Water Mass Transformation in the Greenland Sea during the Period 1986–2016
Bibliographic record
Abstract
Abstract Hydrographic measurements from ships, autonomous profiling floats, and instrumented seals over the period 1986–2016 are used to examine the temporal variability in open-ocean convection in the Greenland Sea during winter. This process replenishes the deep ocean with oxygen and is central to maintaining its thermohaline properties. The deepest and densest mixed layers in the Greenland Sea were located within its cyclonic gyre and exhibited large interannual variability. Beginning in winter 1994, a transition to deeper (>500 m) mixed layers took place. This resulted in the formation of a new, less dense class of intermediate water that has since become the main product of convection in the Greenland Sea. In the preceding winters, convection was limited to <300-m depth, despite strong atmospheric forcing. Sensitivity studies, performed with a one-dimensional mixed layer model, suggest that the deeper convection was primarily the result of reduced water-column stability. While anomalously fresh conditions that increased the stability of the upper part of the water column had previously inhibited convection, the transition to deeper mixed layers was associated with increased near-surface salinities. Our analysis further suggests that the volume of the new class of intermediate water has expanded in line with generally increased depths of convection over the past 10–15 years. The mean export of this water mass from the Greenland Sea gyre from 1994 to present was estimated to be 0.9 ± 0.7 Sv (1 Sv ≡ 106m3s−1), although rates in excess of 1.5 Sv occurred in summers following winters with deep convection.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".