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Title: A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands

Wetlands are the largest global natural methane (CH 4) source, and emissions between 50 and 70° N latitude contribute 10-30 % to this source. Predictive capability of land models for northern wetland CH 4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH 4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) with site- to regional-scale observations. A comparison of the CH 4 fluxes with eddy flux data highlighted needed changes to the model's estimate of aerenchyma area, which we implemented and tested. The model modification substantially reduced biases in CH 4 emissions when compared with CarbonTracker CH 4 predictions. CLM4.5 CH 4 emission predictions agree well with growing season (May–September) CarbonTracker Alaskan regional-level CH 4 predictions and site-level observations. However, CLM4.5 underestimated CH 4 emissions in the cold season (October–April). The monthly atmospheric CH 4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH 4 mole fraction measurements from the Carbon in Arctic Reservoirsmore » Vulnerability Experiment (CARVE) campaign. Both the tower and aircraft analyses confirm the underestimate of cold-season CH 4 emissions by CLM4.5. The greatest uncertainties in predicting the seasonal CH 4 cycle are from the wetland extent, cold-season CH 4 production and CH 4 transport processes. We recommend more cold-season experimental studies in high-latitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH 4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.« less
Authors:
 [1] ;  [1] ;  [1] ;  [2] ;  [3] ;  [4] ;  [5] ;  [6] ;  [7] ;  [8] ;  [9] ;  [10] ;  [11] ;  [12] ;  [1] ;  [13] ;  [14] ;  [4] ;  [15] ;  [16]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division
  2. Univ. of Nebraska, Lincoln, NE (United States). Biological System Engineering Dept.
  3. Harvard Univ., Cambridge, MA (United States). School of Engineering and Applied Sciences (SEAS); Dalhousie Univ., Halifax, Nova Scotia (Canada). Dept. of Physics and Atmospheric Science
  4. Harvard Univ., Cambridge, MA (United States). School of Engineering and Applied Sciences (SEAS)
  5. Univ. of Alaska Fairbanks, Fairbanks, AK (United States). Inst. of Arctic Biology
  6. Dalhousie Univ., Halifax, Nova Scotia (Canada). Dept. of Physics and Atmospheric Science
  7. Univ. of Alaska Fairbanks, Fairbanks, AK (United States). International Arctic Research Center; Osaka Prefecture Univ., Sakai, Osaka (Japan). Graduate School of Life and Environmental Sciences
  8. Univ. of Alaska Fairbanks, Fairbanks, AK (United States). International Arctic Research Center; Shinshu Univ., Matsumoto, Nagano (Japan). Dept. of Environmental Sciences
  9. City Univ. (CUNY), NY (United States). CUNY Environmental Crossroads Initiative and NOAA-CREST Inst., Dept. of Earth and Atmospheric Sciences; California Inst. of Technology (CalTech), La Canada Flintridge, CA (United States). Jet Propulsion Lab.
  10. California Inst. of Technology (CalTech), La Canada Flintridge, CA (United States). Jet Propulsion Lab.
  11. San Diego State Univ., San Diego, CA (United States). Global Change Research Group, Dept. of Biology; Open Univ., Milton Keynes (United Kingdom). Dept. of Environment, Earth and Ecosystems
  12. Montana State Univ., Bozeman, MT (United States). Dept. of Ecology
  13. Univ. of Colorado, Boulder, CO (United States). Cooperative Inst. for Research in Environmental Sciences (CIRES); National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab.
  14. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Earth Sciences Division; Univ. of California, Berkeley, CA (United States)
  15. Montana State Univ., Bozeman, MT (United States). Dept. of Ecology; Swiss Federal Research Inst. WSL, Birmensdorf (Switzerland)
  16. San Diego State Univ., San Diego, CA (United States). Global Change Research Group, Dept. of Biology; Univ. of Sheffield (United Kingdom). Dept. of Animal and Plant Sciences
Publication Date:
Grant/Contract Number:
AC02-05CH11231; SC005160
Type:
Accepted Manuscript
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 13; Journal Issue: 17; Journal ID: ISSN 1726-4189
Publisher:
European Geosciences Union
Research Org:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23); National Science Foundation (NSF); National Aeronautics and Space Administration (NASA)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES
OSTI Identifier:
1377489

Xu, Xiyan, Riley, William J., Koven, Charles D., Billesbach, Dave P., Chang, Rachel Y. -W., Commane, Róisín, Euskirchen, Eugénie S., Hartery, Sean, Harazono, Yoshinobu, Iwata, Hiroki, McDonald, Kyle C., Miller, Charles E., Oechel, Walter C., Poulter, Benjamin, Raz-Yaseef, Naama, Sweeney, Colm, Torn, Margaret, Wofsy, Steven C., Zhang, Zhen, and Zona, Donatella. A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands. United States: N. p., Web. doi:10.5194/bg-13-5043-2016.
