Attributing observed increase in extreme precipitation in China to human influence
Bibliographic record
Abstract
Abstract This paper examines new evidence from observational and detection and attribution studies of changes in extreme precipitation in China since the early 1960s. We have also designed a series of sensitivity tests to explore the robustness of detection and attribution results to the differences in sample size, in extreme precipitation index, and in data processing procedure. Our analyses used the most recent update of observational records as well as simulations conducted with the climate models participated in the Coupled Model Intercomparison Project Phase 6. Based on the existing studies and our additional analyses, we found that human influence is detectable in extreme precipitation in China regardless of the period, extreme precipitation index, or data treatment considered, in both China as a whole and in northern and southern China separately. We also found, as is often encountered in detection and attribution studies, it is difficult to separate the contribution from anthropogenic forcing from that of natural external forcing, and it is also challenging to decompose the anthropogenic component into a greenhouse gas forcing component and a component that reflects other anthropogenic forcing agents (dominantly, aerosols).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".