Global modeling of multicomponent aerosol species: Aerosol optical parameters
Bibliographic record
Abstract
Canadian Aerosol Module (CAM) has been developed to simulate the atmospheric cycling of principal aerosol species, with the Third Generation Canadian Climate Center General Circulation Model (CCC GCM III) as its climatological driver. Adding an aerosol optical module to this modeling framework, the optical parameters of aerosols are simulated and compared to Sun photometer (AERONET) and satellite (MODIS) observations, as well as lidar observations of aerosol vertical profiles. The model captures well the global distribution and seasonal variation of aerosol optical parameters, showing seasonal maxima of optical depth and absorption over desert and biomass‐burning regions. For most sites and months, the modeled optical depths are within MODIS and AERONET means and standard deviations, and modeled single‐scattering albedo (SSA) and asymmetry factor are within 10% of AERONET retrievals. Fairly good agreement is found between modeled and observed optical depths over tropical oceans, where most models show significant underestimation. The model's overestimation of observed optical depths over parts of Europe by about 0.1 is most likely due to overestimates by older emission inventories. Modeled optical depths and SSA above observed means and standard deviations over areas and at sites within Central Africa and Central and South America suggest overprediction of organic carbon contribution by the model. Common to other models, however, our simulations underestimate the strength of the tropical biomass burning season. Modeled aerosol vertical profiles show better agreement with lidar observations at European sites than at an East Asian site, and more so at upper than at lower altitudes.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".