Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic

Abstract

Methane emissions from organic-rich soils in the Arctic have been extensively studied due to their potential to increase the atmospheric methane burden as permafrost thaws1,2,3. However, this methane source might have been overestimated without considering high-affinity methanotrophs (HAMs; methane-oxidizing bacteria) recently identified in Arctic mineral soils4,5,6,7. Herein we find that integrating the dynamics of HAMs and methanogens into a biogeochemistry model8,9,10 that includes permafrost soil organic carbon dynamics3 leads to the upland methane sink doubling (~5.5 Tg CH4 yr−1) north of 50 °N in simulations from 2000–2016. The increase is equivalent to at least half of the difference in net methane emissions estimated between process-based models and observation-based inversions11,12, and the revised estimates better match site-level and regional observations5,7,13,14,15. The new model projects doubled wetland methane emissions between 2017–2100 due to more accessible permafrost carbon16,17,18. However, most of the increase in wetland emissions is offset by a concordant increase in the upland sink, leading to only an 18% increase in net methane emission (from 29 to 35 Tg CH4 yr−1). The projected net methane emissions may decrease further due to different physiological responses between HAMs and methanogens in response to increasing temperature19,20.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Fig. 1: A schematic diagram of XPTEM-XHAM.
Fig. 2: Annual estimates of the Arctic methane budget by three models for 2000–2016.
Fig. 3: The spatial variability of annual net methane flux north of 50 °N for XPTEM-XHAM.
Fig. 4: Projected annual net Arctic methane emissions from 2017 to 2100 north of 50 °N.

Data availability

The data are archived and freely available at the Purdue University Research Repository (PURR) at: https://purr.purdue.edu/publications/3284/1 (https://doi.org/10.4231/Q3R8-SZ17).

Code availability

The code is also archived and freely available at the Purdue University Research Repository (PURR) at: https://purr.purdue.edu/publications/3284/1 (https://doi.org/10.4231/Q3R8-SZ17).

References

  1. 1.

    McGuire, A. D. et al. Dependence of the evolution of carbon dynamics in the northern permafrost region on the trajectory of climate change. Proc. Natl Acad. Sci. USA 115, 3882–3887 (2018).

    Google Scholar 

  2. 2.

    Schuur, E. A. G. et al. Expert assessment of vulnerability of permafrost carbon to climate change. Clim. Change 119, 359–374 (2013).

    CAS  Google Scholar 

  3. 3.

    Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).

    CAS  Google Scholar 

  4. 4.

    Juncher Jørgensen, C., Lund Johansen, K. M., Westergaard-Nielsen, A. & Elberling, B. Net regional methane sink in High Arctic soils of northeast Greenland. Nat. Geosci. 8, 20–23 (2015).

    Google Scholar 

  5. 5.

    Lau, M. C. Y. et al. An active atmospheric methane sink in high Arctic mineral cryosols. ISME J. 9, 1880–1891 (2015).

    CAS  Google Scholar 

  6. 6.

    D’Imperio, L., Nielsen, C. S., Westergaard-Nielsen, A., Michelsen, A. & Elberling, B. Methane oxidation in contrasting soil types: responses to experimental warming with implication for landscape-integrated CH4 budget. Glob. Chang. Biol. 23, 966–976 (2017).

    Google Scholar 

  7. 7.

    Emmerton, C. A. et al. The net exchange of methane with high Arctic landscapes during the summer growing season. Biogeosciences 11, 3095–3106 (2014).

    Google Scholar 

  8. 8.

    Oh, Y. et al. A scalable model for methane consumption in arctic mineral soils. Geophys. Res. Lett. 43, 5143–5150 (2016).

    CAS  Google Scholar 

  9. 9.

    Zhuang, Q. et al. Methane fluxes between terrestrial ecosystems and the atmosphere at northern high latitudes during the past century: A retrospective analysis with a process-based biogeochemistry model. Global Biogeochem. Cy. 18, GB3010 (2004).

    Google Scholar 

  10. 10.

