The effect of model spatial resolution on Secondary Organic Aerosol predictions: a case study at Whistler, BC, Canada
Bibliographic record
Abstract
Abstract. A large fraction of submicron aerosol mass throughout the continental boundary layer consists of secondary organic aerosol (SOA) mass. As such, the ability of chemical transport models to accurately predict continental boundary layer aerosol greatly depends on their ability to predict SOA. Although there has been much recent effort to better describe SOA formation mechanisms in models, little attention has been paid to the effects of model spatial resolution on SOA predictions. The Whistler Aerosol and Cloud Study (WACS 2010), held between 22 June and 28 July 2010 and conducted at Whistler, BC, Canada provides a unique data set for testing simulated SOA predictions. The study consisted of intensive measurements of atmospheric trace gases and particles at several locations strongly influenced by biogenic sources in the region. We test the ability of the global chemical transport model GEOS-Chem to predict the aerosol concentrations during this event and throughout the campaign. Simulations were performed using three different resolutions of the model: 4° × 5° , 2° × 2.5° and 0.5° × 0.667°. Predictions of organic aerosol concentrations at Whistler were greatly dependent on the resolution; the 4° × 5° version of the model significantly under predicts organic aerosol, while the 2° × 2.5° and 0.5° × 0.667° versions are much more closely correlated with measurements. In addition, we performed a comparison between the 3 versions of the model across North America. Comparison simulations were run for both a summer case (July) and Winter case (January). For the summer case, 0.5° × 0.667° simulations predicted on average 19% more SOA than 2° × 2.5° and 32% more than 4° × 5° . For the winter case, the 0.5° × 0.667° simulations predicted 8% more SOA than the 2° × 2.5° and 23% more than the 4° × 5°. This increase in SOA with resolution is largely due to sub-grid variability of organic aerosol (OA) that leads to an increase in the partitioning of secondary organic matter to the aerosol phase at higher resolutions. SOA concentrations were further increased because the shift of secondary organic gases to SOA at higher resolutions increased the lifetime of secondary organic matter (secondary organic gases have a shorter deposition lifetime than SOA in the model). SOA precursor emissions also have smaller, but non-negligible, changes with resolution due to non-linear inputs to the MEGAN biogenic emissions scheme. These results suggest that a portion of the traditional under-prediction of SOA by global models may be due to the effects of coarse grid resolution.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".