Articles | Volume 15, issue 2
Geosci. Model Dev., 15, 803–814, 2022
Geosci. Model Dev., 15, 803–814, 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

Data sets

Training Data for NN-WIFF Christopher Horvat and Lettie Roach

Model output: CICE6 with WIFF Lettie Roach

Model code and software

chhorvat/WIFF-Model: Release 1.0 - Including CICE/ICEPACK code (v1.0b) Christopher Horvat

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.