Radiative forcing and stratospheric ozone changes due to major forest fires and recent volcanic eruptions including Hunga Tonga
Bibliographic record
Abstract
Using the chemistry-climate model EMAC with nudged tropospheric meteorology, we show that organic carbon injected into the stratosphere through forest fire-related pyro-cumulonimbi enhances heterogeneous chlorine activation due to enhanced solubility of HCl in particles containing organic acids and a larger aerosol surface area. After the 2019/2020 Australian mega-bushfires, the upward transport of the pollution plume led to enhanced ozone depletion in the Southern hemispheric lower stratosphere, in agreement with AURA-MLS satellite observations. It reduced total ozone in 2020 and 2021 by up to 28 DU around 70oS, accompanied by a dynamic reduction in August 2020 from the lofting of smoke-filled vortices, reaching 24 DU (total about 40 DU near 65oS). The eruption of Hunga Tonga in January 2022 led to a reduction of total ozone in the entire Southern hemisphere, exceeding 10 DU south of about 55oS in Austral spring of 2022 and 2023. The water vapor injection by the volcano modified only the vertical distribution of ozone loss.The absorbing aerosol from the combined Australian and Canadian forest fire emissions in 2019/2020 caused the largest perturbation in stratospheric optical depth (e.g., seen in OSIRIS data) since the eruption of Pinatubo. It changed the instantaneous stratospheric aerosol forcing -derived at the top of the atmosphere- from -0.2 W/m2 to +0.3 W/m2 in January 2020. In January 2022, the remaining effect was about 0.05 W/m2, reducing the negative forcing by volcanoes. The computed global aerosol radiative forcing caused by the Hunga Tonga eruption in 2022 was about -0.12 W/m2, decreasing to -0.06 W/m2 by December 2023, dominated by the change in stratospheric sulfate aerosols. The positive forcing of the injected water vapor was small (in agreement with other models).
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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 | 0.000 |
| 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) | 0.001 | 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".