Autumn Warming of the Cold Intermediate Layer in the Labrador Shelf
Bibliographic record
Abstract
Abstract The Labrador Shelf is integral to the North Atlantic Ocean's climate system, exerting a significant influence on both regional and global scales. This study examines continuous, high‐temporal resolution hydrographic profiles collected by two Argo floats located on the Labrador Shelf. Our focus centers on the subsurface changes. Among observed seasonal variations, the most pronounced and consistent change is a marked temperature increase during autumn in the Cold Intermediate Layer, which is also confirmed in an eddy resolving global reanalysis data set. Contrary to previous studies that attributed this warming to the autumnal deepening of the mixed layer, our analysis indicates that the warming extends to underneath the deepest mixed layer. Thus, mixed layer development cannot account for the observed warming in the deeper layer. On the other hand, analysis of velocity fields from reanalysis data set reveals active onshore intrusions at several locations, with a section at 58°N emerging as the northernmost hotspot. Budget analysis further indicates that the dominant factor driving autumn warming at Section 58°N is cross‐isobath advection that is associated with the intrusion of slope waters onto the shelf. Subsequently, the positive temperature anomaly at Section 58°N and other enhanced intrusion locations are transported downstream through along‐isobath currents, resulting in lagged yet intensified warming at lower latitudes. Our findings underscore the essential role of cross‐isobath intrusion, in combination with along‐isobath movements in governing seasonal temperature variability in the deep layer of the Labrador Shelf. Incorporating this mechanism is crucial for accurately hindcasting and forecasting bottom environmental conditions in the region.
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 | 0.000 |
| 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".