Comment on egusphere-2025-2981
Bibliographic record
Abstract
Abstract. Recent studies suggest that emissions from large forest fires affect stratospheric chemistry, dynamics, and climate, similar to major volcanic eruptions. Using the chemistry-climate model EMAC, we demonstrate that organic carbon emitted from forest fires, injected into the stratosphere through pyro-cumulonimbi, enhances heterogeneous chlorine activation due to increased solubility of HCl in particles containing organic acids and an augmented aerosol surface area. Following the 2019/2020 Australian megafires, the upward transport of the pollution plumes resulted in enhanced ozone depletion in the Southern Hemisphere lower stratosphere, as corroborated by satellite observations. It diminished column ozone in the following two years, accompanied by a dynamically induced reduction in 2020 due to the lofting of smoke-filled vortices, in total by up to 45 DU. The eruption of the submarine Hunga Tonga volcano in January 2022 caused a decline in total ozone across the entire Southern Hemisphere. The water vapour injection from the volcano altered only the vertical distribution of ozone loss. The sunlight-absorbing aerosol from the combined Australian and Canadian forest fire emissions in 2019/2020 induced the most significant perturbation in stratospheric optical depth since the major eruption of Pinatubo in 1991. It shifted the sign of instantaneous stratospheric aerosol forcing, derived at the top of the atmosphere, from −0.2 Wm-2 to +0.3 Wm-2 in January 2020. The global aerosol radiative forcing resulting from the Hunga Tonga eruption was −0.13 Wm-2, primarily driven by changes in stratospheric sulfate aerosols. The positive radiative forcing from the injected water vapour was minimal.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.008 |
| Insufficient payload (model declined to judge) | 0.509 | 0.356 |
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".