Bibliographic record
Abstract
Abstract. The tropical tropopause layer (TTL) is the main gateway for air transiting from the troposphere to the stratosphere and therefore impacts the chemical composition of the stratosphere. In particular, the cold point tropopause, where air parcels encounter their final dehydration, effectively controls the water vapor content of the lower stratosphere. Given the important role of stratospheric water vapor for the global energy budget, it is crucial to understand the long-term changes in cold point temperature and their impact on water vapor trends. Our study uses GNSS-RO data to show that, there has been no overall cooling trend of the TTL over the past two decades, in contrast to observations prior to 2000. Instead, the cold point is warming, with the strongest trends of up to 0.7 K/decade during boreal winter and spring. The cold point warming shows longitudinal asymmetries with the smallest warming over the central Pacific and the largest warming over the Atlantic. These asymmetries are anti-correlated with patterns of tropospheric warming, and regions of strongest cold point warming are found to show slight cooling trends in the upper troposphere. Overall, the here identified warming of the cold point is consistent with model prediction under global climate change, which attributes the warming trends to radiative effects. The seasonal signals and zonal asymmetries of the cold point temperature and height trends, on the other hand, seemed to be related to dynamical responses to enhanced upper tropospheric heating, changing convection, or trends of the stratospheric circulation. Water vapor observations in the TTL show mostly positive trends for 2004–2021 consistent with cold point warming. We find a decrease of the amplitude of the cold point temperature seasonal cycle by ~7 % driving a reduction of the seasonal cycle in 100 hPa water vapor by 5–6 %. Our analysis shows that this reduction in the seasonal cycle is transported upwards together with the seasonal anomalies and has reduced the amplitude of the well-known tape recorder over the last two decades.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.015 | 0.008 |
| Insufficient payload (model declined to judge) | 0.375 | 0.242 |
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".