Quantification of the factors controlling tropical tropospheric ozone and the South Atlantic maximum
Bibliographic record
Abstract
We quantify the processes controlling the tropical tropospheric ozone burden with particular attention to the tropical Atlantic, using a global chemical transport model (GEOS‐Chem) constrained by satellite and in situ observations of O 3 , NO 2 , and HCHO. Lightning is the dominant contributor to tropical tropospheric O 3 , accounting for more than 37% of the O 3 burden over the Atlantic on annual average. The contributions from biomass burning, soils, and fossil fuels are 4 to 6 times smaller, despite comparable source strengths. This discrepancy can be explained by the tropical ozone production efficiency of lightning (32 mol/mol), soils (14 mol/mol), biomass burning (10 mol/mol), and fossil fuel (13 mol/mol) sources, as calculated using sensitivity simulations with a 1% perturbation. The role of volatile organic compound emissions on the tropical Atlantic ozone burden is negligible (<2.5%). Stratosphere‐troposphere exchange accounts for less than 5% of the regional O 3 burden. The tropical Atlantic O 3 burden is more strongly influenced by nitrogen oxides from Africa (>30%) than from South America (>18%) or the eastern tropics (>11%). Lightning is responsible for more than 39% of the atmospheric oxidation capacity, higher than other sources. The dominant sources of uncertainty in the tropical oxidation rate are the lightning magnitude and the cloud convective parameterization.
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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) | 0.000 | 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) | 0.001 | 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".