Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle

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Abstract

The land and ocean act as a sink for fossil-fuel emissions, thereby slowing the rise of atmospheric carbon dioxide concentrations1. Although the uptake of carbon by oceanic and terrestrial processes has kept pace with accelerating carbon dioxide emissions until now, atmospheric carbon dioxide concentrations exhibit a large variability on interannual timescales2, considered to be driven primarily by terrestrial ecosystem processes dominated by tropical rainforests3. We use a terrestrial biogeochemical model, atmospheric carbon dioxide inversion and global carbon budget accounting methods to investigate the evolution of the terrestrial carbon sink over the past 30 years, with a focus on the underlying mechanisms responsible for the exceptionally large land carbon sink reported in 2011 (ref. 2). Here we show that our three terrestrial carbon sink estimates are in good agreement and support the finding of a 2011 record land carbon sink. Surprisingly, we find that the global carbon sink anomaly was driven by growth of semi-arid vegetation in the Southern Hemisphere, with almost 60 per cent of carbon uptake attributed to Australian ecosystems, where prevalent La Niña conditions caused up to six consecutive seasons of increased precipitation. In addition, since 1981, a six per cent expansion of vegetation cover over Australia was associated with a fourfold increase in the sensitivity of continental net carbon uptake to precipitation. Our findings suggest that the higher turnover rates of carbon pools in semi-arid biomes are an increasingly important driver of global carbon cycle inter-annual variability and that tropical rainforests may become less relevant drivers in the future. More research is needed to identify to what extent the carbon stocks accumulated during wet years are vulnerable to rapid decomposition or loss through fire in subsequent years.

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Figure 1: Interannual variability of NEE and FPAR anomalies.
Figure 2: Global anomalies of NPP and NEE, and the precipitation effect.
Figure 3: Change in climate sensitivity of observations for Australia.
Figure 4: Change in regional climate sensitivity of CMIP5 models.

References

  1. 1

    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)

    CAS  ADS  Article  Google Scholar 

  2. 2

    Le Quéré, C. et al. The global carbon budget 1959–2011. Earth Syst. Sci. Data 5, 1107–1157 (2013)

    ADS  Article  Google Scholar 

  3. 3

    Cox, P. et al. Sensitivity of tropical carbon to climate change constrained by carbon dioxide variability. Nature 494, 341–344 (2013)

    CAS  ADS  Article  Google Scholar 

  4. 4

    Peters, G. P. et al. The challenge to keep global warming below 2 °C. Nature Clim. Change 3, 4–6 (2012)

    ADS  Article  Google Scholar 

  5. 5

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

    CAS  ADS  Article  Google Scholar 

  6. 6

    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)

    CAS  ADS  Article  Google Scholar 

  7. 7

    Sitch, S. et al. Trends and drivers of regional sources and sinks of carbon dioxide over the past two decades. Biogeosci. Disc. 10, 20113–20177 (2013)

    ADS  Article  Google Scholar 

  8. 8

    Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Change Biol. 9, 161–185 (2003)

    ADS  Article  Google Scholar 

  9. 9

    Chevallier, F. et al. CO2 surface fluxes at grid point scale estimated from a global 21 year reanalysis of atmospheric measurements. J. Geophys. Res. D 115, D21307 (2010)

    ADS  Article  Google Scholar 

  10. 10

    Francey, R. J. et al. Atmospheric verification of anthropogenic CO2 emission trends. Nature Clim. Change 3, 520–524 (2013)

    CAS  ADS  Article  Google Scholar 

  11. 11

    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 (2013)

    Article  Google Scholar 

  12. 12

    Bastos, A., Running, S. W., Gouveia, C. & Trigo, R. M. The global NPP dependence on ENSO: La-Niña and the extraordinary year of 2011. J. Geophys. Res. 118, 1247–1255 (2013)

    Article  Google Scholar 

  13. 13

    Haverd, V. et al. Multiple observation types reduce uncertainty in Australia's terrestrial carbon and water cycles. Biogeosciences 10, 2011–2040 (2013)

    ADS  Article  Google Scholar 

  14. 14

    Haverd, V. et al. The Australian terrestrial carbon budget. Biogeosciences 10, 851–869 (2013)

    ADS  Article  Google Scholar 

  15. 15

    Rotenberg, E. & Yakir, D. Contribution of semi-arid forests to the climate system. Science 327, 451–454 (2010)

    CAS  ADS  Article  Google Scholar 

  16. 16

    Zhu, Z. et al. Global Data Sets of Vegetation LAI3g and FPAR3g derived from GIMMS NDVI3g for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013)

