Skip to main content

Thank you for visiting nature.com. 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.

  • Review Article
  • Published:

Plant phenology changes and drivers on the Qinghai–Tibetan Plateau

Nature Reviews Earth & Environment volume 3, pages 633–651 (2022)Cite this article

Subjects

A Publisher Correction to this article was published on 12 August 2022

This article has been updated

Abstract

The ongoing phenological changes in vegetation on the Qinghai–Tibetan Plateau could modify land surface and atmospheric processes. In this Review, we summarize these changes, their drivers and the resulting impacts. The start of the growing season advanced by 9.4 ± 2.2 days during 1982–1999 and 8.3 ± 2.0 days over 2000–2020, and the end of season delayed by 8.2 ± 1.9 days during 2000–2020. The main identified drivers of these changes are warming temperatures and increasing precipitation, but their impacts vary substantially across the Qinghai–Tibetan Plateau. Other factors, such as grazing and nitrogen deposition, also potentially influence phenological changes, but these relationships are poorly constrained. In manipulation experiments, grazing and nitrogen addition have no individual effects on most phenophase timings at the population level, but nitrogen addition markedly delays flowering. Additionally, there are carry-over effects between phenophases that control subsequent temperature and precipitation responses. Phenological changes in turn could alter species interactions, modulate carbon and water cycling, and affect Asian monsoons and spring rainfall over eastern China, but evidence of these interactions is limited. Harmonization of remote-sensing-based and in situ observations, and simultaneous testing of both biotic and abiotic factors, are needed for a mechanistic understanding of Qinghai–Tibetan Plateau phenology dynamics.

Key points

  • Warming and increasing precipitation on the Qinghai–Tibetan Plateau are the main climatic factors driving advances in spring phenology and delays in the end of the growing season.

  • The start of the growing season advanced by 9.4 ± 2.2 and 8.3 ± 2.0 days during 1982–1999 and 2000–2020, respectively, whereas its end was delayed by only 8.2 ± 1.9 days during 2000–2020.

  • Relative to 2000–2014, the start of the growing season is projected to have advanced by 8.8 days and the end of the season delayed by 14.0 days in 2086–2100 under the Shared Socioeconomic Pathway 5-8.5 (SSP5-8.5).

  • Phenophase temperature responses depend on soil water availability, with greater temperature sensitivity of the start and end of the season under wetter conditions.

  • The temperature sensitivities of the start and end of the growing season are greater on the Qinghai–Tibetan Plateau than in Arctic grasslands, but smaller than those of mid-latitude alpine and subalpine grasslands.

  • First flowering date is more sensitive to temperature on the Qinghai–Tibetan Plateau than in Arctic grasslands.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Drivers and impacts of plant phenological changes on the Qinghai–Tibetan Plateau.
Fig. 2: Spatial variations and drivers of phenology.
Fig. 3: Temporal changes in plant phenology.
Fig. 4: Drivers of vegetation phenology.
Fig. 5: Responses of phenological sequences to various treatments.
Fig. 6: The abiotic and carry-over effects on phenophases.
Fig. 7: Temperature sensitivity of phenology on the QTP and other grasslands.

Similar content being viewed by others

Change history

References

  1. Lieth, H. Phenology and Seasonality Modeling Vol. 8 (Springer, 2013).

  2. Piao, S. et al. Plant phenology and global climate change: current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).

    Article  Google Scholar 

  3. Shen, M. et al. Can changes in autumn phenology facilitate earlier green-up date of northern vegetation? Agric. For. Meteorol. 291, 108077 (2020).

    Article  Google Scholar 

  4. Menzel, A. et al. Climate change fingerprints in recent European plant phenology. Glob. Change Biol. 26, 2599–2612 (2020).

    Article  Google Scholar 

  5. Shen, X. et al. Asymmetric effects of daytime and nighttime warming on spring phenology in the temperate grasslands of China. Agric. For. Meteorol. 259, 240–249 (2018).

    Article  Google Scholar 

  6. Rudolf, V. H. W. The role of seasonal timing and phenological shifts for species coexistence. Ecol. Lett. 22, 1324–1338 (2019).

    Google Scholar 

  7. Zhu, J., Zhang, Y. & Wang, W. Interactions between warming and soil moisture increase overlap in reproductive phenology among species in an alpine meadow. Biol. Lett. 12, 20150749 (2016).

    Article  Google Scholar 

  8. Chen, J. et al. Plants with lengthened phenophases increase their dominance under warming in an alpine plant community. Sci. Total Environ. 728, 138891 (2020).

    Article  Google Scholar 

  9. Lian, X. et al. Summer soil drying exacerbated by earlier spring greening of northern vegetation. Sci. Adv. 6, eaax0255 (2020).

    Article  Google Scholar 

  10. Wolkovich, E. M. & Donahue, M. J. How phenological tracking shapes species and communities in non-stationary environments. Biol. Rev. Camb. Philos. Soc. 96, 2810–2827 (2021).

    Article  Google Scholar 

  11. Xu, X., Riley, W. J., Koven, C. D., Jia, G. & Zhang, X. Earlier leaf-out warms air in the north. Nat. Clim. Chang. 10, 370–375 (2020).

    Article  Google Scholar 

  12. D’Amato, G. et al. The effects of climate change on respiratory allergy and asthma induced by pollen and mold allergens. Allergy 75, 2219–2228 (2020).

    Article  Google Scholar 

  13. Garcia-Mozo, H. Poaceae pollen as the leading aeroallergen worldwide: a review. Allergy 72, 1849–1858 (2017).

    Article  Google Scholar 

  14. Ge, Q., Dai, J., Liu, J., Zhong, S. & Liu, H. The effect of climate change on the fall foliage vacation in China. Tour. Manag. 38, 80–84 (2013).

