Assessment of the effects of acid‐coated ice nuclei on the Arctic cloud microstructure, atmospheric dehydration, radiation and temperature during winter
Bibliographic record
Abstract
Abstract Owing to the large‐scale transport of pollution‐derived aerosols from the mid‐latitudes to the Arctic, most of the aerosols are coated with acidic sulfate during winter in the Arctic. Recent laboratory experiments have shown that acid coating on dust particles substantially reduces the ability of these particles to nucleate ice crystals. Simulations performed using the Limited Area version of the Global Multiscale Environmental Model (GEM‐LAM) are used to assess the potential effect of acid‐coated ice nuclei on the Arctic cloud and radiation processes during January and February 2007. Ice nucleation is treated using a new parameterization based on laboratory experiments of ice nucleation on sulphuric acid‐coated and uncoated kaolinite particles. Results show that acid coating on dust particles has an important effect on cloud microstructure, atmospheric dehydration, radiation and temperature over the Central Arctic, which is the coldest part of the Arctic. Mid and upper ice clouds are optically thinner while low‐level mixed‐phase clouds are more frequent and persistent. These changes in the cloud microstructures affect the radiation at the top of the atmosphere with longwave negative cloud forcing values ranging between 0 and − 6 W m−2 over the region covered by the Arctic air mass. Copyright © 2012 Royal Meteorological Society
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".