Greening of the Earth and its drivers

Journal name:
Nature Climate Change
Year published:
Published online

Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with consequences for the functioning of the Earth system and provision of ecosystem services1, 2. Yet how global vegetation is responding to the changing environment is not well established. Here we use three long-term satellite leaf area index (LAI) records and ten global ecosystem models to investigate four key drivers of LAI trends during 1982–2009. We show a persistent and widespread increase of growing season integrated LAI (greening) over 25% to 50% of the global vegetated area, whereas less than 4% of the globe shows decreasing LAI (browning). Factorial simulations with multiple global ecosystem models suggest that CO2 fertilization effects explain 70% of the observed greening trend, followed by nitrogen deposition (9%), climate change (8%) and land cover change (LCC) (4%). CO2 fertilization effects explain most of the greening trends in the tropics, whereas climate change resulted in greening of the high latitudes and the Tibetan Plateau. LCC contributed most to the regional greening observed in southeast China and the eastern United States. The regional effects of unexplained factors suggest that the next generation of ecosystem models will need to explore the impacts of forest demography, differences in regional management intensities for cropland and pastures, and other emerging productivity constraints such as phosphorus availability.

At a glance


  1. Trend in observed growing season integrated LAI.
    Figure 1: Trend in observed growing season integrated LAI.

    ac, Spatial pattern of trends in growing season integrated LAI derived from three remote sensing data sets. a, GIMMS LAI3g. b, GLOBMAP LAI. c, GLASS LAI. All data sets cover the period 1982 to 2009. Regions labelled by black dots indicate trends that are statistically significant (Mann–Kendall test; p < 0.05). d, Probability density function of LAI trends for GIMMS LAI3g, GLASS LAI, GLOBMAP LAI and the average of the three remote sensing data sets (AVG OBS).

  2. Attribution of trend in growing season integrated LAI.
    Figure 2: Attribution of trend in growing season integrated LAI.

    a, Interannual changes in anomalies of growing season integrated LAI estimated by multi-model ensemble mean (MMEM) with all drivers considered (blue line) and the average of the three remote sensing data (red line) for the period 1982–2009, and the interannual changes in anomalies of LAI of GIMMS LAI3g (green line) for the period 1982–2014. The shaded area shows the intensity of EI Niño–Southern Oscillation (ENSO) as defined by the multivariate ENSO index. The black dashed lines label the sensor changing time of the Advanced Very High Resolution Radiometer (AVHRR) satellite series. Two volcanic eruptions (El Chichón eruption and Pinatubo eruption) are indicated with red dashed lines. b, Best estimates of the scaling factors of CO2 fertilization effects (CO2), climate change effects (CLI) and simulated LAI under the four scenarios (see Methods for more details) and their 5–95% uncertainty range from optimal fingerprint analyses of global LAI for 1982–2009. c, Trend in global-averaged LAI derived from satellite observation (OBS) and modelled trends driven by rising CO2 (CO2), climate change (CLI), nitrogen deposition (NDE) and land cover change (LCC) using the Mann–Kendall test. Error bars show the standard deviation of trends derived from satellite data and model simulations. Two asterisks indicate that the trend is statistically significant (p < 0.05).

  3. Spatial pattern of dominant drivers of trend in growing season integrated LAI.
    Figure 3: Spatial pattern of dominant drivers of trend in growing season integrated LAI.

    a,b, Spatial distribution pattern of the trend in growing season integrated LAI for the period 1982–2009. LAI trends were derived from the average of GIMMS, GLOBMAP and GLASS LAI in a and from a multi-model ensemble mean with all drivers considered in b; regions labelled by dots have trends that are statistically significant (p < 0.05). The trend is calculated and evaluated using the Mann–Kendall test at the 5% significance level. c, Dominant driving factors of LAI, defined as the driving factor that contributes the most to the increase (or decrease) in LAI in each vegetated grid cell. The driving factors include rising CO2 (CO2), climate change (CLI), nitrogen deposition (NDE), land cover change (LCC) and other factors (OF), the latter being defined by the non-modelled fraction of observed LAI trend (see text). A prefix ‘+ of the driving factors indicates a positive effect on LAI trends, whereas ‘− indicates a negative effect. d, Fractional area of vegetated land in 15° latitude bands (90°N–60°S) attributed to different factors. The fraction of vegetated area (%) that is dominantly driven by each factor is labelled on top of the bar; + and − have the same meaning as in c.


