Bibliographic record
Abstract
Abstract. Volcanic eruptions are crucial in the Earth's climate system, driving natural variability. Typically, sulfate aerosols generated by major volcanic events have persisted for years, cooling the Earth's surface while warming the stratosphere. The unprecedented submarine eruption of Hunga Tonga Hunga Ha'apai (HTHH) on January 15th, 2022, challenged this “paradigm” and offered a new perspective by injecting material up to the mesosphere. It led to a 10 % increase in the global stratospheric water vapor burden due to seawater injection, warming the Earth’s surface and competing with the sulfate-induced cooling. The resulting Stratospheric Aerosol Optical Depth appears to be much lower than that derived from satellite observations based on previous eruptions. This study shows a unique combination of balloon-borne and satellite-based measurements of the HTHH water-rich aerosol plume. We used an innovative balloon-based sampling technique and ion chromatographic analysis of the collected aerosol samples to suggest the presence of Na+, K+, NH4⁺, Ca2⁺, Cl⁻, and traces of SO42⁻, 8 months after the eruption, indicating its greater complexity than previously assumed. Based upon the chemical and optical balloon-borne and satellite observations, we suggest that marine aerosols played a role in accounting for the larger-than-expected aerosol burden compared to the modest 0.42 Tg SO2 injected. These findings encourage the inclusion of sea salt in addition to sulfate in climate models to correctly simulate the climatic impact of the Hunga eruption.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.019 | 0.009 |
| Insufficient payload (model declined to judge) | 0.348 | 0.226 |
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".