Comment on gmd-2021-172
Bibliographic record
Abstract
Abstract. The photolysis module in Environment and Climate Change Canada's online chemical transport model GEM-MACH (GEM: Global Environmental Multi-scale – MACH: Modelling Air quality and Chemistry) was improved to make use of the online size and composition-resolved representation of atmospheric aerosols and relative humidity in GEM-MACH, to account for aerosol attenuation of radiation in the photolysis calculation. We coupled both the GEM-MACH aerosol module and the MESSy-JVAL (Modular Earth Submodel System) photolysis module, through the use of the online aerosol modeled data and a new Mie lookup table for the model-generated extinction efficiency, absorption and scattering cross sections of each aerosol type. The new algorithm applies a lensing correction factor to the black carbon absorption efficiency (core-shell parameterization) and calculates the scattering and absorption optical depth and asymmetry factor of black carbon, sea salt, dust and other internally mixed components. We carried out a series of simulations with the improved version of MESSy-JVAL and wildfire emission inputs from the Canadian Forest Fire Emissions Prediction System (CFFEPS) for 2 months, compared the model aerosol optical depth (AOD) output to the previous version of MESSy-JVAL, satellite data, ground-based measurements and reanalysis products, and evaluated the effects of AOD calculations and the interactive aerosol feedback on the performance of the GEM-MACH model. The comparison of the improved version of MESSy-JVAL with the previous version showed significant improvements in the model performance with the implementation of the new photolysis module and with adopting the online interactive aerosol concentrations in GEM-MACH. Incorporating these changes to the model resulted in an increase in the correlation coefficient from 0.17 to 0.37 between the GEM-MACH model AOD 1-month hourly output and AERONET (Aerosol Robotic Network) measurements across all the North American sites. Comparisons of the updated model AOD with AERONET measurements for selected Canadian urban and industrial sites, specifically, showed better correlation coefficients for urban AERONET sites and for stations located further south in the domain for both simulation periods (June and January 2018). The predicted monthly averaged AOD using the improved photolysis module followed the spatial patterns of MERRA-2 reanalysis (Modern-Era Retrospective analysis for Research and Applications – version 2), with an overall underprediction of AOD over the common domain for both seasons. Our study also suggests that the domain-wide impacts of direct and indirect effect aerosol feedbacks on the photolysis rates from meteorological changes are considerably greater (3 to 4 times) than the direct aerosol optical effect on the photolysis rate calculations.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.013 | 0.008 |
| Insufficient payload (model declined to judge) | 0.243 | 0.176 |
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".