Quantifying SAGE II (1984–2005) and SAGE III/ISS (2017–2022) observations of smoke in the stratosphere
Bibliographic record
Abstract
Using a common analysis approach for data sets produced by the Stratospheric Aerosol and Gas Experiment instruments SAGE II and SAGE III/ISS, we identify 13 likely smoke events based on enhancements in the aerosol extinction coefficient. Nine of these are sufficiently large compared to ambient aerosol levels to compute mean mid-latitude 1020 nm optical depth enhancements that range from 0.0005 to 0.011. We also note that, for large events, the 525 to 1020 nm aerosol extinction coefficient ratio asymptotes at a high extinction coefficient to values between 2 and 3, suggesting that the aerosol radius is relatively small (< 0.3 µm) and relatively consistent from event to event. Most of these events are primarily confined to the lower stratosphere and rarely can be observed above 20 km. We also infer an increase in the frequency of smoke events between the SAGE II (1984–1991, 1996–2005) and SAGE III/ISS (2017-present) periods by almost a factor of 2 and also note that the two largest events occur in the latter data set. However, given the low frequencies overall, we are not confident that the differences can be attributed to changes between the two periods. We also attempt to disentangle the mixing of aerosol in the Northern Hemisphere summer of 1991 from a pyrocumulus event (Baie-Comeau, Quebec) and Mt. Pinatubo and conclude that, while there is evidence for smoke in the lower stratosphere, virtually all of the enhanced aerosol observations in the northern mid-latitudes in the summer of 1991 are associated with the Mt. Pinatubo eruption.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".