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Existing dynamic global vegetation models (DGVMs) have a limited ability in reproducing phenology and decadal dynamics of vegetation greenness as observed by satellites. These limitations in reproducing observations reflect a poor understanding and 5 description of the environmental controls on phenology, which strongly influence the ability to simulate longer term vegetation dynamics, e.g. carbon allocation. Combining DGVMs with observational data sets can potentially help to revise current modelling approaches and thus to enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenol10 ogy model within the LPJmL (Lund Potsdam Jena managed lands) DGVM and integrated several observational data sets to improve the ability of the model in reproducing satellite-derived time series of vegetation greenness. Specifically, we optimized LPJmL parameters against observational time series of the fraction of absorbed photosynthetic active radiation (FAPAR), albedo and gross primary production to identify the main en15 vironmental controls for seasonal vegetation greenness dynamics. We demonstrated that LPJmL with new phenology and optimized parameters better reproduces seasonality, inter-annual variability and trends of vegetation greenness. Our results indicate that soil water availability is an important control on vegetation phenology not only in water-limited biomes but also in boreal forests and the arctic tundra. Whereas water 20 availability controls phenology in water-limited ecosystems during the entire growing season, water availability co-modulates jointly with temperature the beginning of the growing season in boreal and arctic regions. Additionally, water availability contributes to better explain decadal greening trends in the Sahel and browning trends in boreal forests. These results emphasize the importance of considering water availability in 25 a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal to decadal dynamics of vegetation greenness

Identifying environmental controls on vegetation greenness phenology through model–data integration