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.

  • Article
  • Published:

Global increase in biomass carbon stock dominated by growth of northern young forests over past decade

Nature Geoscience volume 16, pages 886–892 (2023)Cite this article

Subjects

Abstract

Changes in terrestrial carbon storage under environmental and land-use changes remain a critical source of uncertainty in regional and global carbon budgets. We generated global maps of annual live vegetation biomass using L-band microwave vegetation optical depth. Globally, biomass carbon stocks increased from 2010 to 2019 at a rate of 0.50 ± 0.20 PgC yr−1 with a year-to-year variability, closely mirroring the observations of the global atmospheric CO2 growth rate. The main contributors to the global carbon sink are boreal and temperate forests, while wet tropical forests are small carbon sources, from deforestation and agriculture-related disturbances. We found that the tropical deforested and degraded old-growth forests (>140 yr) are nearly carbon neutral whereas temperate and boreal young (< 50 yr) and middle-aged (50–140 yr) forests are the largest sinks. By contrast, dynamic global vegetation models show that all old-growth forests are large sinks and largely ignore the impacts of deforestation and degradation on tropical biomass. Our findings highlight the importance of forest demography when predicting dynamics of future carbon sink under changing climate.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Spatial patterns of changes in total biomass carbon density and forest area fraction during 2010–2019.
Fig. 2: Changes in live biomass carbon density for global and five climatic biomes.
Fig. 3: Relationship between ∆TB and forest age, canopy height.
Fig. 4: Comparison of the IAV in global carbon fluxes and global atmospheric CGR.

Similar content being viewed by others

Data availability

The L-VOD data from this study can be accessed freely through the SMOS-IC website (https://ib.remote-sensing.inrae.fr/). The global forest change product can be accessed from https://earthenginepartners.appspot.com/science-2013-global-forest/download_v1.7.html. The forest disturbance classification map is freely available from https://www.science.org/doi/10.1126/science.aau3445. The forest age datasets are freely available at https://doi.org/10.17871/ForestAgeBGI.2021. The simulations from TRENDY DGVMs are available at https://sites.exeter.ac.uk/trendy.

Code availability

All computer codes for the analysis of the data are available from the corresponding author on reasonable request.

References

  1. Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2020).

    Google Scholar 

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

    Google Scholar 

  3. Ciais, P. et al. Empirical estimates of regional carbon budgets imply reduced global soil heterotrophic respiration. Natl Sci. Rev. 8, nwaa145 (2021).

    Google Scholar 

  4. Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P. & White, J. W. C. Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years. Nature 488, 70–72 (2012).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  7. Grassi, G. et al. The key role of forests in meeting climate targets requires science for credible mitigation. Nat. Clim. Change 7, 220–226 (2017).

    Google Scholar 

  8. Hubau, W. et al. Asynchronous carbon sink saturation in African and Amazonian tropical forests. Nature 579, 80–87 (2020).

    Google Scholar 

  9. Tagesson, T. et al. Recent divergence in the contributions of tropical and boreal forests to the terrestrial carbon sink. Nat. Ecol. Evol. 4, 202–209 (2020).

    Google Scholar 

  10. Tubiello, F. N. et al. Carbon emissions and removals from forests: new estimates, 1990–2020. Earth Syst. Sci. Data 13, 1681–1691 (2021).

    Google Scholar 

  11. Chen, Z. et al. Linking global terrestrial CO2 fluxes and environmental drivers: inferences from the Orbiting Carbon Observatory 2 satellite and terrestrial biospheric models. Atmos. Chem. Phys. 21, 6663–6680 (2021).

    Google Scholar 

  12. Deng, Z. et al. Comparing national greenhouse gas budgets reported in UNFCCC inventories against atmospheric inversions. Earth Syst. Sci. Data 14, 1639–1675 (2022).

    Google Scholar 

  13. Bastos, A. et al. Sources of uncertainty in regional and global terrestrial CO2 exchange estimates. Glob. Biogeochem. Cycles 34, e2019GB006393 (2020).

    Google Scholar 

  14. Rodríguez-Fernández, N. J. et al. An evaluation of SMOS L-band vegetation optical depth (L-VOD) data sets: high sensitivity of L-VOD to above-ground biomass in Africa. Biogeosciences 15, 4627–4645 (2018).

    Google Scholar 

  15. Li, X. et al. Global-scale assessment and inter-comparison of recently developed/reprocessed microwave satellite vegetation optical depth products. Remote Sens. Environ. 253, 112208 (2021).

    Google Scholar 

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

    Google Scholar 

  17. Yang, H. et al. Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño. Proc. Natl Acad. Sci. USA 119, e2101388119 (2022).

