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The evolution of the surface snow microstructure under the influence of wind during precipitation events is hardly understood but crucial for polar and alpine snowpacks. Available statistical models are solely parameterized from field data where conditions are difficult to control. Controlled experiments which exemplify the physical processes underlying the evolution of density or specific surface area (SSA) of surface snow under windy conditions are virtually non-existent. As a remedy, we conducted experiments in a cold laboratory using a ring-shaped wind tunnel with an infinite fetch to systematically investigate wind-induced microstructure modifications under controlled atmospheric, flow and snow conditions and to identify the relevant processes. Airborne snow particles are characterized by high-speed imaging, while deposited snow is characterized by density and SSA measurements. We used a single snow type (dendritic fresh snow) for simulating different precipitation intensities, varied wind speeds at a height of 0.4 m from 3 to 7 m s -1 (for fixed temperature) and varied temperatures from -24 to -2 °C (for fixed wind speed). The measured airborne impact trajectories confirm the consistency of our coefficient of restitution with large-scale saltation, rendering the setup suitable for realistically studying interactions between airborne and deposited snow. Increasing wind speeds resulted in intensified densification and stronger SSA decreases. The most drastic snow density and SSA changes in deposited snow are observed close to the melting point. Our measured densification rates as a function of wind speed show clear deviations from existing statistical models but can be re-parameterized through our data. This study, as a first of its kind, exemplifies a rich nonlinear interplay between airborne and deposited snow particles, which is discussed in view of a multitude of involved processes, i.e., airborne metamorphism, cohesion, particle separation and fragmentation. [ABSTRACT FROM AUTHOR]

Accurate models for the viscous densification of snow (understood here as a density below 550 kgm-3) and firn (a density above 550 kgm-3) under mechanical stress are of primary importance for various applications, including avalanche prediction and the interpretation of ice cores. Formulations of snow and firn compaction in models are still largely empirical instead of using microstructures from micro-computed tomography to numerically compute the mechanical behavior directly from the physics at the microscale. The main difficulty of the latter approach is the choice of the correct rheology/constitutive law governing the deformation of the ice matrix, which is still controversially discussed. Being aware of these uncertainties, we conducted a first systematic attempt of microstructure-based modeling of snow and firn compaction. We employed the finite element suite Elmer FEM using snow and firn microstructures from different sites in the Alps and Antarctica to explore which ice rheologies are able to reproduce observations. We thereby extended the ParStokes solver in Elmer FEM to facilitate parallel computing of transverse isotropic material laws for monocrystalline ice. We found that firn densification can be reasonably well simulated across different sites assuming a polycrystalline rheology (Glen's law) that is traditionally used in glacier or ice sheet modeling. In contrast, for snow, the observations are in contradiction with this rheology. To further comprehend this finding, we conducted a sensitivity study on different ice rheologies. None of the material models is able to explain the observed high compactive viscosity of depth hoar compared to rounded grains having the same density. While, on one hand, our results re-emphasize the limitations of our current mechanical understanding of the ice in snow, they constitute, on the other hand, a confirmation of the common picture of firn as a foam of polycrystalline ice through microstructure-based simulations. [ABSTRACT FROM AUTHOR]

Quantifying the link between microstructure and effective elastic properties of snow, firn, and bubbly ice is essential for many applications in cryospheric sciences. The microstructure of snow and ice can be characterized by different types of fabrics (crystallographic and geometrical), which give rise to macroscopically anisotropic elastic behavior. While the impact of the crystallographic fabric has been extensively studied in deep firn, the present work investigates the influence of the geometrical fabric over the entire range of possible volume fractions. To this end, we have computed the effective elasticity tensor of snow, firn, and ice by finite-element simulations based on 391 X-ray tomography images comprising samples from the laboratory, the Alps, Greenland, and Antarctica. We employed a variant of Eshelby's tensor that has been previously utilized for the parameterization of thermal and dielectric properties of snow and utilized Hashin–Shtrikman bounds to capture the nonlinear interplay between density and geometrical anisotropy. From that we derive a closed-form parameterization for all components of the (transverse isotropic) elasticity tensor for all volume fractions using two fit parameters per tensor component. Finally, we used the Thomsen parameter to compare the geometrical anisotropy to the maximal theoretical crystallographic anisotropy in bubbly ice. While the geometrical anisotropy clearly dominates up to ice volume fractions of ϕ≈0.7 , a thorough understanding of elasticity in bubbly ice may require a coupled elastic theory that includes geometrical and crystallographic anisotropy. [ABSTRACT FROM AUTHOR]

