Indirect effect of sulfate and carbonaceous aerosols: A mechanistic treatment
Bibliographic record
Abstract
The indirect effect of anthropogenic aerosols, whereby aerosol particles change cloud optical properties, is the most uncertain component of climate forcing over the past 100 years. Here we use a mechanistic treatment of droplet nucleation and a prognostic treatment of the number of cloud droplets to study the indirect aerosol effect from changes in carbonaceous and sulfate aerosols. Cloud droplet nucleation is parameterized as a function of total aerosol number concentration, updraft velocity, and an activation parameter, which takes into account the mechanism of sulfate aerosol formation. Where previous studies focussed either on sulfate aerosols or carbonaceous aerosols only, here we estimate the combined effect. The combined indirect aerosol effect amounts to −1.1 W m−2 for an internally mixed aerosol and −1.5 W m−2 for an externally mixed aerosol compared to −1.4 W m−2, which we obtained by empirically relating sulfate mass to cloud droplet number. In the case of an internally mixed aerosol, the contribution from increasing carbonaceous and sulfate aerosols is close to being additive as the individual simulations yield an indirect effect of −0.4 W m−2 due to anthropogenic sulfate aerosols and −0.9 W m−2 due to anthropogenic carbonaceous aerosols. The contribution of anthropogenic sulfate to the indirect effect is close to zero if an externally mixed aerosol is assumed, while the contribution of carbonaceous aerosols increases to −1.3 W m−2. The effect of sulfate in the external mixture approach is much smaller than that of carbonaceous aerosols because its burden only increases by a third of that of carbonaceous aerosols and because the mode radius of sulfate is much larger than that of black and organic carbon.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".