Effects of 20–100 nm particles on liquid clouds in the cleansummertime Arctic
Bibliographic record
Abstract
Abstract. Observations addressing effects of aerosol particles on summertime Arctic clouds are limited. An airborne study, carried out during July 2014 from Resolute Bay, Nunavut, Canada, as part of the Canadian NETCARE project, provides a comprehensive in situ look into some effects of aerosol particles on liquid clouds in the clean environment of the Arctic summer. Median cloud droplet number concentrations (CDNC) from 62 cloud samples are 10 cm−3 for low-altitude cloud (clouds topped below 200 m) and 101 cm−3 for higher-altitude cloud (clouds based above 200 m). The lower activation size of aerosol particles is ≤ 50 nm diameter in about 40 % of the cases. Particles as small as 20 nm activated in the higher-altitude clouds consistent with higher supersaturations (S) for those clouds inferred from comparison of the CDNC with cloud condensation nucleus (CCN) measurements. Over 60 % of the low-altitude cloud samples fall into the CCN-limited regime of Mauritsen et al. (2011), within which increases in CDNC may increase liquid water and warm the surface. These first observations of that CCN-limited regime indicate a positive association of the liquid water content (LWC) and CDNC, but no association of either the CDNC or LWC with aerosol variations. Above the Mauritsen limit, where aerosol indirect cooling may result, changes in particles with diameters from 20 to 100 nm exert a relatively strong influence on the CDNC. Within this exceedingly clean environment, as defined by low carbon monoxide and low concentrations of larger particles, the background CDNC are estimated to range between 16 and 160 cm−3, where higher values are due to activation of particles ≤ 50 nm that likely derive from natural sources. These observations offer the first wide-ranging reference for the aerosol cloud albedo effect in the summertime Arctic.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".