Comment on acp-2022-438
Bibliographic record
Abstract
Abstract. Fires emit a substantial amount of non-methane organic gases (NMOGs), the atmospheric oxidation of which can contribute to ozone and secondary particulate matter formation. However, the abundance and reactivity of these fire NMOGs are uncertain and historically not well constrained. In this work, we expand the representation of fire NMOGs in a global chemical transport model, GEOS-Chem. We update emission factors to Andreae (2019) and the chemical mechanism to include recent aromatic and ethene and ethyne model improvements (Bates et al., 2021; Kwon et al., 2021). We expand the representation of NMOGs by adding lumped furans to the model (including their fire emission and oxidation chemistry) and by adding fire emissions of nine species already included in the model, prioritized for their reactivity using data from the Fire Influence on Regional to Global Environments (FIREX) laboratory studies. Based on quantified emissions factors, we estimate that our improved representation captures 72 % of emitted, identified NMOG carbon mass and 49 % of OH reactivity from savanna and temperate forest fires, a substantial increase from the standard model (49 % of mass, 28 % of OH reactivity). We evaluate fire NMOGs in our model with observations from the Amazon Tall Tower Observatory (ATTO) in Brazil, Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) and DC3 in the US, and Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) in boreal Canada. We show that NMOGs, including furan, are well simulated in the eastern US with some underestimates in the western US and that adding fire emissions improves our ability to simulate ethene in boreal Canada. We estimate that fires provide 15 % of annual mean simulated surface OH reactivity globally, as well as more than 75 % over fire source regions. Over continental regions about half of this simulated fire reactivity comes from NMOG species. We find that furans and ethene are important globally for reactivity, while phenol is more important at a local level in the boreal regions. This is the first global estimate of the impact of fire on atmospheric reactivity.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.019 | 0.010 |
| Insufficient payload (model declined to judge) | 0.463 | 0.360 |
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".