A diagnostic intercomparison of modeled ozone dry deposition over North America and Europe using AQMEII4 regional-scale simulations
Bibliographic record
Abstract
This study analyzes ozone (O3) dry deposition fluxes and velocities (Vd) from twelve regional-scale simulations that were performed over North America and Europe in Phase 4 of the Air Quality Model Evaluation International Initiative (AQMEII4). AQMEII4 collected grid-aggregated and land use (LU)-specific O3 Vd and effective conductances and fluxes for the four major dry deposition pathways. Consistent with recent findings in the AQMEII4 point model intercomparison study, analysis of the grid-aggregated fields shows that grid models with similar Vd can exhibit significant differences in the absolute and relative contributions of the different depositional pathways. Analysis of LU-specific Vd and effective conductances reveals a general increase in model spread compared to grid-aggregated values. This indicates that an analysis of only grid-aggregated deposition diagnostics can mask process-specific differences that exist between schemes. An analysis of AQMEII4 LU distributions across models revealed substantial differences in the spatial patterns and abundance of certain LU categories over both domains, especially for non-forest partially vegetated categories such as agricultural areas, shrubland, and grassland. We demonstrate that these differences can significantly contribute to or even drive differences in LU-specific dry deposition fluxes which can increase the variability in model predictions of ozone. Two recommendations for future deposition-focused modeling studies emerging from the AQMEII4 analyses presented here are to 1) routinely generate diagnostic outputs to advance a process-based understanding of modeled deposition and support impact analyses, and 2) recognize the importance of documenting and analyzing the representation of LU across models and work towards harmonizing this aspect when using air-quality grid models and model ensembles for deposition analyses.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.001 | 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".