Micrometeorological processes driving snow ablation in an Alpine catchment

Mountain snow covers typically become patchy over the course of a melting season. The snow pattern during melt is mainly governed by the end of winter snow depth distribution and the local energy balance. The objective of this study is to investigate micrometeorological processes driving snow ablation in an Alpine catchment. For this purpose we combine a meteorological model (ARPS) with a fully distributed energy balance model (Alpine3D). Turbulent fluxes above melting snow are further investigated by using data from eddy-correlation systems. We compare modelled snow ablation to measured ablation rates as obtained from a series of Terrestrial Laser Scanning campaigns covering a complete ablation season. The measured ablation rates indicate that the advection of sensible heat causes locally increased ablation rates at the upwind edges of the snow patches. The effect, however, appears to be active over rather short distances except for very strong wind conditions. Neglecting this effect, the model is able to capture the mean ablation rates for early ablation periods but strongly overestimates snow ablation once the fraction of snow coverage is below a critical value. While radiation dominates snow ablation early in the season, the turbulent flux contribution becomes important late in the season. Simulation results indicate that the air temperatures appear to overestimate the local air temperature above snow patches once the snow coverage is below a critical value. Measured turbulent fluxes support these findings by suggesting a stable internal boundary layer close to the snow surface causing a strong decrease of the sensible heat flux towards the snow cover. Thus, the existence of a stable internal boundary layer above a patchy snow cover exerts a dominant control on the timing and magnitude of snow ablation for patchy snow covers.<br/

Dominance of grain size impacts on seasonal snow albedo at deforested sites in New Hampshire

Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate

Dominance of grain size impacts on seasonal snow albedo at deforested sites in New Hampshire

Snow cover serves as a major control on the surface energy budget in temperate regions due to its high reflectivity compared to underlying surfaces. Winter in the northeastern United States has changed over the last several decades, resulting in shallower snowpacks, fewer days of snow cover, and increasing precipitation falling as rain in the winter. As these climatic changes occur, it is imperative that we understand current controls on the evolution of seasonal snow albedo in the region. Over three winter seasons between 2013 and 2015, snow characterization measurements were made at three open sites across New Hampshire. These near-daily measurements include spectral albedo, snow optical grain size determined through contact spectroscopy, snow depth, snow density, black carbon content, local meteorological parameters, and analysis of storm trajectories using the Hybrid Single-Particle Lagrangian Integrated Trajectory model. Using analysis of variance, we determine that land-based winter storms result in marginally higher albedo than coastal storms or storms from the Atlantic Ocean. Through multiple regression analysis, we determine that snow grain size is significantly more important in albedo reduction than black carbon content or snow density. And finally, we present a parameterization of albedo based on days since snowfall and temperature that accounts for 52% of variance in albedo over all three sites and years. Our improved understanding of current controls on snow albedo in the region will allow for better assessment of potential response of seasonal snow albedo and snow cover to changing climate

Plant phenology and seasonal nitrogen availability in Arctic snowbed communities

Thesis (M.S.) University of Alaska Fairbanks, 2006This study was part of the International Tundra Experiment (ITEX) and examined the effects of increased winter snow depth and decreased growing season length on the phenology of four arctic plant species (Betula nana, Salix pulchra, Eriophorum vaginatum, and Vaccinium vitis-idaea) and seasonal nitrogen availability in arctic snowbed communities. Increased snow depth had a large effect on the temporal pattern of first date snow-free in spring, bud break, and flowering, but did not affect the rate of plant development. By contrast, snow depth had a large qualitative effect on N mineralization in deep snow zones, causing a shift in the timing and amount of N mineralized compared to ambient snow zones. Nitrogen mineralization in deep snow zones occurred mainly overwinter, whereas N mineralization in ambient snow zones occurred mainly in spring. Concentrations of soil dissolved organic nitrogen (DON) were approximately 5 times greater than concentrations of inorganic nitrogen (DIN) and did not vary significantly over the season. Projected increases in the depth and duration of snow cover in arctic plant communities will likely have minor effects on plant phenology, but potentially large effects on patterns of N cycling

The effects of variation in snow properties on passive microwave snow mass estimation

Estimating snow mass at continental scales is difficult, but important for understanding land-atmosphere interactions, biogeochemical cycles and the hydrology of the Northern latitudes. Remote sensing provides the only consistent global observations, butwith unknown errors. Wetest the theoretical performance of the Chang algorithm for estimating snow mass from passive microwave measurements using the Helsinki University of Technology (HUT) snow microwave emission model. The algorithm's dependence upon assumptions of fixed and uniform snow density and grainsize is determined, and measurements of these properties made at the Cold Land Processes Experiment (CLPX) Colorado field site in 2002–2003 used to quantify the retrieval errors caused by differences between the algorithm assumptions and measurements. Deviation from the Chang algorithm snow density and grainsize assumptions gives rise to an error of a factor of between two and three in calculating snow mass. The possibility that the algorithm performsmore accurately over large areas than at points is tested by simulating emission from a 25 km diameter area of snow with a distribution of properties derived from the snow pitmeasurements, using the Chang algorithm to calculate mean snow-mass from the simulated emission. The snowmass estimation froma site exhibiting the heterogeneity of the CLPX Colorado site proves onlymarginally different than that from a similarly-simulated homogeneous site. The estimation accuracy predictions are tested using the CLPX field measurements of snow mass, and simultaneous SSM/I and AMSR-E measurements

Particulate and water-soluble carbon measured in recent snow at Summit, Greenland

