Concurrent Heatwaves and Droughts in Canada: Spatio‐Temporal Changes, Climate Drivers, and Persistence Properties
Bibliographic record
Abstract
Abstract Extreme weather events that occur concurrently are especially damaging to society, agriculture, the economy, and ecosystems. Here, we investigate the spatial distribution, trends, persistence properties, and temporal shifts of concurrent heatwaves and droughts (CHWDs) across Canada from 1979 to 2018. Our results indicate that the regions of British Columbia and the Prairies are more susceptible to a high number of CHWDs and the Arctic region is affected by less frequent but more intense CHWDs (on average 44 and 25 events respectively). The Arctic regions also have the highest increasing trend of CHWDs due to the higher trends of temperature as compared to other regions. We also explore the relationship of CHWDs with large‐scale climate drivers. The North Atlantic Oscillation has the most influence on the CHWDs affecting the coastal regions and the Arctic. EP‐NP and WP also show a correlation with the CHWD events occurring in central Canada. A relatively high persistence in northeast Canada, coupled with the increasing trend of the total duration of CHWDs, highlights the increasing risk of CHWDs. We note that the timing of CHWDs shifts toward early summer in parts of the Yukon and Northwest Territories and toward late summer in the Canadian Arctic Archipelago. The changes in the number of concurrent events, their total duration, and temporal shifts of occurrence should be incorporated into adaptation and mitigation policies. The rapid variability and inconsistency of CHWDs across Canada emphasize the critical need for region‐specific hazard assessments that incorporate these concurrent extreme events.
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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 | 0.004 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".