Despite being one of the most fundamental microstructural parameters of snow, the specific surface area (SSA) dynamics during temperature gradient metamorphism (TGM) have so far been addressed only within empirical modeling. To surpass this limitation, we propose a rigorous modeling of SSA dynamics using an exact equation for the temporal evolution of the surface area, fed by pore-scale finite-element simulations of the water vapor field coupled with the temperature field on X-ray computed tomography images. The proposed methodology is derived from the first principles of physics and thus does not rely on any empirical parameter. Since the calculated evolution of the SSA is highly sensitive to fluctuations in the experimental data, we quantify the impact of these fluctuations within a stochastic error model. In our simulations, the only poorly constrained physical parameter is the condensation coefficient α. We address this problem by simulating the SSA evolution for a wide range of α values and estimate optimal values by minimizing the differences between simulations and experiments. This methodology suggests that α lies in the intermediate range 10-3<α<10-1 and slightly varies between experiments. Also, our results suggest a transition of the value of α in one TGM experiment, which can be explained by a transition in the underlying surface morphology. Overall, we are able to reproduce very subtle variations in the SSA evolution with correlations of R2=0.95 and 0.99 , respectively, for the two TGM time series considered. Finally, our work highlights the necessity of including kinetic effects and of using realistic microstructures to comprehend the evolution of SSA during TGM. [ABSTRACT FROM AUTHOR]

Despite being one of the most fundamental microstructural parameters of snow, the specific surface area (SSA) dynamics during temperature gradient metamorphism (TGM) have so far been addressed only within empirical modeling. To surpass this limitation, we propose a rigorous modeling of SSA dynamics using an exact equation for the temporal evolution of the surface area, fed by pore-scale finite element simulations of the water vapor field coupled with the temperature field on X-ray computed-tomography images. The proposed methodology derives from physics' first principles and thus does not rely on any empirical parameter. Since the calculated evolution of the SSA is highly sensitive to fluctuations in the experimental data, we address the impact of these fluctuations within a stochastic error model. In our simulations, the only poorly constrained physical parameter is the vapor attachment coefficient α onto ice. We address this problem by simulating the SSA evolution for a wide range of α and estimate optimal values by minimizing the differences between simulations and experiments. This methodology suggests that α lies in the intermediate range 10-3 < α < 10-1 and slightly varies between experiments. Also, our results suggest a transition of the value of α in one TGM experiment, which can be explained by a transition in the underlying surface morphology. Overall, we are able to reproduce very subtle variations in the SSA evolution with correlations of R 2 = 0.95 and 0.99, respectively, for the two considered TGM time series. Finally, our work highlights the necessity of including kinetics effects and of using realistic microstructures to comprehend the evolution of SSA during TGM. [ABSTRACT FROM AUTHOR]

Accurate models for the viscous densification of snow and firn under mechanical stress are of primary importance for various applications, including avalanche prediction and the interpretation of ice cores. Formulations of snow and firn compaction in models are still largely empirical, instead of using microstructures from micro-computed tomography to numerically compute the mechanical behavior directly from the physics at the micro-scale. The main difficulty of the latter approach is the choice of the correct rheology/constitutive law governing the deformation of the ice matrix, which is still controversially discussed. Being aware of these uncertainties, we conducted a first systematic attempt of microstructure-based modeling of snow and firn compaction. We employed the Finite Element suite ElmerFEM using snow and firn microstructures from different sites in the Alps and Antarctica to explore which ice rheologies are able to reproduce observations. We thereby extended the ParStokes solver in ElmerFEM to facilitate parallel computing of transverse isotropic material laws for monocrystalline ice. We found that firn (density above 550 kg m-3) can be reasonably well simulated across different sites assuming a polycrystalline rheology (Glen's law) that is traditionally used in glacier or ice sheet modeling. In contrast, for snow (density below 550 kg m-3) the observations are in contradition with this rheology. To further comprehend this finding, we conducted a sensitivity study on different ice rheologies. None of the material models is able to explain the observed high compactive viscosity of depth hoar compared to rounded grains having the same density. While on one hand our results re-emphasize the limitations of our current mechanical understanding of the ice in snow, they constitute on the other hand a confirmation of the common picture of firn densification as a foam of polycrystalline ice through microstructure-based simulations. [ABSTRACT FROM AUTHOR]

