Aerosol-cloud interactions over the central Arctic Ocean
Bibliographic record
Abstract
The physical and chemical properties of aerosol particles are important for the formation of cloud droplets and ice crystals. This is especially true for pristine regions such as the Arctic, where particle number concentrations are often very low. Observations from these regions are still sparse due to the technical challenges involved. Here, we present recent results of detailed in-situ observations of aerosols and clouds performed on board the Swedish icebreaker Oden over the central Arctic Ocean in 2018. We show that Aitken-mode particles, i.e. particles below 70 nm diameter, contribute significantly to cloud-forming particles (here termed cloud residuals), especially towards autumn with the start of the freeze-up of the sea ice. These cloud-forming Aitken-mode particles coincided with air that spent more time over the ice, while accumulation-mode dominated cloud residuals showed more of an oceanic influence, as shown using air back trajectory analysis. At the same time, the Aitken-mode dominated cloud residuals were associated with changes in the average chemical composition of the accumulation mode showing an increased organic contribution, in contrast to the accumulation-mode dominated cloud residuals, which showed an increased sulfate contribution. The Hoppel-minima in both whole-air and cloud residual size distributions was almost unchanged, suggesting only little addition of aerosol mass due to aqueous-phase cloud processing. Our highly detailed observations of aerosol-cloud interactions over the central Arctic Ocean close to the North Pole provide valuable insights into the properties and the origin of particles that are relevant for cloud formation in this remote region of our planet. This work is currently in review at the Journal of Geophysical Research (JGR).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".