Plant phenology is a sensitive indicator of climate change1,2,3,4 and plays an important role in regulating carbon uptake by plants5,6,7. Previous studies have focused on spring leaf-out by daytime temperature and the onset of snow-melt time8,9, but the drivers controlling leaf senescence date (LSD) in autumn remain largely unknown10,11,12. Using long-term ground phenological records (14,536 time series since the 1900s) and satellite greenness observations dating back to the 1980s, we show that rising pre-season maximum daytime (Tday) and minimum night-time (Tnight) temperatures had contrasting effects on the timing of autumn LSD in the Northern Hemisphere (> 20° N). If higher Tday leads to an earlier or later LSD, an increase in Tnight systematically drives LSD to occur oppositely. Contrasting impacts of daytime and night-time warming on drought stress may be the underlying mechanism. Our LSD model considering these opposite effects improved autumn phenology modelling and predicted an overall earlier autumn LSD by the end of this century compared with traditional projections. These results challenge the notion of prolonged growth under higher autumn temperatures, suggesting instead that leaf senescence in the Northern Hemisphere will begin earlier than currently expected, causing a positive climate feedback.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Data availability

The data that support the findings of this study are available from the corresponding author upon request.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Change history

  • 06 December 2018

    In the version of this Letter originally published, the author Andrew T. Black was mistakenly denoted as being affiliated with the Institute of Geographical Sciences and Natural Resources Research. His affiliation has now been corrected to: Faculty of Land and Food Systems, University of British Columbia, Vancouver, British Columbia, Canada.


  1. 1.

    Fu, Y. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).

  2. 2.

    Xia, J. et al. Joint control of terrestrial gross primary productivity by plant phenology and physiology. Proc. Natl Acad. Sci. USA 112, 2788–2793 (2015).

  3. 3.

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

  4. 4.

    Peñuelas, J., Rutishauser, T. & Filella, I. Phenology feedbacks on climate change. Science 324, 887–888 (2009).

  5. 5.

    Richardson, A. D. et al. Climate change, phenology, and phenological control of vegetation feedbacks to the climate system. Agr. Forest Meteorol. 169, 156–173 (2013).

  6. 6.

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

  7. 7.

    Wu, C. et al. Interannual variability of net ecosystem productivity in forests is explained by carbon flux phenology in autumn. Glob. Ecol. Biogeogr. 22, 994–1006 (2013).

  8. 8.

    Piao, S. et al. Leaf onset in the Northern Hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).

  9. 9.

    Pulliainen, J. et al. Early snowmelt significantly enhances boreal springtime carbon uptake. Proc. Natl Acad. Sci. USA 114, 11081–11086 (2017).

  10. 10.

    Liu, Q. et al. Delayed autumn phenology in the Northern Hemisphere is related to change in both climate and spring phenology. Glob. Change Biol. 22, 3702–3711 (2016).

  11. 11.

    Keenan, T. F. & Richardson, A. D. The timing of autumn senescence is affected by the timing of spring phenology: implications for predictive models. Glob. Change Biol. 21, 2634–2641 (2015).

  12. 12.

    Gill, A. L. et al. Changes in autumn senescence in Northern Hemisphere deciduous trees: a meta-analysis of autumn phenology studies. Ann. Bot. 116, 875–888 (2015).

  13. 13.

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

  14. 14.

    Suni, T. et al. Air temperature triggers the recovery of evergreen boreal forest photosynthesis in spring. Glob. Change Biol. 9, 1410–1426 (2003).

  15. 15.

    Richardson, A. D. et al. Terrestrial biosphere models need better representation of vegetation phenology: results from the North American carbon program site synthesis. Glob. Change Biol. 18, 566–584 (2012).

  16. 16.

    Gallinat, A. S., Primack, R. B. & Wagner, D. L. Autumn, the neglected season in climate change research. Trends Ecol. Evol. 30, 169–176 (2015).

  17. 17.

    Walther, G. R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).

  18. 18.

    Zhu, W. et al. Extension of the growing season due to delayed autumn over mid and high latitudes in North America during 1982–2006. Glob. Ecol. Biogeogr. 21, 260–271 (2012).

  19. 19.

    Garonna, I. et al. Strong contribution of autumn phenology to changes in satellite-derived growing season length estimates across Europe (1982–2011). Glob. Change Biol. 20, 3457–3470 (2014).

  20. 20.

    Yang, Y., Guan, H., Shen, M., Liang, W. & Jiang, L. Changes in autumn vegetation dormancy onset date and the climate controls across temperate ecosystems in China from 1982 to 2010. Glob. Change Biol. 21, 652–665 (2015).

