LPJmL4 - A dynamic global vegetation model with managed land - Part 2: Model evaluation

• Main Authors: Schaphoff, S., Forkel, M., Müller, C., Knauer, J., Von, Bloh, W., Gerten, D., Jägermeyr, J., Lucht, W., Rammig, A., Thonicke, K., Waha, K.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Göttingen : Copernicus GmbH 2018
• Subjects: agricultural modeling; agrometeorology; environmental change; global perspective; in situ measurement; land use change; model validation; permafrost; phenology; satellite data; seasonal variation; spatiotemporal analysis; vegetation dynamics; 550
• Online Access: https://doi.org/10.34657/3739; https://oa.tib.eu/renate/handle/123456789/5110

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. publishedVersion