Satellite-Derived Lagrangian Transport Pathways in the Labrador Sea
Bibliographic record
Abstract
The offshore transport of Greenland coastal waters influenced by freshwater input from ice sheet melting during summer plays an important role in ocean circulation and biological processes in the Labrador Sea. Many previous studies over the last decade have investigated shelfbreak transport processes in the region, primarily using ocean model simulations. Here, we use 27 years of surface geostrophic velocity observations from satellite altimetry, modified to include Ekman dynamics based on atmospheric reanalysis, and virtual particle releases to investigate seasonal and interannual variability in transport of coastal water in the Labrador Sea. Two sets of tracking experiments were pursued, one using geostrophic velocities only, and another using total velocities including the wind effect. Our analysis revealed substantial seasonal variability, even when only geostrophic velocities were considered. Water from coastal southwest Greenland is generally transported northward into Baffin Bay, although westward transport off the west Greenland shelf increases in fall and winter due to winds. Westward offshore transport is increased for water from southeast Greenland so that, in some years, water originating near the east Greenland coast during summer can be transported into the central Labrador Sea and the convection region. When wind forcing is considered, long-term trends suggest decreasing transport of Greenland coastal water during the melting season toward Baffin Bay, and increasing transport into the interior of the Labrador Sea for water originating from southeast Greenland during summer, where it could potentially influence water column stability. Future studies using higher-resolution velocity observations are needed to capture the role of submesoscale variability in transport pathways in the Labrador Sea.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".