Seesaw structure of subsurface temperature anomalies between the Barents Sea and the Labrador Sea
Bibliographic record
Abstract
Using a coupled ocean‐sea ice model of the pan‐Arctic and North Atlantic Ocean, we investigate the response of the Arctic and subarctic thermohaline structure to seasonal extreme atmospheric forcing associated with the winter Arctic Oscillation (AO). During the positive phase of AO, significant surface cooling occurs in the Labrador Sea, but there is no substantial surface warming in the Barents Sea, i.e., no seesaw pattern in sea surface temperature (SST) anomaly between the Barents Sea and Labrador Sea. A possible explanation is that Arctic sea ice export into the Barents Sea melts locally and lowers the SST. However, a seesaw structure in subsurface (below the mixed layer: 40–100 m) water temperature anomaly between the two regions is found, exceeding the 95% significance level. Corresponding to the positive phase of the winter AO, a significant warming of the subsurface water in the Barents Sea and a concurrent cooling in the northwestern Labrador Sea are seen in the model results, which is analogous to the seesaw structure in both surface air temperature (SAT) and sea ice extent anomalies. The mechanism leading to the anomalous subsurface temperature seesaw is consistent with the northward advection of the warm Atlantic Water into the Barents Sea and the southward advection of the cold Arctic and sub‐arctic water into the Labrador Sea from the David Strait and Baffin Bay. Hydrographic data are analyzed and the resulting temperature distribution supports this new finding.
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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 | 0.000 |
| 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".