A climatology of Arctic fog along the coast of East Greenland
Bibliographic record
Abstract
Abstract This study presents a comprehensive climatology of coastal fog from four synoptic weather stations operated by the Danish Meteorological Institute along the entire East Greenland coast between 1958 and 2016. Elements investigated include fog frequency, daily timing, temperature, wind, visibility and radiosonde profiles during fog. The spatiotemporal patterns in fog from the low‐ to high‐Arctic locations were related to varying regional seasonal temperatures, surface and upper‐air wind and sea ice conditions, and to correlations with the North Atlantic Oscillation (NAO) and the Greenland Blocking Index (GBI). Results indicate that ˜70–80% of East Greenland fog occurs in summer (MJJA), and yearly fog onset is near‐coincident with the start of sea ice break‐up. This warm‐season fog has the typical characteristics of advection fog, as shown in the radiosonde profiles and the association with a gentle sea breeze. More than 95% of warm‐season fog is warmer than −10°C, and peaks close to 0°C and, therefore, consists of liquid or supercooled water droplets. In the cold season, mixed‐phase fog prevails in the high‐Arctic locations, accounting for ˜70% of observations. Ice fog (T < −30°C) occurs in only 2% of observations and is limited to Northeast Greenland during the cold season. The cold‐season composite radiosonde fog profiles in the high‐Arctic locations are characterized by deep (˜1000 m) and strong (˜6°C) surface‐based temperature inversions. Visibility during most fog conditions is lowest during the warm season (<500 m) and highest during the cold season (<800 m). In Northeast Greenland, visibility during warm‐season fog has decreased by ˜50 m·dec−1 between 1981 and 2016. In Southeast Greenland, fog visibility is high during low GBI and a positive phase of NAO, but no other correlations with climate indices were found.
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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".