Global simulations of aerosol processing in clouds
Bibliographic record
Abstract
Abstract. An explicit and detailed representation of in-droplet and in-crystal aerosol particles in stratiform clouds has been introduced in the global aerosol-climate model ECHAM5-HAM. The new scheme allows an evaluation of the cloud cycling of aerosols and an estimation of the relative contributions of nucleation and collision scavenging, as opposed to evaporation of hydrometeors in the global aerosol processing by clouds. On average an aerosol particle is cycled through stratiform clouds 0.5 times. The new scheme leads to important changes in the simulated fraction of aerosol scavenged in clouds, and consequently in the aerosol wet deposition. In general, less aerosol is scavenged into clouds with the new prognostic treatment than what is prescribed in standard ECHAM5-HAM. Aerosol concentrations, size distributions, scavenged fractions and cloud droplet concentrations are evaluated and compared to different observations. While the scavenged fraction and the aerosol number concentrations in the marine boundary layer are well represented in the new model, aerosol optical thickness, cloud droplet number concentrations in the marine boundary layer and the aerosol volume in the accumulation and coarse modes over the oceans are overestimated. Sensitivity studies suggest that a better representation of below-cloud scavenging, higher in-cloud collision coefficients, or a reduced water uptake by seasalt aerosols could reduce these biases.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".