Increasing Precipitation Efficiency Amplifies Climate Sensitivity by Enhancing Tropical Circulation Slowdown and Eastern Pacific Warming Pattern
Bibliographic record
Abstract
Abstract The role of precipitation efficiency (PE)—the fraction of column‐integrated condensate that reaches the surface as rain—in the global temperature response to CO 2 rise is yet to be quantified. Here we employ 36 limited‐domain cloud resolving models (CRMs) from the Radiative‐Convective Equilibrium Model Intercomparison Project and find that they strongly imply higher PE at warmer temperatures. We then analyze 35 general circulation models (GCMs) from the Coupled Model Intercomparison Project Phase 6 and find that increasing PE is associated with tropical circulation slowdown and greater eastern equatorial Pacific warming. These changes trigger pan‐tropical positive cloud feedback through stratiform anvil cloud reduction and stratocumulus suppression, resulting in higher Effective Climate Sensitivity (ECS). We find that in 24 of 35 GCMs matching the CRMs in simulating increasing PE with greenhouse warming, mean ECS is 1 K higher than in PE‐decreasing GCMs. Thus, further constraining PE sensitivity to temperature could reduce uncertainty over future climate projections.
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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".