This directory contains volcanic SO2 data derived from limb viewing satellites for the lower stratosphere from 1990 to 2019. The usage of the data is described in Timmreck et al., (2018), datasets VolcDB1 and VolcDB1_3D. We provide 3D-plumes of observed volume mixing ratio perturbations in the lower stratosphere / upper troposphere typically derived from 10-day periods as nc-file and integrated values of injected SO2 mass with peak latitudes and altitudes as Fortran formatted ascii file (33X,A11,5X,6(I3,1X),I4,1X,5(I3,1X),6(I3,1X),I5,1X,4(I3,1X),I3) for at maximum 6 events at one time. Instead of A11 I2,A4,I5 can be used to read in the components of time. The data from Jan. 1990 to Jan. 2002 are based on L2-files of SAGE II (V7.0) provided by the NASA DAAC (Thomason et al., 2008). The data from Jul. 2002 to Mar. 2012 use the updated 5-day time series of MIPAS (Hoepfner et al., 2015), supplemented by SO2 derived from GOMOS extinctions (Bingen et al., 2017, with a corresponding table, scaled for lower resolution). After March 2012 based on OSIRIS (Rieger et al., 2019). volc_SO2-3D-vmr-perturbation-1990-2019.nc: 3D SO2 for 258 days with eruptions in T63L90 resolution (ECHAM-grid in grid-T63L90.nc). Latitude from South to North, for use with ECHAM please reverse. The levels on the hybrid-grid in the grid files are defined as lev(x,y,z)=hyam(z)+hybm(z)*apsave(x,y), in Pa (apsave annual average of surface pressure or orography), surface to 80km (update of VolcDB1_3D). This version contains the factors of Brühl et al. (2018) for MIPAS included in the ascii-file with the integrals and which were missing in Version 2 (SSIRC_2). volc-so2-inventory.ps: plot of zonal averages of SO2 perturbation at 3 altitudes (gaps not shown, widths of bars have no meaning). volc-SO2-mass.txt: integrated SO2 mass injected (in kt), SAGE, ENVISAT and OSIRIS period (update of VolcDB1). The volcano names are in the first column, see also http://www.volcano.si.edu (Smithsonian volcano database), Schallock et al. (2021) and SSIRC_1 (doi:10.1594/WDCC/SSIRC_1). AEROCOM-DIEHL-degassing-volc-SO2.nc: Fluxes from outgassing volcanoes in the troposphere (below 210hPa), taken from AEROCOM (Diehl et al., 2012). Caution, filled with odd climatology after 2009, monthly (subset beginning Jan. 1990). volc-globalforcing-tropo.nc: EMAC results for instanteneous global radiative radiative forcing by stratospheric aerosol near the tropopause (in W/m2), figure see Schallock et al. (2021)

preindustrial Control experiment to be used in VolMIP analyses. The piControl experiment is the CMIP6-DECK piControl experiment described in Eyring et al. (2016). piControl provides initial climate states that are sampled to start most of VolMIP experiments (Zanchettin et al., 2016). The dataset contains monthly values of selected variables spatially averaged over four regions. These are the full globe (GL), the Northern Hemisphere extratropics (30°-90°N, NH), the tropics (30°S-30°N, TR), and the Southern Hemisphere (30°-90°S, hereafter SH). The considered variables have the following cmor names: hfls, hfss, pr, rlds, rldscs, rlus, rlut, rlutcs, rsds, rsdscs, rsdt, rsus, rsut, rsutcs, tas. Additionally, the climate indices NAO and Nino34 are part of the dataset. Considered models are CanESM5, IPSL-CM6A-LR, GISS-E2.1-G, MIROC-ES2L, MPI-ESM1.2-LR (named MPI-ESM-LR in the files of this dataset) and UKESM1. Considered experiments are piControl and volc-pinatubo-full, with initial date and final date as specified for each model in Zanchettin et al. (2021). Different realizations are considered for the participating models depending on availability.

