Decline and partial rebound of the Labrador Current 1993–2004: Monitoring ocean currents from altimetric and conductivity‐temperature‐depth data
Bibliographic record
Abstract
Monitoring and understanding of Labrador Current variability is important because it is intimately linked to the meridional overturning circulation and the marine ecosystem off northeast North America. Nevertheless, knowledge of its decadal variability is inadequate because of scarcity of current meter data. By using a novel synthesis of satellite altimetry with conductivity‐temperature‐depth (CTD) data, we assess the Labrador Current variability north of the Hamilton Bank (56°N) over 1993–2004. Our analysis shows a decline of the surface‐to‐bottom transport of current by 6.3 ± 1.5 Sv (1 Sv = 106 m3 s−1) in the 1990s (significant at the 99% confidence level) and a likely partial rebound of 3.2 ± 1.7 Sv in the early 2000s (significant at the 89% confidence level only). The inferred multiyear changes in the Labrador Current transport seem to be primarily barotropic and positively correlated (at the 99% level) with the North Atlantic Oscillation at zero lag implying a fast response of the regional circulation to the atmospheric forcing variability. The results compare favorably with direct current measurements and recent model‐based findings on the multiyear variability of the subpolar gyre and its underlying mechanisms. The study demonstrates the feasibility of combining altimetry and CTD data for assessing the climatic variability of the boundary currents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".