Matthew Sturm

University of Alaska Fairbanks

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Donald K. Perovich

Dartmouth College

Glen E. Liston

Colorado State University

Jon Holmgren

Cold Regions Research and Engineering Laboratory

Carl S. Benson

University of Alaska Fairbanks

Thomas A. Douglas

Cold Regions Research and Engineering Laboratory

Hans‐Peter Marshall

Boise State University

William R. Simpson

University of Alaska Fairbanks

T. Markus

Goddard Space Flight Center

James A Maslanik

University of Colorado Boulder

Florent Dominé

Université Laval

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University of Alaska Fairbanks

120

Cold Regions Research and Engineering Laboratory

University of Colorado Boulder

United States Army

University of Washington

U.S. Army Engineer Research and Development Center

United States Geological Survey

Goddard Space Flight Center

National Oceanic and Atmospheric Administration

Jet Propulsion Laboratory

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A snow-transport model for complex terrain

Journal of Glaciology (1998)

Glen E. Liston Matthew Sturm

Abstract As part of the winter environment in middle- and high-latitude regions, the interactions between wind, vegetation, topography and snowfall produce snow covers of non-uniform depth and snow water-equivalent distribution. A physically based numerical snow-transport model (SnowTran-3D) is developed and used to simulate this three-dimensional snow-depth evolution over topographically variable terrain. The mass-transport model includes processes related to vegetation snow-holding capacity, topographic modification of wind speeds, snow-cover shear strength, wind-induced surface-shear stress, snow transport resulting from saltation and suspension, snow accumulation and erosion, and sublimation of the blowing and drifting snow. The model simulates the cold-season evolution of snow-depth distribution when forced with inputs of vegetation type and topography, and atmospheric foreings of air temperature, humidity, wind speed and direction, and precipitation. Model outputs include the spatial and temporal evolution of snow depth resulting from variations in precipitation, saltation and suspension transport, and sublimation. Using 4 years of snow-depth distribution observations from the foothills north of the Brooks Range in Arctic Alaska, the model is found to simulate closely the observed snow-depth distribution patterns and the interannual variability.

Snow field

Sublimation

Snow line

Snowmelt

Snowpack

10.3189/s0022143000002021

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Citations (238)

The influence of snow microstructure on dual-frequency radar measurements in a tundra environment

Remote Sensing of Environment (2018)

Joshua King Chris Derksen Peter Toose Alexandre Langlois C. F. Larsen

Recent advancement in the understanding of snow-microwave interactions has helped to isolate the considerable potential for radar-based retrieval of snow water equivalent (SWE). There are however, few datasets available to address spatial uncertainties, such as the influence of snow microstructure, at scales relevant to space-borne application. In this study we introduce measurements from SnowSAR, an airborne, dual-frequency (9.6 and 17.2 GHz) synthetic aperture radar (SAR), to evaluate high resolution (10 m) backscatter within a snow-covered tundra basin. Coincident in situ surveys at two sites characterize a generally thin snowpack (50 cm) interspersed with deeper drift features. Structure of the snowpack is found to be predominantly wind slab (65%) with smaller proportions of depth hoar underlain (35%). Objective estimates of snow microstructure (exponential correlation length; lex), show the slab layers to be 2.8 times smaller than the basal depth hoar. In situ measurements are used to parametrize the Microwave Emission Model of Layered Snowpacks (MEMLS3&a) and compare against collocated SnowSAR backscatter. The evaluation shows a scaling factor (ϕ) between 1.37 and 1.08, when applied to input of lex, minimizes MEMLS root mean squared error to <1.1 dB. Model sensitivity experiments demonstrate contrasting contributions from wind slab and depth hoar components, where wind rounded microstructures are identified as a strong control on observed backscatter. Weak sensitivity of SnowSAR to spatial variations in SWE is explained by the smaller contributing microstructures of the wind slab.

