Evaluate parameterizations of cloud droplet spectral dispersion by using worldwide aircraft data
Bibliographic record
Abstract
Increased aerosol potentially impacts the cloud droplet spectrum, which in turn affects the aerosol-cloud interaction (ACI), known as the dispersion effect (DE). To consider DE in general circulation models (GCMs), many parameterizations have been proposed, but there are relatively few quantitative and global evaluations due to lacking suitable data and methodology. Additionally, both observations and numerical simulations confirmed that DE has opposite effects on ACI in aerosol- and updraft-limited regimes, but whether this effect can be reproduced by parameterizations has no clear conclusion until now. In this study, we used a liquid water content (LWC) binning method and worldwide data (China, Canada, Brazil, and Chile) to evaluate six dispersion parameterizations, namely Martin94, RLiu03, PengL03, Liu08, LiuLi15, and Zhang22. The LWC binning method ensures the difference between ACI values calculated by the cloud droplet number concentration and the effective radius is mainly caused by DE, which makes the quantitative calculation of DE possible. The results show that 1). DE has a weakening effect on ACI in the aerosol-limited regime, but an enhanced effect in the updraft-limited regime; 2). empirical parameterizations (Martin94, RLiu03, PengL03, and Liu08) can only show the weakening effect of DE on ACI, leading to an underestimation (-29% ~ -42%) for calculated ACI, especially for the updraft-limited regime; 3). both LiuLi15 and Zhang22 can reproduce the opposite effects of DE on ACI in different regimes, so we recommend giving priority to the LiuLi15 and the Zhang22 schemes when calculating DE in GCMs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".