Examination of ozonesonde data for trends and trend changes incorporating solar and Arctic oscillation signals
Bibliographic record
Abstract
One major question that arises with the implementation of the Montreal Protocol and its subsequent conventions is our ability to determine that an ozone “recovery” is in process. Toward this we have utilized a statistical model suggested by Reinsel et al. (2002) that utilizes the idea of a trend and a trend change at a specific time and applied it to 12 ozonesonde stations in the midlatitudes of the Northern Hemisphere. The lower stratosphere, in particular, is of significance as this is where the ozone concentration is a maximum and also where heterogeneous ozone losses have been noted. This statistical methodology suffers, however, from the ambiguities of having to select a specific time for the ozone trend to change and the fact that the Mt Pinatubo volcanic aerosols impacted the ozone amount. Within this paper, we analyze the ozonesonde station data utilizing the above model but examine the statistical stability of the computed results by allowing the point of inflection to change from 1995 through 2000 and also exclude varying amounts of data from the post‐Pinatubo period. The results indicate that while the impacts of deleting data and changing the inflection point are nontrivial, the overall results are consistent in that there has been a major change in the ozone trend in the time frame of 1996 and that a reasonable scenario is to utilize a change point in 1996 and exclude 2 years of data after the 1991 Mt. Pinatubo eruption. In addition, we include a term for the Arctic oscillation within the statistical model and demonstrate that it is statistically significant.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".