Comment on gmd-2024-49
Bibliographic record
Abstract
Abstract. We evaluate the vertical turbulent kinetic energy (TKE) mixing scheme of the NEMO-SI3 ocean–sea ice model in sea ice-covered regions of the Arctic Ocean. Specifically, we assess the parameters involved in the TKE mixed layer penetration (MLP) parameterization. This ad-hoc parameterization aims to capture processes like near-inertial oscillations, ocean swells, and waves that impact the ocean surface boundary layer, often not well-represented in the default TKE scheme. We evaluate this parameterization for the first time in three regions of the Arctic Ocean: the Makarov, Eurasian, and Canada Basins. We demonstrate the strong effect of the scaling parameter that accounts for the presence of sea ice. Our results confirm that the TKE MLP must be scaled down below sea ice to avoid unrealistic deep mixed layers. The other parameters evaluated are the percentage of energy penetrating below the mixed layer and the length scale of its decay with depth. All these parameters affect the mixed layer depth and its seasonal cycle, the surface temperature and salinity, as well as the underlying stratification. Shallow mixed layers are associated with stronger stratification and fresh surface anomalies, and deeper mixed layers correspond to weaker stratification and salty surface anomalies. Notably, we observe significant impacts on sea ice thickness across the Arctic Ocean in two scenarios: when the scaling parameter due to sea ice is absent and when the TKE mixed layer penetration process vanishes. In the former case, we observe an increase of several meters in the mixed layer depth together with a reduction in sea ice thickness ranging from 30 to 40 centimeters, reflecting the impact of stronger mixing. Conversely, in the latter case, we notice that a smaller mixed layer depth is accompanied by a moderate increase in sea ice thickness, ranging from 10 to 20 centimeters, as expected from a weaker mixing. Additionally, inter-annual variability suggests that experiments incorporating a scaling parameter based on sea ice concentration display an increased mixed layer depth during periods of reduced sea ice, which is consistent with observed trends. These findings underscore the influence, through specific parameterizations, of enhanced ocean mixing on the physical properties of the upper ocean and sea ice.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.448 | 0.372 |
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".