The contribution of plume-scale nucleation to global and regional aerosol and CCN concentrations: evaluation and sensitivity to emissions changes
Bibliographic record
Abstract
Abstract. We implement the Predicting Particles Produced in Power-Plant Plumes (P6) sub-grid sulphate parameterization for the first time into a global chemical-transport model with online aerosol microphysics, the GEOS-Chem-TOMAS model. Compared to simulations using two other previous treatments of sub-grid sulphate, simulations using P6 sub-grid sulphate predicted similar or smaller increases (depending on other model assumptions) in globally, annually averaged concentrations of particles larger than 80 nm (N80). We test in simulations using P6 sub-grid sulphate the sensitivity of particle number concentrations to changes in SO2 or NOx emissions to represent recent emissions control changes. For global increases of 50% in emissions of either SO2 or NOx, or both SO2 and NOx, we find that globally, annually averaged N80 increase by 9.00, 1.47, or 10.24% respectively. However, both sub-grid and grid-resolved processes contribute to these changes. Finally, we compare the model results against observations of particle number concentrations. Compared with previous treatments of sub-grid sulphate, use of the P6 parameterization generally improves correlation with observed particle number concentrations. The P6 parameterization is able to resolve spatial heterogeneity in new-particle formation and growth that cannot be resolved by any constant assumptions about sub-grid sulphate. However, the differences in annually averaged aerosol size distributions due to the treatment of sub-grid sulphate at the measurement sites examined here are too small to unambiguously establish P6 as providing better agreement with observations.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".