Sea-Ice Concentration Multivariate Assimilation for the Canadian East Coast in a Coupled Sea Ice–Ocean Model
Bibliographic record
Abstract
The present study focuses on the impact of ocean state (i.e., salinity and temperature) updates on the sea-ice analysis and short-term forecast in an assimilative sea ice–ocean coupled system. A relatively simple sea-ice assimilation scheme was applied to the sea ice–ocean coupled North Atlantic Nucleus for European Modelling of the Ocean (NEMO) system with a focus on the Canadian East Coast. In this assimilation scheme the ocean state was updated directly based on the correlations between the model's sea-ice concentration and the upper ocean salinity and temperature. These correlations were based on a limited time ensemble generated by applying random perturbations to the atmospheric forcing fields. High deviations in the sea-ice conditions were found along the ice edge, implying that the sea-ice edge position is sensitive to small atmospheric forcing variations. Assimilation runs with and without ocean state updates (i.e., sea-ice concentration nudging) were conducted and compared for the winter of 2002. Both continuous and intermittent assimilation schemes were examined. In a continuous sea-ice assimilation experiment, the ocean direct update is unnecessary. When the sea-ice updates are introduced intermittently the ocean state has to be altered to accommodate them, or they will be rapidly diminished by the model's dynamics. The correlations between sea-ice concentration and ocean salinity and temperature based on the first 15 days of January were used for corrections during the entire winter season when, in addition to thermodynamic processes, dynamic processes are responsible for, and even dominate, sea-ice evolution on the Labrador and Newfoundland shelves. This was an adequate choice as was demonstrated by the results of the study which showed that the experiments with ocean state adjustments generated more accurate short-term sea-ice forecasts.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".