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false4DOIDB.PIK 10.5880/pik.2017.009 Schaphoff (Ed.), Sibyll Sibyll Schaphoff (Ed.) 0000-0003-1677-8282 Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany von Bloh, Werner Werner von Bloh 0000-0002-7399-2704 Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Rammig, Anja Anja Rammig Technical University of Munich, School of Life Sciences, Munich, Germany Thonicke, Kirsten Kirsten Thonicke 0000-0001-5283-4937 Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Biemans, Hester Hester Biemans Alterra, Wageningen University and Research, Wageningen, Netherlands Forkel, Matthias Matthias Forkel 0000-0003-0363-9697 TU Wien, Climate and Environmental Remote Sensing Group, Department of Geodesy and Geoinformation, Vienna, Austria Gerten, Dieter Dieter Gerten 0000-0002-6214-6991 Potsdam Institute for Climate Impact Research, Potsdam, Germany Humboldt Universität zu Berlin, Department of Geography, Berlin, Germany Heinke, Jens Jens Heinke Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Jägermeyr, Jonas Jonas Jägermeyr Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Knauer, Jürgen Jürgen Knauer 0000-0002-4947-7067 Max Planck Institute for Biogeochemistry, Jena, Germany Langerwisch, Fanny Fanny Langerwisch 0000-0003-4744-7427 Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Lucht, Wolfgang Wolfgang Lucht 0000-0002-3398-8575 Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Humboldt Universität zu Berlin, Department of Geography, Berlin, Germany Müller, Christoph Christoph Müller 0000-0002-9491-3550 Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Rolinski, Susanne Susanne Rolinski Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Waha, Katharina Katharina Waha 0000-0002-8631-8639 Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany CSIRO Agriculture & Food, St. Lucia, Australia GFZ Data Services 2018 soil carbon vegetation carbon global carbon balance permafrost distribution discharge fractional burned area crop yields global dynamic vegetation model vegetation dynamics EARTH SCIENCE > AGRICULTURE > PLANT COMMODITIES > FIELD CROP PRODUCTS EARTH SCIENCE > BIOSPHERE > ECOLOGICAL DYNAMICS > FIRE ECOLOGY > FIRE OCCURRENCE EARTH SCIENCE > BIOSPHERE > VEGETATION > BIOMASS EARTH SCIENCE > BIOSPHERE > VEGETATION > CARBON EARTH SCIENCE > TERRESTRIAL HYDROSPHERE > SURFACE WATER > RUNOFF EARTH SCIENCE SERVICES > MODELS > DYNAMIC VEGETATION/ECOSYSTEM MODELS EARTH SCIENCE SERVICES > MODELS > HYDROLOGIC AND TERRESTRIAL WATER CYCLE MODELS Schaphoff (Ed.), Sibyll Sibyll Schaphoff (Ed.) Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Schaphoff (Ed.), Sibyll Sibyll Schaphoff (Ed.) Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Schaphoff (Ed.), Sibyll Sibyll Schaphoff (Ed.) Potsdam Institute for Climate Impact Research, Telegraphenberg, Potsdam, Germany Schaphoff (Ed.), Sibyll Potsdam Institute for Climate Impact Research, Telegraphenberg, PO Box 60 12 03, 14412 Potsdam, Germany 1901-01-15/2011-12-15 Dataset 10.5194/gmd-11-1343-2018 10.5194/gmd-11-1377-2018 10.5880/pik.2018.002 1.0 CC BY 4.0 LPJmL4 is a process-based model that simulates climate and land-use change impacts on the terrestrial biosphere, the water and carbon cycle and on agricultural production. The LPJmL4 model combines plant physiological relations, generalized empirically established functions and plant trait parameters. The model incorporates dynamic land use at the global scale and is also able to simulate the production of woody and herbaceous short-rotation bio-energy plantations. Grid cells may contain one or several types of natural or agricultural vegetation. A comprehensive description of the model is given by Schaphoff et al. (2017a, http://doi.org/10.5194/gmd-2017-145).


The data presented here represent some standard LPJmL4 model results for the land surface described in Schaphoff et al. (2017a,). Additionally, these results are evaluated in the companion paper of Schaphoff et al. (2017b, http://doi.org/10.5194/gmd-2017-146). The data collection includes some key output variables made with different model setups described by Schaphoff et al. (2017b).


The data cover the entire globe with a spatial resolution of 0.5° and temporal coverage from 1901-2011 on an annual basis for soil, vegetation, aboveground and litter carbon as well as for vegetation distribution, crop yields, sowing dates, maximum thawing depth, and fire carbon emissions. Vegetation distribution is given for each plant functional type (PFT), crop yields, and sowing dates are given for each crop functional type (CFT), respectively. Monthly data are provided for the carbon fluxes (net primary production, gross primary production, soil respiration) and the water fluxes (transpiration, evaporation, interception, runoff, and discharge) and for absorbed photosynthetically active radiation (FAPAR) and albedo.


The data are provided in one netcdf file for each variable and experiment described by Schaphoff et al. (2017b). Crop yields and sowing dates are not provided for the LPJmL4-GSI-GlobFIRE-PNV experiment as this represents natural vegetation only. An overview of all variables and the number of bands are given in the file inventory.
global data in annual and monthly resolution and 0.5° spatial resolution -180 180 -90 90