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Title: WETCHIMP-WSL: Intercomparison of wetland methane emissions models over West Siberia

Abstract

It is known that wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of methane emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This risk is particularly relevant at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over the next 100 years. However, global models disagree as to the magnitude and spatial distribution of emissions, due to uncertainties in wetland area and emissions per unit area and a scarcity of in situ observations. Recent intensive field campaigns across the West Siberian Lowland (WSL) make this an ideal region over which to assess the performance of large-scale process-based wetland models in a high-latitude environment. Here we present the results of a follow-up to the Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP), focused on the West Siberian Lowland (WETCHIMP-WSL). We assessed 21 models and 5 inversions over this domain in terms of total CH4 emissions, simulated wetland areas, and CH4 fluxes per unit wetland area and compared these results to an intensive in situ CH4 flux data set, severalmore » wetland maps, and two satellite surface water products. We found that (a) despite the large scatter of individual estimates, 12-year mean estimates of annual total emissions over the WSL from forward models (5.34 ± 0.54 Tg CH4 yr-1), inversions (6.06 ± 1.22 Tg CH4 yr-1), and in situ observations (3.91 ± 1.29 Tg CH4 yr-1) largely agreed; (b) forward models using surface water products alone to estimate wetland areas suffered from severe biases in CH4 emissions; (c) the interannual time series of models that lacked either soil thermal physics appropriate to the high latitudes or realistic emissions from unsaturated peatlands tended to be dominated by a single environmental driver (inundation or air temperature), unlike those of inversions and more sophisticated forward models; (d) differences in biogeochemical schemes across models had relatively smaller influence over performance; and (e) multiyear or multidecade observational records are crucial for evaluating models' responses to long-term climate change.« less

Authors:
 [1];  [2];  [3];  [4];  [5];  [6];  [7];  [8];  [9];  [10];  [11];  [4];  [12];  [13];  [14];  [15];  [16];  [17];  [18];  [18] more »;  [7];  [19];  [20] « less
  1. Arizona State Univ., Tempe, AZ (United States)
  2. Canadian Centre for Climate Modelling and Analysis, Victoria (Canada)
  3. National Institute for Environmental Studies, Tsukuba (Japan)
  4. Max Planck Institute for Meteorology, Hamburg (Germany)
  5. Univ. of Bern, Bern (Switzerland)
  6. Univ. of Bern, Bern (Switzerland); Imperial College, Ascot (United Kingdom)
  7. Auburn Univ., Auburn, AL (United States)
  8. Purdue Univ., West Lafayette, IN (United States); Colorado State Univ., Fort Collins, CO (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  9. City Univ. of New York, New York, NY (United States); Univ. of Hohenheim, Stuttgart (Germany)
  10. Moscow State Univ., Moscow (Russian Federation); Russain Academy of Sciences, Uspenskoe (Russia); Tomsk State Univ., Tomsk (Russia); Yugra State Univ., Khanty-Mantsiysk (Russia)
  11. National Institute for Environmental Studies, Tsukuba (Japan); Tomsk State Univ., Tomsk (Russia)
  12. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  13. Russian Academy of Sciences (RAS), Moscow (Russian Federation)
  14. Russian Academy of Sciences (RAS), Moscow (Russian Federation); Kazan Federal Univ., Kazan (Russia)
  15. Univ. of Exeter, Exeter (United Kingdom)
  16. City Univ. of New York, New York, NY (United States)
  17. Univ. of Massachusetts, Amherst, MA (United States)
  18. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  19. Purdue Univ., West Lafayette, IN (United States)
  20. Univ. of Lausanne, Lausanne (Switzerland)
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1209490
Alternate Identifier(s):
OSTI ID: 1265354
Grant/Contract Number:  
SC0007007; AC02-05CH11231; AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Biogeosciences (Online)
Additional Journal Information:
Journal Name: Biogeosciences (Online); Journal Volume: 12; Journal Issue: 11; Journal ID: ISSN 1726-4189
Publisher:
European Geosciences Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 58 GEOSCIENCES

