Principal Investigators


At a Glance

Methane (CH4) is the second most important anthropogenic climate forcer after carbon dioxide (CO2). Atmospheric methane has risen to levels roughly 150% above preindustrial concentrations due to human activities and continues to rise despite a short period of stabilization during 1999-2006 (Dean et al., 2018). Since the amount of methane in the atmosphere reflects the balance of chemical and biological processes that produce and consume the gas, efforts to decipher current trends and assess future emissions necessitate accurate accounting of different methane sources and sinks, as well as a clear understanding of their variability across temporal and spatial scales. These remain fundamental challenges for the scientific community. Since 2017, CMI has supported three complementary projects that address the largest unknowns in methane cycling: one experimental project focuses on the critical issue of wetland methane emissions (Project 1), and two global-scale modeling projects aim to quantify the individual sources, sinks, and variations of methane associated with land (Project 2) and the atmosphere (Project 3).

 


Research Highlight

Project 1: Biogeochemical controls on wetland methane emissions.

The CMI Wetland Project, led by Zhang, uses measurements to identify the biological and chemical mechanisms that promote methane emission from wetlands, dominant but highly variable sources of methane that are predicted to play a critical role in carbon-climate feedbacks (Dean et al., 2018). Wetland methane emission 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 remain unknown.

CMI research has identified oxygen transitions related to hydrology as critical for methane production in wetlands (Wilmoth et al., in prep). Exposure of peat to oxygen dramatically enhances methane production compared to peat incubated under continuously anoxic conditions (Figure 7.1). Analyses of peat chemistry and microbiology indicate multiple mechanisms for oxygen-enhanced methane production. Thus, contrary to scientific dogma that microbial methane production is confined to water- logged, oxygen-free, deep wetland zones, CMI research identifies partially saturated wetland soils and peats, located near the water table and exposed to significant oxygen, as critical locations for wetland methane production ( the “emerging model” in Figure 7.1).

CMI research suggests a possible new feedback between increasing temperatures, hydrologic variability, and wetland methane emissions because oxygen levels in wetlands are intimately linked to hydrology (Reddy and Delaune, 2008). Ongoing work aims to evaluate this new carbon-climate feedback using a combination of observational and modeling approaches. Within a policy framework, plans to restore wetlands by rewetting need to consider the possibility that restored wetlands could produce significantly more methane relative to their pre-disturbed forms (Abdalla et al., 2016) due to oxygen-enhanced methane production. Furthermore, our work suggests that carbon mitigation strategies aimed at minimizing wetland water table variability may help limit wetland methane emissions.

Anoxic Phase Methane diagrams
Figure 7.1. Oxic-anoxic transitions promote methane formation. (A) Methane from peat incubated under continuously anoxic conditions (gray bar), under anoxic conditions after exposure to 5% oxygen (yellow bar) or 10% oxygen (orange bar). Error bars, standard error of pooled, replicate incubations of surface (n=3), at the water table (n=3), and deep peat (n=2 or 3). (B) Schematic of emerging and classical models of methane production in wetlands. (Wilmoth et al., in preparation.)

Project 2: Global Model of Methane Emissions from Wetlands

This project, led by Shevliakova, aims to 1) implement and evaluate the capability to simulate CH4 and CO2 emissions from different wetland ecosystems in the terrestrial component of the Geophysical Fluid Dynamics Laboratory (GFDL) Earth System Model (ESM), and 2) to explore how uncertainty in climate and in ecological characteristics affect uncertainty in CH4 and CO2 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 explicitly represents interactions between microbes and soil organic matter in a new vertically resolved soil biogeochemistry model CORPSE (Sulman et al., 2014) that also 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 has a crucial control on 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.

We 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 7.2). We 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. We are now evaluating the coupled soil carbon-water- methane model on individual observational sites and in global stand-alone land simulations.

A full-featured, vertically-resolved hydrological and biogeochemical soil model, which accounts for microbial dynamics, is a critical tool 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 new biogeochemical feedbacks between changes in wetlands and permafrost on climate, which have yet to be included in Earth system projections.

