Articles | Volume 17, issue 7
https://doi.org/10.5194/gmd-17-2509-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-2509-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
04 Apr 2024
Development and technical paper |
| 04 Apr 2024
| 04 Apr 2024
A new temperature–photoperiod coupled phenology module in LPJ-GUESS model v4.1: optimizing estimation of terrestrial carbon and water processes
Shouzhi Chen
College of Water Sciences, Beijing Normal University, Beijing 100875, China
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Plants and Ecosystems, Department of Biology, University of Antwerp, Antwerp, Belgium
Mingwei Li
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Zitong Jia
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Yishuo Cui
College of Water Sciences, Beijing Normal University, Beijing 100875, China
Center for Volatile Interactions, Department of Biology, University of Copenhagen, Copenhagen, Denmark
Related authors
Yishuo Cui, Shouzhi Chen, Yufeng Gong, Mingwei Li, Zitong Jia, Yuyu Zhou, and Yongshuo H. Fu
Earth Syst. Sci. Data, 17, 4005–4022, https://doi.org/10.5194/essd-17-4005-2025, https://doi.org/10.5194/essd-17-4005-2025, 2025
Short summary
Short summary
Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycles. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982–2020 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
Mingwei Li, Shouzhi Chen, Fanghua Hao, Nan Wang, Zhaofei Wu, Yue Xu, Jing Zhang, Yongqiang Zhang, and Yongshuo H. Fu
Hydrol. Earth Syst. Sci., 29, 2081–2095, https://doi.org/10.5194/hess-29-2081-2025, https://doi.org/10.5194/hess-29-2081-2025, 2025
Short summary
Short summary
Climate-driven shifts in vegetation phenology have a significant impact on hydrological processes. In this study, we integrated a process-based phenology module into the SWAT-Carbon model, which led to a substantial improvement in the simulation of vegetation dynamics and hydrological processes in the Jinsha River watershed. Our findings highlight the critical need to incorporate vegetation phenology into hydrological models to achieve a more accurate representation of ecohydrological processes.
Yishuo Cui, Shouzhi Chen, Yufeng Gong, Mingwei Li, Zitong Jia, Yuyu Zhou, and Yongshuo H. Fu
Earth Syst. Sci. Data, 17, 4005–4022, https://doi.org/10.5194/essd-17-4005-2025, https://doi.org/10.5194/essd-17-4005-2025, 2025
Short summary
Short summary
Global changes have significantly altered vegetation phenology, affecting terrestrial carbon cycles. While various remote-sensing-based phenology datasets exist, they often suffer from inconsistencies and uncertainties. To address this, we developed a new phenology dataset spanning 1982–2020 using a reliability ensemble averaging method. Validated against ground data, our dataset demonstrates substantially improved accuracy, providing a novel and reliable source for global ecological studies.
Mingwei Li, Shouzhi Chen, Fanghua Hao, Nan Wang, Zhaofei Wu, Yue Xu, Jing Zhang, Yongqiang Zhang, and Yongshuo H. Fu
Hydrol. Earth Syst. Sci., 29, 2081–2095, https://doi.org/10.5194/hess-29-2081-2025, https://doi.org/10.5194/hess-29-2081-2025, 2025
Short summary
Short summary
Climate-driven shifts in vegetation phenology have a significant impact on hydrological processes. In this study, we integrated a process-based phenology module into the SWAT-Carbon model, which led to a substantial improvement in the simulation of vegetation dynamics and hydrological processes in the Jinsha River watershed. Our findings highlight the critical need to incorporate vegetation phenology into hydrological models to achieve a more accurate representation of ecohydrological processes.
Qi Guan, Jing Tang, Lian Feng, Stefan Olin, and Guy Schurgers
Biogeosciences, 20, 1635–1648, https://doi.org/10.5194/bg-20-1635-2023, https://doi.org/10.5194/bg-20-1635-2023, 2023
Short summary
Short summary
Understanding terrestrial sources of nitrogen is vital to examine lake eutrophication changes. Combining process-based ecosystem modeling and satellite observations, we found that land-leached nitrogen in the Yangtze Plain significantly increased from 1979 to 2018, and terrestrial nutrient sources were positively correlated with eutrophication trends observed in most lakes, demonstrating the necessity of sustainable nitrogen management to control eutrophication.
