Highly Hygroscopic Aerosols Facilitate Summer and Early‐Autumn Cloud Formation at Extremely Low Concentrations Over the Central Arctic Ocean
Bibliographic record
Abstract
Abstract Arctic clouds are sensitive to atmospheric particles since these are sometimes in such low concentrations that clouds cannot always form under supersaturated water vapor conditions. This is especially true in the late summer, when aerosol concentrations are generally very low in the high Arctic. The environment changes rapidly around freeze‐up as the open waters close and snow starts accumulating on ice. We investigated droplet formation during eight significant fog events in the central Arctic Ocean, north of 80°, from August 12 to 19 September 2018 during the Arctic Ocean 2018 expedition onboard the icebreaker Oden. Calculated hygroscopicity parameters (κ) for the entire study were very high (up to κ = 0.85 ± 0.13), notably after freeze‐up, suggesting that atmospheric particles were very cloud condensation nuclei (CCN)‐active. At least one of the events showed that surface clouds were able to form and persist for at least a couple hours at aerosol concentrations less than 10 cm−3, which was previously suggested to be the minimum for cloud formation. Among these events that were considered limited in CCN, effective radii were generally larger than in the high CCN cases. In some of the fog events, droplet residuals particles did not reactivate under supersaturations up to 0.95%, suggesting either in‐droplet reactions decreased hygroscopicity, or an ambient supersaturation above 1%. These results provide insight into droplet formation during the clean late‐summer and fall of the high Arctic with limited influence from continental sources.
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 | 0.000 |
| 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) | 0.001 | 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".