ALBERT

Description: Sea level pressure is a fundamental weather and climate element and the very basis of everyday weather maps. Daily sea level pressure distributions provide information on the influence of high and low pressure systems, air flow, weather activity, and, hence, synoptic conditions. Using sea level pressure distributions from the NCEP/NCAR Reanalysis 1 (Kalnay et al., 1996) and a simplified variant of the weather-typing scheme by Jenkinson and Collison (1977) atmospheric circulation over the North Sea has been classified as to pattern and intensity on a daily basis starting in 1948. A full account of the original weather-typing scheme can be found in Loewe et al. (2005), while the variant scheme has been detailed in Loewe et al. (2006). The analysis has been carried out on the original 16-point grid. Though formally valid at its central point (55°N, 5°E), results are representative of the North Sea region between 50°N-60°N and 0°E-10°E. The modified scheme allows for six weather types, namely four directional (NE=Northeast, SE, SW, NW) and two rotational types (C=cyclonic and A=anticyclonic). The strength of the atmospheric circulation is classified by way of a peak-over-threshold technique, employing re-calibrated thresholds for the gale index G* of 28.3, 36.6, and 44.6 hPa for gale (G), severe gale (SG), and very severe gale (VSG), respectively (Loewe et al., 2013). Technically, the set of weather-typing and gale-classification rules is implemented as a lean FORTRAN code (lwtnssim.f), internally known as "Simple Lamb weather-typing scheme for the North Sea v1". The processing run was done on a Linux server under Debian 10 (Buster). Both, weather types and gale days, form a catalogue of more than 70 annual calendars since 1948 that is presented and continuously updated to the present day at https://www.bsh.de/EN/DATA/Climate-and-Sea/Weather-and-Gales/weather-and-gales_node.html. This catalogue concisely documents synoptic conditions in the North Sea region. Possible benefits are manifold. Special events and episodes in regional-scale atmospheric circulation are easily looked up and traced. Beyond that, the dataset is well suited for frequency, trend, persistence, transition, and extreme-value statistics.

Description: 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.

Description: 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.

Description: The Bias Corrected CESMv1 data for mid-century (2041-2050) 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 (t2m), relative humidity (rh), wind speed (wspd), total precipitation (prec), mean surface shortwave flux (sw), top-of-atmosphere outgoing longwave radiation (lw), mean surface latent (lhf) and sensible (shf) 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 30 files (10 variables x 3 temporal resolutions) 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 30 GB in size. The precipitation files in the older version contained hourly accumulated values for every day. This version contains the correct daily accumulated, monthly accumulated and monthly climatology precipitation data.

Description: The global climate model system MPI-ESM-LR was applied to create an ensemble of 30 members for the historical period 1950-2005 and a continuation of the simulations for the RCP8.5 period 2006-2099. Additionally, a pre-industrial control run was performed for 1950-2099 with atmospheric pCO2 of 1850. All members were subsequently directly regionalized using the regionally coupled MPIOM-REMO climate model system consisting of the global ocean model MPIOM focused with its horizontal resolution on the North Sea and the regional atmospheric model REMO over the EURO CORDEX22 region (euro-cordex.net), which was fully coupled with MPIOM in this region. For extreme value analyses, certain variables were stored with hourly time step. Here, global sea surface height and regional (EURO CORDEX22) u and v wind components at 10 m above ground are available. Further data can be requested from the authors.

Description: In this study, the first gridded hydroclimate dataset in eastern China (EC) during the last millennium was generated. This hydroclimate dataset mainly consisted of two components. The first section was created by interpolating drought/flood grades from 1500 to 2000 using the angular distance weight method. Sampling error estimates were employed to assess the effects of the interpolated dataset. The second section for the hydroclimate dataset during 960-1500 was generated by constructing best subset regression models using selected tree-ring chronologies in the United States/northwestern China through atmospheric teleconnection. The validation parameters of the calibration equations were also derived, including the adjusted R2, predicted R2, RE, and CE. This dataset includes three files named Hydr_EC.mat (43KB), Val_Par.mat (4KB), and SEE.mat (1KB), respectively. In detail, Hydro-EC is the hydroclimate in eastern China during the last millennium; Val-Par and SEE are the dataset’s validation results.

Description: Das GERICS hat für alle 401 deutschen Landkreise, Kreise, Regionalkreise und kreisfreien Städte einen Klimaausblick veröffentlicht. https://www.gerics.de/products_and_publications/fact_sheets/landkreise/index.php.de Jeder Bericht fasst die Ergebnisse für Klimakenngrößen wie z.B. Temperatur, Hitzetage, Trockentage oder Starkregentage auf wenigen Seiten zusammen. Die Ergebnisse zeigen die projizierten Entwicklungen der Klimakenngrößen im Verlauf des 21. Jahrhunderts für ein Szenario mit viel Klimaschutz, ein Szenario mit mäßigem Klimaschutz und ein Szenario ohne wirksamen Klimaschutz. Datengrundlage sind 85 EURO-CORDEX-Simulationen, sowie der HYRAS-Datensatz des Deutschen Wetterdienstes. GERICS has published a climate report for each of the 401 German districts. https://www.gerics.de/products_and_publications/fact_sheets/landkreise/index.php.de Each report summarizes a selection of climate indices like temperature, hot days, dry days or days with heavy precipitation on a few pages. The results show the future development of these indices in the 21st century for three scenarios with strong, medium and weak climate protection, respectively. The data originates from 85 EURO-CORDEX simulations with regional climate models, and the HYRAS dataset of the German Weather Service.

Description: Model runs over Europe were conducted within the ESM project (www.esm-project.net/) for the Frontier Simulations supporting the water and matter fluxes from the European landmass to receiving water bodies (Baltic Sea, Atlantic Ocean and the Mediterranean Sea). Daily discharge from the mesoscale Hydrologic Model (mHM; Samaniego et al., 2010; Kumar et al., 2013; Code version: git.ufz.de/mhm/mhm git version: 35b5cb1) operated at the spatial resolution of 1/16deg for the simulation period from 1.1.1960-31.12.2019 across the European domain (Longitude -11 to 41 Latitude 35 to 72). Model runs were conducted within the ESM project (www.esm-project.net/) for the Frontier Simulations supporting the water and matter fluxes from the European landmass to receiving water bodies (Baltic Sea, Atlantic Ocean and Mediterranian Sea). Special consideration was given to the coastal cells by filtering out those (bordering) grid cells that do not have 100% landmass (i.e., cells with a significant proportion of water bodies/sea/ocean coverage). Meteorological forcing data are based on the E-OBS v21e (daily precipitation, temperature, Hofstra et al. 2009), potential evapotranspiration is based on the Hargreaves-Samani method. Soil characteristics are obtained from the global SoilGrids database (Hengtl et al. 2014; the land cover is derived from the Globcover_V2 (http://due.esrin.esa.int/page_globcover.php); geomorphological features are based on the GMTED2010 (Danielson et al., 2011). Model parameterization was constrained using the observed discharge time series from the GRDC stations (https://portal.grdc.bafg.de/), satisfying the following three conditions: gauge LAT〉48degN, area〉 5000km2, area 〈170000km2. Multi-basin calibration and validation were employed to check the consistency of model simulations following Rakovec et al., 2016 and Samaniego et al. 2019, as follows. Calibration objective function using KGE, DDS algorithm with 500 iterations, to account for uncertainty in the calibration process and the basin selections, 50 random initial conditions were randomly drawn sub-set of basins (N=6basins). The best parameter set in the cross-validations across 1201 basins was selected for the final run (ID: 542). A static 2D file of flow direction over Europe at the routing resolution 1/16deg. Internal upscaling to 1/16deg from the higher resolution (1/512deg) done within mHM (Code version: mesoscale Hydrologic Model (git.ufz.de/mhm/mhm git version: 35b5cb1). Special consideration was given to the coastal cells by filtering out those (bordering) grid cells that do not have 100% landmass (i.e., cells with a significant proportion of water bodies/sea/ocean coverage). Flow direction network (lat,lon) and routed runoff (time,lat,lon) at 1/16deg are provided as separate datasets.

Description: 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.

Description: In order to explore the sensitivity of the climate impact of volcanic eruptions to eruption season and latitude, we simulate volcanic eruptions at different latitudes and in different seasons with the Max Planck Institute Earth System Model (MPI-ESM). We use the same configuration of the MPI-ESM model as used for the historical simulation of CMIP6. An initial run is performed firstly (PINArst). Then we perform 23 and 10 control runs without any volcanic eruption (PINAref and PINAwRef). Two groups of three different latitudinal volcanic eruptions in boreal summer and winter are simulated. We perform 10-member simulations for each eruption case. 9 Tg of total sulfur injection magnitude is prescribed. The eruption latitudes are set to be 0° for the equatorial eruptions (PINAeq and PINAwEQ) and 30° N and 30° S for the northern and southern hemispheric eruptions (PINAnh, PINAwNH, PINAsh and PINAwNH), respectively. For the summer eruptions, the date is set to be the same as the 1991 Pinatubo eruption on June 15, 1991; for the winter eruptions, the date is set to be December 15, 1991.

Description: 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.

