Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 803–814, 2022
https://doi.org/10.5194/gmd-15-803-2022
Geosci. Model Dev., 15, 803–814, 2022
https://doi.org/10.5194/gmd-15-803-2022

Model description paper 28 Jan 2022

Model description paper | 28 Jan 2022

WIFF1.0: a hybrid machine-learning-based parameterization of wave-induced sea ice floe fracture

Christopher Horvat and Lettie A. Roach

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Referee report on gmd-2021-281', Timothy Williams, 14 Sep 2021
  • RC2: 'Comment on gmd-2021-281', Predrag Popovic, 11 Oct 2021
  • CEC1: 'Comment on gmd-2021-281', Juan Antonio Añel, 12 Oct 2021
  • AC1: 'Response to Reviewers', Christopher Horvat, 13 Dec 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Christopher Horvat on behalf of the Authors (20 Dec 2021)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (22 Dec 2021) by Alexander Robel
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Short summary
Sea ice is a composite of individual pieces, called floes, ranging in horizontal size from meters to kilometers. Variations in sea ice geometry are often forced by ocean waves, a process that is an important target of global climate models as it affects the rate of sea ice melting. Yet directly simulating these interactions is computationally expensive. We present a neural-network-based model of wave–ice fracture that allows models to incorporate their effect without added computational cost.