Mean age of stratospheric air derived from AirCore observations
Bibliographic record
Abstract
Abstract. Mean age of stratospheric air can be derived from observations of sufficiently long lived trace gases with approximately linear trends in the troposphere. Mean age can serve as a tracer to investigate stratospheric transport and long term changes in the strength of the overturning Brewer-Dobson circulation of the stratosphere. For this purpose, a low-cost method is required in order to allow for regular observations up to altitudes of about 30 km. Despite the desired low costs, high precision and accuracy are required in order to allow determination of mean age. We present balloon borne AirCore observations from two mid latitude sites: Timmins in Ontario/Canada and Lindenberg in Germany. During the Timmins campaign five AirCores sampled air in parallel from a large stratospheric balloon and were analysed for CO2, CH4 and partly CO. We show that there is good agreement between the different AirCores (better than 0.1 %) especially when vertical gradients are small. The measurements from Lindenberg were performed using small low-cost balloons and yielded very comparable results. We have used the observations to extend our long term data set of mean age observations at Northern Hemi-sphere mid latitudes. The time series now covers more than 40 years and shows a small, statis-tically not significant positive trend of 0.15 ± 0.18 years/decade. This trend is slightly smaller than the previous estimate of 0.24 ± 0.22 years/decade which was based on observations up to the year 2006. These observations are still in contrast to strong negative trends of mean age as derived from some model calculations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".