Using sea‐ice deformation fields to constrain the mechanical strength parameters of geophysical sea ice
Bibliographic record
Abstract
Abstract We investigate the ability of viscous‐plastic (VP) sea‐ice models with an elliptical yield curve and normal flow rule to reproduce the shear and divergence distributions derived from the RADARSAT Geophysical Processor System (RGPS). In particular, we reformulate the VP elliptical rheology to allow independent changes in the ice compressive, shear and isotropic tensile strength parameters ( , respectively) in order to study the sensitivity of the deformation distributions to changes in the ice mechanical strength parameters. Our 10 km VP simulation with standard ice mechanical strength parameters = 27.5 kN m −2 , = 6.9 kN m −2 , and = 0 kN m −2 (ellipse aspect ratio of e = 2) does not reproduce the large shear and divergence deformations observed in the RGPS deformation fields, and specifically lacks well‐defined, active linear kinematic features (LKFs). Probability density functions (PDFs) for the shear and divergence of are nonetheless not Gaussian. Reducing the ice compressive strength (with constant S ∗ and T ∗ ) or increasing the ice shear strength (with constant P ∗ and T ∗ ) both results in shear and divergence PDFs in better agreement with RGPS distributions. The isotropic tensile strength of sea ice does not significantly affect the shear and divergence distributions. When considering additional metrics such as the ice drift error, mean ice thickness fields, and spatial scaling of the total deformations, our results suggest that reducing the ice compressive strength P ∗ (while keeping S ∗ constant, i.e. reducing the ellipse aspect ratio) is a better solution than increasing the shear strength to improve simulations of the Arctic sea‐ice cover with the VP elliptical rheology.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".