The Tropical Tropopause Layer 1960–2100
Bibliographic record
Abstract
Abstract. The representation of the Tropical Tropopause Layer in 13 different Chemistry Climate Models designed to represent the stratosphere is analyzed. Simulations for 1960–present and 1980–2100 are analyzed and compared to reanalysis model output. Results indicate that the models are able to reproduce the basic structure of the TTL. There is a large spread in cold point tropopause temperatures that may be linked to variation in TTL ozone values. The models are generally able to reproduce historical trends in tropopause pressure obtained from reanalysis products. Simulated historical trends in cold point tropopause temperatures and in the meridional extent of the TTL are not consistent across models. The pressure of both the tropical tropopause and the level of main convective outflow appear to be decreasing (increasing altitude) in historical runs. Similar trends are seen in the future. Models consistently predict decreasing tropopause and convective outflow pressure, by several hPa/decade. Tropical cold point temperatures increase by 0.2 K/decade. This indicates that tropospheric warming dominates stratospheric cooling at the tropical tropopause. Stratospheric water vapor at 100 hPa increases by up to 0.5–1 ppmv by 2100. This is less than implied directly by the temperature and methane increases, highlighting the correlation of tropopause temperatures with stratospheric water vapor, but also the complex nature of TTL transport.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 0.001 |
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".