Effect of secondary organic aerosol amount and condensational behavior on global aerosol size distributions
Bibliographic record
Abstract
Recent research has shown that secondary organic aerosols (SOA) are major contributors to ultrafine particle growth to climatically relevant sizes, increasing global cloud condensation nuclei (CCN) concentrations within the continental boundary layer. Many models treat SOA solely as semivolatile, which leads to condensation of SOA proportional to the aerosol mass distribution; however, recent closure studies with field measurements show that a significant fraction of SOA condenses proportional to the aerosol surface area, which suggests a very low volatility. Additionally, while many global models contain only biogenic sources of SOA (with emissions generally 10-30 Tg yr−1), recent studies have shown a need for an additional source of SOA around 100 Tg yr−1 correlated with anthropogenic carbon monoxide (CO) emissions is required to match measurements. Here, we explore the significance of these two findings using the GEOS-Chem-TOMAS global aerosol microphysics model. The percent change in the number of particles of size Dp > 40 nm (N40) within the continental boundary layer between the surface-area-and massdistribution condensation schemes, both with the base biogenic SOA only, yielded a global increase of 8% but exceeds 100% in biogenically active regions. The percent change in N40 within the continental boundary layer between the base simulation (19 Tg yr−1) and the additional SOA (100 Tg yr−1) both using the surface area condensation scheme (very low volatility) yielded a global increase of 14%, and a global decrease in the number of particles of size Dp > 10 nm (N10) of 32%. These model simulations were compared to measured data from Hyytiälä, Finland and other global locations and confirmed a decrease in the model-measurement bias. Thus, treating SOA as very low volatile as well as including additional SOA correlated with anthropogenic CO emissions causes a significant global increase in the number of climatically relevant sized particles, and therefore we must continue to refine our SOA treatments in aerosol microphysics models.
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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 | 0.001 |
| 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".