Determining the Anthropogenic Greenhouse Gas Contribution to the Observed Intensification of Extreme Precipitation
Bibliographic record
Abstract
Abstract This study conducts a detection and attribution analysis of the observed changes in extreme precipitation during 1951–2015. Observed and CMIP6 multimodel simulated changes in annual maximum daily and consecutive 5‐day precipitation are compared using an optimal fingerprinting technique for different spatial scales from global land, Northern Hemisphere extratropics, tropics, three continental regions (North America and western and eastern Eurasia), and global “dry” and “wet” land areas (as defined by their average extreme precipitation intensities). Results indicate that anthropogenic greenhouse gas influence is robustly detected in the observed intensification of extreme precipitation over the global land and most of the subregions considered, all with clear separation from natural and anthropogenic aerosol forcings. Also, the human‐induced greenhouse gas increases are found to be a dominant contributor to the observed increase in extreme precipitation intensity, which largely follows the increased moisture availability under global warming.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".