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  1. Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, CAS Center for Excellence in Tibetan Plateau Earth Science, Chinese Academy of Sciences, Beijing 100085, China

    • Zaichun Zhu &
    • Shilong Piao
  2. Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China

    • Zaichun Zhu,
    • Shilong Piao,
    • Mengtian Huang,
    • Zhenzhong Zeng,
    • Philippe Ciais,
    • Yue Li,
    • Xu Lian,
    • Yongwen Liu,
    • Shushi Peng,
    • Xuhui Wang &
    • Hui Yang
  3. Department of Earth and Environment, Boston University, Boston, Massachusetts 02215, USA

    • Ranga B. Myneni
  4. Global Carbon Project, CSIRO Oceans and Atmosphere, GPO Box 3023, Canberra, Australian Capital Territory 2601, Australia

    • Josep G. Canadell
  5. Laboratoire des Sciences du Climat et de lEnvironnement (LSCE), CEA CNRS UVSQ, 91191 Gif Sur Yvette, France

    • Philippe Ciais &
    • Nicolas Viovy
  6. College of Life and Environmental Sciences, University of Exeter, Exeter EX4 4QF, UK

    • Stephen Sitch
  7. College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK

    • Pierre Friedlingstein
  8. Institute of Meteorology and Climate Research, Atmospheric Environmental Research, Karlsruhe Institute of Technology, 82467 Garmisch-Partenkirchen, Germany

    • Almut Arneth &
    • Thomas A. M. Pugh
  9. State Key Laboratory of Remote Sensing Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

    • Chunxiang Cao
  10. CSIRO Land and Water, Black Mountain, Canberra, Australian Capital Territory 2601, Australia

    • Lei Cheng
  11. Institute of Applied Energy (IAE), Minato-ku, Tokyo 105-0003, Japan

    • Etsushi Kato
  12. Earth Sciences Division, Lawrence Berkeley National Lab, 1 Cyclotron Road, Berkeley, California 94720, USA

    • Charles Koven
  13. LREIS, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

    • Ronggao Liu
  14. Climate Change Science Institute and Environmental Sciences Division, Oak Ridge National Laboratory, Oak Ridge, Tennessee 37831, USA

    • Jiafu Mao
  15. College of Resources Science & Technology, State Key Laboratory of Earth Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China

    • Yaozhong Pan
  16. CSIC, Global Ecology Unit CREAF-CEAB-UAB, Cerdanyola del Vallès, 08193 Catalonia, Spain

    • Josep Peñuelas
  17. CREAF, Cerdanyola del Vallès, 08193 Catalonia, Spain

    • Josep Peñuelas
  18. Montana State University, Institute on Ecosystems and the Department of Ecology, Bozeman, Montana 59717, USA

    • Benjamin Poulter
  19. School of Geography, Earth and Environmental Science, University of Birmingham, Birmingham B15 2TT, UK

    • Thomas A. M. Pugh
  20. Department of Life Sciences, Imperial College London, Silwood Park, Ascot SL5 7PY, UK

    • Benjamin D. Stocker
  21. Climate and Environmental Physics, and Oeschger Centre for Climate Change Research, University of Bern, 3012 Bern, Switzerland

    • Benjamin D. Stocker
  22. CSIRO Oceans and Atmosphere, PMB #1, Aspendale, Victoria 3195, Australia

    • Yingping Wang
  23. State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing 100875, China

    • Zhiqiang Xiao
  24. Max-Planck-Institut für Biogeochemie, PO Box 600164, Hans-Knöll-Str. 10, 07745 Jena, Germany

    • Sönke Zaehle
  25. Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland 20742, USA

    • Ning Zeng


S.Piao, R.B.M. and Z.Zhu designed the study. Z.Zhu performed the analysis. Z.Zhu, S.Piao, J.G.C., P.C. and R.B.M. drafted the paper. Z.Zhu, M.H., Z.Zeng, C.C., Y.Liu, H.Y., X.W., X.L., Y.P., Y.Li, R.L. and Z.X. collected data and prepared figures. S.S., P.F., A.A., B.D.S., B.P., C.K., E.K., J.M., J.P., L.C., N.V., N.Z., S.Peng, S.Z., T.A.M.P., and Y.W. ran the model simulations. All authors contributed to the interpretation of the results and to the text.

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