Observed Climatology and Formation Mechanisms of Sea Fog Along the Trans‐Arctic Shipping Routes
Bibliographic record
Abstract
Abstract As Arctic sea ice rapidly melts, trans‐Arctic shipping routes are emerging with significant economic value and potentially reshaping global shipping patterns. The safety of ships navigating along these routes will be severely threatened by low visibility caused by Arctic sea fog. Here, we utilize ship and coastal site observations spanning 1979–2023 to investigate the climatological features and mechanisms of Arctic sea fog from June to September. We find that the Northern Sea Route (NSR, along the Russian coast) experiences a mean fog frequency of 20.0%, significantly higher than the 11.5% fog frequency observed along the Northwest Passage (NWP, along the Canadian coast). Fog frequency shows null correlation with sea ice but a strong negative correlation (−0.80) with surface air temperature. The lower mean surface air temperature in the NSR region enhances relative humidity and atmospheric stability, which are conducive to fog formation and maintenance. We further reveal that fog formation mechanisms differ between the NSR and NWP. Along the NSR, fog is usually advection fog, associated with upper boundary layer warming and moistening; in contrast, along the NWP, fog is typically radiation fog linked to near‐surface cooling. Our study highlights the distinct fog frequency and dominant mechanisms along the different trans‐Arctic shipping routes, thereby establishing observed reference for the model validation of simulating Arctic sea fog.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".