Evaluation and updates to the oxidized reactive nitrogen trace gas dry deposition parameterization from the GEOS-Chem CTM, including a pathway for ground surface NO <sub>2</sub> hydrolysis
Bibliographic record
Abstract
Abstract. Dry deposition is a major loss pathway for reactive nitrogen species from the atmospheric boundary layer. Represented in chemical transport models (CTMs) as a first-order process, time-varying rate coefficients are parameterized and expressed via species-specific deposition velocities (Vd(x)). We evaluate isolated components of the parameterization for Vd in the GEOS-Chem CTM by extracting the trace gas dry deposition algorithm and reimplementing in single-point-mode to enable more direct comparison to field observations. Resistances to surface uptake follow a modified version of the ‘big-leaf’ Wesely parameterization, which previous studies have shown applies poorly to off-target species such as NO2 under conditions favoring non-stomatal uptake. We evaluate non-stomatal dry deposition of NO2 by comparing to eddy covariance observed nocturnal Vd(NO2) over Harvard Forest. We eliminate a large low bias (-80 %) in simulated nocturnal Vd(NO2) by representing NO2 heterogeneous hydrolysis on deposition surfaces, paying attention to chemical flux divergence, soil NO emission, as well as canopy surface area effects. Finally, we evaluate the updated oxidized reactive nitrogen (NOy) dry deposition parameterization for GEOS-Chem by comparing to eddy covariance observed Vd(NOy) over Harvard Forest, finding a modest nocturnal low bias (-19 %) remains in simulated Vd(NOy) due to the compensating effects of updates to the calculation of molecular diffusivities (28 % reduction in nocturnal Vd(NOy)) and representation of NO2 heterogenous hydrolysis (25 % increase in nocturnal Vd(NOy)). These developments are applicable to models across scales, having important implications for near-surface NO2 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 | 0.001 |
| 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".