Global evaluation of ammonia bidirectional exchange and livestock diurnal variation schemes
Bibliographic record
Abstract
Abstract. Bidirectional air–surface exchange of ammonia (NH3) has been neglected in many air quality models. In this study, we implement the bidirectional exchange of NH3 in the GEOS-Chem global chemical transport model. We also introduce an updated diurnal variability scheme for NH3 livestock emissions and evaluate the recently developed MASAGE_NH3 bottom-up inventory. While updated diurnal variability improves comparison of modeled-to-hourly in situ measurements in the southeastern USA, NH3 concentrations decrease throughout the globe, up to 17 ppb in India and southeastern China, with corresponding decreases in aerosol nitrate by up to 7 μg m−3. The ammonium (NH4+) soil pool in the bidirectional exchange model largely extends the NH3 lifetime in the atmosphere. Including bidirectional exchange generally increases NH3 gross emissions (7.1 %) and surface concentrations (up to 3.9 ppb) throughout the globe in July, except in India and southeastern China. In April and October, it decreases NH3 gross emissions in the Northern Hemisphere (e.g., 43.6 % in April in China) and increases NH3 gross emissions in the Southern Hemisphere. Bidirectional exchange does not largely impact NH4+ wet deposition overall. While bidirectional exchange is fundamentally a better representation of NH3 emissions from fertilizers, emissions from primary sources are still underestimated and thus significant model biases remain when compared to in situ measurements in the USA. The adjoint of bidirectional exchange has also been developed for the GEOS-Chem model and is used to investigate the sensitivity of NH3 concentrations with respect to soil pH and fertilizer application rate. This study thus lays the groundwork for future inverse modeling studies to more directly constrain these physical processes rather than tuning bulk unidirectional NH3 emissions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".