    Google Scholar 

  18. Oliva, R. et al. Status of radio frequency interference (RFI) in the 1400–1427 MHz passive band based on six years of SMOS mission. Remote Sens. Environ. 180, 64–75 (2016).

    Google Scholar 

  19. Konings, A. G., Rao, K. & Steele-Dunne, S. C. Macro to micro: microwave remote sensing of plant water content for physiology and ecology. N. Phytol. 223, 1166–1172 (2019).

    Google Scholar 

  20. Gatti, L. V. et al. Amazonia as a carbon source linked to deforestation and climate change. Nature 595, 388–393 (2021).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  23. Venter, Z. S., Cramer, M. D. & Hawkins, H. J. Drivers of woody plant encroachment over Africa. Nat. Commun. 9, 2272 (2018).

    Google Scholar 

  24. Liu, Y. Y., De Jeu, R. A., McCabe, M. F., Evans, J. P. & Van Dijk, A. I. Global long‐term passive microwave satellite‐based retrievals of vegetation optical depth. Geophys. Res. Lett. 38, L18402 (2011).

    Google Scholar 

  25. Gasser, T. et al. Historical CO2 emissions from land use and land cover change and their uncertainty. Biogeosciences 17, 4075–4101 (2020).

    Google Scholar 

  26. Brandt, M. et al. Reduction of tree cover in West African woodlands and promotion in semi-arid farmlands. Nat. Geosci. 11, 328–333 (2018).

    Google Scholar 

  27. Tong, X. et al. Forest management in southern China generates short term extensive carbon sequestration. Nat. Commun. 11, 129 (2020).

    Google Scholar 

  28. Guillaume, T. et al. Carbon costs and benefits of Indonesian rainforest conversion to plantations. Nat. Commun. 9, 2388 (2018).

    Google Scholar 

  29. Goll, D. S., Joetzjer, E., Huang, M. & Ciais, P. Low phosphorus availability decreases susceptibility of tropical primary productivity to droughts. Geophys. Res. Lett. 45, 8231–8240 (2018).

    Google Scholar 

  30. Pugh, T. A., Arneth, A., Kautz, M., Poulter, B. & Smith, B. Important role of forest disturbances in the global biomass turnover and carbon sinks. Nat. Geosci. 12, 730–735 (2019).

    Google Scholar 

  31. Yang, H. et al. Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates. Glob. Change Biol. 26, 3997–4012 (2020).

    Google Scholar 

  32. Besnard, S. et al. Mapping global forest age from forest inventories, biomass and climate data. Earth Syst. Sci. Data 13, 4881–4896 (2021).

    Google Scholar 

  33. Phillips, O. L. & Brienen, R. J. Carbon uptake by mature Amazon forests has mitigated Amazon nations’ carbon emissions. Carbon Balance Manage. 12, 1 (2017).

    Google Scholar 

  34. Matricardi, E. A. T. et al. Long-term forest degradation surpasses deforestation in the Brazilian Amazon. Science 369, 1378–1382 (2020).

    Google Scholar 

  35. Maxwell, S. L. et al. Degradation and forgone removals increase the carbon impact of intact forest loss by 626%. Sci. Adv. 5, eaax2546 (2019).

    Google Scholar 

  36. Luyssaert, S. et al. Old-growth forests as global carbon sinks. Nature 455, 213–215 (2008).

    Google Scholar 

  37. Lang, N. et al. Global canopy height regression and uncertainty estimation from GEDI LIDAR waveforms with deep ensembles. Remote Sens. Environ. 268, 112760 (2022).

    Google Scholar 

  38. Pugh, T. A. et al. Role of forest regrowth in global carbon sink dynamics. Proc. Natl Acad. Sci. USA 116, 4382–4387 (2019).

    Google Scholar 

  39. Palace, M., Keller, M., Hurtt, G. & Frolking, S. in Tropical Forests (eds Sudarshana, P. et al.) 215–252 (InTech Open, 2012).

  40. Yang, H. et al. Coarse woody debris are buffering mortality-induced carbon losses to the atmosphere in tropical forests. Environ. Res. Lett. 16, 011006 (2021).

    Google Scholar 

  41. Browne, L. et al. Increased mortality of tropical tree seedlings during the extreme 2015–16 El Niño. Glob. Change Biol. 27, 5043–5053 (2021).

    Google Scholar 

  42. Orth, R., Zscheischler, J. & Seneviratne, S. I. Record dry summer in 2015 challenges precipitation projections in Central Europe. Sci. Rep. 6, 28334 (2016).

    Google Scholar 

  43. Marengo, J. A. et al. Two contrasting severe seasonal extremes in tropical South America in 2012: flood in Amazonia and drought in northeast Brazil. J. Clim. 26, 9137–9154 (2013).

