This data set provides atmospheric climate input data to be used as input data for ISIMIP3a (www.isimip.org, Frieler et al. 2023, in prep.). We provide four factual climate input datasets for ISIMIP3a: GSWP3-W5E5, 20CRv3-W5E5, 20CRv3-ERA5, and 20CRv3. All of them have daily temporal and 0.5° spatial resolution. Their temporal coverage varies, with GSWP3-W5E5 and 20CRv3-W5E5 covering 1901-2019, while 20CRv3-ERA5 and 20CRv3 cover 1901-2021 and 1901-2015, respectively. For every factual dataset (obsclim) we provide an associated counterfactual dataset (counterclim) covering the same time period, 100 years worth of climate data to be used for model spin-up (spinclim), and 50 years worth of climate data for the transition from spinclim to counterclim or obsclim (transclim).
Version 1.1 fixes missing values in GSWP3-W5E5 snow fall input data (see https://data.isimip.org/caveats/26/).
Version 1.2 adds extractions of grid cell data for different lake sites.
Version 1.3 adds the years 2022-2024 for 20CRv3-ERA5 and obsclim.

Project: Climate Change and Health in sub-Saharan Africa - This Research Unit (RU) addresses the growing public health concern of accelerated disease burden as a consequence of climate change. So far, there have been very limited concerted efforts by public health scientists, climate change researchers, and social scientists to quantify the climate change impacts on human health, and to design appropriate adaptation strategies. This is particularly true for vulnerable populations in sub-Saharan Africa, despite the facts that rural populations in Africa are strongly affected by climate change and exhibit the lowest adaptive capacity. Indeed, this sub-continent faces an unfinished agenda of combating undernutrition and infectious diseases with all the negative societal and economic consequences. At the same time, non-communicable conditions have been rapidly emerging in sub-Saharan Africa over the past decades, and their management now competes with the limited resources of the local health systems. To date, the additional impacts of climate change on three of these major health problems in the region, namely childhood undernutrition, malaria and cardio-vascular dysfunction have been insufficiently defined.Therefore, this RU aims at i) establishing the causal pathways from weather changes through hydrological, agricultural and economic factors to undernutrition, malaria and heat stress among defined rural populations in Burkina Faso and Kenya, ii) projecting future developments along these pathways, iii) quantifying the effectiveness, the socio-economic costs, and the changes in projections of promising climate-specific adaptation strategies, iv) upscaling the historic and projected scenarios from the local to the national level, and finally, v) identifying broader societal impacts related to long-term health consequences of climate change. This project was funded by the German Research Foundation (DFG).Summary: 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 - r1i1p1f1CNRM-CM6-1 - r1i1p1f2CNRM-ESM2-1 - r1i1p1f2EC-Earth3 - r1i1p1f1GFDL-ESM4 - r1i1p1f1IPSL-CM6A-LR - r1i1p1f1MIROC6 - r1i1p1f1MPI-ESM1-2-HR - r1i1p1f1MRI-ESM2-0 - r1i1p1f1UKESM1-0-LL - r1i1p1f2

This dataset covers additional CMIP6-based and bias-adjusted atmospheric climate input data published as secondary input data for ISIMIP3b. Included are datasets from the 5 CMIP6 global climate models that are included in the ISIMIP3b protocol (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) for experiments which are not part of the ISIMIP3b protocol (hist-nat, ssp119, ssp245, ssp460, ssp534-over). Also included are datasets from 5 additional CMIP6 global climate models (CNRM-CM6-1, CNRM-ESM2-1, CanESM5, EC-Earth3, MIROC6) for the experiments of the ISIMIP3b protocol (picontrol, historical, ssp126, ssp370, ssp585) and hist-nat in some cases. For 4 models (CESM2-WACCM, IITM-ESM, KACE-1-0-G, TaiESM1) we provide this data for a subset of variables.
Version 1.1 of this dataset adds files for the ssp534-over scenario.
Version 1.2 of this dataset adds files for huss, hurs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) and prsn (IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) for the ssp245 scenario.
Version 1.3 of this dataset adds files for the ssp119 scenario (GFDL-ESM4: all variables; IPSL-CM6A-LR and MRI-ESM2-0: hurs, huss, prsn, ps) and the ssp460 scenario (IPSL-CM6A-LR and MRI-ESM2-0: hurs, huss, prsn, ps).
Version 1.4 of this dataset adds ps files for the ssp245 scenario.
Version 1.5 of this dataset adds hurs, pr, sfcwind, tas from 4 additional CMIP6 global climate models (CESM2-WACCM, IITM-ESM, KACE-1-0-G, TaiESM1) for 6 experiments (picontrol, historical, ssp126, ssp245, ssp370, ssp585).

This dataset contains pre-processed and bias-adjusted yield data derived from ISIMIP3b output data from different agriculture models (Jägermeyr et al. 2024). The data is described in detail in Jägermeyer et al. 2021.

This dataset contains the national and gridded historical population data for ISIMIP3a (Frieler et al. 2023). Estimates are available for total as well as urban and rural population counts.
With version 1.1, we provide gridded population data, that is based on HYDE v3.3 (private communication with K. Klein Goldewijk, to be published). Just like the original dataset we provide total, rural and urban population per grid cell. Data for all grid cells of a country have been rescaled such that the country's total population matches the numbers provided by the 2019 revision of the UN World Population Prospects (WPP) database (United Nations, 2019).
Version 1.2 corrects a mistake in the coastal regions that led to a too high population in those grid cells.
Version 1.3 extends the national data to 1850. The additional years were derived from the gridded historic population map by means of the fractional ISIMIP country mask.

