A time series of 21st century observation-based woody vegetation carbon stocks (Xu et al.,2021; https://www.science.org/doi/10.1126/sciadv.abe9829) was assimilated into the bookkeeping model BLUE ('bookkeeping of land use emissions') (Hansis et al.,2015; https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1002/2014GB004997). Two data-integration experiments were performed to isolate anthropogenic and natural carbon fluxes. The 'transient' experiment relies on assimilating the time series of observation-based vegetation carbon stocks at each (annual) time step in BLUE. In the 'transient' experiment, changes in woody vegetation carbon between two time steps are due to the combination of environmental and anthropogenic processes. In the 'fixed' experiment, the time series of observation-based woody vegetation carbon is only assimilated in BLUE at the first time step and changes in woody vegetation carbon between two time steps are only due to anthropogenic processes. The environmental carbon fluxes were subsequently derived by taking the difference in vegetation carbon stocks between the transient and fixed experiments. All spatially explicit variables are on a geographical latitude-longitude grid with a resolution of 0.25°.

This dataset contains reconstructions of land use and land cover from AD 800 to 1992 in global coverage at 30 minute resolution. After AD 1700, the data is based on Ramankutty and Foley (1999), Foley et al. (2003) and Klein Goldewijk (2001); for earlier times, land use is estimated with a country-based method that uses national population data (McEvedy and Jones, 1978) as a proxy for agricultural activity. For each year, a map is provided that contains 14 fields. Each field holds the fraction the respective vegetation type covers in the total grid cell (0-1). The vegetation types comprise three human land use types (crop, C3 pasture and C4 pasture) and 11 natural vegetation types (based on the potential vegetation map of Ramankutty and Foley, 1999). For the time period prior to AD 1700 two additional land cover scenarios are provided (scenmin and scenmax). They quantify the uncertainties associated with this approach, through technological progress in agriculture and uncertainties in population estimates. The additional datasets combine the known uncertainties in a way to give the most extreme range for possible estimates of land use area for each year before 1700. The datasets thus do not represent consistent time series of plausible alternative scenarios, but indicate, for each year, a maximum range outside which estimates of land use area are unrealistic. See citations and references for details. Vegetation types: 1 Tropical evergreen forest 2 Tropical deciduous forest 3 Temperate evergreen broadleaf forest 4 Temperate/boreal deciduous broadleaf forest 5 Temperate/boreal evergreen conifers 6 Temperate/boreal deciduous conifers 7 Raingreen shrubs 8 Summergreen shrubs 9 C3 natural grasses 10 C4 natural grasses 11 Tundra 12 Crop 13 C3 pasture 14 C4 pasture

This dataset comprises MPI-ESM-1.2 output from WP1 scenarios of the LAMACLIMA project. In LAMACLIMA, we analyzed the role of land use for global mitigation and local adaptation strategies and its impacts on local and remote climate and the carbon cycle across Earth system models (ESMs). Work package 1 (WP1) simulations of LAMACLIMA project consist of land-use-induced climate and carbon change sensitivity experiments. The scenarios do not represent realistic or policy relevant realizations. Instead, they simulate globally idealized constant land-use changes under present-day environmental conditions. This approach offers two main advantages. First, large-scale land-use simulations enhance the signal-to-noise ratio, enabling a clearer evaluation of the upper bound of potential impacts, despite their idealized nature. Second, applying global rather than regionally constrained changes permits a more comprehensive and comparative assessment of land-use impacts worldwide. The WP1 simulations consist of the five scenarios (CTL, CROP, FRST, IRR, and HARV); for example, “MPI-ESM-1.2 output of LAMACLIMA work package 1 (WP1): idealized constant global re-/afforestation (FRST) under present-day environmental conditions”. We use the MPI-ESM-1.2-LR climate model, which includes the following model components: - Atmosphere model: ECHAM6.3 (Triangular truncation: T63, approx. 1.88°×1.88°, 200 km; 47 vertical levels) - Land model: JSBACH3.2 (Triangular truncation: T63, approx. 1.88°×1.88°, 200 km) - Ocean dynamical model: MPIOM1.6 (GR1.5, approx. 150 km; 40 vertical levels) - Ocean biogeochemistry model: HAMOCC6 - Coupler: OASIS3-MCT We ran the land model JSBACH3.2 with the following options: - use_dynveg = false - use_disturbance = true - lcc_forcing_type = transitions - lcc_scheme = 2 The MPI-ESM output from the different model components was post-processed using the Climate Model Output Rewriter (CMOR) in accordance with CMIP data conventions. For further details, see: Mauritsen et al., 2019: https://doi.org/10.1029/2018MS001400

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.MPI-M.MPI-ESM1-2-HR.control-1950' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.MPI-M.MPI-ESM1-2-XR.hist-1950' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-XR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T255; 768 x 384 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 50 km, atmos: 50 km, land: 50 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.HighResMIP.MPI-M.MPI-ESM1-2-XR.control-1950' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-XR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T255; 768 x 384 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 50 km, atmos: 50 km, land: 50 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.DAMIP.MPI-M.MPI-ESM1-2-LR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.PMIP.MPI-M.MPI-ESM1-2-LR' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-LR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T63; 192 x 96 longitude/latitude; 47 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (bipolar GR1.5, approximately 1.5deg; 256 x 220 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 250 km, atmos: 250 km, land: 250 km, landIce: none, ocean: 250 km, ocnBgchem: 250 km, seaIce: 250 km.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.DKRZ.MPI-ESM1-2-HR.ssp370' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Deutsches Klimarechenzentrum, Hamburg 20146, Germany (DKRZ) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.CMIP.MPI-M.MPI-ESM1-2-HR.historical' with the full Data Reference Syntax following the template 'mip_era.activity_id.institution_id.source_id.experiment_id.member_id.table_id.variable_id.grid_label.version'. The MPI-ESM1.2-HR climate model, released in 2017, includes the following components: aerosol: none, prescribed MACv2-SP, atmos: ECHAM6.3 (spectral T127; 384 x 192 longitude/latitude; 95 levels; top level 0.01 hPa), land: JSBACH3.20, landIce: none/prescribed, ocean: MPIOM1.63 (tripolar TP04, approximately 0.4deg; 802 x 404 longitude/latitude; 40 levels; top grid cell 0-12 m), ocnBgchem: HAMOCC6, seaIce: unnamed (thermodynamic (Semtner zero-layer) dynamic (Hibler 79) sea ice model). The model was run by the Max Planck Institute for Meteorology, Hamburg 20146, Germany (MPI-M) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, land: 100 km, landIce: none, ocean: 50 km, ocnBgchem: 50 km, seaIce: 50 km.