Trends in weather type frequencies across North America
Bibliographic record
Abstract
Abstract While 20th century changes in many individual meteorological variables are well documented, the trends in multivariate synoptic-scale air masses—or weather types—largely remain unexplored. Utilizing a recently developed gridded weather typing classification system, this research investigates the changes in the frequency of weather types (WTs) across North America, 1979–2017. Averaged across the study domain as a whole, Humid Warm WTs are occurring 22 more days per year, while Dry Warm WTs have increased by 10 days/year. These increases are offset mostly by decreased frequency of Dry Cool (−17 days/year) and Cool WTs (−21 days/year). The largest absolute changes are in the Canadian Archipelago, where the Warm WT is occurring 42 more days/year and the Cool WT is occurring 48 fewer times per year. In western Canada all humid types are occurring more frequently, including a Humid Cool type that is occurring 16 more days/year. The Desert Southwest US and northern Mexico show significant increases in Dry Warm WTs (+33 to +40 days/year). Cold front and warm front passages show increases in most of the US and decreases in most of Canada. Describing these secular changes to the frequency of intuitive weather types may be an effective means of communicating these climate trends to policymakers and the general public, especially considering their large magnitude.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".