Regional Climate Responses in East Asia to the Black Carbon Aerosol Direct Effects from India and China in Summer
Bibliographic record
Abstract
Abstract Black carbon (BC) aerosol is a significant and short-lived climate forcing factor. Here, the direct effects of BC emissions from India (IDBC) and China (CNBC) are investigated in East Asia during summer using the state-of-the-art regional climate model RegCM4. In summer, IDBC and CNBC account for approximately 30% and 46% of the total BC emissions in Asia, respectively. The total BC column burden from the two countries and corresponding TOA effective radiative forcing are 1.58 mg m−2and +1.87 W m−2in East Asia, respectively. The regional air temperature increases over 0.3 K at maximum and precipitation decreases 0.028 mm day−1on average. Individually, IDBC and CNBC each can bring about rather different effects on regional climate. IDBC can result in a cooling perturbation accompanied by a substantially increased cloud amount and scattering aerosol loading, resulting in a complex response in the regional precipitation, while CNBC can lead to regional warming, and further induce a local flood in northern China or drought in southern China depending on the opposite but significant circulation anomalies. CNBC plays a dominant role in modulating the regional climate over East Asia due to its higher magnitude, wider coverage, and stronger climate feedback. The direct effect of the total BC from both countries is not a linear combination of that of IDBC and CNBC individually, suggesting that the regional climate responses are highly nonlinear to the emission intensity or aerosol loading, which may be greatly related to the influences of the perturbed atmospheric circulations and climate feedback.
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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 | 0.000 |
| 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) | 0.001 | 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".