Atmosphere injection of sea salts during the 15 January 2022 submarine eruption of Hunga volcano, Tonga
Bibliographic record
Abstract
The 15 January 2022 submarine eruption at Hunga volcano was the most explosive volcanic eruption in 140 years. It involved exceptional magma and seawater interaction throughout the entire submarine caldera collapse. The submarine volcanic jet breached the sea surface and formed a subaerial eruptive plume that transported volcanic ash, gas, sea salts and seawater up to ~ 57 km, reaching into the mesosphere. We document high concentrations of sea salts in volcanic ash collected shortly after deposition. We discuss the potential climatic consequences of large-scale injection of salts into the upper atmosphere during submarine eruptions. Sodium chloride in these volcanic plumes can reach extreme concentrations, and dehalogenation of chlorides and bromides poses the risk of long-term atmospheric and weather impact. We also discuss high concentrations of Ca-sulfates, originating from both seawater-ash interaction and gas scavenging of SO2 within ash aggregates. The discrepancy between the SO2 measured by satellites and values expected from degassing may be explained by a combination of such SO2 uptake and additional SO2 release to the sea via passive degassing prior to the eruption or with ejecta in submarine gravity currents. The balance between salt loading into the atmosphere versus deposition in ash aggregates is a key factor in understanding the atmospheric and climatic consequences of submarine eruptions.
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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".