David Martín Belda, Peter Anthoni, David Wårlind, Stefan Olin, Guy Schurgers, Jing Tang, Benjamin Smith, and Almut Arneth
Geosci. Model Dev., 15, 6709–6745, https://doi.org/10.5194/gmd-15-6709-2022, https://doi.org/10.5194/gmd-15-6709-2022, 2022
Short summary
Short summary
We present a number of augmentations to the ecosystem model LPJ-GUESS, which will allow us to use it in studies of the interactions between the land biosphere and the climate. The new module enables calculation of fluxes of energy and water into the atmosphere that are consistent with the modelled vegetation processes. The modelled fluxes are in fair agreement with observations across 21 sites from the FLUXNET network.
Cited articles
Ahl, D. E., Gower, S. T., Burrows, S. N., Shabanov, N. V., Myneni, R. B., and Knyazikhin, Y.: Monitoring spring canopy phenology of a deciduous broadleaf forest using MODIS, Remote Sens. Environ., 104, 88–95, 2006.
Ahlström, A., Xia, J., Arneth, A., Luo, Y., and Smith, B.: Importance of vegetation dynamics for future terrestrial carbon cycling, Environ. Res. Lett., 10, 054019, https://doi.org/10.1088/1748-9326/10/5/054019, 2015.
Augspurger, C. K.: Spring 2007 warmth and frost: phenology, damage and refoliation in a temperate deciduous forest, Funct. Ecol., 23, 1031–1039, 2009.
Badeck, F. W., Bondeau, A., Böttcher, K., Doktor, D., Lucht, W., Schaber, J., and Sitch, S.: Responses of spring phenology to climate change, New Phytol., 162, 295–309, 2004.
Bartholome, E. and Belward, A. S.: GLC2000: a new approach to global land cover mapping from Earth observation data, Int. J. Remote Sens., 26, 1959–1977, 2005.
Bigler, C. and Bugmann, H.: Climate-induced shifts in leaf unfolding and frost risk of European trees and shrubs, Sci. Rep., 8, 9865, https://doi.org/10.1038/s41598-018-27893-1, 2018.
Caffarra, A., Donnelly, A., and Chuine, I.: Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models, Clim. Res., 46, 159–170, 2011.
Cao, S., Li, M., Zhu, Z., Wang, Z., Zha, J., Zhao, W., Duanmu, Z., Chen, J., Zheng, Y., Chen, Y., Myneni, R. B., and Piao, S.: Spatiotemporally consistent global dataset of the GIMMS leaf area index (GIMMS LAI4g) from 1982 to 2020, Earth Syst. Sci. Data, 15, 4877–4899, https://doi.org/10.5194/essd-15-4877-2023, 2023.
Chen, S., Fu, Y. H., Hao, F., Li, X., Zhou, S., Liu, C., and Tang, J.: Vegetation phenology and its ecohydrological implications from individual to global scales, Geography and Sustainability, 3, 334–338, https://doi.org/0.1016/j.geosus.2022.10.002, 2022a.
Chen, S., Fu, Y. H., Geng, X., Hao, Z., Tang, J., Zhang, X., Xu, Z., and Hao, F.: Influences of Shifted Vegetation Phenology on Runoff Across a Hydroclimatic Gradient, Front. Plant Sci., 12, 802664, https://doi.org/10.3389/fpls.2021.802664, 2022b.
Chen, S., Fu, Y. H., Wu, Z., Hao, F., Hao, Z., Guo, Y., Geng, X., Li, X., Zhang, X., and Tang, J.: Informing the SWAT model with remote sensing detected vegetation phenology for improved modeling of ecohydrological processes, J. Hydrol., 616, 128817, https://doi.org/10.1016/j.jhydrol.2022.128817, 2023a.
Chen, S., Fu, Y., and Tang, J.: LPJ-GUESS code with a new temperature-photoperiod coupled phenology module, Zenodo [code], https://doi.org/10.5281/zenodo.10416649, 2023b.
