Time-varying trends from Arctic ozonesonde time series in the years 1994–2022
Bibliographic record
Abstract
Although evidence of recovery in Antarctic stratospheric ozone has been found, evidence of recovery in Arctic ozone is still elusive, even though 25 years have passed since the peak in ozone depleting substances. Here we have used a Dynamic Linear Model to derive time-varying trends over 20-year periods in the Arctic ozone time series, measured in-situ by ozonesondes from 6 stations, from 1994 to 2022. The model accounts for seasonality, external forcing and 1st-order correlation in the residuals. As proxies for the external forcing, we have used tropopause pressure (replaced with Arctic Oscillation in the troposphere), eddy heat flux, the volume of polar stratospheric clouds multiplied by effective equivalent stratospheric chlorine, and solar radio flux at 10.7 cm for the 11-year solar cycle. Our results indicate that the ozone recovery in the lower Arctic stratosphere is not detectable. Though significant positive trends have been detected prior to 2017 at some stations, there are no statistically significant positive trends after 2017. Moreover, at a number of stations the trends after 2019 are rather negative and significant, varying between - 0.30 ± 0.25 and - 1.00 ± 0.85% per decade. Furthermore, the Arctic troposphere exhibited only statistically significant negative trends over 20-year periods ending in 2017 or later, varying between - 0.31 ± 0.27 and - 1.76 ± 0.41% per decade. These results highlight the importance of continued monitoring of the Arctic ozone.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".