Detection and attribution of human influence on seasonal extreme precipitation in northern Hemisphere
Bibliographic record
Abstract
• Greenhouse gas forcings dominate the increase in extreme precipitation across the Northern Hemisphere land. • Anthropogenic forcings are detectable in at least one season in over 80% of CMIP6 domains. • Human-induced precipitation increases are more significant in fall and winter compared to spring and summer. • Natural external forcings should be included in seasonal precipitation analyses. Fingerprinting analysis have detected the impact of human activities on annual precipitation extremes. Using simulations of selected climate models of the Coupled Model Intercomparison Project Phase 6 (CMIP6), this study has comprehensively demonstrated human influence on seasonal precipitation extremes of Northern Hemisphere Land (NHL) over different spatial scales from 1950 to 2014. By assessing the impacts of various climatic forcings on the maximum 1-day (Rx1day) and 5-day (Rx5day) precipitation indices, our results show that greenhouse gas (GHG) forcings predominantly drive the increase in observed Rx1day across most of the NHL in all four seasons, with more pronounced effects in fall and winter than in spring and summer. Furthermore, low-risk regions tend to experience greater GHG-induced Rx1day than high-risk regions in all seasons. Anthropogenic aerosol (AER) forcings significantly weaken Rx1day, particularly during winter in regions like India and southern China. Change point analysis reveals a rapid increase in Rx1day under GHG forcings and a slower decrease under AER forcing since the 1980s. However, the abrupt change in Rx1day under both anthropogenic and natural external (ALL) forcings generally began a decade later, around the 1990s. Using optimal fingerprinting techniques, we demonstrate for the first time that discernible anthropogenic forcings (ANT) impact at least one season in over 80% of CMIP6 subregions, with more than 60% of the contributions being attributable to ANT forcings. The number of subregions with detected ANT forcings is twice as high in winter compared to summer. Although seasonal natural (NAT) forcings are undetectable in one-signal analysis, they are detected in similar regions in both two- and three-signal analysis, suggesting that observed changes should be attributed to both anthropogenic and natural forcings.
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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 | 0.001 |
| 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".