Articles | Volume 11, issue 6
Geosci. Model Dev., 11, 2009–2032, 2018
https://doi.org/10.5194/gmd-11-2009-2018
Geosci. Model Dev., 11, 2009–2032, 2018
https://doi.org/10.5194/gmd-11-2009-2018

Model description paper 04 Jun 2018

Model description paper | 04 Jun 2018

Soil Methanotrophy Model (MeMo v1.0): a process-based model to quantify global uptake of atmospheric methane by soil

Fabiola Murguia-Flores et al.

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Cited articles

Adamsen, A. P. S. and King, G. M.: Methane Consumption in Temperate and Subarctic Forest Soils: Rates, Vertical Zonation, and Responses to Water and Nitrogen, Appl. Environ, Microbiol., 59, 485–490, 1993. 
Allan, W., Struthers, H., and Lowe, D. C.: Methane carbon isotope effects caused by atomic chlorine in the marine boundary layer: Global model results compared with Southern Hemisphere measurements, J. Geophys. Res.-Atmos., 112, D04306, https://doi.org/10.1029/2006JD007369, 2007. 
Arndt, S., Jørgensen, B. B., LaRowe, D. E., Middelburg, J. J., Pancost, R. D., and Regnier, P.: Quantifying the degradation of organic matter in marine sediments: A review and synthesis, Earth-Sci. Rev., 123, 53–86, https://doi.org/10.1016/j.earscirev.2013.02.008, 2013. 
Aronson, E. L. and Helliker, B. R.: Methane flux in non-wetland soils in response to nitrogen addition: a meta-analysis, Ecology, 91, 3242–3251, https://doi.org/10.1890/09-2185.1, 2010. 
Bodelier, P. L. E. and Laanbroek, H. J.: Nitrogen as a regulatory factor of methane oxidation in soils and sediments, FEMS Microbiol. Ecol., 47, 265–277, https://doi.org/10.1016/S0168-6496(03)00304-0, 2004. 
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Short summary
Soil bacteria known as methanotrophs are the only biological sink for atmospheric methane (CH4). Their activity depends on climatic and edaphic conditions, thus varies spatially and temporarily. Based on this, we developed a model (MeMo v1.0) to assess the global CH4 consumption by soils. The global CH4 uptake was 33.5 Tg CH4 yr-1 for 1990–2009, with an increasing trend of 0.1 Tg CH4 yr-2. The regional analysis proved that warm and semiarid regions represent the most efficient CH4 sink.