Hemispheric asymmetry in recent stratospheric age of air changes
Bibliographic record
Abstract
Many stratospheric trace gases, including O 3 , HCl, and NO y , have had opposing trends in the Southern Hemisphere (SH) compared to the Northern Hemisphere (NH) during the last 2 decades. Some of this difference is due to hemispherically asymmetric changes in the rate of transport by the Brewer–Dobson circulation (BDC), and some is due to ozone depletion and recovery. The mean age of air (AoA) is a common proxy for the transport rate by the BDC in models; however it cannot be directly measured. We use observations from the Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) along with results from the Chemical Lagrangian Model of the Stratosphere (CLaMS) to derive AoA anomalies and AoA trends. The AoA is derived using observations of N 2 O, CH 4 , and CFC-12, all long-lived trace gases with tropospheric sources. We also consider CLaMS simulations driven with four different reanalyses (ERA5, ERA-Interim, JRA-55, MERRA-2). We find that, irrespective of which trace gas or reanalysis is used, air in the NH aged by up to 0.3 years per decade relative to the SH over 2004–2017. The maximum hemispheric difference in aging occurs in the middle stratosphere, near 30 hPa (∼ 24 km). We also show that the aging rate in the NH becomes smaller when the analysis is extended to 2021. The observed aging in the NH middle stratosphere contradicts model predictions of a decrease in stratospheric AoA in response to rising atmospheric greenhouse gas levels. However, the smaller aging rate during 2004–2021 compared to 2004–2017 provides some evidence that the NH aging is impacted by decadal variability and the limited length of the observation period.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".