On the magnitude of the stratospheric radiative feedback in global warming
Bibliographic record
Abstract
Global warming is amplified by radiative feedbacks. Compared to the feedback in the troposphere, the feedback in the stratosphere is less understood. The stratospheric water vapor (SWV), one of the primary feedbacks in the stratosphere, is argued to be an important contributor to global warming. This, however, is at odds with the finding that the overall stratospheric feedback does not amount to a significant value in global climate models (GCMs). The key to reconciling these seemingly contradictory arguments is to understand the stratospheric temperature (ST) change since the impact of SWV on the top-of-atmosphere (TOA) radiation budget results more from its cooling of the stratosphere than its direct radiative impact on the TOA radiation. Here, we develop a method to decompose the ST change and to quantify the effects of different climate responses associated with SWV on the TOA radiation budget. We find that although the SWV feedback by itself would lead to strong stratospheric cooling, this cooling is strongly offset by the radiative coupling between the stratosphere and troposphere. Such compensation results in an insignificant overall stratospheric feedback. SWV-locking experiments verify that the SWV feedback does not significantly modify the overall climate sensitivity in the GCM global warming simulations.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".