Citation Formats

Bohn, T. J., Melton, J. R., Ito, A., Kleinen, T., Spahni, R., Stocker, B. D., Zhang, B., Zhu, X., Schroeder, R., Glagolev, M. V., Maksyutov, S., Brovkin, V., Chen, G., Denisov, S. N., Eliseev, A. V., Gallego-Sala, A., McDonald, K. C., Rawlins, M. A., Riley, W. J., Subin, Z. M., Tian, H., Zhuang, Q., and Kaplan, J. O. WETCHIMP-WSL: Intercomparison of wetland methane emissions models over West Siberia. United States: N. p., 2015. Web. doi:10.5194/bg-12-3321-2015.
Bohn, T. J., Melton, J. R., Ito, A., Kleinen, T., Spahni, R., Stocker, B. D., Zhang, B., Zhu, X., Schroeder, R., Glagolev, M. V., Maksyutov, S., Brovkin, V., Chen, G., Denisov, S. N., Eliseev, A. V., Gallego-Sala, A., McDonald, K. C., Rawlins, M. A., Riley, W. J., Subin, Z. M., Tian, H., Zhuang, Q., & Kaplan, J. O. WETCHIMP-WSL: Intercomparison of wetland methane emissions models over West Siberia. United States. https://doi.org/10.5194/bg-12-3321-2015
Bohn, T. J., Melton, J. R., Ito, A., Kleinen, T., Spahni, R., Stocker, B. D., Zhang, B., Zhu, X., Schroeder, R., Glagolev, M. V., Maksyutov, S., Brovkin, V., Chen, G., Denisov, S. N., Eliseev, A. V., Gallego-Sala, A., McDonald, K. C., Rawlins, M. A., Riley, W. J., Subin, Z. M., Tian, H., Zhuang, Q., and Kaplan, J. O. Wed . "WETCHIMP-WSL: Intercomparison of wetland methane emissions models over West Siberia". United States. https://doi.org/10.5194/bg-12-3321-2015. https://www.osti.gov/servlets/purl/1209490.
@article{osti_1209490,
title = {WETCHIMP-WSL: Intercomparison of wetland methane emissions models over West Siberia},
author = {Bohn, T. J. and Melton, J. R. and Ito, A. and Kleinen, T. and Spahni, R. and Stocker, B. D. and Zhang, B. and Zhu, X. and Schroeder, R. and Glagolev, M. V. and Maksyutov, S. and Brovkin, V. and Chen, G. and Denisov, S. N. and Eliseev, A. V. and Gallego-Sala, A. and McDonald, K. C. and Rawlins, M. A. and Riley, W. J. and Subin, Z. M. and Tian, H. and Zhuang, Q. and Kaplan, J. O.},
abstractNote = {It is known that wetlands are the world's largest natural source of methane, a powerful greenhouse gas. The strong sensitivity of methane emissions to environmental factors such as soil temperature and moisture has led to concerns about potential positive feedbacks to climate change. This risk is particularly relevant at high latitudes, which have experienced pronounced warming and where thawing permafrost could potentially liberate large amounts of labile carbon over the next 100 years. However, global models disagree as to the magnitude and spatial distribution of emissions, due to uncertainties in wetland area and emissions per unit area and a scarcity of in situ observations. Recent intensive field campaigns across the West Siberian Lowland (WSL) make this an ideal region over which to assess the performance of large-scale process-based wetland models in a high-latitude environment. Here we present the results of a follow-up to the Wetland and Wetland CH4 Intercomparison of Models Project (WETCHIMP), focused on the West Siberian Lowland (WETCHIMP-WSL). We assessed 21 models and 5 inversions over this domain in terms of total CH4 emissions, simulated wetland areas, and CH4 fluxes per unit wetland area and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite surface water products. We found that (a) despite the large scatter of individual estimates, 12-year mean estimates of annual total emissions over the WSL from forward models (5.34 ± 0.54 Tg CH4 yr-1), inversions (6.06 ± 1.22 Tg CH4 yr-1), and in situ observations (3.91 ± 1.29 Tg CH4 yr-1) largely agreed; (b) forward models using surface water products alone to estimate wetland areas suffered from severe biases in CH4 emissions; (c) the interannual time series of models that lacked either soil thermal physics appropriate to the high latitudes or realistic emissions from unsaturated peatlands tended to be dominated by a single environmental driver (inundation or air temperature), unlike those of inversions and more sophisticated forward models; (d) differences in biogeochemical schemes across models had relatively smaller influence over performance; and (e) multiyear or multidecade observational records are crucial for evaluating models' responses to long-term climate change.},
doi = {10.5194/bg-12-3321-2015},
journal = {Biogeosciences (Online)},
number = 11,
volume = 12,
place = {United States},
year = {Wed Jun 03 00:00:00 EDT 2015},
month = {Wed Jun 03 00:00:00 EDT 2015}
}

