Aerosol Number Concentration and Cloud Condensation Nuclei Variability During Warm and Moist Intrusions into the Arctic
Bibliographic record
Abstract
Recent case studies highlight that warm and moist air intrusion events are significant sources of aerosol particles in the Arctic, influencing cloud properties and thus the resulting radiative forcing in the region. However, the contribution of these short-lived events to different aerosol size modes, cloud condensation nuclei (CCN), and droplet number concentrations remains unconstrained. Here, we investigate the multi-annual aspects of intrusion impacts on aerosol properties using data on aerosol number size distributions, CCN, total particle number concentrations, and optical properties from multiple Arctic stations, including Alert, Tiksi, Utqiaġvik, Villum, and Zeppelin, covering the period 2010-2020.Preliminary results suggest that particle concentrations change significantly during intrusion episodes, with variations across seasons and stations. For instance, contrary to previous studies, number size distribution data indicate a distinct decrease in accumulation mode concentrations during wintertime intrusion episodes relative to non-intrusion periods at several Arctic stations. In summer, this pattern reverses, although not uniformly across stations. Additionally, at Zeppelin, the average of the yearly mean CCN concentrations during intrusions is increased by 13% compared to non-intrusion periods, with some years showing increases exceeding 40%.We explore the potential drivers of these observed number size distribution patterns and derive potential source contribution function and removal mechanisms along the trajectories, employing the Lagrangian analysis tool LAGRANTO.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".