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Nature Climate Change | Letter

Recent reversal in loss of global terrestrial biomass

Journal name:
Nature Climate Change
Volume:
5,
Pages:
470–474
Year published:
DOI:
doi:10.1038/nclimate2581
Received
Accepted
Published online

Vegetation change plays a critical role in the Earth’s carbon (C) budget and its associated radiative forcing in response to anthropogenic and natural climate change1, 2, 3, 4. Existing global estimates of aboveground biomass carbon (ABC) based on field survey data provide brief snapshots that are mainly limited to forest ecosystems5, 6, 7, 8. Here we use an entirely new remote sensing approach to derive global ABC estimates for both forest and non-forest biomes during the past two decades from satellite passive microwave observations. We estimate a global average ABC of 362 PgC over the period 1998–2002, of which 65% is in forests and 17% in savannahs. Over the period 1993–2012, an estimated −0.07 PgC yr−1 ABC was lost globally, mostly resulting from the loss of tropical forests (−0.26 PgC yr−1) and net gains in mixed forests over boreal and temperate regions (+0.13 PgC yr−1) and tropical savannahs and shrublands (+0.05 PgC yr−1). Interannual ABC patterns are greatly influenced by the strong response of water-limited ecosystems to rainfall variability, particularly savannahs. From 2003 onwards, forest in Russia and China expanded and tropical deforestation declined. Increased ABC associated with wetter conditions in the savannahs of northern Australia and southern Africa reversed global ABC loss, leading to an overall gain, consistent with trends in the global carbon sink reported in recent studies1, 3, 9.

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  1. Aboveground biomass carbon stores and density by biome.
    Figure 1: Aboveground biomass carbon stores and density by biome.

    a, Total ABC in eight biomes circa 2000 (mean estimate; error bars indicate the 90% confidence interval). ‘Tropical forests’ include those in Southeast Asia, Africa and the Americas (that is, South America, Caribbean countries and Mexico); remaining forests are considered as ‘boreal/temperate’. ‘Shrublands’ includes both open and closed shrublands; ‘Croplands’ includes both ‘croplands’ and ‘cropland/natural vegetation mosaic’ based on the MODIS IGBP land-cover map for 2001 (ref. 16). b, ABC density per unit area circa 2000. The bottom, middle and top band of the box represent the 25th, 50th (median) and 75th percentile, respectively, and the ends of the whiskers represent the 5th and 95th percentile for all corresponding grid cells. c, Annual trends in total biome ABC for 1993–2012 (mean estimate; error bars indicate the 90% confidence interval). The classification relates to year 2001 and grouping of different biomes to the same colour is mainly based on woody vegetation canopy cover, that is, 10–60% for shrublands, savannahs and woody savannahs and less than 10% for grasslands. Croplands with harvest and thus considerable variation in woody components are grouped with grasslands.

  2. Mean annual change in aboveground biomass carbon between 1993 and 2012.
    Figure 2: Mean annual change in aboveground biomass carbon between 1993 and 2012.
  3. Interannual variations in aboveground biomass carbon (ABC) storage.
    Figure 3: Interannual variations in aboveground biomass carbon (ABC) storage.

    a, Time series of annual total ABC for all ecosystems, expressed as the difference from 1993 values. b, Time series of annual total ABC in five biome groups (merged as per colour codes in Fig. 1), with a classification based on MODIS IGBP 2001 (ref. 16). c, Time series of annual tropical forest ABC over the Americas, Southeast Asia and Africa. Southeast Asia here includes Asian countries as well as Papua New Guinea (see Supplementary Fig. 3b). d, Time series of the annual total ABC and normalized rainfall for the savannahs and woody savannahs of southern Africa and northern Australia. Coefficients of determination (r2) between ABC and rainfall are 0.53 and 0.65 for southern Africa and northern Australia, respectively. The solid line represents the mean value and the shadow represents the CI90 range.

