Meteorological and aerosol effects on marine cloud microphysical properties
Bibliographic record
Abstract
Abstract Meteorology and microphysics affect cloud formation, cloud droplet distributions, and shortwave reflectance. The Eastern Pacific Emitted Aerosol Cloud Experiment and the Stratocumulus Observations of Los‐Angeles Emissions Derived Aerosol‐Droplets studies provided measurements in six case studies of cloud thermodynamic properties, initial particle number distribution and composition, and cloud drop distribution. In this study, we use simulations from a chemical and microphysical aerosol‐cloud parcel (ACP) model with explicit kinetic drop activation to reproduce observed cloud droplet distributions of the case studies. Four cases had subadiabatic lapse rates, resulting in fewer activated droplets, lower liquid water content, and higher cloud base height than an adiabatic lapse rate. A weighted ensemble of simulations that reflect measured variation in updraft velocity and cloud base height was used to reproduce observed droplet distributions. Simulations show that organic hygroscopicity in internally mixed cases causes small effects on cloud reflectivity (CR) (<0.01), except for cargo ship and smoke plumes, which increased CR by 0.02 and 0.07, respectively, owing to their high organic mass fraction. Organic hygroscopicity had larger effects on droplet concentrations for cases with higher aerosol concentrations near the critical diameter (namely, polluted cases with a modal peak near 0.1 µm). Differences in simulated droplet spectral widths (k) caused larger differences in CR than organic hygroscopicity in cases with organic mass fractions of 60% or less for the cases shown. Finally, simulations from a numerical parameterization of cloud droplet activation suitable for general circulation models compared well with the ACP model, except under high organic mass fraction.
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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 | 0.001 |
| 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".