Meteorological conditions favourable for strong aerosol impacts on clouds
Bibliographic record
Abstract
Pollution tracks in clouds induced by anthropogenic aerosols (Toll et al 2019, Nature, https://doi.org/10.1038/s41586-019-1423-9) are visually detectable ship-track-like quasi-linear polluted cloud features in satellite imagery. Pollution tracks provide a direct way to study aerosol-cloud interactions, the most uncertain mechanism of anthropogenic climate forcing. Here, we study environmental conditions favourable for pollution tracks’ formation. We use meteorological data from in-situ observations and ERA5 reanalysis and cloud properties derived from MODIS retrievals over the period 2000-2019. We detected pollution track occurrences at the anthropogenic air pollution hot spots of Norilsk and Cherepovec in Russia and Thompson in Canada. In Norilsk, there are large Nickel smelters, in Cherepovec, a steel manufacturing plant, and in Thompson nickel mining and milling operations take place. We compare the meteorological conditions of track-days to cloudy no-track-days. Depending on the studied location, polluted cloud tracks occur 2.7% to 3.5% of the time. Preliminary results show track formation dependence on large-scale dynamical situation, atmospheric stability, unperturbed cloud properties and relative humidity below and above clouds. The track formation could be limited by aerosols, aerosol vertical transport and activation or cloud susceptibility. Our results help to reduce the uncertainty associated with the anthropogenic aerosol impacts on clouds.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 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".