Particle-Continuum Multiscale Modeling of Sea Ice Floes

TL;DR

This paper introduces a multiscale model coupling particle and continuum approaches to simulate sea ice floe dynamics, improving accuracy and computational efficiency in polar climate modeling.

Contribution

It develops a coupled multiscale model using a Boltzmann-type system that links particle and continuum descriptions of sea ice, enhancing simulation fidelity across scales.

Findings

The model effectively captures sea ice dynamics at multiple scales.

Parallelized particle component improves computational efficiency.

The multiscale model approximates the true system linearly.

Abstract

Sea ice profoundly influences the polar environment and the global climate. Traditionally, Sea ice has been modeled as a continuum under Eulerian coordinates to describe its large-scale features, using, for instance, viscous-plastic rheology. Recently, Lagrangian particle models, also known as the discrete element method (DEM) models, have been utilized for characterizing the motion of individual sea ice fragments (called floes) at scales of 10 km and smaller, especially in marginal ice zones. This paper develops a multiscale model that couples the particle and the continuum systems to facilitate an effective representation of the dynamical and statistical features of sea ice across different scales. The multiscale model exploits a Boltzmann-type system that links the particle movement with the continuum equations. For the small-scale dynamics, it describes the motion of each sea ice floe. Then, as the large-scale continuum component, it treats the statistical moments of mass density and linear and angular velocities. The evolution of these statistics affects the motion of individual floes, which in turn provides bulk feedback that adjusts the large-scale dynamics. Notably, the particle model characterizing the sea ice floes is localized and fully parallelized, in a framework that is sometimes called superparameterization, which significantly improves computation efficiency. Numerical examples demonstrate the effective performance of the multiscale model. Additionally, the study demonstrates that the multiscale model has a linear-order approximation to the truth model.