    ADS  Article  Google Scholar 

  17. 17

    Marengo, J. A., Tomasella, J., Alves, L. M., Soares, W. R. & Rodriguez, D. A. The drought of 2010 in the context of historical droughts in the Amazon region. Geophys. Res. Lett. 38, L12703 (2011)

    ADS  Article  Google Scholar 

  18. 18

    Wolter, K. & Timlin, M. S. Monitoring ENSO in COADS with a seasonally adjusted principal component index. In Proc. 17th Climate Diagnostics Workshop 52–57 (NOAA/NMC/CAC, NSSL, Univ. Oklahoma, 1993)

    Google Scholar 

  19. 19

    Myneni, R. B., Los, S. O. & Tucker, C. J. Satellite-based identification of linked vegetation index and sea surface temperate anomaly areas from 1982–1990 for Africa, Australia and South America. Geophys. Res. Lett. 23, 729–732 (1996)

    ADS  Article  Google Scholar 

  20. 20

    Woodward, F. I., Lomas, M. R. & Quaife, T. Global responses of terrestrial productivity to contemporary climatic oscillations. Phil. Trans. R. Soc. Lond. B 363, 2779–2785 (2008)

    CAS  Article  Google Scholar 

  21. 21

    Boening, C. Willis, J. K. Landerer, F. W., Nerem, R. S. & Fasullo, J. The 2011 La Niña: so strong, the oceans fell. Geophys. Res. Lett. 39 http://dx.doi.org/10.1029/2012GL053055 (2012)

  22. 22

    Donohue, R. J., McVicar, T. R. & Roderick, M. L. Climate-related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006. Glob. Change Biol. 15, 1025–1039 (2009)

    ADS  Article  Google Scholar 

  23. 23

    Donohue, R. J., Roderick, M. L., McVicar, T. R. & Farquhar, G. D. CO2 fertilisation has increased maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett. 40, 3031–3035 (2013)

    CAS  ADS  Article  Google Scholar 

  24. 24

    Asner, G. P., Elmore, A. J., Olander, L. P., Martin, R. E. & Harris, A. T. Grazing systems, ecosystem responses, and global change. Annu. Rev. Environ. Resour. 29, 261–299 (2004)

    Article  Google Scholar 

  25. 25

    Jung, M., Reichstein, M. & Bondeau, A. Towards global empirical upscaling of FLUXNET eddy covariance observations: validation of a model tree ensemble approach using a biosphere model. Biogeosciences 6, 5271–5304 (2009)

    Article  Google Scholar 

  26. 26

    Andela, N., Liu, Y. Y., van Dijk, A. I. J. M., de Jeu, R. A. M. & McVicar, T. R. Global changes in dryland vegetation dynamics (1988–2008) assessed by satellite remote sensing: combining a new passive microwave vegetation density record with reflective greenness data. Biogeosci. 10, 6657–6676 (2013)

    ADS  Article  Google Scholar 

  27. 27

    Kang, S. M. et al. Modeling evidence that ozone depletion has impacted extreme precipitation in the austral summer. Geophys. Res. Lett. 40, 4054–4059 (2013)

    ADS  Article  Google Scholar 

  28. 28

    Wang, W. et al. Variations in atmospheric CO2 growth rates controlled by tropical temperature. Proc. Natl Acad. Sci. USA 10.1073/pnas.1219683110 (2013)

  29. 29

    Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of the CMIP5 and the experimental design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012)

    ADS  Article  Google Scholar 

  30. 30

    Le Quéré, C. et al. Global carbon budget 2013. Earth Syst. Sci. Data Discuss. 6, 689–760 (2013)

    ADS  Article  Google Scholar 

  31. 31

    Chapin, F. S. et al. Reconciling carbon-cycle concepts, terminology, and methods. Ecosystems 9, 1041–1050 (2006)

    CAS  Article  Google Scholar 

  32. 32

    Zaehle, S., Sitch, S., Smith, B. & Hattermann, F. Effects of parameter uncertainty on the modeling of terrestrial biosphere dynamics. Glob. Biogeochem. Cycles 19 GB3020 http://dx.doi.org/10.1029/2004GB002395 (2005)

    ADS  Article  Google Scholar 

  33. 33

    Poulter, B., Frank, D., Hodson, E. L., Lischke, H. & Zimmermann, N. E. Impacts of land cover and climate data selection on understanding terrestrial carbon dynamics and the CO2 airborne fraction. Biogeosciences 8, 2027–2036 (2011)

    CAS  ADS  Article  Google Scholar 

  34. 34

    Kaiser, J. W. et al. Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power. Biogeosciences 9, 527–554 (2012)

    CAS  ADS  Article  Google Scholar 

  35. 35

    van der Werf, G. R. et al. Global fire emissions and the contribution of deforestation, savanna, forest, agricultural, and peat fires (1997–2009). Atmos. Chem. Phys. Discuss. 10, 16153–16230 (2010)