Chen, X., Wang, D., Chen, J., Wang, C., and Shen, M.: The mixed pixel effect in land surface phenology: A simulation study, Remote Sens. Environ., 211, 338–344, 2018.
Chuine, I.: A unified model for budburst of trees, J. Theor. Biol., 207, 337–347, 2000.
Chuine, I.: Why does phenology drive species distribution?, Philosophical Transactions of the Royal Society B: Biological Sciences, 365, 3149–3160, 2010.
Cong, N., Piao, S., Chen, A., Wang, X., Lin, X., Chen, S., Han, S., Zhou, G., and Zhang, X.: Spring vegetation green-up date in China inferred from SPOT NDVI data: A multiple model analysis, Agr. Forest Meteorol., 165, 104–113, https://doi.org/10.1016/j.agrformet.2012.06.009, 2012.
Dai, W., Jin, H., Zhou, L., Liu, T., Zhang, Y., Zhou, Z., Fu, Y. H., and Jin, G.: Testing machine learning algorithms on a binary classification phenological model, Global Ecol. Biogeogr., 32, 178–190, 2023.
Delpierre, N., Dufrêne, E., Soudani, K., Ulrich, E., Cecchini, S., Boé, J., and François, C.: Modelling interannual and spatial variability of leaf senescence for three deciduous tree species in France, Agr. Forest Meteorol., 149, 938–948, 2009.
Deng, F., Chen, J. M., Plummer, S., Chen, M., and Pisek, J.: Algorithm for global leaf area index retrieval using satellite imagery, IEEE Trans. Geosci. Remote Sens., 44, 2219–2229, 2006.
Dijkstra, J. A., Westerman, E. L., and Harris, L. G.: The effects of climate change on species composition, succession and phenology: a case study, Glob. Change Biol., 17, 2360–2369, 2011.
Drepper, B., Gobin, A., and Van Orshoven, J.: Spatio-temporal assessment of frost risks during the flowering of pear trees in Belgium for 1971–2068, Agr. Forest Meteorol., 315, 108822, https://doi.org/10.1016/j.agrformet.2022.108822, 2022.
Fang, J. and Lechowicz, M. J.: Climatic limits for the present distribution of beech (Fagus L.) species in the world, J. Biogeogr., 33, 1804–1819, 2006.
Forrest, J., Inouye, D. W., and Thomson, J. D.: Flowering phenology in subalpine meadows: Does climate variation influence community co-flowering patterns?, Ecology, 91, 431–440, 2010.
Fu, Y., Li, X., Zhou, X., Geng, X., Guo, Y., and Zhang, Y.: Progress in plant phenology modeling under global climate change, Science China Earth Sciences, 63, 1237–1247, 2020.
Fu, Y. H., Piao, S., Op de Beeck, M., Cong, N., Zhao, H., Zhang, Y., Menzel, A., and Janssens, I. A.: Recent spring phenology shifts in western C entral E urope based on multiscale observations, Global Ecol. Biogeogr., 23, 1255–1263, 2014.
Fu, Y. H., Zhou, X., Li, X., Zhang, Y., Geng, X., Hao, F., Zhang, X., Hanninen, H., Guo, Y., and De Boeck, H. J.: Decreasing control of precipitation on grassland spring phenology in temperate China, Global Ecol. Biogeogr., 30, 490–499, 2021.
Fu, Y. H., Li, X., Chen, S., Wu, Z., Su, J., Li, X., Li, S., Zhang, J., Tang, J., and Xiao, J.: Soil moisture regulates warming responses of autumn photosynthetic transition dates in subtropical forests, Glob. Change Biol., 28, 4935–4946, 2022.
Fu, Y. H., Geng, X., Chen, S., Wu, H., Hao, F., Zhang, X., Wu, Z., Zhang, J., Tang, J., and Vitasse, Y.: Global warming is increasing the discrepancy between green (actual) and thermal (potential) seasons of temperate trees, Glob. Change Biol., 29, 1377–1389, 2023.