    Article  Google Scholar 

  15. Liu, J., Cheng, H., Jiang, D. & Huang, L. Impact of climate-related changes to the timing of autumn foliage colouration on tourism in Japan. Tour. Manag. 70, 262–272 (2019).

    Article  Google Scholar 

  16. Fan, B. et al. Earlier vegetation green-up has reduced spring dust storms. Sci. Rep. 4, 6749 (2014).

    Article  Google Scholar 

  17. Minoli, S. et al. Global response patterns of major rainfed crops to adaptation by maintaining current growing periods and irrigation. Earths Future 7, 1464–1480 (2019).

    Article  Google Scholar 

  18. Shen, M. et al. Plant phenological responses to climate change on the Tibetan Plateau: research status and challenges. Natl Sci. Rev. 22, 454–467 (2015).

    Article  Google Scholar 

  19. You, Q., Wang, D., Jiang, Z. & Kang, S. Diurnal temperature range in CMIP5 models and observations on the Tibetan Plateau. Q. J. R. Meteorol. Soc. 143, 1978–1989 (2017).

    Article  Google Scholar 

  20. You, Q. et al. Temperature dataset of CMIP6 models over China: evaluation, trend and uncertainty. Clim. Dyn. 57, 17–35 (2021).

    Article  Google Scholar 

  21. Zhu, Y.-Y. & Yang, S. Evaluation of CMIP6 for historical temperature and precipitation over the Tibetan Plateau and its comparison with CMIP5. Adv. Clim. Change Res. 11, 239–251 (2020).

    Article  Google Scholar 

  22. Lun, Y. et al. Assessment of GCMs simulation performance for precipitation and temperature from CMIP5 to CMIP6 over the Tibetan Plateau. Int. J. Climatol. 41, 3994–4018 (2021).

    Article  Google Scholar 

  23. Song, L., Zhuang, Q., Yin, Y., Wu, S. & Zhu, X. Intercomparison of model-estimated potential evapotranspiration on the Tibetan Plateau during 1981–2010. Earth Interact. 21, 1–22 (2017).

    Article  Google Scholar 

  24. You, Q., Min, J. & Kang, S. Rapid warming in the Tibetan Plateau from observations and CMIP5 models in recent decades. Int. J. Climatol. 36, 2660–2670 (2016).

    Article  Google Scholar 

  25. He, J.-S. et al. Above-belowground interactions in alpine ecosystems on the roof of the world. Plant Soil 458, 1–6 (2020).

    Article  Google Scholar 

  26. Kuang, X. & Jiao, J. J. Review on climate change on the Tibetan Plateau during the last half century. J. Geophys. Res. Atmos. 121, 3979–4007 (2016).

    Article  Google Scholar 

  27. Shen, M., Piao, S., Cong, N., Zhang, G. & Jassens, I. A. Precipitation impacts on vegetation spring phenology on the Tibetan Plateau. Glob. Change Biol. 21, 3647–3656 (2015).

    Article  Google Scholar 

  28. Shen, M., Tang, Y., Chen, J., Zhu, X. & Zheng, Y. Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau. Agric. For. Meteorol. 151, 1711–1722 (2011).

    Article  Google Scholar 

  29. Ganjurjav, H. et al. Warming and precipitation addition interact to affect plant spring phenology in alpine meadows on the central Qinghai-Tibetan Plateau. Agric. For. Meteorol. 287, 107943 (2020).

    Article  Google Scholar 

  30. Peng, J., Wu, C., Wang, X. & Lu, L. Spring phenology outweighed climate change in determining autumn phenology on the Tibetan Plateau. Int. J. Climatol. 41, 3725–3742 (2021).

    Article  Google Scholar 

  31. Chen, X., An, S., Inouye, D. W. & Schwartz, M. D. Temperature and snowfall trigger alpine vegetation green-up on the world’s roof. Glob. Change Biol. 21, 3635–3646 (2015).

    Article  Google Scholar 

  32. Zheng, Z. et al. Continuous but diverse advancement of spring-summer phenology in response to climate warming across the Qinghai-Tibetan Plateau. Agric. For. Meteorol. 223, 194–202 (2016).

    Article  Google Scholar 

  33. Zhu, W. et al. Divergent shifts and responses of plant autumn phenology to climate change on the Qinghai-Tibetan Plateau. Agric. For. Meteorol. 239, 166–175 (2017).

    Article  Google Scholar 

  34. Sun, Q., Li, B., Jiang, Y., Chen, X. & Zhou, G. Declined trend in herbaceous plant green-up dates on the Qinghai–Tibetan Plateau caused by spring warming slowdown. Sci. Total Environ. 772, 145039 (2021).

    Article  Google Scholar 

  35. Sun, Q., Li, B., Zhou, G., Jiang, Y. & Yuan, Y. Delayed autumn leaf senescence date prolongs the growing season length of herbaceous plants on the Qinghai–Tibetan Plateau. Agric. For. Meteorol. 284, 107896 (2020).

    Article  Google Scholar 

  36. Jiang, Y. et al. Divergent shifts in flowering phenology of herbaceous plants on the warming Qinghai–Tibetan plateau. Agric. For. Meteorol. 307, 108502 (2021).

    Article  Google Scholar 

  37. Cong, N., Shen, M. & Piao, S. Spatial variations in responses of vegetation autumn phenology to climate change on the Tibetan Plateau. J. Plant Ecol. 10, 744–752 (2016).

