WIFF1.0: A hybrid machine-learning-based parameterization of Wave-Induced sea-ice Floe Fracture

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 is determined by the mutual interaction of ocean waves and floes, as waves prop...

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Bibliographic Details
Main Authors: Horvat, Christopher, Roach, Lettie A.
Format: Text
Language:English
Published: 2021
Subjects:
Online Access:https://doi.org/10.5194/gmd-2021-281
https://gmd.copernicus.org/preprints/gmd-2021-281/
Description
Summary: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 is 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.