Winter Mixed Layer Development in the Central Irminger Sea: The Effect of Strong, Intermittent Wind Events
Bibliographic record
Abstract
Abstract The impact of the Greenland tip jet on the wintertime mixed layer of the southwest Irminger Sea is investigated using in situ moored profiler data and a variety of atmospheric datasets. The mixed layer was observed to reach 400 m in the spring of 2003 and 300 m in the spring of 2004. Both of these winters were mild and characterized by a low North Atlantic Oscillation (NAO) index. A typical tip jet event is associated with a low pressure system that is advected by upper-level steering currents into the region east of Cape Farewell and interacts with the high topography of southern Greenland. Heat flux time series for the mooring site were constructed that include the enhancing influence of the tip jet events. This was used to force a one-dimensional mixed layer model, which was able to reproduce the observed envelope of mixed layer deepening in both winters. The deeper mixed layer of the first winter was largely due to a higher number of robust tip jet events, which in turn was caused by the steering currents focusing more storms adjacent to southern Greenland. Application of the mixed layer model to the winter of 1994–95, a period characterized by a high-NAO index, resulted in convection exceeding 1700 m. This prediction is consistent with hydrographic data collected in summer 1995, supporting the notion that deep convection can occur in the Irminger Sea during strong winters.
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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 | 0.001 |
| 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) | 0.000 | 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".