    Google Scholar 

  38. Shi, C. et al. Effects of warming on chlorophyll degradation and carbohydrate accumulation of Alpine herbaceous species during plant senescence on the Tibetan Plateau. PLoS ONE 9, e107874 (2014).

    Article  Google Scholar 

  39. Morisette, J. T. et al. Tracking the rhythm of the seasons in the face of global change: phenological research in the 21st century. Front. Ecol. Environ. 7, 253–260 (2009).

    Article  Google Scholar 

  40. Kharouba, H. M. et al. Global shifts in the phenological synchrony of species interactions over recent decades. Proc. Natl Acad. Sci. USA 115, 5211–5216 (2018).

    Article  Google Scholar 

  41. Vitasse, Y. et al. Phenological and elevational shifts of plants, animals and fungi under climate change in the European Alps. Biol. Rev. Camb. Philos. Soc. 96, 1816–1835 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  44. Estiarte, M. & Penuelas, J. Alteration of the phenology of leaf senescence and fall in winter deciduous species by climate change: effects on nutrient proficiency. Glob. Change Biol. 21, 1005–1017 (2015).

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  47. Ran, Y., Li, X. & Cheng, G. Climate warming over the past half century has led to thermal degradation of permafrost on the Qinghai–Tibet Plateau. Cryosphere 12, 595–608 (2018).

    Article  Google Scholar 

  48. Gao, T. et al. Accelerating permafrost collapse on the eastern Tibetan Plateau. Environ. Res. Lett. 16, 054023 (2021).

    Article  Google Scholar 

  49. Sun, R. et al. Interannual variability of the North Pacific mixed layer associated with the spring Tibetan Plateau thermal forcing. J. Clim. 32, 3109–3130 (2019).

    Article  Google Scholar 

  50. Zhang, J., Wu, L., Huang, G., Zhu, W. & Zhang, Y. The role of May vegetation greenness on the southeastern Tibetan Plateau for East Asian summer monsoon prediction. J. Geophys. Res. Atmos. 116, D05106 (2011).

    Google Scholar 

  51. Wu, G. et al. Tibetan Plateau climate dynamics: recent research progress and outlook. Natl Sci. Rev. 2, 100–116 (2015).

    Article  Google Scholar 

  52. Wang, Y., Zhao, P., Yu, R. & Rasul, G. Inter-decadal variability of Tibetan spring vegetation and its associations with eastern China spring rainfall. Int. J. Climatol. 30, 856–865 (2010).

    Article  Google Scholar 

  53. Yu, H., Luedeling, E. & Xu, J. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 107, 22151–22156 (2010).

    Article  Google Scholar 

  54. Shen, M. et al. Increasing altitudinal gradient of spring vegetation phenology during the last decade on the Qinghai–Tibetan Plateau. Agric. For. Meteorol. 189-190, 71–80 (2014).

    Article  Google Scholar 

  55. Wang, X. et al. No consistent evidence for advancing or delaying trends in spring phenology on the Tibetan Plateau. J. Geophys. Res. Biogeosci. 122, 3288–3305 (2017).

    Article  Google Scholar 

  56. Wang, C. et al. Assessing phenological change and climatic control of alpine grasslands in the Tibetan Plateau with MODIS time series. Int. J. Biometeorol. 59, 11–23 (2015).

    Article  Google Scholar 

  57. Wang, K. et al. Snow effects on alpine vegetation in the Qinghai-Tibetan Plateau. Int. J. Digit. Earth 8, 58–75 (2013).

    Article  Google Scholar 

  58. Meng, F., Huang, L., Chen, A., Zhang, Y. & Piao, S. Spring and autumn phenology across the Tibetan Plateau inferred from normalized difference vegetation index and solar-induced chlorophyll fluorescence. Big Earth Data 5, 182–200 (2021).

    Article  Google Scholar 

  59. 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 different biomes, and climate drivers. Agric. For. Meteorol. 256–257, 61–74 (2018).

    Article  Google Scholar 

  60. Li, P. et al. Change in autumn vegetation phenology and the climate controls from 1982 to 2012 on the Qinghai–Tibet Plateau. Front. Plant Sci. 10, 1677 (2019).

    Article  Google Scholar 

  61. Zhu, W., Zheng, Z., Jiang, N. & Zhang, D. A comparative analysis of the spatio-temporal variation in the phenologies of two herbaceous species and associated climatic driving factors on the Tibetan Plateau. Agric. For. Meteorol. 248, 177–184 (2018).

    Article  Google Scholar 

  62. Xia, J. et al. Interannual variation in the start of vegetation growing season and its response to climate change in the Qinghai–Tibet Plateau derived from MODIS data during 2001 to 2016. J. Appl. Remote Sens. 13, 048506 (2019).

    Article  Google Scholar 

  63. Huang, K. et al. Impacts of snow cover duration on vegetation spring phenology over the Tibetan Plateau. J. Plant Ecol. 12, 583–592 (2019).

    Article  Google Scholar 

  64. Li, P. et al. Dynamics of vegetation autumn phenology and its response to multiple environmental factors from 1982 to 2012 on Qinghai-Tibetan Plateau in China. Sci. Total Environ. 637-638, 855–864 (2018).

    Article  Google Scholar 

  65. Liu, X. et al. Driving forces of the changes in vegetation phenology in the Qinghai–Tibet Plateau. Remote Sens. 13, 4952 (2021).

    Article  Google Scholar 

  66. Piao, S. et al. Altitude and temperature dependence of change in the spring vegetation green-up date from 1982 to 2006 in the Qinghai–Xizang Plateau. Agric. For. Meteorol. 151, 1599–1608 (2011).

    Article  Google Scholar 

  67. Wang, Z. et al. Causes for the unimodal pattern of biomass and productivity in alpine grasslands along a large altitudinal gradient in semi-arid regions. J. Veg. Sci. 24, 189–201 (2013).

