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  • 1
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    PANGAEA
    In:  Supplement to: Forkel, Matthias; Carvalhais, Nuno; Rödenbeck, Christian; Keeling, Ralph F; Heimann, Martin; Thonicke, Kirsten; Zaehle, Sönke; Reichstein, Markus (2016): Enhanced seasonal CO_2 exchange caused by amplified plant productivity in northern ecosystems. Science, 6274, 696-699, https://doi.org/10.1126/science.aac4971
    Publication Date: 2023-01-13
    Description: Atmospheric monitoring of high northern latitudes (〉 40°N) has shown an enhanced seasonal cycle of carbon dioxide (CO2) since the 1960s but the underlying mechanisms are not yet fully understood. The much stronger increase in high latitudes compared to low ones suggests that northern ecosystems are experiencing large changes in vegetation and carbon cycle dynamics. Here we show that the latitudinal gradient of the increasing CO2 amplitude is mainly driven by positive trends in photosynthetic carbon uptake caused by recent climate change and mediated by changing vegetation cover in northern ecosystems. Our results emphasize the importance of climate-vegetation-carbon cycle feedbacks at high latitudes, and indicate that during the last decades photosynthetic carbon uptake has reacted much more strongly to warming than carbon release processes.
    Type: Dataset
    Format: application/zip, 1.7 GBytes
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  • 2
    Publication Date: 2021-02-08
    Description: 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.
    Type: Article , PeerReviewed
    Format: text
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  • 3
    Publication Date: 2021-02-08
    Description: 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 https://gitlab.pik-potsdam.de/lpjml/LPJmL. 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.
    Type: Article , PeerReviewed
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  • 4
    Publication Date: 2021-09-29
    Description: The productivity of terrestrial vegetation is determined by multiple land surface and atmospheric drivers. Water availability is critical for vegetation productivity, but the role of vertical variability of soil moisture (SM) is largely unknown. Here, we analyze dominant controls of global vegetation productivity represented by sun‐induced fluorescence and spectral vegetation indices at the half‐monthly time scale. We apply random forests to predict vegetation productivity from several hydrometeorological variables including multi‐layer SM and quantify the variable importance. Dominant hydrometeorological controls generally vary with latitudes: temperature in higher latitudes, solar radiation in lower latitudes, and root‐zone SM in between. We find that including vertically resolved SM allows a better understanding of vegetation productivity and reveals extended water‐related control. The deep(er) SM control for semi‐arid grasses and shrubs illustrates the potential of deep(er) rooting systems to adapt to water limitation. This study highlights the potential to infer sub‐surface processes from remote sensing observations.
    Description: Key Points: Vertically resolved soil moisture (SM) improves the understanding of large‐scale vegetation productivity and yields extended water‐related controls. Data‐driven evidence for the meaningfulness of the long‐standing modeling paradigm of vertical soil layer discretization. Comparatively deep SM is most relevant in semi‐arid areas and for grasses and shrubs.
    Description: German Research Foundation
    Description: ESA Living Planet Fellowship
    Keywords: 577.22 ; vegetation productivity ; hydrometeorological controls
    Type: map
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