Principal Investigators

At a Glance

Methane (CH4) is the second most important anthropogenic climate forcer after carbon dioxide. Determining the importance and mechanisms of different methane sources and sinks across temporal and spatial scales remains a fundamental challenge for the scientific community. Three complementary CMI projects address critical unknowns in methane cycling. The first shows how microbial dynamics associated with changes in wetland oxygen content can stimulate wetland CH4 emissions (Highlight 1). This has important implications for ongoing efforts to use wetlands for carbon mitigation as climate change alters hydrology in wetland rich regions. The second highlight uses a NOAA-GFDL state-of-the-art model of methane cycling on land for use in Earth System Models to predict the activity of key microbial groups involved in carbon decomposition and methane emission (Highlight 2). Finally, CMI research with the GFDL chemistry climate model links recent atmospheric methane trends to increasing methane emissions, possibly from energy, agriculture, and waste sectors, and changes in the methane hydroxyl radical sink (Highlight 3).


Research Highlight 1

Atmospheric CH4 has risen to levels roughly 150% above preindustrial concentrations due to human activities. These levels continue to rise despite a short period of stabilization between 1999 and 2006. The CMI Wetland Project uses measurements to identify the biological and chemical mechanisms that promote methane emission from wetlands. Wetlands are dominant but highly variable sources of methane that are predicted to play a critical role in carbon-climate feedbacks. Methane emission in these areas is shaped by a complex and poorly understood interplay of microbial, hydrological, and plant-associated processes which vary in time and space. The set of conditions promoting the greatest methane emission from wetlands remains unknown.

CMI Researchers have investigated the microbial and chemical pathways that regulate methane emissions from acidic wetlands, important carbon reservoirs at high latitudes. By analyzing peat microbiomes during a transition from oxygen rich to poor conditions, they have shown that oxygenation stimulates the growth of key aerobic microorganisms with the capability of liberating carbon from recalcitrant polyphenolic compounds. This leads to a more active methanogenic, microbial food web under subsequent oxygen-poor conditions (Wilmoth et al., in review; Figure 4.1A). This microbially mediated degradation of recalcitrant carbon in wetland peat helps to explain how pulses of oxygen driven by changes in hydrology can unlock a biogeochemical “latch” on downstream carbon flow that ultimately makes wetlands more methanogenic (Figure 4.1B). Ongoing collaborations with the Bourg, Stone, and Porporato groups expand this work by addressing how oxygen variability affects methane emission from diverse wetland types (e.g., temperate, tropical wetland soils). These collaborations focus on how the presence of different mineral and carbon forms can promote or attenuate microbial methane production.

Figure 4.1A-B.
Oxic-anoxic transitions trigger microbial degradation of complex carbon toward methane formation. (A) Metagenome relative contribution of genes involved in degradation of complex organic carbon. Error bars at day 232 are standard errors (n = 2). (B) Schematic of microbially mediated pathways leading to increased methane emissions following oxygenation of peat. Wilmoth et al., (in review).


Disentangling the link between global warming, hydrologic variability, and soil methane emissions hinges on a deeper understanding of how shifting oxygen levels in wetlands affect microbial degradation of complex organic carbon. Importantly, aromatic polyphenolic compounds that are typically viewed as relatively stable forms of organic carbon in soil are vulnerable to degradation and conversion to methane following oscillating aerobic/anaerobic conditions brought about by climate change. Through ongoing work and CMI modeling collaborations (Calabrese et al., in review), the results suggest that minimizing wetland water table variability in acidic wetlands and maintaining critical inundation levels are key factors in developing effective mitigation strategies to limit wetland methane emissions.


Research Highlight 2

This project has two aims: (1) to implement and evaluate the capability to simulate CH4 and CO2 emissions from different wetland ecosystems in the terrestrial component of the GFDL Earth System Model (ESM); and (2) to explore how uncertainty in climate, plant, and microbial characteristics affect uncertainty in methane and carbon dioxide emissions from global wetlands in past and future climates.

The latest GFDL land model, LM4, includes a dynamic representation of vegetation (Weng et al, 2015). LM4 represents interactions between microbes and soil organic matter in a new vertically resolved soil biogeochemistry model CORPSE (Sulman et al., 2014) that captures the effect of land-use on vegetation and soil. Advanced hydrological features of LM4 include frozen soil dynamics, continuous vertical representation of soil water including water table depth, horizontal transport of runoff via rivers to the oceans, and a lake model (Milly et al, 2013). Soil moisture plays an important role in soil carbon storage and methane production. Consistent with Project 1 findings, its impact on methane emissions is highly nonlinear due to complex interactions between levels of anoxia, microbes, and carbon in soils and wetlands.

The researchers developed a new land component with an explicit treatment of the four microbial groups for use with GFDL ESMs, which simulate anaerobic decomposition and methane production (Figure 4.2). They integrated new methane consumption and production components, along with gas diffusion (e.g., O2, CH4 and CO2), through vertical soil layers within the GFDL vertically-resolved soil hydrology model. New numerical approaches enable computation of changes in soil moisture, ice and gas concentrations under a wide range of environmental conditions. The researchers are now evaluating the coupled soil carbon-water-methane model on data from individual observational sites and in global stand-alone land simulations.