Snowpack

Backscatter (email)

10.1016/j.rse.2018.05.028

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Citations (142)

Characteristics and Growth of a Snowdrift in Arctic Alaska, U.S.A.

Arctic Antarctic and Alpine Research (2001)

Matthew Sturm Glen E. Liston Carl S. Benson Jon Holmgren

In arctic Alaska, 15% of the total winter snowpack is contained in large drifts. Stratigraphic sections reveal that these can form during as few as five weather events during winter, while comparison of stratigraphy and weather records show that significant deposition (up to 43% of the total drift volume) can occur during a single event of short duration (<72 h). Based on three years of wind, snowfall, and snow transport records, a set of rules was developed for predicting when periods of drift growth would occur. The rules were: 10-m wind speed >5.3 m s−1 for at least 3 h, wind direction within 30° of the normal to drift trap axis, and recent snowfall available for transport. When used, these rules successfully identified all drift-growth events, plus a few "extra" events that did not contribute substantially to drift growth. The extra events were invariably periods when there was sufficient wind to move snow, but insufficient snow for transport. In arctic Alaska drift size currently appears to be limited by precipitation rather than wind, leading us to speculate that an increase in precipitation could increase drift size and intensify the ecological, hydrological, and climatic impact of drifts on this arctic system.

Snowpack

10.1080/15230430.2001.12003436

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Citations (54)

Thermal conductivity and heat transfer through the snow on the ice of the Beaufort Sea

Journal of Geophysical Research Atmospheres (2002)

Matthew Sturm Donald K. Perovich Jon Holmgren

Eighty‐nine point measurements of the thermal conductivity ( k s ) of the snow on the sea ice of the Beaufort Sea were made using a heated needle probe. Average values ranged from 0.078 W m −1 K −1 for new snow to 0.290 W m −1 K −1 for an ubiquitous wind slab. k s increased with increasing density, consistent with published equations, but could also be reliably estimated from the metamorphic state of the snow. Using measured values of k s and snow stratigraphy, the average bulk value for the full snowpack was 0.14 W m −1 K −1 . In contrast, k s inferred from ice growth and temperature gradients in the snow was 0.33 W m −1 K −1 . The mismatch arises in part because the second estimate is based on measurements from an aggregate scale that includes enhanced heat flow due to two‐ and three‐dimensional snow and ice geometry. A finite element model suggests that the complex geometry produces areas of concentrated heat loss at the snow surface. These “hot spots,” however, increase the apparent conductivity only by a factor of 1.4, not enough to fully explain the mismatch. Nonconductive heat transfer mechanisms, like natural and forced air convection, may also be operating in the snowpack, though the ubiquitous presence of low permeability wind slabs potentially limits their effectiveness. The relative contributions of effects due to snow and ice geometric and nonconductive processes within the snowpack remain uncertain.

Snowpack

Firn

Liquid water content

10.1029/2000jc000409

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Citations (256)

Snow on Antarctic sea ice

Reviews of Geophysics (2001)

RA Massom Hajo Eicken Christian Hass Martin O. Jeffries Mark R. Drinkwater

Snow on Antarctic sea ice plays a complex and highly variable role in air‐sea‐ice interaction processes and the Earth's climate system. Using data collected mostly during the past 10 years, this paper reviews the following topics: snow thickness and snow type and their geographical and seasonal variations; snow grain size, density, and salinity; frequency of occurrence of slush; thermal conductivity, snow surface temperature, and temperature gradients within snow; and the effect of snow thickness on albedo. Major findings include large regional and seasonal differences in snow properties and thicknesses; the consequences of thicker snow and thinner ice in the Antarctic relative to the Arctic (e.g., the importance of flooding and snow‐ice formation); the potential impact of increasing snowfall resulting from global climate change; lower observed values of snow thermal conductivity than those typically used in models; periodic large‐scale melt in winter; and the contrast in summer melt processes between the Arctic and the Antarctic. Both climate modeling and remote sensing would benefit by taking account of the differences between the two polar regions.

