Subpolar gyre decadal variability explains the recent oxygenation in the Irminger Sea
Bibliographic record
Abstract
The North Atlantic is one of the hot-spot for ocean oxygen ventilation due to cold surface water and strong winter convection. This region is subjected to large interannual to multidecadal variability, which is suspected to strongly impact the regional and temporal oxygen ventilation and inventory. Here we investigate the oxygen variability over 1991-2018 and driving mechanisms of the two main water masses of the Irminger Sea: the Labrador Sea Water (LSW) and the Island Scotland Overflow Water (ISOW). For this, we combined the most recent Argo dataset with ship-based hydrographic data in the Irminger Sea. The dissolved oxygen concentration of the LSW oscillated between 300 mu mol/kg in the early 90's and between 2016 and 2018, and 280 mu mol/kg in the period 2002-2015. The temporal changes in oxygen concentration are less pronounced in the underlying Iceland Scotland Overflow Water (ISOW). We show that, while solubility changes partly explain the variability of the dissolved oxygen concentration within the Labrador Sea Water (LSW), the main driver of oxygen variability is the Apparent Oxygen Utilisation (AOU). In the early 90's and between 2015 and 2018, the deep convection was more intense and led to less stratified, thicker, colder, and more oxygenated LSW than during the period 1995-2015. This was attributed to larger ocean heat loss, stronger wind stress, and colder subpolar gyre under positive NAO conditions. The observed oxygen variability in the Irminger Sea between 1991 and 2018 does not show any significant linear trend. This study provides the first observational evidence of the impact of the subpolar gyre decadal variability on the oxygen ventilation in the Irminger Sea and advocates for continuing the monitoring of oxygen concentration and content in the subpolar gyre to separate any possible warming-induced long-term changes from the large decadal natural variability.
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".