We utilize the ICON version 2.6.3 with upper-atmosphere extension as distributed by the German weather service (DWD). The ICON model is a collaborative project of DWD and the Max Planck Institute for Meteorology, striving at providing a unified modeling system to seamlessly allow simulations from climatological time scales to large-eddy simulations as well as for global numerical weather prediction (Zangl et al., 2015). In addition to the upper-atmosphere physics package implemented in UA-ICON, the dynamical core is extended from the shallow to deep atmosphere dynamics (Borchert et al., 2019). In our setup, the UA-ICON model is set up with the horizontal resolution of R2B4, which corresponds to a grid mesh of approximately 160 km with 120 levels up to a height of approximately 147 km. The time step of the simulation is 360s. The data output interval is set to 6 h, which is essential in the calculations of the Eliassen-Palm (EP) flux divergence. The mid-monthly sea surface temperature (SST) and sea ice concentration (SIC) values produced by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for the Atmosphere Model Intercomparison Project (AMIP, Taylor et al. (2000)) served as lower boundary condition data. Interactive chemistry was not used in the current simulation. Instead, the concentrations of CO2, CH4, N2O, CFC-11, and CFC-12 were taken from the historical greenhouse gas volume mixing ratios for CMIP6 (Meinshausen et al., 2017). The atmospheric ozone concentrations were prescribed based on the input4MIPs project (https://esgf-node.llnl.gov/search/input4mips). Here we conduct 30-year long time-slice experiments with the UA-ICON model by employing repeated annual cycles of SST, SIC, and greenhouse gases of the year 1985. This year is appointed as both El-Nino southern oscillation and Pacific decadal oscillation were in their neutral phase and no major volcano eruption has occurred, hence conditions in this year can serve as a useful proxy for the multi-year mean conditions and an estimate of their internal variability. The control run is carried out where both the sub-grid scale orography (SSO) scheme and non-orographic gravity waves scheme are used. In UA-ICON, the entire SSO drag is treated after Lott and Miller (1997), and the non-orographic GW drag parameterization is based on Warner and McIntyre (1996) and Scinocca (2003). It is worthwhile to mention that in addition to the orographic GWD, the SSO scheme used in UA-ICON also contains the effect of low-level blocking and wake drag. As the simulations are the time-slice experiments, only the months , days and hours have their true/usual meanings and years in the name of output files do not have their true/usual meaning. In other words, all the outputs files with different years (1986, 1987, ..., 2015) are identical to year 1985 as identical boundary conditions as year 1985 are used to simulate them. For example, 0001 and 0002 represent the first and second years of simulations, respectively and 0030 is the last (30) year.

We utilize the ICON version 2.6.3 with upper-atmosphere extension as distributed by the German weather service (DWD). The ICON model is a collaborative project of DWD and the Max Planck Institute for Meteorology, striving at providing a unified modeling system to seamlessly allow simulations from climatological time scales to large-eddy simulations as well as for global numerical weather prediction (Zangl et al., 2015). In addition to the upper-atmosphere physics package implemented in UA-ICON, the dynamical core is extended from the shallow to deep atmosphere dynamics (Borchert et al., 2019). In our setup, the UA-ICON model is set up with the horizontal resolution of R2B4, which corresponds to a grid mesh of approximately 160 km with 120 levels up to a height of approximately 147 km. The time step of the simulation is 360s. The data output interval is set to 6 h, which is essential in the calculations of the Eliassen-Palm (EP) flux divergence. The mid-monthly sea surface temperature (SST) and sea ice concentration (SIC) values produced by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for the Atmosphere Model Intercomparison Project (AMIP, Taylor et al. (2000)) served as lower boundary condition data. Interactive chemistry was not used in the current simulation. Instead, the concentrations of CO2, CH4, N2O, CFC-11, and CFC-12 were taken from the historical greenhouse gas volume mixing ratios for CMIP6 (Meinshausen et al., 2017). The atmospheric ozone concentrations were prescribed based on the input4MIPs project (https://esgf-node.llnl.gov/search/input4mips). Here we conduct 30-year long time-slice experiments with the UA-ICON model by employing repeated annual cycles of SST, SIC, and greenhouse gases of the year 1985. This year is appointed as both El-Nino southern oscillation and Pacific decadal oscillation were in their neutral phase and no major volcano eruption has occurred, hence conditions in this year can serve as a useful proxy for the multi-year mean conditions and an estimate of their internal variability. The non-orographic gravity waves scheme is not used in this simulation. In UA-ICON, the entire SSO drag is treated after Lott and Miller (1997), and the non-orographic GW drag parameterization is based on Warner and McIntyre (1996) and Scinocca (2003). It is worthwhile to mention that in addition to the orographic GWD, the SSO scheme used in UA-ICON also contains the effect of low-level blocking and wake drag. As the simulations are the time-slice experiments, only the months, days and hours have their true/usual meanings and years in the name of output files do not have their true/usual meaning. In other words, all the outputs files with different years (1986, 1987, ..., 2015) are identical to year 1985 as identical boundary conditions as year 1985 are used to simulate them. For example, 0001 and 0002 represent the first and second years of simulations, respectively and 0030 is the last (30) year.

