The impact of uncertainty in black carbon's refractive index on simulated optical depth and radiative forcing
Bibliographic record
Abstract
The radiative forcing of black carbon (BC) is subject to many complex, interconnected sources of uncertainty. Here we isolate the role of the refractive index, which determines the extent to which BC absorbs and scatters radiation. We compare four refractive index schemes: three that are commonly used in Earth system models and a fourth more recent estimate with higher absorption. With other parameterizations held constant, changing BC's spectrally varying refractive index from the least- to most-absorbing estimate commonly used in Earth system models ( m 550 nm =1.75–0.44 i to m 550 nm =1.95–0.79 i ) increases simulated absorbing aerosol optical depth (AAOD) by 42 % and the effective radiative forcing from BC–radiation interactions (BC ERFari) by 47 %. The more recent estimate, m 532 nm =1.48–0.84 i , increases AAOD and BC ERFari by 59 % and 100 % respectively relative to the low-absorption case. The AAOD increases are comparable to those from recent updates to aerosol emission inventories and, in BC source regions, up to two-thirds as large as the difference in AAOD retrieved from MISR (Multi-angle Imaging SpectroRadiometer) and POLDER-GRASP (Polarization and Directionality of the Earth's Reflectances instrument with the Generalized Retrieval of Atmosphere and Surface Properties algorithm) satellites. The BC ERFari increases are comparable to previous assessments of overall uncertainties in BC ERFari, even though this source of uncertainty is typically overlooked. Although model sensitivity to the choice of BC refractive index is known to be modulated by other parameterization choices, our results highlight the importance of considering refractive index diversity in model intercomparison projects.
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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 | 0.002 | 0.004 |
| 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 | 0.001 |
| 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".