    Google Scholar 

  44. Baranowski, K., Faust, C. L., Eby, P. & Bharti, N. Quantifying the impacts of Australian bushfires on native forests and gray-headed flying foxes. Glob. Ecol. Conserv. 27, e01566 (2021).

    Google Scholar 

  45. Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).

    Google Scholar 

  46. Konings, A. G., Holtzman, N. M., Rao, K., Xu, L. & Saatchi, S. S. Interannual variations of vegetation optical depth are due to both water stress and biomass changes. Geophys. Res. Lett. 48, e2021GL095267 (2021).

    Google Scholar 

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

    Google Scholar 

  48. Wigneron, J. P. et al. Tropical forests did not recover from the strong 2015–2016 El Niño event. Sci. Adv. 6, eaay4603 (2020).

    Google Scholar 

  49. Tootchi, A., Jost, A. & Ducharne, A. Multi-source global wetland maps combining surface water imagery and groundwater constraints. Earth Syst. Sci. Data 11, 189–220 (2019).

    Google Scholar 

  50. Melton, J. R. et al. A map of global peatland extent created using machine learning (Peat-ML). Geosci. Model Dev. 15, 4709–4738 (2022).

    Google Scholar 

  51. Thoning, K. W., Tans, P. P. & Komhyr, W. D. Atmospheric carbon dioxide at Mauna Loa Observatory: 2. Analysis of the NOAA GMCC data, 1974–1985. J. Geophys. Res. Atmos. 94, 8549–8565 (1989).

    Google Scholar 

  52. 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).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  55. Santoro, M. et al. A detailed portrait of the forest aboveground biomass pool for the year 2010 obtained from multiple remote sensing observations. Geophys. Res. Abstr. 20, EGU2018–EGU18932 (2018).

    Google Scholar 

  56. Spawn, S. A., Sullivan, C. C., Lark, T. J. & Gibbs, H. K. Harmonized global maps of above and belowground biomass carbon density in the year 2010. Sci. Data 7, 112 (2020).

    Google Scholar 

  57. Chevallier, F. et al. Objective evaluation of surface- and satellite-driven carbon dioxide atmospheric inversions. Atmos. Chem. Phys. 19, 14233–14251 (2019).

    Google Scholar 

  58. Wernick, I. K. et al. Quantifying forest change in the European Union. Nature 592, E13–E14 (2021).

    Google Scholar 

  59. Curtis, P. G., Slay, C. M., Harris, N. L., Tyukavina, A. & Hansen, M. C. Classifying drivers of global forest loss. Science 361, 1108–1111 (2018).

    Google Scholar 

  60. Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Climatic Change 109, 117–161 (2011).

    Google Scholar 

Download references

Acknowledgements

This research has been funded by the European Space Agency Climate Change Initiative (ESA-CCI) Biomass project (ESA ESRIN/ 4000123662) and RECCAP2 project 1190 (ESA ESRIN/ 4000123002/18/I-NB). H.Y. was supported by the CCI Biomass project of the Climate Change Initiative (ESA ESRIN/ 4000123662) funded by ESA and the Project Office BIOMASS (grant number 50EE1904) funded by the German Federal Ministry of Economics and Technology. J.-P.W. acknowledges support from the CNES (Centre National d’Etudes Spatiales) TOSCA programme. L.F. acknowledges support from the National Natural Science Foundation of China (Grant No. 42171339).

Author information

Authors and Affiliations

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

    Hui Yang, Philippe Ciais & Simon Bowring

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

    Hui Yang

  3. UMR ISPA, Université de Bordeaux, Villenave d’Ornon, France

    Frédéric Frappart, Xiaojun Li & Jean-Pierre Wigneron

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

    Martin Brandt & Rasmus Fensholt

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

    Lei Fan

  6. Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA

    Sassan Saatchi

  7. Institute of Environment and Sustainability, University of California, Los Angeles, CA, USA

    Sassan Saatchi

  8. Section 1.4 Remote Sensing and Geoinformatics, Telegrafenberg, Helmholtz Center Potsdam GFZ German Research Centre for Geosciences, Potsdam, Germany

    Simon Besnard

  9. Department of Earth System Science, Tsinghua University, Beijing, China

    Zhu Deng

Authors
  1. Hui Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Philippe Ciais
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Frédéric Frappart
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Xiaojun Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Martin Brandt
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rasmus Fensholt
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lei Fan
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Sassan Saatchi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Simon Besnard
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Zhu Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Simon Bowring
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jean-Pierre Wigneron
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

P.C., J-P.W. and H.Y. designed the research. H.Y. performed all of the analyses. F.F., X.L. and J-P.W. prepared the raw SMOS-IC L-VOD data. S. Besnard prepared the forest age product. Z.D. prepared the national inventory data. H.Y., J.-P.W., P.C., M.B., R.F., S.S., L.F. and S. Bowring interpreted the results and provided comments on the discussion. The manuscript was drafted by H.Y., J.-P.W, and P.C. with contributions by all co-authors.

