Sea ice kinetic energy dissipation with different yield curves
Bibliographic record
Abstract
Wind imparts energy via surface stress to sea ice where it leads to internal stress and motion. The ocean also exerts a drag that slows down ice motion, but the internal stress dissipates part of the energy in convergent and shear motion (ridging). This internal dissipation is an important part of the energy balance. Floe-floe interactions within sea ice play an essential role in the kinetic energy dissipation in winter when the sea-ice is compact. In large-scale sea ice models, these interactions are parameterized by the rheology. The main goal of this work is to investigate the influence of the viscous plastic rheology, in particular the shape of the yield curve on the kinetic energy dissipation within sea ice. Different yield curves (standard ellipse, Mohr–Coulomb with an elliptic plastic potential, Truncated Ellipse Method, and teardrop) are implemented in a sea ice model with viscous-plastic rheology and a grid spacing of 4.5 km. Also, the impact of model resolution is explored for one rheological model with simulations with grid spacings of 36, 9 and 4.5 km. The results suggest that a yield curve with more shear strength leads to smaller sea ice drift, and thus, to smaller wind energy input and energy loss due to ocean drag. Furthermore, in simulations with the elliptical yield curve with tensile strength, the sea ice is thicker than in those without tensile strength. The simulations with the Teardrop yield curve and the Mohr–Coulomb yield curve have the largest frictional dissipation in shearing and ridge deformation, respectively. In summary, the impact of the different yield curve on the net energy dissipation is small, but simulations with similar yield curves have similar kinetic energy dissipation within the ice. Finally, the higher the resolution of the simulation, the more the deformation and hence the dissipation is localized along shear lines. More localization leads to smaller mean drift and hence less kinetic energy input and loss by ocean drag. Because of the smaller energy input, the net dissipation by internal stress is also reduced for higher resolution.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".