A parameterization of the specific surface area of seasonal snow for field use and for models of snowpack evolution

Abstract

[1] The specific surface area (SSA) of snow is needed to model air-snow exchange of chemical species. SSA is related to many snow physical properties, such as albedo and permeability. However, it is not described in models of snowpack evolution, in part because it is difficult to measure. Snowpack models often predict snow grain shape and snow density, and the goal of this paper is to propose parameterizations of snow SSA, based on snow density and grain shape. SSA values of 345 snow samples from snowpacks of the Alpine, maritime, tundra and taiga types are presented. Samples are regrouped into three main types: fresh (F), recent (R), and aged (A) snows, with several subtypes referring to grain shapes. Overall, there is a clear inverse correlation between SSA and density, d. Empirical equations of the form SSA = A ln(d) + B are proposed for the F and R types. For aged snows, separate correlations are proposed for subtypes A1 (rounded grains), A2 (faceted crystals), A3 (depth hoar), and A4 (lightly melted snow). Within subtypes A1, A2, and A3, more elaborate classifications are made by considering the snowpack type (Alpine, taiga, or tundra). For A1, A2, and A3 types, different trends are related to different intensities of wind action, which increases in the order taiga, Alpine, and tundra. We finally propose three parameterizations of snow SSA with increasing sophistication, by correlating SSA to snow type, then to snow type and density, and finally to snow type, density, and snowpack type.Citation: Domine, F., A.-S. Taillandier, and W. R. Simpson (2007), A parameterization of the specific surface area of seasonal snow for field use and for models of snowpack evolution,

Evolution of the Snow Area Index of the Subarctic Snowpack in Central Alaska over a Whole Season. Consequences for the Air to Snow Transfer of Pollutants

“…After the release of Legagneux et al (14), an experimental artifact due to CH 4 adsorption on the stainless steel walls of the container holding the snow was detected, as already mentioned by Legagneux et al (14) and detailed in Domine et al (25). Its effects were quantified by measuring the adsorption isotherm of CH 4 on the empty container and corrected by considering simultaneously both adsorption equilibria on stainless steel and snow.…”

Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm

“…SSA=S/M=S/ρ ice V , where S is the surface area of a given mass of snow particles, M its mass, V is the volume of the snow particles, and ρ ice is the density of ice (917 kg m −3 at 0 • C). SSA values range typically from 2 m 2 kg −1 for melt-freeze layers to 156 m 2 kg −1 for fresh dendritic snow (Domine et al, 2007a).…”

“…Most published SSA measurements to date have been obtained using methane adsorption at 77 K Domine et al, 2007a). Briefly, snow placed in a vacuum container is immersed in liquid nitrogen and the adsorption isotherm of methane on snow is measured, allowing the determination of snow SSA.…”

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

“…Due to the demand for global monitoring of the cryosphere and its change, numerous algorithms have been developed to retrieve geophysical information on snow cover extent (Grody and Basist, 1996;Nghiem and Tsai, 2001), snow depth, and snow water equivalent on both land (Josberger and Mognard, 2002;Kelly and Chang, 2003;Derksen et al, 2003) and sea ice (Comiso et al, 2003;Cavalieri et al, 2012), snow accumulation on ice sheets (Abdalati and Steffen, 1998;Vaughan et al, 1999;Drinkwater et al, 2001;Winebrenner et al, 2001;Flach et al, 2005;Arthern et al, 2006;Dierking et al, 2012) wet snow (Zwally, 1977;Shi and Dozier, 1995;Abdalati and Steffen, 1997;Nagler and Rott, 2000;Steffen, 2004;Picard et al, 2007), snow temperature (Shuman et al, 1995;Schneider and Steig, 2002;Schneider et al, 2004), snow grain size (Brucker et al, 2010;Picard et al, 2012), and snow density Champollion et al, 2013). Even though many applications still rely on empirical approaches to relate snowpack properties (e.g., snow water equivalent, SWE) and measured signals, it is generally accepted that a physical understanding of the interaction between snow and electromagnetic waves is necessary to improve the accuracy and overcome inherent difficulties of the retrieval as an underdetermined problem.…”