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Abstract. The net rate of snow accumulation b is predicted to increase over large areas of the Antarctic and Greenland ice sheets as the climate warms. Models disagree on how this will affect the thickness of the firn layer – the relatively low-density upper layer of the ice sheets that influences altimetric observations of ice sheet mass change and palaeo-climate reconstructions from ice cores. Here we examine how b influences firn compaction and porosity in a simplified model that accounts for mass conservation, dry firn compaction, grain-size evolution, and the impact of grain size on firn compaction. Treating b as a boundary condition and employing an Eulerian reference frame helps to untangle the factors controlling the b dependence of firn thickness. We present numerical simulations using the model, as well as simplified steady-state approximations to the full model, to demonstrate how the downward advection of porosity and grain size are both affected by b but have opposing impacts on firn thickness. The net result is that firn thickness increases with b and that the strength of this dependence increases with increasing surface grain size. We also quantify the circumstances under which porosity advection and grain-size advection balance exactly, which counterintuitively renders steady-state firn thickness independent of b. These findings are qualitatively independent of the stress-dependence of firn compaction and whether the thickness of the ice sheet is increasing, decreasing, or steady. They do depend on the grain-size dependence of firn compaction. Firn models usually ignore grain-size evolution, but we highlight the complex effect it can have on firn thickness when included in a simplified model. This work motivates future efforts to better observationally constrain the rheological effect of grain size in firn.
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Deep Learning on Airborne Radar Echograms for Tracing Snow Accumulation Layers of the Greenland Ice Sheet
Climate change is extensively affecting ice sheets resulting in accelerating mass loss in recent decades. Assessment of this reduction and its causes is required to project future ice mass loss. Annual snow accumulation is an important component of the surface mass balance of ice sheets. While in situ snow accumulation measurements are temporally and spatially limited due to their high cost, airborne radar sounders can achieve ice sheet wide coverage by capturing and tracking annual snow layers in the radar images or echograms. In this paper, we use deep learning to uniquely identify the position of each annual snow layer in the Snow Radar echograms taken across different regions over the Greenland ice sheet. We train with more than 15,000 images generated from radar echograms and estimate the thickness of each snow layer within a mean absolute error of 0.54 to 7.28 pixels, depending on dataset. A highly precise snow layer thickness can help improve weather models and, thus, support glaciological studies. Such a well-trained deep learning model can be used with ever-growing datasets to aid in the accurate assessment of snow accumulation on the dynamically changing ice sheets.
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- Award ID(s):
- 1947584
- PAR ID:
- 10285759
- Date Published:
- Journal Name:
- Remote Sensing
- Volume:
- 13
- Issue:
- 14
- ISSN:
- 2072-4292
- Page Range / eLocation ID:
- 2707
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/rs13142707