Idealized volcanic-forcing coupled climate model experiment using the 1991 Pinatubo forcing as used in the CMIP6 historical simulations. It is a Tier 1 (mandatory) VolMIP experiment based on a large ensemble of short-term “Pinatubo” climate simulations aimed at accurately estimating simulated responses to volcanic forcing that may be comparable to the amplitude of internal interannual climate variability. Initialization is based on equally distributed predefined states of ENSO (cold/neutral/warm states) and of the North Atlantic Oscillation (NAO, negative/neutral/positive states). Sampling of an eastern phase of the Quasi-Biennial Oscillation (QBO), as observed after the 1991 Pinatubo eruption, is preferred for those models that spontaneously generate such mode of stratospheric variability. VIRF diagnostics must be calculated for this experiment for the whole integration and for all ensemble members, as these are required for the “volc-pinatubo-strat”/“surf” experiments. A minimum length of integration of 3 years is requested. Details about the experiment are provided by Zanchettin et al. (2016). The dataset contains monthly values of selected variables spatially averaged over four regions. These are the full globe (GL), the Northern Hemisphere extratropics (30°-90°N, NH), the tropics (30°S-30°N, TR), and the Southern Hemisphere (30°-90°S, hereafter SH). The considered variables have the following cmor names: hfls, hfss, pr, rlds, rldscs, rlus, rlut, rlutcs, rsds, rsdscs, rsdt, rsus, rsut, rsutcs, tas. Additionally, the climate indices NAO and Nino34 are part of the dataset. Considered models are CanESM5, IPSL-CM6A-LR, GISS-E2.1-G, MIROC-ES2L, MPI-ESM1.2-LR (named MPI-ESM-LR in the files of this dataset) and UKESM1. Considered experiments are piControl and volc-pinatubo-full, with initial date and final date as specified for each model in Zanchettin et al. (2021). Different realizations are considered for the participating models depending on availability.

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) and Regional Earth System Models (RESMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary from region to region. Besides land-surface processes, the most crucial processes to be parameterized in ESMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model (Version v3.9) for the period 2006-2010 in a resolution of 0.1 degree horizontal resolution. Conclusions about suitable physical parameterization schemes may vary within the study area. We therefore want to stimulate the development of own performance evaluation studies for climate simulations or subsequent impact studies over specific (sub-)regions in NSSA. For this reason, selected climate surface variables of the physics ensemble (i.e. the 16 experiments from 2006-2010) are provided. For more information about the setup of the experiments, please see: Laux et al., 2021: A high-resolution regional climate model physics ensemble for Northern sub-Saharan Africa. Frontiers in Earth Science (under revision).

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) and Regional Earth System Models (RESMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary from region to region. Besides land-surface processes, the most crucial processes to be parameterized in ESMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model (Version v3.9) for the period 2006-2010 in a resolution of 0.1 degree horizontal resolution. Conclusions about suitable physical parameterization schemes may vary within the study area. We therefore want to stimulate the development of own performance evaluation studies for climate simulations or subsequent impact studies over specific (sub-)regions in NSSA. For this reason, selected climate surface variables of the physics ensemble (i.e. the 16 experiments from 2006-2010) are provided. For more information about the setup of the experiments, please see: Laux et al., 2021: A high-resolution regional climate model physics ensemble for Northern sub-Saharan Africa. Frontiers in Earth Science (under revision).

While climate information from General Circulation Models (GCMs) are usually too coarse for climate impact modelers or decision makers from various disciplines (e.g., hydrology, agriculture), Regional Climate Models (RCMs) and Regional Earth System Models (RESMs) provide feasible solutions for downscaling GCM output to finer spatiotemporal scales. However, it is well known that the model performance depends largely on the choice of the physical parameterization schemes, but optimal configurations may vary from region to region. Besides land-surface processes, the most crucial processes to be parameterized in ESMs include radiation (RA), cumulus convection (CU), cloud microphysics (MP), and planetary boundary layer (PBL), partly with complex interactions. Before conducting long-term climate simulations, it is therefore indispensable to identify a suitable combination of physics parameterization schemes for these processes. Using the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis product ERA-Interim as lateral boundary conditions, we derived an ensemble of 16 physics parameterization runs for a larger domain in Northern sub-Saharan Africa (NSSA), northwards of the equator, using two different CU-, MP-, PBL-, and RA schemes, respectively, using the Weather Research and Forecasting (WRF) model (Version v3.9) for the period 2006-2010 in a resolution of 0.1 degree horizontal resolution. Conclusions about suitable physical parameterization schemes may vary within the study area. We therefore want to stimulate the development of own performance evaluation studies for climate simulations or subsequent impact studies over specific (sub-)regions in NSSA. For this reason, selected climate surface variables of the physics ensemble (i.e. the 16 experiments from 2006-2010) are provided. For more information about the setup of the experiments, please see: Laux et al., 2021: A high-resolution regional climate model physics ensemble for Northern sub-Saharan Africa. Frontiers in Earth Science (under revision).

