Idealized modeling of anticyclonic plumes from wildfires and volcanic eruptions in the stratosphere
Bibliographic record
Abstract
Anticyclonically-trapped plumes were first discovered following the 2020 Australian fires. Since then, they have been reported after several extreme wildfires and volcanic eruptions, including the 2017 Canadian wildfires, the 2019 Raikoke and the 2022 Hunga Tonga-Hunga Ha’apai eruptions. They appear as coherent plumes of aerosols and combustion/volcanic compounds confined within mesoscale anticyclones (100s to 1000 km diameter), which for several months resist dispersion and dilution by the large-scale flow. Due to their unusual composition, large radiative forcing is prevailing inside the plumes, generating significant diabatic responses in terms of vertical motions and potential vorticity.In this presentation, we propose a conceptual model of the anticyclonic plumes. We will explore ramifications through idealized numerical simulations and theoretical investigations. Particular focus will be put on the condition of their formation and the dynamics of their maintenance and diabatic motions in the stratosphere.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".