On the mean precipitation characteristics of North American heatwaves
Bibliographic record
Abstract
Precipitation—or a lack thereof—can be both cause and consequence of heatwaves. Whereas most heatwave-precipitation research has emphasized summer heatwaves and extreme precipitation, heatwaves outside of summer are relatively more important for driving streamflow. Further, there is increasing recognition of how heatwaves impact river basin hydrology via their influence on the cryosphere, but fewer studies have considered the hydrological impacts of heatwaves via precipitation. Here, we aim to link and address these knowledge gaps by offering an analysis of a set of seasonally- and spatially-varying precipitation characteristics of heatwaves. We consider: (1) how the frequency of heatwave precipitation varies by precipitation intensity; (2) how the frequency and magnitude of precipitation differs between heatwave and non-heatwave periods; (3) how the frequency and magnitude of precipitation varies throughout a heatwave; and (4) how precipitation varies between periods before, during, and after heatwaves. We assess these characteristics for 14 425 basins across North America for winter, spring, summer, and autumn heatwaves. We find that there is a high degree of spatial and seasonal variability in all characteristics assessed. Overall, heatwaves are wetter in autumn and winter but are drier in spring and summer. We find that there are drier conditions overall in the continental interior and wetter conditions overall along the northwest and east coasts. Precipitation is generally greater and more frequent on the final day of heatwaves relative to other days, except for the west and east coast regions in winter. For those cases, heatwaves are generally wetter than periods that occur immediately before and after, indicating strong links between precipitation and relative warmth. These findings have implications for the processes that drive streamflow during heatwaves, and offer insights into the role that extreme temperature events play in modifying river basin hydrology.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".