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Siberian carbon sink reduced by forest disturbances

Nature Geoscience volume 16, pages 56–62 (2023)Cite this article

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Abstract

Siberian forests are generally thought to have acted as an important carbon sink over recent decades, but exposure to severe droughts and fire disturbances may have impacted their carbon dynamics. Limited available forest inventories mean the carbon balance remains uncertain. Here we analyse annual live and dead above-ground carbon changes derived from low-frequency passive microwave observations from 2010 to 2019. We find that during this period, the carbon balance of Siberian forests was close to neutral, with the forests acting as a small carbon sink of \(+ 0.02_{ + 0.01}^{ + 0.03}\) PgC yr−1. Carbon storage in dead wood increased, but this was largely offset by a decrease in live biomass. Substantial losses of live above-ground carbon are attributed to fire and drought, such as the widespread fires in northern Siberia in 2012 and extreme drought in eastern Siberia in 2015. These live above-ground carbon losses contrast with ‘greening’ trends seen in leaf area index over the same period, a decoupling explained by faster post-disturbance recovery of leaf area than live above-ground carbon. Our study highlights the vulnerability of large forest carbon stores in Siberia to climate-induced disturbances, challenging the persistence of the carbon sink in this region of the globe.

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Fig. 1: Temporal variations in annual AGC and LAI over Siberian forests.
Fig. 2: Spatial patterns of net changes in AGC, CWDC and LAI.
Fig. 3: Interannual variation of AGClive, LAI and hydrological indices for a single 25 km grid cell.
Fig. 4: The responses of AGClive and LAI to wildfire.

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Data availability

L-VOD and soil moisture data from this study are freely available from the SMOS-IC website (https://ib.remote-sensing.inrae.fr/). AGCtot, AGClive and CWDC products are freely available from https://doi.org/10.11888/Terre.tpdc.272842. The Saatchi biomass map is available upon request from Dr. S. Saatchi (sasan.s.saatchi@jpl.nasa.gov). Tree species maps are available upon request from D. Schepaschenko (schepd@iiasa.ac.at) or from http://webarchive.iiasa.ac.at/Research/FOR/forest_cdrom/english/for_prod_en.html. Additional data used in the paper are publicly available, with their locations provided in the respective references.

References

  1. Keenan, R. J. et al. Dynamics of global forest area: results from the FAO Global Forest Resources Assessment 2015. For. Ecol. Manage. 352, 9–20 (2015).

    Article  Google Scholar 

  2. Arneth, A. et al. in Special Report on Climate Change and Land (eds Shukla, P. R. et al.) Ch. 1 (IPCC, 2019).

  3. Piao, S. et al. Growing season extension and its impact on terrestrial carbon cycle in the Northern Hemisphere over the past 2 decades. Glob. Biogeochem. Cycles 21, GB3018 (2007).

    Article  Google Scholar 

  4. Chen, C. et al. China and India lead in greening of the world through land-use management. Nat. Sustain. 2, 122–129 (2019).

    Article  Google Scholar 

  5. Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2020).

    Article  Google Scholar 

  6. Liu, Y. Y. et al. Recent reversal in loss of global terrestrial biomass. Nat. Clim. Change 5, 470–474 (2015).

    Article  Google Scholar 

  7. Chen, J. M. et al. Vegetation structural change since 1981 significantly enhanced the terrestrial carbon sink. Nat. Commun. 10, 4259 (2019).

    Article  Google Scholar 

  8. Myneni, R. B. et al. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).

    Article  Google Scholar 

  9. Filipchuk, A. et al. Russian forests: a new approach to the assessment of carbon stocks and sequestration capacity. Environ. Dev. 26, 68–75 (2018).

    Article  Google Scholar 

  10. Goodale, C. L. et al. Forest carbon sinks in the Northern Hemisphere. Ecol. Appl. 12, 891–899 (2002).

    Article  Google Scholar 

  11. Tchebakova, N. M. et al. Energy and mass exchange and the productivity of main Siberian ecosystems (from eddy covariance measurements). 2. Carbon exchange and productivity. Biol. Bull. 42, 579–588 (2015).

