RCM forcing data from the 1st realisation (r1i1p1f1) of the CMIP6/ScenarioMIP experiment ssp126, conducted with the MPI-ESM1-2-HR on the Mistral supercomputer of the DKRZ. The experiment covers the years 2015 to 2100 and branches from realisations of the CMIP6/CMIP historical experiment. The file format is gzip-compressed GRIB (*.grb.gz). ScenarioMIP website: https://cmip.ucar.edu/scenario-mip ScenarioMIP paper: https://doi.org/10.5194/gmd-9-3461-2016 Experiment description ssp126: SSP-based RCP scenario with low radiative forcing by the end of the century. Following approximately RCP2.6 global forcing pathway with SSP1 socioeconomic conditions. Radiative forcing reaches a level of 2.6 W/m2 in 2100. Concentration-driven.

RCM forcing data from the 2nd realisation (r2i1p1f1) of the CMIP6/ScenarioMIP experiment ssp126, conducted with the MPI-ESM1-2-HR on the Cray supercomputer of the DWD Offenbach. The experiment covers the years 2015 to 2100 and branches from realisations of the CMIP6/CMIP historical experiment. The file format is gzip-compressed GRIB (*.grb.gz). ScenarioMIP website: https://cmip.ucar.edu/scenario-mip ScenarioMIP paper: https://doi.org/10.5194/gmd-9-3461-2016 Experiment description ssp126: SSP-based RCP scenario with low radiative forcing by the end of the century. Following approximately RCP2.6 global forcing pathway with SSP1 socioeconomic conditions. Radiative forcing reaches a level of 2.6 W/m2 in 2100. Concentration-driven.

nuArctic aims at increasing our understanding of the remineralization of nutrients and carbon in the Arctic Ocean and its feedbacks with the Earth System, i.e. the capacity of the Arctic Ocean to be productive and to act as a carbon sink into the future. To do so, the project is proposing modeling advances to increase the robustness of model projections. This project includes global model simulations with the global multi-resolution Finite Volume Sea Ice-Ocean Model (FESOM version 2.1) coupled to the Regulated Ecosystem Model (REcoM version 3, Gürses et al. 2024). For this project, model simulations include a representation of terrigenous inputs from both rivers and coastal erosion and were run from 1970-2100 on a model grid with eddy-permitting (4.5 km) resolution at pan-Arctic scale. The ocean-only model simulations were forced at the ocean surface with 3-hourly atmospheric output from the AWI Climate Model (Semmler et al. 2020). The project includes model experiments under four “Shared Socioeconomic Pathways” emission scenarios, a control run and sensitivity experiments. This work lead to the publication of Oziel et al. 2025 ("Climate change and terrigenous inputs decrease the efficiency of the future Arctic Ocean’s biological carbon pump » in 2025 in Nature Climate Change) which comprises a detailed description of the methods, model experiments and setups but also a publication of the source code and post-processing scripts (Oziel, 2024).

RCM forcing data from the 20 realisations (r11i1p1f1-r30i1p1f1) of the CMIP6/ScenarioMIP experiment ssp126, conducted with the MPI-ESM1-2-LR on the Mistral supercomputer of the DKRZ. The experiment covers the years 2015 to 2100 and branches from realisations of the CMIP6/CMIP historical experiment. The file format is gzip-compressed GRIB (*.grb.gz). ScenarioMIP website: https://cmip.ucar.edu/scenario-mip ScenarioMIP paper: https://doi.org/10.5194/gmd-9-3461-2016 Experiment description ssp126: SSP-based RCP scenario with low radiative forcing by the end of the century. Following approximately RCP2.6 global forcing pathway with SSP1 socioeconomic conditions. Radiative forcing reaches a level of 2.6 W/m2 in 2100. Concentration-driven.