    Article  Google Scholar 

  68. Du, M. et al. in Proc. MODSIM 2007 Int. Congr. Model. Simul. (eds Oxley, L. & Kulasiri, D.) 2146–2152 (Modelling and Simulation Society of Australia and New Zealand, 2007).

  69. Wang, S. P. et al. Asymmetric sensitivity of first flowering date to warming and cooling in alpine plants. Ecology 95, 3387–3398 (2014).

    Article  Google Scholar 

  70. Che, M. et al. Spatial and temporal variations in the end date of the vegetation growing season throughout the Qinghai–Tibetan Plateau from 1982 to 2011. Agric. For. Meteorol. 189–190, 81–90 (2014).

    Article  Google Scholar 

  71. Zhang, G., Zhang, Y., Dong, J. & Xiao, X. Green-up dates in the Tibetan Plateau have continuously advanced from 1982 to 2011. Proc. Natl Acad. Sci. USA 110, 4309–4314 (2013).

    Article  Google Scholar 

  72. Maisongrande, P., Duchemin, B. & Dedieu, G. VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products. Int. J. Remote Sens. 25, 9–14 (2010).

    Article  Google Scholar 

  73. Didan, K., Munoz, A. B., Solano, R. & Huete, A. MODIS vegetation index user’s guide (MOD13 series) version 3.00, June 2015 (collection 6) (Univ. Arizona, 2015).

  74. Beck, H. E. et al. Global evaluation of four AVHRR–NDVI data sets: intercomparison and assessment against Landsat imagery. Remote Sens. Environ. 115, 2547–2563 (2011).

    Article  Google Scholar 

  75. Zhang, Y., Song, C., Band, L. E., Sun, G. & Li, J. Reanalysis of global terrestrial vegetation trends from MODIS products: browning or greening? Remote Sens. Environ. 191, 145–155 (2017).

    Article  Google Scholar 

  76. Zhang, Y., Joiner, J., Alemohammad, S. H., Zhou, S. & Gentine, P. A global spatially contiguous solar-induced fluorescence (CSIF) dataset using neural networks. Biogeosciences 15, 5779–5800 (2018).

    Article  Google Scholar 

  77. Ding, M. et al. Temperature dependence of variations in the end of the growing season from 1982 to 2012 on the Qinghai–Tibetan Plateau. GISci. Remote Sens. 53, 147–163 (2015).

    Article  Google Scholar 

  78. Cheng, M., Jin, J. & Jiang, H. Strong impacts of autumn phenology on grassland ecosystem water use efficiency on the Tibetan Plateau. Ecol. Indic. 126, 107682 (2021).

    Article  Google Scholar 

  79. Pedelty, J. et al. in Proc. 2007 IEEE Int. Geosci. Remote Sensing Symp. 1021–1025 (IEEE, 2007).

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

    Article  Google Scholar 

  81. Liu, Y., Liu, R. & Chen, J. M. Retrospective retrieval of long-term consistent global leaf area index (1981–2011) from combined AVHRR and MODIS data. J. Geophys. Res. Biogeosci. 117, G04003 (2012).

    Article  Google Scholar 

  82. Yang, B. et al. New perspective on spring vegetation phenology and global climate change based on Tibetan Plateau tree-ring data. Proc. Natl Acad. Sci. USA 114, 6966–6971 (2017).

    Article  Google Scholar 

  83. Shishov, V. V. et al. VS-oscilloscope: a new tool to parameterize tree radial growth based on climate conditions. Dendrochronologia 39, 42–50 (2016).

    Article  Google Scholar 

  84. Zhao, Y., Zhou, T., Zhang, W. & Li, J. Change in precipitation over the Tibetan Plateau projected by weighted CMIP6 models. Adv. Atmos. Sci. 39, 1133–1150 (2022).

    Article  Google Scholar 

  85. Lalande, M., Ménégoz, M., Krinner, G., Naegeli, K. & Wunderle, S. Climate change in the High Mountain Asia in CMIP6. Earth Syst. Dyn. 12, 1061–1098 (2021).

    Article  Google Scholar 

  86. Jin, Z. et al. Temporal variability in the thermal requirements for vegetation phenology on the Tibetan plateau and its implications for carbon dynamics. Clim. Change 138, 617–632 (2016).

    Article  Google Scholar 

  87. Eyring, V. et al. Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958 (2016).

    Article  Google Scholar 

  88. Cao, R., Shen, M., Zhou, J. & Chen, J. Modeling vegetation green-up dates across the Tibetan Plateau by including both seasonal and daily temperature and precipitation. Agric. For. Meteorol. 249, 176–186 (2018).

    Article  Google Scholar 

  89. Li, P. et al. Combined control of multiple extreme climate stressors on autumn vegetation phenology on the Tibetan Plateau under past and future climate change. Agric. For. Meteorol. 308–309, 108571 (2021).

    Article  Google Scholar 

  90. Lang, W., Chen, X., Qian, S., Liu, G. & Piao, S. A new process-based model for predicting autumn phenology: how is leaf senescence controlled by photoperiod and temperature coupling? Agric. For. Meteorol. 268, 124–135 (2019).

    Article  Google Scholar 

  91. Yang, Z. et al. Phylogenetic conservatism in heat requirement of leaf-out phenology, rather than temperature sensitivity, in Tibetan Plateau. Agric. For. Meteorol. 304-305, 108413 (2021).

    Article  Google Scholar 

  92. Gao, B., Li, J. & Wang, X. Impact of frozen soil changes on vegetation phenology in the source region of the Yellow River from 2003 to 2015. Theor. Appl. Climatol. 141, 1219–1234 (2020).

