Understanding global secondary organic aerosol amount and size-resolved condensational behavior
Bibliographic record
Abstract
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 (BL). However, there are three recent developments regarding the condensation of SOA that lead to uncertainties in the contribution of SOA to particle growth and CCN concentrations: (1) while many global models contain only biogenic sources of SOA (with annual production rates generally 10–30 Tg yr−1), recent studies have shown that an additional source of SOA around 100 Tg yr−1 correlated with anthropogenic carbon monoxide (CO) emissions may be required to match measurements. (2) Many models treat SOA solely as semi-volatile, which leads to condensation of SOA proportional to the aerosol mass distribution; however, recent closure studies with field measurements show nucleation mode growth can be captured only if it is assumed that a significant fraction of SOA condenses proportional to the Fuchs-corrected aerosol surface area. This suggests a very low volatility of the condensing vapors. (3) Other recent studies of particle growth show that SOA condensation deviates from Fuchs-corrected surface-area condensation at sizes smaller than 10 nm and that size-dependent growth rate parameterizations (GRP) are needed to match measurements. We explore the significance of these three findings using GEOS-Chem-TOMAS global aerosol microphysics model and observations of aerosol size distributions around the globe. The change in the concentration of particles of size Dp > 40 nm (N40) within the BL assuming surface-area condensation compared to mass-distribution net condensation yielded a global increase of 11% but exceeded 100% in biogenically active regions. The percent change in N40 within the BL with the inclusion of the additional 100 Tg SOA yr−1 compared to the base simulation solely with biogenic SOA emissions (19 Tg yr−1) both using surface area condensation yielded a global increase of 13.7%, but exceeded 50% in regions with large CO emissions. The inclusion of two different GRPs in the additional-SOA case both yielded a global increase in N40 of < 1%, however exceeded 5% in some locations in the most extreme case. All of the model simulations were compared to measured data obtained from diverse locations around the globe and the results confirmed a decrease in the model-measurement bias and improved slope for comparing modeled to measured CCN number concentration when non-volatile SOA was assumed and the extra SOA was included.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.023 | 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 teacher head, 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".