Interactions Between Anthropogenic Greenhouse‐Gas and Aerosol Emissions Will Shape Extreme Precipitations Over the Qinghai‐Tibet Plateau
Bibliographic record
Abstract
Abstract Attributing intensification extreme precipitation to anthropogenic factors on the regional scale is challenging, given the large fluctuations and the complexity of quantifying interactions among these anthropogenic factors. Here, we propose a new variance‐based method to investigate the roles of human‐induced greenhouse gas (GHG), aerosol (AER), and their interactions (GA) in shaping extreme precipitation on the Qinghai‐Tibet Plateau (QTP) at stational scale. In terms of contribution, GHG has the greatest impact on total wet‐day precipitation and simple daily intensity. In terms of significance, GA, and AER exert significant effects on all 10 extreme indices (P; < 0.05) over 48.3% and 44.8% of all stations, while GHG affects less (25.9%). Overall, GHG is not the only dominant factor, and GA and AER are expected to play vital roles in intensifying extreme precipitation over the QTP under SSP2‐4.5. These findings challenge the conventional insights that GHG is the primary anthropogenic driver of extreme precipitation.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".