Xu, Xiyan, Riley, William J., Koven, Charles D., Billesbach, Dave P., Chang, Rachel Y. -W., Commane, Róisín, Euskirchen, Eugénie S., Hartery, Sean, Harazono, Yoshinobu, Iwata, Hiroki, McDonald, Kyle C., Miller, Charles E., Oechel, Walter C., Poulter, Benjamin, Raz-Yaseef, Naama, Sweeney, Colm, Torn, Margaret, Wofsy, Steven C., Zhang, Zhen, & Zona, Donatella. A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands. United States. doi:10.5194/bg-13-5043-2016.
Xu, Xiyan, Riley, William J., Koven, Charles D., Billesbach, Dave P., Chang, Rachel Y. -W., Commane, Róisín, Euskirchen, Eugénie S., Hartery, Sean, Harazono, Yoshinobu, Iwata, Hiroki, McDonald, Kyle C., Miller, Charles E., Oechel, Walter C., Poulter, Benjamin, Raz-Yaseef, Naama, Sweeney, Colm, Torn, Margaret, Wofsy, Steven C., Zhang, Zhen, and Zona, Donatella. 2016. "A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands". United States. doi:10.5194/bg-13-5043-2016. https://www.osti.gov/servlets/purl/1377489.
@article{osti_1377489,
title = {A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands},
author = {Xu, Xiyan and Riley, William J. and Koven, Charles D. and Billesbach, Dave P. and Chang, Rachel Y. -W. and Commane, Róisín and Euskirchen, Eugénie S. and Hartery, Sean and Harazono, Yoshinobu and Iwata, Hiroki and McDonald, Kyle C. and Miller, Charles E. and Oechel, Walter C. and Poulter, Benjamin and Raz-Yaseef, Naama and Sweeney, Colm and Torn, Margaret and Wofsy, Steven C. and Zhang, Zhen and Zona, Donatella},
abstractNote = {Wetlands are the largest global natural methane (CH4) source, and emissions between 50 and 70° N latitude contribute 10-30 % to this source. Predictive capability of land models for northern wetland CH4 emissions is still low due to limited site measurements, strong spatial and temporal variability in emissions, and complex hydrological and biogeochemical dynamics. To explore this issue, we compare wetland CH4 emission predictions from the Community Land Model 4.5 (CLM4.5-BGC) 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 we implemented and tested. The model modification substantially reduced biases in CH4 emissions when compared with CarbonTracker CH4 predictions. CLM4.5 CH4 emission predictions agree well with growing season (May–September) CarbonTracker Alaskan regional-level CH4 predictions and site-level observations. However, CLM4.5 underestimated CH4 emissions in the cold season (October–April). The monthly atmospheric CH4 mole fraction enhancements due to wetland emissions are also assessed using the Weather Research and Forecasting-Stochastic Time-Inverted Lagrangian Transport (WRF-STILT) model coupled with daily emissions from CLM4.5 and compared with aircraft CH4 mole fraction 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 by CLM4.5. The greatest uncertainties in predicting the seasonal CH4 cycle are from the wetland extent, cold-season CH4 production and CH4 transport processes. We recommend more cold-season experimental studies in high-latitude systems, which could improve the understanding and parameterization of ecosystem structure and function during this period. Predicted CH4 emissions remain uncertain, but we show here that benchmarking against observations across spatial scales can inform model structural and parameter improvements.},
doi = {10.5194/bg-13-5043-2016},
journal = {Biogeosciences (Online)},
number = 17,
volume = 13,
place = {United States},
year = {2016},
month = {9}
}