The evolution of the surface snow microstructure under the influence of wind is hardly understood but crucial for polar and alpine snowpacks. Available statistical models are solely parameterized from field data where conditions are difficult to control. Controlled experiments which exemplify the physical processes underlying the evolution of density or specific surface area (SSA) of surface snow under wind are virtually non-existing. As a remedy, we conducted experiments in a cold 5 laboratory using a ring-shaped wind tunnel with an infinite fetch to systematically investigate wind-induced microstructure modifications under controlled atmospheric, flow and snow conditions. Airborne snow particles are characterized by high-speed imaging, while deposited snow is characterized by density and SSA measurements. We used a single snow type (dendritic fresh snow), cover wind speeds from 3 ms-1 to 7 ms-1 (for fixed temperature) and vary temperatures from -24°C to -2°C (for fixed wind speed). The measured airborne impact trajectories confirm the consistency of our coefficient of restitution with 10 large scale saltation, rendering the setup suitable to realistically study interactions between airborne and deposited snow. Our measured densification rates in the deposit as a function of wind speed show clear deviations from existing statistical models, but can be re-parameterized through our data. The most drastic changes in densification and SSA rates of deposited snow are observed close to the melting point. This study, as a first of its kind, exemplifies a rich non-linear interplay between airborne and deposited snow particles which is discussed in view of a multitude of involved processes, i.e. airborne metamorphism, 15 cohesion, particle separation and fragmentation. [ABSTRACT FROM AUTHOR]

Quantifying the link between microstructure and effective elastic properties of snow, firn, and bubbly ice is essential for many applications in cryospheric sciences. The microstructure of snow and ice can be characterized by different types of fabrics (crystallographic, geometrical) that gives rise to macroscopically anisotropic elastic behavior. While the impact of the crystallographic fabric has been extensively studied in deep firn, the present work investigates the influence of the geometrical fabric over the entire range of possible volume fractions. To this end we have computed the effective elasticity tensor of snow, firn, and ice by finite element simulations based on 395 X-ray tomography images comprising samples from the laboratory, Alps, Greenland, and Antarctica. We employed a variant of the Eshelby tensor that has been previously utilized for the parametrization of thermal and dielectric properties of snow and utilized Hashin-Shtrikman bounds to capture the nonlinear interplay between density and geometrical anisotropy. From that we derive a closed-form parametrization for all components of the (transverse isotropic) elastic tensor for all volume fractions using 2 fit parameters per tensor component. Finally we used the Thomsen parameter to compare the geometrical anisotropy to the crystallographic anisotropy in bubbly ice. While the geometrical anisotropy is clearly dominating up to ice volume fractions of Ø ≈ 0.7, a thorough understanding of elasticity in bubbly ice may require a coupled elastic theory that includes geometrical and crystallographic anisotropy. [ABSTRACT FROM AUTHOR]

Snow significantly impacts the seasonal growth of Arctic sea ice due to its thermally insulating properties. Various measurements and parametrizations of thermal properties exist, but an assessment of the entire seasonal evolution of thermal conductivity and snow resistance is hitherto lacking. Using the comprehensive snow data set from the MOSAiC expedition, we have evaluated for the first time the seasonal evolution of the snow's thermal conductivity and thermal resistance on different ice ages (leads, first and second-year ice) and topographic features (ridges). Combining different measurement parametrizations and assessing the robustness against spatial variability, we infer and quantify a hitherto undocumented feature in the seasonal dynamics of snow on sea ice. We observe an increase in thermal conductivity up to March and a decrease thereafter, both on first-year and second-year ice before the melt period started. Since a similar non-monotonic behaviour is extracted for the snow depth, the thermal resistance of snow on level sea ice remains approximately constant with a value of 515 ± 404 m2 K W−1 on first-year ice and 660 ± 475m2 K W−1 on second-year ice. We found approximately three times higher thermal resistance on ridges (1411 ± 910 m2 K W−1). Our findings are that the micropenetrometer-derived thermal conductivities give accurate values, and confirm that spatial variability of the snow cover is vertically and horizontally large. The implications of our findings for Arctic sea ice are discussed. [ABSTRACT FROM AUTHOR]