Unraveling the optical shape of snow

Abstract

The reflection of sunlight off the snow is a major driver of the Earth’s climate. This reflection is governed by the shape and arrangement of ice crystals at the micrometer scale, called snow microstructure. However, snow optical models overlook the complexity of this microstructure by using simple shapes, and mainly spheres. The use of these various shapes leads to large uncertainties in climate modeling, which could reach 1.2 K in global air temperature. Here, we accurately simulate light propagation in three-dimensional images of natural snow at the micrometer scale, revealing the optical shape of snow. This optical shape is neither spherical nor close to the other idealized shapes commonly used in models. Instead, it more closely approximates a collection of convex particles without symmetry. Besides providing a more realistic representation of snow in the visible and near-infrared spectral region (400 to 1400 nm), this breakthrough can be directly used in climate models, reducing by 3 the uncertainties in global air temperature related to the optical shape of snow.

Simulation of snow albedo and solar irradiance profile with the two-stream radiative transfer in snow (TARTES) v2.0 model

“…which stems from the recent work on random media and is now the recommended and default option (Malinka, 2014;Robledano et al, 2023). The last option gives the possibility to the user to prescribe a constant value or a value per wavelength.…”

“…For instance the specific surface area (SSA) (the ratio between the air-interface surface area and the mass of ice, Domine et al, 2006) advantageously replaces the grain size as it can be rigorously defined and calculated for any porous medium made of two phases (ice and air here). Regarding the shape, the situation is less advanced but geometrical metrics related to the chord length distribution can provide useful information (Malinka, 2014;Krol and Löwe, 2016;Dumont et al, 2021;Robledano et al, 2023).…”

“…Conversely three-dimensional RT models are necessary to account for snowpack with 3D structures, embedded objects or light sources. Some examples are models for rough surfaces (Warren et al, 1998;Larue et al, 2020;Robledano et al, 2022), for explicit photon trajectory calculation in the snow microstructure (Kaempfer et al, 2005;Picard et al, 2009;Xiong and Shi, 2014;Letcher et al, 2022;Robledano et al, 2023) and for interaction with embedded objects or instruments (Gallet et al, 2009;Picard et al, 2016).…”

“…Libois et al, 2013;Shao et al, 2018;van Dalum et al, 2019;Tuzet et al, 2020;Manninen et al, 2021;Veillon et al, 2021), this paper aims at a first comprehensive and formal description of the model. Some minor but significant adjustments using the latest results on microstructure (Robledano et al, 2023) are also included, as well as a brief presentation of the ecosystem of tools relevant to snow optical computations built around TARTES.…”

The Effect of Physically Based Ice Radiative Processes on Greenland Ice Sheet Albedo and Surface Mass Balance in E3SM

“…Significant progress has been made on our ability to model bare ice surfaces and estimate the total impact of bare ice exposure on GrIS melt. Previous work has explicitly represented bare ice using the physical microstructure of the ice, including the size distribution of air bubbles within the ice, the density of the ice, and a refractive boundary that represents the transition in the index of refraction between air media and ice media (Briegleb & Light, 2007; Dadic et al., 2013; Gardner & Sharp, 2010; Mullen & Warren, 1988; Robledano et al., 2023; Whicker et al., 2022). Subsequent work further developed an offline snow radiative transfer model to accurately simulate a spectrally resolved cryospheric column of snow and ice with a refractive boundary and incorporates LACs, including black carbon, and algae (Whicker et al., 2022).…”