The data contains the emission variation simulations which build the lookup-tables for TransClim. Eleven emission regions are defined: Germany, Western Europe, Northern Europe, Eastern Europe, Southern Europe, China, India, Southeast Asia, Japan/South Korea, North America and South America. In each of these emission regions, the road traffic emissions of nitrogen oxide (NOx), volatile organic compounds (VOC) and carbon monooxide (CO) are varied and the resulting climate response is calculated with the global chemistry climate model EMAC.

The data of this experiment have been used in (Hagemann et al., 2020). It comprise daily data of surface runoff and subsurface runoff (drainage) from JSBACH and MPI-HM and simulated daily discharges (river runoff). To generate river runoff, the Hydrological discharge (HD) model (Hagemann et al., 2020; Hagemann and Ho-Hagemann, 2021) was used that was operated at 5 arc minutes horizontal resolution. Different to the published version of HD model parameters (5.0) on Zenodo, an earlier version (4.0) of flow directions and model parameters has been used that is provided as an auxiliary data file. The HD model was set up over the European domain covering the land areas between -11°W to 69°E and 27°N to 72°N. First, the respective forcing data of surface and sub-surface runoff were interpolated to the HD model domain using conservative remapping. Then, daily discharges were simulated with the HD model for the period 1979-2009 (1999-2009 for HD5-MESCAN). In addition, daily discharges were analogously simulated using only JSBACH forcing with the global 0.5° version 1.10 of the HD model. The associated flow directions and model parameters of vs. 1.10 are provided as an auxiliary data file. The HD forcing data are: a) HD5-JSBACH In order to generate daily input fields of surface runoff and drainage, the land surface scheme JSBACH (vs. 3 + frozen soil physics; (Ekici et al., 2014)) was forced globally at 0.5° with daily atmospheric forcing data based on the Interim Re-Analysis of the European Centre for Medium-Range Weather Forecast (ERA-Interim; (Dee et al., 2011)). These forcing data are bias-corrected (see (Beer et al., 2014)) towards the so-called WATCH forcing data (WFD; (Weedon et al., 2011)) that have been generated in the EU project WATCH. b) HD5-MPIHM The MPI-M hydrology model MPI-HM (Stacke and Hagemann, 2012) was driven by daily WATCH forcing data based on ERA-Interim (WFDEI; (Weedon et al., 2014)) from 1979-2009 to generate daily input fields of surface runoff and drainage at global 0.5° resolution. c) HD5-MESCAN Six hourly data of surface runoff and drainage (variable name: percolation) were retrieved from the MESCAN-SURFEX regional surface reanalysis (Bazile et al., 2017) created in the EU project UERRA (Uncertainties in Ensembles of Regional ReAnalysis; www.uerra.eu). SURFEX (Masson et al., 2013) is a land surface platform that was driven by atmospheric forcing at 5.5 km. The forcing comprises 24h-precipitation, near-surface temperature and relative humidity analyzed by the MESCAN surface analysis system as well as radiative fluxes and wind downscaled at 5.5 km from the 3DVar re-analysis conducted with the HARMONIE system at 11 km (Ridal et al., 2017). The latter has been generated using six-hourly fields of the ERA-Interim reanalysis as boundary conditions and covers a domain comprising Europe and parts of the Atlantic, which is similar to the European domain of the Coordinated Downscaling Experiment (CORDEX) at 11 km.

