The effect of snow: How to better model ground surface temperatures

We present an inverse modeling approach for reconstructing the effective thermal conductivity of snow on a daily basis using air temperature, ground temperature and snow depth measurements. The method is applied to four sites in Alaska. To validate the method we used measured snow densities and snow water equivalents. The modeled thermal conductivities of snow for the two interior Alaska sites have relatively low values and reach their maximum near the end of the snow season, while the conductivities at the two sites on the Alaskan North Slope are higher and reach their maximum earlier in the snow season. We show that the reconstructed daily thermal conductivities allow for more accurate modeling of ground surface temperatures when compared to applying a constant thermal conductivity for the snow layer.