On the formulation of snow thermal conductivity in large‐scale sea ice models
Why this work is in the frame
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Bibliographic record
Abstract
An assessment of the performance of a state‐of‐the‐art large‐scale coupled sea ice‐ocean model, including a new snow multilayer thermodynamic scheme, is performed. Four 29 year long simulations are compared against each other and against sea ice thickness and extent observations. Each simulation uses a separate parameterization for snow thermophysical properties. The first simulation uses a constant thermal conductivity and prescribed density profiles. The second and third parameterizations use typical power‐law relationships linking thermal conductivity directly to density (prescribed as in the first simulation). The fourth parameterization is newly developed and consists of a set of two linear equations relating the snow thermal conductivity and density to the mean seasonal wind speed. Results show that simulation 1 leads to a significant overestimation of the sea ice thickness due to overestimated thermal conductivity, particularly in the Northern Hemisphere. Parameterizations 2 and 4 lead to a realistic simulation of the Arctic sea ice mean state. Simulation 3 results in the underestimation of the sea ice basal growth in both hemispheres, but is partly compensated by lateral growth and snow ice formation in the Southern Hemisphere. Finally, parameterization 4 improves the simulated Snow Depth Distributions by including snow packing by wind, and shows potential for being used in future works. The intercomparison of all simulations suggests that the sea ice model is more sensitive to the snow representation in the Arctic than it is in the Southern Ocean, where the sea ice thickness is not driven by temperature profiles in the snow.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it