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

Horvat, Christopher; Roach, Lettie A.

doi:https://doi.org/10.5194/gmd-15-803-2022

Articles | Volume 15, issue 2

https://doi.org/10.5194/gmd-15-803-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-15-803-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 15, issue 2

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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Final revised paper (published on 28 Jan 2022)
Preprint (discussion started on 25 Aug 2021)

Interactive discussion

Status: closed

RC1: 'Referee report on gmd-2021-281', Timothy Williams, 14 Sep 2021

The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2021-281/gmd-2021-281-RC1-supplement.pdf

Citation: https://doi.org/10.5194/gmd-2021-281-RC1
RC2: 'Comment on gmd-2021-281', Predrag Popovic, 11 Oct 2021

The comment was uploaded in the form of a supplement: https://gmd.copernicus.org/preprints/gmd-2021-281/gmd-2021-281-RC2-supplement.pdf

Citation: https://doi.org/10.5194/gmd-2021-281-RC2
CEC1: 'Comment on gmd-2021-281', Juan Antonio Añel, 12 Oct 2021

Dear authors,
Checking your manuscript, I have seen that you have done a good job making the relevant data available in Zenodo. However, you have archived your code in GitHub. However, GitHub is not a suitable repository. GitHub itself instructs authors to use other alternatives for long-term archival and publishing, such as Zenodo. Therefore, please, publish your code in one of the appropriate repositories.

In this way, before the Discussions stage is closed, you must reply to this comment with the link to the repository for the code and the corresponding DOI. Also, you must include in a potential reviewed version of your manuscript the modified 'Code and Data Availability' section and the DOI of the code.
Juan A. Añel

Geosc. Mod. Dev. Executive Editor

Citation: https://doi.org/10.5194/gmd-2021-281-CEC1
AC1: 'Response to Reviewers', Christopher Horvat, 13 Dec 2021

Please see attached response letter to reviewer 1 and 2 comments.

Citation: https://doi.org/10.5194/gmd-2021-281-AC1

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

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.