Quantifying stratospheric ozone trends: Complications due to stratospheric cooling
Bibliographic record
Abstract
[1] Recent studies suggest that ozone turnaround (the second stage of ozone recovery) is near. Determining precisely when this occurs, however, will be complicated by greenhouse gas‐induced stratospheric cooling as ozone trends derived from profile data in different units and/or vertical co‐ordinates will not agree. Stratospheric cooling leads to simultaneous trends in air density and layer thicknesses, confounding the interpretation of ozone trends. A simple model suggests that instruments measuring ozone in different units may differ as to the onset of turnaround by a decade, with some indicting a continued decline while others an increase. This concept was illustrated by examining the long‐term (1979–2005) ozone trends in the SAGE (Stratospheric Aerosol and Gas Experiment) and SBUV (Solar Backscatter Ultraviolet) time series. Trends from SAGE, which measures number density as a function of altitude, and SBUV, which measures partial column as a function of pressure, are known to differ by 4–6%/decade in the upper stratosphere. It is shown that this long‐ standing difference can be reconciled to within 2%/decade when the trend in temperature is properly accounted for.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".