Modelling wave-ice interactions in three-dimensions in the marginal ice zone
Bibliographic record
Abstract
<!--!introduction!--> The study and forecasting of Arctic storms, and their ocean surface waves, are important issues, particularly with climate change, and decreasing sea ice. Our focus is three different modern wave-ice models, with particular attention for three-dimensional wave-scattering, which is challenging to implement because of required energy redistribution. These models have only recently been implemented in WAVEWATCHIII wave model, with sufficient efficiency for operational forecasts, as described by Perrie et al. (2022, doi.org/10.1098/rsta.2021.0263). We perform the simulation of large-scale ocean waves and controlled inter-comparisons, for these wave-ice models for a simple hypothetical ocean and Marginal Ice Zone (MIZ); test cases for wave development and attenuation are driven by constant winds, to identify model weaknesses. Our follow-on work compares model simulations with field measurements using relatively high-quality data, collected by wave buoys from the Sea State Boundary Layer Experiment of 2015 in the Beaufort Sea. This experiment includes several storm events and a variety of wave systems and MIZ situations, with differing ice floe sizes, concentrations and thicknesses. Results are given in this presentation. Regarding the wave-ice models, one is the Bedford Institute of Oceanography (BIO) model which involves 3-dimensional wave-ice interactions for wave attenuation and three-dimensional wave scattering. The second model (MBS) uses the full integration of the scattering kernel, (like BIO), and includes flexible ice floes, based on papers by Meylan, Bennetts and Squire. The third is like MBS, with additional energy dissipation. MBS and MBS’ models assume no ice floe submergence; BIO has rigid MIZ floes that submerge partially.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".