GEM-AQ/EC, an on-line global multi-scale chemical weather modelling system: model development and evaluation of global aerosol climatology
Bibliographic record
Abstract
Abstract. A global air quality modeling system GEM-AQ/EC was developed by implementing tropospheric chemistry and aerosol processes on-line into the Global Environmental Multiscale weather prediction model – GEM. Due to the multi-scale features of the GEM, the integrated model, GEM-AQ/EC, is able to investigate chemical weather at scales from global to urban domains. The current chemical mechanism is comprised of 50 gas-phase species, 116 chemical and 19 photolysis reactions, and is complemented by a sectional aerosol module CAM (The Canadian Aerosol Module) with 5 aerosols types: sulphate, black carbon, organic carbon, sea-salt and soil dust. Monthly emission inventories of black carbon and organic carbon from boreal and temperate vegetation fires were assembled using the most reliable areas burned datasets by countries, from statistical databases and derived from remote sensing products of 1995–2004. The model was run for ten years from from 1995–2004 with re-analyzed meteorology on a global uniform 1° × 1° horizontal resolution domain and 28 hybrid levels extending up to 10 hPa. The simulating results were compared with various observations including surface network around the globe and satellite data. Regional features of global aerosols are reasonably captured including emission, surface concentrations and aerosol optical depth. For various types of aerosols, satisfactory correlations were achieved between modeled and observed with some degree of systematic bias possibly due to large uncertainties in the emissions used in this study. A global distribution of natural aerosol contributions to the total aerosols is obtained and compared with observations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 0.001 |
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".