Upper Troposphere Warming Amplification over the Tibetan Plateau
Bibliographic record
Abstract
Abstract The Tibetan Plateau (TP) has experienced significant warming since 1980, with greater surface warming than the global average. While previous studies have focused primarily on surface temperature, we identified significant warming amplification in the upper troposphere over the TP, centered around 250 hPa, with a warming rate of approximately 0.31 K decade −1 , faster than the rates observed at comparable latitudes. Using fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5), Japanese 55-year Reanalysis (JRA-55), and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis datasets and the energy budget analysis method, we attribute this warming amplification to various physical processes, with convection contributing about 0.24 K decade −1 and clouds adding about 0.13 K decade −1 . In contrast, water vapor and dynamical processes exert a substantial cooling effect that partially offsets the warming. Upper-tropospheric warming is evident in all four seasons, contributing to the overall annual-mean warming trend, with the greatest contribution occurring in autumn, reaching 0.37 K decade −1 , and the smallest in winter at 0.25 K decade −1 . Although the warming magnitudes across the four seasons are similar, the underlying mechanisms differ. In spring and summer, convection is the primary driver, while in autumn and winter, dynamical processes contribute most. Despite differences in the specific values of each contributing factor, all three datasets consistently show that convection plays a significant role in shaping the temperature patterns over the TP. Improving convection simulation in models is crucial for producing more accurate projections of future temperature trends in this region.
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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 | 0.000 | 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 teacher head, 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".