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Published March 1, 2023 | public
Journal Article

Level Set Discrete Element Method for modeling sea ice floes


Understanding and projecting seasonal variations in sea ice is necessary to improve global climate predictions. However, accurately capturing changes in sea ice and its interactions with ocean and atmosphere variability remains a challenge for models, notably due to its complex behavior at the floe scale. In this work, we introduce a method to capture the floe-like behavior of sea ice, named the 'Level Set Discrete Element Method for Sea Ice' (LS-ICE). This model can resolve individual sea ice floes with realistic shapes, and represent their physical interactions by leveraging level-set functions for detecting contact between floes. LS-ICE can also be coupled to heat and momentum forcings from the atmosphere and the ocean, and simulate associated melt and breakage processes. The discrete representation of sea ice floes reveals melt dynamics, associated with their shapes and thickness distributions, which are currently not well represented by continuum models. We illustrate the model capabilities for two different years involving the spring to summer transition in Baffin Bay, where the sea ice concentration declines from approximately 80% to 0% between the months of June and July. Satellite imagery, along with oceanographic reanalysis data based on field measurements, are used to initialize the model and validate its subsequent evolution during these months. For an appropriate set of parameters, the model can reproduce the evolution of sea ice concentration, floe size distribution, oceanic temperature and mean sea ice thickness, despite only a small number of tunable parameters. This study identifies the potential for LS-ICE to simulate the interaction between floe shape, melt and breakage, to enhance seasonal scale forecasts for sea ice floes.

Additional Information

© 2023 Elsevier. R.M.L. and J.A.'s research was funded by the support of ARO, United States Grant W911NF-19-1-0245 and the NSF, United States grant JEA.NSFCMMIECI-1-NSF.2033779. M.G. and A.F.T. were supported by award NSF-OCE 1829969 and the Office of Naval Research (ONR) Multidisciplinary University Research Initiative (MURI) on Mathematics and Data Science for Physical Modeling and Prediction of Sea Ice, United States , N00014-19-1-2421. All of the authors declare not having any type of conflict of interests with respect to the results of this paper. Data availability. MODIS images can be accessed from the NASA Worldview website [43]. Copernicus data from the TOPAZ4 Arctic Ocean Physics Analysis and Forecast was obtained from [45]. To access LS-DEM and LS-ICE, the corresponding author can be contacted directly. The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jose E. Andrade reports financial support was provided by National Science Foundation.

Additional details

August 22, 2023
October 25, 2023