Bibliographic record
Abstract
Abstract. The tropical tropopause layer (TTL) is the transition region between the well-mixed convective troposphere and the radiatively controlled stratosphere and plays a crucial role for air mass transport between these layers. In this paper, we present updated trends of TTL and lower stratospheric temperature from radiosonde and Global Navigation Satellite System – Radio Occultation (GNSS-RO) data and evaluate temperature trends in the reanalysis data sets ERA5, JRA-3Q, and MERRA-2. Given its importance in determining the concentration of water vapor entering the stratosphere, we focused in particular on temperature trends at the cold point tropopause, which we determined from radiosonde observations by removing time-varying bias effects from trends based on unadjusted data. From 1980 to 2023, cold point tropopause cooling is shown in radiosondes, overestimated by JRA-3Q and underestimated by MERRA-2 and ERA5. Splitting into two periods reveals a shift in TTL temperature trends: cooling (1980–2001) to warming (2002–2023) across all datasets, highlighting post-2002 changes in tropical tropopause dynamics. Tropical upwelling estimates from the three reanalyses show opposite trends for 1980–2001 compared to 2002–2023 consistent with the cold point and lower stratosphere temperature trends. While the vertical residual circulation increased before 2000 consistent with cold point cooling, the circulation trends turned to zero (MERRA-2, JRA-3Q) or became positive (ERA5) after 2000 consistent with cold point warming. Between 2002–2023, GNSS-RO and reanalysis show significant warming at the cold point and lower stratosphere, aligning with observed patterns and seasonality. Warming trends anticorrelate with tropospheric cooling, strongest where upper troposphere cooling appears.
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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.018 | 0.017 |
| Insufficient payload (model declined to judge) | 0.191 | 0.151 |
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".