Representing sub-grid scale variations in nitrogen deposition associated with land use in a global Earth system model: implications for present and future nitrogen deposition fluxes over North America
Bibliographic record
Abstract
Abstract. Reactive nitrogen (N) emissions have increased over the last 150 years as a result of greater fossil fuel combustion and food production. The resulting increase in N deposition can alter the function of ecosystems, but characterizing its ecological impacts remains challenging, in part because of uncertainties in model-based estimates of N dry deposition. Here, we use the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric chemistry–climate model (AM3) coupled with the GFDL land model (LM3) to estimate dry deposition velocities. We leverage the tiled structure of LM3 to represent the impact of physical, hydrological, and ecological heterogeneities on the surface removal of chemical tracers. We show that this framework can be used to estimate N deposition at more ecologically relevant scales (e.g., natural vegetation, water bodies) than from the coarse-resolution global model AM3. Focusing on North America, we show that the faster removal of N over forested ecosystems relative to cropland and pasture implies that coarse-resolution estimates of N deposition from global models systematically underestimate N deposition to natural vegetation by 10 % to 30 % in the central and eastern US. Neglecting the sub-grid scale heterogeneity of dry deposition velocities also results in an underestimate (overestimate) of the amount of reduced (oxidized) nitrogen deposited to water bodies. Overall, changes in land cover associated with human activities are found to slow down the removal of N from the atmosphere, causing a reduction in the dry oxidized, dry reduced, and total (wet+dry) N deposition over the contiguous US of 8 %, 26 %, and 6 %, respectively. We also find that the reduction in the overall rate of removal of N associated with land-use change tends to increase N deposition on the remaining natural vegetation and facilitate N export to Canada. We show that sub-grid scale differences in the surface removal of oxidized and reduced nitrogen imply that projected near-term (2010–2050) changes in oxidized (−47 %) and reduced (+40 %) US N emissions will cause opposite changes in N deposition to water bodies (increase) and natural vegetation (decrease) in the eastern US, with potential implications for acidification and ecosystems.
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) | 0.000 | 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".