Geng, X., Zhou, X., Yin, G., Hao, F., Zhang, X., Hao, Z., Singh, V. P., and Fu, Y. H.: Extended growing season reduced river runoff in Luanhe River basin, J. Hydrol., 582, 124538, https://doi.org/10.1016/j.jhydrol.2019.124538, 2020.
Guan, K., Pan, M., Li, H., Wolf, A., Wu, J., Medvigy, D., Caylor, K. K., Sheffield, J., Wood, E. F., and Malhi, Y.: Photosynthetic seasonality of global tropical forests constrained by hydroclimate, Nat. Geosci., 8, 284–289, 2015.
Hänninen, H.: Modelling bud dormancy release in trees from cool and temperate regions, Acta Forestalia Fennica, Finnish Forest Research Institute, Helsinki, Finland, No. 213, 47 pp., 1990.
Hickler, T., Smith, B., Sykes, M. T., Davis, M. B., Sugita, S., and Walker, K.: Using a generalized vegetation model to simulate vegetation dynamics in northeastern USA, Ecology, 85, 519–530, 2004.
Hmimina, G., Dufrêne, E., Pontailler, J.-Y., Delpierre, N., Aubinet, M., Caquet, B., De Grandcourt, A., Burban, B., Flechard, C., and Granier, A.: Evaluation of the potential of MODIS satellite data to predict vegetation phenology in different biomes: An investigation using ground-based NDVI measurements, Remote Sens. Environ., 132, 145–158, 2013.
Huang, M., Piao, S., Janssens, I. A., Zhu, Z., Wang, T., Wu, D., Ciais, P., Myneni, R. B., Peaucelle, M., and Peng, S.: Velocity of change in vegetation productivity over northern high latitudes, Nat. Ecol. Evol., 1, 1649–1654, 2017.
Jain, A. K. and Yang, X.: Modeling the effects of two different land cover change data sets on the carbon stocks of plants and soils in concert with CO2 and climate change, Global Biogeochem. Cy., 19, GB2015, https://doi.org/10.1029/2004GB002349, 2005.
Kaufmann, R. K., Zhou, L., Knyazikhin, Y., Shabanov, V., Myneni, R. B., and Tucker, C. J.: Effect of orbital drift and sensor changes on the time series of AVHRR vegetation index data, IEEE T. Geosci. Remote Sens., 38, 2584–2597, 2000.
Keenan, T. F. and 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.
Keenan, T. F., Gray, J., Friedl, M. A., Toomey, M., Bohrer, G., Hollinger, D. Y., Munger, J. W., O'Keefe, J., Schmid, H. P., SueWing, I., Yang, B., and Richardson, A. D.: Net carbon uptake has increased through warming-induced changes in temperate forest phenology, Nat. Clim. Change, 4, 598–604, https://doi.org/10.1038/Nclimate2253, 2014.
Kim, J. H., Hwang, T., Yang, Y., Schaaf, C. L., Boose, E., and Munger, J. W.: Warming-induced earlier greenup leads to reduced stream discharge in a temperate mixed forest catchment, J. Geophys. Res.-Biogeo., 123, 1960–1975, 2018.
Kramer, K.: Selecting a model to predict the onset of growth of Fagus sylvatica, J. Appl. Ecol., 31,, 172–181, https://doi.org/10.2307/2404609, 1994.
Krinner, G., Viovy, N., de Noblet-Ducoudré, N., Ogée, J., Polcher, J., Friedlingstein, P., Ciais, P., Sitch, S., and Prentice, I. C.: A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system, Global Biogeochem. Cy., 19, GB1015, https://doi.org/10.1029/2003GB002199, 2005.
Kucharik, C. J., Barford, C. C., El Maayar, M., Wofsy, S. C., Monson, R. K., and Baldocchi, D. D.: A multiyear evaluation of a Dynamic Global Vegetation Model at three AmeriFlux forest sites: Vegetation structure, phenology, soil temperature, and CO2 and H2O vapor exchange, Ecol. Modell., 196, 1–31, https://doi.org/10.1016/j.ecolmodel.2005.11.031, 2006.
