Linking marine fog variability in Atlantic Canada to changes in large-scale atmospheric and marine features
Bibliographic record
Abstract
Marine and coastal fog forms mainly from the cooling of warm and moist air advected over a colder sea surface. Atlantic Canada is one of the foggiest regions of the world due to the strong temperature contrast between the two oceanic currents in the vicinity. Recurring periods of low visibility notably disrupt off-shore operations and marine traffic, but also land and air transportation. On longer time-scales, marine fog variability also has a significant impact on the global radiative budget. Clouds, including fog, are the greatest source of uncertainty in the current climate projections because of their complex feedback mechanisms. Meteorological records indicate a significant negative trend in the occurrence of foggy conditions over the past six decades at most airports in Atlantic Canada, with large internal variability, including interannual and interdecadal variations. Using the airport observations, reanalysis data and climate model outputs, we investigated the various variabilities on the trend, at interannual and interdecadal scales, and attempted to address what caused these changes in fog frequency. Our results show that the strength and position of the North Atlantic Subtropical High as well as the sea-surface temperature of the cold and warm waters near Atlantic Canada were highly correlated with fog occurrence. We applied the derived fog indices on climate model outputs and projected the fog trends and variability in the different future climate scenarios. The results from this study will be compared with those obtained from other methods and the implications will be discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".