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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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.
The authors declare no competing financial interests.
Extended data figures and tables
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).
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.
The full climate attribution of the global land sink simulation by the LPJ DGVM is shown in the bar graph. PDO, Pacific Decadal Oscillation.
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.
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.
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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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