We utilize the ICON version 2.6.3 with upper-atmosphere extension as distributed by the German weather service (DWD). The ICON model is a collaborative project of DWD and the Max Planck Institute for Meteorology, striving at providing a unified modeling system to seamlessly allow simulations from climatological time scales to large-eddy simulations as well as for global numerical weather prediction (Zangl et al., 2015). In addition to the upper-atmosphere physics package implemented in UA-ICON, the dynamical core is extended from the shallow to deep atmosphere dynamics (Borchert et al., 2019). In our setup, the UA-ICON model is set up with the horizontal resolution of R2B4, which corresponds to a grid mesh of approximately 160 km with 120 levels up to a height of approximately 147 km. The time step of the simulation is 360s. The data output interval is set to 6 h, which is essential in the calculations of the Eliassen-Palm (EP) flux divergence. The mid-monthly sea surface temperature (SST) and sea ice concentration (SIC) values produced by the Program for Climate Model Diagnosis and Intercomparison (PCMDI) for the Atmosphere Model Intercomparison Project (AMIP, Taylor et al. (2000)) served as lower boundary condition data. Interactive chemistry was not used in the current simulation. Instead, the concentrations of CO2, CH4, N2O, CFC-11, and CFC-12 were taken from the historical greenhouse gas volume mixing ratios for CMIP6 (Meinshausen et al., 2017). The atmospheric ozone concentrations were prescribed based on the input4MIPs project (https://esgf-node.llnl.gov/search/input4mips). Here we conduct 30-year long time-slice experiments with the UA-ICON model by employing repeated annual cycles of SST, SIC, and greenhouse gases of the year 1985. This year is appointed as both El-Nino southern oscillation and Pacific decadal oscillation were in their neutral phase and no major volcano eruption has occurred, hence conditions in this year can serve as a useful proxy for the multi-year mean conditions and an estimate of their internal variability. The non-orographic gravity waves scheme is not used in this simulation. In UA-ICON, the entire SSO drag is treated after Lott and Miller (1997), and the non-orographic GW drag parameterization is based on Warner and McIntyre (1996) and Scinocca (2003). It is worthwhile to mention that in addition to the orographic GWD, the SSO scheme used in UA-ICON also contains the effect of low-level blocking and wake drag. As the simulations are the time-slice experiments, only the months , days and hours have their true/usual meanings and years in the name of output files do not have their true/usual meaning. In other words, all the outputs files with different years (1986, 1987, ..., 2015) are identical to year 1985 as identical boundary conditions as year 1985 are used to simulate them. For example, 0001 and 0002 represent the first and second years of simulations, respectively and 0030 is the last (30) year.

This experiment provides output from a six-member ensemble simulation using the UA-ICON (Upper Atmosphere ICON) general circulation model, version 2.6.6. The model setup uses a high-top configuration with 120 vertical levels extending to ~150 km altitude and a horizontal resolution of ICON R2B4 (~160 km), allowing detailed representation of processes in the troposphere, stratosphere, mesosphere, and lower thermosphere. The experiment uses a time step of 360 seconds and includes a comprehensive physics package (ICON numerical weather prediction, NWP, package), notably gravity wave (GW) parameterizations for both subgrid-scale orographic (SSO) and non-orographic (NO) sources. Radiative transfer is handled via the ecRad radiation scheme. The experiment includes six ensemble members to account for internal atmospheric variability. Initial conditions are based on ERA5 climatology (1979–2022). Ensemble 1 uses the mean January 1 state from ERA5 data (1979–2022), while ensembles 2–6 each exclude one year (1984, 1992, 2000, 2008, or 2016) to introduce slight variations in the initial conditions. Each ensemble simulation spans 30 years, with the first year treated as spin-up and excluded from output, resulting in a data range from 1991-01-01 to 2019-12-31 (arbitrarily numbered dates). All simulations are conducted under seasonally repeating boundary conditions to represent a stationary present-day climate. Sea surface temperature and sea ice are based on ERA5 climatology (1979–2022), greenhouse gas concentrations follow CMIP6 historical means (1979–2020), and ozone climatology is derived from MACC and GEMS datasets. The control simulation (C) was designed with no transient external forcing, serving as a reference dataset for analyzing current-climate circulation patterns and variability, especially in the middle and upper atmosphere. It also provides a foundation for sensitivity experiments targeting the role of regional orographic and non-orographic gravity wave forcing. Daily-mean atmospheric fields are stored as monthly NetCDF files over the global domain on model levels. Variables include 3D fields (temperature, winds, pressure, vertical velocity), GW drag tendencies (SSO and NO), and surface values such as 2-meter temperature and surface pressure.

