Effects of 20–100 nanometre particles on liquid clouds in the clean summertime Arctic
Bibliographic record
Abstract
Abstract. Aerosol-cloud research in the Arctic has largely focused on the springtime resulting in relatively little information about the effects of the atmospheric aerosol on Arctic clouds during summer. An airborne study, carried out during July, 2014 from Resolute Bay, Nunavut, Canada, provides a comprehensive in-situ look into some effects of aerosol particles on liquid clouds in the clean environment of the Arctic summer. The median cloud droplet number concentrations (CDNC) are 10 cm−3 and 101 cm−3 for low altitude cloud (LA: clouds topped below 200 m) and higher altitude cloud (HA: clouds based above 200 m), respectively. The mean lower activation size of aerosol particles is ≤ 50 nm diameter in 40 % of the cases, and particles as small as 20 nm activated in the HA clouds consistent with the higher supersaturations inferred for those clouds. Over 60 % of the LA cloud cases fall into the cloud condensation nuclei (CCN)-limited regime of Mauritsen et al. (2011) within which increases in CDNC may increase liquid water and warm the surface. In that CCN-limited regime, the liquid water contents (LWC) and the CDNC are positively correlated, but there is no dependence of changes in either the CDNC or LWC on the aerosol, suggesting no aerosol limitation. Above the Mauritsen limit, where indirect cooling may result, particles with diameters from 20 nm to 100 nm exert a strong influence on the CDNC. Based on CO concentrations, the background CDNC are estimated to range between 16 cm−3 and 160 cm−3, implying a large uncertainty for the baseline of the aerosol cloud albedo effect.
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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 | 0.000 |
| 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".