Skip to main content

Thank you for visiting 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.

Human-induced greening of the northern extratropical land surface


Significant land greening in the northern extratropical latitudes (NEL) has been documented through satellite observations during the past three decades1,2,3,4,5. This enhanced vegetation growth has broad implications for surface energy, water and carbon budgets, and ecosystem services across multiple scales6,7,8. Discernible human impacts on the Earth’s climate system have been revealed by using statistical frameworks of detection–attribution9,10,11. These impacts, however, were not previously identified on the NEL greening signal, owing to the lack of long-term observational records, possible bias of satellite data, different algorithms used to calculate vegetation greenness, and the lack of suitable simulations from coupled Earth system models (ESMs). Here we have overcome these challenges to attribute recent changes in NEL vegetation activity. We used two 30-year-long remote-sensing-based leaf area index (LAI) data sets12,13, simulations from 19 coupled ESMs with interactive vegetation, and a formal detection and attribution algorithm14,15. Our findings reveal that the observed greening record is consistent with an assumption of anthropogenic forcings, where greenhouse gases play a dominant role, but is not consistent with simulations that include only natural forcings and internal climate variability. These results provide the first clear evidence of a discernible human fingerprint on physiological vegetation changes other than phenology and range shifts11.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Figure 1: Spatial distribution of LAI trends for 1982–2011.
Figure 2: Observed and simulated 1982–2011 time series of LAI anomalies.
Figure 3: Parameterized frequency distributions of LAI 1982–2011 30-year-long trends.
Figure 4: Results from optimal D&A for 1982–2011 time series of LAI anomalies.


  1. 1

    Myneni, R. B., Keeling, C., Tucker, C., Asrar, G. & Nemani, R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).

    CAS  Article  Google Scholar 

  2. 2

    Zhou, L. et al. Variations in northern vegetation activity inferred from satellite data of vegetation index during 1981 to 1999. J. Geophys. Res. 106, 20069–20083 (2001).

    Article  Google Scholar 

  3. 3

    Lucht, W. et al. Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science 296, 1687–1689 (2002).

    CAS  Article  Google Scholar 

  4. 4

    Mao, J. et al. Global latitudinal-asymmetric vegetation growth trends and their driving mechanisms: 1982–2009. Remote Sens. 5, 1484–1497 (2013).

    Article  Google Scholar 

  5. 5

    Buitenwerf, R., Rose, L. & Higgins, S. I. Three decades of multi-dimensional change in global leaf phenology. Nature Clim. Change 5, 364–368 (2015).

    Article  Google Scholar 

  6. 6

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

    CAS  Article  Google Scholar 

  7. 7

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

    Article  Google Scholar 

  8. 8

    Ukkola, A. M. et al. Reduced streamflow in water-stressed climates consistent with CO2 effects on vegetation. Nature Clim. Change 6, 75–78 (2015).

    Article  Google Scholar 

  9. 9

    Hegerl, G. C. et al. in Climate Change 2007: The Physical Science Basis (eds Solomon, S. et al.) 667–732 (IPCC, Cambridge Univ. Press, 2007).

    Google Scholar 

  10. 10

    Bindoff, N. L. et al. in Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) 867–931 (IPCC, Cambridge Univ. Press, 2013).

    Google Scholar 

  11. 11

    Cramer, W. et al. in Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) 979–1037 (IPCC, Cambridge Univ. Press, 2014).

    Google Scholar 

  12. 12

    Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI) 3g and fraction of photosynthetically active radiation (FPAR) 3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2011. Remote Sens. 5, 927–948 (2013).

    Article  Google Scholar 

  13. 13

    Baret, F. et al. GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part 1: Principles of development and production. Remote Sens. Environ. 137, 299–309 (2013).

    Article  Google Scholar 

  14. 14

    Allen, M. & Stott, P. Estimating signal amplitudes in optimal fingerprinting, part I: theory. Clim. Dynam. 21, 477–491 (2003).

    Article  Google Scholar 

  15. 15

    Ribes, A., Planton, S. & Terray, L. Application of regularised optimal fingerprinting to attribution. part I: method, properties and idealised analysis. Clim. Dynam. 41, 2817–2836 (2013).

    Article  Google Scholar 

  16. 16

    Lamarque, J. F. et al. Multi-model mean nitrogen and sulfur deposition from the Atmospheric Chemistry and Climate Model Intercomparison Project (ACCMIP): evaluation of historical and projected future changes. Atmos. Chem. Phys. 13, 7997–8018 (2013).

    Article  Google Scholar 

  17. 17

    Anav, A. et al. Evaluation of land surface models in reproducing satellite derived leaf area index over the high-latitude northern hemisphere. part II: Earth system models. Remote Sens. 5, 3637–3661 (2013).