    Article  Google Scholar 

  12. Vaganov, E. A. et al. Forests and swamps of Siberia in the global carbon cycle. Contemp. Probl. Ecol. 1, 168–182 (2008).

    Article  Google Scholar 

  13. Schepaschenko, D. et al. Russian forest sequesters substantially more carbon than previously reported. Sci. Rep. 11, 12825 (2021).

    Article  Google Scholar 

  14. Shvidenko, A. & Schepaschenko, D. Climate change and wildfires in Russia. Contemp. Probl. Ecol. 6, 683–692 (2013).

    Article  Google Scholar 

  15. Bradshaw, C. J. A. & Warkentin, I. G. Global estimates of boreal forest carbon stocks and flux. Glob. Planet. Change 128, 24–30 (2015).

    Article  Google Scholar 

  16. Curtis, P. G. et al. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    Article  Google Scholar 

  17. Sukhinin, A. I. et al. AVHRR-based mapping of fires in Russia: new products for fire management and carbon cycle studies. Remote Sens. Environ. 93, 546–564 (2004).

    Article  Google Scholar 

  18. Soja, A. J. et al. Climate-induced boreal forest change: predictions versus current observations. Glob. Planet. Change 56, 274–296 (2007).

    Article  Google Scholar 

  19. Dolman, A. J. et al. An estimate of the terrestrial carbon budget of Russia using inventory-based, eddy covariance and inversion methods. Biogeosciences 9, 5323–5340 (2012).

    Article  Google Scholar 

  20. Schaphoff, S. et al. Tamm review: Observed and projected climate change impacts on Russia’s forests and its carbon balance. For. Ecol. Manage. 361, 432–444 (2016).

    Article  Google Scholar 

  21. de Jong, R. et al. Trend changes in global greening and browning: contribution of short-term trends to longer-term change. Glob. Change Biol. 18, 642–655 (2012).

    Article  Google Scholar 

  22. Buermann, W. et al. Recent shift in Eurasian boreal forest greening response may be associated with warmer and drier summers. Geophys. Res. Lett. 41, 1995–2002 (2014).

    Article  Google Scholar 

  23. Rödig, E. et al. Spatial heterogeneity of biomass and forest structure of the Amazon rain forest: Linking remote sensing, forest modelling and field inventory. Glob. Ecol. Biogeogr. 26, 1292–1302 (2017).

    Article  Google Scholar 

  24. Quegan, S. et al. Estimating the carbon balance of central Siberia using a landscape-ecosystem approach, atmospheric inversion and dynamic global vegetation models. Glob. Change Biol. 17, 351–365 (2011).

    Article  Google Scholar 

  25. Gurney, K. R. et al. Interannual variations in continental-scale net carbon exchange and sensitivity to observing networks estimated from atmospheric CO2 inversions for the period 1980 to 2005. Glob. Biogeochem. Cycles 22, GB3025 (2008).

    Article  Google Scholar 

  26. Stephens, B. B. et al. Weak northern and strong tropical land carbon uptake from vertical profiles of atmospheric CO2. Science 316, 1732–1735 (2007).

    Article  Google Scholar 

  27. Leskinen, P. et al. Russian Forests and Climate Change: What Science Can Tell Us 11 (EFI, 2020); https://doi.org/10.36333/wsctu11

  28. Myers-Smith, I. H. et al. Complexity revealed in the greening of the Arctic. Nat. Clim. Change 10, 106–117 (2020).

    Article  Google Scholar 

  29. Stow, D. A. et al. Remote sensing of vegetation and land-cover change in Arctic tundra ecosystems. Remote Sens. Environ. 89, 281–308 (2004).

    Article  Google Scholar 

  30. Karlsen, S. R. et al. A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high Arctic plant productivity. Environ. Res. Lett. 13, 025011 (2018).

    Article  Google Scholar 

  31. Ding, Z. et al. Nearly half of global vegetated area experienced inconsistent vegetation growth in terms of greenness, cover, and productivity. Earths Future 8, e2020EF001618 (2020).

    Article  Google Scholar 

  32. Fan, L. et al. Satellite-observed pantropical carbon dynamics. Nat. Plants 5, 944–951 (2019).

    Article  Google Scholar 

  33. Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).

