Radiative characteristics of aerosol during extreme fire event over Siberia in summer 2012
Bibliographic record
Abstract
Abstract. Microphysical and optical properties of aerosol were studied during a mega-fire event in summer 2012 over Siberia using ground-based measurements of spectral solar radiation at the AERONET site in Tomsk and satellite observations. The data were analysed using multi-year (2003–2013) measurements of aerosol characteristics under background conditions and for less intense fires, differing in burning biomass type, stage of fire, remoteness from observation site, etc. (ordinary smoke). In June–August 2012, the average aerosol optical depth (AOD, 500 nm) had been 0.95 ± 0.86, about a factor of 6 larger than background values (0.16 ± 0.08), and a factor of 2.5 larger than in ordinary smoke. The AOD values were extremely high on 24–28 July and reached 3–5. A comparison with satellite observations showed that ground-based measurements in the region of Tomsk not only reflect the local AOD features, but are also characteristic for the territory of Western Siberia as a whole. Single scattering albedo (SSA, 440 nm) in this period ranged from 0.91 to 0.99 with an average of ∼ 0.96 in the entire wavelength range of 440–1020 nm. The increase in absorptance of aerosol particles (SSA(440 nm) = 0.92) and decrease in SSA with wavelength observed in ordinary smoke agree with the data from multi-year observations in analogous situations in the boreal zone of USA and Canada. Volume aerosol size distribution in extreme and ordinary smoke had a bimodal character with significant prevalence of fine-mode particles, but in summer 2012 the mean median radius and the width of the fine-mode distribution somewhat increased. In contrast to data from multi-year observations, in summer 2012 an increase in the volume concentration and median radius of the coarse mode was observed with growing AOD. The calculations of the average radiative effects of smoke and background aerosol are presented. Compared to background conditions and ordinary smoke, under the extreme smoke conditions the cooling effect of aerosol considerably intensifies: direct radiative effects (DRE) at the bottom (BOA) and at the top of the atmosphere (TOA) are −13, −35, and −60 W m−2 and −5, −14, and −35 W m−2 respectively. The maximal values of DRE were observed on 27 July (AOD(500 nm) = 3.5), when DRE(BOA) reached −150 W m−2, while DRE(TOA) and DRE of the atmosphere were −75 W m−2. During the fire event in summer 2012 the direct radiative effect efficiency varied in range: at the BOA it was −80–−40 W m−2, at the TOA it was −50–−20 W m−2 and in the atmosphere it was −35–−20 W m−2.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 teacher head, 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".