Rapid flips between warm and cold extremes in a warming world
Bibliographic record
Abstract
Rapid temperature flips are sudden shifts from extreme warm to cold or vice versa–both challenge humans and ecosystems by leaving a very short time to mitigate two contrasting extremes, but are yet to be understood. Here, we provide a global assessment of rapid temperature flips from 1961 to 2100. Warm-to-cold flips favorably follow wetter and cloudier conditions, while cold-to-warm flips exhibit an opposite feature. Of the global areas defined by the Intergovernmental Panel on Climate Change, over 60% have experienced more frequent, intense, and rapid flips since 1961, and this trend will expand to most areas in the future. During 2071–2100 under SSP5-8.5, we detect increases of 6.73–8.03% in flip frequency (relative to 1961–1990), 7.16–7.32% increases in intensity, and 2.47–3.24% decreases in transition duration. Global population exposure will increase over onefold, which is exacerbated in low-income countries (4.08–6.49 times above the global average). Our findings underscore the urgency to understand and mitigate the accelerating hazard flips under global warming. Rapid temperature flips between hot and cold extremes will become more frequent, more intense, and more rapid globally by the end of the twenty-first century, which is exacerbated in world’s breadbasket regions and low-income countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".