Air pollution slows down surface warming over the Tibetan Plateau
Bibliographic record
Abstract
The Tibetan Plateau (TP) plays a vital role in regional and global climate change. The TP has been undergoing significant surface warming starting from 1850, with an air temperature increase of 1.39 K and surface solar dimming resulting from decreased incident solar radiation. The causes and impacts of solar dimming on surface warming are unclear. In this study, long-term (from 1850 to 2015) surface downward radiation datasets over the TP are developed by integrating 18 Coupled Model Intercomparison Project phase 5 (CMIP5) models and satellite products. The validation results from two ground measurement networks show that the generated downward surface radiation datasets have a higher accuracy than the mean of multiple CMIP5 datasets and the fused datasets of reanalysis and satellite products. After analyzing the generated radiation data with four air temperature datasets, we found that downward shortwave radiation (DSR) remained stable before 1950 and then declined rapidly at a rate of −0.53 W m −2 per decade, and that the fastest decrease in DSR occurs in the southeastern TP. Evidence from site measurements, satellite observations, reanalysis, and model simulations suggested that the TP solar dimming was primarily driven by increased anthropogenic aerosols. The TP solar dimming is stronger in summer, at the same time that the increasing magnitude of the surface air temperature is the smallest. The cooling effect of solar dimming offsets surface warming on the TP by 0.80±0.28 K (48.6±17.3 %) in summer since 1850. It helps us understand the role of anthropogenic aerosols in climate warming and highlights the need for additional studies to be conducted to quantify the influence of air pollution on regional climate change over the TP.
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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 | 0.000 | 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".