Modeling the Evolution of the Structural Anisotropy of Snow
The structural anisotropy of snow that originates from a spatially anisotropic distribution of the ice matrix and the pore space, is a key quantity to understand physical snow properties and to improve their parameterizations. To this end we propose a minimal empirical model to describe the temporal...
| Main Authors: | , , , |
|---|---|
| Format: | Text |
| Language: | English |
| Published: |
2019
|
| Subjects: | |
| Online Access: | https://doi.org/10.5194/tc-2019-63 https://www.the-cryosphere-discuss.net/tc-2019-63/ |
| Summary: | The structural anisotropy of snow that originates from a spatially anisotropic distribution of the ice matrix and the pore space, is a key quantity to understand physical snow properties and to improve their parameterizations. To this end we propose a minimal empirical model to describe the temporal evolution of the structural anisotropy and publish the extensive, calibration dataset consisting of meteorological, radar, and micro computer tomography (CT) data. The dataset was acquired near the town of Sodankylä in Northern Finland. The model is tailored to immediate implementation into common snow pack models driven by meteorological data as its parametrization is solely based on macroscopic, thermodynamic fields. Here we use output data of the physical model SNOWPACK to drive our model. The model implements rate equations for each snow layer and accounts for snow settling and temperature gradient metamorphism, which are taken to be the main drivers of the temporal evolution of the structural anisotropy. The model is calibrated with available time series of anisotropy measurements spanning four different winter seasons. The calibration measurements were obtained from polarimetric radar data which were analyzed with respect to the dielectric anisotropy of snow. From the detailed comparison between simulated anisotropy and radar time series we identify settling as the main mechanism causing horizontal structures in the snow pack. The comparison also confirms temperature gradient metamorphism as the main mechanism for vertical structures. For validation of the model we use full-depth profiles of anisotropy measurements obtained from CT data. The results show that the model can predict the measured CT profiles quite accurately. For depth hoar, differences between modeled anisotropy and the anisotropy derived from exponential correlation lengths are observed and discussed in view of potential limitations. |
|---|