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journal, November 2010


Works referencing / citing this record:

Increasing Methane Emissions From Natural Land Ecosystems due to Sea‐Level Rise
journal, May 2018

  • Lu, Xiaoliang; Zhou, Yuyu; Zhuang, Qianlai
  • Journal of Geophysical Research: Biogeosciences, Vol. 123, Issue 5
  • DOI: 10.1029/2017jg004273

Cold season emissions dominate the Arctic tundra methane budget
journal, December 2015

  • Zona, Donatella; Gioli, Beniamino; Commane, Róisín
  • Proceedings of the National Academy of Sciences, Vol. 113, Issue 1
  • DOI: 10.1073/pnas.1516017113

Attribution of changes in global wetland methane emissions from pre-industrial to present using CLM4.5-BGC
journal, March 2016


Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics
journal, September 2017

  • Poulter, Benjamin; Bousquet, Philippe; Canadell, Josep G.
  • Environmental Research Letters, Vol. 12, Issue 9
  • DOI: 10.1088/1748-9326/aa8391

Analysis of the Diurnal, Weekly, and Seasonal Cycles and Annual Trends in Atmospheric CO2 and CH4 at Tower Network in Siberia from 2005 to 2016
journal, November 2019

  • Belikov, Dmitry; Arshinov, Mikhail; Belan, Boris
  • Atmosphere, Vol. 10, Issue 11
  • DOI: 10.3390/atmos10110689

Methane Content in Ground Ice and Sediments of the Kara Sea Coast
journal, November 2018


Soil Moisture Monitoring in a Temperate Peatland Using Multi-Sensor Remote Sensing and Linear Mixed Effects
journal, June 2018

  • Millard, Koreen; Thompson, Dan; Parisien, Marc-André
  • Remote Sensing, Vol. 10, Issue 6
  • DOI: 10.3390/rs10060903

A High-Resolution Airborne Color-Infrared Camera Water Mask for the NASA ABoVE Campaign
journal, September 2019

  • Kyzivat, Ethan D.; Smith, Laurence C.; Pitcher, Lincoln H.
  • Remote Sensing, Vol. 11, Issue 18
  • DOI: 10.3390/rs11182163

Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data
journal, December 2015

  • Schroeder, Ronny; McDonald, Kyle; Chapman, Bruce
  • Remote Sensing, Vol. 7, Issue 12
  • DOI: 10.3390/rs71215843

Using δ 13 C-CH 4 and δ D-CH 4 to constrain Arctic methane emissions
journal, January 2016

  • Warwick, Nicola J.; Cain, Michelle L.; Fisher, Rebecca
  • Atmospheric Chemistry and Physics, Vol. 16, Issue 23
  • DOI: 10.5194/acp-16-14891-2016

Atmospheric constraints on the methane emissions from the East Siberian Shelf
journal, January 2016

  • Berchet, Antoine; Bousquet, Philippe; Pison, Isabelle
  • Atmospheric Chemistry and Physics, Vol. 16, Issue 6
  • DOI: 10.5194/acp-16-4147-2016

Methane fluxes in the high northern latitudes for 2005–2013 estimated using a Bayesian atmospheric inversion
journal, January 2017