    Article  Google Scholar 

  93. Jiang, H. et al. The impacts of soil freeze/thaw dynamics on soil water transfer and spring phenology in the Tibetan Plateau. Arct. Antarct. Alp. Res. 50, e1439155 (2018).

    Article  Google Scholar 

  94. Li, G., Jiang, C., Cheng, T. & Bai, J. Grazing alters the phenology of alpine steppe by changing the surface physical environment on the northeast Qinghai-Tibet Plateau, China. J. Environ. Manage. 248, 109257 (2019).

    Article  Google Scholar 

  95. Du, J. et al. Interacting effects of temperature and precipitation on climatic sensitivity of spring vegetation green-up in arid mountains of China. Agric. For. Meteorol. 269–270, 71–77 (2019).

    Article  Google Scholar 

  96. Liu, L. et al. Effects of elevation on spring phenological sensitivity to temperature in Tibetan Plateau grasslands. Chin. Sci. Bull. 59, 4856–4863 (2014).

    Article  Google Scholar 

  97. Cong, N. et al. Little change in heat requirement for vegetation green-up on the Tibetan Plateau over the warming period of 1998–2012. Agric. For. Meteorol. 232, 650–658 (2017).

    Article  Google Scholar 

  98. Shen, M. et al. Strong impacts of daily minimum temperature on the green-up date and summer greenness of the Tibetan Plateau. Glob. Change Biol. 22, 3057–3066 (2016).

    Article  Google Scholar 

  99. Du, J. et al. Daily minimum temperature and precipitation control on spring phenology in arid-mountain ecosystems in China. Int. J. Climatol. 40, 2568–2579 (2020).

    Article  Google Scholar 

  100. Shen, M. Spring phenology was not consistently related to winter warming on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 108, E91–E92 (2011).

    Article  Google Scholar 

  101. An, S. et al. Precipitation and minimum temperature are primary climatic controls of alpine grassland autumn phenology on the Qinghai-Tibet Plateau. Remote Sens. 12, 431 (2020).

    Article  Google Scholar 

  102. Zu, J. et al. Biological and climate factors co-regulated spatial-temporal dynamics of vegetation autumn phenology on the Tibetan Plateau. Int. J. Appl. Earth Obs. Geoinf. 69, 198–205 (2018).

    Google Scholar 

  103. Qiao, C. et al. Vegetation phenology in the Qilian mountains and its response to temperature from 1982 to 2014. Remote Sens. 13, 286 (2021).

    Article  Google Scholar 

  104. Yang, Z. et al. Asymmetric responses of the end of growing season to daily maximum and minimum temperatures on the Tibetan Plateau. J. Geophys. Res. Atmos. 122, 13,78–13,287 (2017).

    Article  Google Scholar 

  105. Dorji, T. et al. Plant functional traits mediate reproductive phenology and success in response to experimental warming and snow addition in Tibet. Glob. Change Biol. 19, 459–472 (2013).

    Article  Google Scholar 

  106. Li, X., Zhang, L. & Luo, T. Rainy season onset mainly drives the spatiotemporal variability of spring vegetation green-up across alpine dry ecosystems on the Tibetan Plateau. Sci. Rep. 10, 18797 (2020).

    Article  Google Scholar 

  107. Zhang, X. et al. Effects of climate change on the growing season of alpine grassland in Northern Tibet, China. Glob. Ecol. Conserv. 23, e01126 (2020).

    Article  Google Scholar 

  108. Sun, Q. et al. A prognostic phenology model for alpine meadows on the Qinghai–Tibetan Plateau. Ecol. Indic. 93, 1089–1100 (2018).

    Article  Google Scholar 

  109. Zhu, J., Zhang, Y. & Jiang, L. Experimental warming drives a seasonal shift of ecosystem carbon exchange in Tibetan alpine meadow. Agric. For. Meteorol. 233, 242–249 (2017).

    Article  Google Scholar 

  110. Shen, M. et al. No evidence of continuously advanced green-up dates in the Tibetan Plateau over the last decade. Proc. Natl Acad. Sci. USA 110, E2329 (2013).

    Article  Google Scholar 

  111. 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).

    Article  Google Scholar 

  112. 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 

  113. 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 

  114. Meng, F. D. et al. Changes in flowering functional group affect responses of community phenological sequences to temperature change. Ecology 98, 734–740 (2017).

    Article  Google Scholar 

  115. Wang, S. et al. Timing and duration of phenological sequences of alpine plants along an elevation gradient on the Tibetan plateau. Agric. For. Meteorol. 189–190, 220–228 (2014).

    Article  Google Scholar 

  116. Jiang, L. L. et al. Relatively stable response of fruiting stage to warming and cooling relative to other phenological events. Ecology 97, 1961–1969 (2016).

    Article  Google Scholar 

  117. Li, X. et al. Responses of sequential and hierarchical phenological events to warming and cooling in alpine meadows. Nat. Commun. 7, 12489 (2016).

    Article  Google Scholar 

  118. Meng, F. et al. Nonlinear responses of temperature sensitivities of community phenophases to warming and cooling events are mirroring plant functional diversity. Agric. For. Meteorol. 253–254, 31–37 (2018).

    Article  Google Scholar 

  119. Meng, F. et al. Divergent responses of community reproductive and vegetative phenology to warming and cooling: asymmetry versus symmetry. Front. Plant Sci. 10, 1310 (2019).

    Article  Google Scholar 

  120. Zhang, Z., Niu, K., Liu, X., Jia, P. & Du, G. Linking flowering and reproductive allocation in response to nitrogen addition in an alpine meadow. J. Plant Ecol. 7, 231–239 (2013).

