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Increased drought effects on the phenology of autumn leaf senescence


Global warming delays the autumn date of foliar senescence (DFS) in recent decades, with positive implications for growing season length and therefore global carbon storage. However, warming-associated drought, leading to water limitation, may conversely stimulate earlier DFS. Using ground observations since 1940s and 34 years of satellite greenness data (1982‒2015) over the Northern Hemisphere (>30° N), we show the increased impact of drought on DFS. Earlier DFS is linked to decreased precipitation under warming and weaker drought resistance associated with various plant functional traits. For example, isohydric plants with strict regulation of water status may drop leaves fast during droughts. We derive an improved set of phenology models based on this influence and project earlier DFS by the end of the century, particularly at high latitudes (>50° N). Our results limit uncertainties in the later end of plant growth with warming, aiding estimation of carbon uptake of terrestrial ecosystems.

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Fig. 1: Temporal changes in the sensitivity of the autumnal foliar senescence date (DFS) to drought.
Fig. 2: Underlying mechanisms for enhanced drought effects.
Fig. 3: Accuracy comparisons with the enhanced drought effects.
Fig. 4: DFS difference predicted by the SIAMS and SIAM (SIAMS minus SIAM) under SSP 1-2.6 and SSP 5-8.5 scenarios.

Data availability

All data used in this study are included in the article and the Supplementary Information. The specific link for each dataset can be found in Supplementary Table 1.

Code availability

All the data analyses and modelling were performed using MATLAB. The codes for Tsen calculation and the eight phenology models as well as the satellite LUD and DFS data used in our study are available at Other codes and relevant data are available upon request to the corresponding authors.


  1. 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 

  2. 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 

  3. Piao, S. L. et al. Leaf onset in the northern hemisphere triggered by daytime temperature. Nat. Commun. 6, 6911 (2015).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  5. 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 

  6. Piao, S. L. et al. Net carbon dioxide losses of northern ecosystems in response to autumn warming. Nature 451, 49–52 (2008).

    CAS  Article  Google Scholar 

  7. Zhao, Y. et al. ABA receptor PYL9 promotes drought resistance and leaf senescence. Proc. Natl Acad. Sci. USA 113, 1949–1954 (2016).

    CAS  Article  Google Scholar 

  8. Keskitalo, J., Bergquist, G., Gardestrom, P. & Jansson, S. A cellular timetable of autumn senescence. Plant Physiol. 139, 1635–1648 (2005).

    CAS  Article  Google Scholar 

  9. 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 

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

    CAS  Article  Google Scholar 

  11. Zani, D., Crowther, T. W., Mo, L., Renner, S. S. & Zohner, C. M. Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees. Science 370, 1066–1071 (2020).

    CAS  Article  Google Scholar 

  12. Zhang, Y., Parazoo, N. C., Williams, A. P., Zhou, S. & Gentine, P. Large and projected strengthening moisture limitation on end-of-season photosynthesis. Proc. Natl Acad. Sci. USA 117, 9216–9222 (2020).

    CAS  Article  Google Scholar 

  13. Grossiord, C. et al. Plant responses to rising vapor pressure deficit. New Phytol. 226, 1550–1566 (2020).

    Article  Google Scholar 

  14. Ciais, P. et al. Europe-wide reduction in primary productivity caused by the heat and drought in 2003. Nature 437, 529–533 (2005).

    CAS  Article  Google Scholar 

  15. 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 

  16. Liu, L. B. et al. Soil moisture dominates dryness stress on ecosystem production globally. Nat. Commun. 11, 4892 (2020).

    CAS  Article  Google Scholar 

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

    Article  Google Scholar 

  18. Piao, S. L. et al. Weakening temperature control on the interannual variations of spring carbon uptake across northern lands. Nat. Clim. Change 7, 359–363 (2017).

    CAS  Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  20. Seastedt, T. R. & Knapp, A. K. Consequences of nonequilibrium resource availability across multiple time scales: the transient maxima hypothesis. Am. Nat. 141, 621–633 (1993).

    CAS  Article  Google Scholar 

  21. Korner, C. Paradigm shift in plant growth control. Curr. Opin. Plant Biol. 25, 107–114 (2015).

    CAS  Article  Google Scholar 

  22. Huxman, T. E. et al. Convergence across biomes to a common rain-use efficiency. Nature 429, 651–654 (2004).

    CAS  Article  Google Scholar 

  23. McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought. New Phytol. 178, 719–739 (2008).

    Article  Google Scholar 

  24. Nolan, R. H. et al. Differences in osmotic adjustment, foliar abscisic acid dynamics, and stomatal regulation between an isohydric and anisohydric woody angiosperm during drought. Plant Cell Environ. 40, 3122–3134 (2017).

    CAS  Article  Google Scholar 

  25. Fan, Y., Miguez-Macho, G., Jobbágy, E. G., Jackson, R. B. & Otero-Casal, C. Hydrologic regulation of plant rooting depth. Proc. Natl Acad. Sci. USA 114, 10572–10577 (2017).

    CAS  Article  Google Scholar 

  26. Choat, B. et al. Triggers of tree mortality under drought. Nature 558, 531–539 (2018).

    CAS  Article  Google Scholar 

  27. Giardina, F. et al. Tall Amazonian forests are less sensitive to precipitation variability. Nat. Geosci. 11, 405–409 (2018).

    CAS  Article  Google Scholar 

  28. Kannenberg, S. A., Driscoll, A. W., Szejner, P., Anderegg, W. R. L. & Ehleringer, J. R. Rapid increases in shrubland and forest intrinsic water-use efficiency during an ongoing megadrought. Proc. Natl Acad. Sci. USA (2021).

