowest SWE accumulation and land relief (e.g. the Interior Plains of northern BC), while poorer performance was seen in regions of high accumulation and complex topography (Columbia and Rocky Mountains and Coast Mountains). Overall, the performance ranking order is ERA-Interim/Land (best), GLDAS2, MERRA, CMC, GLDAS1, MERRA-Land, GlobSnow and ERA-Interim. [ABSTRACT FROM AUTHOR]

The snow surface temperature (SST) is essential for estimating longwave radiation fluxes from snow. SST can be diagnosed using finescale multilayer snow physics models that track changes in snow properties and internal energy; however, these models are heavily parameterized, have high predictive uncertainty, and require continuous simulation to estimate prognostic state variables. Here, a relatively simple model to estimate SST that is not reliant on prognostic state variables is proposed. The model assumes that the snow surface is poorly connected thermally to the underlying snowpack and largely transparent for most of the shortwave radiation spectrum, such that a snow surface energy balance among only sensible heat, latent heat, longwave radiation, and near-infrared radiation is possible and is called the radiative psychrometric model (RPM). The RPM SST is sensitive to air temperature, humidity, ventilation, and longwave irradiance and is secondarily affected by absorption of near-infrared radiation at the snow surface that was higher where atmospheric deposition of particulates was more likely to be higher. The model was implemented with neutral stability, an implicit windless exchange coefficient, and constant shortwave absorption factors and aerodynamic roughness lengths. It was evaluated against radiative SST measurements from the Canadian Prairies and Rocky Mountains, French Alps, and Bolivian Andes. With optimized and global shortwave absorption and aerodynamic roughness length parameters, the model is shown to accurately predict SST under a wide range of conditions, providing superior predictions when compared to air temperature, dewpoint, or ice bulb calculation approaches. [ABSTRACT FROM AUTHOR]

We investigate the problem of balancing model complexity and input data requirements in snow hydrology. For this purpose, we analyze the performance of two models of different complexity in estimating variables of interest in snow hydrology applications. These are snow depth, bulk snow density, snow water equivalent and snowmelt run-off. We quantify the differences between data and model prediction using 18 years of measurements from an experimental site in the French Alps (Col de Porte, 1325 m AMSL). The models involved in this comparison are a one-layer temperature-index model (HyS) and a multilayer model (Crocus). Results show that the expected loss in performance in the one-layer temperature-index model with respect to the multilayer model is low when considering snow depth, snow water equivalent and bulk snow density. As for run-off, the comparison returns less clear indications for identification of a balance. In particular, differences between the models' prediction and data with an hourly resolution are higher when considering the Crocus model than the HyS model. However, Crocus is better at reproducing sub-daily cycles in this variable. In terms of daily run-off, the multilayer physically based model seems to be a better choice, while results in terms of cumulative run-off are comparable. The better reproduction of daily and sub-daily variability of run-off suggests that use of the multilayer model may be preferable for this purpose. Variation in performance is discussed as a function of both the calibration solution chosen and the time of year. Copyright © 2016 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The global prevalence of industrial lead (Pb) contamination was exemplified decades ago by the predominance of anthropogenic Pb in samples of Antarctic surface ice and in Southern Ocean surface waters. Decreases in environmental Pb contamination corresponding with the near-global phase-out of leaded automobile gasoline beginning in the 1970s have since been observed. Measurements of Pb concentration in snow and ice core samples from Antarctica show that recent fluxes of industrial Pb to Antarctica have similarly declined. Here, we present measurements of Pb concentrations and isotopic compositions in seawater and surface sediments from the Amundsen Sea continental shelf including the Amundsen Sea Polynya. Both sets of measurements show that most (~60-95%) of the Pb at our sites, at the time of sampling, is natural in source: that is, derived from the weathering of Antarctic continental rocks. These fluxes of natural Pb then become entrained into polynya waters either from sediment resuspension or from the transport of sediment-laden glacial melt waters to the polynya. [ABSTRACT FROM AUTHOR]

Dry deposition of atmospheric 18 polycyclic aromatic hydrocarbon (PAH) components was investigated in the scope of the study by using surrogate snow samplers at 49 different sampling points in and around the city center of Erzurum, Turkey. Snow was sampled twice, the first of which was taken immediately after the first fresh snow cover and placed into aluminum trays to obtain dry deposition surface while the second sample was taken from the snow cover (accumulated snow) exposed to an 8-day dry deposition period and then analyzed and extracted. All the samples taken from the samplers were extracted using solid and liquid phase extraction and analyzed through GC-MS. It was observed that at the end of an 8-day dry period, snow samples enriched 5.5 times more in PAH components than the baseline. PAH deposition was determined to be influenced mainly by coal, mixed source, traffic, diesel fuel, and petrol fuel at 43, 27, 20, 8, and 2 % of sampling points, respectively. Local polluting sources were found to be effective on the spatial distribution of dry deposition of PAH components in urban area. [ABSTRACT FROM AUTHOR]

The ability of the Canadian Seasonal to Interannual Prediction System (CanSIPS) to provide realistic forecast initial conditions for snow cover is assessed using in situ measurements and gridded snow analyses. Forecast initial conditions for snow in CanCM3 and CanCM4 employed by CanSIPS are determined by the response of the Canadian Land Surface Scheme (CLASS) used in both models to forcing from model atmospheric fields constrained by assimilation of 6-hourly reanalysis data. These snow initial conditions are found to be representative of the daily climatology of snow water equivalent (SWE) as well as interannual variations in maximum SWE and the timing of snow onset and snowmelt observed at eight in situ measurement sites located across Canada. The level of this agreement is similar to that of three independent gridded snow analyses (MERRA, the European Space Agency's GlobSnow, and an offline forced version of CLASS). Total Northern Hemisphere snow mass generated by the CanSIPS initialization procedure is larger for both models (especially CanCM3) than in MERRA, mostly because of higher SWE in regions of common snow cover. Globally, the interannual variability of initial SWE is found to correlate highly with that of MERRA in locations with appreciable snow. These initial values are compared to SWE in freely running CanCM3 and CanCM4 simulations produced without data assimilation of atmospheric fields. Differences in climatological SWE relative to MERRA are similar in the freely running and assimilating CanCM3 and CanCM4 simulations, suggesting that inherent model biases are a major contributor to biases in CanSIPS snow initial conditions. [ABSTRACT FROM AUTHOR]

The influence of temperature and time of snow sample melting on the measurement of mass concentration and size distribution of black carbon (BC) in snow was evaluated experimentally. In the experiments, fresh (Shirouma) and aged (Hakusan) snow samples were melted at different temperatures or at different time lengths, and the BC mass concentration and size distribution in the melted snow samples were measured using a nebulizer and a single-particle soot photometer (SP2). In the experiment where melting temperature was varied, the BC mass concentration in the liquid decreased at a melting temperature of 70°C. This decrease was 8.0% for the Shirouma sample and 46.4% for the Hakusan sample and depended on BC particle size, with a significant decrease found at BC diameters less than 350 nm. A similar decrease in BC mass concentration was found when the Hakusan snow sample that had been melted at 5°C was heated to 70°C. The experiment in which melting time was varied indicated that BC mass concentration in the liquid did not change for the Shirouma sample but decreased significantly with a longer melting time for the Hakusan sample (38.6%). These results indicate that melting of snow samples at high temperatures or over long time periods can significantly affect the measurement of BC mass and its size distribution, especially for aged snow samples. [ABSTRACT FROM AUTHOR]