Mixing state of black carbon and its impact on optical properties and radiative forcing over East Asia
Bibliographic record
Abstract
Abstract. BC mixing state, one of essential aerosol microphysical properties modulating its optical properties and radiative forcing, and climatic and environmental effect, has often been assumed in numerical models previously. In this study, by employing a nested version of GEOS-Chem-APM model with predicted BC mixing state, we carefully examined the effect of BC mixing state on aerosol optical properties, radiative forcing, and heating rate over East Asia (EA) and particularly in East-Central China (ECC). The modelling results show that the mass of both BC core and coating by secondary species (coated SS) are significantly high in ECC and India due to strong anthropogenic emissions but quite low in the other regions. The ratio of total mass (BC + coated SS) to the BC over ECC can be doubled compared to pure BC core mass, indicating quite large coating over the regions with high anthropogenic emissions. Absorptive aerosol optical depth (AAOD) enhances ~ 40 % over ECC once core-shell mixing, rather than external mixing, is taken into account, despite AOD only changes slightly, and the resulting absorption amplification (Aa) normally varies from 1.1 to 1.8 over ECC. Aerosol direct radiative forcing (DRF) under clear sky and all sky indicate that DRF at top of atmosphere (DRFTOA) become weaker but DRF at surface (DRFSRF) become stronger when core-shell mixing, instead of external mixing, is taken in to account in the simulation. The simulation with core-shell mixing presented that over ECC, the regional-averaged atmospheric absorption enhances 39 % while DRFTOA and DRFSRF are reported as −8.3 and −17.8 W m-2, compared to −9.4 and −16.2 W m-2 in the simulation with external mixing state. The heating rate in entire column enhances with core-shell mixing state due to absorption amplification, compared to external mixing state. The heating rate at surface over ECC increases about 43 %, i.e. increase from 0.7 K day-1 (external) to 0.9 K day-1 (core-shell) during the study period. The results in three cities (Beijing, Nanjing, Chengdu), representative of North China Plain (NCP), Yangtze River Delta (YRD), and Sichuan Basin (SCB), major anthropogenic aerosol emissions regions over EA, are also discussed in the study.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".