    Zhuang, Q. et al. Response of global soil consumption of atmospheric methane to changes in atmospheric climate and nitrogen deposition. Global Biogeochem. Cy. 27, 650–663 (2013).

    CAS  Google Scholar 

  11. 11.

    Bruhwiler, L. et al. CarbonTracker-CH4: an assimilation system for estimating emissions of atmospheric methane. Atmos. Chem. Phys. 14, 8269–8293 (2014).

    Google Scholar 

  12. 12.

    Saunois, M. et al. The global methane budget 2000–2012. Earth Syst. Sci. Data 8, 697–751 (2016).

    Google Scholar 

  13. 13.

    Bloom, A. A., Palmer, P. I., Fraser, A., Reay, D. S. & Frankenberg, C. Large-scale controls of methanogenesis inferred from methane and gravity spaceborne data. Science 327, 322–325 (2010).

    CAS  Google Scholar 

  14. 14.

    Bohn, T. J. et al. WETCHIMP-WSL: intercomparison of wetland methane emissions models over West Siberia. Biogeosciences 12, 3321–3349 (2015).

    Google Scholar 

  15. 15.

    Miller, S. M. et al. A multiyear estimate of methane fluxes in Alaska from CARVE atmospheric observations. Global Biogeochem. Cy. 30, 1441–1453 (2016).

    CAS  Google Scholar 

  16. 16.

    Hugelius, G. et al. A new data set for estimating organic carbon storage to 3 m depth in soils of the northern circumpolar permafrost region. Earth Syst. Sci. Data 5, 393–402 (2013).

    Google Scholar 

  17. 17.

    Koven, C. D. et al. Permafrost carbon-climate feedbacks accelerate global warming. Proc. Natl Acad. Sci. USA 108, 14769–14774 (2011).

    CAS  Google Scholar 

  18. 18.

    Lawrence, D. M., Koven, C. D., Swenson, S. C., Riley, W. J. & Slater, A. G. Permafrost thaw and resulting soil moisture changes regulate projected high-latitude CO2 and CH4 emissions. Environ. Res. Lett. 10, 094011 (2015).

  19. 19.

    Hagerty, S. B. et al. Accelerated microbial turnover but constant growth efficiency with warming in soil. Nat. Clim. Change 4, 903–906 (2014).

    CAS  Google Scholar 

  20. 20.

    Trimmer, M. et al. Riverbed methanotrophy sustained by high carbon conversion efficiency. ISME J. 9, 2304–2314 (2015).

    CAS  Google Scholar 

  21. 21.

    Schuur, E. A. G. et al. The effect of permafrost thaw on old carbon release and net carbon exchange from tundra. Nature 459, 556–559 (2009).

    CAS  Google Scholar 

  22. 22.

    Christiansen, J. R. et al. Methane fluxes and the functional groups of methanotrophs and methanogens in a young Arctic landscape on Disko Island, West Greenland. Biogeochemistry 122, 15–33 (2015).

    CAS  Google Scholar 

  23. 23.

    Baani, M. & Liesack, W. Two isozymes of particulate methane monooxygenase with different methane oxidation kinetics are found in Methylocystis sp. strain SC2. Proc. Natl Acad. Sci. USA 105, 10203–10208 (2008).

    CAS  Google Scholar 

  24. 24.

    Tveit, A. T. et al. Widespread soil bacterium that oxidizes atmospheric methane. Proc. Natl Acad. Sci. USA 116, 8515–8524 (2019).

    CAS  Google Scholar 

  25. 25.

    Segers, R. Methane production and methane consumption: a review of processes underlying wetland methane fluxes. Biogeochemistry 41, 23–51 (1998).

    CAS  Google Scholar 

  26. 26.

    Wieder, W. R., Bonan, G. B. & Allison, S. D. Global soil carbon projections are improved by modelling microbial processes. Nat. Clim. Change 3, 909–912 (2013).

    CAS  Google Scholar 

  27. 27.

    Von Stockar, U. & Liu, J. S. Does microbial life always feed on negative entropy? Thermodynamic analysis of microbial growth. Biochim. Biophys. Acta Bioenerg. 1412, 191–211 (1999).

