Cloud Longwave Scattering Effect and Its Impact on Climate Simulation
Bibliographic record
Abstract
The cloud longwave (LW) scattering effect has been ignored in most current climate models. To investigate its climate impact, we apply an eight-stream DIScrete Ordinates Radiative Transfer (DISORT) scheme to include the cloud LW scattering in the General circulation model version of the LongWave Rapid Radiative Transfer Model (RRTMG_LW) and the Community Atmospheric Model Version 5 (CAM5). Results from the standalone RRTMG_LW and from diagnostic runs of CAM5 (no climate feedback) show that the cloud LW scattering reduces the upward flux at the top of the atmosphere and leads to an extra warming effect in the atmosphere. In the interactive runs with climate feedback included in CAM5, the cloud LW scattering effect is amplified by the water vapor-temperature feedback in a warmer atmosphere and has substantial influences on cloud fraction and specific humidity. The thermodynamic feedbacks are more significant in the northern hemisphere and the resulting meridional temperature gradient is different between the two hemispheres, which strengthens the southern branch of Hadley circulation, and modulates the westerly jet near 50° S and the upper part of Walker circulation. Our study concludes that the cloud LW scattering effect could have complex impacts on the global energy budget and shall be properly treated in future climate models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".