Ozone trend profiles in the stratosphere: combining ground-based data over Central Europe to consider uncertainties
Bibliographic record
Abstract
Abstract. Observing stratospheric ozone is essential to assess if the Montreal Protocol has succeeded to save the ozone layer by banning ozone depleting substances. Recent studies have reported positive trends indicating that ozone is recovering in the upper stratosphere at mid-latitudes, but the trend magnitudes differ and uncertainties are still high. Trends and their uncertainties are influenced by factors such as instrumental drifts, sampling patterns, discontinuities, biases, or short-term anomalies that all might mask a potential ozone recovery. The present study investigates how anomalies, temporal measurement sampling rates and trend period lengths influence resulting trends. We present an approach for handling suspicious anomalies in trend estimations to improve the derived trend profiles. The approach was applied to data from a Ground-based Millimetre-wave Ozone Spectrometer (GROMOS) located in Bern, Switzerland. We compare our improved GROMOS trend estimate with results from other ground stations (lidars, ozonesondes, and microwave radiometers) in Central Europe. The data indicate positive trends of 1 to 3 % per decade at an altitude of about 40 km (3 hPa), providing a confirmation of ozone recovery in the upper stratosphere in agreement to satellite observations. At lower altitudes, the ground station data show inconsistent trend results, which emphasize the importance of ongoing research on lower stratospheric ozone trends. Our presented method of a combined analysis of ground station data provides a useful approach to recognize and to reduce uncertainties in stratospheric ozone trends by considering anomalies in the trend estimation. We conclude that stratospheric trend estimations still need improvement and that our approach provides a tool that can also be useful for other data sets.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".