Evolution of the Climate Forcing During the Two Years after the Hunga Tonga-Hunga Ha'apai Eruption
Bibliographic record
Abstract
We calculate the climate forcing for the two years after the January 15, 2022, Hunga Tonga-Hunga Ha’apai (Hunga) eruption. We use satellite observations of stratospheric aerosols, trace gases and temperatures to compute the tropopause radiative flux changes relative to climatology. Overall, the net downward radiative flux decreased compared to climatology. Although the Hunga stratospheric water vapor anomaly increases the downward infrared radiative flux, the solar flux reduction due to Hunga aerosol shroud dominates the net flux over most of the two-year period. Decreases in temperature produced by the Hunga stratospheric circulation changes contributes to the decrease in downward flux; however, the Hunga induced decrease in ozone increases the net short-wave downward flux creating small sub-tropical net flux increase in late 2022. Coincident with the aerosols settling out, the water vapor anomaly disperses, and circulation changes disappear so that the contrasting forcings all decrease together. By the end of 2023, most of the Hunga induced radiative forcing changes have disappeared. There is some disagreement in the satellite stratospheric aerosol optical depth (SAOD) which we view as a measure of the uncertainty; however, SAOD uncertainty does not alter our conclusion that, overall, aerosols dominate the radiative flux changes followed by temperature and ozone.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".