Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil
Bibliographic record
Abstract
Abstract. Soil bacteria known as methanotrophs are the sole biological sink for atmospheric methane (CH4), a potent greenhouse gas that is responsible for ∼ 20 % of the human-driven increase in radiative forcing since pre-industrial times. Soil methanotrophy is controlled by a plethora of factors, including temperature, soil texture, moisture and nitrogen content, resulting in spatially and temporally heterogeneous rates of soil methanotrophy. As a consequence, the exact magnitude of the global soil sink, as well as its temporal and spatial variability, remains poorly constrained. We developed a process-based model (Methanotrophy Model; MeMo v1.0) to simulate and quantify the uptake of atmospheric CH4 by soils at the global scale. MeMo builds on previous models by Ridgwell et al. (1999) and Curry (2007) by introducing several advances, including (1) a general analytical solution of the one-dimensional diffusion–reaction equation in porous media, (2) a refined representation of nitrogen inhibition on soil methanotrophy, (3) updated factors governing the influence of soil moisture and temperature on CH4 oxidation rates and (4) the ability to evaluate the impact of autochthonous soil CH4 sources on uptake of atmospheric CH4. We show that the improved structural and parametric representation of key drivers of soil methanotrophy in MeMo results in a better fit to observational data. A global simulation of soil methanotrophy for the period 1990–2009 using MeMo yielded an average annual sink of 33.5 ± 0.6 Tg CH4 yr−1. Warm and semi-arid regions (tropical deciduous forest and open shrubland) had the highest CH4 uptake rates of 602 and 518 mg CH4 m−2 yr−1, respectively. In these regions, favourable annual soil moisture content (∼ 20 % saturation) and low seasonal temperature variations (variations < ∼ 6 ∘C) provided optimal conditions for soil methanotrophy and soil–atmosphere gas exchange. In contrast to previous model analyses, but in agreement with recent observational data, MeMo predicted low fluxes in wet tropical regions because of refinements in formulation of the influence of excess soil moisture on methanotrophy. Tundra and mixed forest had the lowest simulated CH4 uptake rates of 176 and 182 mg CH4 m−2 yr−1, respectively, due to their marked seasonality driven by temperature. Global soil uptake of atmospheric CH4 was decreased by 4 % by the effect of nitrogen inputs to the system; however, the direct addition of fertilizers attenuated the flux by 72 % in regions with high agricultural intensity (i.e. China, India and Europe) and by 4–10 % in agriculture areas receiving low rates of N input (e.g. South America). Globally, nitrogen inputs reduced soil uptake of atmospheric CH4 by 1.38 Tg yr−1, which is 2–5 times smaller than reported previously. In addition to improved characterization of the contemporary soil sink for atmospheric CH4, MeMo provides an opportunity to quantify more accurately the relative importance of soil methanotrophy in the global CH4 cycle in the past and its capacity to contribute to reduction of atmospheric CH4 levels under future global change scenarios.
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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) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".