Age of air from ACE-FTS measurements of sulfur hexafluoride
Bibliographic record
Abstract
The Brewer-Dobson Circulation (BDC) is one of the main determinants of trace gas distributions in the atmosphere. Climate models predict that atmospheric warming will cause the BDC to accelerate, modifying where greenhouse gases are most active and impacting the radiative properties of the atmosphere, resulting in a feedback effect. This acceleration is difficult to verify with observations because the speed of the BDC cannot be measured directly. However, changes in stratospheric transport can be identified using the stratospheric age of air, defined as the time since an air parcel entered the stratosphere from the troposphere. A decrease in age of air at higher latitudes would suggest a reduction in transit times, signifying an acceleration of the BDC. Age of air can be calculated using long-lived “clock tracers” such as sulfur hexafluoride (SF6), an industrial gas that is produced in the troposphere, has a negligible seasonal cycle, and has no stratospheric sinks. Due to its small concentrations, measurements have been historically limited, but detecting changes in age of air derived from SF6 requires a long-term, and ideally consistent (i.e., measured by the same instrument), time series. The Atmospheric Chemistry Experiment Fourier Transform Spectrometer (ACE-FTS) provides the longest available vertically-resolved record of SF6, spanning 2004 to the present. This study presents a new age of air product derived from the ACE-FTS SF6 dataset using an updated version of the method used for the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) SF6 dataset, which spans the 2002-2012 period. In this presentation, the method for age of air calculation will be presented along with comparisons with other age of air profile datasets derived from MIPAS and balloon measurements. The long-term trend in age of air will be estimated using this new product with the goal of corroborating the predictions made by climate models.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".