Description: Regional climate simulations with the COSMO-CLM V. 5.0-CLM6 + TERRA_URB V. 2.0 model by KULeuven. Dynamical downscaling on the CORDEX EUR-11 domain and HRes domain over Belgium at convection-permitting scale. Model name: COSMO-CLM V. 5.0-CLM6 + TERRA_URB V. 2.0 Important reference: Wouters et al. 2016: https://dx.doi.org/10.5194/gmd-9-3027-2016 Resolution: RCM: 12.5km; LAM: 2.8 km Nr. vertical levels: 40 Time step (s): 80 (12.5 km); 20 (2.8 km) Important scheme: TERRA_URB Focal time series / severity index: Precipitation, UHI Host GCM: ERA-Interim / EC-EARTH Non-hydrostatic: yes

Description: Regional climate simulations with the MAR V 3.9 model by University of Liège. Dynamical downscaling on the CORDEX EUR-11 domain and HRes domain over Belgium at convection-permitting scale. Model name: MAR V. 3.9 Important reference: Wyard et al. 2017: https://dx.doi.org/10.1002/joc.4879 Resolution: RCM: 50 km and 12.5 km; LAM: 5 km Nr. vertical levels: 30 Time step (s): Important scheme: Snow variables Focal time series / severity index: Snowfall events and snowmelt events inducing floods Host GCM: ERA-Interim / various Non-hydrostatic: no

Description: Regional climate simulations with the ALARO-0 model by the Royal Meteorological Institute of Belgium and Ghent University (RMIB-UGent). Dynamical downscaling on the CORDEX EUR-11 domain and HRes domain over Belgium at convection-permitting scale. Model name: ALARO-0 Important reference: Giot et al. 2016: https://dx.doi.org/10.5194/gmd-9-1143-2016 Resolution: RCM: 50 km and 12.5 km; LAM: 4 km Nr. vertical levels: 46 Time step (s): 900 (50 km); 300 (12.5 km); 180 (4 km) Important scheme: 3MT Focal time series / severity index: (Sub-)hourly precipitation Host GCM: ERA-Interim / ARPEGE Non-hydrostatic: no

Description: MPI-ESM1-2-LR’s CMIP6 CovidMIP baseline simulations are based on simulations forced with CO2 emissions allowing interactive carbon cycle. The baseline simulations (ssp245-cov-baseline, publish here) is a reference to the CovidMIP simulations (ssp245-covid, ssp245-cov-fossil, ssp245-cov-strgreen, and ssp245-cov-modgreen, published under CMIP6 CovidMIP) to investigate the effects of COVID-19 induced emission reductions on global carbon cycle, climate change and feedbacks. As presented in Jones et al. (2021), the radiative and climate responses of MPI-ESM1-2-LR are within the range of multi-model simulation results. have 10 ensemble members of the simulation named from r1i1p1f99 to r10i1p1f99. Here f99 is used in the file name *r*i1p1f99* of all CovidMIP simulations because of the updated aerosol forcing (Fiedler et al. 2021). Fiedler, S.; Wyser, K.; Rogelj, J. & van Noije, T. (2021): Radiative effects of reduced aerosol emissions during the COVID-19 pandemic and the future recovery, Atmospheric Research, 264, 105866, https://doi.org/10.1016/j.atmosres.2021.105866 Jones, C. D., Hickman, J. E., Rumbold, S. T., Walton, J., Lamboll, R. D., Skeie, R. B., ... & Ziehn, T. (2021). The climate response to emissions reductions due to COVID‐19: Initial results from CovidMIP. Geophysical research letters, 48(8), e2020GL091883.

Description: The data contains the code of TransClim: written in Python 2.

Description: The LUCAS LUC future dataset consists of annual land use and land cover maps from 2016 to 2100. It is based on land cover data from the LANDMATE PFT dataset for the year 2015. The LANDMATE PFT consists of 16 plant functional types and non-vegetated classes that were converted from the ESA-CCI LC land cover data according to the method of Reinhart et al. (2021). The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2.1f, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2021). The projected land use change information was derived for different Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) combinations used in the framework of the 6th phase of Coupled Modelling Intercomparison Project (CMIP6). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within EURO-CORDEX. Plant functional types and non-vegetated classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

Description: The LANDMATE PFT dataset provides a land cover map for Europe for the year 2015 in 0.1° (~10km) and 0.018° (~2km) resolution. The dataset is based on land cover data of the ESA Climate Change Initiative (ESA-CCI, native resolution: 300m) which is translated into 16 plant functional types (PFTs) and non-vegetated classes employing the cross-walking procedure introduced by Reinhart et al. (2021). The translation is done under consideration of the Holdridge Life Zones (HLZs), a system, that classifies land areas based on bioclimatic properties. Through the HLZs, regional distinction of the individual PFT distribution can be achieved. The land cover information is given as fractions per grid cell where each fraction represents the area covered by the respective land cover within each grid cell (0-1). The dataset is available in two different horizontal resolutions, 0.1° (~10km) and 0.018° (~2km), whereby the land cover information is resampled using a fractional approach to achieve the desired resolution. The LANDMATE PFT dataset was carefully developed and designed to meet the present and future requirements of regional climate models and is therefore recommended to be used for regional climate modeling over the European Continent. The LANDMATE PFT dataset (0.1° resolution) serves as basemap for the historical and future land use and land cover dataset LUCAS LUC developed by Hoffmann et al. (2021).

Description: The LUCAS LUC historical dataset consists of annual land use and land cover maps from 1950 to 2015. It is based on land cover data from the LANDMATE PFT dataset that was generated from ESA-CCI LC data. The ESA-CCI LC land cover classes are converted into 16 plant functional types and non-vegetated classes employing the method of Reinhart et al. (2021). The land use change information from the Land-Use Harmonization Data Set version 2 (LUH2 v2h, Hurtt et al. 2020) were imposed using the land use translator developed by Hoffmann et al. (2021). For each year, a map is provided that contains 16 fields. Each field holds the fraction the respective plant functional types and non-vegetated classes in the total grid cell (0-1). The LUCAS LUC dataset was constructed within the HICSS project LANDMATE and the WCRP flagship pilot study LUCAS to meet the requirements of downscaling experiments within EURO-CORDEX. Plant functional types and non-vegetated classes: 1 - Tropical broadleaf evergreen trees 2 - Tropical deciduous trees 3 - Temperate broadleaf evergreen trees 4 - Temperate deciduous trees 5 - Evergreen coniferous trees 6 - Deciduous coniferous trees 7 - Coniferous shrubs 8 - Deciduous shrubs 9 - C3 grass 10 - C4 grass 11 - Tundra 12 - Swamp 13 - Non-irrigated crops 14 - Irrigated crops 15 - Urban 16 - Bare

Description: Source code of the Max Planck Institute atmospheric general circulation model (ECHAM6) adopted to the project PalMod for the concurrent execution of radiative transfer on HPC-systems.

Description: 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.

Description: The hydrodynamic model TRIM-NP in a barotropic mode is used to simulate the strong storm tide in March 1906 forced by ECMWF ERA-20C and CERA-20C ensemble of coupled climate reanalyses (https://www.ecmwf.int). The model area covers the region of 20W to 30E and 42N to 65N with a spatial resolution of 12.8x12.8 km for grid 1. At the lateral boundaries of grid 1, the water level is calculated with tide model FES2004. TRIM-NP calculates one way nested with higher resolution the North Sea (with 6.4km, grid2), southern North Sea (with 3.2km, grid3) and the German Bight (with 1.6km, grid4). In this data bank, the datasets are available hourly for grid 2 and grid 4. Please contact the authors for grid 1 and grid 3.

Description: The hydrodynamic model TRIM-NP in a barotropic mode is used to simulate the strong storm tide in March 1906 forced by NOAA-CIRES-DOE Twentieth Century Reanalysis (20CR) version 2c and 3. datasets (https://portal.nersc.gov/project/20C_Reanalysis/). The model area covers the region of 20W to 30E and 42N to 65N with a spatial resolution of 12.8x12.8 km for grid 1. At the lateral boundaries of grid 1, the water level is calculated with tide model FES2004. TRIM-NP calculates one way nested with higher resolution the North Sea (with 6.4km, grid2), southern North Sea (with 3.2km, grid3) and the German Bight (with 1.6km, grid4). In this data bank, the datasets are available hourly for grid 2 and grid 4. Please contact the authors for grid 1 and grid 3.

Description: Source code of the Max Planck Institute Earth System Model (MPI-ESM1.2) adopted to the project PRIMAVERA for the comparison of four different ocean vertical mixing schemes.

Description: ICON 2.5 km simulations over the tropical Atlantic ([65W:15E],[10S:20N] for the months of December 2013 (NARVAL1 : 30 days) and August 2016 (NARVAL2 : 30 days). The grid spacing, computed as the square root of the triangular grid cells, amounts to 2.5 km. In the vertical, a stretched vertical coordinate is used with 75 layers, whereby 12 layers are located in the first kilometer. The simulations are conducted for the months of December 2013 and July 2016. They are started every day at 00 UTC from the analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) and integrated for 36 hours. Boundary data are taken from the ECMWF forecasts and updated every 3 hours. At the bottom boundary, the Sea Surface Temperature (SST) is taken from the ECMWF analysis. It is kept fixed at its initial value during the 36-h integration period. The simulations were conducted using the ICOsahedral Non-hydrostatic (ICON) model (Zängl et al., 2015). Given the horizontal grid spacing, no convective parameterization is employed and convection is explicitly resolved by the bulk microphysics scheme that predicts cloud water, rain, snow, ice and graupel (Baldauf et al., 2011). The parameterizations for gravity wave drag and subgrid-scale orography are also switched off, otherwise the model employs the same parameterizations as the operational model version in use at the German Weather Service (DWD), see Zängl et al. (2015) and Klocke et al. (2017) for further details.

Description: ICON 2.5 km simulations over the tropical Atlantic ([65W:15E],[10S:20N] for the months of December 2013 (NARVAL1 : 30 days) and August 2016 (NARVAL2 : 30 days). The grid spacing, computed as the square root of the triangular grid cells, amounts to 2.5 km. In the vertical, a stretched vertical coordinate is used with 75 layers, whereby 12 layers are located in the first kilometer. The simulations are conducted for the months of December 2013 and July 2016. They are started every day at 00 UTC from the analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) and integrated for 36 hours. Boundary data are taken from the ECMWF forecasts and updated every 3 hours. At the bottom boundary, the Sea Surface Temperature (SST) is taken from the ECMWF analysis. It is kept fixed at its initial value during the 36-h integration period. The simulations were conducted using the ICOsahedral Non-hydrostatic (ICON) model (Zängl et al., 2015). Given the horizontal grid spacing, no convective parameterization is employed and convection is explicitly resolved by the bulk microphysics scheme that predicts cloud water, rain, snow, ice and graupel (Baldauf et al., 2011). The parameterizations for gravity wave drag and subgrid-scale orography are also switched off, otherwise the model employs the same parameterizations as the operational model version in use at the German Weather Service (DWD), see Zängl et al. (2015) and Klocke et al. (2017) for further details.

