Decadal warming and increased precipitation interactively affect <scp> N <sub>2</sub> O </scp> emissions based on a long‐term field experiment and a meta‐analysis
Bibliographic record
Abstract
Abstract Global warming and altered precipitation affect nitrous oxide (N 2 O) emissions from terrestrial ecosystems; however, the interactive effects of these global change factors on N 2 O emissions remain unclear. Here, we use data from a decade‐long field experiment in an alpine meadow on the Qinghai‐Tibetan Plateau and a global meta‐analysis to examine how warming (W) and increased precipitation (P) interactively drive N 2 O emissions. Our field experiment showed that W and P enhanced N 2 O emissions by 36.2% and 23.9%, respectively. However, P dampened the W effect on N 2 O emissions, suggesting an antagonistic interaction between the two global change factors. The N 2 O emissions were jointly regulated by edaphic properties (such as soil moisture content, temperature and pH) and denitrifying microbial communities, with soil denitrifiers (particularly nir S abundance and community composition) driving N 2 O fluxes, underscoring the microbial mechanisms regulating N 2 O emissions under climate change. The meta‐analysis, synthesizing 75 observations, revealed that N 2 O emissions were enhanced by W (+4.5%), P (+18.6%) and WP (+9.4%). Our findings indicate that W and P exhibit positive feedbacks to climate change and the interactive effects of W and P call for more multi‐factor experiments to provide data for improving Earth system models to better assess climate change effects on future greenhouse gas fluxes. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.017 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 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".