Modeling the marginal ice zone in a coupled wave-ice model: insights from RADARSAT and CryoSat-2-derived floe size
Bibliographic record
Abstract
The shift toward a seasonal sea-ice cover has motivated scientific interest in the marginal ice zone (MIZ).The understanding of the processes at play in wave-ice interaction remains, however, rudimentary as coupled wave-ice models are poorly constrained by observations.Here, we couple the CICE sea-ice model to WAVEWATCHIII (WW3), including a prognostic equation for the floe-size distribution, a flexural ice-breaking scheme, and wave attenuation to simulate the MIZ extent using two definitions: floe size (MIZ-FSD) and sea-ice concentration (MIZ-SIC).We assess the realism of the simulated MIZ-FSD with comparison to the mean floe diameter derived from low-resolution (25 km) altimetric floe chord measurements (CryoSat-2) and higher resolution (10 km) RADARSAT synthetic aperture radar analysis from the Canadian Ice Service (CIS).When compared to CIS, the MIZ-FSD is shown to be overestimated by CryoSat-2 because of the lack of freeboard detection in the small floe range (0-1 km), in low-concentration regions and along the coastline.Then, results show that the simulated MIZ-FSD extent is systematically larger than the MIZ-SIC as the wave fracture affects the entire width of the MIZ-SIC in contrast to both of the observational datasets.Finally, we test the model's sensitivity to various wave attenuation schemes, showing that a strong floe-dependent attenuation is required to reproduce a more realistic MIZ-FSD by reducing wave-induced ice fracture and by increasing the formation of large floes in the pack.Those results point to the need for a universal wave fracture criterion and a better representation of the processes affecting the floe size distribution over the full observed floe range (0-10 km) to further improve the representation of the MIZ in fully coupled wave-ice models.i Mon grand-père était conseiller en orientation : l'éducation est donc une valeur qu'il avait à coeur.D'ailleur, comme cadeaux d'anniversaires il préférait placer de l'argent dans un régime d'épargne d'étude.C'est entre autres grâce à lui que j'ai eu le privilège de choisir et de poursuivre mes études dans un domaine qui me passionne.Quand j'ai commencé ma maîtrise, il m'a montré fièrement le brouillon de sa propre thèse, un vieux document papier griffonné en rouge de commentaires indéchiffrables par son superviseur, me rappelant qu'il y a des choses qui ne change pas.Mon grand-père est décédé le 23 octobre 2022.Faute de pouvoir lui faire lire à mon tour le fruit de mon travail, je lui dédis cette thèse, car sans lui, rien n'aurait été possible.Merci pour tout papy xxx.Je remercie mon superviseur Bruno Tremblay pour son encadrement, mais aussi pour sa confiance et le caractère exploratoire de ce projet.Je suis aussi reconnaissant de sa patience, de son cerveau bouillonnant d'idée, des opportunités qui m'ont été offertes et de ses passes sur la palette.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".