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

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

This datasets contains simulation output for the global hydrological models HydroPy and MPI-HM. Both used meteorological forcing from the GSWP3 dataset for the period 1979-2014 and a 50 years spinup period. The analysis of this simulations is published at https://doi.org/10.5194/gmd-2021-53 .

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.

Sentinel-3 OLCI images processed with the Atmospheric Correction for Optical Water Types, A4O [Hieronymi et al. in prep & 2023], and the water algorithm OLCI Neural Network Swarm, ONNS [Hieronymi et al., 2017]. ONNS derives inherent optical properties (IOPs) from which the concentrations of water constituents are estimated. In addition, the results of an Optical Water Type (OWT) classification based on A4O reflectances are provided [Bi and Hieronymi, 2024]. All available satellite data of a day for the region of interest are merged in a common grid at approximately original resolution. Information about the variables are given in the attached Additional Info.

The module allows for taking into account wind farms in atmospheric modelling via the wind farm parametrization by Fitch et al, 2012 in the regional climate model COSMO-CLM. Prerequisite is a wind farm mask file. Further details are given in the " Step-by-step implementation" document. Version 2.0: Update of wind farm parametrization

1 Dataset description In ocean model or Earth System model applications, the riverine freshwater inflow is an important flux affecting salinity and marine stratification in coastal areas. However, in climate change studies, the river runoff based on climate model output often has large biases on local, regional or even basin wide scales. If these biases are too large, the ocean model forced by the runoff will drift into a different climate state compared to the observed state, which is especially relevant for semi-enclosed seas like the Baltic Sea. In order to fulfil the demands for low biases in river runoff, a three-part bias correction was developed by Hagemann et al. (in prep.) that comprises different correction factors for low, medium and high percentile ranges of river runoff over Europe. First, we utilized the global hydrology model HydroPy (Stacke and Hagemann 2021) and the Hydrological Discharge (HD) model (Hagemann et al. 2020) to simulate daily discharge time series over the European domain at 1/12° horizontal resolution Sect. 1.1) from 1901-2019. Then, we bias-corrected these time series as described in Sect. 1.2 to generate bias-corrected discharges at coastal ocean boxes of the European HD model domain from 1901-2019. 1.1 Century-long high-resolution discharge simulation over Europe Analogous to Hagemann and Stacke (2022), the global hydrology model HydroPy (Vs. 1.0.2 Stacke and Hagemann 2021) and the Hydrological Discharge (HD) model (Vs. 5.2.0, Hagemann et al. 2023) were used to simulate daily discharge time series over the European domain at 1/12° horizontal resolution. Daily data of two atmospheric datasets were utilized to force HydroPy that provided the input to the HD model. The Global Soil Wetness Project Phase 3 (GWSP3; Dirmeyer et al. 2006; Kim 2017) dataset is available at 0.5° resolution from 1901-2014. Here, we used the data from 1901-1978, and then the simulated time series were continued by using the WFDE5 dataset (Cucchi et al. 2020; 0.5° resolution) from 1979-2019. 1.2 Generation of bias corrected HD discharge data In order to apply the bias correction of Hagemann et al. (in prep.) to the simulated time series of daily discharge from 1901-2019, two sets of bias correction factors were derived. The first set uses the WFDE5-based discharges and discharge station observations for the period 1979-2014. This set was used to bias-correct the simulated discharge at HD river mouths from 1979-2019. The second set uses a further discharge simulation where we continued the GSWP3-based simulation with GSWP3 forcing until 2014. Again, the set of bias-correction factors was derived for the period 1979-2014 using discharge station observations. Then, this set was applied to bias-correct the simulated discharge at HD river mouths from 1901-1978. Detailed information you can find in the specified sections of the attached PDF (https://www.wdc-climate.de/ui/entry?acronym=Biasc_hr_riverro_Eu_AdI_v1_1). Recently, a bug has been discovered in the part of the bias correction procedure, which transfers the bias correction factors from the station locations to the river mouths. Here, accidentally the bias correction factors from a previous simulation, which had utilized GSWP3 data, HydroPy and the HD model, were transferred to the river mouths for the whole considered period from 1901-2019. It can be noted that these factors still have improved the simulated inflows for most of the basins compared to the uncorrected HD model discharges. However, fixing this bug has led to general improvement for most of the basins. Fig. 1 in the attached PDF (https://www.wdc-climate.de/ui/entry?acronym=Biasc_hr_riverro_Eu_AdI_v1_1) provides an example for the major Baltic Sea sub-basins and shows the inflow biases compared to HELCOM observational estimates. Note that the other datasets of Version 1.0 (https://doi.org/10.26050/WDCC/Biasc_hr_riverro_Eu) did not change.

