Variability and long-term changes in tropical cold-point temperature
Bibliographic record
Abstract
The tropical tropopause layer (TTL) serves as a crucial boundary for air exchange between the troposphere and stratosphere, influencing the chemical composition and radiative balance of the lower stratosphere. Specifically, the cold-point tropopause, where air parcels undergo final dehydration, plays a key role in determining stratospheric water vapor content, which has significant implications for the global energy budget.Our research utilizes Global Navigation Satellite System – Radio Occultation (GNSS-RO) and radiosonde data to investigate long-term changes in cold-point temperature and their impact on water vapor trends. We present evidence of a shift from pre-2000 cooling to post-2000 warming in TTL and lower stratospheric temperatures. Between 2002 and 2023, the cold point exhibits significant warming trends, reaching up to 0.7 K per decade during boreal winter and spring, with pronounced longitudinal asymmetries. These trends are strongest over the Atlantic and weakest over the central Pacific and are anti-correlated with upper tropospheric temperature trends. Our analysis shows a decrease in the seasonal cycle of cold-point temperature by ∼7%, driving a corresponding reduction of 6% in the seasonal cycle of water vapor at 100 hPa. This decrease of the water vapor seasonal cycle is transported upwards weakening the amplitude of the well-known stratospheric tape recorder signal.Our findings are reproduced by reanalysis data (ERA5, JRA-55, MERRA-2), which accurately capture the spatial and seasonal variations in temperature trends. The reanalyses also highlight an important connection between TTL temperatures and tropical upwelling with a pre-2000 increase in tropical upwelling consistent with observed cold-point cooling and a post-2000 decrease in upwelling consistent with observed cold-point warming.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".