  21. 21.

    Jeong, S. J., Ho, C. H., Gim, H. J. & Brown, M. E. Phenology shifts at start vs. end of growing season in temperate vegetation over the Northern Hemisphere for the period 1982–2008. Glob. Change Biol. 17, 2385–2399 (2011).

  22. 22.

    Peng, S. et al. Asymmetric effects of daytime and night-time warming on Northern Hemisphere vegetation. Nature 501, 88–92 (2013).

  23. 23.

    Beguería, S., Vicente-Serrano, S. M., Reig, F. & Latorre, B. Standardized precipitation evapotranspiration index (SPEI) revisited: parameter fitting, evapotranspiration models, tools, datasets and drought monitoring. Int. J. Climatol. 34, 3001–3023 (2014).

  24. 24.

    Meng, T. T., Ni, J. & Harrison, S. P. Plant morphometric traits and climate gradients in northern China: a meta-analysis using quadrat and flora data. Ann. Bot. 104, 1217–1229 (2009).

  25. 25.

    Prasad, V. K., Badarinath, K. V. S. & Eaturu, A. Spatial patterns of vegetation phenology metrics and related climatic controls of eight contrasting forest types in India—analysis from remote sensing datasets. Theor. Appl. Climatol. 89, 95–107 (2007).

  26. 26.

    Peñuelas, J. et al. Evidence of current impact of climate change on life: a walk from genes to the biosphere. Glob. Change Biol. 19, 2303–2338 (2013).

  27. 27.

    Wolf, A. A., Zavaleta, E. S. & Selmants, P. C. Flowering phenology shifts in response to biodiversity loss. Proc. Natl Acad. Sci. USA 114, 3463–3468 (2017).

  28. 28.

    Fu, Y. S. et al. Variation in leaf flushing date influences autumnal senescence and next year’s flushing date in two temperate tree species. Proc. Natl Acad. Sci. USA 111, 7355–7360 (2014).

  29. 29.

    Wolf, S. et al. Warm spring reduced carbon cycle impact of the 2012 US summer drought. Proc. Natl Acad. Sci. USA 113, 5880–5885 (2016).

  30. 30.

    Peñuelas, J. et al. Shifting from a fertilization-dominated to a warming-dominated period. Nat. Ecol. Evol. 1, 1438–1445 (2017).

  31. 31.

    Templ, B. et al. Pan European Phenological database (PEP725): a single point of access for European data. Int. J. Biometeorol. 62, 1109–1113 (2018).

  32. 32.

    Ge, Q., Wang, H., Rutishauser, T. & Dai, J. Phenological response to climate change in China: a meta-analysis. Glob. Change Biol. 21, 265–274 (2015).

  33. 33.

    Park, C. K., Ho, C.-H., Jeong, S.-J., Lee, E. J. & Kim, J. Spatial and temporal changes in leaf coloring date of Acer palmatum and Ginkgo biloba in response to temperature increases in South Korea. PLoS ONE 12, e0174390 (2017).

  34. 34.

    Schaber, J. & Badeck, F. W. Evaluation of methods for the combination of phenological time series and outlier detection. Tree Physiol. 22, 973–982 (2002).

  35. 35.

    Huete, A. et al. Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens. Environ. 83, 195–213 (2002).

  36. 36.

    Zhang, X. et al. Monitoring vegetation phenology using MODIS. Remote Sens. Environ. 84, 471–475 (2003).

  37. 37.

    Shen, M. et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau. Agr. Forest Meteorol. 189, 71–80 (2014).

  38. 38.

    Chen, J. et al. A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky–Golay filter. Remote Sens. Environ. 91, 332–344 (2004).

  39. 39.

    Piao, S. et al. Changes in satellite-derived vegetation growth trend in temperate and boreal Eurasia from 1982 to 2006. Glob. Change Biol. 17, 3228–3239 (2011).

  40. 40.

    White, M. A., Thornton, P. E. & Running, S. W. A continental phenology model for monitoring vegetation responses to interannual climatic variability. Glob. Biogeochem. Cycles 11, 217–234 (1997).

  41. 41.

    Elmore, A. J., Guinn, S. M., Minsley, B. J. & Richardson, A. D. Landscape controls on the timing of spring, autumn, and growing season length in mid-Atlantic forests. Glob. Change Biol. 18, 656–674 (2012).

  42. 42.

