Interpretation of measured aerosol mass scattering efficiency over North America using a chemical transport model
Bibliographic record
Abstract
Aerosol mass scattering efficiency affects climate forcing calculations, atmospheric visibility, and the interpretation of satellite observations of aerosol optical depth. We evaluated the representation of aerosol mass scattering efficiency ( α sp ) in the GEOS-Chem chemical transport model over North America using collocated measurements of aerosol scatter and mass from IMPROVE network sites between 2000 and 2010. We found a positive bias in mass scattering efficiency given current assumptions of aerosol size distributions and particle hygroscopicity in the model. We found that overestimation of mass scattering efficiency was most significant in dry (RH < 35 %) and midrange humidity (35 % < RH < 65 %) conditions, with biases of 82 % and 40 %, respectively. To address these biases, we investigated assumptions surrounding the two largest contributors to fine aerosol mass, organic (OA) and secondary inorganic aerosols (SIA). Inhibiting hygroscopic growth of SIA below 35 % RH and decreasing the dry geometric mean radius, from 0.069 µm for SIA and 0.073 µm for OA to 0.058 µm for both aerosol types, significantly decreased the overall bias observed at IMPROVE sites in dry conditions from 82 % to 9 %. Implementation of a widely used alternative representation of hygroscopic growth following κ -Kohler theory for secondary inorganic (hygroscopicity parameter κ =0.61) and organic ( κ =0.10) aerosols eliminated the remaining overall bias in α sp . Incorporating these changes in aerosol size and hygroscopicity into the GEOS-Chem model resulted in an increase of 16 % in simulated annual average α sp over North America, with larger increases of 25 % to 45 % in northern regions with high RH and hygroscopic aerosol fractions, and decreases in α sp up to 15 % in the southwestern U.S. where RH is low.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".