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Urban−rural gradients reveal joint control of elevated CO2 and temperature on extended photosynthetic seasons



Photosynthetic phenology has large effects on the land–atmosphere carbon exchange. Due to limited experimental assessments, a comprehensive understanding of the variations of photosynthetic phenology under future climate and its associated controlling factors is still missing, despite its high sensitivities to climate. Here, we develop an approach that uses cities as natural laboratories, since plants in urban areas are often exposed to higher temperatures and carbon dioxide (CO2) concentrations, which reflect expected future environmental conditions. Using more than 880 urban–rural gradients across the Northern Hemisphere (≥30° N), combined with concurrent satellite retrievals of Sun-induced chlorophyll fluorescence (SIF) and atmospheric CO2, we investigated the combined impacts of elevated CO2 and temperature on photosynthetic phenology at the large scale. The results showed that, under urban conditions of elevated CO2 and temperature, vegetation photosynthetic activity began earlier (−5.6 ± 0.7 d), peaked earlier (−4.9  ± 0.9 d) and ended later (4.6 ± 0.8 d) than in neighbouring rural areas, with a striking two- to fourfold higher climate sensitivity than greenness phenology. The earlier start and peak of season were sensitive to both the enhancements of CO2 and temperature, whereas the delayed end of season was mainly attributed to CO2 enrichments. We used these sensitivities to project phenology shifts under four Representative Concentration Pathway climate scenarios, predicting that vegetation will have prolonged photosynthetic seasons in the coming two decades. This observation-driven study indicates that realistic urban environments, together with SIF observations, provide a promising method for studying vegetation physiology under future climate change.

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Data availability

OCO-2 SIF and XCO2 data are available at MODIS EVI and LST data are available at Precipitation data can be obtained from Global Historical Climatology Network (version 2) and Climate Anomaly Monitoring System air temperature data are available at Climate Change Initiative land cover data are available at GTOPO30 digital elevation model data are available at NO2 and O3 data are available at Projected CO2 concentrations can be obtained from the RCP Database ( Model results and the urban clusters are available at

Code availability

The codes used to estimate the phenological indicators in this study are available at

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The authors declare no competing interests.

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  1. 1.

    Canadell, J. G. et al. Contributions to accelerating atmospheric CO2 growth from economic activity, carbon intensity, and efficiency of natural sinks. Proc. Natl Acad. Sci. USA 104, 18866–18870 (2007).

  2. 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).

  3. 3.

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

  4. 4.

    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).

  5. 5.

    Richardson, A. D. et al. Ecosystem warming extends vegetation activity but heightens vulnerability to cold temperatures. Nature 560, 368–371 (2018).

  6. 6.

    Peñuelas, J. & Filella, I. Responses to a warming world. Science 294, 793–795 (2001).

  7. 7.

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

  8. 8.

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

  9. 9.

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

  10. 10.

    IPCC Climate Change 2014: Synthesis Report (eds Core Writing Team, Pachauri, R. K. & Meyer, L. A.) (IPCC, 2014).

  11. 11.

    Calfapietra, C. et al. Challenges in elevated CO2 experiments on forests. Trends Plant Sci. 15, 5–10 (2010).

  12. 12.

    Fu, Y. H. et al. Larger temperature response of autumn leaf senescence than spring leaf-out phenology. Glob. Change Biol. 24, 2159–2168 (2017).

  13. 13.

    Wolkovich, E. M. et al. Warming experiments underpredict plant phenological responses to climate change. Nature 485, 494–497 (2012).

  14. 14.

    Calfapietra, C., Peñuelas, J. & Niinemets, Ü. Urban plant physiology: adaptation-mitigation strategies under permanent stress. Trends Plant Sci. 20, 72–75 (2015).

  15. 15.

    Peng, S. et al. Surface urban heat island across 419 global big cities. Environ. Sci. Technol. 46, 696–703 (2011).

  16. 16.

    Schwandner, F. M. et al. Spaceborne detection of localized carbon dioxide sources. Science 358, eaam5782 (2017).

  17. 17.

    Zhao, S., Liu, S. & Zhou, D. Prevalent vegetation growth enhancement in urban environment. Proc. Natl Acad. Sci. USA 113, 6313–6318 (2016).

  18. 18.

