Regional surface temperature changes in China caused by reduced air pollution and halogenated greenhouse gases
Bibliographic record
Abstract
China's vast territory across a large latitude makes it an ideal country to investigate the mechanisms causing regional climate changes. Here, we showed that the temporal patterns in regional surface temperature are very different between low- and high latitude regions and between lightly and severely polluted regions, and that a reversal in surface temperature occurs earlier at higher-latitude regions. The latter is affected by recent drastic reductions in air pollution, which give rise to positive net radiative forcings that are the primary cause for China's regional temperature rises in the last decade. These regional climate patterns are in good agreement with both the cosmic-ray driven electron-induced reaction (CRE) theory of ozone depletion and the physics model of warming caused by halogen-containing greenhouse gases (halo-GHGs, mainly chlorofluorocarbons (CFCs)). Using the IPCC-given globally averaged radiative forcings of aerosols and ozone, our calculated results by the CFC-warming physics model showed good agreement with the observed regional surface temperature changes since 1990, giving correlation coefficients of 0.70–0.96. In lightly polluted regions, such as northeast and northwest China (Heilongjiang, Xinjiang and Inner Mongolia), Hainan and Guangdong, our calculations reproduced close observations, while underestimating temperatures in highly polluted regions such as Beijing (Hebei), Fujian, and Jiangsu. This discrepancy is explained by larger reductions in post-2013 air pollution, causing greater positive radiative forcings. Our results revealed the mechanisms for regional and global climate change.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".