Sensitivity of convective overturning and turbulent mixing of dissolved gases in the Labrador Sea to atmospheric forcing
Bibliographic record
Abstract
Deep oceanic convection occurs in few locations around the globe. One such location is found in the Labrador Sea where dense waters can subside to depths in excess of 2km below the surface. The weak stratification preconditions the water column for deep convection, triggered by wintertime surface cooling associated with high wind speed events. The convected water brings with it dissolved gases, such as Carbon Dioxide, which are in constant flux between ocean and atmosphere. It is thought that this process of turbulent boundary layer interactions coupled with deep convection is responsible for mixing these gases into the deep ocean, making the ocean the largest sink of anthropogenic carbon. The convective overturning process depends on the temperature and salinity profiles which, together dictate density and thus the static stability of the water column. We have adapted a widely used one-dimensional mixed-layer model, referred to as PWP, to include a parameterization of the air-sea flux of gases such as Oxygen and Carbon Dioxide. The model is forced with surface meteorological fields from the ERA5 reanalysis as well as the higher resolution operational reanalysis from the ECMWF. With the model, we investigate the sensitivity of deep-water formation and the vertical profile of these gases to various atmospheric forcing scenarios. Overturning in the Labrador Sea is most active during the winter months when heat flux out of the ocean is at its maximum. It is found that overturning is far more sensitive to thermal forcing than it is to freshwater forcing within the range of forcings typical to the Labrador Sea. We explore the impact of this sensitivity, including the dependence of the atmospheric forcing on modes of climate variability such as the NAO, has on the role that the Labrador Sea plays as a marine sink for anthropogenic carbon.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".