    Zeng, H. & Jia, G. Impacts of snow cover on vegetation phenology in the Arctic from satellite data. Adv. Atmos. Sci. 30, 1421–1432 (2013).

  43. 43.

    Wang, X., Wu, C., Peng, D., Gonsamo, A. & Liu, Z. Snow cover phenology affects alpine vegetation growth dynamics on the Tibetan Plateau: satellite observed evidence, impacts of biomes, and climate drivers. Agr. Forest Meteorol. 256, 61–74 (2018).

  44. 44.

    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 (2014).

  45. 45.

    Silva, A. A. & de Souza Echer, M. P. Ground-based measurements of local cloud cover. Meteorol. Atmos. Phys. 120, 201–212 (2013).

  46. 46.

    Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth. Bioscience 51, 933–938 (2001).

  47. 47.

    Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).

  48. 48.

    Delpierre, N. et al. Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France. Agr. Forest Meteorol. 149, 938–948 (2009).

  49. 49.

    Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Glob. Biogeochem. Cycles 19, GB1015 (2005).

Download references


This work was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19040103), International Cooperation and Exchange Programs of National Science Foundation of China (Sino-German, 41761134082), National Natural Science Foundation of China (41522109) and the Key Research Program of Frontier Sciences, CAS (QYZDB-SSW-DQC011). J.P. and P.C. were funded by European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P. A.R.D. acknowledges support from the Ned P. Smith Professorship of Climatology, University of Wisconsin–Madison.

Author information


  1. The Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    • Chaoyang Wu
    • , Xiaoyue Wang
    • , Huanjiong Wang
    •  & Quansheng Ge
  2. University of the Chinese Academy of Sciences, Beijing, China

    • Chaoyang Wu
    • , Xiaoyue Wang
    • , Huanjiong Wang
    •  & Quansheng Ge
  3. Laboratoire des Sciences du Climat et de l’Environnement, IPSL-LSCE CEA CNRS UVSQ, Gif-sur-Yvette, France

    • Philippe Ciais
  4. CSIC, Global Ecology Unit CREAF-CSIC-UAB, Barcelona, Spain

    • Josep Peñuelas
  5. CREAF, Barcelona, Spain

    • Josep Peñuelas
  6. Department of Earth and Environment, Boston University, Boston, MA, USA

    • Ranga B. Myneni
  7. Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, WI, USA

    • Ankur R. Desai
  8. Department of Biology, Virginia Commonwealth University, Richmond, VA, USA

    • Christopher M. Gough
  9. Department of Geography and Planning, University of Toronto, Toronto, Ontario, Canada

    • Alemu Gonsamo
    •  & Jing M. Chen
  10. Faculty of Land and Food Systems, University of British Columbia, Vancouver, British Columbia, Canada

    • Andrew T. Black
    •  & Rachhpal S. Jassal
  11. International Institute for Earth System Science, Nanjing University, Nanjing, China

    • Weimin Ju
  12. School of Atmospheric Sciences, Center for Monsoon and Environment Research, Sun Yat-Sen University, Guangzhou, China

    • Wenping Yuan
  13. College of Water Sciences, Beijing Normal University, Beijing, China

    • Yongshuo Fu
  14. CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China

    • Miaogen Shen
  15. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China

    • Shihua Li
  16. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China

    • Ronggao Liu


  1. Search for Chaoyang Wu in:

  2. Search for Xiaoyue Wang in:

  3. Search for Huanjiong Wang in:

  4. Search for Philippe Ciais in:

  5. Search for Josep Peñuelas in:

  6. Search for Ranga B. Myneni in:

  7. Search for Ankur R. Desai in:

  8. Search for Christopher M. Gough in:

  9. Search for Alemu Gonsamo in:

  10. Search for Andrew T. Black in:

  11. Search for Rachhpal S. Jassal in:

  12. Search for Weimin Ju in:

  13. Search for Wenping Yuan in:

  14. Search for Yongshuo Fu in:

  15. Search for Miaogen Shen in:

  16. Search for Shihua Li in:

  17. Search for Ronggao Liu in:

  18. Search for Jing M. Chen in:

  19. Search for Quansheng Ge in:


C.W., H.W. and Q.G. designed the research. C.W. wrote the first draft of the paper. J.P. and P.C. extensively revised the writing. H.W. performed the site model simulations. X.W. performed remote-sensing model simulations. All the authors contributed to writing the paper.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Chaoyang Wu or Huanjiong Wang or Quansheng Ge.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–21, Supplementary Tables 13

About this article

Publication history