Figure 4.2.
Structure of the new methane production and consumption component of the GFDL land model. DOC stands for dissolved organic carbon.

A full-featured, vertically-resolved hydrological and biogeochemical soil model, which accounts for microbial dynamics, is critical to resolve temporal and spatial variability of methane sources and sinks and to make projections about future greenhouse gas under changing climate and land use. Interactive land–atmosphere methane fluxes will enable evaluation of new biogeochemical feedbacks between changes in wetlands and permafrost on climate, which have yet to be included in Earth system projections.


Research Highlight 3

This project addresses the key question – what are the drivers of atmospheric methane trends and variability at the decadal to centennial time scales? An imbalance in methane sources and sinks leads to changes in atmospheric methane levels. Observations reveal complex temporal variations in atmospheric methane growth over the past three decades, which have challenged attempts to attribute these variations to specific methane sources or sinks (Nisbet et al., 2019). The GFDL atmospheric chemistry group has applied a process-based global chemistry-climate model (GFDL-AM4.1) that simulates changes in methane sources as well as the primary methane sink in a unified framework. The goal is to explore how individual sources and sinks affect the observed trends and variability in methane from 1980 to 2017.

CMI research with GFDL-AM4.1 model shows that the methane stabilization during the period between 1999 and 2006 was mainly due to increasing methane emissions balanced by increasing methane OH sink. In the period following 2006, increasing emissions outweighed any changes in sink, resulting in the renewed growth of methane as shown in Figure 4.3A (He et al., 2020). Ongoing work applying observed methane isotopic (i.e., δ13CH4) constraints, together with model simulations, suggests energy, agriculture, and waste sectors are likely the major contributors to the renewed growth in methane after 2006 as shown in Figure 4.3B (He et al., in preparation). A reanalysis of atmospheric data has prompted ongoing work on model simulations. This work suggests non-negligible impacts on OH levels due to different meteorological forcing would introduce uncertainty in estimating methane budget (He et al., in preparation).

Figure 4.3.
(A) Atmospheric methane trends driven by an imbalance in global methane budget. The methane budget estimates for four decades are presented in bar charts (plotted on the left y axis). Sources are positive and sinks are negative. The black dots represent observed global monthly mean atmospheric CH4 dry-air mole fractions and the solid black line represents the simulated global monthly means (plotted on the right y axis). (B) Renewed growth in methane after 2006 is mainly driven by tracers from energy (ENE), agriculture (AGR), and waste sectors (WST). The y-axis shows estimated linear trend (ppb yr-1) in methane source tagged tracers based on the sector optimization using observed δ13CH4 constraints.

A quantitative understanding of how individual sources and sinks drive methane variability is a crucial precursor to designing effective strategies to mitigate near-term climate warming. Accurate bottom-up estimates of methane emissions are needed to improve quantitative analyses of the global methane budget and prediction of atmospheric methane. The most important sources of uncertainty in emissions are wetlands and freshwater systems (Saunois et al., 2019). Future work involving the coupling of improved terrestrial wetland emissions model (Project 2 with input from Project 1) with GFDL’s Earth System Model (ESM4) will advance the characterization of the drivers of atmospheric methane variability.



Calabrese, S., J.L. Wilmoth, X. Zhang, and A. Porporato. A critical inundation level for methane emissions from wetlands. Science (in review).

Milly, P.C.D., S.L. Malyshev, E. Shevliakova, K.A. Dunne, K.L. Findell, T. Gleeson, Z. Liang, P. Phillips, R.J. Stouffer, and S. Swenson, 2013. Enhanced representation of land physics for earth-system modeling. J. Hydrometeorology 15:1739-1761.

Nisbet, E. G., M.R. Manning, E.J. Dlugokencky, R.E. Fisher, D. Lowry, S.E. Michel, et al., 2019. Very strong atmospheric methane growth in the 4 years 2014–2017: Implications for the Paris Agreement. Global Biogeochemical Cycles, 33: 318–342.

Saunois, M., A.R. Stavert, B. Poulter, P. Bousquet, J.G. Canadell, R.B. Jackson, P.A. Raymond, E.J. Dlugokencky, et al. 2019. The Global Methane Budget 2000–2017. Earth Syst, Sci. Data Discuss (in review).

Sulman, B., R. Phillips, A. Oishi, E. Shevliakova, S. Pacala, 2014. Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2. Nature Climate Change 4:1099-1102.

Weng, E.S., S.L. Malyshev, J.W. Lichstein, C.E. Farrior, R. Dybzinski, T. Zhang, E. Shevliakova, and S.W. Pacala, 2015. Scaling from individual trees to forests in an Earth system modeling framework using a mathematically tractable model of height-structured competition. Biogeosciences, 12(9), pp.2655-2694.

Wilmoth, J.L., J. Schaefer, D. Schlesinger, S. Roth, P. Hatcher, J. Shoemaker, and X. Zhang. The role of oxygen in stimulating methane production in wetlands. Global Change Biology (in review).