    Sun, Y. et al. Overview of solar-induced chlorophyll fluorescence (SIF) from the Orbiting Carbon Observatory-2: retrieval, cross-mission comparison, and global monitoring for GPP. Remote Sens. Environ. 209, 808–823 (2018).

  19. 19.

    Guanter, L. et al. Global and time-resolved monitoring of crop photosynthesis with chlorophyll fluorescence. Proc. Natl Acad. Sci. USA 111, E1327–E1333 (2014).

  20. 20.

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

  21. 21.

    Zhou, D., Zhao, S., Zhang, L. & Liu, S. Remotely sensed assessment of urbanization effects on vegetation phenology in China’s 32 major cities. Remote Sens. Environ. 176, 272–281 (2016).

  22. 22.

    Zhou, D., Zhao, S., Liu, S., Zhang, L. & Zhu, C. Surface urban heat island in China’s 32 major cities: spatial patterns and drivers. Remote Sens. Environ. 152, 51–61 (2014).

  23. 23.

    Chuine, I., Morin, X. & Bugmann, H. Warming, photoperiods, and tree phenology. Science 329, 277–278 (2010).

  24. 24.

    Fu, Y. H., Campioli, M., Deckmyn, G. & Janssens, I. A. Sensitivity of leaf unfolding to experimental warming in three temperate tree species. Agric. For. Meteorol. 181, 125–132 (2013).

  25. 25.

    Jeong, S.-J. et al. Application of satellite solar-induced chlorophyll fluorescence to understanding large-scale variations in vegetation phenology and function over northern high latitude forests. Remote Sens. Environ. 190, 178–187 (2017).

  26. 26.

    Kikuzawa, K. Phenological and morphological adaptations to the light environment in two woody and two herbaceous plant species. Funct. Ecol. 17, 29–38 (2003).

  27. 27.

    Daumard, F. et al. A field platform for continuous measurement of canopy fluorescence. IEEE Trans. Geosci. Remote Sens. 48, 3358–3368 (2010).

  28. 28.

    Suni, T. et al. Interannual variability and timing of growing-season CO2 exchange in a boreal forest. J. Geophys. Res. Atmos. 108, 4265 (2003).

  29. 29.

    Medvigy, D., Jeong, S. J., Clark, K. L., Skowronski, N. S. & Schäfer, K. V. Effects of seasonal variation of photosynthetic capacity on the carbon fluxes of a temperate deciduous forest. J. Geophys. Res. Biogeosci. 118, 1703–1714 (2013).

  30. 30.

    Chen, X., Wang, D., Chen, J., Wang, C. & Shen, M. The mixed pixel effect in land surface phenology: a simulation study. Remote Sens. Environ. 211, 338–344 (2018).

  31. 31.

    Filella, I., Penuelas, J., Llorens, L. & Estiarte, M. Reflectance assessment of seasonal and annual changes in biomass and CO2 uptake of a Mediterranean shrubland submitted to experimental warming and drought. Remote Sens. Environ. 90, 308–318 (2004).

  32. 32.

    Hilker, T. et al. Remote sensing of photosynthetic light-use efficiency across two forested biomes: spatial scaling. Remote Sens. Environ. 114, 2863–2874 (2010).

  33. 33.

    Walther, S. et al. Satellite chlorophyll fluorescence measurements reveal large‐scale decoupling of photosynthesis and greenness dynamics in boreal evergreen forests. Glob. Change Biol. 22, 2979–2996 (2016).

  34. 34.

    Porcar-Castell, A. et al. Linking chlorophyll a fluorescence to photosynthesis for remote sensing applications: mechanisms and challenges. J. Exp. Bot. 65, 4065–4095 (2014).

  35. 35.

    Baker, N. R. Chlorophyll fluorescence: a probe of photosynthesis in vivo. Annu. Rev. Plant Biol. 59, 89–113 (2008).

  36. 36.

    Norton, A. J., Rayner, P. J., Koffi, E. N. & Scholze, M. Assimilating solar-induced chlorophyll fluorescence into the terrestrial biosphere model BETHY-SCOPE v1.0: model description and information content. Geosci. Model Dev. 11, 1517–1536 (2018).

  37. 37.

