Volcanic Plume Aging During Passive Degassing and Low Eruptive Events of Etna and Stromboli Volcanoes
Bibliographic record
Abstract
Abstract Volcanic gases and aerosols emissions from passive degassing or low eruptive events are now included in most climate models despite large uncertainties still exist about their injection height and their temporal and spatial variability. The aim of this study is to quantify the evolution of the gas and aerosols inside volcanic plumes with high kilometric‐resolution simulations. With online chemistry and aerosols, these simulations are carried out together with in situ measurements of aerosol and gas‐phase properties to assess the impact of Etna and Stromboli volcanic plumes produced by passive degassing and regular Strombolian activity, respectively. Comparison between simulation and observations show that the simulation reproduces the main characteristics of the volcanic plume evolution and confirms that volcanic plumes produced by passive degassing or low eruptive events have a strong impact on cloud condensation nuclei (CCN) formation increasing the number of CCN by a factor of 5. It was also shown that depending on the plume location, the aerosols will act as CCN at different distance from the vent. In the marine atmospheric boundary layer, the aerosols will act as CCN at proximity to the vent (less than 50 km) because of strong condensation sink inhibiting nucleation. In comparison, in the free troposphere, aerosols will act as CCN far from the vent, at more than 200 km. To the best of our knowledge, this study using in situ measurements as well as subkilometric simulations is unique.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".