Bibliographic record
Abstract
Abstract The Tibetan Plateau (TP) is the world's highest and largest plateau. By acting as an elevated thermal source as well as a topographic barrier, the TP has a profound impact on both local weather and global climate. The TP has recently been warming at a faster rate than the entire Northern Hemisphere. However, the lack of instrumental records prior to the 1950s limits our ability to place this recent warming in a longer‐term context. Here we show that over the plateau, because of its high elevation, the surface pressure is a proxy for surface air temperature and that since the 1870s there has been a statistically significant increase in surface pressure over the TP, that has undergone an acceleration since the 1980s. There is also a compensatory decrease in surface pressure in the surrounding lower elevation region. Furthermore, we show that since the 1870s the surface pressure over the plateau is correlated with the Northern Hemisphere temperature record at a statistical significance that exceeds the 99 percentile. Finally, the trend in surface pressure over the plateau is consistent with an increase in annual mean surface air temperature of ∼0.4°C since the 1870s and an increase of ∼1°C since the 1980s. The long‐term warming over the TP derived from the surface pressure trend is of the same order as the long‐term hemispheric warming. This suggests that the increased rate of warming recently observed over the plateau is not a long‐term phenomenon. Copyright © 2012 Royal Meteorological Society
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.002 | 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".