Inhomogeneous radiative forcing of homogeneous greenhouse gases
Bibliographic record
Abstract
Abstract Radiative forcing of a homogeneous greenhouse gas (HGG) can be very inhomogeneous because the forcing is dependent on other atmospheric and surface variables. In the case of doubling CO 2 , the monthly mean instantaneous forcing at the top of the atmosphere is found to vary geographically and temporally from positive to negative values, with the range (−2.5–5.1 W m −2 ) being more than 3 times the magnitude of the global mean value (2.3 W m −2 ). The vertical temperature change across the atmospheric column (temperature lapse rate) is found to be the best single predictor for explaining forcing variation. In addition, the masking effects of clouds and water vapor also contribute to forcing inhomogeneity. A regression model that predicts forcing from geophysical variables is constructed. This model can explain more than 90% of the variance of the forcing. Applying this model to analyzing the forcing variation in the Climate Model Intercomparison Project Phase 5 models, we find that intermodel discrepancy in CO 2 forcing caused by model climatology leads to considerable discrepancy in their projected change in poleward energy transport.
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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) | 0.000 | 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".