The Surface Warming Attributable to Stratospheric Water Vapor in CO<sub>2</sub>‐Caused Global Warming
Bibliographic record
Abstract
Abstract Stratospheric water vapor (SWV) is recognized as a potentially important positive feedback in global warming. The SWV change induces significant downward radiative flux perturbation at the tropopause and therefore is hypothesized to substantially amplify the surface warming. To test this hypothesis, we use a global climate model to quantify the surface warming contributed by the SWV change in the context of the quadrupled CO2. By prescribing the SWV increase as an external forcing, we find that SWV only accounts for 0.42 K surface warming, making up merely 5.4% of the total CO2‐caused surface warming (7.7 K). The efficacy of the stratosphere‐adjusted SWV forcing is small (38%), where the efficacy is defined as the ratio of the global temperature response per unit radiative forcing relative to that of the CO2 forcing. With the aid of a series of auxiliary experiments, we find that although the stratosphere‐adjusted SWV forcing at the top of atmosphere (TOA) is significant (1.13 W m−2), more than half of the forcing is offset by a high‐cloud decrease and an upper tropospheric warming in the tropospheric adjustment. The direct radiative impact of the SWV increase on surface temperature is negligible, and the SWV‐induced surface temperature change is a result of interactions between the radiative and nonradiative processes.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".