Increasing global precipitation whiplash due to anthropogenic greenhouse gas emissions
Bibliographic record
Abstract
Abstract Precipitation whiplash, including abrupt shifts between wet and dry extremes, can cause large adverse impacts on human and natural systems. Here we quantify observed and projected changes in characteristics of sub-seasonal precipitation whiplash and investigate the role of individual anthropogenic influences on these changes. Results show that the occurrence frequency of global precipitation whiplash is projected to be 2.56 ± 0.16 times higher than in 1979–2019 by the end of the 21 st Century, with increasingly rapid and intense transitions between two extremes. The most dramatic increases of whiplash show in the polar and monsoon regions. Changes in precipitation whiplash show a much higher percentage change than precipitation totals. In historical simulations, anthropogenic greenhouse gas (GHG) and aerosol emissions have increased and decreased precipitation whiplash occurrences, respectively. By 2079, anthropogenic GHGs are projected to increase 55 ± 4% of the occurrences risk of precipitation whiplash, which is driven by shifts in circulation patterns conducive to precipitation extremes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".