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Glen E. Liston
and
Jan-Gunnar Winther

Abstract

This paper presents modeled surface and subsurface melt fluxes across near-coastal Antarctica. Simulations were performed using a physical-based energy balance model developed in conjunction with detailed field measurements in a mixed snow and blue-ice area of Dronning Maud Land, Antarctica. The model was combined with a satellite-derived map of Antarctic snow and blue-ice areas, 10 yr (1991–2000) of Antarctic meteorological station data, and a high-resolution meteorological distribution model, to provide daily simulated melt values on a 1-km grid covering Antarctica. Model simulations showed that 11.8% and 21.6% of the Antarctic continent experienced surface and subsurface melt, respectively. In addition, the simulations produced 10-yr averaged subsurface meltwater production fluxes of 316.5 and 57.4 km3 yr−1 for snow-covered and blue-ice areas, respectively. The corresponding figures for surface melt were 46.0 and 2.0 km3 yr−1, respectively, thus demonstrating the dominant role of subsurface over surface meltwater production. In total, computed surface and subsurface meltwater production values equal 31 mm yr−1 if evenly distributed over all of Antarctica. While, at any given location, meltwater production rates were highest in blue-ice areas, total annual Antarctic meltwater production was highest for snow-covered areas due to its larger spatial extent. The simulations also showed higher interannual meltwater variations for surface melt than subsurface melt. Since most of the produced meltwater refreezes near where it was produced, the simulated melt has little effect on the Antarctic mass balance. However, the melt contribution is important for the surface energy balance and in modifying surface and near-surface snow and ice properties such as density and grain size.

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Matthew Sturm
and
Glen E. Liston

Abstract

Twenty-five years ago, we published a global seasonal snow classification now widely used in snow research, physical geography, and as a mission planning tool for remote sensing snow studies. Performing the classification requires global datasets of air temperature, precipitation, and land cover. When introduced in 1995, the finest-resolution global datasets of these variables were on a 0.5° × 0.5° latitude–longitude grid (approximately 50 km). Here we revisit the snow classification system and, using new datasets and methods, present a revised classification on a 10-arc-s × 10-arc-s latitude–longitude grid (approximately 300 m). We downscaled 0.1° × 0.1° latitude–longitude (approximately 10 km) gridded meteorological climatologies [1981–2019, European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis, 5th Generation Land (ERA5-Land)] using MicroMet, a spatially distributed, high-resolution, micrometeorological model. The resulting air temperature and precipitation datasets were combined with European Space Agency (ESA) Climate Change Initiative (CCI) GlobCover land-cover data (as a surrogate for wind speed) to produce the updated classification, which we have applied to all of Earth’s terrestrial areas. We describe this new, high-resolution snow classification dataset, highlight the improvements added to the classification system since its inception, and discuss the utility of the climatological snow classes at this much higher resolution. The snow class dataset (Global Seasonal-Snow Classification, Version 1) and the tools used to develop the data are publicly available online at the National Snow and Ice Data Center (NSIDC).

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Glen E. Liston
and
Matthew Sturm

Abstract

A blowing-snow model (SnowTran-3D) was combined with field measurements of end-of-winter snow depth and density to simulate solid (winter) precipitation, snow transport, and sublimation distributions over a 20 000-km2 arctic Alaska domain. The domain included rolling uplands and a flat coastal plain. Simulations were produced for the winters of 1994/95, 1995/96, and 1996/97. The model, which accounts for spatial and temporal variations in blowing-snow sublimation, as well as saltation and turbulent-suspended transport, was driven with interpolated fields of observed temperature, humidity, and wind speed and direction. Model outputs include local (a few hundreds of meters) to regional (several tens of kilometers) distributions of winter snow-water-equivalent depths and blowing-snow sublimation losses, from which the regional winter precipitation distributions are computed. At regional scales, the end-of-winter snow depth is largely equal to the difference between winter precipitation and moisture loss due to sublimation. While letting SnowTran-3D simulate the blowing-snow sublimation fluxes, the precipitation fields were determined by forcing the regional variation in model-simulated snow depths to match measured values. Averaged over the entire domain and the three simulation years, the winter precipitation was 17.6 cm, with uplands values averaging 19.0 cm and coastal values averaging 15.3 cm. On average, 21% of the precipitation was returned to the atmosphere by blowing-snow sublimation, while in the windier coastal regions 34% of the winter precipitation sublimated.

