Anthropogenic influence on altitudinally amplified temperature change in the Tibetan Plateau
Bibliographic record
Abstract
Abstract As the highest plateau on the Earth, the Tibetan Plateau (TP) has experienced rapid warming in the last decades, affecting natural ecosystem and water resources extending far beyond the plateau itself. A distinctive characteristic known as elevation-dependent warming (EDW) in the high mountain regions was particularly pronounced in the TP, whereby the magnitude of temperature warming was amplified with increasing altitudes. Different mechanisms have been proposed to explain this phenomenon, however, the link between the root cause of warming, human activities, and the EDW remains poorly understood. Here we used the homogenized observation and simulations by the newest climate models to discern human influence on both mean and extreme temperatures within the region. An optimal fingerprinting method was applied in a vertical space rather than in traditional horizontal space. We found that the long-term trends in mean and extreme temperature amplified with increasing elevation, with larger magnitude of trends at higher elevations. The response to external forcing, primarily driven by human activities, was robustly detected in altitudinal amplification of temperature increase, providing clear evidence of human causes of EDW. As warming increases, the EDW in the region will continue, with more pronounced EDW corresponding to larger magnitude of warming under a high emission scenario. These findings mark the first evidence of human influence on temperature across different vertical altitudes of climate system.
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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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; both teacher heads agree on what is shown here.
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".