Articles | Volume 15, issue 2
https://doi.org/10.5194/gmd-15-803-2022
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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Cited articles

Aksenov, Y., Popova, E. E., Yool, A., Nurser, A. J., Williams, T. D., Bertino, L., and Bergh, J.: On the future navigability of Arctic sea routes: High-resolution projections of the Arctic Ocean and sea ice, Mar. Policy, 75, 300–317, https://doi.org/10.1016/j.marpol.2015.12.027, 2017. a
Asplin, M. G., Galley, R., Barber, D. G., and Prinsenberg, S.: Fracture of summer perennial sea ice by ocean swell as a result of Arctic storms, J. Geophys. Res.-Oceans, 117, 1–12, https://doi.org/10.1029/2011JC007221, 2012. a
Asplin, M. G., Scharien, R., Else, B., Howell, S., Barber, D. G., Papakyriakou, T., and Prinsenberg, S.: Implications of fractured Arctic perennial ice cover on thermodynamic and dynamic sea ice processes, J. Geophys. Res.-Oceans, 119, 2327–2343, https://doi.org/10.1002/2013JC009557, 2014. a
Bateson, A. W., Feltham, D. L., Schröder, D., Hosekova, L., Ridley, J. K., and Aksenov, Y.: Impact of sea ice floe size distribution on seasonal fragmentation and melt of Arctic sea ice, The Cryosphere, 14, 403–428, https://doi.org/10.5194/tc-14-403-2020, 2020. a, b
Boutin, G., Ardhuin, F., Dumont, D., Sévigny, C., Girard-Ardhuin, F., and Accensi, M.: Floe size effect on wave-ice interactions: possible effects, implementation in wave model, and evaluation, J. Geophys. Res.-Oceans, 123, 4779–4805, https://doi.org/10.1029/2017JC013622, 2018. a
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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.