    Google Scholar 

  28. 28.

    Tijhuis, L., Van Loosdrecht, M. C. M. & Heijnen, J. J. A thermodynamically based correlation for maintenance gibbs energy requirements in aerobic and anaerobic chemotrophic growth. Biotechnol. Bioeng. 42, 509–519 (1993).

    CAS  Google Scholar 

  29. 29.

    Knoblauch, C., Spott, O., Evgrafova, S., Kutzbach, L. & Pfeiffer, E. Regulation of methane production, oxidation, and emission by vascular plants and bryophytes in ponds of the northeast Siberian polygonal tundra. J. Geophys. Res. Biogeosci. 120, 2525–2541 (2015).

    CAS  Google Scholar 

  30. 30.

    Throckmorton, H. M. et al. Active layer hydrology in an arctic tundra ecosystem: quantifying water sources and cycling using water stable isotopes. Hydrol. Process. 30, 4972–4986 (2016).

    Google Scholar 

  31. 31.

    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 Dataset. Int. J. Climatol. 34, 623–642 (2014).

    Google Scholar 

  32. 32.

    Meinshausen, M. et al. The RCP greenhouse gas concentrations and their extensions from 1765 to 2300. Clim. Change 109, 213–241 (2011).

    CAS  Google Scholar 

  33. 33.

    Matthews, Elaine & Fung, I. Methane emission from natural wetlands: global distribution, area, and environmental characteristics of sources. Global Biogeochem. Cy. 1, 61–86 (1987).

    CAS  Google Scholar 

  34. 34.

    Poulter, B. et al. Global wetland contribution to 2000–2012 atmospheric methane growth rate dynamics. Environ. Res. Lett. 12, 094013 (2017).

  35. 35.

    Lawrence, D. et al. Technical Description of Version 5.0 of the Community Land Model (CLM) 4245–4287 (The National Center for Atmospheric Research, 2018).

  36. 36.

    Sepulveda-Jauregui, A., Walter Anthony, K. M., Martinez-Cruz, K., Greene, S. & Thalasso, F. Methane and carbon dioxide emissions from 40 lakes along a north-south latitudinal transect in Alaska. Biogeosciences 12, 3197–3223 (2015).

    CAS  Google Scholar 

  37. 37.

    McCalley, C. K. et al. Methane dynamics regulated by microbial community response to permafrost thaw. Nature 514, 478–481 (2014).

    CAS  Google Scholar 

  38. 38.

    Liljedahl, A. K. et al. Pan-Arctic ice-wedge degradation in warming permafrost and its influence on tundra hydrology. Nat. Geosci. 9, 312–318 (2016).

    CAS  Google Scholar 

  39. 39.

    Nauta, A. L. et al. Permafrost collapse after shrub removal shifts tundra ecosystem to a methane source. Nat. Clim. Change 5, 67–70 (2015).

    CAS  Google Scholar 

  40. 40.

    Wik, M., Varner, R. K., Anthony, K. W., MacIntyre, S. & Bastviken, D. Climate-sensitive northern lakes and ponds are critical components of methane release. Nat. Geosci. 9, 99–105 (2016).

    CAS  Google Scholar 

  41. 41.

    Pedersen, E. P., Michelsen, A. & Elberling, B. In situ CH4 oxidation inhibition and 13CH4 labeling reveal methane oxidation and emission patterns in a subarctic heath ecosystem. Biogeochemistry 138, 197–213 (2018).

    CAS  Google Scholar 

  42. 42.

    Zhuang, Q. et al. Modeling soil thermal and carbon dynamics of a fire chronosequence in interior Alaska. J. Geophys. Res. D 108, 8147 (2003).

  43. 43.

    Walter, B. P. & Heimann, M. A process‐based, climate‐sensitive model to derive methane emissions from natural wetlands: application to five wetland sites, sensitivity to model parameters, and climate. Global Biogeochem. Cy. 14, 745–765 (2000).

    CAS  Google Scholar 

  44. 44.

