Long-term trends and radiative impact in vertically resolved stratospheric water vapour from ESA WV_cci data records
Bibliographic record
Abstract
Water vapour in the upper troposphere and stratosphere has a significant impact both on the radiative and chemical properties of the atmosphere. Reliable water vapour climate data records (CDRs) are essential for use in climate research, to assess vertically resolved trends and associated radiative impacts. Within the ESA Water Vapour Climate Change Initiative (WV_cci), new vertically resolved water vapour CDRs in the stratosphere and UTLS were merged from a range of satellite observations. In this contribution, we provide an overview of these CDRs, highlighting innovations in the merging methodologies and results from a detailed quality assessment. In particular, the long-term trends derived from the new water vapour CDRs are compared to other merged datasets, reanalyses, and simulations from chemistry-climate models, with the ESA WV_cci CDRs deemed to be valuable new datasets for climate studies. We conclude that, mostly driven by dynamical variability, the derived water vapour trends vary significantly depending on the dataset used, chosen time period and location in the atmosphere. Using an off-line radiative transfer model, we estimate the consequence of these differences on the radiative forcing from water vapour changes in the upper troposphere and stratosphere over the past 30+ years.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".