MLS measurements of stratospheric hydrogen cyanide during the2015–16 El Niño event
Bibliographic record
Abstract
Abstract. It is known from ground-based measurements made during the 1982–83 and 1997–98 El Niño events that atmospheric HCN tends to be higher than usual during such years. The Microwave Limb Sounder (MLS) on Aura has been measuring HCN mixing ratios since launch in 2004; the measurements are ongoing at the time of writing. The winter of 2015–16 has seen the largest El Niño event since 1997–98. We present MLS measurements of HCN in the lower stratosphere for the Aura mission to date, comparing the 2015–16 El Niño period to the rest of the mission. HCN in 2015–16 is higher than at any other time during the mission, but ground based measurements suggest that it may have been even more elevated in 1997–98. As the MLS HCN data are essentially un-validated, we show them alongside data from the MIPAS and ACE-FTS instruments; the three instruments agree reasonably well in the tropical lower stratosphere. Global HCN emissions calculated from the GFED (V4.1) database are very much greater during large El Niño events and are greater in 1997–98 than in 2015–16, thereby showing good qualitative agreement with the measurements. Correlation between ENSO indices, measured HCN and GFED HCN emissions is less clear away from the 2015–16 event. In particular, the 2009–10 winter had fairly strong El Niño conditions and fairly large GFED HCN emissions, but very little effect is observed in the MLS HCN.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".