The Regional Impact of Wet and Windy Extremes Over Europe, Following North American Cold Spells
Bibliographic record
Abstract
Due to the compounding nature of co-occurring weather extremes, these events can be highly detrimental to economies, damaging to infrastructure and result in loss of life. Previous work has established a connection between cold spells over North America and extreme wet and windy weather over Europe. This work attempts to identify a statistical link between the regional impact of wet and windy extremes over Europe based on the regional impact of cold spells over North America. We identify cold spells for 41 overlapping regions over North America for full winter (DJF) seasons between 1979 and 2020 using ERA5 data, employing 4 methodologies for the computation of onset dates. The impact of extreme precipitation and wind events over 6 regions of western and central Europe is analysed. Consistent across all methodologies, cold spells over eastern and mid USA are followed by significant wind extremes over Iberia, whilst cold spells over eastern Canada are followed by significant wind extremes over northern Europe and the British Isles. The regional impact of precipitation extremes shows much greater variance, though we find significant Iberian and southern European precipitation for cold spells over eastern USA, consistent with that found for wind extremes. The majority of extreme precipitation and some significant wind extremes also precede the peak of the cold spell. We show also that the frequency of extreme precipitation and wind events over Iberia increases by 1.5 to more than 2 times the climatological frequency, following cold spells in most North American regions.
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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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".