Description: HadCRU_MLE_v1.0 is a dataset of monthly gridded surface temperatures for the Earth during the instrumental period (since 1850). The name ‘HadCRU_MLE_v1.0’ reflects the dataset’s use of maximum likelihood estimation and observational data primarily from the Met Office Hadley Centre and the Climate Research Unit of the University of East Anglia. Source datasets used to create HadCRU_MLE_v1.0 include land surface air temperature anomalies of HadCRUT4, sea surface temperature anomalies of HadSST4, sea ice coverage of HadISST2, the surface temperature climatology of Jones et al. (1999), the sea surface temperature climatology of HadSST3, land mask data of OSTIA, surface elevation data of GMTED2010, and climate model output of CCSM4 for a pre-industrial control scenario. HadCRU_MLE_v1.0 was generated using information from the Met Office Hadley Centre, the Climate Research Unit of the University of East Anglia, the E.U. Copernicus Marine Service, the U.S. Geological Survey, and the University Corporation of Atmospheric Research. The primary motivation to develop HadCRU_MLE_v1.0 was to correct for two biases that may exist in global instrumental temperature datasets. The first bias is an amplification bias caused by not adequately accounting for the tendency of different regions of the planet to warm at different rates. The second bias is a sea ice bias caused by not adequately accounting for changes in sea ice coverage during the instrumental period. Corrections to these biases increased the estimate of global mean surface temperature change during the instrumental period. The new dataset has improvements compared to the Cowtan and Way version 2 dataset, including an improved statistical foundation for estimating model parameters, taking advantage of temporal correlations of observations, taking advantage of correlations between land and sea observations, and accounting for more sources of uncertainty. To properly correct for amplification bias, HadCRU_MLE_v1.0 incorporates the behaviour of the El Niño Southern Oscillation. HadCRU_MLE_v1.0 includes mean surface temperature anomalies for each month from 1850 to 2018 and for each 5° latitude by 5° longitude grid cell. Future versions of HadCRU_MLE may become available to extend the temporal coverage beyond 2018. The maximum likelihood estimation approach allows for the estimated field of surface temperature anomalies to be temporally and spatially complete for the entire instrumental period and for the entire surface of the Earth. A 5° by 5° gridded 1961-1990 temperature climatology for HadCRU_MLE_v1.0 is available, although caution is advised when interpreting this temperature climatology since the source datasets used for temperature climatologies do not correspond perfectly with the source datasets used for temperature anomalies. Other information of HadCRU_MLE_v1.0 is available, including the estimated local amplification factors, the magnitude of the corrections for sea ice bias, and the impact of the El Niño Southern Oscillation on surface temperature anomalies.

Description: The hydrodynamic model TRIM-NP in a barotropic mode is used to simulate the strong storm tide in March 1906 forced by reconstructed weather data by the Deutsche Wetterdienst (DWD) and Helmholtz-Zentrum Geesthacht. From georeferenced historical station data, pressure maps are drawn, digitised, and wind speed calculated from them. The model area covers the region of 20W to 30E and 42N to 65N with a spatial resolution of 12.8x12.8 km for grid 1. At the lateral boundaries of grid 1, the water level is calculated with tide model FES2004. TRIM-NP calculates one way nested with higher resolution the North Sea (with 6.4km, grid2), southern North Sea (with 3.2km, grid3) and the German Bight (with 1.6km, grid4). In this data bank, the datasets are available hourly for grid 2 and grid 4. Please contact the authors for grid 1 and grid 3. The datasets are visualised https://doi.org/10.5446/49529 or https://www.dkrz.de/projects-and-partners/projects/focus/stormtide1906. In additional experiments, the tides at the lateral boundaries are shifted backwards (up to minus six hours) or forward (up to plus six hours) in time to calculate the peak of the storm tide. The atmospheric forcing is not changed. Only the water levels from grid4 of this experiment are stored.

Description: 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

Description: This is version v1.1 of the hydrographic part of the "Baltic and North Sea Climatology (BNSC)". It turned out that the original hydrographic data product of the BNSC (BNSClim hydrographic part (Version 1.0)) was erroneous. The errors occurred by accidentally reading obsolete files in two of the intermediate steps of the production procedure. By this, the basis of observations was altered. This happened after the quality control and interpolation of the observations on standard depths, in the step where the observations are sorted into the chosen grid (this affects temperature and salinity) and in the following step, the correction of the temporal sampling error (this affects only salinity). These errors were corrected in this Version 1.1. The parameters provided are water temperature and salinity on 105 depth levels. The data product comprises the time period from 1873-2015 and is based on more than one million observational profiles, which were obtained from several different data sources in the region of the Baltic, the North Sea and adjacent areas of the North Atlantic Ocean (15°W-30°E, 47°N-66°N). Intersection of observational data from different data sources is avoided and the in situ data were objected to an elaborate automatic quality control to identify erroneous observations that would bias the data product. Additionally, a correction of the temporal sampling error was applied to minimize the impact of the temporal distribution of the observations on the created temporal mean fields. The data product consists of gridded mean fields of water temperature and salinity. The spatial resolution is 0.25° in meridional and zonal direction. The depth levels are irregularly distributed: for the depth interval from 0 to 50m the distance between the single depth levels is 5m. Below 50m, the distance increases progressively by 1m to the last depth level of 4985m. The dimensions of the data product are 180*76*105 (longitude, latitude, depth). The BNSC climatology consists, on the one hand, of time series of monthly and annual mean values of the hydrographic parameters as fields of box averages. Grid boxes that show no observations are left empty. Based on these time series, decadal monthly mean fields are created for the decades 1956-1965, 1966-1975, 1976-1985, 1986-1995, 1996-2005, 2006-2015 as another part of the data product. Again, gaps remain in observational data-void regions. The third part of the data product results from above mentioned decadal mean fields: horizontally interpolated fields by application of the method of objective analysis. Consequently, this subset does not contain gaps. Available parameters: box averages: monthly and annual mean, resp. standard deviation, number of observations decadal box averages: decadal monthly mean, resp. standard deviation, mean year, standard deviation to mean year, number of years decadal interpolated mean: interpolated monthly mean, absolute median deviation, number of bins, first guess, relative interpolation error, mean year, mean distance The products and a description of the differences between v1.0 and v1.1 are publicly available at the ICDC portal ( https://icdc.cen.uni-hamburg.de/1/daten/ocean/bnsc/)

Description: Global paleoclimate simulations are carried out on the basis of the so-called time slice technique. The simulations are performed with the state-of-the-art global circulation model ECHAM5 (Roeckner et al., 2003) at a spectral resolution of T106 (∼1.125°×1.125°) and 19 vertical levels. Different time slices are selected at a time interval of approx. 1000 years from each other, from 6000 years ago to pre-industrial times. For each time slice a simulation is carried out over a period of 30 years. As boundary conditions prescribed sea ice fraction and sea surface temperatures were used, which were derived from a continuous simulation with transient periods. This simulation was performed with the coupled atmosphere-ocean circulation model ECHO-G, consisting of the ECHAM4 (Roeckner et al., 1996) and the ocean model HOPE (Wolff et al., 1997), at a spectral resolution of T30 (∼3.75◦×3.75◦). Further information on simulation realization can be found in Wagner et al. (2007). Detailed information on the model set-up can be found in Russo and Cubasch (2016). Russo, E. and Cubasch, U.: Mid-to-late Holocene temperature evolution and atmospheric dynamics over Europe in regional model simulations, Clim. Past, 12, 1645-1662, https://doi.org/10.5194/cp-12-1645-2016, 2016.

Description: High-resolution simulations of the palaeoclimate are carried out throughout Europe. A set of climate simulations will be performed, based on the so-called time slicing technique. The simulations are performed with the state-of-the-art regional climate model COSMO-CLM (cosmo_4.8_clm19) at a horizontal resolution of 0.44° longitude and 40 vertical levels. The COSMO-CLM is a non-hydrostatic RCM with rotated geographical coordinates and a terrain following height coordinate (Rockel et al., 2008), developed by the German Weather Service (DWD) of the COSMO model (Doms and Schättler, 2003). The ECHAM5 output is used as a boundary data set for the dynamic downscaling approach. Detailed information on the model set-up can be found in Russo and Cubasch (2016). Russo, E. and Cubasch, U.: Mid-to-late Holocene temperature evolution and atmospheric dynamics over Europe in regional model simulations, Clim. Past, 12, 1645-1662, https://doi.org/10.5194/cp-12-1645-2016, 2016.

Description: Historical monthly models of mean minimum temperature and maximum temperature, and total precipitation

Description: RCM forcing data from 10 realisations (r*i1p1f1) of the CMIP6/ScenarioMIP experiment ssp370, 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 ssp370: Gap: Baseline scenario with a medium to high radiative forcing by the end of century. Following approximately RCP7.0 global forcing pathway with SSP3 socioeconomic conditions. Radiative forcing reaches a level of 7.0 W/m2 in 2100. Concentration-driven.

Description: RCM forcing data from 2 realisations (r2i1p1f1, r3i1p1f1) of the CMIP6/CMIP DECK experiment amip, conducted with the MPI-ESM1-2-HR on the Mistral supercomputer of the DKRZ. The experiment covers the years 1979 to 2014. The two variants have been created by modulating the horizontal diffusion coefficient of the top model layer by a factor 1.00001 (amip_r2i1p1f1) and 0.99999 (amip_r3i1p1f1) for the year 1979. The file format is gzip-compressed GRIB (*.grb.gz). CMIP6 website: https://www.wcrp-climate.org/wgcm-cmip/wgcm-cmip6 CMIP6 paper: https://www.geosci-model-dev.net/9/1937/2016/gmd-9-1937-2016.html Experiment description amip: An atmosphere only climate simulation using prescribed sea surface temperature and sea ice concentrations but with other conditions as in the Historical simulation.

Description: Polar regions are data sparse regions. Research ships operating in polar regions often record sea-ice conditions during their transects through ice infested waters. Such observations of the sea-ice conditions are often the only information that can be provided in addition to satellite-based estimates of the sea-ice conditions, such as sea-ice concentration or sea-ice thickness. Such observations have been carried out and gathered using two protocols. For the Antarctic, this is the so-called ASPeCt protocol [Worby and Allison, 1999; Worby and Dirita, 1999; Worby et al., 2008]. For the Arctic, this is the so-called ASSIST/IceWatch protocol [Hutchings et al., 2018]. The latter builds on the ASPeCt protocol, incorporating surface melt conditions being more ubiquitous in the Arctic. Ship-based observations of the sea-ice conditions are conducted manually, visually, i.e. by eye, regularly every hour taking into account an area around the ship of about one kilometer radius. Note that this area distorts to an elliptically shaped area as a function of observers' experience, ships' cruising speed and ice and visibility conditions. Each observation comprises the total sea-ice concentration, and the concentration, level ice thickness, level ice snow depth, fraction and height of ridges, ice type, snow type, and floe size for the up to three thickest ice types. For the Arctic, melt-pond fraction and stage-of-melt are also part of the observables. In addition to the ships' position often auxiliary parameters such as visibility, wind speed and direction, or air and water temperature are recorded. For development and evaluation of satellite-based sea-ice products, such ship-based observations are of great value. Because of this, within the ESA-CCI sea-ice ECV project (ESA-SICCI), phase 2, a standardized data set of such ship-based observations was generated for both polar regions. It comprises data from June 2002 through December 2015. This time period is motivated by the purpose to evaluate sea-ice concentration data retrieved from AMSR-E and AMSR2 brightness temperature measurements which, at the time the project was initiated, were planned to be retrieved until the end of 2015. The data set incorporates observational data from various collections, e.g. a part of the original ASPeCt collection [Worby et al., 2008], which ended in May 2005. More information about all data sources is given below. All data have been manually standardized to the same format (i.e., number of decimals, unit), using the same value to describe missing data, using the same temporal ordering, and filling gaps with the respective missing-data value. Double data entries have been removed. The data set is split into two ascii text files, one for the Arctic, one for the Antarctic. It has been successfully used to evaluate sea-ice concentration and thickness products of the ESA-SICCI phase 2 project.

