Ensemble of MPI-ESM1-2-HR CMIP6 historical simulations without solar and ozone variability (i.e., set to the year 1850). The simulations are performed within the BMBF project "Solar contribution to climate change on decadal to centennial timescales" (SOLCHECK) of the "Role of the middle atmosphere in climate" (ROMIC II: https://romic2.iap-kborn.de/en/romic/strategy). The experimental setup is identical to the MPI-ESM1-2-HR historical CMIP6 simulations except for the solar and ozone variability. Please refrain from using the following variables since their computations where either erroneous or do not comply with the CMIP6 protocol: Eyr_fracLut, 6hrPlevPt_sfcWind, Amon_mc, CFday_mc, CFmon_dmc, CFmon_smc, CFmon_mcd, CFmon_mcu, Omon_o2sat, Oyr_o2sat, Omon_uo, Omon_umo, Omon_hfx Omon_tauuo Technical details: Ensemble run on bullx B700 Mistral at DKRZ

Ensemble of MPI-ESM1-2-HR CMIP6 historical simulations with low-pass filtered solar and ozone variability (i.e., using a 33-years running-mean). The simulations are performed within the BMBF project "Solar contribution to climate change on decadal to centennial timescales" (SOLCHECK) of the "Role of the middle atmosphere in climate" (ROMIC II: https://romic2.iap-kborn.de/en/romic/strategy). The experimental setup is identical to the MPI-ESM1-2-HR historical CMIP6 simulations except for the solar and ozone variability.

Ensemble of MPI-ESM1-2-HR CMIP6 historical simulations with low-pass filtered solar and ozone variability (i.e., using a 33-years running-mean). The simulations are performed within the BMBF project "Solar contribution to climate change on decadal to centennial timescales" (SOLCHECK) of the "Role of the middle atmosphere in climate" (ROMIC II: https://romic2.iap-kborn.de/en/romic/strategy). The experimental setup is identical to the MPI-ESM1-2-HR historical CMIP6 simulations except for the solar and ozone variability.

Ensemble of MPI-ESM1-2-HR CMIP6 historical simulations without solar and ozone variability (i.e., set to the year 1850). The simulations are performed within the BMBF project "Solar contribution to climate change on decadal to centennial timescales" (SOLCHECK) of the "Role of the middle atmosphere in climate" (ROMIC II: https://romic2.iap-kborn.de/en/romic/strategy). The experimental setup is identical to the MPI-ESM1-2-HR historical CMIP6 simulations except for the solar and ozone variability. Please refrain from using the following variables since their computations where either erroneous or do not comply with the CMIP6 protocol: Eyr_fracLut, 6hrPlevPt_sfcWind, Amon_mc, CFday_mc, CFmon_dmc, CFmon_smc, CFmon_mcd, CFmon_mcu, Omon_o2sat, Oyr_o2sat, Omon_uo, Omon_umo, Omon_hfx Omon_tauuo Technical details: Ensemble run on bullx B700 Mistral at DKRZ

Climate data for adaptation and vulnerability assessments — northwest (ClimAVA-NW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-NW dataset encompasses the geopolitical boundaries of the five states in the northwestern United States: Idaho, Oregon, Wyoming, Montana, and Washington. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

Climate data for adaptation and vulnerability assessments — southwest (ClimAVA-SW) provides bias-corrected, downscaled daily climatic data at ~4km spatial resolution from 17 CMIP6 GCMs, three different climatic variables (pr, tasmax, and tasmin), and three different shared socioeconomic pathways (SSP245, SSP370, and SSP585). Historical runs span from January 1, 1981, to December 31, 2014. Future scenarios span from January 1, 2015, to December 31, 2100. The ClimAVA-SW dataset encompasses the geopolitical boundaries of the six states in the southwestern United States: California, Nevada, Arizona, New Mexico, Utah, and Colorado, as well as watersheds that run into these states. Employing the Spatial Pattern Interactions Downscaling (SPID) method, ClimAVA ensures high-quality downscaling using machine learning models. These models capture the relationship between spatial patterns at Global Circulation Model (GCM) resolution and fine-resolution pixel values derived from the reference data (PRISM 4K). A random forest model is trained for each pixel, using the finer reference data as a predictand and nine pixels from the spatially resampled (coarser) version of the reference data as predictors. These models are then utilized to downscale the bias-corrected GCM data. Results from this method have proven to maintain climate realism and greatly represent extreme events.

The experiment includes the source code, compile and run scripts for ICON-ESM-V1.0 in the configuration “Ruby-0”, the initialization data for ICON-ESM-V1 in the configuration “Ruby-0”, and scripts, libraries, and input data used to produce figures.

These data include supplemental datasets for 'CMIP6.CMIP.AWI.AWI-CM-1-1-MR' 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. Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

These data include supplemental datasets for 'CMIP6.ScenarioMIP.AWI.AWI-CM-1-1-MR' 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. Individuals using the data must abide by terms of use for CMIP6 data (https://pcmdi.llnl.gov/CMIP6/TermsOfUse). The original license restrictions on these datasets were recorded as global attributes in the data files, but these may have been subsequently updated.

The experiment includes the source code, compile and run scripts for ICON-ESM-V1.0 in the configuration “Ruby-0”, the initialization data for ICON-ESM-V1 in the configuration “Ruby-0”, and scripts, libraries, and input data used to produce figures.