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Glen E. Liston
and
Kelly Elder

Abstract

An intermediate-complexity, quasi–physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are distributed: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, surface pressure, and precipitation. To produce these distributions, MicroMet assumes that at least one value of each of the following meteorological variables are available for each time step, somewhere within, or near, the simulation domain: air temperature, relative humidity, wind speed, wind direction, and precipitation. These variables are collected at most meteorological stations. For the incoming solar and longwave radiation, and surface pressure, either MicroMet can use its submodels to generate these fields, or it can create the distributions from observations as part of a data assimilation procedure. MicroMet includes a preprocessor component that analyzes meteorological data, then identifies and corrects potential deficiencies. Since providing temporally and spatially continuous atmospheric forcing data for terrestrial models is a core objective of MicroMet, the preprocessor also fills in any missing data segments with realistic values. Data filling is achieved by employing a variety of procedures, including an autoregressive integrated moving average calculation for diurnally varying variables (e.g., air temperature). To create the distributed atmospheric fields, spatial interpolations are performed using the Barnes objective analysis scheme, and subsequent corrections are made to the interpolated fields using known temperature–elevation, wind–topography, humidity–cloudiness, and radiation–cloud–topography relationships.

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Brian Taras
,
Matthew Sturm
, and
Glen E. Liston

Abstract

Air and snow–ground interface temperatures were measured during two winters at 33 stations spanning the 180-km-long Kuparuk basin in arctic Alaska. Interface temperatures averaged 7.5°C higher than air temperatures and varied in a manner that was more complex, and on a spatial scale more than 100 times smaller, than the air temperature. Within the basin, two distinct thermal regimes could be identified, with the division at the boundary between coastal and uplands provinces. When each station was classified into one of three snow exposure classes (exposed, intermediate, or sheltered), accounting for variations in snow depth and thermal properties, 87% of the variation in the average interface temperature could be predicted from air temperature. Individual station interface temperature records were fit using a beta curve that captured the slow decrease in autumn and the rapid rise in spring. Beta curves were specified by three parameters (α, β, and γ) that could be predicted if province and snow exposure class were known. A model based on the beta curves was developed and applied over the basin to predict interface temperatures in both time and space. Tests of the model against data from within the domain and from arctic Alaskan locations outside the domain suggest an accuracy of ±2°C when simulating average winter interface temperatures, and ±3°C when simulating daily interface temperatures.

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Matthew Sturm
,
Jon Holmgren
, and
Glen E. Liston

Abstract

A new classification system for seasonal snow covers is proposed. It has six classes (tundra, taiga, alpine, maritime, prairie, and ephemeral, each class defined by a unique ensemble of textural and stratigraphic characteristics including the sequence of snow layers, their thickness, density, and the crystal morphology and grain characteristics within each layer. The classes can also be derived using a binary system of three climate variables: wind, precipitation, and air temperature. Using this classification system, the Northern Hemisphere distribution of the snow cover classes is mapped on a 0.5° lat × 0.5° long grid. These maps are compared to maps prepared from snow cover data collected in the former Soviet Union and Alaska. For these areas where both climatologically based and texturally based snow cover maps are available, there is 62% and 90% agreement, respectively. Five of the six snow classes are found in Alaska. From 1989 through 1992, hourly measurements, consisting of 40 thermal and physical parameters, including snow depth, the temperature distribution in the snow, and basal heat flow, were made on four of these classes. In addition, snow stratigraphy and texture were measured every six weeks. Factor analysis indicates that the snow classes can be readily discriminated using four or more winter average thermal or physical parameters. Further, analysis of hourly time series indicates that 84% of the time, spot measurements of the parameters are sufficient to correctly differentiate the snow cover class. Using the new snow classification system, 1) classes can readily be distinguished using observations of simple thermal parameters, 2) physical and thermal attributes of the snow can be inferred, and 3) classes can be mapped from climate data for use in regional and global climate modeling.

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Ulrich Strasser
,
Michael Warscher
, and
Glen E. Liston

Abstract

Snow interception in a coniferous forest canopy is an important hydrological feature, producing complex mass and energy exchanges with the surrounding atmosphere and the snowpack below. Subcanopy snowpack accumulation and ablation depends on the effects of canopy architecture on meteorological conditions and on interception storage by stems, branches, and needles. Mountain forests are primarily composed of evergreen conifer species that retain their needles throughout the year and hence intercept snow efficiently during winter. Canopy-intercepted snow can melt, fall to the ground, and/or sublimate into the air masses above and within the canopy. To improve the understanding of snow–canopy interception processes and the associated influences on the snowpack below, a series of model experiments using a detailed, physically based snow–canopy and snowpack evolution model [Alpine Multiscale Numerical Distributed Simulation Engine (AMUNDSEN)] driven with observed meteorological forcing was conducted. A cone-shaped idealized mountain covered with a geometrically regular pattern of coniferous forest stands and clearings was constructed. The model was applied for three winter seasons with different snowfall intensities and distributions. Results show the effects of snow–canopy processes and interactions on the pattern of ground snow cover, its duration, and the amount of meltwater release, in addition to showing under what conditions the protective effect of a forest canopy overbalances the reduced accumulation of snow on the ground. The simulations show considerable amounts of canopy-intercepted snowfall can sublimate, leading to reduced snow accumulation beneath the forest canopy. In addition, the canopy produces a shadowing effect beneath the trees that leads to reduced radiative energy reaching the ground, reduced below-canopy snowmelt rates, and increased snow-cover duration relative to nonforested areas. During snow-rich winters, the shadowing effect of the canopy dominates and snow lasts longer inside the forest than in the open, but during winters with little snow, snow sublimation losses dominate and snow lasts longer in the open areas than inside the forest. Because of the strong solar radiation influence on snowmelt rates, the details of these relationships vary for northern and southern radiation exposures and time of year. In early and high winter, the radiation protection effect of shadowing by the canopy is small. If little snow is available, an intermittent melt out of the snow cover inside the forest can occur. In late winter and spring, the shadowing effect becomes more efficient and snowmelt is delayed relative to nonforested areas.

