Arctic sea ice and freshwater sensitivity to the treatment of the atmosphere‐ice‐ocean surface layer
Bibliographic record
Abstract
Abstract Global simulations are presented focusing on the atmosphere‐ice‐ocean (AIO) surface layer (SL) in the Arctic. Results are produced using an ocean model (NEMO) coupled to two different sea ice models: the Louvain‐La‐Neuve single‐category model (LIM2) and the Los Alamos multicategory model (CICE4). A more objective way to adjust the sea ice‐ocean drag is proposed compared to a coefficient tuning approach. The air‐ice drag is also adjusted to be more consistent with the atmospheric forcing data set. Improving the AIO SL treatment leads to more realistic results, having a significant impact on the sea ice volume trend, sea ice thickness, and the Arctic freshwater (FW) budget. The physical mechanisms explaining this sensitivity are studied. Improved sea ice drift speeds result in less sea ice accumulation in the Beaufort Sea, correcting a typical ice thickness bias. Sea ice thickness and drag parameters affect how atmospheric stress is transferred to the ocean, thereby influencing Ekman transport and FW retention in the Beaufort Gyre (BG). Increasing sea ice‐ocean roughness reduces sea ice growth in winter by reducing ice deformation and lead fractions in the BG. It also increases the total Arctic FW content by reducing sea ice export through Fram Strait. Similarly, increasing air‐ice roughness increases the total Arctic FW content by increasing FW retention in the BG.
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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 | 0.002 | 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 | 0.000 |
| 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".