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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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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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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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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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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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Matthew Sturm, Brian Taras, Glen E. Liston, Chris Derksen, Tobias Jonas, and Jon Lea

Abstract

In many practical applications snow depth is known, but snow water equivalent (SWE) is needed as well. Measuring SWE takes ∼20 times as long as measuring depth, which in part is why depth measurements outnumber SWE measurements worldwide. Here a method of estimating snow bulk density is presented and then used to convert snow depth to SWE. The method is grounded in the fact that depth varies over a range that is many times greater than that of bulk density. Consequently, estimates derived from measured depths and modeled densities generally fall close to measured values of SWE. Knowledge of snow climate classes is used to improve the accuracy of the estimation procedure. A statistical model based on a Bayesian analysis of a set of 25 688 depth–density–SWE data collected in the United States, Canada, and Switzerland takes snow depth, day of the year, and the climate class of snow at a selected location from which it produces a local bulk density estimate. When converted to SWE and tested against two continental-scale datasets, 90% of the computed SWE values fell within ±8 cm of the measured values, with most estimates falling much closer.

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Chris Derksen, Arvids Silis, Matthew Sturm, Jon Holmgren, Glen E. Liston, Henry Huntington, and Daniel Solie

Abstract

During April 2007, a coordinated series of snow measurements was made across the Northwest Territories and Nunavut, Canada, during a snowmobile traverse from Fairbanks, Alaska, to Baker Lake, Nunavut. The purpose of the measurements was to document the general nature of the snowpack across this region for the evaluation of satellite- and model-derived estimates of snow water equivalent (SWE). Although detailed, local snow measurements have been made as part of ongoing studies at tundra field sites (e.g., Daring Lake and Trail Valley Creek in the Northwest Territories; Toolik Lake and the Kuparak River basin in Alaska), systematic measurements at the regional scale have not been previously collected across this region of northern Canada. The snow cover consisted of depth hoar and wind slab with small and ephemeral fractions of new, recent, and icy snow. The snow was shallow (<40 cm deep), usually with fewer than six layers. Where snow was deposited on lake and river ice, it was shallower, denser, and more metamorphosed than where it was deposited on tundra. Although highly variable locally, no longitudinal gradients in snow distribution, magnitude, or structure were detected. This regional homogeneity allowed us to identify that the observed spatial variability in passive microwave brightness temperatures was related to subgrid fractional lake cover. Correlation analysis between lake fraction and Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature showed frequency dependent, seasonally evolving relationships consistent with lake ice drivers. Simulations of lake ice thickness and snow depth on lake ice produced from the Canadian Lake Ice Model (CLIMo) indicated that at low frequencies (6.9, 10.7 GHz), correlations with lake fraction were consistent through the winter season, whereas at higher frequencies (18.7, 36.5 GHz), the strength and direction of the correlations evolved consistently with the penetration depth as the influence of the subice water was replaced by emissions from the ice and snowpack. A regional rain-on-snow event created a surface ice lens that was detectable using the AMSR-E 36.5-GHz polarization gradient due to a strong response at the horizontal polarization. The appropriate polarization for remote sensing of the tundra snowpack depends on the application: horizontal measurements are suitable for ice lens detection; vertically polarized measurements are appropriate for deriving SWE estimates.

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Matthew Sturm, Jon Holmgren, Joseph P. McFadden, Glen E. Liston, F. Stuart Chapin III, and Charles H. Racine

Abstract

In the Arctic, where wind transport of snow is common, the depth and insulative properties of the snow cover can be determined as much by the wind as by spatial variations in precipitation. Where shrubs are more abundant and larger, greater amounts of drifting snow are trapped and suffer less loss due to sublimation. The snow in shrub patches is both thicker and a better thermal insulator per unit thickness than the snow outside of shrub patches. As a consequence, winter soil surface temperatures are substantially higher, a condition that can promote greater winter decomposition and nutrient release, thereby providing a positive feedback that could enhance shrub growth. If the abundance, size, and coverage of arctic shrubs increases in response to climate warming, as is expected, snow–shrub interactions could cause a widespread increase (estimated 10%–25%) in the winter snow depth. This would increase spring runoff, winter soil temperatures, and probably winter CO2 emissions. The balance between these winter effects and changes in the summer energy balance associated with the increase in shrubs probably depends on shrub density, with the threshold for winter snow trapping occurring at lower densities than the threshold for summer effects such as shading. It is suggested that snow–shrub interactions warrant further investigation as a possible factor contributing to the transition of the arctic land surface from moist graminoid tundra to shrub tundra in response to climatic warming.

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