Parameterization of cloud optical properties for semidirect radiative forcing
Bibliographic record
Abstract
[1] A parameterization of liquid cloud optical properties with a mixture of black carbon is proposed. It is found that the changes in cloud optical properties due to mixture of black carbon can be treated as a perturbation to existing cloud optical property parameterizations in climate models. The advantage of the proposed scheme is that current cloud optical property parameterizations used in climate models can be kept. It is shown that the dominant factor with respect to radiative forcing due to the inclusion of black carbon in cloud droplets is the resulting change in single-scattering albedo values. Therefore, a simple scheme to modify only the single-scattering albedo is considered. The additional consideration of the modification of asymmetry factor only applies to the case of very large black carbon volume fraction. The results in a one-dimensional radiation model show that internal mixtures of black carbon can have a significant impact on solar flux and heating rates. For a black carbon volume fraction of 10−7, the reduction in solar flux at the top of the atmosphere can be over 0.5 Wm−2 and the heating rate can increase by about 0.08 Kd−1 for a solar zenith angle of 53°.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".