Water-soluble organic carbon (WSOC), waterinsoluble particulate organic carbon (WIOC), and particulate elemental carbon (EC) were measured simultaneously for the first time on the Greenland Ice Sheet in surface snow and in a 3-meter snow pit. Snow pit concentrations reveal that, on average, WSOC makes up the majority (89%) of carbonaceous species, followed by WIOC (10%) and EC (1%). The enhancement of OC relative to EC (ratio 99:1) in Greenland snow suggests that, along with atmospheric particulate matter, gaseous organics contribute to snow-phase OC. Comparison of summer surface snow concentrations in 2006 with past summer snow pit layers (2002 – 2005) found a significant depletion in WSOC (20 – 82%) and WIOC (46 – 65%) relative to EC for 3 of the 4 years. The apparent substantial loss of WSOC and WIOC in aged snow suggests that post-depositional processes, such as photochemical reactions, need to be considered in linking ice core records of organics to atmospheric concentrations. Citation: Hagler, G. S. W., M. H. Bergin, E. A. Smith, J. E. Dibb, C. Anderson, and E. J. Steig (2007), Particulate and water-soluble carbon measured in recent snow at Summit, Greenland, Geophys. Res. Lett., 34, L16505, doi:10.1029/2007GL030110

Soluble species in aerosol and snow and their relationship at Glacier 1, Tien Shan, China

Simultaneous sampling of aerosol (n = 20) and snow (n = 114) was made at Glacier 1, Tien Shan, between May 19 and June 29, 1996. Similar temporal patterns of some major ion (calcium, magnesium, potassium, sodium, chloride, and sulfate) concentrations between snow and aerosol show that snow chemistry basically reflects changes in the chemistry of the atmosphere. This gives us confidence in the reconstruction of past atmospheric change using some snow data. There are no significant correlations between aerosol and snow samples for ammonium and nitrate. This suggests that post-depositional and/or post-collection processes may alter ammonium and nitrate concentrations in snow. The fact that the measured cations in aerosol and snow always exceed the measured anions suggests that the atmosphere is alkaline over Glacier 1, Tien Shan. In aerosol and snow samples, calcium is the dominant cationic species, with sulfate and presumed carbonate being the dominant anions. There is a very good inverse relationship (r = 0.96) between the equivalence ratio of calcium to sulfate and the ratio of ammonium to sulfate in aerosols, but this relationship does not hold for snow. This further suggests that post depositional and/or post collection processes exert important controls on ammonium concentrations in snow. Although melt-freeze cycles might increase the concentration of all crustal species through progressive dissolution of dust, these cycles seem most important for magnesium and carbonate

Snow Cover in Alaska: Comprehensive Review

This report presents the results of a statistical analysis of snow cover in Alaska using historical data acquired from the Global Historical Climate Network. Measurements of snow depth and snow water equivalence were collected for Alaska stations between 1950 and 2017. Data cleaning and a distribution analysis were completed for all stations. Finally regression equations were developed to estimate snow water equivalence using recorded snow depth data from Alaska stations. The project is partially supported by ConocoPhillips Arctic Science and Engineering Foundation, UAA, and the Structural Engineers Association of Alaska (SEAAK).University of Alaska Anchorage ConocoPhillips Arctic Science and Engineering Foundation Structural Engineers Association of AlaskaAbstract / Introduction / Methodology / Discussion / Conclusion / References / Appendix 1 Predicted 50-year WESD Stations's snow laods / Appendix 2 Calcuated 50-Year SNWD Station's snow loads / Appendix 3 Distribution Assignment for WESD and SNWD Stations / Appendix 4 Station Plot

A simple model for predicting snow albedo decay using observations from the Community Collaborative Rain, Hail, and Snow-Albedo (CoCoRAHS-Albedo) Network

The albedo of seasonal snow cover plays an important role in the global climate system due to its influence on Earth’s radiation budget and energy balance. Volunteer CoCoRaHS-Albedo observers collected 3,249 individual daily albedo, snow depth, and density measurements using standardized techniques at dozens of sites across New Hampshire, USA over four winter seasons. The data show that albedo increases rapidly with snow depth up to ~ 0.14 m. Multiple linear regression models using snowpack age, snow depth or density, and air temperature provide reasonable approximations of surface snow albedo during times of albedo decay. However, the linear models also reveal systematic biases that highlight an important non-linearity in snow albedo decay. Modeled albedo values are reasonably accurate within the range of 0.6 to 0.9, but exhibit a tendency to over-estimate lower albedo values and under-estimate higher albedo values. We hypothesize that rapid reduction in high albedo fresh snow results from a decrease in snow specific surface area, while during melt-events the presence of liquid water in the snowpack accelerates metamorphism and grain growth. We conclude that the CoCoRaHS-Albedo volunteer observer network provides useful snow albedo, depth, and density measurements and serves as an effective model for future measurement campaigns

Simulations of snow distribution and hydrology in a mountain basin

We applied a version of the Regional Hydro‐Ecologic Simulation System (RHESSys) that implements snow redistribution, elevation partitioning, and wind‐driven sublimation to Loch Vale Watershed (LVWS), an alpine‐subalpine Rocky Mountain catchment where snow accumulation and ablation dominate the hydrologic cycle. We compared simulated discharge to measured discharge and the simulated snow distribution to photogrammetrically rectified aerial (remotely sensed) images. Snow redistribution was governed by a topographic similarity index. We subdivided each hillslope into elevation bands that had homogeneous climate extrapolated from observed climate. We created a distributed wind speed field that was used in conjunction with daily measured wind speeds to estimate sublimation. Modeling snow redistribution was critical to estimating the timing and magnitude of discharge. Incorporating elevation partitioning improved estimated timing of discharge but did not improve patterns of snow cover since wind was the dominant controller of areal snow patterns. Simulating wind‐driven sublimation was necessary to predict moisture losses