Description: The eVolv2k database includes estimates of the magnitudes and approximate source latitudes of major volcanic stratospheric sulfur injection (VSSI) events from 500 BCE to 1900 CE. The VSSI estimates incorporate recent improvements to the ice core records in terms of synchronization and dating, refinements to the methods used to estimate VSSI from ice core records, and includes estimates of the random uncertainties in VSSI values. Ice core-derived volcanic sulfate deposition composites for Antarctica (Sigl et al., 2014) and Greenland (Sigl et al., 2015, Zielinski et al., 1995) are scaled to volcanic stratospheric sulfur injection based on a method similar to that of Gao et al. (2007). More details are described by Toohey and Sigl (2017). Compared to version 2, this update includes reassignment of eruption region for minor events in 1654, 1414, 1381, 688, 379 and -430. Also, minimum flux threshold adjusted downwards so as to include small Greenland flux for events in 1463, -190 and -430. Finally, events with 0 VSSI removed. In addition, a reconstruction of stratospheric aerosol optical depth (AOD) using the VSSI estimates and the EVA v1.2 volcanic forcing generator (Toohey et al., 2016) is provided. Complete optical properties (extinction, single scattering albedo, scattering asymmetry factor) as a function of height, latitude and time can be produced using the eVolv2k VSSI database and the EVA forcing generator. EVA version 1.2 includes a fix of a minor bug which affected the spatiotemporal distribution of AOD, most notably for extratropical eruptions. Gao, C., Oman, L., Robock, A. and Stenchikov, G. L.: Atmospheric volcanic loading derived from bipolar ice cores: Accounting for the spatial distribution of volcanic deposition, J. Geophys. Res., 112(D9), doi:10.1029/2006JD007461, 2007. Sigl, M., Winstrup, M., McConnell, J. R., Welten, K. C., Plunkett, G., Ludlow, F., Büntgen, U., Caffee, M., Chellman, N., Dahl-Jensen, D., Fischer, H., Kipfstuhl, S., Kostick, C., Maselli, O. J., Mekhaldi, F., Mulvaney, R., Muscheler, R., Pasteris, D. R., Pilcher, J. R., Salzer, M., Schüpbach, S., Steffensen, J. P., Vinther, B. M. and Woodruff, T. E.: Timing and climate forcing of volcanic eruptions for the past 2,500 years, Nature, 523, 543¿549, doi:10.1038/nature14565, 2015. Sigl, M., McConnell, J. R., Toohey, M., Curran, M., Das, S. B., Edwards, R., Isaksson, E., Kawamura, K., Kipfstuhl, S., Krüger, K., Layman, L., Maselli, O. J., Motizuki, Y., Motoyama, H., Pasteris, D. R. and Severi, M.: Insights from Antarctica on volcanic forcing during the Common Era, Nat. Clim. Chang., 4, 693-697, doi:10.1038/nclimate2293, 2014. Toohey, M. and Sigl, M.: Volcanic stratospheric sulfur injections and aerosol optical depth from 500 BCE to 1900 CE, Earth Syst. Sci. Data, 9(2), 809–831, doi:10.5194/essd-9-809-2017, 2017. Toohey, M., Stevens, B., Schmidt, H. and Timmreck, C.: Easy Volcanic Aerosol (EVA v1.0): an idealized forcing generator for climate simulations, Geosci. Model Dev., 9(11), 4049–4070, doi:10.5194/GMD-9-4049-2016, 2016.

Description: A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the year 2012 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1999 and, then, from 2000 to 2011 with tagging (see below; three years would have been sufficient). The model output has been validated with measurement data of the "IOW Baltic Monitoring and long-term data program" (https://www.io-warnemuende.de/iowdb.html; IOW: Leibniz Institute for Baltic Sea Research Warnemünde) and from the HELCOM database (http://ocean.ices.dk/helcom/Helcom.aspx; HELCOM: Helsinki Commission). A publication is in preparation. The model simulation was forced by coastDat2 COSMO-CLM data (doi:10.1594/WDCC/coastDat-2_COSMO-CLM). Atmospheric nitrogen deposition data of 16x16 km2 horizontal resolution were provided by the Helmholtz-Zentrum Geesthacht within the EU BONUS SHEBA Project (Karl et al., 2019, doi:10.5194/acp-19-7019-2019). Nitrogen from atmospheric deposition of nitrogen from shipping emissions and from all emission sectors has been tagged in the model simulation according to a method by Menésguen et al. (2006, doi: 10.4319/lo.2006.51.1_part_2.0591). Therefore, all nitrogen-containing model variables exist three times in the output: once as regular variables and once per tagged nitrogen source (total atmospheric and shipping-related). The simulation was performed at the North-German Supercomputing Alliance (HLRN, project id: mvk00054, zulassung.hlrn.de/kurzbeschreibungen/mvk00054.pdf). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A).

Description: A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the years 2003 to 2014 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1999 and, then, from 2000 to 2002 with tagging (see below). The model output has been validated with measurement data of the "IOW Baltic Monitoring and long-term data program" (https://www.io-warnemuende.de/iowdb.html; IOW: Leibniz Institute for Baltic Sea Research Warnemünde) and from the HELCOM database (http://ocean.ices.dk/helcom/Helcom.aspx; HELCOM: Helsinki Commission). A publication is in preparation. The model simulation was forced by coastDat2 COSMO-CLM data (doi:10.1594/WDCC/coastDat-2_COSMO-CLM). Atmospheric nitrogen deposition data of 0.1° x 0.1° spatial resolution were taken from the 2018 reporting of the European Measurement and Evaluation Programme (EMEP) as presented in EMEP (2018, url: http://emep.int/publ/reports/2018/EMEP_Status_Report_1_2018.pdf) and available from the Norwegian Meteorological Institute (2018, url: http://thredds.met.no/thredds/catalog/data/EMEP/2018_Reporting/catalog.html). Nitrogen from atmospheric deposition of nitrogen from livestock/agricultural emissions (estimated, see documentation) and from all emission sectors has been tagged in the model simulation according to a method by Menésguen et al. (2006, doi:10.4319/lo.2006.51.1_part_2.0591). Therefore, all nitrogen-containing model variables exist three times in the output: once as regular variables and once per tagged nitrogen source (total atmospheric and agriculturally-related). The simulation was performed at the North-German Supercomputing Alliance (HLRN, project id: mvk00054, zulassung.hlrn.de/kurzbeschreibungen/mvk00054.pdf). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A).

Description: A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the years 1995 to 2014 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1984 and, then, from 1985 to 1994 with tagging (see below). The model output has been validated with measurement data of the "IOW Baltic Monitoring and long-term data program" (https://www.io-warnemuende.de/iowdb.html; IOW: Leibniz Institute for Baltic Sea Research Warnemünde) and from the HELCOM database (http://ocean.ices.dk/helcom/Helcom.aspx; HELCOM: Helsinki Commission). The model simulation was forced by coastDat2 COSMO-CLM data (doi:10.1594/WDCC/coastDat-2_COSMO-CLM). Riverine phosphorus input of the Warnow River was calculated with the Soil & Water Assessment Tool (SWAT; Bauwe et al., 2019, doi:10.1016/j.ecohyd.2019.03.003). Phosphorus from the Warnow River has been tagged in the model simulation according to a method by Menésguen et al. (2006, doi:10.4319/lo.2006.51.1_part_2.0591). Therefore, all phosphorus-containing model variables exist twice in the output: once as regular variables and once as tagged variable. The default phosphorus input by the Warnow River based on real phosphorus release patterns and real atmospheric conditions was used ("base scenario"; PhosWaM SWAT case "ist"). The turnover of phosphorus compounds in the Unterwarnow was calculated based on the "Unterwarnow turnover estimation v04" (see final project report of PhosWaM for details). The simulation was performed at the North-German Supercomputing Alliance (HLRN). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A).

Description: A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the years 1995 to 2014 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1984 and, then, from 1985 to 1994 with tagging (see below). The model output has been validated with measurement data of the "IOW Baltic Monitoring and long-term data program" (https://www.io-warnemuende.de/iowdb.html; IOW: Leibniz Institute for Baltic Sea Research Warnemünde) and from the HELCOM database (http://ocean.ices.dk/helcom/Helcom.aspx; HELCOM: Helsinki Commission). The model simulation was forced by coastDat2 COSMO-CLM data (doi:10.1594/WDCC/coastDat-2_COSMO-CLM). Riverine phosphorus input of the Warnow River was calculated with the Soil & Water Assessment Tool (SWAT; Bauwe et al., 2019, doi:10.1016/j.ecohyd.2019.03.003). Phosphorus from the Warnow River has been tagged in the model simulation according to a method by Menésguen et al. (2006, doi:10.4319/lo.2006.51.1_part_2.0591). Therefore, all phosphorus-containing model variables exist twice in the output: once as regular variables and once as tagged variable. The default phosphorus input by the Warnow River based on real phosphorus release patterns and real atmospheric conditions was used (PhosWaM SWAT case "ist"). The turnover of phosphorus compounds in the Unterwarnow was calculated based on the "Unterwarnow turnover estimation v05" (see final project report of PhosWaM for details). The simulation was performed at the North-German Supercomputing Alliance (HLRN). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A).

Description: A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the years 1995 to 2014 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1984 and, then, from 1985 to 1994 with tagging (see below). The model output has been validated with measurement data of the "IOW Baltic Monitoring and long-term data program" (https://www.io-warnemuende.de/iowdb.html; IOW: Leibniz Institute for Baltic Sea Research Warnemünde) and from the HELCOM database (http://ocean.ices.dk/helcom/Helcom.aspx; HELCOM: Helsinki Commission). The model simulation was forced by coastDat2 COSMO-CLM data (doi:10.1594/WDCC/coastDat-2_COSMO-CLM). Riverine phosphorus input of the Warnow River was calculated with the Soil & Water Assessment Tool (SWAT; Bauwe et al., 2019, doi:10.1016/j.ecohyd.2019.03.003). Phosphorus from the Warnow River has been tagged in the model simulation according to a method by Menésguen et al. (2006, doi:10.4319/lo.2006.51.1_part_2.0591). Therefore, all phosphorus-containing model variables exist twice in the output: once as regular variables and once as tagged variable. The phosphorus input by the Warnow River based on real phosphorus release patterns and real atmospheric conditions was modified in order to comply with BASP (Baltic Sea Action Plan) targets (PhosWaM SWAT case "15"). The turnover of phosphorus compounds in the Unterwarnow was calculated based on the "Unterwarnow turnover estimation v04" (see final project report of PhosWaM for details). The simulation was performed at the North-German Supercomputing Alliance (HLRN). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A).

