Comparing heterogeneity of sea-ice models with viscous-plastic and Maxwell elasto-brittle rheology
Bibliographic record
Abstract
Abstract Classical sea-ice models in climate model resolution do not resolve the small-scale physics of sea ice. New methods to address this problem include modifications to established viscous-plastic (VP) rheology models, sub-gridscale parameterizations or new rheologies such as the Maxwell elasto-brittle (MEB) rheology. Here, we investigate differences in gridscale dynamics simulated by the VP and MEB models, their dependency on tunable model parameters and their response to added stochastic perturbations of material parameters in a new implementation in the Massachusetts Institute of Technology general circulation model. Idealized simulations are used to demonstrate that material parameters can be tuned so that both VP and MEB rheologies lead to similar cohesive stress states, arching behaviour and heterogeneity in the deformation fields. As expected, simulations with MEB rheology generally show more heterogeneity than the VP model as measured by the number of simulated linear kinematic features (LKFs). For both rheologies, the cohesion determines the emergence of LKFs. Introducing gridscale heterogeneity by random model parameter perturbation, however, leads to a larger increase of LKF numbers in the VP simulations than in the MEB simulations and similar heterogeneity between VP and MEB models.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".