References

  1. Le Quéré, C. et al. Global carbon budget 2013. Earth Syst. Sci. Data 6, 235–263 (2014).
  2. Le Quéré, C. et al. Trends in the sources and sinks of carbon dioxide. Nature Geosci. 2, 831–836 (2009).
  3. Canadell, J. G. et al. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Natl Acad. Sci. USA 104, 18866–18870 (2007).
  4. Peters, G. P. et al. The challenge to keep global warming below 2 °C. Nature Clim. Change 3, 4–6 (2013).
  5. Pan, Y. D. et al. A large and persistent carbon sink in the world’s forests. Science 333, 988–993 (2011).
  6. 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).
  7. Baccini, A. et al. Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps. Nature Clim. Change 2, 182–185 (2012).
  8. Harris, N. L. et al. Baseline map of carbon emissions from deforestation in tropical regions. Science 336, 1573–1576 (2012).
  9. Peylin, P. et al. Global atmospheric carbon budget: Results from an ensemble of atmospheric CO2 inversions. Biogeosciences 10, 6699–6720 (2013).
  10. Jackson, T. J. & Schmugge, T. J. Vegetation effects on the microwave emission of soils. Remote Sens. Environ. 36, 203–212 (1991).
  11. Kerr, Y. H. & Njoku, E. G. A semiempirical model for interpreting microwave emission from semiarid land surfaces as seen from space. IEEE Trans. Geosci. Remote Sensing 28, 384–393 (1990).
  12. Guglielmetti, M. et al. Measured microwave radiative transfer properties of a deciduous forest canopy. Remote Sens. Environ. 109, 523–532 (2007).
  13. Owe, M., De Jeu, R. & Walker, J. A methodology for surface soil moisture and vegetation optical depth retrieval using the microwave polarization difference index. IEEE Trans. Geosci. Remote Sensing 39, 1643–1654 (2001).
  14. Liu, Y. Y., de Jeu, R. A. M., McCabe, M. F., Evans, J. P. & van Dijk, A. I. J. M. Global long-term passive microwave satellite-based retrievals of vegetation optical depth. Geophys. Res. Lett. 38, L18402 (2011).
  15. Sellers, P. J. Canopy reflectance, photosynthesis and transpiration. Int. J. Remote Sensing 6, 1335–1372 (1985).
  16. Friedl, M. A. et al. MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sens. Environ. 114, 168–182 (2010).
  17. Liu, Y. Y., van Dijk, A. I. J. M., McCabe, M. F., Evans, J. P. & de Jeu, R. A. M. Global vegetation biomass change (1988–2008) and attribution to environmental and human drivers. Glob. Ecol. Biogeogr. 22, 692–705 (2013).
  18. Koren, I., Remer, L. A. & Longo, K. Reversal of trend of biomass burning in the Amazon. Geophys. Res. Lett. 34, L20404 (2007).
  19. Zamolodchikov, D. G., Grabovskii, V. I. & Kraev, G. N. A twenty year retrospective on the forest carbon dynamics in Russia. Contemp. Probl. Ecol. 4, 706–715 (2011).
  20. Fang, J. et al. Forest biomass carbon sinks in East Asia, with special reference to the relative contributions of forest expansion and forest growth. Glob. Change Biol. 20, 2019–2030 (2014).
  21. Liu, Y., de Jeu, R. A. M., van Dijk, A. I. J. M. & Owe, M. TRMM-TMI satellite observed soil moisture and vegetation density (1998–2005) show strong connection with El Niño in eastern Australia. Geophys. Res. Lett. 34, L15401 (2007).
  22. Miralles, D. G. et al. El Niño–La Niña cycle and recent trends in continental evaporation. Nature Clim. Change 4, 122–126 (2014).
  23. Kim, W., Yeh, S. W., Kim, J. H., Kug, J. S. & Kwon, M. The unique 2009–2010 El Niño event: A fast phase transition of warm pool El Niño to La Niña. Geophys. Res. Lett. 38, L15809 (2011).
  24. Bond, W. J. & Midgley, G. F. Carbon dioxide and the uneasy interactions of trees and savannah grasses. Phil. Trans. R. Soc. B 367, 601–612 (2012).
  25. Friedlingstein, P. et al. Update on CO2 emissions. Nature Geosci. 3, 811–812 (2010).
  26. Robinson, D. Implications of a large global root biomass for carbon sink estimates and for soil carbon dynamics. Phil. Trans. R. Soc. B 274, 2753–2759 (2007).
  27. Buitenwerf, R., Bond, W. J., Stevens, N. & Trollope, W. S. W. Increased tree densities in South African savannas: > 50 years of data suggests CO2 as a driver. Glob. Change Biol. 18, 675–684 (2012).
  28. Poulter, B. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014).
  29. Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).
  30. Schneider, U. et al. GPCC Full Data Reanalysis Version 6.0 at 1.0°: Monthly Land-Surface Precipitation from Rain-Gauges built on GTS-based and Historic Data (GPCC, 2011); http://dx.doi.org/10.5676/DWD_GPCC/FD_M_V6_100

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Author information

Affiliations

  1. ARC Centre of Excellence for Climate Systems Science & Climate Change Research Centre, University of New South Wales, Sydney, New South Wales 2052, Australia

    • Yi Y. Liu &
    • Jason P. Evans

  2. Water Research Centre, School of Civil and Environmental Engineering, University of New South Wales, Sydney, New South Wales 2052, Australia

    • Yi Y. Liu

  3. Fenner School of Environment & Society, Australian National University, Canberra, Australian Capital Territory 0200, Australia

    • Albert I. J. M. van Dijk

  4. CSIRO Land and Water Flagship, Canberra, Australian Capital Territory 2601, Australia

    • Albert I. J. M. van Dijk

  5. Earth and Climate Cluster, Department of Earth Sciences, Faculty of Earth and Life Sciences, VU University Amsterdam, Amsterdam 1081 HV, Netherlands

    • Richard A. M. de Jeu

  6. Global Carbon Project, CSIRO Oceans and Atmosphere Flagship, Canberra, Australian Capital Territory 2601, Australia

    • Josep G. Canadell

  7. Water Desalination and Reuse Center, Division of Biological and Environmental Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Saudi Arabia

    • Matthew F. McCabe

  8. School of Geography and Remote Sensing, Nanjing University of Information Science and Technology, Nanjing 210044, China

    • Guojie Wang

Contributions

All authors contributed to the development of the paper. Y.Y.L. and A.I.J.M.v.D. designed the study. R.A.M.d.J., Y.Y.L. and G.W. prepared the VOD data set. Y.Y.L. conducted the analysis and wrote the Supplementary Information. A.I.J.M.v.D. and J.G.C. summarized the results and wrote the first draft of the paper, with subsequent addition and improvement by all authors.

Competing financial interests

The authors declare no competing financial interests.

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