Articles | Volume 18, issue 11
https://doi.org/10.5194/tc-18-5365-2024
https://doi.org/10.5194/tc-18-5365-2024
Research article
 | 
21 Nov 2024
Research article |  | 21 Nov 2024

Bounded and categorized: targeting data assimilation for sea ice fractional coverage and nonnegative quantities in a single-column multi-category sea ice model

Molly M. Wieringa, Christopher Riedel, Jeffrey L. Anderson, and Cecilia M. Bitz

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Short summary
Statistically combining models and observations with data assimilation (DA) can improve sea ice forecasts but must address several challenges, including irregularity in ice thickness and coverage over the ocean. Using a sea ice column model, we show that novel, bounds-aware DA methods outperform traditional methods for sea ice. Additionally, thickness observations at sub-grid scales improve modeled ice estimates of both thick and thin ice, a finding relevant for forecasting applications.