    White, M. A., Nemani, R. R., Thornton, P. E. & Running, S. W. Satellite evidence of phenological differences between urbanized and rural areas of the eastern United States deciduous broadleaf forest. Ecosystems 5, 260–273 (2002).

  38. 38.

    Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H. & Schneider, A. The footprint of urban climates on vegetation phenology. Geophys. Res. Lett. 31, L12209 (2004).

  39. 39.

    Han, G. & Xu, J. Land surface phenology and land surface temperature changes along an urban–rural gradient in Yangtze River Delta, China. Environ. Manag. 52, 234–249 (2013).

  40. 40.

    Cong, N. et al. Spring vegetation green-up date in China inferred from SPOT NDVI data: a multiple model analysis. Agric. For. Meteorol. 165, 104–113 (2012).

  41. 41.

    Li, X. et al. Response of vegetation phenology to urbanization in the conterminous United States. Glob. Change Biol. 23, 2818–2830 (2017).

  42. 42.

    Niu, S. et al. Seasonal hysteresis of net ecosystem exchange in response to temperature change: patterns and causes. Glob. Change Biol. 17, 3102–3114 (2011).

  43. 43.

    Van der Tol, C., Verhoef, W. & Rosema, A. A model for chlorophyll fluorescence and photosynthesis at leaf scale. Agric. For. Meteorol. 149, 96–105 (2009).

  44. 44.

    Sun, Y. et al. OCO-2 advances photosynthesis observation from space via solar-induced chlorophyll fluorescence. Science 358, eaam5747 (2017).

  45. 45.

    Zarco-Tejada, P., Morales, A., Testi, L. & Villalobos, F. Spatio-temporal patterns of chlorophyll fluorescence and physiological and structural indices acquired from hyperspectral imagery as compared with carbon fluxes measured with eddy covariance. Remote Sens. Environ. 133, 102–115 (2013).

  46. 46.

    Yang, X. et al. Solar-induced chlorophyll fluorescence that correlates with canopy photosynthesis on diurnal and seasonal scales in a temperate deciduous forest. Geophys. Res. Lett. 42, 2977–2987 (2015).

  47. 47.

    Joiner, J. et al. The seasonal cycle of satellite chlorophyll fluorescence observations and its relationship to vegetation phenology and ecosystem atmosphere carbon exchange. Remote Sens. Environ. 152, 375–391 (2014).

  48. 48.

    CaraDonna, P. J., Iler, A. M. & Inouye, D. W. Shifts in flowering phenology reshape a subalpine plant community. Proc. Natl Acad. Sci. USA 111, 4916–4921 (2014).

  49. 49.

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

  50. 50.

    Thompson, R. & Clark, R. Is spring starting earlier? Holocene 18, 95–104 (2008).

  51. 51.

    Miller-Rushing, A. J. & Primack, R. B. Global warming and flowering times in Thoreau’s Concord: a community perspective. Ecology 89, 332–341 (2008).

  52. 52.

    Vitasse, Y. et al. Leaf phenology sensitivity to temperature in European trees: do within-species populations exhibit similar responses? Agric. For. Meteorol. 149, 735–744 (2009).

  53. 53.

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

  54. 54.

    Körner, C. & Basler, D. Phenology under global warming. Science 327, 1461–1462 (2010).

  55. 55.

    Way, D. A. & Montgomery, R. A. Photoperiod constraints on tree phenology, performance and migration in a warming world. Plant Cell Environ. 38, 1725–1736 (2015).

  56. 56.

    Gonsamo, A., Chen, J. M. & Ooi, Y. W. Peak season plant activity shift towards spring is reflected by increasing carbon uptake by extratropical ecosystems. Glob. Change Biol. 24, 2117–2128 (2017).

  57. 57.

    Xu, C., Liu, H., Williams, A. P., Yin, Y. & Wu, X. Trends toward an earlier peak of the growing season in Northern Hemisphere mid-latitudes. Glob. Change Biol. 22, 2852–2860 (2016).

  58. 58.

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

  59. 59.

    Marchin, R. M., Salk, C. F., Hoffmann, W. A. & Dunn, R. R. Temperature alone does not explain phenological variation of diverse temperate plants under experimental warming. Glob. Change Biol. 21, 3138–3151 (2015).

  60. 60.

