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The Cryosphere An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 09 Jul 2019

Submitted as: research article | 09 Jul 2019

Review status
This discussion paper is a preprint. A revision of the manuscript is under review for the journal The Cryosphere (TC).

Estimating Early-Winter Antarctic Sea Ice Thickness From Deformed Ice Morphology

M. Jeffrey Mei1,2, Ted Maksym1, and Hanumant Singh3 M. Jeffrey Mei et al.
  • 1Department of Applied Ocean Science and Engineering, Woods Hole Oceanographic Institution, MA 02540, USA
  • 2Department of Mechanical Engineering, Massachusetts Institute of Technology, MA 02139, USA
  • 3Department of Electrical and Computer Engineering, Northeastern University, MA 02115, USA

Abstract. Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness can be estimated from surface elevation measurements, such as those from airborne/satellite LiDAR, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic sea ice thickness have errors as high as ~ 50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate sea ice thickness at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate sea ice thickness at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ~ 15 %). Moreover, the neural network does not require specifying snow depth/density, which increases its potential applications to other LiDAR datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season, which is much lower than the mean relative error for a linear fit (errors up to 47 %). This method may be extended to lower-resolution, larger-footprint data such as such as IceBridge, and suggests a possible avenue to reduce errors in satellite estimates of Antarctic sea ice thickness from ICESat-2 over current methods, especially at smaller scale.

M. Jeffrey Mei et al.
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M. Jeffrey Mei et al.
M. Jeffrey Mei et al.
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Short summary
Sea ice thickness is hard to measure directly, and current datasets are very limited to sporadic-conducted drill lines. However, surface elevation is much easier to measure. Converting surface elevation to ice thickness requires making assumptions about snow depth and density, which leads to large errors (and also may not generalize to new datasets). A deep learning method is presented that uses the surface morphology as a direct predictor of sea ice thickness, with testing errors of < 20 %.
Sea ice thickness is hard to measure directly, and current datasets are very limited to...