Journal cover Journal topic
Biogeosciences An interactive open-access journal of the European Geosciences Union
Biogeosciences, 12, 3321-3349, 2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Research article
03 Jun 2015
WETCHIMP-WSL: intercomparison of wetland methane emissions models over West Siberia
T. J. Bohn1, J. R. Melton2, A. Ito3, T. Kleinen4, R. Spahni5,6, B. D. Stocker5,7, B. Zhang8, X. Zhu9,10,11, R. Schroeder12,13, M. V. Glagolev14,15,16,17, S. Maksyutov3,16, V. Brovkin4, G. Chen18, S. N. Denisov19, A. V. Eliseev19,20, A. Gallego-Sala21, K. C. McDonald12, M.A. Rawlins22, W. J. Riley11, Z. M. Subin11, H. Tian8, Q. Zhuang9, and J. O. Kaplan23 1School of Earth and Space Exploration, Arizona State University, Tempe, AZ, USA
2Canadian Centre for Climate Modelling and Analysis, Environment Canada, Victoria, BC, Canada
3National Institute for Environmental Studies, Tsukuba, Japan
4Max Planck Institute for Meteorology, Hamburg, Germany
5Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland
6Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
7Department of Life Sciences, Imperial College, Silwood Park Campus, Ascot, UK
8International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, USA
9Department of Earth, Atmospheric, and Planetary Sciences, Purdue University, West Lafayette, IN, USA
10Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA
11Earth Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
12City College of New York, City University of New York, New York, NY, USA
13Institute of Botany, University of Hohenheim, Stuttgart, Germany
14Moscow State University, Moscow, Russia
15Institute of Forest Science, Russian Academy of Sciences, Uspenskoe, Russia
16Laboratory of Computational Geophysics, Tomsk State University, Tomsk, Russia
17Yugra State University, Khanty-Mantsiysk, Russia
18Oak Ridge National Laboratory, Oak Ridge, TN, USA
19A. M. Obukhov Institute of Atmospheric Physics, Russian Academy of Sciences, Moscow, Russia
20Kazan Federal University, Kazan, Russia
21Department of Geography, University of Exeter, Exeter, UK
22Department of Geosciences, University of Massachusetts, Amherst, MA, USA
23Institute of Earth Surface Dynamics, University of Lausanne, Lausanne, Switzerland
Abstract. 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.

Citation: 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, Biogeosciences, 12, 3321-3349,, 2015.
Publications Copernicus
Short summary
We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions and simulated wetland areas and compared these results to an intensive in situ CH4 flux data set, several wetland maps, and two satellite inundation products. In addition to assembling a definitive collection of methane emissions estimates for the region, we were able to identify the types of wetland maps and model features necessary for accurate simulations of high-latitude wetlands.
We evaluated 21 forward models and 5 inversions over western Siberia in terms of CH4 emissions...