Data-Constrained Projections of Methane Fluxes in a Northern Minnesota Peatland in Response to Elevated CO2 and Warming

Shuang Ma, Jiang Jiang, Yuanyuan Huang, Zheng Shi, Rachel M. Wilson, Daniel Ricciuto, Stephen D. Sebestyen, Paul J. Hanson, Yiqi Luo

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39 Scopus citations

Abstract

Large uncertainties exist in predicting responses of wetland methane (CH4) fluxes to future climate change. However, sources of the uncertainty have not been clearly identified despite the fact that methane production and emission processes have been extensively explored. In this study, we took advantage of manual CH4 flux measurements under ambient environment from 2011 to 2014 at the Spruce and Peatland Responses Under Changing Environments (SPRUCE) experimental site and developed a data-informed process-based methane module. The module was incorporated into the Terrestrial ECOsystem (TECO) model before its parameters were constrained with multiple years of methane flux data for forecasting CH4 emission under five warming and two elevated CO2 treatments at SPRUCE. We found that 9°C warming treatments significantly increased methane emission by approximately 400%, and elevated CO2 treatments stimulated methane emission by 10.4%–23.6% in comparison with ambient conditions. The relative contribution of plant-mediated transport to methane emission decreased from 96% at the control to 92% at the 9°C warming, largely to compensate for an increase in ebullition. The uncertainty in plant-mediated transportation and ebullition increased with warming and contributed to the overall changes of emissions uncertainties. At the same time, our modeling results indicated a significant increase in the emitted CH4:CO2 ratio. This result, together with the larger warming potential of CH4, will lead to a strong positive feedback from terrestrial ecosystems to climate warming. The model-data fusion approach used in this study enabled parameter estimation and uncertainty quantification for forecasting methane fluxes.

Original languageEnglish
Pages (from-to)2841-2861
Number of pages21
JournalJournal of Geophysical Research: Biogeosciences
Volume122
Issue number11
DOIs
StatePublished - Nov 2017

Funding

We thank Russell Doughty for his English language editing of this manuscript. Xingjie Lu and Junyi Liang offered technical help on coding and debugging. This work was primarily founded by subcontract 4000144122 from Oak Ridge National Laboratory (ORNL) to the University of Oklahoma. Oak Ridge National Laboratory is managed by UT-Battelle, LLC, for the U.S. Department of Energy under contract DE-AC05-00OR22725. Research in Yiqi Luo's EcoLab was also financially supported by U.S. DOE grants DE-SC0008270 and DE-SC00114085 and U.S. National Science Foundation (NSF) grants EF 1137293 and OIA-1301789. All data sets from this study are available upon request. Relevant measurements were obtained from the SPRUCE Webpage (http://mnspruce.ornl.gov/), the archival ftp site (ftp://sprucedata.ornl.gov), or from the USDA Forest Service. Data of the TECO_SPRUCE_ME model are available at http://dx.doi.org/10.3334/ CDIAC/spruce.046. This manuscript has been co-authored by a Federal employee. The United States Government retains, and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes.

Keywords

  • climate change
  • data-model fusion
  • forecasting
  • methane
  • uncertainty
  • wetland

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