Preprints
https://doi.org/10.5194/egusphere-2025-6379
https://doi.org/10.5194/egusphere-2025-6379
21 Jan 2026
 | 21 Jan 2026
Status: this preprint is open for discussion and under review for The Cryosphere (TC).

Arctic sea ice predictability on daily-to-weekly timescales: sensitivity to initial positional errors under different rheology formulations

Lohenn Fiol, Stephanie Leroux, Pierre Rampal, and Jean-Michel Brankart

Abstract. We investigates short-term (daily-to-weekly) winter Arctic sea-ice predictability using a coupled ice–ocean model, and focusing on how sensitive forecasts are to initial uncertainty in the location of sea ice features (e.g., leads, ridges, etc.). In this context, two rheologies are compared: elastic–viscous–plastic (aEVP) and brittle Bingham–Maxwell (BBM). For January–March 1997, we conduct 10-day ensemble forecasts, initialized by applying displacement perturbations to all sea-ice fields to  represent initial  positional errors, while keeping atmospheric forcing identical for all the ensemble members. Potential predictability is evaluated using a “perfect model” framework and probabilistic metrics for the ice-edge position errors, local state-variable errors (concentration, thickness, drift, deformation), and the spread of virtual drifters. Ice-edge forecasts are found to be largely insensitive to initial positional errors for both rheologies, indicating dominance of thermodynamic forcing rather than ice dynamics at short lead times. In contrast, BBM exhibits strong nonlinear sensitivity in pack ice: predictability is limited to 1–5 days for drift and deformation and 5–10 days for concentration. The aEVP model, on the other hand, quickly damps small-scale heterogeneities, yielding more convergent, and thus more predictable solutions. These findings have concrete implications: the BBM model produces larger regions with high probability of intense deformation and the spread of Lagrangian drifters up to an order of magnitude greater than in the aEVP model. Our results underscore the importance of ensemble forecasting for quantifying risks in a highly nonlinear and weakly predictable sea-ice system.

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Lohenn Fiol, Stephanie Leroux, Pierre Rampal, and Jean-Michel Brankart

Status: open (until 04 Mar 2026)

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Lohenn Fiol, Stephanie Leroux, Pierre Rampal, and Jean-Michel Brankart
Lohenn Fiol, Stephanie Leroux, Pierre Rampal, and Jean-Michel Brankart
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Short summary
We examine how uncertainty in the initial position of sea ice features (leads, ridges), affects daily-to-weekly winter sea-ice forecasts. Using ensemble simulations with a sea ice–ocean model, we compare two formulations of sea ice mechanics. We show that pack-ice dynamics are highly sensitive to this choice: one formulation strongly amplifies small initial errors, while the other damps them. Our results highlight the need for ensemble forecasts to capture uncertainty and risks in the Arctic.
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