This experiment provides output from a six-member ensemble simulation using the UA-ICON (Upper Atmosphere ICON) general circulation model, version 2.6.6. The model setup uses a high-top configuration with 120 vertical levels extending to ~150 km altitude and a horizontal resolution of ICON R2B4 (~160 km), allowing detailed representation of processes in the troposphere, stratosphere, mesosphere, and lower thermosphere. The experiment uses a time step of 360 seconds and includes a comprehensive physics package (ICON numerical weather prediction, NWP, package), notably gravity wave (GW) parameterizations for both subgrid-scale orographic (SSO) and non-orographic (NO) sources. Radiative transfer is handled via the ecRad radiation scheme. The experiment includes six ensemble members to account for internal atmospheric variability. Initial conditions are based on ERA5 climatology (1979–2022). Ensemble 1 uses the mean January 1 state from ERA5 data (1979–2022), while ensembles 2–6 each exclude one year (1984, 1992, 2000, 2008, or 2016) to introduce slight variations in the initial conditions. Each ensemble simulation spans 30 years, with the first year treated as spin-up and excluded from output, resulting in a data range from 1991-01-01 to 2019-12-31 (arbitrarily numbered dates). All simulations are conducted under seasonally repeating boundary conditions to represent a stationary present-day climate. Sea surface temperature and sea ice are based on ERA5 climatology (1979–2022), greenhouse gas concentrations follow CMIP6 historical means (1979–2020), and ozone climatology is derived from MACC and GEMS datasets. The key experimental perturbation is an artificial tenfold enhancement of the stratospheric SSO drag component within the East Asia region (EA: 30°–60°N, 110°–175°E). The scaling factor of 10 was determined experimentally, ensuring that the enhanced drag remains within the range of natural variability and preserves realistic dynamical forcing. It is the only applied external perturbation in this experiment. This approach is designed to isolate the influence of this known GW hotspot on atmospheric dynamics and circulation patterns. The dataset is well-suited for studying East Asia stratospheric GW forcing effects on stratospheric variability, polar vortex dynamics, and for quantifying signal-to-noise characteristics via ensemble analysis. Daily-mean atmospheric fields are stored as monthly NetCDF files over the global domain on model levels. Variables include 3D fields (temperature, winds, pressure, vertical velocity), GW drag tendencies (SSO and NO), and surface values such as 2-meter temperature and surface pressure.

This experiment provides output from a six-member ensemble simulation using the UA-ICON (Upper Atmosphere ICON) general circulation model, version 2.6.6. The model setup uses a high-top configuration with 120 vertical levels extending to ~150 km altitude and a horizontal resolution of ICON R2B4 (~160 km), allowing detailed representation of processes in the troposphere, stratosphere, mesosphere, and lower thermosphere. The experiment uses a time step of 360 seconds and includes a comprehensive physics package (ICON numerical weather prediction, NWP, package), notably gravity wave (GW) parameterizations for both subgrid-scale orographic (SSO) and non-orographic (NO) sources. Radiative transfer is handled via the ecRad radiation scheme. The experiment includes six ensemble members to account for internal atmospheric variability. Initial conditions are based on ERA5 climatology (1979–2022). Ensemble 1 uses the mean January 1 state from ERA5 data (1979–2022), while ensembles 2–6 each exclude one year (1984, 1992, 2000, 2008, or 2016) to introduce slight variations in the initial conditions. Each ensemble simulation spans 30 years, with the first year treated as spin-up and excluded from output, resulting in a data range from 1991-01-01 to 2019-12-31 (arbitrarily numbered dates). All simulations are conducted under seasonally repeating boundary conditions to represent a stationary present-day climate. Sea surface temperature and sea ice are based on ERA5 climatology (1979–2022), greenhouse gas concentrations follow CMIP6 historical means (1979–2020), and ozone climatology is derived from MACC and GEMS datasets. The key experimental perturbation is an artificial tenfold enhancement of the stratospheric SSO drag component within the Himalayan region (HI: 25°–45°N, 70°–100°E). The scaling factor of 10 was determined experimentally, ensuring that the enhanced drag remains within the range of natural variability and preserves realistic dynamical forcing. It is the only applied external perturbation in this experiment. This approach is designed to isolate the influence of this known GW hotspot on atmospheric dynamics and circulation patterns. The dataset is well-suited for studying Himalayan stratospheric gravity wave forcing effects on stratospheric variability, polar vortex dynamics, and for quantifying signal-to-noise characteristics via ensemble analysis. Daily-mean atmospheric fields are stored as monthly NetCDF files over the global domain on model levels. Variables include 3D fields (temperature, winds, pressure, vertical velocity), GW drag tendencies (SSO and NO), and surface values such as 2-meter temperature and surface pressure.

