EGU21-7626, updated on 04 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Snow microstructure on sea ice: Importance for remote sensing applications

Amy R. Macfarlane1, Stefanie Arndt2, Ruzica Dadic3, Carolina Gabarró4, Bonnie Light5, Mallik Mahmud6, Reza Naderpour1, Randall Scharien7, Madison Smith5, Gunnar Spreen8, Julienne Stroeve9,10, Aikaterini Tavri7, David N. Wagner1,11, and Martin Schneebeli1
Amy R. Macfarlane et al.
  • 1WSL Swiss Federal Institute for Forest, Snow and Landscape Research
  • 2Alfred Wegener Institute, 27570 Bremerhaven, Germany
  • 3Antarctic Research Centre, Victoria University of Wellington
  • 4BEC & Institute of Marine Science, CSIC, Barcelona, Spain
  • 5Applied Physics Laboratory, University of Washington, Seattle, WA, USA
  • 6University of Calgary, AB Canada
  • 7University of Victoria, BC Canada
  • 8University of Bremen, Institute of Environmental Physics, 28359 Bremen, Germany
  • 9University of Manitoba, Winnipeg, MB, R3T 2N2
  • 10UCL Earth Sciences, Bloomsbury, London WC1E 6BT
  • 11CRYOS, School of Architecture, Civil and Environmental Engineering, EPFL, 1015 Lausanne, Switzerland

Snow plays a key role in interpreting satellite remote sensing data from both active and passive sensors in the high Arctic and therefore impacts retrieved sea ice variables from these systems ( e.g., sea ice extent, thickness and age). Because there is high spatial and temporal variability in snow properties, this porous layer adds uncertainty to the interpretation of signals from spaceborne optical sensors, microwave radiometers, and radars (scatterometers, SAR, altimeters). We therefore need to improve our understanding of physical snow properties, including the snow specific surface area, snow wetness and the stratigraphy of the snowpack on different ages of sea ice in the high Arctic.

The MOSAiC expedition provided a unique opportunity to deploy equivalent remote sensing sensors in-situ on the sea ice similar to those mounted on satellite platforms. To aid in the interpretation of the in situ remote sensing data collected, we used a micro computed tomography (micro-CT) device. This instrument was installed on board the Polarstern and was used to evaluate geometric and physical snow properties of in-situ snow samples.  This allowed us to relate the snow samples directly to the data from the remote sensing instruments, with the goal of improving interpretation of satellite retrievals. Our data covers the full annual evolution of the snow cover properties on multiple ice types and ice topographies including level first-year (FYI), level multi-year ice (MYI) and ridges.

First analysis of the data reveals possible uncertainties in the retrieved remote sensing data products related to previously unknown seasonal processes in the snowpack. For example, the refrozen porous summer ice surface, known as surface scattering layer, caused the formation of a hard layer at the multiyear ice/snow interface in the winter months, leading to significant differences in the snow stratigraphy and remote sensing signals from first-year ice, which has not experienced summer melt, and multiyear ice. Furthermore, liquid water dominates the extreme coarsening of snow grains in the summer months and in winter the temporally large temperature gradients caused strong metamorphism, leading to brine inclusions in the snowpack and large depth hoar structures, all this significantly influences the signal response of remote sensing instruments.

How to cite: Macfarlane, A. R., Arndt, S., Dadic, R., Gabarró, C., Light, B., Mahmud, M., Naderpour, R., Scharien, R., Smith, M., Spreen, G., Stroeve, J., Tavri, A., Wagner, D. N., and Schneebeli, M.: Snow microstructure on sea ice: Importance for remote sensing applications, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-7626,, 2021.

Corresponding presentation materials formerly uploaded have been withdrawn.