    Article  Google Scholar 

  121. Xi, Y. et al. Nitrogen addition alters the phenology of a dominant alpine plant in Northern Tibet. Arct. Antarct. Alp. Res. 47, 511–518 (2018).

    Article  Google Scholar 

  122. Yin, T.-F., Zheng, L.-L., Cao, G.-M., Song, M.-H. & Yu, F.-H. Species-specific phenological responses to long-term nitrogen fertilization in an alpine meadow. J. Plant Ecol. 10, 301–309 (2016).

    Google Scholar 

  123. Liu, L. et al. Altered precipitation patterns and simulated nitrogen deposition effects on phenology of common plant species in a Tibetan Plateau alpine meadow. Agric. For. Meteorol. 236, 36–47 (2017).

    Article  Google Scholar 

  124. Liu, Y. et al. Effects of nitrogen addition and mowing on reproductive phenology of three early-flowering forb species in a Tibetan alpine meadow. Ecol. Eng. 99, 119–125 (2017).

    Article  Google Scholar 

  125. Zhu, J., Zhang, Y. & Liu, Y. Effects of short-term grazing exclusion on plant phenology and reproductive succession in a Tibetan alpine meadow. Sci. Rep. 6, 27781 (2016).

    Article  Google Scholar 

  126. Li, Y. et al. The effects of grazing regimes on phenological stages, intervals and divergences of alpine plants on the Qinghai–Tibetan Plateau. J. Veg. Sci. 30, 134–145 (2019).

    Article  Google Scholar 

  127. Dorji, T. et al. Impacts of climate change on flowering phenology and production in alpine plants: the importance of end of flowering. Agric. Ecosyst. Environ. 291, 106795 (2020).

    Article  Google Scholar 

  128. Meng, F. et al. Opposite effects of winter day and night temperature changes on early phenophases. Ecology 100, e02775 (2019).

    Article  Google Scholar 

  129. Meng, F. et al. Temperature sensitivity thresholds to warming and cooling in phenophases of alpine plants. Clim. Change 139, 579–590 (2016).

    Article  Google Scholar 

  130. Suonan, J., Classen, A. T., Sanders, N. J. & He, J. S. Plant phenological sensitivity to climate change on the Tibetan Plateau and relative to other areas of the world. Ecosphere 10, e02543 (2019).

    Article  Google Scholar 

  131. Ganjurjav, H. et al. Phenological changes offset the warming effects on biomass production in an alpine meadow on the Qinghai–Tibetan Plateau. J. Ecol. 109, 1014–1025 (2020).

    Article  Google Scholar 

  132. Jiang, Z. et al. Extreme climate events in China: IPCC-AR4 model evaluation and projection. Clim. Change 110, 385–401 (2011).

    Article  Google Scholar 

  133. Huang, X. et al. Spatiotemporal dynamics of snow cover based on multi-source remote sensing data in China. Cryosphere 10, 2453–2463 (2016).

    Article  Google Scholar 

  134. Piao, S. et al. Characteristics, drivers and feedbacks of global greening. Nat. Rev. Earth Environ. 1, 14–27 (2019).

    Article  Google Scholar 

  135. Wang, C. & Tang, Y. Responses of plant phenology to nitrogen addition: a meta-analysis. Oikos 128, 1243–1253 (2019).

    Article  Google Scholar 

  136. Chen, H., Zhu, Q., Wu, N., Wang, Y. & Peng, C. H. Delayed spring phenology on the Tibetan Plateau may also be attributable to other factors than winter and spring warming. Proc. Natl Acad. Sci. USA 108, E93 (2011).

    Google Scholar 

  137. Zhang, L. et al. Effect of warming and degradation on phenophases of Kobresia pygmaea and Potentilla multifida on the Tibetan Plateau. Agric. Ecosyst. Environ. 300, 106998 (2020).

    Article  Google Scholar 

  138. Lin, X. et al. Fluxes of CO2, CH4, and N2O in an alpine meadow affected by yak excreta on the Qinghai-Tibetan plateau during summer grazing periods. Soil Biol. Biochem. 41, 718–725 (2009).

    Article  Google Scholar 

  139. Sa, C. et al. Spatiotemporal variation in snow cover and its effects on grassland phenology on the Mongolian Plateau. J. Arid Land 13, 332–349 (2021).

    Article  Google Scholar 

  140. Zheng, J., Xu, X., Jia, G. & Wu, W. Understanding the spring phenology of Arctic tundra using multiple satellite data products and ground observations. Sci. China Earth Sci. 63, 1599–1612 (2020).

    Article  Google Scholar 

  141. Wu, W., Sun, Y., Xiao, K. & Xin, Q. Development of a global annual land surface phenology dataset for 1982–2018 from the AVHRR data by implementing multiple phenology retrieving methods. Int. J. Appl. Earth Obs. Geoinf. 103, 102487 (2021).

    Google Scholar 

  142. Karkauskaite, P., Tagesson, T. & Fensholt, R. Evaluation of the plant phenology index (PPI), NDVI and EVI for start-of-season trend analysis of the Northern Hemisphere boreal zone. Remote Sens. 9, 485 (2017).

    Article  Google Scholar 

  143. 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 

  144. Zhang, J. et al. Comparison of land surface phenology in the Northern Hemisphere based on AVHRR GIMMS3g and MODIS datasets. ISPRS J. Photogramm. Remote Sens. 169, 1–16 (2020).

    Article  Google Scholar 

  145. Shen, M. et al. Earlier-season vegetation has greater temperature sensitivity of spring phenology in northern hemisphere. PLoS ONE 9, e88178 (2014).

