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 uses laboratory experiments to show how microbial dynamics control wetland CH4 emissions and how methane stable isotopes can be used to identify a novel biological methane source (Highlight 1a, 1b). The second highlight uses a National Oceanic and Atmospheric Administration-Geophysical Fluid Dynamics Laboratory (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 1a

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 and 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 that vary in time and space. The factors responsible for the greatest methane emission from wetlands remain unknown.

CMI Researchers are investigating 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 compounds that are more difficult to degrade by biology. This leads to a more active methane-producing (methanogenic), microbial food web under subsequent oxygen-poor conditions (Wilmoth et al., in review). 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 microbial “latch” on downstream carbon flow that ultimately makes wetlands more methanogenic (Figure 2.1, Reji et al., in prep).

Figure 2.1.
Transient oxygen exposure triggers a shift in microbial community succession during microbial degradation of complex aromatic peat carbon that promotes methane formation. (Reji et al., in preparation).

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 peat 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 press), 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.

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). CMI collaborations (Yang et al., 2020) also address how the presence of different mineral and carbon forms can promote or attenuate microbial methane production.


Research Highlight 1b

The 2018 discovery of carbon dioxide reduction into methane by nitrogenases, enzymes responsible for natural nitrogen input into ecosystems, revealed a second biological mechanism for methane production on Earth. However, the importance of this pathway for methane production in the environment remains unknown because scientists lack information on how to distinguish nitrogenase-associated methane from other sources. Inspired by ongoing research in the group on the stable isotope signatures of nitrogenase enzymes (Zhang et al., 2014) and the application of stable isotopes of methane to constrain methane budgets, the researchers hypothesized that nitrogenase-derived methane could feature a carbon or hydrogen stable isotope composition distinctive from methane generated by other processes. Using pure culture systems, the researchers demonstrate that hydrogen stable isotopic composition distinguishes methane produced by nitrogenase from other sources (Figure 2.2, Luxem et al., 2020). With this new fingerprint, it will be possible to improve our understanding of different methane sources and their interactions with nitrogen cycling in nature.

Figure 2.2.
Nitrogenase-derived methane has a unique isotopic composition. The stable isotopic composition of methane produced by nitrogenase (yellow) can be distinguished from other natural methane sources due to its more depleted hydrogen isotopic composition. Individual data points from this study are shown as diamonds (n = 31). The observed ranges for fermentative (green), hydrogenotrophic (blue), and geological (red) methane sources are also shown. (From Luxem et al., 2020).

Research Highlight 2

To assess how field and laboratory data scale up to affect regional and global CH4 trends, the researchers (1) implement and evaluate the capability of the terrestrial component of the GFDL Earth System Model (ESM) to simulate CH4 and CO2 emissions from different wetland ecosystems; and (2) explore how uncertainty in climate, plant, 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 landuse 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 findings outlined under Highlight 1, the impact of soil moisture 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 model component with an explicit treatment of the four microbial groups for use with GFDL ESMs, which simulate anaerobic decomposition and methane production (Figure 2.3). 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 2.3.
Structure of the new methane production and consumption component of the GFDL land model. DOC, dissolved organic carbon.

With the support of CMI, the researchers are developing 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 make projections about future greenhouse gas under changing climate and land use. Interactive land–atmosphere methane fluxes will enable evaluation of biogeochemical feedbacks between changes in wetlands and permafrost on climate, which have yet to be included in Earth system projections.


Research Highlight 3

An imbalance in methane sources and sinks leads to changes in atmospheric methane levels. Observations reveal complex temporal variation in atmospheric methane growth over the past three decades. These have challenged the often controversial attempt 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 to determine the drivers of atmospheric methane trends and variability at the decadal to centennial time scales. The primary goal is to explore how individual sources and sinks affect the observed trends and variability in methane from 1980 to 2017.

CMI research with the GFDL-AM4.1 model shows that the methane stabilization observed between 1999 and 2006 was mainly due to increasing methane emissions balanced by an increasing methane OH sink. In the period following 2006, increasing emissions outweighed any changes in the sink, resulting in the renewed growth of methane (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 2.4 (He et al., in preparation). Furthermore, a reanalysis of atmospheric meteorological data has prompted investigation of the uncertainties in OH distribution, trends, and variability, and the resulting impacts on methane budget and lifetime due to differences in meteorology (He et al., in preparation).

Figure 2.4.
Source Contributions to Atmospheric Methane from 1983-2017. 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 (From He et al, in preparation).

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. Future work involving the coupling of improved terrestrial wetland emissions model (Highlight 2 with input from Highlight 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. In press, Environmental Research Letters.

He, J., V. Naik, L.W. Horowitz, E. Dlugokencky, and K. Thoning, 2020. Investigation of the global methane budget over 1980– 2017 using GFDL-AM4.1, Atmos. Chem. Phys., 20, 805–827. (

He et al.: Interpreting changes in global methane budget using a global model constrained with isotopic observations, in preparation.

He et al.: Modeling hydroxyl radical (OH) response to meteorological forcing: Implications for methane budget estimate, in preparation.

Luxem K., W. Leavitt,and X. Zhang, 2020. Large hydrogen isotope fractionation distinguishes nitrogenase derived methane from other sources. Applied and Environmental Microbiology. (DOI: 10.1128/AEM.00849-20).

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., et al., 2019. Global Biogeochemical Cycles, 33: 318-342. (

Reji, L., and X. Zhang. Functional ecology of uncultured Acidobacteria in redox oscillated sphagnum peat. In preparation for Environmental Microbiology.

Sulman, B., R. Phillips, A. Oishi, E. Shevliakova, and 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. 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. In review at Global Change Biology.

Yang, Q., X. Zhang, I. Bourg, and H. Stone, 2020. 4D imaging reveals mechanisms of clay-carbon protection and release. In press, Nature Communications.

Zhang X., D.M. Sigman, F.M.M. Morel, and A.M.L. Kraepiel, 2014. Nitrogen isotope fractionation by alternative nitrogenases and past ocean anoxia. Proceedings of the National Academy of Sciences USA 111(13):4782-4787. (DOI: 10.1073/pnas.1402976111).