Uncertainties of simulated aerosol optical properties induced by assumptions on aerosol physical and chemical properties: An AQMEII-2 perspective
Bibliographic record
Abstract
The calculation of aerosol optical properties from aerosol mass is a process subject to uncertainty related to necessary assumptions on the treatment of the chemical species mixing state, density, refractive index, and hygroscopic growth. In the framework of the AQMEII-2 model intercomparison, we used the bulk mass profiles of aerosol chemical species sampled over the locations of AERONET stations across Europe and North America to calculate the aerosol optical properties under a range of common assumptions for all models. Several simulations with parameters perturbed within a range of observed values are carried out for July 2010 and compared in order to infer the assumptions that have the largest impact on the calculated aerosol optical properties. We calculate that the most important factor of uncertainty is the assumption about the mixing state, for which we estimate an uncertainty of 30–35% on the simulated aerosol optical depth (AOD) and single scattering albedo (SSA). The choice of the core composition in the core–shell representation is of minor importance for calculation of AOD, while it is critical for the SSA. The uncertainty introduced by the choice of mixing state choice on the calculation of the asymmetry parameter is the order of 10%. Other factors of uncertainty tested here have a maximum average impact of 10% each on calculated AOD, and an impact of a few percent on SSA and g. It is thus recommended to focus further research on a more accurate representation of the aerosol mixing state in models, in order to have a less uncertain simulation of the related optical properties.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".