Structure of the new methane production and consumption component
Figure 7.2. Structure of the new methane production and consumption component of the GFDL land model. DOC is dissolved organic carbon, simulated by LM4.

Project 3: Global Chemistry-Climate Modeling of Atmospheric Methane Cycle

This project, led by Naik, 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 have revealed complex temporal variations in atmospheric methane growth over the past three decades that have been challenging and often controversial to attribute to specific methane sources or sinks (Crill & Thornton, 2017; Dean et al., 2018). The GFDL atmospheric chemistry group has applied a process-based global chemistry-climate model (GFDL-AM4) that simulates changes in methane sources as well as the primary methane sink within a unified framework to explore the contribution of individual sources and sinks on observed trends and variability in methane from 1980 to 2014.

GFDL-AM4 model results suggest that the methane stabilization during the period of 1999-2006 was mainly due to increasing methane emissions balanced by increasing methane sink, primarily due to its reaction with hydroxyl (OH) radical. Post-2006, increasing emissions outweighed any changes in sink, resulting in the renewed growth of methane as shown in Figure 7.3 (He et al., in preparation). Ongoing work using methane isotope observations aims to further pin down the rise in methane to specific source types (biogenic, anthropogenic, or pyrogenic).

A quantitative understanding of the roles of individual sources and sinks in driving methane variability is a crucial precursor to designing effective mitigation strategies to address near-term climate warming.

Model results imply the need for accurate bottom-up estimates of methane emissions 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., 2016). Future work involving the coupling of improved terrestrial wetland emissions model (Project 2 with input from Project 1) with GFDL’s chemistry-climate model will advance the characterization of the drivers of atmospheric methane variability.

Atmospheric methane trends graph
Figure 7.3. Atmospheric methane trends driven by an imbalance in global methane budget. The left y-axis shows global average surface methane concentration from observations (NOAA Earth System Research Laboratory) and that simulated by the NOAA GFDL-AM4 model (black lines). The right y-axis shows the net methane flux calculated as the difference between optimized global total methane emissions and total methane sink in the model (red line).

References

Abdalla, M. et al., 2016. Emissions of methane from northern peatlands: a review of management impacts and implications for future management options. Ecology and Evolution, 6(19): 7080–7102. doi. org/10.1002/ece3.2469.

Crill, P. M. and B.F. Thornton, 2017. Wither methan in the IPCC process? Nature Climate Change, 7(10): 678-680. doi.org/10.1038/nclimate3403

Dean, J.F., et al., 2018. Methane feedbacks to the global climate system in a warmer world. Reviews of Geophysics, 56(1): 207-250. doi.org/10.1002/2017RG000559.

He., J., V. Naik, L. Horowitz, E. Dlugokencky, and K. Thoning, 2019. Evolution of the global methane budget over the 1980-2014 period using the GFDL-AM4, in preparation.

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, 2014. Enhanced Representation of Land Physics for Earth-System Modeling. J. Hydrometeorology 15:1739-1761. doi.org/10.1175/JHM-D-13-0162.1

Reddy, K. and R. DeLaune, 2008. Biogeochemistry of Wetlands. Boca Raton: CRC Press.

Saunois, M. et al., 2016. The global methane budget 2000-2012. Earth Syst, Sci. Data, 8(2): 697-751. doi. org/10.5194/essd-8-697-201.

Saunois, M. et al., 2017. Variability and quasi-decadal changes in the methane budge over the period 2000-2012. Atmos. Phys. Chem., 17(18): 11135-11161. doi.org/10.5194/acp-17-11135-2017.

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. doi. org/10.1038/nclimate2436.

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): 2655-2694. doi. org/10.5194/bg-12-2655-2015.

Wilmoth, J.L., J. Schaefer, D. Schlesinger, J. Shoemaker, P. Hatcher, S. Myneni, and X. Zhang. Oxygen variability stimulates methane production by wetland peats. In preparation.