What caused the frequent and widespread occurrences of noctilucent clouds at middle latitudes in 2020?
Bibliographic record
Abstract
Abstract. The 2020 summer season has revealed frequent occurrences of noctilucent clouds (NLCs) around the Northern hemisphere at middle latitudes (45–55° N), with the lowest latitude at which NLCs were seen being 34.1° N. In order to investigate a reason for this NLC extraordinary summer season, we have analyzed long-term Aura/MLS satellite data for all available summer periods from 2005 to 2020. Both Aura/MLS summer temperature and water vapor in the upper mesosphere and the mesopause region, between 74 and 89 km altitude, have been considered. We have found that there has been a moderate decrease in the upper mesosphere temperature between 2016 and 2020 and no dramatic changes have been observed in temperature in the summer of 2020 at the middle latitude mesopause. At the same time, water vapor concentration has significantly increased (by about 12–15 %) in the zonal mean H2O value in the 2020 summer compared to 2017, meaning that the summer mesopause at middle latitudes has become more wet. At the same time, no increase in water vapor has been detected at the high latitude high altitude mesopause. A combination of lower mesopause temperature and water vapor concentration maximum at middle latitudes is the main reason for frequent and widespread occurrences of NLCs seen around the globe at middle latitudes in the summer of 2020. The 24th solar cycle minimum cannot explain the H2O maximum in 2020 since the correlation between Lyman-α flux and the amount of water vapor is low. The increase in volcanic activity from 2013 to 2015 (and its recent maximum occurred in 2015) explains the increased amount of water vapor in the upper mesosphere for the past years and its maximum in 2020 due to volcanic water vapor being injected into the atmosphere and transported into the upper mesosphere. The 5-year delay between volcanic activity and water vapor maximum is well explained by a general meridional-vertical atmospheric circulation.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".