Corresponding author

Correspondence to Hui Yang.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Geoscience thanks David Chaparro and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Xujia Jiang, in collaboration with the Nature Geoscience team.

Additional information

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

Extended data

Extended Data Fig. 1 Comparison with the results of an Amazon inversion study.

(a) The right-hand side panel shows the air samples collected at the four sites influenced by the regions (light blue lines). The left-hand side panel is a comparison of the average of total carbon flux for four sites from Gatti et al. (ref. 20) and the average of total biomass changes from L-VOD for the influence regions of four sites. Error-bars of carbon flux are uncertainties related to the background and travel time trajectories for each sample used to compose first the monthly mean and later the annual means, while error-bars of ∆TB are the standard deviation of total biomass changes from L-VOD for the influence regions. (b) L-VOD derived mean annual changes in total live biomass carbon density over 2010-2019. (c) Mean annual changes in total live biomass carbon density over 2010-2019 (data from ref. 6). (d) Total forest gross loss fraction over 2010-2019 using the Landsat-based Global Forest Change Data. Panel a reproduced with permission from ref. 20, Springer Nature Limited.

Extended Data Fig. 2 The fraction of zero/minor loss, deforestation, agriculture, forestry, wildfire, and urbanization.

The fraction of zero/minor loss (a), deforestation (b), agriculture (c), forestry (d), wildfire (e), and urbanization (f) with a spatial resolution of 25 ×25 km, aggregated from the forest disturbance map by Curtis et al. (ref. 59).

Extended Data Fig. 3 Changes in forest fraction from 2010 to 2018 from the TRENDY model ensembles.

The fraction of forest for each year was calculated using plant functional type fractions from each DGVM.

Extended Data Fig. 4 Changes in ∆TB density from L-VOD and from TRENDY DGVMs partitioned to different classes of canopy height (2 m intervals).

The averaged changes in total live biomass (∆TB) density from L-VOD (a) and from TRENDY DGVMs (b) during 2010-2019 partitioned to different classes of forest ages (20-year intervals). The width of colored bars represents the forest area of different forest classes. The total ∆TB density of intact forests is shown as white bars (the width of white bars represents the area of intact forests) in panel a.

Extended Data Fig. 5 Relationship between total live biomass changes from each TRENDY DGVM and forest age.

The averaged changes in total live biomass (∆TB) density during 2010-2019 partitioned to different classes of forest ages (20-year intervals). Total ∆TB density in each class area TRENDY DGVMs. The width of colored bars represents the forest area of different forest classes.

Extended Data Fig. 6 Global patterns of carbon fluxes for the year of 2012 and 2015.

The carbon fluxes, which are the loss or gains in total live biomass within one year, are calculated using the difference in L-VOD derived live biomass between year n and year n + 1.

Extended Data Fig. 7 Interannual variability of regional carbon fluxes from L-VOD without and with coarse woody debris (CWD) buffering over 2012 – 2019.

The regional carbon fluxes were calculated for five biomes, that is, wet tropics, dry tropics, arid, temperate and boreal. Panel a displays the interannual variability without CWD buffering, while panel b presents the interannual variability with CWD buffering.

Extended Data Fig. 8 Mean annual changes in above-ground biomass carbon density over 2010-2019.

(a) The annual values of live biomass were calculated as the averages of November, December, January and February (that is, Jan 1-centered averages). (b) The annual values of live biomass were calculated as the averages of May, June, July, and August (that is, July 1-centered averages). (c) Difference in AGB change between Jan 1-centered averages and July 1-centered averages.

Extended Data Fig. 9 Mean net biome productivity for global and five climatic biomes.

Mean values of annual net biome productivity (NBP) are estimated using two inversion models (SURF, and GOSAT), and pink bars presents the adjusted NBP estimates using the river-to-estuary carbon flux and crop & wood carbon productions from Deng et al. (2021) (ref. 12 see Supplementary Table S3). NBP were calculated for global (a), and five climatic biomes: wet tropics (b), dry tropics (c), arid (d), temperate (e), and boreal (f). The error-bars denote the standard deviation of annual NBP over the periods (2010-2018 for SURF and 2010-2015 for GOSAT).

Supplementary information

Supplementary Information

Supplementary Figs. 1–11, Tables 1–3 and Texts 1–4.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yang, H., Ciais, P., Frappart, F. et al. Global increase in biomass carbon stock dominated by growth of northern young forests over past decade. Nat. Geosci. 16, 886–892 (2023). https://doi.org/10.1038/s41561-023-01274-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41561-023-01274-4

This article is cited by

Search

Advanced 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