Contrasting responses of autumn-leaf senescence to daytime and night-time warming

An Author Correction to this article was published on 09 January 2019

A Publisher Correction to this article was published on 06 December 2018

This article has been updated


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.

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Fig. 1: Frequency of the partial correlation coefficient between LSD and Tday and Tnight.
Fig. 2: Spatial distributions of the partial correlation coefficient between LSD and Tday and Tnight.
Fig. 3: The partial correlation coefficient between the SPEI, evapotranspiration, Tday and Tnight.
Fig. 4: LSD differences from the weighted DNGDD and traditional GDD (LSDDNGDD − LSDGDD) models under two RCP scenarios.

Data availability

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

Change history

  • 09 January 2019

    In the version of this Letter originally published, there were errors in Fig. 1a. The sites denoted purple were described in the legend as ‘Pday>0.05 & Pnight>0.05’, but should have been labelled ‘Pday<0.05 & Pnight>0.05’. The sites denoted green were described in the legend as ‘Pday>0.05 & Pnight>0.05’, but should have been labelled ‘Pday>0.05 & Pnight<0.05’. The sites denoted orange were described in the legend as ‘Pday>0.05 & Pnight>0.05’, but should have been labelled ‘Pday<0.05 & Pnight<0.05’. These errors have now been corrected.

  • 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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  4. 4.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  8. 8.

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

    CAS  Article  Google Scholar 

  9. 9.

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  17. 17.

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  22. 22.

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

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  30. 30.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  36. 36.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    CAS  Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  45. 45.

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

    Article  Google Scholar 

  46. 46.

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

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  Google Scholar 

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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




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.

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Correspondence to Chaoyang Wu or Huanjiong Wang or Quansheng Ge.

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Wu, C., Wang, X., Wang, H. et al. Contrasting responses of autumn-leaf senescence to daytime and night-time warming. Nature Clim Change 8, 1092–1096 (2018).

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