Introduction Conclusions References

The thermal conductivity of snow determines the temperature gradient, and by this, it has a direct effect on the rate of snow metamorphism. It is therefore a key property of snow. However, thermal conductivities measured with the transient needle probe and the steady-state, heat flux plate differ. In addition, the anisotropy of thermal conductivity plays an important role in the accuracy of thermal conductivity measurements. In this study, we investigated three independent methods to measure snow thermal conductivity and its anisotropy: a needle probe with a long heating time, a guarded heat flux plate, and direct numerical simulation at the microstructural level of the pore and ice structure. The three methods were applied to identical snow samples. We analyzed the consistency and the difference between these methods. As already shown in former studies, we observed a distinct difference between the anisotropy of thermal conductivity in small rounded grains and in depth hoar. Indeed, the anisotropy between vertical and horizontal thermal conductivity components ranges between 0.5–2. This can cause a difference in thermal conductivity measurements carried out with needle probes of up to –25 % to +25 % if the thermal conductivity is calculated only from a horizontally inserted needle probe. Based on our measurements and the comparison of the three methods studied here, the direct numerical simulation is the most reliable method, as the tensorial components of the thermal conductivity can be calculated and the corresponding microstructure is precisely known.

Release of trace gases from surface snow on earth drives atmospheric chemistry, especially in the polar regions. The gas-phase diffusion of methanol and of acetone through the interstitial air of snow was investigated in a well-controlled laboratory study in the temperature range of 223 to 263 K. The aim of this study was to evaluate how the structure of the snowpack, the interaction of the trace gases with the snow surface, and the grain boundaries influence the diffusion on timescales up to 1 h. The diffusive loss of these two volatile organics into packed snow samples was measured using a chemical ionization mass spectrometer. The structure of the snow was analysed by means of X-ray-computed micro-tomography. The observed diffusion profiles could be well described based on gas-phase diffusion and the known structure of the snow sample at temperatures ≥ 253 K. At colder temperatures, surface interactions start to dominate the diffusive transport. Parameterizing these interactions in terms of adsorption to the solid ice surface, i.e. using temperature-dependent air–ice partitioning coefficients, better described the observed diffusion profiles than the use of air–liquid partitioning coefficients. No changes in the diffusive fluxes were observed by increasing the number of grain boundaries in the snow sample by a factor of 7, indicating that for these volatile organic trace gases, uptake into grain boundaries does not play a role on the timescale of diffusion through porous surface snow. For this, a snow sample with an artificially high amount of ice grains was produced and the grain boundary surface measured using thin sections. In conclusion, we have shown that the diffusivity can be predicted when the structure of the snowpack and the partitioning of the trace gas to solid ice is known.

Classification of snow samplesIn the following we provide a detailed listing of used samples.The isothermal experiments are listed in Tab. 1.The time series of temperature gradient experiments (TGM-2, TGM17) were conducted in the µ-CT and listed in Tab.2,3.The temperature gradient experiments (DH-1, DH-2) were carried out in a metamorphism box and listed in Tab. 4. The set of 37 individual samples (DIV) comprising various types of snow is described in Tab. 5.Besides the ice volume fraction φ i (density) (conservative error estimate: ±0.01) and the specific surface area (SSA) (conservative error estimate ±10%) we provide a measure of "grain size": The mean thickness Sp and Th of the ice matrix and pore space, respectively have been computed by the method [2] based on the inscription of spheres.The respective errors denote the standard deviation of the thickness distribution.In addition, a grain classification has been carried out from tomography images according to [1].For time-lapse metamorphism experiments also the time step is given.

The quantification of the structural properties of snow is traditionally based on model-based stereology. Model-based stereology requires assumptions about the shape of the investigated structure. Here, we show how the density, specific surface area, and grain boundary area can be measured using a design-based method, where no assumptions about structural properties are necessary. The stereological results were also compared to X-ray tomography to control the accuracy of the method. The specific surface area calculated with the stereological method was 19.8±12.3% smaller than with X-ray tomography. For the density, the stereological method gave results that were 11.7±12.1% larger than X-ray tomography. The statistical analysis of the estimates confirmed that the stereological method and the sampling used are accurate. This stereological method was successfully tested on artificially produced ice beads but also on several snow types. Combining stereology and polarisation microscopy provides a good estimate of grain boundary areas in ice beads and in natural snow, with some limitations.

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Abstarct Snow hides its true structure from easy visual observation. One reason is that ice is transparent, so the three-dimensional (3-D) structure is impossible to disentangle. 3-D reconstruction is essential to understand the physical and mechanical properties of snow. In recent years, the techniques to measure and visualize snow in 3-D have improved tremendously. X-ray microtomography is much more user-friendly than the older microtome sectioning techniques. We show different techniques to measure cast and natural snow samples and the steps necessary to produce high-quality data. The simplest way is 3-D renderings. Imaging snow in 3-D definitively challenges our previous view of a snowpack consisting of particles as traditionally seen on a crystal card.