Extratropical Cloud Feedbacks
Bibliographic record
Abstract
The extratropics are the cloudiest region on Earth. Changes in clouds in this region in response to warming have the potential to substantially affect global mean cloud feedback and by extension climate sensitivity. Global climate models (GCMs) predict a relatively small, but consistent positive longwave (LW) cloud feedback throughout much of the extratropics. The bulk of GCMs transition from positive subtropical shortwave (SW) cloud feedback to negative extratropical SW cloud feedback is driven by increasing cloud optical depth. However, the strength of the negative feedback in the extratropics is not agreed upon by GCMs. Recent shifts in extratropical SW cloud feedback toward more positive values in the most recent generation of GCMs have led to the emergence of several high ECS ( K) GCMs. Thus, understanding and constraining the processes that drive extratropical cloud feedback has global implications and constraint of the SW extratropical cloud feedback has garnered significant attention in the literature. In this chapter, we summarize recent literature and present the processes important for extratropical cloud feedback in the context of meteorological regimes and global climate.
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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 | 0.001 | 0.001 |
| 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) | 0.017 | 0.002 |
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".