[1] We carried out numerical simulations of the conductivity of snow using microtomographic images. The full tensor of the effective thermal conductivity (k eff ) was computed from 30 three-dimensional images of the snow microstructure, spanning all types of seasonal snow. Only conduction through ice and interstitial air were considered. The obtained values are strongly correlated to snow density. The main cause for the slight scatter around the regression curve to snow density is the anisotropy of k eff : the vertical component of k eff of facetted crystals and depth hoar samples is up to 1.5 times larger than the horizontal component, while rounded grains sampled deeply in the snowpack exhibit the inverse behavior. Results of simulations neglecting the conduction in the interstitial air indicate that this phase plays a vital role in heat conduction through snow. The computed effective thermal conductivity is found to increase with decreasing temperature, mostly following the temperature dependency of the thermal conductivity of ice. The results are compared to experimental data obtained either with the needle-probe technique or using combined measurements of the vertical heat flux and the corresponding temperature gradient. Needle-probe measurements are systematically significantly lower than those from the two other techniques. The observed discrepancies between the three methods are investigated and briefly discussed.

We used three-dimensional (3-D) images of snow microstructure to carry out numerical estimations of the full tensor of the intrinsic permeability of snow (<b>K</b>). This study was performed on 35 snow samples, spanning a wide range of seasonal snow types. For several snow samples, a significant anisotropy of permeability was detected and is consistent with that observed for the effective thermal conductivity obtained from the same samples. The anisotropy coefficient, defined as the ratio of the vertical over the horizontal components of <b>K</b>, ranges from 0.74 for a sample of decomposing precipitation particles collected in the field to 1.66 for a depth hoar specimen. Because the permeability is related to a characteristic length, we introduced a dimensionless tensor <b>K</b>*=<b>K</b>/<i>r</i>_es², where the equivalent sphere radius of ice grains (<i>r</i>_es) is computed from the specific surface area of snow (SSA) and the ice density (&rho;_i) as follows: <i>r</i>_es=3/(SSA&times;&rho;_i. We define <i>K</i> and <i>K</i>* as the average of the diagonal components of <b>K</b> and <b>K</b>*, respectively. The 35 values of <i>K</i>* were fitted to snow density (&rho;_s) and provide the following regression: <i>K</i> = (3.0 &pm; 0.3) <i>r</i>_es² exp((&minus;0.0130 &pm; 0.0003)&rho;_s). We noted that the anisotropy of permeability does not affect significantly the proposed equation. This regression curve was applied to several independent datasets from the literature and compared to other existing regression curves or analytical models. The results show that it is probably the best currently available simple relationship linking the average value of permeability, <i>K</i>, to snow density and specific surface area