Rising nitrogen deposition leads to only a minor increase in CO2 uptake in Earth system models
Bibliographic record
Abstract
Current frameworks for evaluating biogeochemical climate change feedbacks in Earth System Models lack an explicit consideration of nitrogen cycling in the land and ocean spheres despite its vital role in limiting primary productivity. As coupled carbon-nitrogen cycling becomes the norm, a better understanding of the role of nitrogen cycling is needed. Here we develop a new framework for quantifying carbon-nitrogen feedbacks in Earth System Models and show that rising nitrogen deposition acts as a negative feedback over both land and ocean, enhancing carbon dioxide (CO2) fertilisation in a model ensemble. However, increased CO2 uptake due to rising nitrogen deposition is small relative to the large reduction in CO2 uptake when coupled carbon-nitrogen cycling is implemented in Earth System Models. Altogether, rising nitrogen deposition leads to only a minor increase in CO2 uptake but also enhances nitrous oxide (N2O) emissions over land and ocean, contributing only marginally to mitigating climate change. Rising nitrogen deposition only marginally promotes carbon dioxide uptake but also enhances nitrous oxide emissions, minimally contributing to mitigating climate change, according to simulations using multiple Earth system models.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".