Sensitivity of Modeled Soil NOx Emissions to Soil Moisture
Bibliographic record
Abstract
Abstract As emissions of nitrogen oxides (NOx) from fossil fuel combustion decrease, the relative contribution of NOx emissions from managed and unmanaged soils (S NOx ) is increasing. Modeling S NOx presents a challenge as it requires proper characterization of emission dynamics in response to environmental conditions. S NOx is often represented using the Berkeley Dalhousie Soil NOx Parameterization (BDSNP), which relies upon static relationships between soil moisture and S NOx for arid and non‐arid lands. However, soil chamber and atmospheric studies have shown that emission characteristics are more dynamic, with peak emissions often occurring at higher soil moisture content. Here, to better capture observational studies, we update BDSNP by creating a dynamic S NOx response to soil moisture based on a normalized soil moisture index. We compare the standard and updated parameterizations over the contiguous United States (U.S.) for 2011–2020 using input soil moisture data from ERA5‐Land, MERRA‐2 and NLDAS2‐Mosaic and evaluate S NOx across these different input drivers as well as between the standard and updated parameterizations. The standard parametrization exhibits strong sensitivity to different input soil moisture products, with annual U.S. S NOx differences of up to 0.28 Tg N yr −1 . In contrast, the updated parameterization provides a robust representation of S NOx with reduced sensitivity to input soil moisture product with differences of at most 0.03 Tg N yr −1 . The updated parameterization simulates a broad increase in S NOx in non‐arid regions, including much of the Eastern U.S., indicating that this region may be more sensitive to climatically‐driven S NOx as anthropogenic NOx emissions continue to decline.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".