Strong Downslope Wind Events in Ammassalik, Southeast Greenland
Bibliographic record
Abstract
Abstract Ammassalik in southeast Greenland is known for strong wind events that can reach hurricane intensity and cause severe destruction in the local town. Yet, these winds and their impact on the nearby fjord and shelf region have not been studied in detail. Here, data from two meteorological stations and the European Centre for Medium-Range Weather Forecasts Interim Re-Analysis (ERA-Interim) are used to identify and characterize these strong downslope wind events, which are especially pronounced at a major east Greenland fjord, Sermilik Fjord, within Ammassalik. Their local and regional characteristics, their dynamics and their impacts on the regional sea ice cover, and air–sea fluxes are described. Based on a composite of the events it is concluded that wind events last for approximately a day, and seven to eight events occur each winter. Downslope wind events are associated with a deep synoptic-scale cyclone between Iceland and Greenland. During the events, cold dry air is advected down the ice sheet. The downslope flow is accelerated by gravitational acceleration, flow convergence inside the Ammassalik valley, and near the coast by an additional thermal and synoptic-scale pressure gradient acceleration. Wind events are associated with a large buoyancy loss over the Irminger Sea, and it is estimated that they drive one-fifth of the net wintertime loss. Also, the extreme winds drive sea ice out of the fjord and away from the shelf.
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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 | 0.000 |
| 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".