Human influence on historical heaviest precipitation events in the Yangtze River Valley
Bibliographic record
Abstract
Abstract With the recurrence of high-impact extreme events and the growing public demands to understand the causes of the events, event attribution has emerged as a frontier of climate change research. Typically, an event attribution study focuses on one individual extreme event that has just occurred. Studies rarely examine human influence on multiple extreme events in different times of the past. Here we conduct a comprehensive attribution analysis on the four heaviest precipitation events in the Yangtze River Valley during the past 100 years. We start by defining extreme precipitation events as the heaviest precipitation over a fixed size area that is of direct relevance to flood preparedness and management. When examining the events over the historical time, we allow the precise location of the area to change in different years. By definition, four extremely strong events are identified, and they happened in the summer of 1931, 1954, 1998 and 2020. We find that the impacts of greenhouse gases (GHGs) and anthropogenic aerosols (AAs) on these events show clear difference in different time period. The impacts were negligible in the early period and became more and more discernible since the late 20th century. The GHGs have gradually increased the occurrence probability of extreme precipitaiton while the AAs have decreased the occurrence of extrem precipitation. These competing effects from the GHGs and AAs have led to a slight and then gradually increasing human influence on extreme precipitation over time. GHGs have exerted a larger influence on short-duration precipitation events while AAs have had a larger influence on monthly mean precipitation. The more extreme the precipitation event, the clearer the anthropogenic influence.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.004 |
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".