    ADS  Article  Google Scholar 

  36. 36

    van der Werf, G. R. et al. Interannual variability in global biomass burning emissions from 1997-2004. Atmos. Chem. Phys. 6, 3423–3441 (2006)

    CAS  ADS  Article  Google Scholar 

  37. 37

    Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010)

    CAS  ADS  Article  Google Scholar 

  38. 38

    Moss, R. H. et al. The next generation of scenarios for climate change research and assessment. Nature 463, 747–756 (2010)

    CAS  ADS  Article  Google Scholar 

  39. 39

    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. 6, 692–705 (2012)

    Google Scholar 

  40. 40

    Kanamitsu, M. et al. NCEP-DEO AMIP-II Reanalysis (R-2). Bull. Am. Meteorol. Soc. 83, 1631–1643 (2002)

    ADS  Article  Google Scholar 

Download references

Acknowledgements

We acknowledge support from the EU FP7 GEOCARBON programme (283080), and thank the researchers involved with collecting and maintaining the climate data at the Climate Research Unit, University of East Anglia, UK, and the National Center for Atmospheric Research, USA. We also acknowledge the World Climate Research Programme’s Working Group on Coupled Modelling, which is responsible for CMIP. We thank the climate modelling groups for producing and making available their model output. For CMIP the US Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. We also thank M. Jung for providing the ‘upscaled’ NEE data used in our analysis. J.G.C. acknowledges the support of the Australian Climate Change Science Program. R.B.M. and S.W.R. were funded by the NASA Earth Science Division. C. Le Quéré and L. Cernusak provided comments and suggestions that improved the manuscript. This paper is a contribution to the Global Carbon Budget activity of the Global Carbon Project.

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Authors

Contributions

B.P., D.F., P.C. and R.B.M. designed the analyses. J.B., F.C., G.B., D.F., R.B.M., S.W.R., S.S., G.R.v.d.W., J.G.C., Y.Y.L. and N.A. contributed data to the analyses. B.P., F.C., R.B.M., S.R. and D.F. conducted the analyses. All authors contributed to the writing of the manuscript.

Corresponding author

Correspondence to Benjamin Poulter.

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The authors declare no competing financial interests.

Extended data figures and tables

Extended Data Figure 1

The thirteen regions used throughout the analysis, 11 from TRANSCOM, and two additional regions for the African continent that are semi-arid (see Methods Summary).

Extended Data Figure 2 Seasonal AVHRR FPAR anomalies (z score) for the year 2011.

The z score is calculated relative to the long-term seasonal mean and standard deviation of FPAR (1982–2011); see legend to Fig. 1c. The seasons DJF, MAM, JJA and SON are defined by the first letter of each month.

Extended Data Figure 3 Forcing contribution to NEE and the MEI and PDO indices.

The full climate attribution of the global land sink simulation by the LPJ DGVM is shown in the bar graph. PDO, Pacific Decadal Oscillation.

Extended Data Figure 4 r and FPAR correlations between climate modes for NEE at given seasons.

Shown is r (on the colour scales) between climate modes and MAM (a), and JJA (b) NEE simulated by LPJ for each of the TransCom regions. FPAR correlations between climate modes are shown for MAM (c) and JJA (d). The correlations were made for 1982–2011. Blank boxes indicate correlation between −0.1 and 0.1.

Extended Data Figure 5 Global climate anomalies for air temperature and precipitation.

a, Global temperature and precipitation anomalies from CRU TS3.21 data. The anomalies are with respect to 1979–2012 seasonal means. b, Seasonal precipitation anomalies (z score) for year 2010. c, Seasonal precipitation anomalies (z score) for year 2011. The z score for b and c is calculated relative to the long-term seasonal mean and standard deviation of precipitation (1979–2011).

Extended Data Figure 6 Spatial pattern of the contribution of precipitation to NEE exchange in 2011.

This is calculated as the difference between NPP (a) and Rh (b) with the all-climate forcing varied and NEE simulated with the precipitation climatology. This is the same as in Fig. 2c but for component fluxes of NEE.

Extended Data Table 1 Global summary of annual NEE
Extended Data Table 2 Annual LPJ-derived NEE
Extended Data Table 3 Total carbon emissions from wildfire for each TransCom region
Extended Data Table 4 CMIP5 Earth system models from PCMDI node 9 that were accessed and where the RCP8.5 scenario (2005–2099) was merged with the historical simulation (1860–2005)

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Poulter, B., Frank, D., Ciais, P. et al. Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle. Nature 509, 600–603 (2014). https://doi.org/10.1038/nature13376

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