Impact of non-normal flow rule on linear kinematic features in pan-Arctic ice-ocean simulations
Bibliographic record
Abstract
Abstract. One important conclusion of the Sea Ice Rheology Experiment (SIREx) is that continuum based sea ice models, with different spatial discretizations and/or sea ice rheologies, all simulate intersection angles between linear kinematic features (LKFs) that are too wide compared to observations. The peak of the probability density function (PDF) of simulated intersection angles is around 90° while the PDF for observed angles rather exhibits a peak around 45°. Ringeisen et al. 2021 proposed to remedy this issue for viscous-plastic (VP) and elastic-VP (EVP) models by introducing a non-normal flow rule specified by a plastic potential. We implemented the plastic potential approach of Ringeisen et al. 2021 in the CICE sea ice model. In pan-Arctic simulations, the non-normal flow rule also leads to a peak of the PDF around 90°. We show that this peak at 90° is at least partly a consequence of many LKFs that are aligned with the computational grid. Nevertheless, the non-normal flow rule brings an interesting capability: it could be used to independently optimize simulated LKFs and more generally deformations while parameters defining the yield curve could serve for modifying simulated landfast ice and to a lesser extent sea ice drift.
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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 | 0.000 | 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".