    Article  Google Scholar 

  146. Zhang, H., Yuan, W., Liu, S., Dong, W. & Fu, Y. Sensitivity of flowering phenology to changing temperature in China. J. Geophys. Res. Biogeosci. 120, 1658–1665 (2015).

    Article  Google Scholar 

  147. Cook, B. I. et al. Sensitivity of spring phenology to warming across temporal and spatial climate gradients in two independent databases. Ecosystems 15, 1283–1294 (2012).

    Article  Google Scholar 

  148. Wang, C., Cao, R., Chen, J., Rao, Y. & Tang, Y. Temperature sensitivity of spring vegetation phenology correlates to within-spring warming speed over the Northern Hemisphere. Ecol. Indic. 50, 62–68 (2015).

    Article  Google Scholar 

  149. Gao, M. et al. Three-dimensional change in temperature sensitivity of northern vegetation phenology. Glob. Change Biol. 26, 5189–5201 (2020).

    Article  Google Scholar 

  150. Zohner, C. M., Benito, B. M., Fridley, J. D., Svenning, J. C. & Renner, S. S. Spring predictability explains different leaf-out strategies in the woody floras of North America, Europe and East Asia. Ecol. Lett. 20, 452–460 (2017).

    Article  Google Scholar 

  151. Fu, Y. H. et al. Daylength helps temperate deciduous trees to leaf-out at the optimal time. Glob. Change Biol. 25, 2410–2418 (2019).

    Article  Google Scholar 

  152. Huang, J. G. et al. Photoperiod and temperature as dominant environmental drivers triggering secondary growth resumption in Northern Hemisphere conifers. Proc. Natl Acad. Sci. USA 117, 20645–20652 (2020).

    Article  Google Scholar 

  153. Iler, A. M., CaraDonna, P. J., Forrest, J. R. K. & Post, E. Demographic consequences of phenological shifts in response to climate change. Annu. Rev. Ecol. Evol. Syst. 52, 221–245 (2021).

    Article  Google Scholar 

  154. Chen, S., Huang, Y., Gao, S. & Wang, G. Impact of physiological and phenological change on carbon uptake on the Tibetan Plateau revealed through GPP estimation based on spaceborne solar-induced fluorescence. Sci. Total Environ. 663, 45–59 (2019).

    Article  Google Scholar 

  155. Jin, J. et al. Grassland production in response to changes in biological metrics over the Tibetan Plateau. Sci. Total Environ. 666, 641–651 (2019).

    Article  Google Scholar 

  156. Kang, X. et al. Variability and changes in climate, phenology, and gross primary production of an alpine wetland ecosystem. Remote Sens. 8, 391 (2016).

    Article  Google Scholar 

  157. Zheng, Z., Zhu, W. & Zhang, Y. Direct and lagged effects of spring phenology on net primary productivity in the alpine grasslands on the Tibetan Plateau. Remote Sens. 12, 1223 (2020).

    Article  Google Scholar 

  158. Wang, S. et al. Responses of net primary productivity to phenological dynamics in the Tibetan Plateau, China. Agric. For. Meteorol. 232, 235–246 (2017).

    Article  Google Scholar 

  159. Li, S., Zhang, H., Zhou, X., Yu, H. & Li, W. Enhancing protected areas for biodiversity and ecosystem services in the Qinghai–Tibet Plateau. Ecosyst. Serv. 43, 101090 (2020).

    Article  Google Scholar 

  160. Meng, F. et al. Enhanced spring temperature sensitivity of carbon emission links to earlier phenology. Sci. Total Environ. 745, 140999 (2020).

    Article  Google Scholar 

  161. Hu, G. et al. The divergent impact of phenology change on the productivity of alpine grassland due to different timing of drought on the Tibetan Plateau. Land Degrad. Dev. 32, 4033–4041 (2021).

    Article  Google Scholar 

  162. Li, P., Zhu, W. & Xie, Z. Diverse and divergent influences of phenology on herbaceous aboveground biomass across the Tibetan Plateau alpine grasslands. Ecol. Indic. 121, 107036 (2021).

    Article  Google Scholar 

  163. He, M. et al. Relationships between wood formation and cambium phenology on the Tibetan Plateau during 1960–2014. Forests 9, 86 (2018).

    Article  Google Scholar 

  164. Wang, J., Li, M., Yu, C. & Fu, G. The change in environmental variables linked to climate change has a stronger effect on aboveground net primary productivity than does phenological change in alpine grasslands. Front. Plant Sci. 12, 798633 (2022).

    Article  Google Scholar 

  165. Shen, W., Zhang, L. & Luo, T. Causes for the increase of early-season freezing events under a warmer climate at alpine treelines in southeast Tibet. Agric. For. Meteorol. 316, 108863 (2022).

    Article  Google Scholar 

  166. Ye, D.-Z. & Wu, G.-X. The role of the heat source of the Tibetan Plateau in the general circulation. Meteorol. Atmos. Phys. 67, 181–198 (1998).

    Article  Google Scholar 

  167. Cao, R., Feng, Y., Liu, X., Shen, M. & Zhou, J. Uncertainty of vegetation green-up date estimated from vegetation indices due to snowmelt at northern middle and high latitudes. Remote Sens. 12, 190 (2020).

    Article  Google Scholar 

  168. Zeng, L., Wardlow, B. D., Xiang, D., Hu, S. & Li, D. A review of vegetation phenological metrics extraction using time-series, multispectral satellite data. Remote Sens. Environ. 237, 111511 (2020).

    Article  Google Scholar 

  169. Cao, R. et al. A simple method to improve the quality of NDVI time-series data by integrating spatiotemporal information with the Savitzky-Golay filter. Remote Sens. Environ. 217, 244–257 (2018).