Snow field

Snow line

Albedo (alchemy)

Firn

10.1029/2000rg000085

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Citations (352)

Extensive measurements of snow depth using FM-CW radar

Cold Regions Science and Technology (1998)

Jon Holmgren Matthew Sturm Norbert E. Yankielun Gary Koh

10.1016/s0165-232x(97)00020-7

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Citations (50)

Simulation of the specific surface area of snow using a one-dimensional physical snowpack model: implementation and evaluation for subarctic snow in Alaska

Hans‐Werner Jacobi Florent Dominé William R. Simpson Thomas A. Douglas Matthew Sturm

Abstract. The specific surface area (SSA) of the snow constitutes a powerful parameter to quantify the exchange of matter and energy between the snow and the atmosphere. However, currently no snow physics model can simulate the SSA. Therefore, two different types of empirical parameterizations of the specific surface area (SSA) of snow are implemented into the existing one-dimensional snow physics model CROCUS. The parameterizations are either based on diagnostic equations relating the SSA to parameters like snow type and density or on prognostic equations that describe the change of SSA depending on snow age, snowpack temperature, and the temperature gradient within the snowpack. Simulations with the upgraded CROCUS model were performed for a subarctic snowpack, for which an extensive data set including SSA measurements is available at Fairbanks, Alaska for the winter season 2003/2004. While a reasonable agreement between simulated and observed SSA values is obtained using both parameterizations, the model tends to overestimate the SSA. This overestimation is more pronounced using the diagnostic equations compared to the results of the prognostic equations. Parts of the SSA deviations using both parameterizations can be attributed to differences between simulated and observed snow heights, densities, and temperatures. Therefore, further sensitivity studies regarding the thermal budget of the snowpack were performed. They revealed that reducing the heat conductivity of the snow or increasing the turbulent fluxes at the snow surfaces leads to a slight improvement of the simulated thermal budget of the snowpack compared to the observations. However, their impact on further simulated parameters like snow height and SSA remains small. Including additional physical processes in the snow model may have the potential to advance the simulations of the thermal budget of the snowpack and, thus, the SSA simulations.

Snowpack

Snowmelt

10.5194/tcd-3-681-2009

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Citations (1)

The smoothing of landscapes during snowfall with no wind

Journal of Glaciology (2019)

Simon Filhol Matthew Sturm

ABSTRACT Every winter, snowy landscapes are smoothed by snow deposition in calm conditions (no wind). In this study, we investigated how vertically falling snow attenuates topographic relief at horizontal scales less than or approximately equal to snow depth (e.g., 0.1–10 m). In a set of three experiments under natural snowfall, we observed the particle-scale mechanisms by which smoothing is achieved, and we examined the cumulative effect at the snowpack scale. The experiments consisted of (a) a strobe-light box for tracking the trajectories of snowflakes at deposition, (b) allowing snow to fall through a narrow gap (40 mm) and examining snow accumulation above and below the gap, and (c) allowing snow to accumulate over a set of artificial surfaces. At the particle scale, we observed mechanisms enhancing (bouncing, rolling, ejection, breakage, creep, metamorphism) and retarding (interlocking, cohesion, adhesion, sintering) the rate of smoothing. The cumulative effect of these mechanisms is found to be driven by snowpack surface curvature, introducing a directional bias in the lateral transport of snow particles. Our findings suggest that better quantification of the mechanisms behind smoothing by snow could provide insights into the evolution of snow depth variability, and snow-vegetation interactions.

Snowpack

Smoothing

Meltwater

10.1017/jog.2018.104

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Citations (25)

Evidence and Implications of Recent Climate Change in Northern Alaska and Other Arctic Regions

AGUFM (2004)

L. D. Hinzman Neil D. Bettez William Bolton F. Stuart Chapin Mark B. Dyurgerov

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Citations (17)