  • Thompson, Rona L.; Sasakawa, Motoki; Machida, Toshinobu
  • Atmospheric Chemistry and Physics, Vol. 17, Issue 5
  • DOI: 10.5194/acp-17-3553-2017

Reviews and syntheses: Four decades of modeling methane cycling in terrestrial ecosystems
journal, January 2016


A multi-scale comparison of modeled and observed seasonal methane emissions in northern wetlands
journal, January 2016


Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations
journal, January 2019


Calibrating the sqHIMMELI v1.0 wetland methane emission model with hierarchical modeling and adaptive MCMC
journal, January 2018

  • Susiluoto, Jouni; Raivonen, Maarit; Backman, Leif
  • Geoscientific Model Development, Vol. 11, Issue 3
  • DOI: 10.5194/gmd-11-1199-2018

Consumption of atmospheric methane by the Qinghai–Tibet Plateau alpine steppe ecosystem
journal, January 2018


Space‐Based Observations for Understanding Changes in the Arctic‐Boreal Zone
journal, January 2020

  • Duncan, Bryan N.; Ott, Lesley E.; Abshire, James B.
  • Reviews of Geophysics, Vol. 58, Issue 1
  • DOI: 10.1029/2019rg000652

Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic
journal, March 2020


Assessment of the theoretical limit in instrumental detectability of northern high-latitude methane sources using δ13CCH4 atmospheric signals
journal, January 2019

  • Thonat, Thibaud; Saunois, Marielle; Pison, Isabelle
  • Atmospheric Chemistry and Physics, Vol. 19, Issue 19
  • DOI: 10.5194/acp-19-12141-2019

Modeling micro-topographic controls on boreal peatland hydrology and methane fluxes
journal, January 2015


Year-round simulated methane emissions from a permafrost ecosystem in Northeast Siberia
journal, January 2018

  • Castro-Morales, Karel; Kleinen, Thomas; Kaiser, Sonja
  • Biogeosciences, Vol. 15, Issue 9
  • DOI: 10.5194/bg-15-2691-2018

The global methane budget 2000–2012
journal, January 2016

  • Saunois, Marielle; Bousquet, Philippe; Poulter, Ben
  • Earth System Science Data, Vol. 8, Issue 2
  • DOI: 10.5194/essd-8-697-2016

Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics
text, January 2017

  • Poulter, Benjamin; Bousquet, Philippe; Canadell, Josep G.
  • IOP Publishing
  • DOI: 10.7892/boris.106533

Variability and quasi-decadal changes in the methane budget over the period 2000–2012
text, January 2017

  • Saunois, Marielle; Bousquet, Philippe; Poulter, Ben
  • European Geosciences Union
  • DOI: 10.7892/boris.106534

Monthly gridded data product of northern wetland methane emissions based on upscaling eddy covariance observations
text, January 2019


A New Process-Based Soil Methane Scheme: Evaluation Over Arctic Field Sites With the ISBA Land Surface Model
journal, January 2019

  • Morel, X.; Decharme, B.; Delire, C.
  • Journal of Advances in Modeling Earth Systems, Vol. 11, Issue 1
  • DOI: 10.1029/2018ms001329

Northern Eurasia Future Initiative (NEFI): facing the challenges and pathways of global change in the twenty-first century
journal, December 2017

  • Groisman, Pavel; Shugart, Herman; Kicklighter, David
  • Progress in Earth and Planetary Science, Vol. 4, Issue 1
  • DOI: 10.1186/s40645-017-0154-5

An assessment of natural methane fluxes simulated by the CLASS-CTEM model
journal, January 2018


The global methane budget 2000--2012
text, January 2016

  • Saunois, Marielle; Bousquet, Philippe; Poulter, Ben
  • Copernicus Publications
  • DOI: 10.7892/boris.91436

A New Process-Based Soil Methane Scheme: Evaluation Over Arctic Field Sites With the ISBA Land Surface Model
journal, January 2019

  • Morel, X.; Decharme, B.; Delire, C.
  • Journal of Advances in Modeling Earth Systems, Vol. 11, Issue 1
  • DOI: 10.1029/2018ms001329