Sensitivity studies of the effect of increased aerosol concentrations and snow crystal shape on the snowfall rate in the Arctic
Bibliographic record
Abstract
The mesoscale model GESIMA is used to simulate microphysical properties of Arctic clouds and their effect on radiation. Different case studies during the FIRE ACE/SHEBA project show that GESIMA is able to simulate the cloud boundaries, ice and liquid water content, and effective radii in good agreement with observations. For two different aerosol scenarios, the simulation results show that the anthropogenic aerosol can alter microphysical properties of Arctic clouds, and consequently modify surface precipitation. Borys et al. [2000] proposed that anthropogenically induced decreases in cloud droplet size inhibit the riming process. On the contrary, we find that the accretion of snow crystals with cloud droplets is increased in the polluted cloud owing to its higher cloud droplet number concentration. Instead, the autoconversion rate of cloud droplets and accretion of drizzle by snow decreases caused by the shutdown of the collision‐coalescence process in the polluted cloud. The amount of precipitation reaching the surface as snow depends crucially on the crystal shape. If aggregates are assumed, then a tenfold increase in aerosol concentration leads to an increase in accumulated snow by 40% after 7 hours of simulation whereas the snow amount decreases by 30% when planar crystals are assumed because of the larger accretion efficiency of snow crystals with cloud droplets in case of aggregates.
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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 | 0.004 | 0.004 |
| 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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".