2021 North American Heat Wave Fueled by Climate-Change-Driven Nonlinear Interactions
Bibliographic record
Abstract
Extreme heat conditions in the Pacific Northwest US and Southwestern Canada in summer 2021 were of unprecedented severity. Constituting a 5-sigma anomaly, the heatwave affected millions, likely led to thousands of excess deaths, and promoted wildfires that decreased air quality throughout the continent. Even as global warming causes an increase in the severity and frequency of heatwaves both locally and globally, this event’s magnitude went beyond what many would have considered plausible under current climate conditions. It is thus important to attribute such an exceptional event to specific physical drivers and assess its relation to climate change, to improve projection and prediction of future extreme heat events. A particularly pressing question is whether any changing variability of atmospheric dynamics or land-atmosphere interaction is implicated in amplifying current and future heat extremes. Using ERA5 reanalysis, we find that slow- and fast-moving components of the atmospheric circulation interacted to trigger extreme geopotential height anomalies during this event. We additionally identify anomalously low soil moisture levels as a critical event driver: we find that land-atmosphere feedbacks drove nonlinear amplification of its temperature anomaly by 40% (contributing 3K of the 10K peak regional-mean anomaly), catalyzed by multidecadal temperature and soil moisture trends. This is supported by a model experiment demonstrating that soil moisture interaction may increase the likelihood of the observed monthly-scale regional temperature anomaly by O(10)x. We estimate that over the four recent decades of gradual warming, the event’s temperature anomaly has become 10–100 times more likely, transforming from a ~10,000-year to a 100–1,000-year occurrence. Its likelihood continues to increase, roughly exponentially, and it is projected to recur ~20-yearly by 2060 based on continued warming at a constant rate. Our results therefore suggest an important role of atmospheric dynamics and nonlinear land-atmosphere interactions in driving this exceptional heat extreme, promoted by a long-term warming trend due to anthropogenic climate change that will continue to increase the likelihood of such extremes under continued emissions.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".