Extratropical age of air trends and causative factors in climate projection simulations
Bibliographic record
Abstract
Climate model simulations show an acceleration of the Brewer–Dobson circulation (BDC) in response to climate change. While the general mechanisms for the BDC strengthening are widely understood, there are still open questions concerning the influence of the details of the wave driving. Mean age of stratospheric air (AoA) is a useful transport diagnostic for assessing changes in the BDC. Analyzing AoA from a subset of Chemistry–Climate Model Initiative part 1 climate projection simulations, we find a remarkable agreement between most of the models in simulating the largest negative AoA trends in the extratropical lower to middle stratosphere of both hemispheres (approximately between 20 and 25 geopotential kilometers (gpkm) and 20–50 ∘ N and S). We show that the occurrence of AoA trend minima in those regions is directly related to the climatological AoA distribution, which is sensitive to an upward shift of the circulation in response to climate change. Also other factors like a reduction of aging by mixing (AbM) and residual circulation transit times (RCTTs) contribute to the AoA distribution changes by widening the AoA isolines. Furthermore, we analyze the time evolution of AbM and RCTT trends in the extratropics and examine the connection to possible drivers focusing on local residual circulation strength, net tropical upwelling and wave driving. However, after the correction for a vertical shift of pressure levels, we find only seasonally significant trends of residual circulation strength and zonal mean wave forcing (resolved and unresolved) without a clear relation between the trends of the analyzed quantities. This indicates that additional causative factors may influence the AoA, RCTT and AbM trends. In this study, we postulate that the shrinkage of the stratosphere has the potential to influence the RCTT and AbM trends and thereby cause additional AoA changes over time.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".