Strong wind events across Greenland�s coast and their influence on the ice sheet, sea ice and ocean
Bibliographic record
Abstract
In winter, Greenland's coastline adjacent to the subpolar North Atlantic and Nordic Seas is characterized by a large land-sea temperature contrast.Therefore, winds across the coast advect air across a horizontal temperature gradient and can result in significant surface heat fluxes both over the ice sheet (during onshore winds) and over the ocean (during offshore winds).Despite their importance, these winds have not been investigated in detail, and this thesis includes the first comprehensive study of their characteristics, dynamics and impacts.Using an atmospheric reanalysis, observations from local weather stations, and remote sensing data, it is suggested that high-speed wind events across the coast are triggered by the superposition of an upper level potential vorticity anomaly on a stationary topographic Rossby wave over Greenland, and that they intensify through baroclinic instability.Onshore winds across Greenland's coast can result in increased melting, and offshore winds drive large heat losses over major ocean convection sites.Strong offshore winds across the southeast coast are unique over Greenland, because the flow is funneled from the vast ice sheet inland into the narrow valley of Ammassalik at the coast, where it can reach hurricane intensity.In this region, the cold air, which formed over the northern ice sheet, is suddenly released during intense downslope wind events and spills over the Irminger Sea where the cold and strong winds can drive heat fluxes of up to 1000 W m -2 , with potential implications for deep water formation.Moreover, the winds advect sea ice away from the coast and out of a major glacial fjord.Simulations of these wind events in Ammassalik with the atmospheric Weather Research and Forecast Model show that mountain wave dynamics contribute to the acceleration of the downslope flow.In order to capture these dynamics, a high model First I would like to thank my advisor Fiamma Straneo, for her extraordinary support throughout my time in the Joint Program.She has given me countless helpful advice both with regard to my research and my career, has always encouraged me, and made time for me even when her schedule was already crammed.Fiamma has given me great academic freedom, inspired me in my research and beyond, and given me confidence.I am very grateful that I could be part of her group, and I know that I will benefit from this time in my future work and life.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".