Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 15, issue 2
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

Viewed

Total article views: 3,588 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,709 787 92 3,588 97 138
  • HTML: 2,709
  • PDF: 787
  • XML: 92
  • Total: 3,588
  • BibTeX: 97
  • EndNote: 138
Views and downloads (calculated since 25 Aug 2021)
Cumulative views and downloads (calculated since 25 Aug 2021)

Viewed (geographical distribution)

Total article views: 3,588 (including HTML, PDF, and XML) Thereof 3,380 with geography defined and 208 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 18 Nov 2025
Download
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.
Read more
Share