    Article  Google Scholar 

  34. Giglio, L. et al. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).

    Article  Google Scholar 

  35. Blunden, J. & Arndt, D. S. State of the climate in 2015. Bull. Am. Meteorol. Soc. 97, Si–S275 (2016).

    Article  Google Scholar 

  36. Bastos, A. et al. Was the extreme Northern Hemisphere greening in 2015 predictable? Environ. Res. Lett. 12, 044016 (2017).

    Article  Google Scholar 

  37. Pan, Y. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).

    Article  Google Scholar 

  38. Kukavskaya, E. A. et al. Biomass dynamics of central Siberian Scots pine forests following surface fires of varying severity. Int. J. Wildland Fire 23, 872–886 (2014).

    Article  Google Scholar 

  39. Gauthier, S. et al. Boreal forest health and global change. Science 349, 819 (2015).

    Article  Google Scholar 

  40. Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573 (2012).

    Article  Google Scholar 

  41. Qin, Y. et al. Carbon loss from forest degradation exceeds that from deforestation in the Brazilian Amazon. Nat. Clim. Change 11, 442–448 (2021).

    Article  Google Scholar 

  42. Rogers, B. M. et al. Influence of tree species on continental differences in boreal fires and climate feedbacks. Nat. Geosci. 8, 228–234 (2015).

    Article  Google Scholar 

  43. Shvetsov, E. G. et al. Assessment of post-fire vegetation recovery in southern Siberia using remote sensing observations. Environ. Res. Lett. 14, 055001 (2019).

    Article  Google Scholar 

  44. Wang, J. A. et al. Disturbance suppresses the aboveground carbon sink in North American boreal forests. Nat. Clim. Change 11, 435–441 (2021).

    Article  Google Scholar 

  45. Xu, L. et al. Changes in global terrestrial live biomass over the 21st century. Sci. Adv. 7, eabe9829 (2021).

    Article  Google Scholar 

  46. Shuman, J. K. et al. Forest forecasting with vegetation models across Russia. Can. J. For. Res. 45, 175–184 (2014).

    Article  Google Scholar 

  47. Flannigan, M. et al. Impacts of climate change on fire activity and fire management in the circumboreal forest. Glob. Change Biol. 15, 549–560 (2009).

    Article  Google Scholar 

  48. Yuan, W. et al. Differentiating moss from higher plants is critical in studying the carbon cycle of the boreal biome. Nat. Commun. 5, 4270 (2014).

    Article  Google Scholar 

  49. Harris, N. L. et al. Global maps of twenty-first century forest carbon fluxes. Nat. Clim. Change 11, 234–240 (2021).

    Article  Google Scholar 

  50. Larjavaara, M. et al. Post-fire carbon and nitrogen accumulation and succession in Central Siberia. Sci. Rep. 7, 12776 (2017).

    Article  Google Scholar 

  51. Berner, L. T. et al. Cajander larch (Larix cajanderi) biomass distribution, fire regime and post-fire recovery in northeastern Siberia. Biogeosciences 9, 3943–3959 (2012).

    Article  Google Scholar 

  52. Myneni, R. et al. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid v.006 (LAADS DAAC, 2015).

  53. Houghton, R. A. et al. Mapping Russian forest biomass with data from satellites and forest inventories. Environ. Res. Lett. 2, 045032 (2007).

    Article  Google Scholar 

  54. DiMiceli, C. et al. Annual Global Automated MODIS Vegetation Continuous Fields (MOD44B) at 250 m Spatial Resolution for Data Years Beginning Day 65, 2000–2014, Collection 5 Percent Tree Cover v.6 (University of Maryland, 2017).

  55. Simard, M. et al. Mapping forest canopy height globally with spaceborne lidar. J. Geophys. Res. 116, G04021 (2011).

    Google Scholar 

  56. Broxton, P. et al. A global land cover climatology using MODIS data. J. Appl. Meteorol. Climatol. 53, 1593–1605 (2014).

    Article  Google Scholar 

  57. Saatchi, S. S. et al. Benchmark map of forest carbon stocks in tropical regions across three continents. Proc. Natl Acad. Sci. USA 108, 9899–9904 (2011).

