Impact of non-normal flow rule on linear kinematic features in pan-Arctic ice-ocean simulations
Bibliographic record
Abstract
The standard sea ice viscous-plastic (VP) rheology is based on an elliptical yield curve and a normal flow rule. This formulation implies that the post-failure deformations are always normal to the yield curve. A drawback of this is that modifications to the yield curve also lead to notable changes to the deformations. We implemented the plastic potential approach of Ringeisen et al. (2021) in the CICE sea ice model. With this formulation, deformations are normal to an elliptical plastic potential which is defined independently from the yield curve. This an interesting capability as it allows to independently optimize deformations while parameters defining the yield curve could serve to adjust landfast ice and to a lesser extent sea ice drift. We investigated the impact of a non-normal flow rule in pan-Arctic simulations. Compared to the standard VP rheology, the non-normal flow rule leads to a more active sea ice cover with narrower linear kinematic features (LKFs) and a higher LKF density. The higher divergence with the non-normal flow rule causes an enhanced ice growth and larger Arctic sea ice volume. In idealized experiments, Ringeisen et al. (2021) showed that the non-normal flow rule can correct the unrealistic (too large) intersection angles between LKFs. However, in our pan-Arctic simulations, the non-normal flow rule does not correct the unrealistic intersection angles which are often around 90°. Results suggest that these frequent 90° angles are partly caused by the alignment of LKFs with the computational grid.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".