Radiative forcing and stratospheric ozone changes due to recent volcanic eruptions and major forest fires
Bibliographic record
Abstract
The chemistry-climate model EMAC was used to simulate the period 2019 to 2023 with tropospheric meteorology slightly nudged to ERA5 data. Volcanic SO2 injections were derived from aerosol extinction observations by OSIRIS and OMPS-LP which were also used for evaluation of the simulated aerosol, which includes organic particles from major forest fires that can linger in the lower stratosphere for more than 2 years. Our simulations consider several hundred explosive volcanic eruptions. The simulations of ozone chemistry include enhanced surface area density and fast heterogeneous chlorine activation on organic particles and will be compared with AURA-MLS observations. The effects of the major water vapour injection by the eruption of Hunga Tonga in 2022 on radiative transfer and chemistry were also analysed (as a contribution to SSIRC Hunga Tonga). For example, in 2022 the Hunga Tonga eruption increased the depth of the calculated Antarctic ozone hole by about 12 DU. The Australian bushfire emissions enhanced the aerosol surface area which deepened the 2020 ozone hole by about 7 DU, with the largest changes near the vortex edge. The smoke effect is expected to increase with updated heterogeneous chemistry.The computed global instantaneous aerosol radiative forcing by Hunga Tonga at the top of the atmosphere was about -0.12 W/m2 in 2022. The injected water vapour by Hunga Tonga exerted a radiative forcing of about +0.04 W/m2 in the first four months after the eruption. By the end of 2022, it nearly vanished due to dynamical and chemical adjustments. The absorbing aerosol from the Australian and Canadian forest fire emissions changed the stratospheric aerosol forcing from -0.2 W/m2 to +0.3 W/m2 in January 2020, and in January 2022 the remaining effect was about 0.05 W/m2, reducing the negative forcing by the volcanoes. Continued interesting effects of the Hunga Tonga eruptions are expected for 2023, based on results from ongoing simulations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".