Anthropogenic aerosols turn liquid cloud droplets into ice crystals, produce snow and eat holes in the clouds
Bibliographic record
Abstract
Living downwind of a cement-producing or a metallurgical plant could mean you get more snow, fewer clouds and more sunshine compared to nearby areas. We use satellite observations to reveal the glaciation of supercooled stratiform liquid-phase clouds by anthropogenic aerosols acting as ice-nucleating particles. There are strong indications that glaciation is caused by aerosols emitted from oil refineries, coal-fired power plants, cement, metal smelting and processing, chemical plants, and other anthropogenic air pollution sources in Europe, Asia, North America and Australia. Heavily polluted areas derived by simulating aerosol dispersion from strong anthropogenic aerosol point sources overlap with the areas of glaciation, snowfall, and decreased cloud cover strikingly well. Moreover, the polluted areas with decreased cloud cover are plume-shaped, with a distinctive head pointing towards the pollution source, similar to aerosol-polluted cloud tracks in liquid-water clouds (Toll et al 2019 Nature https://doi.org/10.1038/s41586-019-1423-9).Glaciation-induced snowfall downwind of aerosol sources is observed using ground-based precipitation radars, and tracks of snow are also seen on the ground in satellite imagery. Aerosol-induced glaciation and snowfall lead to reduced cloud cover. At multiple aerosol sources, glaciation events are more frequent than polluted tracks in liquid-phase clouds. Thus, at least locally at some aerosol sources in the middle and high latitudes, the warming effect induced by aerosols acting as ice-nucleating particles likely exceeds the cooling effect induced by aerosols acting as cloud condensation nuclei. Further research is needed to quantify the global radiative forcing by anthropogenic ice nucleating particles.
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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 | 0.000 |
| 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".