Evaluation of and updates to the oxidized reactive nitrogen gaseous dry-deposition parameterization from the GEOS-Chem model, including a pathway for ground surface NO <sub>2</sub> hydrolysis
Bibliographic record
Abstract
Dry deposition is a major loss pathway for reactive nitrogen species from the atmospheric boundary layer. We evaluate isolated components of the parameterization for species-specific gaseous dry-deposition velocity V d ( x ) for HNO 3 and NO 2 from the GEOS-Chem chemical transport model by running a stand-alone version of V d code in single-point mode to enable a more direct comparison to field observations. Improved measurement–model agreement results mainly from (i) updates to the calculation of molecular diffusivities and (ii) the representation of ground surface NO 2 hydrolysis in the formulation of non-stomatal uptake. We evaluate the parameterization for non-stomatal dry deposition of NO 2 by comparing to eddy-covariance-inferred nocturnal V d (NO 2 ) over Harvard Forest. We address a large low bias (−80 %) in simulated nocturnal V d (NO 2 ) by representing NO 2 heterogeneous hydrolysis on deposition surfaces, paying attention to chemical flux divergence, soil NO emission, and canopy surface area effects. Finally, we evaluate the updated oxidized reactive nitrogen (NO y ) dry-deposition parameterization by comparing to eddy-covariance-inferred V d (NO y ) over Harvard Forest, finding that a modest nocturnal low bias (−19 %) remains in simulated V d (NO y ) due to the compensating effects of updates to the calculation of molecular diffusivities (28 % reduction in nocturnal V d (NO y )) and the representation of NO 2 heterogeneous hydrolysis (25 % increase in nocturnal V d (NO y )). These developments are a first step towards a tractable representation of NO 2 hydrolysis in a dry-deposition scheme and have important implications for the near-surface NO 2 lifetime through a mechanism involving HONO emission.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".