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.

Highlights

  • Due to the significant decline of sea ice volume and the increase in maritime activity in the Arctic Ocean over the past decades, an accurate sea ice hindcast/forecast has become an important component of the data assimilation systems for the region

  • The major objective of our study is to find a numerically feasible method for simultaneous optimization of multiple model parameters including the spatially varying rheological parameters (RPs), initial conditions and forcing fields in the sea ice models based on EVP solvers (e.g., CICE model)

  • We extend the investigation to analyze the feasibility of RP optimization within a more advanced twodimensional (2D) sea ice model based on the EVP rheology formulation of Lemieux et al (2016)

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Summary

Introduction

Due to the significant decline of sea ice volume and the increase in maritime activity in the Arctic Ocean over the past decades, an accurate sea ice hindcast/forecast has become an important component of the data assimilation systems for the region. The typical values of P ∗ determined from sea ice drift were diagnosed to vary within 27.5 kN/m2 (Hibler and Walsh, 1982), 15–20 kN/m2 (Kreyscher et al, 1997, 2000) and 30–45 kN/m2 (Tremblay and Hakakian, 2006) These studies indicate the existence of significant variations of P ∗ estimates, which may be attributed to both nonphysical considerations (such as spatially variable model resolution) and spatiotemporal variations of Arctic sea ice. The numerical experiments of Lemieux et al (2016) using a coarse-resolution pan-Arctic CICE-NEMO model have shown that kT = 0.2 provides the best agreement with landfast strength observations in the Kara Sea, when the ellipse axes ratio ranges within 1.2–1.4.

Sea ice model and its 4D-Var implementation
Formulation
Numerical scheme
Strong constraint formulation
Adjoint and tangent linear models
Simulated observations and cost functions
Arching: optimization of kT
Objective
Grounding effect: optimization of k2
Optimization of the ice strength and axes ratio fields
PIZ OSSE
Findings
Summary and discussion
Full Text
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