Age of Stratospheric Air: Progress on Processes, Observations, and Long‐Term Trends
Bibliographic record
Abstract
Abstract Age of stratospheric air is a well established metric for the stratospheric transport circulation. Rooted in a robust theoretical framework, this approach offers the benefit of being deducible from observations of trace gases. Given potential climate‐induced changes, observational constraints on stratospheric circulation are crucial. In the past two decades, scientific progress has been made in three main areas: (a) Enhanced process understanding and the development of process diagnostics led to better quantification of individual transport processes from observations and to a better understanding of model deficits. (b) The global age of air climatology is now well constrained by observations thanks to improved quality and quantity of data, including global satellite data, and through improved and consistent age calculation methods. (c) It is well established and understood that global models predict a decrease in age, that is, an accelerating stratospheric circulation, in response to forcing by greenhouse gases and ozone depleting substances. Observational records now confirm long‐term forced trends in mean age in the lower stratosphere. However, in the mid‐stratosphere, uncertainties in observational records are too large to confirm or disprove the model predictions. Continuous monitoring of stratospheric trace gases and further improved methods to derive age from those tracers will be crucial to better constrain variability and long‐term trends from observations. Future work on mean age as a metric for stratospheric transport will be important due to its potential to enhance the understanding of stratospheric composition changes, address climate model biases, and assess the impacts of proposed climate geoengineering methods.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".