Weather whiplash: Trends in rapid temperature changes in a warming climate
Bibliographic record
Abstract
Abstract Both research and media attention has shown an increasing interest in rapidly changing weather, colloquially termed “weather whiplash” events. This research examines the spatial and seasonal variability of trends in seasonally standardized short‐term temperature ranges across the globe. Trends are calculated for three different “range windows”: 7‐day ranges, 1‐day departure, and diurnal (24‐hr) temperature ranges. Results show that globally, over the 70‐year period of record 7‐ and 1‐day ranges are increasing substantially in all seasons, while diurnal trends are only changing (decreasing) significantly in boreal autumn. Since 1985, however, ranges at all three time windows have increased significantly. The most widespread changes are occurring as significant increases in these ranges in the Southern Ocean, Africa, and South America and in regions of coastal upwelling. Significant decreases in these ranges are noted mostly at the Arctic Ocean confluence with the Pacific and Atlantic Oceans, especially in the Greenland, Iceland, and Norwegian Seas, and more recently, in northeastern Canada. Oceanic trends appear driven by changes in wind speeds, especially in the Southern Hemisphere where increasing open‐ocean winds are nearly ubiquitous. Trends in temperature variability over land are largely inverse of the long‐term changes in cloud cover. This research adds to a growing body of climate change literature examining temperature variability trends, and it represents the first examination of full 70‐year trends of many variables contained in the recently released ERA5 reanalysis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".