Conditional attribution of cold extremes in Canada: The role of atmospheric circulation in a changing climate
Bibliographic record
Abstract
This study examines the influence of large-scale atmospheric circulation patterns, specifically the Pacific-North American (PNA), Asian-Bering-North American (ABNA), and North Atlantic Oscillation (NAO) climate indices, on the likelihood of cold extremes across Canada, utilizing ERA5 data and CMIP6 model simulations. The analysis reveals that PNA is closely linked to cold anomalies in western Canada, ABNA influences the west and central regions, and NAO impacts eastern Canada. A decrease in the likelihood of cold extremes is attributed to human-induced climate change, using both unconditional event attribution and event attribution conditioned on the observed circulation patterns. Under similar atmospheric circulation patterns as observed, human-induced climate change reduced the likelihood of recent cold extremes by 3 to 10 times across Canadian regions in the current climate compared to the pre-industrial climate. Under both the current and pre-industrial climates, negative PNA/ABNA phases and positive NAO phases can increase the likelihood of regular cold extremes, with synergies between indices significantly amplifying risks. Conversely, the opposite phases can reinforce the climate signal, further reducing the probability of cold extremes. These findings highlight the critical role of natural variability in cold extreme dynamics, offering valuable insights for improved climate prediction, attribution, and regional adaptation strategies in Canada.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".