Description: A marine physical biogeochemical model simulation was performed with the model MOM-ERGOM for the years 1995 to 2014 covering the Baltic Sea. Previously, MOM-ERGOM had been initialized for several decades without tagging until 1984 and, then, from 1985 to 1994 with tagging (see below). The model output has been validated with measurement data of the "IOW Baltic Monitoring and long-term data program" (https://www.io-warnemuende.de/iowdb.html; IOW: Leibniz Institute for Baltic Sea Research Warnemünde) and from the HELCOM database (http://ocean.ices.dk/helcom/Helcom.aspx; HELCOM: Helsinki Commission). The model simulation was forced by coastDat2 COSMO-CLM data (doi:10.1594/WDCC/coastDat-2_COSMO-CLM). Riverine phosphorus input of the Warnow River was calculated with the Soil & Water Assessment Tool (SWAT; Bauwe et al., 2019, doi:10.1016/j.ecohyd.2019.03.003). Phosphorus from the Warnow River has been tagged in the model simulation according to a method by Menésguen et al. (2006, doi:10.4319/lo.2006.51.1_part_2.0591). Therefore, all phosphorus-containing model variables exist twice in the output: once as regular variables and once as tagged variable. The phosphorus input by the Warnow River based on real phosphorus release patterns and real atmospheric conditions was calculated and a Maximum Technical Feasible Reduction (MTFR) approach was applied (PhosWaM SWAT case "35"). The turnover of phosphorus compounds in the Unterwarnow was calculated based on the "Unterwarnow turnover estimation v04" (see final project report of PhosWaM for details). The simulation was performed at the North-German Supercomputing Alliance (HLRN). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A).

Description: This is an atmospheric hindcast for Western Europe and the North Atlantic using COSMO-CLM version 5.0 with spectral nudging from 2002-2017. MERRA2 reanalysis data are used as forcing. Additionally transient and monthly aerosol data of the MACv2 climatology are prescribed. The model uses a rotated grid with 566 x 481 grid points and a grid point distance of 0.0625 degrees, the rotated North pole is located at 162.0 W, 39.25 N. The published data excludes the sponge zone and have 526 x 441 grid points. In rotated coordinates the published simulation data extends from 22.64 W to 10.18 E, 11.2 S to 16.3 N, in geographical coordinates this corresponds to about 12 W to 30 E, 39 N to 60 N. institution: Helmholtz-Zentrum Geesthacht, Institute of Coastal Research, Germany source: int2lm_131101_2.00_clm4, COSMO-CLM5.0_clm14_aerosol_gust (available at DKRZ's LTA WDCC service), http://www.cosmo-model.org/content/model/documentation/core/default.htm contact: http://coastmod.hzg.de originator: Ronny Petrik crs: EPSG:4326

Description: Products of liquid water path (LWP), rain water path (RWP) and integrated water vapor (IWV, also called precipitable water vapor (PWV)) are retrieved from microwave radiometer observations with auxiliary measurements from backscatter lidar and cloud radar. The nadir measurements were taken by the German High Altitude and Long range research aircraft (HALO) during the Next generation Advanced Remote sensing for VALidation campaign South (NARVAL-South) in December 2013. Products are provided over tropical Atlantic east of Barbados. This experiment provides column integrated quantities as seen from satellite perspective but with higher spatially resolution (about 1 km footprint) than available from microwave satellites.

Description: Products of liquid water path (LWP), rain water path (RWP) and integrated water vapor (IWV, also called precipitable water vapor (PWV)) are retrieved from microwave radiometer observations with auxiliary measurements from backscatter lidar and cloud radar. The nadir measurements were taken by the German High Altitude and Long range research aircraft (HALO) during the Next generation Advanced Remote sensing for VALidation campaign 2 (NARVAL2) in August 2016. Products are provided over tropical Atlantic east of Barbados. This experiment provides column integrated quantities as seen from satellite perspective but with higher spatially resolution (about 1 km footprint) than available from microwave satellites.

Description: We developed a global dataset of downscaled future projections developed by applying a statistical method for climate model downscaling and bias correction. To develop the dataset, we applied the delta method, which comprises the sum of interpolated anomalies of each GCM to the WorldClim 1-km spatial resolution dataset. The GCMs were the 35 Coupled Model Intercomparison Project Phase 5 (CMIP5) models, for four representative concentrations pathways (RCPs). For each of these, we used the 30-year future periods named as 2030s (mean of 2020-2049), 2050s (2040-2069), 2070s (2060-2089) and 2080s (2070-2099) with three climate variables (mean monthly maximum and minimum temperatures and monthly rainfall). From these, we also derive a set of bioclimatic indices.

Description: A marine physical biogeochemical model simulation was performed for the year 2012 covering the North Sea and Baltic Sea. Only data for the western Baltic Sea are provided here. The model output has been validated in Neumann et al. (2018a, doi: 10.5194/os-2018-71). The work was funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI, FKZ 50EW1601, https://www.io-warnemuende.de/meramo-en.html). The simulation was performed at the North-German Supercomputing Alliance (HLRN, project id: mvk00054, zulassung.hlrn.de/kurzbeschreibungen/mvk00054.pdf). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A). The model simulation was forced by operational meteorological data of the German Weather Service (DWD). Atmospheric nitrogen deposition data of high spatial resolution of 4x4 km2 were provided by the Helmholtz-Zentrum Geesthacht within the EU BONUS SHEBA Project (Karl et al., 2019, doi: 10.5194/acp-2018-1317). Information on the riverine inputs, boundary conditions, and the model itself are provided in detail in Neumann et al. (2018b, doi: 10.5194/bg-2018-364). Nitrogen from atmospheric deposition of shipping-related nitrogen has been tagged in the model simulation according to a method by Menésguen et al. (2006, 10.4319/lo.2006.51.1_part_2.0591). Therefore, all nitrogen-containing model variables exist twice in the output: once as regular variables and once as nitrogen content from shipping-related activities. The concentrations of all prognostic biogeochemical model variables are given in nitrogen units according to the Redfield ratio.

Description: A marine physical biogeochemical model simulation was performed for the year 2012 covering the North Sea and Baltic Sea. Only data for the western Baltic Sea are provided here. The model output has been validated in Neumann et al. (2018a, doi: 10.5194/os-2018-71). The work was funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI, FKZ 50EW1601, https://www.io-warnemuende.de/meramo-en.html). The simulation was performed at the North-German Supercomputing Alliance (HLRN, project id: mvk00054, zulassung.hlrn.de/kurzbeschreibungen/mvk00054.pdf). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A). The model simulation was forced by operational meteorological data of the German Weather Service (DWD). Atmospheric nitrogen deposition data of medium spatial resolution of 16x16 km2 were provided by the Helmholtz-Zentrum Geesthacht within the EU BONUS SHEBA Project (Karl et al., 2019, doi: 10.5194/acp-2018-1317). Information on the riverine inputs, boundary conditions, and the model itself are provided in detail in Neumann et al. (2018b, doi: 10.5194/bg-2018-364). Nitrogen from atmospheric deposition of shipping-related nitrogen, agricultural-related nitrogen, and total nitrogen has been tagged in the model simulation according to a method by Menésguen et al. (2006, 10.4319/lo.2006.51.1_part_2.0591). Therefore, all nitrogen-containing model variables exist four times in the output: once as regular variables and once per tagged nitrogen source (total, shipping-related, agricultural-related). The concentrations of all prognostic biogeochemical model variables are given in nitrogen units according to the Redfield ratio.

Description: A marine physical biogeochemical model simulation was performed for the year 2012 covering the North Sea and Baltic Sea. Only data for the western Baltic Sea are provided here. The model output has been validated in Neumann et al. (2018a, doi: 10.5194/os-2018-71). The work was funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI, FKZ 50EW1601, https://www.io-warnemuende.de/meramo-en.html). The simulation was performed at the North-German Supercomputing Alliance (HLRN, project id: mvk00054, zulassung.hlrn.de/kurzbeschreibungen/mvk00054.pdf). The model output data were processed and evaluated on servers provided by the project 'PROSO - Prozesse von Spurenstoffen in der Ostsee' (FKZ 03F0779A). The model simulation was forced by operational meteorological data of the German Weather Service (DWD). Atmospheric nitrogen deposition data of 50x50 km2 spatial resolution were taken from the 2016 reporting of the European Measurement and Evaluation Programme (EMEP) as presented in EMEP (2016, url: http://emep.int/publ/reports/2016/EMEP_Status_Report_1_2016.pdf) and available from the Norwegian Meteorological Institute (2016, http://thredds.met.no/thredds/catalog/data/EMEP/2016_Reporting/catalog.html). Information on the riverine inputs, boundary conditions, and the model itself are provided in detail in Neumann et al. (2018b, doi: 10.5194/bg-2018-364). The concentrations of all prognostic biogeochemical model variables are given in nitrogen units according to the Redfield ratio.