This experiment comprises data that have been used in Hagemann et al. (submitted). It comprises daily data of surface runoff and subsurface runoff from HydroPy and simulated daily discharges (river runoff) of the HD model. The discharge data close the water cycle at the land-ocean interface so that the discharges can be used as lateral freshwater input for ocean models applied in the European region. a) HD5-ERA5 ERA5 is the fifth generation of atmospheric reanalysis (Hersbach et al., 2020) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). It provides hourly data on many atmospheric, land-surface, and sea-state parameters at about 31 km resolution. The global hydrology model HydroPy (Stacke and Hagemann, 2021) was driven by daily ERA5 forcing data from 1979-2018 to generate daily input fields of surface and subsurface runoff at the ERA5 resolution. It uses precipitation and 2m temperature directly from the ERA5 dataset. Furthermore, potential evapotranspiration (PET) was calculated from ERA5 data in a pre-processing step and used as an additional forcing for HydroPy. Here, we applied the Penman-Monteith equation to calculate a reference evapotranspiration following (Allen et al., 1998) that was improved by replacing the constant value for albedo with a distributed field from the LSP2 dataset (Hagemann, 2002). In order to initialize the storages in the HydroPy model and to avoid any drift during the actual simulation period, we conducted a 50-years spin-up simulation by repeatedly using year 1979 of the ERA5 dataset as forcing. To generate river runoff, the Hydrological discharge (HD) model (Hagemann et al., 2020; Hagemann and Ho-Hagemann, 2021) was used that was operated at 5 arc minutes horizontal resolution. The HD model was set up over the European domain covering the land areas between -11°W to 69°E and 27°N to 72°N. First, the forcing data of surface and sub-surface runoff simulated by HydroPy were interpolated to the HD model grid. Then, daily discharges were simulated with the HD model. b) HD5-EOBS The E-OBS dataset (Cornes et al., 2018) comprises several daily gridded surface variables at 0.1° and 0.25° resolution over Europe covering the area 25°N-71.5°N x 25°W-45°E. The dataset has been derived from station data collated by the ECA&D (European Climate Assessment & Dataset) initiative (Klein Tank et al., 2002; Klok and Klein Tank, 2009). In the present study, we use the best-guess fields of precipitation and 2m temperature of vs. 22 (EOBS22) at 0.1° resolution for the years 1950-2018. HydroPy was driven by daily EOBS22 data of temperature and precipitation at 0.1° resolution from 1950-2019. The potential evapotranspiration (PET) was calculated following the approach proposed by (Thornthwaite, 1948) including an average day length at a given location. As for HD5-ERA5, the forcing data of surface and sub-surface runoff simulated by HydroPy were first interpolated to the HD model grid. Then, daily discharges were simulated with the HD model. Main reference: Hagemann, S., Stacke, T. Complementing ERA5 and E-OBS with high-resolution river discharge over Europe. Oceanologia. Submitted.

The Bias Corrected CESMv1 data for current (2006-2015) and future (2091-2100) for RCP8.5 emission scenario at coarser resolution has been downscaled to 10km resolution over India using the Weather Research and Forecasting (WRF) model. The climate variables included are 2m Temperature, relative humidity, wind speed, total precipitation, mean surface shortwave flux, top-of-atmosphere outgoing longwave radiation, mean surface latent and sensible heat fluxes along with the latitude, longitude, and time information. The dataset covers the Indian National Territory region at a 369 x 369 grid. The data is available at three temporal resolutions: Daily TS, Monthly TS, and Monthly Climatology. The dataset has been structured into a total of 60 files (10 variables x 3 temporal resolutions x 2 periods packed in self-explanatory NetCDF format. The daily, monthly, and monthly climatology files contain 369x369x3650, 369x369x30, and 369x369x12 data points, respectively. The entire dataset is about 100 GB in size. The WRF version used for this project is WRF 3.8.1. . The WRF-ARW source codes and suitable tutorials are available free to users as an open-source model in the NCAR’s https://www2.mmm.ucar.edu/wrf/users/download/get_sources.html website.