Parameter optimization in sea ice models with elastic–viscoplastic rheology

Abstract

Abstract. The modern sea ice models include multiple parameters which strongly affect model solution. As an example, in the CICE6 community model, rheology and landfast grounding/arching effects are simulated by functions of the sea ice thickness and concentration with a set of fixed parameters empirically adjusted to optimize the model performance. In this study, we consider the extension of a two-dimensional elastic–viscoplastic (EVP) sea ice model using a spatially variable representation of these parameters. The feasibility of optimization of the landfast sea ice parameters and rheological parameters is assessed via idealized variational data assimilation experiments with synthetic observations of ice concentration, thickness and velocity. The experiments are configured for a 3 d data assimilation window in a rectangular basin with variable wind forcing. The tangent linear and adjoint models featuring EVP rheology are found to be unstable but can be stabilized by adding a Newtonian damping term into the adjoint equations. A set of observation system simulation experiments shows that landfast parameter distributions can be reconstructed after 5–10 iterations of the minimization procedure. Optimization of sea ice initial conditions and spatially varying parameters in the stress tensor equation requires more computation but provides a better hindcast of the sea ice state and the internal stress tensor. Analysis of inaccuracy in the wind forcing and errors in sea ice thickness observations show reasonable robustness of the variational DA approach and the feasibility of its application to available and incoming observations.

Arctic sea ice and snow from different ice models: A CICE–SI3 intercomparison study

“…The tuning in the models was done manually testing varying each of the albedo parameters one by one and comparing the result to total sea ice volume. With the snow and ice layer changing through out the seasons and several parameters at play which have different effect in different regions depending on the season it would be advisable to use a more sophisticated tuning method as for example Panteleev et al (2020) or Massonnet et al (2014).…”

Lagrangian Data Assimilation and Parameter Estimation of an Idealized Sea Ice Discrete Element Model

“…The existing data assimilation techniques are also more applicable to Eulerian models. Recent development of the Eulerian data assimilation and parameter estimation can be found in Fenty and Heimbach (2013), Fletcher (2010), Heimbach et al (2005), Kevlahan et al (2015), Panteleev et al (2020), and Toyoda et al (2019).…”