    Lau, M. C. Y. et al. An oligotrophic deep-subsurface community dependent on syntrophy is dominated by sulfur-driven autotrophic denitrifiers. Proc. Natl Acad. Sci. USA 113, E7927–E7936 (2016).

    CAS  Google Scholar 

  45. 45.

    Stackhouse, B. T. et al. Effects of simulated spring thaw of permafrost from mineral cryosol on CO2 emissions and atmospheric CH4 uptake. J. Geophys. Res. Biogeosciences 120, 1764–1784 (2015).

    CAS  Google Scholar 

  46. 46.

    Thauer, R. K., Kaster, A. K., Seedorf, H., Buckel, W. & Hedderich, R. Methanogenic archaea: ecologically relevant differences in energy conservation. Nat. Rev. Microbiol. 6, 579–591 (2008).

    CAS  Google Scholar 

  47. 47.

    Gottschalk, G. Bacterial Metabolism (Springer Science & Business Media, 2012).

  48. 48.

    Von Stockar, U., Maskow, T., Liu, J., Marison, I. W. & Patiño, R. Thermodynamics of microbial growth and metabolism: an analysis of the current situation. J. Biotechnol. 121, 517–533 (2006).

    Google Scholar 

  49. 49.

    Stackhouse, B. et al. Atmospheric CH4 oxidation by Arctic permafrost and mineral cryosols as a function of water saturation and temperature. Geobiology 15, 94–111 (2017).

    CAS  Google Scholar 

  50. 50.

    Conrad, R. The global methane cycle: recent advances in understanding the microbial processes involved. Environ. Microbiol. Rep. 1, 285–292 (2009).

    CAS  Google Scholar 

  51. 51.

    Sellers, P. J. et al. BOREAS in 1997: experiment overview, scientific results, and future directions. J. Geophys. Res. Atmos. 102, 28731–28769 (1997).

    Google Scholar 

  52. 52.

    Harazono, Y. et al. Temporal and spatial differences of methane flux at arctic tundra in Alaska. Mem. Natl Inst. Polar Res. 59, 79–95 (2006).

    CAS  Google Scholar 

  53. 53.

    Dinsmore, K. J. et al. Growing season CH4 and N2O fluxes from a subarctic landscape in northern Finland; from chamber to landscape scale. Biogeosciences 14, 799–815 (2017).

    CAS  Google Scholar 

  54. 54.

    Duan, Q. Y., Gupta, V. K. & Sorooshian, S. Shuffled complex evolution approach for effective and efficient global minimization. J. Optim. Theory Appl. 76, 501–521 (1993).

    Google Scholar 

  55. 55.

    Melillo, J. M. et al. Global climate change and terrestrial net primary production. Nature 363, 234–240 (1993).

    Google Scholar 

  56. 56.

    Global Soil Data Task (IGBP-DIS, ISO-image of CD). (International Geosphere-Biosphere Program, PANGAEA, 2000); https://doi.org/10.1594/PANGAEA.869912

  57. 57.

    Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).

    Google Scholar 

  58. 58.

    Peters, W. et al. An ensemble data assimilation system to estimate CO2 surface fluxes from atmospheric trace gas observations. J. Geophys. Res. Atmos. 110, D24304 (2005).

    Google Scholar 

  59. 59.

    Krol, M. et al. The two-way nested global chemistry-transport zoom model TM5: algorithm and applications. Atmos. Chem. Phys. 5, 417–432 (2005).

    CAS  Google Scholar 

  60. 60.

    Seinfeld, J. H., Pandis, S. N. & Noone, K. Atmospheric chemistry and physics: from air pollution to climate change. Phys. Today 51, 88 (1998).

    Google Scholar 

Download references

Acknowledgements

This work was supported by NASA Earth and Space Science Fellowship Program (grant no. 80NSSC17K0368 P00001) and NASA Interdisciplinary Research in Earth Science (grant no. NNX17AK20G). B.E. acknowledges Danish National Research Foundation (grant no. CENPERM DNRF100) for financial support. We also thank W. Wieder for providing model results and valuable discussions.

