Thermal Conductivity of Snow, Firn, and Porous Ice From 3‐D Image‐Based Computations

Calonne, Neige; Milliancourt, Lucas; Burr, Alexis; Philip, Armelle; Martín, Christophe; Flin, Frédéric; Geindreau, Christian

doi:10.1029/2019gl085228

Cited by 62 publications

(74 citation statements)

References 64 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specific surface area values are however systematically higher than the ones from microCT, especially in the first 0.4 m (about 25 m 2 kg −1 vs. 15 m 2 kg −1 ). Similar deviations were reported in Calonne et al (2019), suggesting possible biases in the specific surface area measurement methods. In our case, the deviations might have been likewise caused by equi-temperature metamorphism in the snow blocks during transport and storage, leading to a specific surface area decrease, the higher the initial specific surface area, the higher the evolution rate.…”

Section: Structural and Crystallographic Fabric Profilessupporting

confidence: 81%

“…Despite a reduced structural fabric anisotropy with depth (Calonne et al, 2019), the layered structure of the snowpack inherited from anisotropy variations may still survive and impact the lock-in zone characteristics (thickness and depth) down to the close off depth (Fujita et al, 2009(Fujita et al, , 2014Gregory et al, 2014;Fourteau et al, 2019). In particular, the existence of a seasonal signal in the structural fabric strengthens the interpretation of Gregory et al (2014) that hypothesizes an impact of the microstructure on the lock-in processes.…”

Section: The Necessity Of Quantifying Both Fabric Tensorsmentioning

confidence: 91%

“…Grain boundary migration mechanisms associated with normal grain growth, which govern the microstructure evolution in firn (de la Chapelle et al, 1998), likely reduce the anisotropy inherited from metamorphism. Both mechanisms (densification and normal grain growth) favor evolution toward a weaker structural and a weaker crystallographic anisotropy with depth (Montagnat et al, 2011;Calonne et al, 2019).…”

Section: The Necessity Of Quantifying Both Fabric Tensorsmentioning

confidence: 99%

See 2 more Smart Citations

On the Birth of Structural and Crystallographic Fabric Signals in Polar Snow: A Case Study From the EastGRIP Snowpack

Montagnat

Löwe²,

Calonne

et al. 2020

Front. Earth Sci.

Self Cite

View full text Add to dashboard Cite

The role of near-surface snow processes for the formation of climate signals through densification into deep polar firn is still barely understood. To this end we have analyzed a shallow snow pit (0-3 meters) from EastGRIP (Greenland) and derived high-resolution profiles of different types of mechanically relevant fabric tensors. The structural fabric, which characterizes the anisotropic geometry of ice matrix and pore space, was obtained by X-ray tomography. The crystallographic fabric, which characterizes the anisotropic distribution of the c-axis (or optical axis) orientations of snow crystals, was obtained from automatic analysis of thin sections. The structural fabric profile unambiguously reveals the seasonal cycles at EastGRIP, as a consequence of temperature gradient metamorphism, and in contrast to featureless signals of parameters like density or specific surface area. The crystallographic fabric profile unambiguously reveals a signal of cluster-type texture already at shallow depth. We make use of order of magnitude estimates for the formation time of both fabric signals and discuss potential coupling effects in the context of snow and firn densification.

show abstract

Section: Structural and Crystallographic Fabric Profilessupporting

confidence: 81%

Section: The Necessity Of Quantifying Both Fabric Tensorsmentioning

confidence: 91%

Section: The Necessity Of Quantifying Both Fabric Tensorsmentioning

confidence: 99%

See 1 more Smart Citation

On the Birth of Structural and Crystallographic Fabric Signals in Polar Snow: A Case Study From the EastGRIP Snowpack

Montagnat

Löwe²,

Calonne

et al. 2020

Front. Earth Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…Previous studies have shown that vertical anisotropy in the snowpack results from temperature-gradient metamorphism (Srivastava et al, 2010;Calonne et al, 2017). The effective heat conductivity of snow results from several aspects such as structure, density, history of the snow pack, and applied temperature (Satyawali et al, 2008;Calonne et al, 2019). The latter varies on a daily and seasonal time scale as recorded by the AWS (Figure 5).…”