    Article  Google Scholar 

  170. 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 

  171. Wang, C. et al. A snow-free vegetation index for improved monitoring of vegetation spring green-up date in deciduous ecosystems. Remote Sens. Environ. 196, 1–12 (2017).

    Article  Google Scholar 

  172. Yang, W. et al. A semi-analytical snow-free vegetation index for improving estimation of plant phenology in tundra and grassland ecosystems. Remote Sens. Environ. 228, 31–44 (2019).

    Article  Google Scholar 

  173. Wang, C., Chen, J., Tang, Y., Black, T. A. & Zhu, K. A novel method for removing snow melting-induced fluctuation in GIMMS NDVI3g data for vegetation phenology monitoring: a case study in deciduous forests of North America. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 11, 800–807 (2018).

    Article  Google Scholar 

  174. Helman, D. Land surface phenology: What do we really ‘see’ from space? Sci. Total Environ. 618, 665–673 (2018).

    Article  Google Scholar 

  175. Steltzer, H. & Post, E. Ecology. Seasons and life cycles. Science 324, 886–887 (2009).

    Article  Google Scholar 

  176. Liang, L., Schwartz, M. D. & Fei, S. Validating satellite phenology through intensive ground observation and landscape scaling in a mixed seasonal forest. Remote Sens. Environ. 115, 143–157 (2011).

    Article  Google Scholar 

  177. Li, R. et al. Leaf unfolding of Tibetan alpine meadows captures the arrival of monsoon rainfall. Sci. Rep. 6, 20985 (2016).

    Article  Google Scholar 

  178. Tang, J. et al. Emerging opportunities and challenges in phenology: a review. Ecosphere 7, e01436 (2016).

    Article  Google Scholar 

  179. Van Nuland, M. E. et al. Natural soil microbiome variation affects spring foliar phenology with consequences for plant productivity and climate-driven range shifts. New Phytol. 232, 762–775 (2021).

    Article  Google Scholar 

  180. Mutz, J., McClory, R., van Dijk, L. J. A., Ehrlen, J. & Tack, A. J. M. Pathogen infection influences the relationship between spring and autumn phenology at the seedling and leaf level. Oecologia 197, 447–457 (2021).

    Article  Google Scholar 

  181. Radville, L., McCormack, M. L., Post, E. & Eissenstat, D. M. Root phenology in a changing climate. J. Exp. Bot. 67, 3617–3628 (2016).

    Article  Google Scholar 

  182. Gao, M. et al. Divergent changes in the elevational gradient of vegetation activities over the last 30 years. Nat. Commun. 10, 2970 (2019).

    Article  Google Scholar 

Download references

Acknowledgements

This research was supported by the following funding: the Second Tibetan Plateau Scientific Expedition and Research Program (grant no. 2019QZKK0405), the National Natural Science Foundation of China (grant nos. 41731175, 41988101 and 41875107) and the Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

  1. State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing, China

    Miaogen Shen, Nan Jiang, Lihao Zhang, Wenwu Zhao & Bojie Fu

  2. State Key Laboratory of Tibetan Plateau Earth System, Resources and Environment (TPESRE), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China

    Miaogen Shen, Shiping Wang, Jianping Sun, Bo Fang, Lei Zhang, Wangwang Lv, Tsechoe Dorji & Shilong Piao

  3. School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, China

    Ruyin Cao & Xiaofang Ling

  4. Key Laboratory of Regional Climate-Environment for Temperate East Asia, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China

    Xiyan Xu

  5. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China

    Baolin Li

  6. School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai, China

    Qingling Sun & Zhenming Ji

  7. Department of Ecology, College of Urban and Environmental Sciences, and Key Laboratory for Earth Surface Processes of the Ministry of Education, Peking University, Beijing, China

    Fandong Meng

  8. Academy of Forest Inventory and Planning, National Forestry and Grassland Administration, Beijing, China

    Yuhao Jiang

  9. College of Water Sciences, Beijing Normal University, Beijing, China

    Yongshuo Fu

  10. Rocky Mountain Biological Laboratory, Crested Butte, CO, USA

    Amy Iler

  11. Forest Dynamics Unit, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland

    Yann Vitasse

  12. Environment & Sustainability Department, Fort Lewis College, Durango, CO, USA

    Heidi Steltzer

Authors
  1. Miaogen Shen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shiping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nan Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jianping Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ruyin Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiaofang Ling
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Bo Fang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lei Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Lihao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xiyan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Wangwang Lv
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Baolin Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Qingling Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Fandong Meng
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Yuhao Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Tsechoe Dorji
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yongshuo Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Amy Iler
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Yann Vitasse
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Heidi Steltzer
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Zhenming Ji
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Wenwu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Shilong Piao
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. Bojie Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.S., S.W. and B.Fu formulated the Review. N.J., J.S., R.C., X.L., B.Fang, L.Z., L.Z. and Y.J. performed the analyses and drafted the figures. M.S., R.C., F.M. and S.W. wrote the first draft. M.S., S.W., A.I. and Y.V. reviewed and edited the manuscript. All authors contributed to the discussion of content.

Corresponding authors

Correspondence to Miaogen Shen, Shiping Wang or Bojie Fu.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Earth & Environment thanks Shikui Dong, Gang Fu and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Shen, M., Wang, S., Jiang, N. et al. Plant phenology changes and drivers on the Qinghai–Tibetan Plateau. Nat Rev Earth Environ 3, 633–651 (2022). https://doi.org/10.1038/s43017-022-00317-5

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s43017-022-00317-5

This article is cited by

Search

Advanced search

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