Bibliographic record
Abstract
Abstract The radiative feedbacks of such climate variables as air temperature, water vapor, and clouds influence the energy budget of the climate system. Measuring the strength of each feedback is of critical importance for understanding the climate sensitivity and its spread in climate models. Most feedback analyses to‐date, such as those using the kernel method, have been based on a linear decomposition of the radiation budget and neglect the nonlinear effects between the feedbacks. In this work, we quantify the coupling effects between different longwave radiative feedbacks based on partial radiative perturbations using a radiative transfer model. Two climate change scenarios, the El niño‐Southern Oscillation (ENSO) and the quadrupling CO2 (4xCO2), are examined. We find that the coupling effect between water vapor and cloud is the strongest among all the coupling effects and can amount to 50% or greater of the univariate cloud feedback. This significant coupling effect results from the masking effect of the two feedbacks on each other and can be well explained by a simple analytic model.
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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 | 0.002 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".