The record-shattering 2021 Pacific Northwest heatwave – challenges and opportunities for attribution and event storylines
Bibliographic record
Abstract
Parts of western North America experienced a heatwave in late June 2021that many would have conceived impossible based on observations so far. In Lytton, Canada, temperatures peaked at 49.6°C, and the area-average daily maximum temperature record across the Pacific Northwest was broken by nearly 5°C. Given the exceptional intensity of the eventsome media outlets and scientists raised the questions whether heat extremes intensify faster than previously projected based on climate models, or whether current generations of climate models miss crucial processes and are thus unable to even reproduce such an event.. Here I address these questions and highlight some of the challenges for widely methods in model evaluation and attribution.First, I review some of the recent literature detailing the key physical mechanisms driving the Pacific Northwest heatwave. I address some of the key scientific challenges regarding the quantification of return periods, event attribution, model evaluation and near-term projections. Widely used methods estimating stationary return periods based on the observational record up to the year before imply that such an event had an infinite return period, i.e., that it would never happen. Even when taking into account the non-stationarity of a warming climate, the exceedance probability would be zeroor nearly zero depending on the estimation of the confidence intervals, the duration of the event and whether the event itself is included in the fit. I discuss some potential ways forward in addressing the above challenges and in quantifying the potential intensity of record-shattering events in the near future.
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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 | 0.032 | 0.139 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".