Spatial and temporal patterns of CH <sub>4</sub> and N <sub>2</sub> O fluxes in terrestrial ecosystems of North America during 1979–2008: application of a global biogeochemistry model
Bibliographic record
Abstract
Abstract. Continental-scale estimations of terrestrial methane (CH4) and nitrous oxide (N2O) fluxes over a long time period are crucial to accurately assess the global balance of greenhouse gases and enhance our understanding and prediction of global climate change and terrestrial ecosystem feedbacks. Using a process-based global biogeochemical model, the Dynamic Land Ecosystem Model (DLEM), we quantified simultaneously CH4 and N2O fluxes in North America's terrestrial ecosystems from 1979 to 2008. During the past 30 years, approximately 14.69 ± 1.64 T g C a−1 (1 T g = 1012 g) of CH4, and 1.94 ± 0.1 T g N a−1 of N2O were released from terrestrial ecosystems in North America. At the country level, both the US and Canada acted as CH4 sources to the atmosphere, but Mexico mainly oxidized and consumed CH4 from the atmosphere. Wetlands in North America contributed predominantly to the regional CH4 source, while all other ecosystems acted as sinks for atmospheric CH4, of which forests accounted for 36.8%. Regarding N2O emission in North America, the US, Canada, and Mexico contributed 56.19%, 18.23%, and 25.58%, respectively, to the continental source over the past 30 years. Forests and croplands were the two ecosystems that contributed most to continental N2O emission. The inter-annual variations of CH4 and N2O fluxes in North America were mainly attributed to year-to-year climatic variability. While only annual precipitation was found to have a significant effect on annual CH4 flux, both mean annual temperature and annual precipitation were significantly correlated to annual N2O flux. The regional estimates and spatiotemporal patterns of terrestrial ecosystem CH4 and N2O fluxes in North America generated in this study provide useful information for global change research and policy making.
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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 | 0.000 |
| 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 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".