    Sigurdsson, B. D. Elevated CO2 and nutrient status modified leaf phenology and growth rhythm of young Populus trichocarpa trees in a 3-year field study. Trees 15, 403–413 (2001).

  61. 61.

    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).

  62. 62.

    Cleland, E. E., Chiariello, N. R., Loarie, S. R., Mooney, H. A. & Field, C. B. Diverse responses of phenology to global changes in a grassland ecosystem. Proc. Natl Acad. Sci. USA 103, 13740–13744 (2006).

  63. 63.

    Jach, M. E. & Ceulemans, R. Effects of elevated atmospheric CO2 on phenology, growth and crown structure of Scots pine (Pinus sylvestris) seedlings after two years of exposure in the field. Tree Physiol. 19, 289–300 (1999).

  64. 64.

    Taylor, G. et al. Future atmospheric CO2 leads to delayed autumnal senescence. Glob. Change Biol. 14, 264–275 (2008).

  65. 65.

    State of the Climate: Global Climate Report for May 2018 (NOAA National Centers for Environmental Information, 2018);

  66. 66.

    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).

  67. 67.

    Barichivich, J. et al. Large-scale variations in the vegetation growing season and annual cycle of atmospheric CO2 at high northern latitudes from 1950 to 2011. Glob. Change Biol. 19, 3167–3183 (2013).

  68. 68.

    Buyantuyev, A. & Wu, J. Urbanization diversifies land surface phenology in arid environments: interactions among vegetation, climatic variation, and land use pattern in the Phoenix metropolitan region, USA. Landsc. Urban Plan. 105, 149–159 (2012).

  69. 69.

    Decina, S. M., Templer, P. H. & Hutyra, L. R. Atmospheric inputs of nitrogen, carbon, and phosphorus across an urban area: unaccounted fluxes and canopy influences. Earths Future 6, 134–148 (2018).

  70. 70.

    Gregg, J. W., Jones, C. G. & Dawson, T. E. Urbanization effects on tree growth in the vicinity of New York City. Nature 424, 183–187 (2003).

  71. 71.

    Zhang, X., Friedl, M. A., Schaaf, C. B., Strahler, A. H. & Liu, Z. Monitoring the response of vegetation phenology to precipitation in Africa by coupling MODIS and TRMM instruments. J. Geophys. Res. Atmos. 110, D12103 (2005).

  72. 72.

    Do, F. C. et al. Environmental influence on canopy phenology in the dry tropics. For. Ecol. Manag. 215, 319–328 (2005).

  73. 73.

    Peñuelas, J. et al. Complex spatiotemporal phenological shifts as a response to rainfall changes. New Phytol. 161, 837–846 (2004).

  74. 74.

    Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Environ. Resour. 39, 91–123 (2014).

  75. 75.

    Zhou, Y. et al. A cluster-based method to map urban area from DMSP/OLS nightlights. Remote Sens. Environ. 147, 173–185 (2014).

  76. 76.

    Zhou, Y. et al. A global map of urban extent from nightlights. Environ. Res. Lett. 10, 054011 (2015).

  77. 77.

    Huang, X., Schneider, A. & Friedl, M. A. Mapping sub-pixel urban expansion in China using MODIS and DMSP/OLS nighttime lights. Remote Sens. Environ. 175, 92–108 (2016).

  78. 78.

    Zhang, Y. et al. On the relationship between sub-daily instantaneous and daily total gross primary production: implications for interpreting satellite-based SIF retrievals. Remote Sens. Environ. 205, 276–289 (2018).

  79. 79.

    Frankenberg, C. et al. Prospects for chlorophyll fluorescence remote sensing from the Orbiting Carbon Observatory-2. Remote Sens. Environ. 147, 1–12 (2014).

  80. 80.

    Crisp, D. et al. The on-orbit performance of the Orbiting Carbon Observatory-2 (OCO-2) instrument and its radiometrically calibrated products. Atmos. Meas. Tech. 10, 59–81 (2017).

  81. 81.

    Wunch, D. et al. Comparisons of the Orbiting Carbon Observatory-2 (OCO-2) XCO2 measurements with TCCON. Atmos. Meas. Tech. 10, 2209–2238 (2017).

  82. 82.

    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).

  83. 83.