The CCH project (Climate Change and Health in sub-Saharan Africa, https://cch-africa.de) focuses on the rising health impacts of climate change, particularly in sub-Saharan Africa where vulnerable populations are most at risk. Despite the urgency, there has been little collaboration across disciplines to assess these effects or develop effective adaptation strategies. Key health challenges - childhood undernutrition, malaria, and cardiovascular dysfunction—remain under-researched in the context of climate change. As part of this experiment, we generated a global dataset of Wet-Bulb Globe Temperature (WBGT) projections to serve as a bioclimatic indicator for quantifying the potential health impacts of climate change. An ensemble of daily average WBGT estimates based on the primary and secondary ISIMIP3b (https://www.isimip.org) model ensemble (10 models) is provided. The ensemble includes the historical (1850–2014), SSP1-2.6 (2015–2100), SSP3-7.0 (2015–2100), and SSP5-8.5 (2015–2100) experiments. WBGT was estimated on an hourly basis using the PyWBGT Python package (Kong and Huber, 2022, doi:10.1029/2021EF002334), based on the Liljegren method (Liljegren et al., 2008, doi:10.1080/15459620802310770). This method estimates WBGT from 2 m air temperature (tas), near-surface relative humidity (hurs), surface pressure (ps), 10 m wind speed (sfcWind), and surface downward solar radiation (rsds). Hourly values were derived from daily values using an average diurnal cycle calculated separately for each Julian day. WBGT was estimated for each hour and then averaged to obtain daily values. The dataset covers the entire globe, excluding Antarctica. The following 10 models from the ISIMIP3b projects are used: CanESM5 - r1i1p1f1 CNRM-CM6-1 - r1i1p1f2 CNRM-ESM2-1 - r1i1p1f2 EC-Earth3 - r1i1p1f1 GFDL-ESM4 - r1i1p1f1 IPSL-CM6A-LR - r1i1p1f1 MIROC6 - r1i1p1f1 MPI-ESM1-2-HR - r1i1p1f1 MRI-ESM2-0 - r1i1p1f1 UKESM1-0-LL - r1i1p1f2

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.MPI-M.MPI-ESM1-2-LR.ssp126' 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.

RCM forcing data from the 1st realisation (r1i1p1f1) of the CMIP6/ScenarioMIP experiment ssp126, conducted with the MPI-ESM1-2-HR on the Mistral supercomputer of the DKRZ. The experiment covers the years 2015 to 2100 and branches from realisations of the CMIP6/CMIP historical experiment. The file format is gzip-compressed GRIB (*.grb.gz). ScenarioMIP website: https://cmip.ucar.edu/scenario-mip ScenarioMIP paper: https://doi.org/10.5194/gmd-9-3461-2016 Experiment description ssp126: SSP-based RCP scenario with low radiative forcing by the end of the century. Following approximately RCP2.6 global forcing pathway with SSP1 socioeconomic conditions. Radiative forcing reaches a level of 2.6 W/m2 in 2100. Concentration-driven.

RCM forcing data from the 2nd realisation (r2i1p1f1) of the CMIP6/ScenarioMIP experiment ssp126, conducted with the MPI-ESM1-2-HR on the Cray supercomputer of the DWD Offenbach. The experiment covers the years 2015 to 2100 and branches from realisations of the CMIP6/CMIP historical experiment. The file format is gzip-compressed GRIB (*.grb.gz). ScenarioMIP website: https://cmip.ucar.edu/scenario-mip ScenarioMIP paper: https://doi.org/10.5194/gmd-9-3461-2016 Experiment description ssp126: SSP-based RCP scenario with low radiative forcing by the end of the century. Following approximately RCP2.6 global forcing pathway with SSP1 socioeconomic conditions. Radiative forcing reaches a level of 2.6 W/m2 in 2100. Concentration-driven.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.AS-RCEC.TaiESM1.ssp126' 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 Taiwan Earth System Model 1.0 climate model, released in 2018, includes the following components: aerosol: SNAP (same grid as atmos), atmos: TaiAM1 (0.9x1.25 degree; 288 x 192 longitude/latitude; 30 levels; top level ~2 hPa), atmosChem: SNAP (same grid as atmos), land: CLM4.0 (same grid as atmos), ocean: POP2 (320x384 longitude/latitude; 60 levels; top grid cell 0-10 m), seaIce: CICE4. The model was run by the Research Center for Environmental Changes, Academia Sinica, Nankang, Taipei 11529, Taiwan (AS-RCEC) in native nominal resolutions: aerosol: 100 km, atmos: 100 km, atmosChem: 100 km, land: 100 km, ocean: 100 km, seaIce: 50 km.

These data include the subset used by IPCC AR6 WGI authors of the datasets originally published in ESGF for 'CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR.ssp126' 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 AWI-CM 1.1 MR climate model, released in 2018, includes the following components: atmos: ECHAM6.3.04p1 (T127L95 native atmosphere T127 gaussian grid; 384 x 192 longitude/latitude; 95 levels; top level 80 km), land: JSBACH 3.20, ocean: FESOM 1.4 (unstructured grid in the horizontal with 830305 wet nodes; 46 levels; top grid cell 0-5 m), seaIce: FESOM 1.4. The model was run by the Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Am Handelshafen 12, 27570 Bremerhaven, Germany (AWI) in native nominal resolutions: atmos: 100 km, land: 100 km, ocean: 25 km, seaIce: 25 km.