On the phenomenon of the blue sun
Bibliographic record
Abstract
Abstract. This study examines the cause of the blue colour of the sun as observed after the eruption of Krakatao in 1883 as well as other volcanic eruptions or massive forest fire events. Aerosol particles, e.g. volcanic ash or products of biomass burning, are believed to be able to modify the spectral distribution of transmitted solar radiation making it appear blue or green to a human observer. Previous studies already showed that narrow aerosol particle size distributions with radii on the order of about 500 nm can lead to anomalous scattering; i.e. scattering cross sections increasing with increasing wavelength in the visible spectral range. In this work we treat the effect of Rayleigh scattering on the shape of the transmitted solar spectrum correctly employing radiative transfer (RT) simulations with the SCIATRAN RT-model. The colour associated with solar transmission spectra is determined based on the CIE (International Commission on Illumination) colour matching functions and CIE chromaticity values. It is shown that a blue sun can be simulated for aerosol optical depths (at 550 nm) of about τ=0.5 (or higher) if Rayleigh scattering is taken into account. Without considering Rayleigh scattering – as in most of the previous studies – a blue sun is in principle produced with aerosol optical depths as low as about τ=0.1 (at 550 nm) if the aerosol particle size distribution is chosen to maximize anomalous scattering in the visible spectral range. It is demonstrated that Rayleigh scattering – as expected – has a strong impact on the transmission spectrum, particularly at low solar-elevation angles, and needs to be considered for a correct determination of the perceived colour of the sun. We also test the hypothesis that the blue sun after the eruption of Krakatao was caused by large abundances of water vapour in the atmosphere, as proposed in earlier studies. In addition, we present a case study on a particularly noteworthy blue sun event in the past, i.e. the one related to the large Canadian forest fires in September 1950.
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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 | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".