ORCA-LIM global coupled sea ice–ocean model
Bibliographic record
Abstract
The dynamic–thermodynamic Louvain-la-Neuve sea ice model (LIM) has been coupled to the OPA primitive equation ocean general circulation model. In the ORCA2-LIM configuration, the model is run on a global domain with 2mean resolution. Model runs are forced with a combined dataset consisting of daily NCEP/NCAR reanalysis data and various climatologies. The models performance is evaluated with re-spect to the representation of sea ice and the high latitude oceans. The annual cycle of sea ice growth and decay is realistically captured in both hemispheres, with ice extent, thickness and drift in close agreement with observations. The location of the main sites of deep convection (Labrador and Greenland Seas; continental shelves of marginal seas of the Southern Ocean) is well reproduced. Model deficiencies include a slight overestimation of summer ice extent in the Arctic, and a significant underestimation of multi-year ice in the Weddell Sea. Furthermore, the width of the Arctic Ocean Boundary Current and the Antarctic Circumpolar Current is overestimated. Sensitivity studies indicate that the use of the combined forcing dataset is crucial to achieve a reasonable summer sea ice coverage and that the direct use of the NCEP/ NCAR wind stress data leads to an overestimation of sea ice drift velocities. A restoring of sea surface salinity is necessary to avoid spurious open ocean convection in the Weddell Sea.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".