Impacts of Cyclones on Arctic Clouds during Autumn in the Early 21st Century
Bibliographic record
Abstract
Our study shows that, during 2001–2017, when the sea ice was melting rapidly, cyclone days accounted for more than 50% of the total autumn days at the sounding stations in the Arctic marginal seas north of the Eurasian continent and almost 50% of the total autumn days at the sounding station on the northern coast of Canada. It is necessary to investigate the influence of Arctic cyclones on the cloud fraction in autumn when the sea ice refreezes from its summer minimum and the infrared cloud radiative effect becomes increasingly important. Cyclones at the selected stations are characterized by a narrow maximum rising zone with vertically consistent high relative humidity (RH) and a broad region outside the high RH zone with low RH air from the middle troposphere covering the low troposphere’s high relative humidity air. Consequently, on approximately 40% of the cyclone days, the cloud formation condition was improved from the near surface to the upper troposphere due to the cooling of strong rising warm humid air. Therefore, cyclones lead to middle cloud increases and sometimes high cloud increases, since the climatological Arctic autumn clouds are mainly low clouds. On approximately 60% of the cyclone days, only low cloud formed, but the low cloud formation condition was suppressed due to the mixing ratio decrease induced by cold dry air sinking. As a result, cyclones generally lead to a decrease in low clouds. However, the correlation between the cyclones and low clouds is complex and varies with surface ice conditions.
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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 | 0.001 |
| 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 | 0.001 |
| 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 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".