Dominant roles of subgrid‐scale cloud structures in model diversity of cloud radiative effects
Bibliographic record
Abstract
Today, large model discrepancies exist in estimated cloud radiative effects (CREs) and irradiances across 1‐D radiative transfer schemes aimed for climate models. The primary purpose of this study is to understand physical causes of such model discrepancies, especially in CREs under partly cloudy sky. To achieve this goal, the unique Cloud‐Aerosol‐Radiation (CAR) ensemble modeling system was employed, offline driven by the ERA‐Interim global data for July 2004 with no feedback considered. For evaluating each individual contribution from the existing scheme diversity of cloud horizontal inhomogeneity, cloud optical properties, cloud vertical overlap, and gas absorptions, several sets of numerical experiments were conducted. It is the first time to explicitly demonstrate that after removing most of the disagreement in cloud fields, model spreads of CREs among the CAR's seven major radiation schemes, as well as those of radiative fluxes, dramatically diminish. Taking global mean CREs for example, their current model ranges can decrease to <4 W m−2 from about 10 W m−2 for shortwave and also to <4 W m−2 from 5–8 W m−2 for longwave. Dominant roles of subgrid‐scale cloud structures (including vertical overlap and horizontal variability) were proven in general, explaining about 40–75% of the total model spreads. We have also found that model spreads of CREs are very sensitive to cloud cover fractions. Such nonlinear sensitivity can be largely reduced after removing the model difference in the treatments of cloud vertical overlap.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".