Seasonality of pCO2 and air-sea CO2 fluxes in the Central Labrador Sea
Bibliographic record
Abstract
The Labrador Sea in the subpolar North Atlantic is known for its large air-to-sea CO2 fluxes, which can be around 40% higher than in other regions of intense ocean uptake like the Eastern Pacific and within the Northwest Atlantic. This region is also a hot-spot for storage of anthropogenic CO2. Deep water is formed here, so that dissolved gas uptake by the surface ocean directly connects to deeper waters, helping to determine how much atmospheric CO2 may be sequestered (or released) by the deep ocean. Currently, the Central Labrador Sea acts as a year-round sink of atmospheric CO2, with intensification of uptake driven by biological production in spring and lasting through summer and fall. Observational estimates of air-sea CO2 fluxes in the region rely upon very limited, scattered data with a distinct lack of wintertime observations. Here, we compile surface ocean observations of pCO2 from moorings and underway measurements, including previously unreported data, between 2000 and 2020, to create a baseline seasonal climatology for the Central Labrador Sea. This is used as a reference to compare against other observational-based and statistical estimates of regional surface pCO2 and air-sea fluxes from a collection of global products. The comparison reveals systematic differences in the representation of the seasonal cycle of pCO2 and uncertainties in the magnitude of air-sea CO2 fluxes. The analysis reveals the paramount importance of long-term, seasonally-resolved data coverage in this region in order to accurately quantify the size of the present ocean sink for atmospheric CO2 and its sensitivity to climate perturbations.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".