Particle sedimentation and diffusive convection in volcanic ash‐clouds
Bibliographic record
Abstract
Understanding the longevity of volcanic ash‐clouds generated by powerful explosive eruptions is a long standing problem for assessing volcanic hazards and the nature and time scale of volcanic forcings on climate change. It is well known that the lateral spreading and longevity of these clouds is influenced by stratospheric winds, particle settling and turbulent diffusion. Observations of the recent 2010 Eyjafjallajökull and 2011 Grimsvötn umbrella clouds, as well as the structure of atmospheric aerosol clouds from the 1991 Mt Pinatubo event, suggest that an additional key process governing the cloud dynamics is the production of internal layering. Here, we use analog experiments on turbulent particle‐laden umbrella clouds to show that this layering occurs where natural convection driven by particle sedimentation and the differential diffusion of primarily heat and fine particles give rise to a large scale instability. Where umbrella clouds are particularly enriched in fine ash, this “particle diffusive convection” strongly influences the cloud longevity. More generally, cloud residence time will depend on fluxes due to both individual settling and diffusive convection. We develop a new sedimentation model that includes both sedimentation processes, and which is found to capture real‐time measurements of the rate of change of particle concentration in the 1982 El Chichon, 1991 Mt Pinatubo and 1992 Mt Spurr ash‐clouds. A key result is that these combined sedimentation processes enhance the fallout of fine particles relative to expectations from individual settling suggesting that particle aggregation is not the only mechanism required to explain volcanic umbrella longevity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".