Li, X., Fu, Y. H., Chen, S., Xiao, J., Yin, G., Li, X., Zhang, X., Geng, X., Wu, Z., and Zhou, X.: Increasing importance of precipitation in spring phenology with decreasing latitudes in subtropical forest area in China, Agr. Forest Meteorol., 304, 108427, https://doi.org/10.1016/j.agrformet.2021.108427, 2021.
Liu, Q., Fu, Y. H., Liu, Y., Janssens, I. A., and Piao, S.: Simulating the onset of spring vegetation growth across the Northern Hemisphere, Glob. Change Biol., 24, 1342–1356, 2018a.
Liu, Q., Piao, S., Janssens, I. A., Fu, Y., Peng, S., Lian, X., Ciais, P., Myneni, R. B., Peñuelas, J., and Wang, T.: Extension of the growing season increases vegetation exposure to frost, Nat. Commun., 9, 426, https://doi.org/10.1038/s41467-017-02690-y, 2018b.
Lu, J., Wang, G., Chen, T., Li, S., Hagan, D. F. T., Kattel, G., Peng, J., Jiang, T., and Su, B.: A harmonized global land evaporation dataset from model-based products covering 1980–2017, Earth Syst. Sci. Data, 13, 5879–5898, https://doi.org/10.5194/essd-13-5879-2021, 2021a.
Lu, J., Wang, G., Chen, T., Li, S., Hagan, D. F. T., Kattel, G., Peng, J., Jiang, T., and Su, B.: A Harmonized Global Land Evaporation Dataset from Model-based Products Covering 1980–2017, Zenodo [data set], https://doi.org/10.5281/zenodo.4595941, 2021b.
Marini, F. and Walczak, B.: Particle swarm optimization (PSO). A tutorial, Chemometr. Intell. Lab., 149, 153–165, 2015.
Medvigy, D., Wofsy, S., Munger, J., Hollinger, D., and Moorcroft, P.: Mechanistic scaling of ecosystem function and dynamics in space and time: Ecosystem Demography model version 2, J. Geophys. Res.-Biogeo., 114, G01002, https://doi.org/10.1029/2008JG000812, 2009.
Morales, P., Sykes, M. T., Prentice, I. C., Smith, P., Smith, B., Bugmann, H., Zierl, B., Friedlingstein, P., Viovy, N., and Sabaté, S.: Comparing and evaluating process-based ecosystem model predictions of carbon and water fluxes in major European forest biomes, Glob. Change Biol., 11, 2211–2233, 2005.
Morisette, J. T., Richardson, A. D., Knapp, A. K., Fisher, J. I., Graham, E. A., Abatzoglou, J., Wilson, B. E., Breshears, D. D., Henebry, G. M., and Hanes, J. M.: 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.
Piao, S., Fang, J., Zhou, L., Ciais, P., and Zhu, B.: Variations in satellite-derived phenology in China's temperate vegetation, Glob. Change Biol., 12, 672–685, 2006.
Piao, S., Liu, Q., Chen, A., Janssens, I. A., Fu, Y., Dai, J., Liu, L., Lian, X., Shen, M., and Zhu, X.: Plant phenology and global climate change: Current progresses and challenges, Glob. Change Biol., 25, 1922–1940, 2019.
Pinzon, J. E. and Tucker, C. J.: A non-stationary 1981–2012 AVHRR NDVI3g time series, Remote Sens., 6, 6929–6960, 2014.
Poli, R., Kennedy, J., and Blackwell, T.: Particle swarm optimization: An overview, Swarm Intell., 1, 33–57, 2007.
Prevéy, J., Vellend, M., Rüger, N., Hollister, R. D., Bjorkman, A. D., Myers-Smith, I. H., Elmendorf, S. C., Clark, K., Cooper, E. J., and Elberling, B.: Greater temperature sensitivity of plant phenology at colder sites: implications for convergence across northern latitudes, Glob. Change Biol., 23, 2660–2671, 2017.
Reed, B. C., Brown, J. F., VanderZee, D., Loveland, T. R., Merchant, J. W., and Ohlen, D. O.: Measuring phenological variability from satellite imagery, J. Veg. Sci., 5, 703–714, 1994.
