Accounting for black carbon refractive index in atmospheric radiation
Bibliographic record
Abstract
Abstract Because of the fractal aggregated structure of black carbon (BC), black carbon refractive index measurements are difficult. There are substantial differences among the over 40 existing measurement schemes and no two schemes are the same. Three typical BC refractive index schemes are chosen to explore the difference in black carbon optical properties and the consequences of the radiative effect. Two schemes are widely used in climate models, and the third is from a newer measurement in 2016. It is shown that black carbon optical properties are sensitive to different refractive indices. The relative differences in extinction coefficient and single scattering albedo can be over 100%. In addition, by using Maxwell–Garnett and Bruggeman mixing rules, it has been found that the effect of internal mixing on aerosol optical properties depends strongly on the choice of refractive index. Using a one‐dimensional radiative transfer model under clear‐sky conditions, we demonstrate that the choice of black carbon refractive index influences the inferred radiative effect. Using the more recent (2016) scheme for pure black carbon can increase the top‐of‐atmosphere radiative effect by 20% relative to the currently widely used lowest‐absorbing scheme. For internally mixed aerosol, the sign of the radiative effect can change depending on which refractive index is used.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".