    Article  Google Scholar 

  58. Santoro, M. et al. The global forest above-ground biomass pool for 2010 estimated from high-resolution satellite observations. Earth Syst. Sci. Data. 13, 3927–3950 (2021).

    Article  Google Scholar 

  59. Carreiras, J. M. B. et al. Coverage of high biomass forests by the ESA BIOMASS mission under defense restrictions. Remote Sens. Environ. 196, 154–162 (2017).

    Article  Google Scholar 

  60. Penman, J. et al. Good Practice Guidance for Land Use, Land-Use Change and Forestry (IGES, 2013).

  61. Avitabile, V. et al. An integrated pan-tropical biomass map using multiple reference datasets. Glob. Change Biol. 22, 1406–1420 (2016).

    Article  Google Scholar 

  62. Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nat. Clim. Change 2, 182–185 (2012).

    Article  Google Scholar 

  63. Fernandez-Moran, R. et al. SMOS-IC: an alternative SMOS soil moisture and vegetation optical depth product. Remote Sens. 9, 457 (2017).

    Article  Google Scholar 

  64. Wigneron, J.-P. et al. SMOS-IC data record of soil moisture and L-VOD: historical development, applications and perspectives. Remote Sens. Environ. 254, 112238 (2021).

    Article  Google Scholar 

  65. Mitchard, E. T. A. et al. Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites. Glob. Ecol. Biogeogr. 23, 935–946 (2014).

    Article  Google Scholar 

  66. Mitchard, E. T. A. et al. Uncertainty in the spatial distribution of tropical forest biomass: a comparison of pan-tropical maps. Carbon Balance Manage. 8, 10 (2013).

    Article  Google Scholar 

  67. Harmon, M. E. et al. Release of coarse woody detritus-related carbon: a synthesis across forest biomes. Carbon Balance Manage. 15, 1 (2020).

    Article  Google Scholar 

  68. Bartalev, S. A. & Stytsenko, F. V. Assessment of forest-stand destruction by fires based on remote-sensing data on the seasonal distribution of burned areas. Contemp. Probl. Ecol. 14, 711–716 (2021).

    Article  Google Scholar 

  69. van Wees, D. et al. The role of fire in global forest loss dynamics. Glob. Change Biol. 27, 2377–2391 (2021).

    Article  Google Scholar 

  70. Vicente‐Serrano, S. M. et al. A multiscalar drought index sensitive to global warming: the Standardized Precipitation Evapotranspiration Index. J. Clim. 23, 1696–1718 (2010).

    Article  Google Scholar 

  71. Schepaschenko, D. et al. A new hybrid land cover dataset for Russia: a methodology for integrating statistics, remote sensing and in situ information. J. Land Use Sci. 6, 245–259 (2011).

    Article  Google Scholar 

  72. Du, J. et al. A global satellite environmental data record derived from AMSR-E and AMSR2 microwave Earth observations. Earth Syst. Sci. Data. 9, 791–808 (2017).

    Article  Google Scholar 

  73. Brandt, M. et al. Satellite passive microwaves reveal recent climate-induced carbon losses in African drylands. Nat. Ecol. Evol. 2, 827–835 (2018).

    Article  Google Scholar 

  74. De Grandpré, L. et al. Long-term post-fire changes in the northeastern boreal forest of Quebec. J. Veg. Sci. 11, 791–800 (2000).

    Article  Google Scholar 

Download references

Acknowledgements

This study is supported in part by research grants from the National Natural Science Foundation of China (grant no. 41830648, 42171339), the Fundamental Research Funds for the Central Universities (SWU020016) and Innovation Research 2035 Pilot Plan of Southwest University (SWUPilotPlan031). J.-P.W acknowledges funding support from the CNES (Centre National d’Etudes Spatiales, France) TOSCA programme. P.C. acknowledges the support from the European Space Agency (ESA) Climate Change Initiative (CCI) Biomass project (contract no. 4000123662/18/I-NB). P.C. and S.S. have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 821003 (project 4 C). P.C., A.B., S.S. and J.-P.W acknowledge support from the ESA CCI RECCAP2-A project (ESRIN/4000123002/18/I-NB). Y.Q. and X.X. are supported by research grants from US NSF (OIA-1946093, 1911955) and NASA (GeoCarb contract #80LARC17C0001). Tree species information preparation and pre-processing were financially supported by the Russian Science Foundation (project no. 21-46-07002). The data on dead-wood stock and decomposition rate were collected and processed with support by the State Assignment of V.N. Sukachev Institute of Forest SB RAS no. 0287-2021-0008 (state registration number 121031500339-0). M.B. was funded by the European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 947757 TOFDRY) and DFF Sapere Aude (grant no. 9064–00049B). C.Y. acknowledges support from the National Science Foundation of China (U20A2090). D.v.W. acknowledges support from Dutch Research Council (NWO) Vici scheme research programme (no. 016.160.324) We also acknowledge that S. Saatchi provided the Saatchi biomass map.

