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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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.