Richardson, A. D., Anderson, R. S., Arain, M. A., Barr, A. G., Bohrer, G., Chen, G., Chen, J. M., Ciais, P., Davis, K. J., and Desai, A. R.: 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.
Rinnan, R., Iversen, L. L., Tang, J., Vedel-Petersen, I., Schollert, M., and Schurgers, G.: Separating direct and indirect effects of rising temperatures on biogenic volatile emissions in the Arctic, P. Natl. Acad. Sci. USA, 117, 32476–32483, https://doi.org/10.1073/pnas.2008901117, 2020.
Roberts, A. M., Tansey, C., Smithers, R. J., and Phillimore, A. B.: Predicting a change in the order of spring phenology in temperate forests, Glob. Change Biol., 21, 2603–2611, 2015.
Rollinson, C. R. and Kaye, M. W.: Experimental warming alters spring phenology of certain plant functional groups in an early successional forest community, Glob. Change Biol., 18, 1108–1116, 2012.
Ryu, S.-R., Chen, J., Noormets, A., Bresee, M. K., and Ollinger, S. V.: Comparisons between PnET-Day and eddy covariance based gross ecosystem production in two Northern Wisconsin forests, Agr. Forest Meteorol., 148, 247–256, 2008.
Sarvas, R.: Investigations on the annual cycle of development of forest trees. Active period, 76, Metsantutkimuslaitoksen Julkaisuja, 110 pp., 1972.
Savitzky, A. and Golay, M. J.: Smoothing and differentiation of data by simplified least squares procedures, Anal. Chem., 36, 1627–1639, 1964.
Schaefer, K., Collatz, G. J., Tans, P., Denning, A. S., Baker, I., Berry, J., Prihodko, L., Suits, N., and Philpott, A.: Combined simple biosphere/Carnegie-Ames-Stanford approach terrestrial carbon cycle model, J. Geophys. Res.-Biogeo., 113, G03034, https://doi.org/10.1029/2007JG000603, 2008.
Sellers, P., Mintz, Y., Sud, Y. E. A., and Dalcher, A.: A simple biosphere model (SiB) for use within general circulation models, J. Atmos. Sci., 43, 505–531, 1986.
Sellers, P., Randall, D., Collatz, G., Berry, J., Field, C., Dazlich, D., Zhang, C., Collelo, G., and Bounoua, L.: A revised land surface parameterization (SiB2) for atmospheric GCMs. Part I: Model formulation, J. Climate, 9, 676–705, 1996.
Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Kaplan, J. O., Levis, S., Lucht, W., and Sykes, M. T.: Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model, Glob. Change Biol., 9, 161–185, 2003.
Smith, B., Prentice, I. C., and Sykes, M. T.: Representation of vegetation dynamics in the modelling of terrestrial ecosystems: comparing two contrasting approaches within European climate space, Global Ecol. Biogeogr., 10, 621–637, 2001.
Sykes, M. T., Prentice, I. C., and Cramer, W.: A bioclimatic model for the potential distributions of north European tree species under present and future climates, J. Biogeogr., 23, 203–233, 1996.
Tang, J., Zhou, P., Miller, P. A., Schurgers, G., Gustafson, A., Makkonen, R., Fu, Y. H., and Rinnan, R.: High-latitude vegetation changes will determine future plant volatile impacts on atmospheric organic aerosols, npj Climate and Atmospheric Science, 6, 147, https://doi.org/10.1038/s41612-023-00463-7, 2023.
Thornton, P. E., Law, B. E., Gholz, H. L., Clark, K. L., Falge, E., Ellsworth, D. S., Goldstein, A. H., Monson, R. K., Hollinger, D., and Falk, M.: Modeling and measuring the effects of disturbance history and climate on carbon and water budgets in evergreen needleleaf forests, Agr. Forest Meteorol., 113, 185–222, 2002.
Tremblay, N. O. and Larocque, G. R.: Seasonal dynamics of understory vegetation in four eastern Canadian forest types, Int. J. Plant Sci., 162, 271–286, 2001.
