Greater than averages: how metrics of extreme weather are trending differently than averages would suggest
Bibliographic record
Abstract
Upon the backdrop of steadily rising global average temperatures, it is the extreme weather events that are arguably more important and impactful than changing averages – especially on human health. This research examines trends in North America of three different parameters of extreme temperature events important to human thermal comfort and public health: their frequency, duration, and spatial extent. Most of the changes are expected; that is, with warmer temperatures there are more frequent extreme heat events that are lasting longer and covering more area. However, we highlight some intriguing divergences from this pattern. For example, despite quickly rising autumn temperatures in northern Canada, a concurrent decrease in temperature variability is resulting in extreme heat events remaining stable and is instead manifest more as significant decreases in extreme cold events. In parts of the western US, even though there is no significant trend in autumn mean temperatures, there is a significant rise in extreme cold events. And, in the southern High Plains in summer, despite little trend in averages, a more negative skew to the distribution is nonetheless leading to significant increases in heat events. Seasonal and geographic variability in the trends of extreme dew point events is also explored. For example, increases in extreme humidity events are ubiquitous throughout most of Canada, particularly in summer; but the US has a northeast (increasing humid events) to southwest (increasing dry events) dichotomy that is strongest in winter. While such nuances might complicate our efforts to broadly generalize the message of climate change, these distinctions suggest a renewed emphasis on local- to regional-scale analyses (rather than larger scales) when providing actionable climate information for planners and policymakers.
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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 | 0.009 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.018 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".