The 2021 Western North American Heatwave and Its Subseasonal Predictions
Bibliographic record
Abstract
Abstract Record‐breaking above‐normal temperatures were observed across western North America in June–July 2021. In this study, our ability to predict this heatwave 2–3 weeks in advance is assessed based on 10 Subseasonal‐to‐Seasonal prediction models. It is found that the above normal temperature in Western Canada during June 28–July 4 was predicted by most models as early as June 10. However, for the forecasts initialized earlier than June 17, not a single ensemble member of all the models is able to capture the magnitude of the observed temperature anomaly. We identify two important processes: an upper tropospheric wave train associated with the boreal summer intraseasonal oscillation in Southeast Asia and an anomalous North Pacific atmospheric river leading to high moisture conditions. Most models are able to predict the wave train across the North Pacific. A realistic representation of moisture transport and its pattern appears crucial for the extended‐range forecast of this heatwave.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".