The role of Rossby wave dynamics in spatially compounding heatwaves in mid-summer 2023
Bibliographic record
Abstract
Abstract In July 2023, a series of heat extremes hit the Northern Hemisphere, which posed threats to vulnerable populations and societal infrastructure in Eastern Canada, the Mediterranean, and Central Asia. However, whether these record-shattering extremes were connected to each other remains unknown. Here we identify a dynamical linkage behind the spatiotemporal compounding nature of heatwaves over those three regions. By investigating the 2023 case and conducting historical analysis, we show that the Northern Hemispheric concurrent heatwaves in July 2023 can be attributed to a recurrent wave-6 pattern. In particular, pre-existing warmth and drought over Eastern Canada in early-July intensified the wave-6 teleconnection; which then led to extreme heatwaves over the Mediterranean and Central Asia in mid-July 2023. Furthermore, we reveal that the wave train was generated by early-July convection over the subtropical western Pacific. This, combined with the lowest May snow cover over North America in the past 40 years helped to warm Eastern Canada. Multiple models from the Coupled Model Intercomparison Project 6 are able to simulate those compound extremes connected by the wave-6 pattern with a high inter-model agreement. Our research offers insights into record-breaking compounding heatwaves in disparate parts of world during the mid-summer of 2023, with implications for disaster decision-making and risk management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".