    Article  Google Scholar 

  18. 18

    Mahowald, N. et al. Projections of leaf area index in Earth system models. Earth Syst. Dynam. 7, 211–229 (2016).

    Article  Google Scholar 

  19. 19

    Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).

    Article  Google Scholar 

  20. 20

    Zeng, F., Collatz, G., Pinzon, J. & Ivanoff, A. Evaluating and quantifying the climate-driven interannual variability in Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) at global scales. Remote Sens. 5, 3918–3950 (2013).

    Article  Google Scholar 

  21. 21

    Piao, S. et al. Evidence for a weakening relationship between interannual temperature variability and northern vegetation activity. Nature Commun. 5, 5018 (2014).

    CAS  Article  Google Scholar 

  22. 22

    Anav, A. et al. Spatio-temporal patterns of terrestrial gross primary production: a review. Rev. Geophys. 53, 785–818 (2015).

    Article  Google Scholar 

  23. 23

    McDowell, N. G. et al. Multi-scale predictions of massive conifer mortality due to chronic temperature rise. Nature Clim. Change 6, 295–300 (2016).

    Article  Google Scholar 

  24. 24

    Di Vittorio, A. V. et al. From land use to land cover: restoring the afforestation signal in a coupled integrated assessment–Earth system model and the implications for CMIP5 RCP simulations. Biogeosciences 11, 6435–6450 (2014).

    Article  Google Scholar 

  25. 25

    Kunstler, G. et al. Plant functional traits have globally consistent effects on competition. Nature 529, 204–207 (2016).

    CAS  Article  Google Scholar 

  26. 26

    Zhang, X. et al. Detection of human influence on twentieth-century precipitation trends. Nature 448, 461–465 (2007).

    CAS  Article  Google Scholar 

  27. 27

    Wu, P., Christidis, N. & Stott, P. Anthropogenic impact on Earth’s hydrological cycle. Nature Clim. Change 3, 807–810 (2013).

    Article  Google Scholar 

  28. 28

    Keenan, T. F. et al. Net carbon uptake has increased through warming-induced changes in temperate forest phenology. Nature Clim. Change 4, 598–604 (2014).

    CAS  Article  Google Scholar 

  29. 29

    Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model. Dev. Discuss. 8, 10539–10583 (2015).

    Article  Google Scholar 

  30. 30

    Pinzon, J. E. & Tucker, C. J. A non-stationary 1981–2012 AVHRR NDVI3g time series. Remote Sens. 6, 6929–6960 (2014).

    Article  Google Scholar 

  31. 31

    Allen, M. R. & Tett, S. F. Checking for model consistency in optimal fingerprinting. Clim. Dynam. 15, 419–434 (1999).

    Article  Google Scholar 

  32. 32

    Stott, P. A. et al. Observational constraints on past attributable warming and predictions of future global warming. J. Clim. 19, 3055–3069 (2006).

    Article  Google Scholar 

Download references


This work is supported by the Biogeochemistry-Climate Feedbacks Scientific Focus Area project funded through the Regional and Global Climate Modeling Program, and the Terrestrial Ecosystem Science Scientific Focus Area project funded through the Terrestrial Ecosystem Science Program, with additional support from the Accelerated Climate Modeling for Energy project, in the Climate and Environmental Sciences Division (CESD) of the Biological and Environmental Research (BER) Program in the US Department of Energy Office of Science. Oak Ridge National Laboratory is managed by UT-BATTELLE for DOE under contract DE-AC05-00OR22725. This work is supported in part by the Fondation STAE, via the project Chavana. R.S. thanks the H2020 project CRESCENDO ‘Coordinated Research in Earth Systems and Climate: Experiments, kNowledge, Dissemination and Outreach’, which received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 641816. R.B.M. is supported by NASA Earth Science Division through MODIS and VIIRS grants. B.W. is supported by the National Basic Research Program of China (Grant no. 2014CB441302). P.C. thanks the ERC SyG project IMBALANCE-P Effects of phosphorus limitations on Life, Earth system and Society Grant agreement no. 610028.

Author information




J.M. conceived the study. J.M., A.R., B.Y., X.S., P.E.T. and R.S. performed diagnostics and wrote the text, with comments and edits from all authors.

Corresponding author

Correspondence to Jiafu Mao.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

Supplementary Information (PDF 7014 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Mao, J., Ribes, A., Yan, B. et al. Human-induced greening of the northern extratropical land surface. Nature Clim Change 6, 959–963 (2016).

Download citation

Further reading


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