Robust Acceleration of Stratospheric Moistening and Its Radiative Feedback Under Greenhouse Warming
Bibliographic record
Abstract
Abstract Stratospheric water vapor (SWV) changes, in response to increasing CO2, as a feedback component may play an important role in the Earth's energy budget. It has drawn extensive studies in the past decade. Here, we calculate the SWV climate feedback using the 150‐year CO2 forcing (1pctCO2) simulations in the CMIP6 ensemble of models. All models robustly show a moistening of the stratosphere, causing a positive radiative feedback to surface warming. We find that the stratospheric moistening rate and the SWV feedback both increase with surface warming. The moistening occurs at a rate of 0.9 ± 0.1 ppmv K−1 and its radiative feedback measured by the fixed dynamical heating method is 0.11 ± 0.02 W m−2 K−1 in the first 50 model years; the moistening rate increases to 1.2 ± 0.2 ppmv K−1 and the feedback increases to 0.16 ± 0.03 W m−2 K−1 in the last 50 model years when the global‐mean surface temperature is 3.3 K warmer. These increases are found to be caused by an amplified rate of tropical tropopause warming with respect to surface warming, which is 0.6 and 1.1 K K−1 for the two 50‐year periods, respectively. We conclude that the SWV feedback is strengthening with surface warming, which can contribute to increasing climate sensitivity in the future under global 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".