Representing the Dynamic Response of Vegetation to Nitrogen Limitation in the CLASSIC Land Model
Bibliographic record
Abstract
Despite its pivotal feedback to carbon cycling, representing the dynamic response of vegetation to nitrogen limitation is a key challenge for simulating the terrestrial carbon sink in land models. Here, we explore a representation of this dynamic response of vegetation to nitrogen limitation with a novel representation of biological nitrogen fixation and nitrogen cycling in the Canadian Land Surface Scheme Including Biogeochemical Cycles (CLASSIC) model. First, we assess how incorporating this dynamic response of vegetation to nitrogen limitation via biological nitrogen fixation influences carbon sequestration for CO 2 and nitrogen fertilisation experiments, comparing simulations against observation-based estimates from meta-analyses. This evaluates whether underlying mechanisms are realistically represented. Second, we assess how incorporating the dynamic response of vegetation to nitrogen limitation via biological nitrogen fixation affects carbon sequestration over the late 20 th and early 21 st century, examining the effects of global change drivers (CO 2 , nitrogen deposition, climate, and land use change) acting both individually and concurrently. Including nitrogen cycling reduces the terrestrial carbon sink driven by elevated atmospheric CO 2 concentration over the historical period. Representing the dynamic response of vegetation to nitrogen limitation via biological nitrogen fixation increases the present-day terrestrial carbon sink by 0.2 Pg C yr -1 because the upregulation of biological nitrogen fixation driven by stronger nitrogen limitation under elevated atmospheric CO 2 concentration alleviates nitrogen limitation. Our results highlight the importance of the dynamic response of vegetation to nitrogen limitation for realistically projecting the future terrestrial carbon sink under global change with land models.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".