Improving the simulation of landfast ice by combining tensile strength and a parameterization for grounded ridges
Bibliographic record
Abstract
Abstract In some coastal regions of the Arctic Ocean, grounded ice ridges contribute to stabilizing and maintaining a landfast ice cover. Recently, a grounding scheme representing this effect on sea ice dynamics was introduced and tested in a viscous‐plastic sea ice model. This grounding scheme, based on a basal stress parameterization, improves the simulation of landfast ice in many regions such as in the East Siberian Sea, the Laptev Sea, and along the coast of Alaska. Nevertheless, in some regions like the Kara Sea, the area of landfast ice is systematically underestimated. This indicates that another mechanism such as ice arching is at play for maintaining the ice cover fast. To address this problem, the combination of the basal stress parameterization and tensile strength is investigated using a 0.25° Pan‐Arctic CICE‐NEMO configuration. Both uniaxial and isotropic tensile strengths notably improve the simulation of landfast ice in the Kara Sea but also in the Laptev Sea. However, the simulated landfast ice season for the Kara Sea is too short compared to observations. This is especially obvious for the onset of the landfast ice season which systematically occurs later in the model and with a slower build up. This suggests that improvements to the sea ice thermodynamics could reduce these discrepancies with the data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".