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

• Published in: Geoscientific Model Development
• Main Authors: Horvat, Christopher, Roach, Lettie A.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; Sea ice
• Online Access: https://doi.org/10.5194/gmd-15-803-2022; https://noa.gwlb.de/receive/cop_mods_00060041; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00059690/gmd-15-803-2022.pdf; https://gmd.copernicus.org/articles/15/803/2022/gmd-15-803-2022.pdf

Ocean surface waves play an important role in maintaining the marginal ice zone, a heterogenous region occupied by sea ice floes with variable horizontal sizes. The location, width, and evolution of the marginal ice zone are determined by the mutual interaction of ocean waves and floes, as waves propagate into the ice, bend it, and fracture it. In previous work, we developed a one-dimensional “superparameterized” scheme to simulate the interaction between the stochastic ocean surface wave field and sea ice. As this method is computationally expensive and not bitwise reproducible, here we use a pair of neural networks to accelerate this parameterization, delivering an adaptable, computationally inexpensive, reproducible approach for simulating stochastic wave–ice interactions. Implemented in the sea ice model CICE, this accelerated code reproduces global statistics resulting from the full wave fracture code without increasing computational overheads. The combined model, Wave-Induced Floe Fracture (WIFF v1.0), is publicly available and may be incorporated into climate models that seek to represent the effect of waves fracturing sea ice.