Association of Climate-related Total Atmospheric Energy Anomalies in the Tibetan Plateau with Haze in Eastern China
Bibliographic record
Abstract
As climate warming is widespread over the world, the Tibetan Plateau is particularly sensitive to climate warming impacts owing to its high elevations and complex topography, receiving worldwide attention for its significant feedbacks through affecting the atmosphere circulation, and thus the Asia climate system even to global. In this study, the new metric “total atmospheric energy”, is considered to investigate the association of thermal anomalies on the Tibetan Plateau with haze events in China. The total atmospheric energy (TPE) of the Tibetan Plateau, including sensible heat energy, potential energy, kinetic energy, and latent heat energy, was calculated using US National Center for Environmental Prediction (NCEP) meteorological reanalysis data from 1980 to 2016 to characterize the atmospheric thermal forcing of the large topography of the Tibetan Plateau. Results show that TPE has displayed periods of increase levels in recent decades, and inter-annual variations of TPE are significantly positively correlated with winter haze days over northern China but are negatively associated with haze events in southern China. Further diagnostic analysis and simulated results using the Weather Research and Forecasting (WRF)-Chem model indicate that the anomalous increase of TPE leads to enhanced thermal stability of the lower atmosphere, a weakened East Asian winter monsoonal wind in northern China, and increased near-surface wind speed in southern China, which are conductive to positive PM2.5 anomalies in northern China and negative PM2.5 anomalies in southern China. This implies that meteorological changes induced by TPE anomalies may play an important role in wintertime haze pollution over different regions of eastern China. Understanding the climate-related TPE modulating the favourable meteorological conditions for winter haze pollution over Eastern China could contribute to long-term planning for air pollution control.
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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 | 0.002 | 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 | 0.000 | 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".