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    Identifying environmental controls on vegetation greenness phenology through model-data integration
    (München : European Geopyhsical Union, 2014) Forkel, M.; Carvalhais, N.; Schaphoff, S.; v. Bloh, W.; Migliavacca, M.; Thurner, M.; Thonicke, K.
    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 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 enhance the understanding of processes that control seasonal to long-term vegetation greenness dynamics. Here we implemented a new phenology 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 environmental 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 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 a new generation of phenology modules in DGVMs in order to correctly reproduce observed seasonal-to-decadal dynamics of vegetation greenness.
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    LPJmL4 - A dynamic global vegetation model with managed land - Part 2: Model evaluation
    (Göttingen : Copernicus GmbH, 2018) Schaphoff, S.; Forkel, M.; Müller, C.; Knauer, J.; Von, Bloh, W.; Gerten, D.; Jägermeyr, J.; Lucht, W.; Rammig, A.; Thonicke, K.; Waha, K.
    The dynamic global vegetation model LPJmL4 is a process-based model that simulates climate and land use change impacts on the terrestrial biosphere, agricultural production, and the water and carbon cycle. Different versions of the model have been developed and applied to evaluate the role of natural and managed ecosystems in the Earth system and the potential impacts of global environmental change. A comprehensive model description of the new model version, LPJmL4, is provided in a companion paper (Schaphoff et al., 2018c). Here, we provide a full picture of the model performance, going beyond standard benchmark procedures and give hints on the strengths and shortcomings of the model to identify the need for further model improvement. Specifically, we evaluate LPJmL4 against various datasets from in situ measurement sites, satellite observations, and agricultural yield statistics. We apply a range of metrics to evaluate the quality of the model to simulate stocks and flows of carbon and water in natural and managed ecosystems at different temporal and spatial scales. We show that an advanced phenology scheme improves the simulation of seasonal fluctuations in the atmospheric CO2 concentration, while the permafrost scheme improves estimates of carbon stocks. The full LPJmL4 code including the new developments will be supplied open source through . We hope that this will lead to new model developments and applications that improve the model performance and possibly build up a new understanding of the terrestrial biosphere.
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    LPJmL4 - A dynamic global vegetation model with managed land - Part 1: Model description
    (Göttingen : Copernicus GmbH, 2018) Schaphoff, S.; Von Bloh, W.; Rammig, A.; Thonicke, K.; Biemans, H.; Forkel, M.; Gerten, D.; Heinke, J.; Jägermeyr, J.; Knauer, J.; Langerwisch, F.; Lucht, W.; Müller, C.; Rolinski, S.; Waha, K.
    This paper provides a comprehensive description of the newest version of the Dynamic Global Vegetation Model with managed Land, LPJmL4. This model simulates - internally consistently - the growth and productivity of both natural and agricultural vegetation as coherently linked through their water, carbon, and energy fluxes. These features render LPJmL4 suitable for assessing a broad range of feedbacks within and impacts upon the terrestrial biosphere as increasingly shaped by human activities such as climate change and land use change. Here we describe the core model structure, including recently developed modules now unified in LPJmL4. Thereby, we also review LPJmL model developments and evaluations in the field of permafrost, human and ecological water demand, and improved representation of crop types. We summarize and discuss LPJmL model applications dealing with the impacts of historical and future environmental change on the terrestrial biosphere at regional and global scale and provide a comprehensive overview of LPJmL publications since the first model description in 2007. To demonstrate the main features of the LPJmL4 model, we display reference simulation results for key processes such as the current global distribution of natural and managed ecosystems, their productivities, and associated water fluxes. A thorough evaluation of the model is provided in a companion paper. By making the model source code freely available at https://gitlab.pik-potsdam.de/lpjml/LPJmL we hope to stimulate the application and further development of LPJmL4 across scientific communities in support of major activities such as the IPCC and SDG process.