Section: Seasonal Anisotropy and Depositionmentioning

confidence: 97%

Microstructure of Snow and Its Link to Trace Elements and Isotopic Composition at Kohnen Station, Dronning Maud Land, Antarctica

et al. 2020

View full text Add to dashboard Cite

Understanding the deposition history and signal formation in ice cores from polar ice sheets is fundamental for the interpretation of paleoclimate reconstruction based on climate proxies. Polar surface snow responds to environmental changes on a seasonal time scale by snow metamorphism, displayed in the snow microstructure and archived in the snowpack. However, the seasonality of snow metamorphism and accumulation rate is poorly constrained for low-accumulation regions, such as the East Antarctic Plateau. Here, we apply core-scale microfocus X-ray computer tomography to continuously measure snow microstructure of a 3-m deep snow core from Kohnen Station, Antarctica. We compare the derived microstructural properties to discretely measured trace components and stable water isotopes, commonly used as climate proxies. Temperature and snow height data from an automatic weather station are used for further constraints. Dating of the snow profile by combining non-sea-salt sulfate and density crusts reveals a seasonal pattern in the geometrical anisotropy. Considering seasonally varying temperature-gradient metamorphism in the surface snow and the timing of the anisotropy pattern observed in the snow profile, we propose the anisotropy to display the deposition history of the site. An annually varying fraction of deposition during summer months, ranging from no or negative deposition to large deposition events, leads to the observed microstructure and affects trace components as well as stable water isotopes.

show abstract

“…The transition between these two behaviors lies around 350 to 400 kg m −3 . Note that Calonne et al (2019) report that a similar transition between the low and high-density samples also exists under limited kinetics, but occurs at a much lower density of about 100 kg m −3 .…”

Section: Effective Thermal Conductivity and Diffusion Coefficient As mentioning

confidence: 79%

Impact of water vapor diffusion and latent heat on the effective thermal conductivity of snow

Fourteau¹,

Dominé²,

Hagenmuller³

2020

Preprint

View full text Add to dashboard Cite

Abstract. Heat transport in snowpacks is generally thought to occur through two independent processes: heat conduction and latent heat transport carried by water vapor. This paper investigates the coupling between both these processes in snow, with an emphasis on the impacts of the kinetics of the sublimation and deposition of water vapor onto ice. In the case where kinetics is fast, latent heat exchanges at ice surfaces modify their temperature, and therefore the thermal gradient within ice crystals and the heat conduction through the entire microstructure. Furthermore, in this case, the effective thermal conductivity of snow can be expressed by a purely conductive term complemented by a term directly proportional to the effective diffusion coefficient of water vapor in snow, which illustrates the inextricable coupling between heat conduction and water vapor transport. Numerical simulations on measured three-dimensional snow microstructures reveal that the effective thermal conductivity of snow can be significantly larger, up to about 50 % for low-density snow, than if water vapor transport is neglected. Comparison of our numerical simulations with literature data suggests that the fast kinetics hypothesis could be a reasonable assumption to model snow physical properties. Lastly, we demonstrate that under the fast kinetics hypothesis the effective diffusion coefficient of water vapor is related to the effective thermal conductivity by a simple linear relationship. Under such condition, the effective diffusion coefficient of water vapor is expected to lie in the narrow 100 % to about 80 % range of the value of the diffusion coefficient of water vapor in air for most seasonal snows. This may greatly facilitate the parameterization of water vapor diffusion of snow in models.

show abstract

Thermal Conductivity of Snow, Firn, and Porous Ice From 3‐D Image‐Based Computations

Cited by 62 publications

References 64 publications

On the Birth of Structural and Crystallographic Fabric Signals in Polar Snow: A Case Study From the EastGRIP Snowpack

On the Birth of Structural and Crystallographic Fabric Signals in Polar Snow: A Case Study From the EastGRIP Snowpack

Microstructure of Snow and Its Link to Trace Elements and Isotopic Composition at Kohnen Station, Dronning Maud Land, Antarctica

Impact of water vapor diffusion and latent heat on the effective thermal conductivity of snow

Contact Info

Product

Resources

About