Superfloe Parameterization with Physics Constraints for Uncertainty Quantification of Sea Ice Floes

TL;DR

This paper introduces a superfloe parameterization method that reduces computational costs in sea ice modeling by approximating numerous floes with physics-constrained superfloes, improving uncertainty quantification and data assimilation.

Contribution

The paper presents a novel superfloe parameterization scheme that preserves key physics constraints, enabling efficient and accurate uncertainty quantification in sea ice DEM models.

Findings

Effective reduction in computational cost.

Accurate uncertainty quantification of sea ice dynamics.

Enhanced data assimilation for ocean and sea ice fields.

Abstract

The discrete element method (DEM) is providing a new modeling approach for describing sea ice dynamics. It exploits particle-based methods to characterize the physical quantities of each sea ice floe along its trajectory under Lagrangian coordinates. One major challenge in applying the DEM models is the heavy computational cost when the number of floes becomes large. In this paper, an efficient Lagrangian parameterization algorithm is developed, which aims at reducing the computational cost of simulating the DEM models while preserving the key features of the sea ice. The new parameterization takes advantage of a small number of artificial ice floes, named the superfloes, to effectively approximate a considerable number of the floes, where the parameterization scheme satisfies several important physics constraints. The physics constraints guarantee the superfloe parameterized system will have similar short-term dynamical behavior as the full system. These constraints also allow the superfloe parameterized system to accurately quantify the long-range uncertainty, especially the non-Gaussian statistical features, of the full system. In addition, the superfloe parameterization facilitates a systematic noise inflation strategy that significantly advances an ensemble-based data assimilation algorithm for recovering the unobserved ocean field underneath the sea ice. Such a new noise inflation method avoids ad hoc tunings as in many traditional algorithms and is computationally extremely efficient. Numerical experiments based on an idealized DEM model with multiscale features illustrate the success of the superfloe parameterization in quantifying the uncertainty and assimilating both the sea ice and the associated ocean field.