Seasonal persistence of ozone and zonal wind anomalies in the equatorial stratosphere
Bibliographic record
Abstract
Analysis of the variability of equatorial ozone profiles in the Satellite Aerosol and Gas Experiment‐corrected Solar Backscatter Ultraviolet data set demonstrates a strong seasonal persistence of interannual ozone anomalies, revealing a seasonal dependence to equatorial ozone variability. In the lower stratosphere (40–25 hPa) and in the upper stratosphere (6–4 hPa), ozone anomalies persist from approximately November until June of the following year, while ozone anomalies in the layer between 16 and 10 hPa persist from June to December. Analysis of zonal wind fields in the lower stratosphere and temperature fields in the upper stratosphere reveals a similar seasonal persistence of the zonal wind and temperature anomalies associated with the quasi‐biennial oscillation (QBO). Thus, the persistence of interannual ozone anomalies in the lower and upper equatorial stratosphere, which are mainly associated with the well‐known QBO ozone signal through the QBO‐induced meridional circulation, is related to a newly identified seasonal persistence of the QBO itself. The upper stratospheric QBO ozone signal is argued to arise from a combination of QBO‐induced temperature and NOx perturbations, with the former dominating at 5 hPa and the latter at 10 hPa. Ozone anomalies in the transition zone between dynamical and photochemical control of ozone (16–10 hPa) are less influenced by the QBO signal and show a quite different seasonal persistence compared to the regions above and below.
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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 | 0.000 |
| 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".