Superparameterisation of Arctic sea ice floes

Abstract: In this talk, I will start with some quick facts about Arctic sea ice floes and then give a quick review of the evolution of sea ice models. The first models are Eulerian continuum models that describe the sea ice floes as viscous-plastics (Hilber 1979). Lagrangian particle models have been developed recently, showing improved model performance, especially in ice-marginal zones where sea ice is fragmented. The most successful one is the discrete element method (DEM). It characterises the physical quantities of each sea ice floe along its trajectory under the Lagrangian coordinates. The major challenges are 1) model coupling in different frames of reference (Lagrangian for sea ice while Eulerian for the ocean and atmosphere dynamics); 2) the heavy computational cost when the number of the floes is large; and 3) inaccurate floe parameterisation when the floe distribution has multiscale features. In this talk, I will present a superfloe parameterisation to reduce the computational cost and a superparameterisation to capture the multiscale features. The superfloe parameterisation algorithm generates a small number of superfloes that effectively approximate a considerable number of the floes. The parameterisation scheme satisfies several important physics constraints that guarantee similar short-term dynamical behaviour while maintaining long-range uncertainties, especially the non-Gaussian statistical features, of the full system. In addition, the superfloe parameterisation facilitates noise inflation in data assimilation that recovers the unobserved ocean field underneath the sea ice. To capture the multiscale features, we follow the derivation of the Boltzmann equation for particles and superparameterise the sea ice floes as continuity equations governing the statistical moments of mass density and linear and angular velocities. This leads to a particle-continuum coupled model. The continuum part captures the large scales and the particle part captures the small scales. The particle model is localised and fully parallelised for computation efficiency. I will present several numerical experiments to demonstrate the success of the proposed schemes. This is joint work with Nan Chen (UW-Madison) and Sam Stechmann (UW-Madison).

computational biologycomputational engineering, finance, and sciencenumerical analysiscomputational physics

Audience: researchers in the topic

ANU Mathematics and Computational Sciences Seminar