Description: Global HAPPI-MIP protocol data based on the CAM4[1]/CLM4 [2] (CAM4-2degree) AGCM developed by the National Center for Atmospheric Research – Department of Energy (NCAR-DOE). CAM4/CLM4 is part of the CESM 1.0.4 model release [3]. This CERA experiment includes data of 521 AMIP simulations within the period 1979-2016, 501 AMIP-like simulations of 2106-2116 representing a climate warmer by 1.5°C than under pre-industrial conditions (1861-1880) and 501 AMIP-like simulations of 2106-2116 representing a climate warmer by 2.0°C than under pre-industrial conditions (1861-1880). Reference: [1] Neale, R. B., Richter, J. H., Conley, A. J., Park, S., Lauritzen, P. H., Gettelman, A., Williamson, D. L., Rasch, P. J., Vavrus, S. J., Taylor, M. A., Collins, W. D., Zhang, M., and Lin, S.-J.: Description of the NCAR Community Atmosphere Model (CAM4). NCAR Tech. Note NCAR/TN-485+STR, National Center for Atmospheric Research, Boulder, CO, 120 pp., 2011. [2] Oleson, K. W., Lawrence, D. M., Bonan, G. B., Flanner, M. G., Kluzek, E., Lawrence, P. J., Levis, S., Swenson, S. C., Thornton, P. E., Dai, A., Decker, M., Dickinson, R., Feddema, J., Heald, C. L., Hoffman, F., Lamarque, J.-F., Mahowald, N., Niu, G.-Y., Qian, T., Randerson, J., Running, S., Sakaguchi, K., Slater, A., Stockli, R., Wang, A., Yang, Z.-L., Zeng, Xi., and Zeng, Xu.: Technical Description of version 4.0 of the Community Land Model (CLM), NCAR Tech. Note NCAR/TN-478+STR, National Center for Atmospheric Research, Boulder, CO, 257 pp., 2010. [3] http://www.cesm.ucar.edu/models/cesm1.0/tags/index.html#CESM1_0_4

Description: Global HAPPI-MIP protocol data based on the MIROC5 [1] AGCM developed by the University of Tokyo, NIES and JAMSTEC (MIROC). This CERA experiment includes data of 160 AMIP simulations within the period 1950-2016, 100 AMIP-like simulations of 2106-2116 representing a climate warmer by 1.5°C than under pre-industrial conditions (1861-1880) and 100 AMIP-like simulations of 2106-2116 representing a climate warmer by 2.0°C than under pre-industrial conditions. Reference: [1] https://doi.org/10.1175/2010JCLI3679.1

Description: Global HAPPI-MIP protocol data based on the CanAM4 [1] AGCM developed by the Canadian Centre for Climate Modelling and Analysis (CCCma). This CERA experiment includes data of 100 AMIP simulations of the period 2006-2015, 100 AMIP-like simulations of 2106-2115 representing a climate warmer by 1.5°C than under pre-industrial conditions (1861-1880) and 100 AMIP-like simulations of 2106-2115 representing a climate warmer by 2.0°C than under pre-industrial conditions (1861-1880). Reference: [1] https://doi.org/10.1080/07055900.2012.755610

Description: Global HAPPI-MIP protocol data based on the NorESM1-HAPPI [1] AGCM developed by the NCC (NorESM Climate modeling Consortium). This CERA experiment includes data of 135 AMIP simulations within the period 1959-2016, 125 AMIP-like simulations of 2106-2116 representing a climate warmer by 1.5°C than under pre-industrial conditions (1861-1880) and 125 AMIP-like simulations of 2106-2116 representing a climate warmer by 2.0°C than under pre-industrial conditions. Reference: [1] https://doi.org/10.5194/esd-2017-115

Description: Global HAPPI-MIP protocol data based on the ECHAM6.3 [1] AGCM developed by the MPI-M (Max Planck Institute for Meteorology). This CERA experiment includes data of 100 AMIP simulations of the period 2006-2015, five AMIP simulations of 1959-2015, 100 AMIP-like simulations of 2106-2115 representing a climate warmer by 1.5°C than under pre-industrial conditions (1861-1880) and 100 AMIP-like simulations of 2106-2115 representing a climate warmer by 2.0°C than under pre-industrial conditions. The data of the current decade and of the 57-year long simulations are copies of those in https://doi.org/10.1594/WDCC/HAPPI-MIP-global-ECHAM6.3. The other data therein are flawed and are replaced herein by new versions. Reference: [1] doi:10.1002/jame.20015

Description: NARVAL-North: Measurements of microwave radiometer brightness temperatures, radar reflectivity and linear depolarisation ratio, and dropsonde atmospheric profiles from aircraft campaign over mid-latitude Atlantic out of Iceland. Aim of the campaign was the observation of clouds associated with the cold sector of mid-latitude cyclones.

Description: NARVAL-South: Measurements of microwave radiometer brightness temperatures, radar reflectivity and linear depolarisation ratio, and dropsonde atmospheric profiles from aircraft campaign over tropical Atlantic out of Barbados. Aim of the campaign was the observation of shallow convection in the trade wind region east of Barbados. website: http://www.mpimet.mpg.de/en/science/the-atmosphere-in-the-earth-system/working-groups/tropical-cloud-observation/halo/missions/

Description: NAWDEX: Measurements of microwave radiometer brightness temperatures, radar reflectivity and linear depolarisation ratio, and dropsonde atmospheric profiles from aircraft campaign over mid-latitude Atlantic out of Iceland. The campaign explored the impact of diabatic processes on disturbances of the jet stream and their influence on downstream high-impact weather through the deployment of four research aircraft. Website: http://www.pa.op.dlr.de/nawdex/

Description: NARVAL2: Measurements of microwave radiometer brightness temperatures, radar reflectivity and linear depolarisation ratio, and dropsonde atmospheric profiles from aircraft campaign over tropical Atlantic out of Barbados. During this campaign, a broad range of states of convection were observed from suppressed and shallow convection in relatively dry surroundings to deep convection in the ITCZ. website: https://www.mpimet.mpg.de/en/science/the-atmosphere-in-the-earth-system/working-groups/tropical-cloud-observation/halo/missions/

Description: Daily files of Doppler lidar measurements. Located at Hambach during the HOPE campaign.

Description: Daily files of meteorological data (near surface and soil). Stations were located during the HOPE campaign: Station 1 (sebs00) at Hambach together with mets00, Station 2 (sebs01) at Wasserwerk together with mets01.

Description: Daily files of KITCube microwave radiometer measurements. The instrument was located at Hambach during the HOPE campaign.

Description: Pyranometer network consisting of 45 members were set up during the Intensive Observation Campaign at Lindenberg / Falkenberg, 2014.

Description: Daily files of meteorological near surface measurements. Located with energy balance: station 1 (mets00) at Hambach during HOPE, station 2 (mets01) at Wasserwerk during the HOPE campaign.

Description: Daily files of Doppler Lidar (coherent measurement) data. The instrument is part of the LACROS facility that was installed on the area of a sewage treatment plant in Krauthausen, south of Juelich.

Description: Daily files of Jenoptik CHM15k ceilometer data. The instrument is part of the LACROS facility that was installed on the area of a sewage treatment plant in Krauthausen, south of Juelich during the HOPE campaign.

Description: Daily files of LACROS microwave radiometer measurements. The instrument is part of the LACROS facility that was installed on the area of a sewage treatment plant in Krauthausen, south of Juelich during the HOPE campaign.

Description: Daily files of humidity mixing ratio profiles from a combination of Raman lidar and microwave radiometer. Both instruments are part of the LACROS facility that was installed on the area of a sewage treatment plant in Krauthausen, south of Juelich during the HOPE campaign.

Description: Daily files of LACROS disdrometer data. The instrument is part of the LACROS facility that was installed on the area of a sewage treatment plant in Krauthausen, south of Juelich.

Description: Daily files of Cloudnet products. The instruments were parts of LACROS that was located on the area of a sewage treatment plant in Krauthausen, south of Juelich during the HOPE campaign.

Description: Daily files of Doppler spectrum moments based on cloud radar measurements. The instrument is part of the LACROS facility that was installed on the area of a sewage treatment plant in Krauthausen, south of Juelich during the HOPE campaign.

Description: Daily files of absolute humidity from UHOH DIAL measurements. UHOH DIAL was located at the supersite KITCube/UHOH during the HOPE campaign.

Description: Radio soundings. Starting location at Hambach during the HOPE campaign.

Description: Daily files of mean temperature profiles from UHOH Raman lidar. UHOH RL was located at the supersite KITcube during the HOPE campaign, located in small truck.

Description: Daily files of data from pyranometer network (supplemented with a micro-module for RH) consisting of 99 stations spatially distributed in 10 x 12 square km domain during HOPE-Juelich campaign.

Description: Observation data during HOPE Melpitz of the LACROS facility that was installed at the TROPOS field site of Melpitz, approximately 40 km NE of Leipzig.

Description: Airborne measurements with ACTOS during HOPE Melpitz. The airborne measurements above the measurement site Melpitz (51°31'31.18N 12°55'47.20E), ACTOS took off from airfield Beilrode (51°34'17.96N 13°3'33.15E).

Description: This version of the fully coupled catchment simulation features the atmospheric model COSMO run at 1.1km (0.01°rotlat/lon grid), the land surface model CLM and the groundwater model Parflow, both run at 400m (regular lat/lon grid). Coupled with OASIS3-MCT.

Description: Daily observations of pyranometer network consisting of 50 stations spatially distributed in 3 x 3 square km domain during HOPE-Melpitz campaign.

Description: This is the first version (v1.0) of the hydrographic part of the "Baltic and North Sea Climatology (BNSC)". The parameters provided here are water temperature and salinity on 105 depth levels. The data product comprises the time period from 1873-2015 and is based on more than one million observational profiles, which were obtained from several different data sources in the region of the Baltic, the North Sea and adjacent areas of the North Atlantic Ocean (15°W-30°E, 47°N-66°N). Intersection of observational data from different data sources is avoided and the in situ data were objected to an elaborate automatic quality control to identify erroneous observations that would bias the data product. Additionally, a correction of the temporal sampling error was applied to minimize the impact of the temporal distribution of the observations on the created temporal mean fields. The data product consists of gridded mean fields of water temperature and salinity. The spatial resolution is 0.25° in meridional and zonal direction. The depth levels are irregularly distributed: for the depth interval from 0 to 50m the distance between the single depth levels is 5m. Below 50m, the distance increases progressively by 1m to the last depth level of 4985m. The dimensions of the data product are 180*76*105 (longitude, latitude, depth). The BNSC climatology consists, on the one hand, of time series of monthly and annual mean values of the hydrographic parameters as fields of box averages. Grid boxes that show no observations are left empty. Based on these time series, decadal monthly mean fields are created for the decades 1956-1965, 1966-1975, 1976-1985, 1986-1995, 1996-2005, 2006-2015 as another part of the data product. Again, gaps remain in observational data-void regions. The third part of the data product results from above mentioned decadal mean fields: horizontally interpolated fields by application of the method of objective analysis. Consequently, this subset does not contain gaps. Available parameters: box averages: monthly and annual mean, resp. standard deviation, number of observations decadal box averages: decadal monthly mean, resp. standard deviation, mean year, standard deviation to mean year, number of years decadal interpolated mean: interpolated monthly mean, absolute median deviation, number of bins, first guess, relative interpolation error, mean year, mean distance The products are publicly available at the ICDC portal ( https://icdc.cen.uni-hamburg.de/1/daten/ocean/bnsc/)