Tucker, C. J., Pinzon, J. E., Brown, M. E., Slayback, D. A., Pak, E. W., Mahoney, R., Vermote, E. F., and El Saleous, N.: An extended AVHRR 8-km NDVI dataset compatible with MODIS and SPOT vegetation NDVI data, Int. J. Remote Sens., 26, 4485–4498, 2005.
Viovy, N.: CRUNCEP Version 7 – Atmospheric Forcing Data for the Community Land Model, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/PZ8F-F017, 2018.
White, M. A., Thornton, P. E., and Running, S. W.: A continental phenology model for monitoring vegetation responses to interannual climatic variability, Global Biogeochem. Cy., 11, 217–234, 1997.
White, M. A., de Beurs, K. M., Didan, K., Inouye, D. W., Richardson, A. D., Jensen, O. P., O'keefe, J., Zhang, G., Nemani, R. R., and van Leeuwen, W. J.: Intercomparison, interpretation, and assessment of spring phenology in North America estimated from remote sensing for 1982–2006, Glob. Change Biol., 15, 2335–2359, 2009.
Wolkovich, E. M., Cook, B. I., Allen, J. M., Crimmins, T., Betancourt, J. L., Travers, S. E., Pau, S., Regetz, J., Davies, T. J., and Kraft, N. J.: Warming experiments underpredict plant phenological responses to climate change, Nature, 485, 494–497, 2012.
Zani, D., Crowther, T. W., Mo, L., Renner, S. S., and Zohner, C. M.: Increased growing-season productivity drives earlier autumn leaf senescence in temperate trees, Science, 370, 1066–1071, 2020.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., and Dong, J.: A global moderate resolution dataset of gross primary production of vegetation for 2000–2016, Sci. Data, 4, 170165, https://doi.org/10.1038/sdata.2017.165, 2017a.
Zhang, Y., Xiao, X., Wu, X., Zhou, S., Zhang, G., Qin, Y., et al.: A global moderate resolution dataset of gross primary production of vegetation for 2000–2016, figshare [data set], https://doi.org/10.6084/m9.figshare.c.3789814.v1, 2017b.
Zhang, Y., Commane, R., Zhou, S., Williams, A. P., and Gentine, P.: Light limitation regulates the response of autumn terrestrial carbon uptake to warming, Nat. Clim. Change, 10, 739–743, 2020.
Zheng, J., Jia, G., and Xu, X.: Earlier snowmelt predominates advanced spring vegetation greenup in Alaska, Agr. Forest Meteorol., 315, 108828, 2022.
Zhou, X., Geng, X., Yin, G., Hänninen, H., Hao, F., Zhang, X., and Fu, Y. H.: Legacy effect of spring phenology on vegetation growth in temperate China, Agr. Forest Meteorol., 281, 107845, https://doi.org/10.1016/j.agrformet.2019.107845, 2020.
Zhu, Z., Piao, S., Myneni, R. B., Huang, M., Zeng, Z., Canadell, J. G., Ciais, P., Sitch, S., Friedlingstein, P., and Arneth, A.: Greening of the Earth and its drivers, Nat. Clim. Change, 6, 791–795, 2016.
Zohner, C. M., Mirzagholi, L., Renner, S. S., Mo, L., Rebindaine, D., Bucher, R., Palouš, D., Vitasse, Y., Fu, Y. H., and Stocker, B. D.: Effect of climate warming on the timing of autumn leaf senescence reverses after the summer solstice, Science, 381, eadf5098, https://doi.org/10.1126/science.adf5098, 2023.
Short summary
It is still a challenge to achieve an accurate simulation of vegetation phenology in the dynamic global vegetation models (DGVMs). We implemented and coupled the spring and autumn phenology models into one of the DGVMs, LPJ-GUESS, and substantially improved the accuracy in capturing the start and end dates of growing seasons. Our study highlights the importance of getting accurate phenology estimations to reduce the uncertainties in plant distribution and terrestrial carbon and water cycling.
It is still a challenge to achieve an accurate simulation of vegetation phenology in the dynamic...
