Black Carbon and Brown Carbon and Selective Smoke Aerosol Absorption during Massive Wildfires in Alaska in 2019 and Canada in 2023
Bibliographic record
Abstract
Abstract Smoky haze which occurs during large-scale wildfires essentially transforms the radiative regime of the atmosphere over large territories. The variability of shortwave radiation fluxes in a smoke-laden atmosphere is driven by variations in the optical and microphysical properties of smoke aerosols, including the spectral dependences of the imaginary part of the refractive index. These dependences are determined by the presence of black carbon, brown carbon, and radiation-selective absorbing organic compounds in aerosol particles. This study analyzes the aforementioned spectral dependences based on AERONET data during large-scale wildfires in Alaska in 2019 and Canada in 2023. The analysis includes the cases of extreme radiation absorption by black and brown carbon, where the imaginary part of the refractive index at a wavelength of 440 nm attained 0.50 and 0.27, respectively. Variations in the spectral dependence of the imaginary part of the refractive index under moderate manifestations of selective absorption of smoke aerosol during massive fires in Alaska and Canada are analyzed. Approximations for the spectral dependence of the imaginary part of the refractive index are suggested. The aerosol radiative forcing at the top of the atmosphere is estimated for the cases of extreme radiation absorption by black carbon and brown carbon in the visible and near-infrared spectral regions and of anomalous selective absorption. The results can be useful in monitoring of the radiative regime of the atmosphere and for the development of atmospheric remote sounding techniques.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".