On Pan-Atlantic cold, wet and windy compound extremes
Bibliographic record
Abstract
The co-occurrence of extreme weather in geographically distinct regions can result in larger impacts than the sum of the individual events they comprise. Previous work has proposed a connection between extreme cold temperatures over North America and wet and windy weather over Europe. Here, we present a systematic statistical analysis of this link, focusing on extreme occurrences in both continents. We identify wintertime cold air outbreaks (CAOs) for 40 overlapping domains over North America between 1979 and 2020 using ERA5 data, employing different CAO definitions. The occurrence of these regional CAOs is then matched to extreme precipitation and wind events over 6 domains in western and central Europe. Consistent across all methodologies, CAOs over the eastern and central USA are followed by significant wind extremes over Iberia, whilst CAOs over Canada are followed by significant wind extremes over France, northern Europe and the British Isles. Precipitation extremes show a greater variability, though we find significant Iberian and southern European precipitation extremes occurring in conjunction with CAOs over the eastern USA, consistent with that found for wind extremes. Indeed, Iberia is one of the hotspot regions for wet and windy extremes co-occurring with cold spells in North America: when conditioning on the latter, the frequency of extreme precipitation and wind events over Iberia locally more than doubles relative to climatology. Physically, the pan-Atlantic extremes are associated with a zonalisation of the North Atlantic storm track, which acts as a dynamical connector between North America and Europe.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".