This experiment provides output from a six-member ensemble simulation using the UA-ICON (Upper Atmosphere ICON) general circulation model, version 2.6.6. The model setup uses a high-top configuration with 120 vertical levels extending to ~150 km altitude and a horizontal resolution of ICON R2B4 (~160 km), allowing detailed representation of processes in the troposphere, stratosphere, mesosphere, and lower thermosphere. The experiment uses a time step of 360 seconds and includes a comprehensive physics package (ICON numerical weather prediction, NWP, package), notably gravity wave (GW) parameterizations for both subgrid-scale orographic (SSO) and non-orographic (NO) sources. Radiative transfer is handled via the ecRad radiation scheme. The experiment includes six ensemble members to account for internal atmospheric variability. Initial conditions are based on ERA5 climatology (1979–2022). Ensemble 1 uses the mean January 1 state from ERA5 data (1979–2022), while ensembles 2–6 each exclude one year (1984, 1992, 2000, 2008, or 2016) to introduce slight variations in the initial conditions. Each ensemble simulation spans 30 years, with the first year treated as spin-up and excluded from output, resulting in a data range from 1991-01-01 to 2019-12-31 (arbitrarily numbered dates). All simulations are conducted under seasonally repeating boundary conditions to represent a stationary present-day climate. Sea surface temperature and sea ice are based on ERA5 climatology (1979–2022), greenhouse gas concentrations follow CMIP6 historical means (1979–2020), and ozone climatology is derived from MACC and GEMS datasets. The key experimental perturbation is an artificial tenfold enhancement of the stratospheric SSO drag component within the Northwest America region (NA: 30°–60°N, 100°–130°W). The scaling factor of 10 was determined experimentally, ensuring that the enhanced drag remains within the range of natural variability and preserves realistic dynamical forcing. It is the only applied external perturbation in this experiment. This approach is designed to isolate the influence of this known GW hotspot on atmospheric dynamics and circulation patterns. In this experiment, Unlike the other ensemble members, which span 30 years, ensemble member 5 terminated early in 2005 due to numerical instability. the output of the ensemble is archived from 1991 onward, excluding the spin-up year. The dataset is well-suited for studying Northwest America stratospheric GW forcing effects on stratospheric variability, polar vortex dynamics, and for quantifying signal-to-noise characteristics via ensemble analysis. Daily-mean atmospheric fields are stored as monthly NetCDF files over the global domain on model levels. Variables include 3D fields (temperature, winds, pressure, vertical velocity), GW drag tendencies (SSO and NO), and surface values such as 2-meter temperature and surface pressure.

