Articles | Volume 13, issue 11
Research article
08 Nov 2019
Research article |  | 08 Nov 2019

Estimating early-winter Antarctic sea ice thickness from deformed ice morphology

M. Jeffrey Mei, Ted Maksym, Blake Weissling, and Hanumant Singh


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (20 Sep 2019) by John Yackel
AR by M. Jeffrey Mei on behalf of the Authors (20 Sep 2019)  Author's response    Manuscript
ED: Publish as is (07 Oct 2019) by John Yackel
Short summary
Sea ice thickness is hard to measure directly, and current datasets are very limited to sporadically 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 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 %.