On Effective Radiative Forcing of Partial Internally and Externally Mixed Aerosols and Their Effects on Global Climate
Bibliographic record
Abstract
Abstract The total effective radiative forcing (ERF) due to partial internally mixed (PIM) and externally mixed (EM) anthropogenic aerosols, as well as their climatic effects since the year of 1850, was evaluated and compared using the aerosol‐climate online coupled model of BCC_AGCM2.0_CUACE/Aero. The influences of internal mixing (IM) on aerosol hygroscopicity parameter, optical properties, and concentration were considered. Generally, IM could markedly weaken the negative ERF and cooling effects of anthropogenic aerosols. The global annual mean ERF of EM anthropogenic aerosols from 1850 to 2010 was −1.87 W m−2, of which the aerosol‐radiation interactive ERF (ERFari) and aerosol‐cloud interactive ERF (ERFaci) were −0.49 and −1.38 W m−2, respectively. The global annual mean ERF due to PIM anthropogenic aerosols from 1850 to 2010 was −1.23 W m−2, with ERFari and ERFaci of −0.23 and −1.01 W m−2, respectively. The global annual mean surface temperature and water evaporation and precipitation were reduced by 1.74 K and 0.14 mm d−1 for EM scheme and 1.28 K and 0.11 mm d−1 for PIM scheme, respectively. However, the relative humidity near the surface was slightly increased for both mixing cases. The Intertropical Convergence Zone was southwardly shifted for both EM and PIM cases but was less southwardly shifted in PIM scheme due to the less reduction in atmospheric temperature in the midlatitude and low latitude of the Northern Hemisphere.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".