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Sebastian H. Mernild
,
Glen E. Liston
, and
Christopher A. Hiemstra

Abstract

Mass changes and mass contribution to sea level rise from glaciers and ice caps (GIC) are key components of the earth’s changing sea level. GIC surface mass balance (SMB) magnitudes and individual and regional mean conditions and trends (1979–2009) were simulated for all GIC having areas greater or equal to 0.5 km2 in the Northern Hemisphere north of 25°N latitude (excluding the Greenland Ice Sheet). Recent datasets, including the Randolph Glacier Inventory (RGI; v. 2.0), the NOAA Global Land One-km Base Elevation Project (GLOBE), and the NASA Modern-Era Retrospective Analysis for Research and Applications (MERRA) products, together with recent SnowModel developments, allowed relatively high-resolution (1-km horizontal grid; 3-h time step) simulations of GIC surface air temperature, precipitation, sublimation, evaporation, surface runoff, and SMB. Simulated SMB outputs were calibrated against 1422 direct glaciological annual SMB observations of 78 GIC. The overall GIC mean annual and mean summer air temperature, runoff, and SMB loss increased during the simulation period. The cumulative GIC SMB was negative for all regions. The SMB contribution to sea level rise was largest from Alaska and smallest from the Caucasus. On average, the contribution to sea level rise was 0.51 ± 0.16 mm sea level equivalent (SLE) yr−1 for 1979–2009 and ~40% higher 0.71 ± 0.15 mm SLE yr−1 for the last decade, 1999–2009.

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Kelly Elder
,
Don Cline
,
Glen E. Liston
, and
Richard Armstrong

Abstract

A field measurement program was undertaken as part NASA’s Cold Land Processes Experiment (CLPX). Extensive snowpack and soil measurements were taken at field sites in Colorado over four study periods during the two study years (2002 and 2003). Measurements included snow depth, density, temperature, grain type and size, surface wetness, surface roughness, and canopy cover. Soil moisture measurements were made in the near-surface layer in snow pits. Measurements were taken in the Fraser valley, North Park, and Rabbit Ears Pass areas of Colorado. Sites were chosen to gain a wide representation of snowpack types and physiographies typical of seasonally snow-covered regions of the world. The data have been collected with rigorous protocol to ensure consistency and quality, and they have undergone several levels of quality assurance to produce a high-quality spatial dataset for continued cold lands hydrological research. The dataset is archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

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Xiaogang Shi
,
Matthew Sturm
,
Glen E. Liston
,
Rachel E. Jordan
, and
Dennis P. Lettenmaier

Abstract

The lateral and vertical variability of snow stratigraphy was investigated through the comparison of the measured profiles of snow density, temperature, and grain size obtained during the Snow Science Traverse—Alaska Region (SnowSTAR2002) 1200-km transect from Nome to Barrow with model reconstructions from the Snow Thermal Model (SNTHERM), a multilayered energy and mass balance snow model. Model profiles were simulated at the SnowSTAR2002 observation sites using the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) as meteorological forcing. ERA-40 precipitation was rescaled so that the total snow water equivalent (SWE) on the SnowSTAR2002 observation dates equaled the observed values. The mean absolute error (MAE) of measured and simulated snow properties shows that SNTHERM was able to produce good simulations for snowpack temperature but larger errors for grain size and density. A spatial similarity analysis using semivariograms of measured profiles shows that there is diverse lateral and vertical variability for snow properties along the SnowSTAR2002 transect resulting from differences in initial snow deposition, influenced by wind, vegetation, topography, and postdepositional mechanical and thermal metamorphism. The correlation length in snow density (42 km) is quite low, whereas it is slightly longer for snow grain size (125 km) and longer still for snow temperature (130 km). An important practical question that the observed and reconstructed profiles allow to be addressed is the implications of model errors in the observed snow properties for simulated microwave emissions signatures. The Microwave Emission Model for Layered Snowpacks (MEMLS) was used to simulate 19- and 37-GHz brightness temperatures. Comparison of SNTHERM–MEMLS and SnowSTAR2002–MEMLS brightness temperatures showed a very good match occurs at 19 GHz [a root-mean-square error (RMSE) of 1.5 K (8.7 K) for vertical (horizontal) polarization] and somewhat larger [5.9 K (6.2 K) for vertical (horizontal) polarization] at 37 GHz. These results imply that the simulation of snow microphysical profiles is a viable strategy for passive microwave satellite–based retrievals of SWE.

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