Determining concentrations of cloud condensation nuclei (CCN) is one of the first steps in the chain in analysis of cloud droplet formation, the direct microphysical link between aerosols and cloud droplets, a process key for aerosol-cloud interactions (ACI). However, due to sparse coverage of in-situ measurements and difficulties associated with retrievals from satellites, a global exploration of their magnitude, source, temporal and spatial distribution cannot be easily obtained. Thus, a better representation of CCN is one of the goals for quantifying ACI processes and achieving uncertainty reduced estimates of their associated radiative forcing. Here, we introduce a new CCN dataset which is derived based on aerosol mass mixing ratios from the latest Copernicus Atmosphere Monitoring Service (CAMS) reanalysis (RA: EAC4) in a diagnostic model that uses CAMSRA aerosol properties and a simplified kappa-Köhler framework suitable for global models. The emitted aerosols in CAMS are not only based on input from emission inventories using aerosol observations, they also have a strong tie to satellite-retrieved aerosol optical depth (AOD) as this is assimilated as a constraining factor in the reanalysis. Furthermore, the reanalysis interpolates for cases of poor or missing retrievals and thus allows for a full spatio-temporal quantification of CCN. Therefore, the CCN retrieved from the CAMS aerosol reanalysis succeed the sole use of AOD as a proxy for global CCN. This CCN dataset features CCN concentrations of global coverage for various supersaturations and aerosol species covering the years from 2003 to 2024 with daily frequency and a spatial resolution of 0.75×0.75 degree and 60 vertical levels. Apart from the CAMSRA data, which is available every 3 hours, CCN are currently only computed once a day at 00:00 UTC. The data comprises 3-D fields of total CCN computed for six different supersaturations (s: 0.1, 0.2, 0.4, 0.6, 0.8 and 1 %) and 3-D CCN fields containing aerosol species CCN from sulfate (SO4), hydrophilic black carbon (BCh) and organic matter (OMh) and three size bins of sea salt aerosol (SS) computed for two supersaturations (s: 0.02 % and 0.8 %) comprising additional aerosol information in the lower and upper supersaturation range, respectively. The current choice of data frequency, resolution and variable dependencies such as supersaturation is made regarding general interest and suitability as well as file size, data storage and computational costs. This dataset offers the opportunity to be used for evaluation of general circulation and earth system models as well as in studies of aerosol-cloud interactions. The file name of the data sets is composed as follows. <project>_<experiment>_<version>_<dataset>_<year><mon>.nc project: QUAERERE (Quantifying aerosol-cloud-climate effects by regime) experiment: CCNCAMS (Cloud condensation nuclei derived from the CAMS reanalysis) version: v1 dataset: Total_CCN (total cloud condensation nuclei) and Aerosol_species_CCN (aerosol species cloud condensation nuclei) year: 2003 to 2024 mon: 1 to 12 Acknowledgement: This dataset was generated using Copernicus Atmosphere Monitoring Service information [2003-2024]. Neither the European Commission nor ECMWF is responsible for any use that may be made of the Copernicus information or data it contains. The source data is downloaded from the Copernicus Atmosphere Monitoring Service (CAMS) Atmosphere Data Store (ADS) (https://ads.atmosphere.copernicus.eu/cdsapp#!/dataset/cams-global-reanalysis-eac4?tab=overview)

This experiment contains the model output from a series of sensitivity simulations, called "rain", carried out with the global aerosol-climate model ECHAM6-HAM2 (model version ECHAM6.1-HAM2.2-MOZ0.9). The simulations were performed within the scope of the AeroCom project (https://aerocom.met.no/). In general, the "rain" sensitivity study aims to provide a process-based observational constraint on the cloud lifetime effect by examining the parameterized precipitation stemming from warm rain. Aerosol (precursor) emission estimates of the year 2000 from the AEROCOM-II ACCMIP dataset were used as forcing. Details can be found in the associated publication of Mülmenstädt et al., (2020).  The sensitivity simulations aim at investigating the effect of changing the autoconversion tuning factor (gamma) and the critical effective radius (rc) in the parameterization of autoconversion (see Fig.3 and Eq. 3 in Mülmenstädt et al., (2020). The respective setting of these parameters is indicated in the dataset group name (e.g. AeroCom ECHAM6-HAM2 warm rain sensitivity simulation gamma${x_gamma} rc${x_rc}. In the default setting of ECHAM6-HAM2, gamma is 4 and rc is -1. rc=-1 is used for simulations where no critical impact radius is applied. In addition to microphysical variables, the model output includes simulated radar reflections from CloudSat created with the satellite simulator COSP (Cloud Feedback Model Intercomparison Project Observational Simulator Package, see Bodas-Salcedo et al., (2011)). The radar reflectivities are outputted on so-called subcolumns to include information on the subgrid variability of hydrometeors. The model output is provided as global fields on a reduced Gaussian Grid (N48) with 3-hourly temporal resolution and covers the period January 2000 to December 2004.  The dataset is well suited for evaluating the sensitivity of warm rain parameterization in ECHAM6-HAM2. The data publication is standardized according to the ATMODAT Standard (v3.0) (Ganske et al. 2021). The data standardization was funded within the framework of “Forschungsvorhaben zur Entwicklung und Erprobung von Kurationskriterien und Qualitätsstandards von Forschungsdaten” by the German Federal Ministry of Education and Research (BMBF; FKZ: 16QK02B).