Author information

Affiliations

Authors

Contributions

Y.O., Q.Z., M.C.L., T.C.O. and D.M. conceived the study. Y.O., Q.Z. and L.L. built the model. L.D., B.E. and G.H. provided unpublished or raw data. Y.O. conducted the model runs. All authors contributed to data interpretation and preparation of manuscript text.

Corresponding author

Correspondence to Qianlai Zhuang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Climate Change thanks Lauren Hale and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Pan-Arctic monthly mean methane fluxes for XPTEM-XHAM and PTEM-HAM from 2000-2016 north of 50°N.

Estimates of pan-arctic (a, c) monthly wetland methane emission and (b, d) monthly upland methane consumption in mg m-2 day-1 for (a, b) XPTEM-XHAM and (c, d) PTEM-HAM model. The blue line is monthly averages over 2000-2016, and grey lines represent values of each year.

Extended Data Fig. 2 Inter-annual variability of methane fluxes from 2000 – 2016 north of 50°N.

(Left) Annual estimates of pan-arctic (a) wetland methane emission, (b) upland methane consumption, and (c) net methane emission for XPTEM-XHAM (blue), PTEM-HAM (yellow), and TEM (red) in TgCH4yr-1 from 2000-2016. The shaded area represents one standard deviation of models determined by varying the optimized parameters. (Right) Mean and one standard deviation averaged over the simulation period for each metric are given by the bars. Panel (c) additionally shows mean and one standard deviation of previous estimates of net methane emission estimated by top-down inversions (times symbol) by the bars.

Extended Data Fig. 3 Spatial variability of soil and vegetation properties north of 50°N.

(a) annual top 10-cm soil temperature in °C, (b) annual top 10-cm soil moisture in % volume, (c) monthly net primary productivity in gC m-2 month-1, and (d) permafrost SOC stored in the top 3-m in kg m-23,16. The soil temperature, moisture, and net primary productivity were averaged over the contemporary period during 2000-2016. The dotted longitudinal lines are at 30° intervals, and the latitudinal line is at 65°N.

Extended Data Fig. 4 Inter-annual variability of methane fluxes using time-varying inundation fraction from 2000 – 2012 north of 50°N.

Annual estimates of pan-arctic (a) net methane emission, (b) wetland methane emission, and (c) upland methane consumption for XPTEM-XHAM model using static inundation fraction33 (blue) and time-varying inundation fraction from SWAMPS-GLWD34 (green) in TgCH4yr-1. The shaded area represents one standard deviation determined by varying the optimized parameters.

Extended Data Fig. 5 Model-data comparison of methane fluxes using site-level data.

Comparison of (a) wetland methane emission and (b) upland methane consumption of data from 46 in situ measurements (supplementary table 5) with simulation results from XPTEM-XHAM (blue), PTEM-HAM (yellow), and TEM (red).

Extended Data Fig. 6 Inter-annual variability of methane fluxes using time-varying inundation fraction from 2017 – 2100 north of 50°N.

Annual estimates of pan-Arctic (a) net methane emission, (b) wetland methane emission, and (c) upland methane consumption for XPTEM-XHAM model using static inundation fraction (blue) and dynamic inundation fraction (green) in TgCH4yr-1 using RCP 2.6 (dotted), RCP 4.5 (dashed), and RCP 8.5 (solid).

Extended Data Fig. 7 Future Arctic methane feedbacks.

Previous studies predicted a positive feedback between temperature increase and methane emission (circles 1–2). However, because high-affinity methanotrophs may respond strongly to temperature and less strongly to soil moisture due to uncertain Arctic hydrology (circles 3–4), this feedback may be partially suppressed. Moreover, explicit modeling of microbial dynamics (circle 5) will facilitate future model developments that include effects of microbial physiology (modified Fig. 5 of Oh et al., 8).

Supplementary information

Supplementary Information

Supplementary Methods 1–6, Figs. 1–16, Tables 1–8 and references.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Oh, Y., Zhuang, Q., Liu, L. et al. Reduced net methane emissions due to microbial methane oxidation in a warmer Arctic. Nat. Clim. Chang. 10, 317–321 (2020). https://doi.org/10.1038/s41558-020-0734-z

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing