Anomalous Arctic Warming Linked with Severe Winter Weather in Northern Hemisphere Continents
Bibliographic record
Abstract
We have extended a recently developed index of accumulated winter season severity index (AWSSI), originally based on temperature and snowfall observations from weather stations in the United States only, to the entire Northern Hemisphere using reanalysis output. The expanded index (rAWSSI) is analyzed to reveal relationships between Arctic air temperatures/geopotential heights and the probability of severe winter weather across the midlatitudes. Cold temperatures dominate the index, while snowfall contributes mainly over high elevations. We find a direct and linear relationship between anomalously high Arctic temperatures/geopotential heights and increased severe winter weather, especially in northern and eastern continental regions. Positive temperature trends in specific Arctic regions are associated with increasing trends in severe winter weather in particular midlatitude areas. These trends are more robust during recent decades when Arctic warming has accelerated, exceeding the pace of global-average warming by a factor of two to four. We also explore trends in the variability of daily rAWSSI. During the era of rapid Arctic warming, variability has decreased over the Arctic Ocean and Europe – suggesting less volatile winter weather -- while it has increased along the United States (US)/Canadian border, western Canada, and northeast Asia, indicating more pronounced shifts in weather conditions. This finding suggests an increased tendency for volatile weather swings known as weather whiplash. Finally, we find that when the stratospheric polar vortex is weak (anomalously warm stratosphere), the rAWSSI tends to increase, suggesting an association between disruptions in the polar vortex and severe winter weather across certain regions of the Northern Hemisphere continents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".