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Title: Data-Constrained Projections of Methane Fluxes in a Northern Minnesota Peatland in Response to Elevated CO 2 and Warming

Large uncertainties exist in predicting responses of wetland methane (CH 4) 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 CH 4 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 CH 4 emission under five warming and two elevated CO 2 treatments at SPRUCE. We found that 9°C warming treatments significantly increased methane emission by approximately 400%, and elevated CO 2 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 amore » significant increase in the emitted CH 4:CO 2 ratio. This result, together with the larger warming potential of CH 4, will lead to a strong positive feedback from terrestrial ecosystems to climate warming. In conclusion, the model-data fusion approach used in this study enabled parameter estimation and uncertainty quantification for forecasting methane fluxes.« less
ORCiD logo [1] ;  [2] ;  [3] ; ORCiD logo [4] ; ORCiD logo [5] ; ORCiD logo [6] ; ORCiD logo [7] ; ORCiD logo [6] ;  [8]
  1. Northern Arizona Univ., Flagstaff, AZ (United States)
  2. Nanjing Forestry Univ., Nanjing (China); Univ. of Oklahoma, Norman, OK (United States)
  3. Univ. of Oklahoma, Norman, OK (United States); Lab. des Sciences du Climat et de l'Environnement, Gif-sur-Yvette (France)
  4. Univ. of Oklahoma, Norman, OK (United States)
  5. Florida State Univ., Tallahassee, FL (United States)
  6. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  7. U.S. Forest Service, Grand Rapids, MN (United States)
  8. Northern Arizona Univ., Flagstaff, AZ (United States); Tsinghua Univ., Beijing (China)
Publication Date:
Grant/Contract Number:
AC05-00OR22725; 4000144122
Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Volume: 122; Journal Issue: 11; Journal ID: ISSN 2169-8953
American Geophysical Union
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Country of Publication:
United States
58 GEOSCIENCES; data-model fusion; uncertainty; forecasting; methane; wetland; climate change
OSTI Identifier:
Alternate Identifier(s):
OSTI ID: 1407820