This dataset contains the national and gridded historical population data for ISIMIP3b (Frieler et al. 2024, in prep.). Estimates are available for total as well as urban and rural population counts.
With version 1.1, we provide gridded population data, that is based on HYDE v3.3 (private communication with K. Klein Goldewijk, to be published) rescaled to the national population counts from the 2019 revision of the UN World Population Prospects (WPP) database (United Nations, 2019). Just like the original dataset we provide total, rural and urban population per grid cell. The data for the historical period is identical to the corresponding ISIMIP3a data, adjusted for the different start and end years.
Version 1.2 extends the national data to 1850. The additional years were derived from the gridded historic population map by means of the fractional ISIMIP country mask.
Note: Combining time series of the gridded population from ISIMIP3b with the historical gridded population data provided under ISIMIP3a needs to be done with caution: On the grid-cell level inconsistencies in the transition between the observational period and the projections (2015-2016) arise, due to differences in the spatial population distribution in HYDE v3.3 and the observational basis dataset (Gridded Population of the World version 3) used for the NCAR projections.

This dataset covers additional CMIP6-based and bias-adjusted atmospheric climate input data published as secondary input data for ISIMIP3b. Included are datasets from the 5 CMIP6 global climate models that are included in the ISIMIP3b protocol (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) for experiments which are not part of the ISIMIP3b protocol (hist-nat, ssp119, ssp245, ssp460, ssp534-over). Also included are datasets from 5 additional CMIP6 global climate models (CNRM-CM6-1, CNRM-ESM2-1, CanESM5, EC-Earth3, MIROC6) for the experiments of the ISIMIP3b protocol (piControl, historical, SSP126, SSP370, SSP585) and hist-nat in some cases.
Version 1.1 of this dataset adds files for the ssp534-over scenario.
Version 1.2 of this dataset adds files for huss, hurs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) and prsn (IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) for the ssp245 scenario.
Version 1.3 of this dataset adds files for the ssp119 scenario (GFDL-ESM4: all variables; IPSL-CM6A-LR and MRI-ESM2-0: hurs, huss, prsn, ps) and the ssp460 scenario (IPSL-CM6A-LR and MRI-ESM2-0: hurs, huss, prsn, ps).
Version 1.4 of this dataset adds ps files for the ssp245 scenario.

This dataset covers additional CMIP6-based and bias-adjusted atmospheric climate input data published as secondary input data for ISIMIP3b. Included are datasets from the 5 CMIP6 global climate models that are included in the ISIMIP3b protocol (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) for experiments which are not part of the ISIMIP3b protocol (hist-nat, ssp119, ssp245, ssp460, ssp534-over). Also included are datasets from 5 additional CMIP6 global climate models (CNRM-CM6-1, CNRM-ESM2-1, CanESM5, EC-Earth3, MIROC6) for the experiments of the ISIMIP3b protocol (piControl, historical, SSP126, SSP370, SSP585) and hist-nat in some cases.
Version 1.1 of this dataset adds files for the ssp534-over scenario.
Version 1.2 of this dataset adds files for huss, hurs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) and prsn (IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) for the ssp245 scenario.
Version 1.3 of this dataset adds files for the ssp119 scenario (GFDL-ESM4: all variables; IPSL-CM6A-LR and MRI-ESM2-0: hurs, huss, prsn, ps) and the ssp460 scenario (IPSL-CM6A-LR and MRI-ESM2-0: hurs, huss, prsn, ps).

This dataset covers additional CMIP6-based and bias-adjusted atmospheric climate input data published as secondary input data for ISIMIP3b. Included are datasets from the 5 CMIP6 global climate models that are included in the ISIMIP3b protocol (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) for experiments which are not part of the ISIMIP3b protocol (hist-nat, ssp119, ssp245, ssp460, ssp534-over). Also included are datasets from 5 additional CMIP6 global climate models (CNRM-CM6-1, CNRM-ESM2-1, CanESM5, EC-Earth3, MIROC6) for the experiments of the ISIMIP3b protocol (piControl, historical, SSP126, SSP370, SSP585) and hist-nat in some cases.
Version 1.1 of this dataset adds files for the ssp534-over scenario.
Version 1.2 of this dataset adds files for the huss, hurs (GFDL-ESM4, IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) and prsn (IPSL-CM6A-LR, MPI-ESM1-2-HR, MRI-ESM2-0, UKESM1-0-LL) for the ssp245 scenario.

This data set provides atmospheric climate input data to be used as input data for ISIMIP3a (www.isimip.org, Frieler et al. 2023, in prep.). We provide four factual climate input datasets for ISIMIP3a: GSWP3-W5E5, 20CRv3-W5E5, 20CRv3-ERA5, and 20CRv3. All of them have daily temporal and 0.5° spatial resolution. Their temporal coverage varies, with GSWP3-W5E5 and 20CRv3-W5E5 covering 1901-2019, while 20CRv3-ERA5 and 20CRv3 cover 1901-2021 and 1901-2015, respectively. For every factual dataset (obsclim) we provide an associated counterfactual dataset (counterclim) covering the same time period, 100 years worth of climate data to be used for model spin-up (spinclim), and 50 years worth of climate data for the transition from spinclim to counterclim or obsclim (transclim).
Version 1.1 fixes missing values in GSWP3-W5E5 snow fall input data (see https://data.isimip.org/caveats/26/).
Version 1.2 adds extractions of grid cell data for different lake sites.