Description: This experiment comprises 5 different simulations: - hind-cast simulations with specified dynamics from 1979 to 2013 - ERA-Interim SSTs/SICs RC1SD-base-07 T42L90MA “wave zero” (i.e. the global mean) temperature included for the Newtonian relaxation RC1SD-base-08 T42L47MA global mean temperature (wave 0) included for the Newtonian relaxation RC1SD-base-09 T42L47MA global mean temperature (wave 0) not included for the Newtonian relaxation RC1SD-base-10 T42L90MA global mean temperature (wave 0) not included for the Newtonian relaxation RC1SD-base-10a (years 2000-2014) T42L90MA global mean temperature (wave 0) not included for the Newtonian relaxation with corrected road traffic emissions and stratospheric aerosol optical properties For further studies based on simulations of the ESCiMo project and on the EMAC model please also refer to: https://www.atmos-chem-phys.net/special_issue812.html https://gmd.copernicus.org/articles/special_issue10_22.html https://www.atmos-chem-phys.net/special_issue22.html http://www.pa.op.dlr.de/~PatrickJoeckel/ESCiMo/publications/escimo_publications.html MESSy version 2.50.5 http://www.messy-interface.org

Description: Simulation with most recent version of MADE3 into the atmospheric chemistry general circulation model EMAC, including a detailed evaluation of a ten-year aerosol simulation with MADE3 as part of EMAC. Model details and setup specification are described in Kaiser et al. (Geosci. Model Dev., 2018). We compare simulation output to station network measurements of near-surface aerosol component mass concentrations, to airborne measurements of aerosol mass mixing ratio and number concentration vertical profiles, to ground-based and airborne measurements of particle size distributions, and to station network and satellite measurements of aerosol optical depth. Furthermore, we describe and apply a new evaluation method, which allows a comparison of model output to size-resolved electron microscopy measurements of particle composition. Although there are indications that fine mode particle deposition may be underestimated by the model, we obtained satisfactory agreement with the observations. Remaining deviations are of similar size as those identified in other global aerosol model studies. Thus, MADE3 can be considered ready for application within EMAC. Due to its detailed representation of aerosol mixing state, it is especially useful for simulating wet and dry removal of aerosol particles, aerosol-induced formation of cloud droplets and ice crystals as well as aerosol-radiation interactions. Besides studies on these fundamental processes, we also plan to use MADE3 for a reassessment of the climate effects of anthropogenic aerosol perturbations. Please cite Kaiser et al. (Geosci. Model Dev., 2018) if using the data.

Description: Simulation with most recent version of MADE3 into the atmospheric chemistry general circulation model EMAC, including a detailed evaluation of a ten-year aerosol simulation with MADE3 as part of EMAC. Model details and setup specification are described in Kaiser et al. (Geosci. Model Dev., 2018). We compare simulation output to station network measurements of near-surface aerosol component mass concentrations, to airborne measurements of aerosol mass mixing ratio and number concentration vertical profiles, to ground-based and airborne measurements of particle size distributions, and to station network and satellite measurements of aerosol optical depth. Furthermore, we describe and apply a new evaluation method, which allows a comparison of model output to size-resolved electron microscopy measurements of particle composition. Although there are indications that fine mode particle deposition may be underestimated by the model, we obtained satisfactory agreement with the observations. Remaining deviations are of similar size as those identified in other global aerosol model studies. Thus, MADE3 can be considered ready for application within EMAC. Due to its detailed representation of aerosol mixing state, it is especially useful for simulating wet and dry removal of aerosol particles, aerosol-induced formation of cloud droplets and ice crystals as well as aerosol-radiation interactions. Besides studies on these fundamental processes, we also plan to use MADE3 for a reassessment of the climate effects of anthropogenic aerosol perturbations. Please cite Kaiser et al. (Geosci. Model Dev., 2018) if using the data.

Description: This is the Baltic and North Sea Climatology (BNSC) for the Baltic Sea and the North Sea in the range 47 ° N to 66 ° N and 15 ° W to 30 ° E. It is the follow-up project to the KNSC climatology. The climatology was first made available to the public in March 2018 by ICDC and is published here in a slightly revised version 2. It contains the monthly averages of mean air pressure at sea level, and air temperature, and dew point temperature at 2 meter height. It is available on a 1 ° x 1 ° grid for the period from 1950 to 2015. For the calculation of the mean values, all available quality-controlled data of the DWD (German Meteorological Service) of ship observations and buoy measurements were taken into account during this period. Additional dew point values were calculated from relative humidity and air temperature if available. Climatologies were calculated for the WMO standard periods 1951-1980, 1961-1990, 1971-2000 and 1981-2010 (monthly mean values). As a prerequisite for the calculation of the 30-year-climatology, at least 25 out of 30 (five-sixths) valid monthly means had to be present in the respective grid box. For the long-term climatology from 1950 to 2015, at least four-fifths valid monthly means had to be available. Two methods were used (in combination) to calculate the monthly averages, to account for the small number of measurements per grid box and their uneven spatial and temporal distribution: 1. For parameters with a detectable annual cycle in the data (air temperature, dew point temperature), a 2nd order polynomial was fitted to the data to reduce the variation within a month and reduce the uncertainty of the calculated averages. In addition, for the mean value of air temperature, the daily temperature cycle was removed from the data. In the case of air pressure, which has no annual cycle, in version 2 per month and grid box no data gaps longer than 14 days were allowed for the calculation of a monthly mean and standard deviation. This method differs from KNSC and BNSC version 1, where mean and standard deviation were calculated from 6-day windows means. 2. If the number of observations fell below a certain threshold, which was 20 observations per grid box and month for the air temperature as well as for the dew point temperature, and 500 per box and month for the air pressure, data from the adjacent boxes was used for the calculation. The neighbouring boxes were used in two steps (the nearest 8 boxes, and if the number was still below the threshold, the next sourrounding 16 boxes) to calculate the mean value of the center box. Thus, the spatial resolution of the parameters is reduced at certain points and, instead of 1 ° x 1 °, if neighboring values are taken into account, data from an area of 5 ° x 5 ° can also be considered, which are then averaged into a grid box value. This was especially used for air pressure, where the 24 values of the neighboring boxes were included in the averaging for most grid boxes. The mean value, the number of measurements, the standard deviation and the number of grid boxes used to calculate the mean values are available as parameters in the products. The calculated monthly and annual means were allocated to the centers of the grid boxes: Latitudes: 47.5, 48.5, ... Longitudes: -14.5, -13.5, … In order to remove any existing values over land, a land-sea mask was used, which is also provided in 1 ° x 1 ° resolution. In this version 2 of the BNSC, a slightly different database was used, than for the KNSC, which resulted in small changes (less than 1 K) in the means and standard deviations of the 2-meter air temperature and dew point temperature. The changes in mean sea level pressure values and the associated standard deviations are in the range of a few hPa, compared to the KNSC. The parameter names and units have been adjusted to meet the CF 1.6 standard.

Description: The data include daily, monthly, and seasonal variables of CORDEX experiments scaled down onto a ca. 12.5 km grid over Europe (acronym: EUR-44) based on the ALARO-0 regional climate model. Each file contains a single variable and is formatted according to the CORDEX data protocol - meaning NetCDF-4 compressed, CF-1.4 compliant, with attributes and filenames according to the specified DRS. The experiments provided are evaluation, historical, rcp85, rcp45 and rcp26 using forcing data derived from the CMIP5 model CNRM-CM5. The data are provided on the model computational (native) grid.

Description: The data include daily, monthly, and seasonal variables of CORDEX experiments scaled down onto a ca. 50 km grid over Europe (acronym: EUR-44) based on the ALARO-0 regional climate model. Each file contains a single variable and is formatted according to the CORDEX data protocol - meaning NetCDF-4 compressed, CF-1.4 compliant, with attributes and filenames according to the specified DRS. The experiments provided are evaluation, historical, rcp85, rcp45 and rcp26 using forcing data derived from the CMIP5 model CNRM-CM5. The data are provided on the model computational (native) grid.

Description: EARLINET climatological lidar observations are performed on a regular schedule of one daytime measurement per week around noon (on Monday), when the boundary layer is usually well developed, and two night-time measurements per week (on Monday and Thursday), with low background light, in order to perform Raman extinction measurements. This regular schedule for observations minimizes the bias in the dataset possibly related to specific measurement conditions. The resulting dataset is used to obtain unbiased data for climatological studies. This dataset contains profiles of aerosol extinction, backscatter and lidar ratio. Several aerosol extinction/backscatter datasets can be present for the same climatological measurement in order to provide profiles either with a better temporal resolution or with an extended height range by using a larger temporal average. This is by far the largest ground based dataset on the aerosol vertical distribution, and it is the only one which is collected systematically and is covering a whole continent.

Description: Since the beginning of CALIPSO observations in June 2006 EARLINET has performed correlative measurements during nearby overpasses of the satellite at individual stations following a dedicated observational strategy. The EARLINET-CALIPSO correlative measurement plan considers the criteria established in the CALIPSO validation plan (http://calipsovalidation.hamptonu.edu). Participating EARLINET stations perform measurements, as close in time as possible and for a period of at least 30 min up to several hours, when CALIPSO overpasses their location within a horizontal radius of 100 km. Within the 16-day observational cycle of CALIPSO each station is overpassed within this distance 1-2 times during daytime (typically between 1100 and 1400 UTC) and 1-2 times during night time (typically between 0000 and 0300 UTC). Additional measurements are performed, mainly on a non-regular basis, when CALIPSO overpasses a neighboring station in order to study the horizontal variability of the aerosol distribution. The time schedule for correlative observations is calculated starting from the high-resolution ground-track data provided by NASA, and is updated and distributed to whole network weekly. The EARLINET-CALIPSO correlative dataset represents a statistically significant data set to be used for the validation and full exploitation of the CALIPSO mission, for studying the representativeness of cross sections along an orbit against network observations on a continental scale, and for supporting the continuous, harmonized observation of aerosol and clouds with remote-sensing techniques from space over long time periods.

Description: This collection contains all measurements that have been performed in the frame of the EARLINET project during the period April 2000 - December 2015. Some of these measurements are also part of the collections 'Calipso', 'Climatology', 'SaharanDust' or 'VolcanicEruption'. In addition this collection also contains measurements from the categories 'Cirrus', 'DiurnalCycles', 'ForestFires', 'Photosmog', 'RuralUrban', and 'Stratosphere'. This collection also contains measurements not devoted to any of the above categories. More information about these categories and the contributing stations can be found in the file 'EARLINET_general_introduction.pdf' accompanying this dataset.

