Bibliographic record
Abstract
Abstract. Ice clouds in the lowermost stratosphere affect stratospheric water vapour and the Earth's radiation budget. The knowledge of its occurrence and driving forces is limited. To assess the distribution and possible formation mechanisms of stratospheric ice clouds (SICs) over North America, we analysed SIC occurrence frequencies observed by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) instrument during the years 2006 to 2018. Possible driving forces such as deep convection are assessed based on Atmospheric Infrared Sounder (AIRS) observations during the same time. Results show that at nighttime, SICs are most frequently observed during the thunderstorm season over the Great Plains from May to August (MJJA) with a maximum occurrence frequency of 6.2 %. During the months from November to February (NDJF), the highest SICs occurrence frequencies are 5.5 % over the north-eastern Pacific and western Canada and 4.4 % over the western North Atlantic. Occurrence frequencies of deep convection from AIRS, which includes storm systems, fronts, mesoscale convective systems, and mesoscale convective complexes at midlatitude and high latitude, show similar hotspots like the SICs, with highest occurrence frequencies being observed over the Great Plains in MJJA (4.4 %) and over the north-eastern Pacific, western Canada, and the western North Atlantic in NDJF (∼ 2.5 %). Both, seasonal patterns and daily time series of SICs and deep convection show a high degree of spatial and temporal relation. Further analysis indicates that the maximum fraction of SICs related to deep convection is 74 % over the Great Plains in MJJA and about 50 % over the western North Atlantic, the north-eastern Pacific, and western Canada in NDJF. We conclude that, locally and regionally, deep convection is the leading factor related to the occurrence of SICs over North America. In this study, we also analysed the impact of gravity waves as another important factor related to the occurrence of SICs, as the Great Plains is a well-known hotspot for stratospheric gravity waves. In the cases where SICs are not directly linked to deep convection, we found that stratospheric gravity wave observations correlate with SICs with as much as 30 % of the cases over the Great Plains in MJJA, about 50 % over the north-eastern Pacific and western Canada, and up to 90 % over eastern Canada and the north-west Atlantic in NDJF. Our results provide a better understanding of the physical processes and climate variability related to SICs and will be of interest for modellers as SIC sources such as deep convection and gravity waves are small-scale processes that are difficult to represent in global general circulation models.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.024 | 0.012 |
| Insufficient payload (model declined to judge) | 0.394 | 0.374 |
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".