Characterizing the hygroscopicity of growing particles in the Canadian Arctic summer
Bibliographic record
Abstract
The impact of aerosols on clouds is a well-studied, although still poorly constrained, part of the atmospheric system. New particle formation (NPF) is thought to contribute 40 %–80 % of the global cloud droplet number concentration, although it is extremely difficult to observe an air mass from NPF to cloud formation. NPF and growth occurs frequently in the Canadian Arctic summer atmosphere, although only a few studies have characterized the source and properties of these aerosols. This study presents cloud condensation nuclei (CCN) concentrations measured on board the CCGS Amundsen in the eastern Canadian Arctic Archipelago from 23 July to 23 August 2016 as part of the Network on Climate and Aerosols: Addressing Uncertainties in Remote Canadian Environments (NETCARE). The study was dominated by frequent ultrafine particle and/or growth events, and particles smaller than 100 nm dominated the size distribution for 92 % of the study period. Using κ -Köhler theory and aerosol size distributions, the mean hygroscopicity parameter ( κ ) calculated for the entire study was 0.12 (0.06–0.12, 25th–75th percentile), suggesting that the condensable vapours that led to particle growth were primarily slightly hygroscopic, which we infer to be organic. Based on past measurement and modelling studies from NETCARE and the Canadian Arctic, it seems likely that the source of these slightly hygroscopic, organic, vapours is the ocean. Examining specific growth events suggests that the mode diameter ( D max ) had to exceed 40 nm before CCN concentrations at 0.99 % supersaturation (SS) started to increase, although a statistical analysis shows that CCN concentrations increased 13–274 cm −3 during all ultrafine particle and/or growth times (total particle concentrations >500 cm −3 , D max <100 nm) compared with background times (total concentrations <500 cm −3 ) at SS of 0.26 %–0.99 %. This value increased to 25–425 cm −3 if the growth times were limited to times when D max was also larger than 40 nm. These results support past results from NETCARE by showing that the frequently observed ultrafine particle and growth events are dominated by a slightly hygroscopic fraction, which we interpret to be organic vapours originating from the ocean, and that these growing particles can increase the background CCN concentrations at SS as low as 0.26 %, thus pointing to their potential contribution to cloud properties and thus climate through the radiation balance.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".