Interaction between the Black Carbon Aerosol Warming Effect and East Asian Monsoon Using RegCM4
Bibliographic record
Abstract
Abstract Black carbon aerosol (BC) has a significant influence on regional climate changes because of its warming effect. Such changes will feed back to BC loadings. Here, the interactions between the BC warming effect and the East Asian monsoon (EAM) in both winter (EAWM) and summer (EASM) are investigated using a regional climate model, RegCM4, that essentially captures the EAM features and the BC variations in China. The seasonal mean BC optical depth is 0.021 over East Asia during winter, which is 10.5% higher than that during summer. Nevertheless, the BC direct radiative forcing is 32% stronger during summer (+1.85 W m−2). The BC direct effect would induce lower air to warm by 0.11–0.12 K, which causes a meridional circulation anomaly associated with a cyclone at 20°–30°N and southerly anomalies at 850 hPa over East Asia. Consequently, the EAM circulation is weakened during winter but enhanced during summer. Precipitation is likely increased, especially in southern China during summer (by 3.73%). Relative to BC changes that result from EAM interannual variations, BC changes from its warming effect are as important but are weaker. BC surface concentrations are decreased by 1%–3% during both winter and summer, whereas the columnar BC is increased in south China during winter. During the strongest monsoon years, the BC loadings are higher at lower latitudes than those during the weakest years, resulting in more southerly meridional circulation anomalies and BC feedbacks during both winter and summer. However, the interactions between the BC warming effect and EAWM/EASM are more intense during the weakest monsoon years.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".