Quantifying transport into the lowermost stratosphere using simultaneous in-situ measurements of SF <sub>6</sub> and CO <sub>2</sub>
Bibliographic record
Abstract
Abstract. The seasonality of transport and mixing of air into the lowermost stratosphere (LMS) is studied using distributions of mean age of air and a~mass balance approach, based on in-situ observations of SF6 and CO2 during the SPURT (Spurenstofftransport in der Tropopausenregion, trace gas transport in the tropopause region) aircraft campaigns. Combining the information of the mean age of air and the water vapour distributions we demonstrate that the tropospheric air transported into the LMS above the extratropical tropopause layer (ExTL) originates predominantly from the tropical tropopause layer (TTL). The concept of our mass balance is based on simultaneous measurements of the two passive tracers and the assumption that transport into the LMS can be described by age spectra which are superposition of two different modes. Based on this concept we conclude that the stratospheric influence on LMS composition is strongest in April with tropospheric fractions (α1) below 20% and that the strongest tropospheric signatures are found in October with (α1 greater than 80%. Beyond the fractions, our mass balance concept allows to calculate the associated transit times for transport of tropospheric air from the tropics into the LMS. The shortest transit times (<0.3 years) are derived for the summer, continuously increasing up to 0.8 years by the end of spring. These findings suggest that strong quasi-horizontal mixing across the weak subtropical jet from summer to mid of autumn and the considerably shorter residual transport time-scales within the lower branch of the Brewer-Dobson circulation in summer than in winter dominates the tropospheric influence in the LMS until the beginning of next year's summer.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".