The Role of Cloud Processing for the Ice Nucleating Ability of Organic Aerosol and Coal Fly Ash Particles
Bibliographic record
Abstract
Abstract Ice nucleating particles are a minor fraction of tropospheric aerosol, yet they play a key role for cloud microphysical processes. One poorly understood process is the impact of atmospheric aging of aerosol particles on ice nucleation. Here we study the impact of cloud processing on the ice nucleation abilities of two physicochemically different aerosol particles by taking two model systems for atmospheric organic aerosol (OA), as well as coal fly ash (CFA) particles representing an inorganic aerosol type. The ice nucleation activity of the unprocessed particles is compared to aerosol particles that are first exposed to conditions mimicking trajectories though cirrus clouds (CC) and mixed‐phase clouds (MPC) prior to testing their ice nucleation activity at temperatures below 243 K. We observed that unprocessed OA do not exhibit heterogeneous ice nucleation, requiring homogeneous freezing conditions of solution droplets to form ice. However, after CC processing raffinose particles showed heterogeneous ice nucleation activity at 218 K and a water saturation ratio of 0.68–0.82, reaching activated fractions of up to 0.3. This enhancement compared to unprocessed raffinose particles results from an increase in particle viscosity upon CC processing. We also present new results of unprocessed CFA particles exhibiting strong heterogeneous ice nucleation activity at temperatures below 235 K in the deposition and/or pore condensation and freezing mode. In contrast to the OA, the CFA show a decrease in ice nucleation activity after both MPC and CC processing. Furthermore, cloud processing and generating CFA particles from aqueous suspensions do not have the same effect on their ice nucleation ability.
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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) | 0.001 | 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".