Seasonal stratospheric ozone trends over 2000–2018 derived from several merged data sets
Bibliographic record
Abstract
Abstract. In this work, we analyse the seasonal dependence of ozone trends in the stratosphere using four long-term merged datasets: SAGE-CCI-OMPS, SAGE-OSIRIS-OMPS, GOZCARDS and SWOOSH which provide more than 30 years of monthly zonal mean ozone profiles in the stratosphere. We focus here on trends between 2000 and 2018. All datasets show similar results, although some discrepancies are observed. In the upper stratosphere, the trends are positive throughout all seasons and the majority of latitudes. The largest upper stratospheric ozone trends are observed during local winter (up to 6 % dec−1) and equinox (up to 3 % dec−1) at mid-latitudes. In the equatorial region, we find a very strong seasonal dependence of ozone trends at all altitudes: the trends vary from positive to negative, with the sign of transition depending on altitude and season. The trends are negative in the upper stratospheric winter (−1 to −2 % dec−1) and in the lower stratospheric spring (−2 to −4 % dec−1), but positive (2–3 % dec−1) at 30–35 km in spring, while the opposite pattern is observed in summer. The tropical trends below 25 km are negative and maximize during summer (up to −2 % dec−1) and spring (up to −3 % dec−1). In the lower mid-latitude stratosphere, our analysis indicates hemispheric asymmetry: during local summers and equinoxes, positive trends are observed in the South (+1 to +2 % dec−1) while negative trends are observed in the North (−1 to −2 % dec−1). We compare the seasonal dependence of ozone trends with available analyses of the seasonal dependence of stratospheric temperature trends. We find that ozone and temperature trends show positive correlation in the dynamically controlled lower stratosphere, and negative correlation above 30 km, where photochemistry dominates. Seasonal trend analysis gives information beyond that contained in annual mean trends, which can be helpful in order to better understand the role of dynamical variability in short-term trends and future ozone recovery predictions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".