The importance of interstitial particle scavenging by cloud droplets in shaping the remote aerosol size distribution and global aerosol-climate effects
Bibliographic record
Abstract
Abstract. In this paper, we investigate the coagulation of interstitial aerosol particles (particles too small to activate to cloud droplets) with cloud drops, a process often ignored in aerosol-climate models. We use the GEOS-Chem-TOMAS (Goddard Earth Observing System-Chemistry TwO-Moment Aerosol Sectional) global chemical transport model with aerosol microphysics to calculate the changes in the aerosol size distribution, cloud-albedo aerosol indirect effect, and direct aerosol effect due to the interstitial coagulation process. We find that inclusion of interstitial coagulation in clouds lowers total particle number concentrations by 15–21% globally, where the range is due to varying assumptions regarding activation diameter, cloud droplet size, and ice cloud physics. The interstitial coagulation process lowers the concentration of particles with dry diameters larger than 80 nm (a proxy for larger CCN) by 10–12%. These 80 nm particles are not directly removed by the interstitial coagulation but are reduced in concentration because fewer smaller particles grow to diameters larger than 80 nm. The global aerosol indirect effect of adding interstitial coagulation varies from +0.4 to +1.3 W m−2 where again the range depends on our cloud assumptions. Thus, the aerosol indirect effect of this process is significant, but the magnitude depends greatly on assumptions regarding activation diameter, cloud droplet size, and ice cloud physics. The aerosol direct effect of the interstitial coagulation process is minor (< 0.01 W m−2) due to the shift in the aerosol size distribution at sizes where scattering is most effective being small. We recommend that this interstitial scavenging process be considered in aerosol models when the size distribution and aerosol indirect effects are important.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".