Description: The geographical distribution of the EARLINET stations is particularlysuitable for dust observation, with stations located all around the Mediterranean(from the Iberian Peninsula in the West to the Greece and Bulgaria and Romania in the East) and in the center of the Mediterranean (Italian stations) where dust intrusions are frequent, and with several stations in the central Europe where dust penetrates occasionally. A suitable observing methodology has been established within the network, based on Saharan dust alerts distributed to all EARLINET stations. The dust alert is based on the operational outputs (aerosol dust load) of the SDS-WAS (Sand and Dust Storm- Warning and Advisory System of WMO), and the Skiron models. The alerts are diffused 24 to 36 hours prior to the arrival of dust aerosols over the EARLINET sites. Runs of measurements longer than 3-hour observations, typical for the EARLINET climatological measurements are performed at the EARLINET stations in order to investigate the temporal evolution of the dust events. All aerosol backscatter and extinction profiles related to observations of Saharan dust layers are collected in the "Saharan dust" category of the EARLINET database.

Description: Aerosols originating from volcanic emissions have an impact on the climate: sulfate and ash particles from volcanic emissions reflect solar radiation, act as cloud condensation and ice nuclei, and modify the radiative properties and lifetime of clouds, and therefore influence the precipitation cycle. These volcanic particles can also have an impact on environmental conditions and could be very dangerous for aircraft in flight. In addition to the routine measurements, further EARLINET observations are devoted to monitor volcano eruptions. The EARLINET volcanic dataset includes extended observations related to two different volcanoes in Europe Mt. Etna (2001 and 2002 eruptions), and the Eyjafjallajökull volcano in Iceland (April - May 2010 eruption). This dataset includes also events of volcanic eruptions in the North Pacific region (2008-2010) that emitted sulfuric acid droplets into the upper troposphere lower stratosphere (UTLS) height region of the northern hemisphere. The EARLINET volcanic observations in the UTLS are complemented by the long-term stratospheric aerosol observations collected in the Stratosphere category.

Description: Data contain dominating soil type in a grid cell for the whole world at 30 arc-second (~1km) horizontal resolution. The data are based on Harmonized World Soil Database (HWSD), but reclassified according to the State Soil Geographic (STATSGO) classification table of the Weather Research and Forecasting (WRF) Model for NOAH and NOAH-MP Land Surface Models (LSMs). The source of the data is HWSD version 1.21, provided by the Food and Agriculture Organization of the United Nations (FAO), the International Institute for Applied Systems Analysis (IIASA), International Soil Reference and Information Centre (ISRIC), Institute of Soil Science - Chinese Academy of Sciences (ISSCAS) and Joint Research Centre of the European Commission (JRC) in 2012 (FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.21). FAO, Rome, Italy and IIASA, Laxenburg, Austria). Horizontal resolution: 0.0083333333333333° Type/units: categorical/1-16 categories Missing_value: -9999(ascii file), 241.0 (WRF-bin) Projection: regular latitude longitude

Description: Attenuated backscatter profiles from the CALIOP satellite lidar are used to estimate cloud base heights of lower-troposphere liquid clouds (cloud base height below approximately 3 km). Even when clouds are thick enough to attenuate the lidar beam (optical thickness 〉 5), the technique provides cloud base heights by treating the cloud base height of nearby thinner clouds as representative of the surrounding cloud field. Using ground-based ceilometer data, uncertainty estimates for the cloud base height product at retrieval resolution are derived as a function of various properties of the CALIOP lidar profiles. Evaluation of the predicted cloud base heights and their predicted uncertainty using a second, statistically independent, ceilometer dataset shows that cloud base heights and uncertainties are biased by less than 10%. CBASE provides two files for each CALIOP VFM input file: one using a 40 km window to detect the cloud field base height, and one using a 100 km window. (The input CALIOP VFM dataset is organized by the daytime/nighttime half of each orbit.) The file name pattern is CBASE〈resolution〉_〈date〉T〈time〉〈day/night〉.nc (identical to the input CALIOP VFM file name with the exception of the product name). Files are organized into subdirectories by half-orbit start date.

Description: The "climate" water vapor product developed within ESA's "GOME-Evolution" project provides a consistent time series of monthly mean global maps of total column water vapor derived from the satellite instruments GOME, SCIAMACHY, and GOME-2 (Metop-A). Consistency amongst the different instruments (including cloud treatment) is substantially improved by (1) merging SCIAMACHY and GOME-2 observations to GOME pixel size, and (2) reducing the GOME-2 swath width to GOME/SCIAMACHY swath, thereby mimicking GOME-like observation conditions for all three sensors. This is Version 2.2

Description: This directory (experiment) contains volcanic SO2 data derived from limb viewing satellites for the lower stratosphere from 1998 to 2012. 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-files and integrated values of injected SO2 mass with peak latitudes and altitudes as Fortran formatted ascii files (A11,5(1X,I3),I4,4(1X,I3),5(1X,I2),I3,4(1X,I2)) for at maximum 5 events at one time. Instead of A11 I2,I5,I5 can be used to read in the components of time. The data from Dec. 1997 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). SO2volc3D_pap_T42L90r.nc: 3D SO2 for 131 events in T42L90 resolution (ECHAM-grid in grid_T42L90.nc) surface to about 80km).. SO2volc3D_pap_T63L90r.nc: same in T63L90 resolution (ECHAM-grid in grid_T63L90.nc). Here a downscaling by 0.7 for low latitude eruptions is recommended because of less removal by overshooting convection (The data in the T42 file and in the table in Bingen et al 2017 were upscaled within the measurement uncertainty to overcome the model artifact in low resolution, this applies only for the ENVISAT part from Jul. 2002 on). 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). Volcano_or_region_echam_merged_dd_mm_yyyy.txt: integrated SO2 mass injected (in kt), SAGE and ENVISAT period. The postscript-file is an example on the T42 grid, the *doc-file includes the volcano names for the data in the *.txt Files, see also http://wwww.volcano.si.edu (Smithsonian volcano database). AEROCOM-DIEHL_UMZ1_tropo11.nc: Fluxes from outgassing volcanoes in the troposphere (below 200hPa), taken from AEROCOM (Diehl, 2012; Caution, filled with odd climatology after 2009, monthly, beginning in Jan. 1950) AEROCOM-DIEHL_1297-0312_tropo11.nc: Subset beginning Dec. 1997.

Description: Please note that data representing a warmer future climate are flawed by the use of incorrect sea ice coverage data. All data of the 1.5°C and the 2.0°C experiment of this CERA experiment have been replaced by and all data of the current decade and the 57-year-long AMIP experiment have been copied to http://cera-www.dkrz.de/WDCC/ui/Compact.jsp?acronym=HAPPI-global-ECHAM6.3_v2. For detailed information refer to that experiment. Global HAPPI-MIP protocol data based on the ECHAM6.3 AGCM developed by the MPI-M (Max Planck Institute for Meteorology). This CERA experiment includes data of five AMIP simulations of the period 1959-2015 and 100 AMIP simulations of 2006-2015. In addition it includes data of 100 AMIP-like simulations of 2106-2115 representing a climate warmer by 1.5°C than under pre-industrial conditions (1861-1880) and 100 AMIP-like simulations of 2106-2115 representing a climate warmer by 2.0°C than under pre-industrial conditions.

Description: This data of the project CORDEX includes CORDEX experiments for the domain for Europe in high resolution (EUR-11) based on DHMZ's RegCM4-2 model. Each file contains a single variable in the format NetCDF-4 compressed with CF standard names (CF-1.4). The data are provided on the model computational (native) grid. Information on additional project naming conventions is specified in the project description. The data include several daily, monthly and seasonal variables from the DHMZ-RegCM4-2 simulation forced by the ERA-Interim reanalysis. Data format follows CORDEX data protocol. Please contact ivan.guettler@cirus.dhz.hr for any relevant requests for this simulation.

Description: This data of the project CORDEX includes CORDEX experiments for the domain for Europe in high resolution (EUR-11) based on DHMZ's RegCM4-2 model. Each file contains a single variable in the format NetCDF-4 compressed with CF standard names (CF-1.4). The data are provided on the interpolated (geographical) grid. Information on additional project naming conventions is specified in the project description. The data include several daily, monthly and seasonal variables from the DHMZ-RegCM4-2 simulation forced by the ERA-Interim reanalysis. Data format follows CORDEX data protocol. Please contact ivan.guettler@cirus.dhz.hr for any relevant requests for this simulation.

Description: purpose: This map shows the total organic carbon content (TOC) of surface sediments in the North Sea. It was produced by interpolation of legacy data from more than 3000 samples collected between 1960 and 2014. The distribution of this map allows the user to visualize an important marine habitat characteristic and to exploit the dataset for ecological and biogeochemical modelling. abstract: Weight percent total organic carbon (TOC) is one of the most commonly used descriptors for marine sediments. It is used to judge primary productivity of the overlying water column and refers to the amount of organic matter preserved within sediment. TOC has a major influence on biogeochemical processes occurring in sediments, including the regulation of the behavior of the other chemical species such as metals and organic pollutants. Therefore, determination of TOC is an essential component of environmental characterization analysis.This map conveys information on the weight percent TOC of seabed sediments in the North Sea. It has been produced with multivariate geostatistics (external drift kriging) using the percentage mud content as a trend variable. The underlying data set is a compilation of over 3,000 sediment samples from many national and European surveys conducted between 1960 and 2014. Due to the vintage of some samples in the database, users are advised to consider the dynamic nature of the seafloor when using the data and when creating derived surrogate based habitat maps. Also, due to the diversity of sources for the point data, users should be aware of the differing methods by which the TOC analyses were conducted. As a consequence, map confidence is not necessarily uniform and thus areas not always comparable, even though the interpolation surface may look continuous.

Description: The MPIC/DLR "climate" water vapor product, developed within ESA's "GOME Evolution" project, provides a consistent time series of monthly mean H2O columns from the satellite instruments GOME, SCIAMACHY, and GOME-2 (MetopA). Consistency amongst the different instruments (including cloud treatment) is substantially improved by (1) merging SCIAMACHY and GOME-2 observations to GOME pixel size, and (2) reducing the GOME-2 swath width to GOME/SCIAMACHY swath, thereby mimicking GOME-like observation conditions for all three sensors. WARNING: Version 1.0 is based on spectral analysis settings which have slightly changed during the GOME-2 timeseries, introducing a small but clear "jump" in the TCWV timeseries at the turn of the years 2012/2013. This version should not be used any more! Use version 〉2.2 instead! doi:10.1594/WDCC/GOME-EVL_water_vapor_clim_v2.2