  29. Liu, Q. et al. Extension of the growing season increases vegetation exposure to frost. Nat. Commun. (2018).

  30. Schuur, E. A. G. et al. Climate change and the permafrost carbon feedback. Nature 520, 171–179 (2015).

    CAS  Article  Google Scholar 

  31. Samaniego, L. et al. Anthropogenic warming exacerbates European soil moisture droughts. Nat. Clim. Change 8, 421–426 (2018).

    Article  Google Scholar 

  32. 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 

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

    Article  Google Scholar 

  34. Zhang, X. Y. Reconstruction of a complete global time series of daily vegetation index trajectory from long-term AVHRR data. Remote Sens. Environ. 156, 457–472 (2015).

    Article  Google Scholar 

  35. 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 

  36. White, M. A. et al. Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006. Glob. Change Biol. 15, 2335–2359 (2009).

    Article  Google Scholar 

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

    Article  Google Scholar 

  38. Gonsamo, A., Chen, J. M., Price, D. T., Kurz, W. A. & Wu, C. Y. Land surface phenology from optical satellite measurement and CO2 eddy covariance technique. J. Geophys. Res. 117, G03032 (2012).

    Google Scholar 

  39. Muñoz, S. ERA5-Land Monthly Averaged Data from 1981 to Present (C3S CDS, date accessed:10-8-2021);

  40. Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).

    Article  Google Scholar 

  41. Müller, W. A. et al. A Higher-resolution version of the Max Planck Institute Earth System Model (MPI-ESM1.2-HR). J. Adv. Model. Earth Syst. 10, 1383–1413 (2018).

    Article  Google Scholar 

  42. Vicente-Serrano, S. M. et al. Response of vegetation to drought time-scales across global land biomes. Proc. Natl Acad. Sci. USA 110, 52–57 (2013).

    CAS  Article  Google Scholar 

  43. Allen, R. G., Smith, M., Pereira, L. S. & Perrier, A. An update for the calculation of reference evapotranspiration. ICID Bull. 43, 64–92 (1994).

    Google Scholar 

  44. Gampe, D. et al. Increasing impact of warm droughts on northern ecosystem productivity over recent decades. Nat. Clim. Change (2021).

  45. Sheffield, J., Wood, E. F. & Roderick, M. L. Little change in global drought over the past 60 years. Nature 491, 435–438 (2012).

    CAS  Article  Google Scholar 

  46. Peng, J., Wu, C. Y., Zhang, X. Y., Wang, X. Y. & Gonsamo, A. Satellite detection of cumulative and lagged effects of drought on autumn leaf senescence over the Northern Hemisphere. Glob. Change Biol. 25, 2174–2188 (2019).

    Article  Google Scholar 

  47. 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 

  48. Beaudoing, H., Rodell, M. & NASA/GSFC/HSL. GLDAS Noah Land Surface Model L4 3 Hourly 0.25×0.25 Degree Version 2.0 (GES DISC, 2015);

  49. Beaudoing, H., Rodell, M. & NASA/GSFC/HSL. GLDAS Noah Land Surface Model L4 3 Hourly 0.25 ×0.25 Degree Version 2.1 (GES DISC, 2016);

  50. Zheng, Y. et al. Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017. Earth Syst. Sci. Data 12, 2725–2746 (2020).

    Article  Google Scholar 

  51. Zhang, K. et al. Vegetation greening and climate change promote multidecadal rises of global land evapotranspiration. Sci. Rep. (2015).

  52. Li, Y. et al. Estimating global ecosystem isohydry/anisohydry using active and passive microwave satellite data. J. Geophys. Res. 122, 3306–3321 (2017).

    Article  Google Scholar 

  53. Moesinger, L. et al. The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA). Earth Syst. Sci. Data 12, 177–196 (2020).

  54. Gupta, H. V., Kling, H., Yilmaz, K. K. & Martinez, G. F. Decomposition of the mean squared error and NSE performance criteria: implications for improving hydrological modelling. J. Hydrol. 377, 80–91 (2009).

    Article  Google Scholar 

  55. Botta, A., Viovy, N., Ciais, P., Friedlingstein, P. & Monfray, P. A global prognostic scheme of leaf onset using satellite data. Glob. Change Biol. 6, 709–725 (2000).

    Article  Google Scholar 

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This work was funded by the Strategic Priority Research Programme of the Chinese Academy of Sciences (XDA19040103), the National Natural Science Foundation of China (42125101) and the CAS Interdisciplinary Innovation Team (JCTD-2020-05). J.P. and P.C. were funded by European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P. J.P. was also financially supported by the Fundación Ramon Areces grant ELEMENTAL-CLIMATE, the Spanish Government grant PID2019-110521GB-I00 and the Catalan Government grant SGR 2017-1005.

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Authors and Affiliations



C.W., J.P. and Q.G. designed the research. C.W. and J.P. wrote the first draft of the manuscript. J.P. performed data analyses and remote-sensing model simulations. X.W. and H.H. contributed to model simulation. P.C., J.P. and S.B. substantially revised the manuscript with intensive suggestions. H.W., A.B., R.J., X.Z., W.Y., E.L., R.L., W.J. and Y.F. contributed to the writing of the manuscript.

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Correspondence to Chaoyang Wu, Jie Peng or Quansheng Ge.

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Nature Climate Change thanks Ainong Li, Constantin Zohner and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Wu, C., Peng, J., Ciais, P. et al. Increased drought effects on the phenology of autumn leaf senescence. Nat. Clim. Chang. (2022).

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