Influence of Arctic Microlayers and Algal Cultures on Sea Spray Hygroscopicity and the Possible Implications for Mixed‐Phase Clouds
Bibliographic record
Abstract
Abstract As Arctic sea ice cover diminishes, sea spray aerosols (SSA) have a larger potential to be emitted into the Arctic atmosphere. Emitted SSA can contain organic material, but how it affects the ability of particles to act as cloud condensation nuclei (CCN) is still not well understood. Here we measure the CCN‐derived hygroscopicity of three different types of aerosol particles: (1) Sea salt aerosols made from artificial seawater, (2) aerosol generated from artificial seawater spiked with diatom species cultured in the laboratory, and (3) aerosols made from samples of sea surface microlayer (SML) collected during field campaigns in the North Atlantic and Arctic Ocean. Samples are aerosolized using a sea spray simulation tank (plunging jet) or an atomizer. We show that SSA containing diatom and microlayer exhibit similar CCN activity to inorganic sea salt with a κ value of ∼1.0. Large‐eddy simulation (LES) is then used to evaluate the general role of aerosol hygroscopicity in governing mixed‐phase low‐level cloud properties in the high Arctic. For accumulation mode aerosol, the simulated mixed‐phase cloud properties do not depend strongly on κ, unless the values are lower than 0.4. For Aitken mode aerosol, the hygroscopicity is more important; the particles can sustain the cloud if the hygroscopicity is equal to or higher than 0.4, but not otherwise. The experimental and model results combined suggest that the internal mixing of biogenic organic components in SSA does not have a substantial impact on the cloud droplet activation process and the cloud lifetime in Arctic mixed‐phase clouds.
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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 | 0.000 | 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".