The Science

Wetlands are the largest global natural methane (CH₄) source, yet predictive capability of land models is low. In a recent study, researchers improved the methane module in the Community Land Model (CLM) and Accelerated Climate Modeling for Energy (ACME) Land Model (ALM) and compared predictions with tower and aircraft observations and atmospheric inversions. The findings highlight new observations and model requirements to improve global CH₄ predictions.

The Impact

Model changes substantially improved CH₄ emission predictions compared to observations. Cold season CH₄ emissions estimates remain biased low, motivating more observations during this period. Large CH₄ emissions uncertainties are also generated by uncertainties in wetland hydrology.

Summary

The study compared wetland CH₄ emission model predictions with site- to regional-scale observations. A comparison of the CH₄ fluxes with eddy flux data highlighted needed changes to the model’s estimate of aerenchyma area, which were implemented and tested. The model modifications substantially reduced biases in CH₄ emissions when compared with CarbonTracker CH₄ predictions. CLM4.5 CH₄ emission predictions agree well with Alaskan growing season (May–September) CarbonTracker CH₄ predictions and site-level observations. However, the model underestimated CH₄ emissions in the cold season (October–April). The monthly atmospheric CH₄ mole fraction enhancements due to wetland emissions also were assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model and compared with measurements from the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold season CH₄ emissions. The greatest uncertainties in predicting the seasonal CH₄ cycle are from the wetland extent, cold season CH₄ production, and CH₄ transport processes. Predicted CH₄ emissions remain uncertain, but the study’s findings show that benchmarking against observations across spatial scales can inform model structural and parameter improvements.

Principal Investigator

William J. Riley
Lawrence Berkeley National Laboratory
wjriley@lbl.gov

Program Manager

Daniel Stover
U.S. Department of Energy, Biological and Environmental Research (SC-33)
Environmental System Science
daniel.stover@science.doe.gov

Funding

Funding for this study was provided by the Regional and Global Climate Model (RGCM) program and Next-Generation Ecosystem Experiments (NGEE)–Arctic project of the Office of Biological and Environmental Research, within the U.S. Department of Energy (DOE) Office of Science, under the under contract # DE-AC02-05CH11231.

References