    Collier, M. A., et al. The CSIRO-Mk3.6.0 Atmosphere-Ocean GCM: Participation in CMIP5 and data publication in 19th International Congress on Modelling and Simulation, Perth, Australia, 2691–2697 (2011).

  84. 84.

    Griffies, S. M. et al. The GFDL CM3 coupled climate model: characteristics of the ocean and sea ice simulations. J. Clim. 24, 3520–3544 (2011).

  85. 85.

    Schmidt, G. A. et al. Configuration and assessment of the GISS ModelE2 contributions to the CMIP5 archive. J. Adv. Model. Earth Syst. 6, 141–184 (2014).

  86. 86.

    Bentsen, M. et al. The Norwegian Earth system model, NorESM1-M—part 1: description and basic evaluation of the physical climate. Geosci. Model Dev. 6, 687–720 (2013).

  87. 87.

    Gonsamo, A., Chen, J. M. & D’Odorico, P. Deriving land surface phenology indicators from CO2 eddy covariance measurements. Ecol. Indic. 29, 203–207 (2013).

  88. 88.

    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).

  89. 89.

    Hird, J. N. & McDermid, G. J. Noise reduction of NDVI time series: an empirical comparison of selected techniques. Remote Sens. Environ. 113, 248–258 (2009).

  90. 90.

    Duren, R. M. & Miller, C. E. Measuring the carbon emissions of megacities. Nat. Clim. Change 2, 560–562 (2012).

  91. 91.

    Bréon, F. et al. An attempt at estimating Paris area CO2 emissions from atmospheric concentration measurements. Atmos. Chem. Phys. 15, 1707–1724 (2015).

  92. 92.

    Chen, F. et al. The integrated WRF/urban modelling system: development, evaluation, and applications to urban environmental problems. Int. J. Climatol. 31, 273–288 (2011).

  93. 93.

    Chevallier, F. et al. Toward robust and consistent regional CO2 flux estimates from in situ and spaceborne measurements of atmospheric CO2. Geophys. Res. Lett. 41, 1065–1070 (2014).

  94. 94.

    Peters, W. et al. An atmospheric perspective on North American carbon dioxide exchange: CarbonTracker. Proc. Natl Acad. Sci. USA 104, 18925–18930 (2007).

  95. 95.

    Liu, M. et al. Spatial variation of near-surface CO2 concentration during spring in Shanghai. Atmos. Pollut. Res. 7, 31–39 (2016).

  96. 96.

    Zhou, D., Zhao, S., Zhang, L., Sun, G. & Liu, Y. The footprint of urban heat island effect in China. Sci. Rep. 5, 11160 (2015).

  97. 97.

    Alkama, R. & Cescatti, A. Biophysical climate impacts of recent changes in global forest cover. Science 351, 600–604 (2016).

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This research was financially supported by the National Key R&D Program of China (2016YFA0600202), Strategic Priority Research Program of the Chinese Academy of Sciences (under grant XDA19040500), Jiangsu Provincial Natural Science Fund for Distinguished Young Scholars of China (BK20170018), International Cooperation and Exchange Programs between NSFC and DFG (41761134082) and General Program of National Science Foundation of China (41671421). J.P. acknowledges financial support from the European Research Council Synergy grant ERC-SyG-2013-610028 IMBALANCE-P. A.H. acknowledges financial support from Australian Research Council Discovery Program grant DP170101630. S.H.W. was supported by the Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX18_0037) and the Key Research Program of the Chinese Academy of Sciences (grant number KFZD-SW-310).

Author information

Y. Zhang designed the research. S.W. performed the analysis. S.W., Y. Zhang and W.J. drafted the paper. J.P. and A.C. contributed to interpreting the results and writing the paper. A.H., Y. Zhou and Y.F. contributed to writing the paper. Y. Zhou and M.L. provided the data.

Competing interests

The authors declare no competing interests.

Correspondence to Yongguang Zhang.

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Fig. 1: Mean urban–rural phenological differences based on SIF and EVI.
Fig. 2: Spatial distributions of the four urban–rural phenological differences based on SIF.
Fig. 3: Controlling factors of urban–rural phenological differences.
Fig. 4: Temperature and CO2 associations with the phenological gradients.
Fig. 5: Future projections of photosynthetic phenology shifts.