Interactive Aerosol Feedbacks on Photolysis Rates in the GEM-MACH Air Quality Model for an Athabasca Oil-Sands Modelling Study
Bibliographic record
Abstract
The radiative transfer module of an on-line chemical transport models requires input data from aerosol extinction efficiency, single scatter albedo and asymmetry factor, in order to predict the radiative state of the atmosphere. These aerosol optical properties (aerosol optical depth, AOD), may be integrated vertically for comparison to satellite observations. These optical effects may also influence the shorter wavelengths associated with atmospheric gas photolysis, influencing atmospheric reactivity. These processes may be harmonized in an on-line reaction transport model, such as Environment and Climate Change Canada’s GEM-MACH (GEM: Global Environmental Multi-scale – MACH: Modelling Air quality and Chemistry). Previous photolysis routine in the radiative transfer module, MESSY-JVAL (Modular Earth Sub-Model System), in GEM-MACH, made use of a climatology of aerosol optical properties, and the previous on-line version made use of a homogeneous mixture Mie code for meteorological radiative transfer calculations. We calculated a new lookup table for the extinction efficiency, absorption and scattering cross sections of each aerosol type. The new version of MESSY-JVAL uses GEM-MACH predicted aerosol size distributions, chemical composition and relative humidity in each vertical column at each time step as input, reads aerosol absorption and scattering cross section data from the new lookup table and calculates aerosol optical properties, that are then used to modify both photolysis and meteorological radiative transfer calculations. In order to evaluate these modifications to the model, we performed a series of simulations with GEM-MACH with wildfire emissions inputs from the Canadian Forest Fire Emissions Prediction System (CFFEPS) and compared the model AOD output with satellite and AERONET (Aerosol Robotic Network) measurement data. Comparison of the hourly AERONET and monthly-averaged satellite AOD demonstrates major improvements in the revised model AOD predictions. The impact of the updated photolysis rates and meteorological radiative transfer calculations on predictions of oxidant mixing ratios and rates of pollutant oxidation (nitrogen dioxide conversion to nitric acid) will be assessed both within and below the forest fire plume.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 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".