Author information

Authors and Affiliations

  1. Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing, China

    Lei Fan & Mingguo Ma

  2. INRAE, Bordeaux Sciences Agro, UMR 1391 ISPA, Villenave-d’Ornon, France

    Jean-Pierre Wigneron, Xiaojun Li, Xiangzhuo Liu, Mengjia Wang & Frédéric Frappart

  3. Université de Bordeaux, Talence, France

    Jean-Pierre Wigneron, Xiaojun Li, Xiangzhuo Liu & Frédéric Frappart

  4. Laboratoire des Sciences du Climat et de l’Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France

    Philippe Ciais

  5. Laboratoire Evolution and Diversité Biologique, CNRS, IRD, UT3, Université Paul Sabatier, Toulouse, France

    Jérome Chave

  6. Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen, Denmark

    Martin Brandt & Rasmus Fensholt

  7. College of Life and Environmental Sciences, University of Exeter, Exeter, UK

    Stephen Sitch

  8. State Key Laboratory of Soil Erosion and Dryland Farming on the Loess Plateau, Northwest A&F University, Yangling, China

    Chao Yue

  9. Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany

    Ana Bastos & Hui Yang

  10. National Tibetan Plateau Data Center (TPDC), State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Xin Li

  11. Department of Microbiology and Plant Biology, Center for Earth Observation and Modeling, University of Oklahoma, Norman, OK, USA

    Yuanwei Qin & Xiangming Xiao

  12. Guangdong Province Key Laboratory for Climate Change and Natural Disaster Studies, School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou, China

    Wenping Yuan & Xiuzhi Chen

  13. International Institute for Applied Systems Analysis, Laxenburg, Austria

    Dmitry Schepaschenko

  14. V.N. Sukachev Institute of Forest, Siberian Branch of the Russian Academy of Science, Krasnoyarsk, Russia

    Dmitry Schepaschenko & Liudmila Mukhortova

  15. Siberian Federal University, Krasnoyarsk, Russia

    Dmitry Schepaschenko

  16. Department of Geography and Program in Planning, University of Toronto, Toronto, Ontario, Canada

    Jingming Chen

  17. State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

    Jianguang Wen

  18. Department of Earth Sciences, Vrije Universiteit, Amsterdam, the Netherlands

    Dave van Wees

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  1. Lei Fan
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Contributions

L.F., J.-P.W and P.C. designed the overall study plan. L.F. prepared the SMOS-IC SM, AGClive, CWDC dataset. D.S. prepared the tree species dataset. D.v.W prepared the stand-replacing fire product. L.F., Y.Q., X. Li, X. Liu and M.W. carried out data processing and analysis. L.F., J.-P.W, P.C., J. Chave, R.F., M.B., S.S., D.S. and L.M. interpreted the results. C.Y., A.B., X. Li, W.Y., F.F., X.X., M.M., J.W., X.C., H.Y. and J. Chen provided comments on the discussion. The manuscript was drafted by L.F., J.-P.W, P.C., J. Chave, R.F., M.B., and S.S. with contributions by all co-authors.

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Correspondence to Lei Fan or Jean-Pierre Wigneron.

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Nature Geoscience thanks Wenru Xu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Tom Richardson, in collaboration with the Nature Geoscience team.

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Fan, L., Wigneron, JP., Ciais, P. et al. Siberian carbon sink reduced by forest disturbances. Nat. Geosci. 16, 56–62 (2023). https://doi.org/10.1038/s41561-022-01087-x

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