Global Uptake of Atmospheric Methane by Soil From 1900 to 2100
Bibliographic record
Abstract
Abstract Soil methanotrophy is the only biological process that removes methane (CH4) from the atmosphere. There is good agreement about the size of the global sink but great uncertainty about its interannual variability and regional responses to changes in key environmental drivers. We used the process‐based methanotrophy model Methanotrophy Model (MeMo) v1.0 and output from global climate models to simulate regional and global changes in soil uptake of atmospheric CH4 from 1900 to 2100. The annual global uptake doubled from 17.1 ± 2.4 to 37.2 ± 3.3 Tg yr−1 from 1900‐2015 and could increase further to 82.7 ± 4.4 Tg yr−1 by 2100 (RCP8.5), primarily due to enhanced diffusion of CH4 into soil as a result of increases in atmospheric CH4 mole fraction. We show that during the period 1980–2015 temperature became an important influence on the increasing rates of soil methanotrophy, particularly in the Northern Hemisphere. In RCP‐forced simulations the relative influence of temperature on changes in the uptake continues to increase, enhancing the soil sink through higher rates of methanotrophic metabolic activity, increases in the global area of active soil methanotrophy and length of active season. During the late 21st century under RCP6.0, temperature is predicted to become the dominant driver of changes in global mean soil uptake rates for the first time. Regionally, in Europe and Asia, nitrogen inputs dominate changes in soil methanotrophy, while soil moisture is the most important influence in tropical South America. These findings highlight that the soil sink could change in response to drivers other than atmospheric CH4 mole fraction.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".