For the Helsinki Commission (HELCOM), annual waterborne basin inflows of total nitrogen (N) and total phosphorus (P) were compiled for the seven main Baltic Sea sub-basins (Sect. 1.1). In order to allow the utilization within a regional Earth System or ocean modelling framework, we redistributed these nutrient loads spatially and temporally using a dataset of bias corrected discharge that was generated with the Hydrological Discharge (HD) model (Sect. 1.2). Following the spatial and temporal downscaling procedure described in Sect. 1.3, we generated a dataset of daily riverine and annual direct nutrient loads (N and P) into the Baltic Sea at 1/12° resolution from 1901-2019. Detailed information you can find in the specified sections of the attached PDF https://www.wdc-climate.de/ui/entry?acronym=HELCOM_HD_info In November 2023, the bias corrected discharges were improved after a bug was found (see "Bias corrected high resolution river runoff over Europe (Version 1.0)", https://doi.org/10.26050/WDCC/Biasc_hr_riverro_Eu). Consequently, the redistributed HELCOM loads of nitrogen (N) and phosphorus (P) were also updated using the improved bias corrected discharge dataset. However, changes in the N and P loads into the Baltic Sea are marginal. The annual basin sums are the same, only daily values may have slightly changed.

Regional coupled ecosystem simulation of the Southern North Sea with the fully coupled Modular System for Shelves and Coasts (MOSSCO v1.0.2), an application layer of the Earth System Modeling Framework (ESMF). Here, we couple (1) the General Estuarine Transport Model (GETM) hydrodynamics and local waves with (2) the Model for Adaptive ECoSystems (MAECS) in the pelagic through the Framework for Aquatic Biogeochemical Models (FABM), and (3) the Ocean Margin Experiment Diagenesis (OMExDia) with added phosphorous cycle in the benthic through FABM. Forcing and boundary conditions are provided by (1) zero-gradient open boundary dissolved and particulate nutrients from a North Atlantic shelf simulation with the Ecosystem Model Hamburg (ECOHAM), (2) astronomical forcing of tides as boundary sea surface elevation, (3) surface winds from the CoastDat2 Climate Limited Area (CLM) hindcast, (4) sediment porosity from the North Sea Observation and Assessment of Habitats (NOAH) atlas, and (5) river fluxes and nutrient loads from the Hereon River database. The simulation covers the period 1 Feb 1960 to 31 Jan 2013, where the first year 1960 should be considered spin-up, such that analysis should be performed on complete production years 1961 to 2012. The simulation is performed on a curvilinear grid of the Southern North Sea, represented by a 98 x 139 logically rectangular grid, with varying spatial resolution of 3.7-66 sqkm per grid cell, and highest resolution in the Elbe Estuary. Vertical resolution is 20 layers in the pelagic on terrain-following sigma coordinates, and 15 z-levels resolving the ocean floor down to 20 cm. The output format is netCDF in the Climate and Forecast (CF) convention as much as possible. Complete three-dimensional data are available at 36-hour intervals. The coupled model system and the model setup are described in detail in Lemmen et al. (2018). Validations of the ecosystem coupling were performed, amongst others by Wirtz (2019, also describing the ecosystem model) and Slavik et al. (2019) using the same setup. Coupling to sediment processes is described by Nasermoaddeli et al. (2018) and to bentho-pelagic filtration by Lemmen (2018). The results specific to this long-term simulation have already been used by Xu et al. (2022). The model system and all of its components are available as free and open source and available from https://codebase.helmholtz.cloud/mossco/code.

Regional coupled ecosystem simulation of the Southern North Sea with the fully coupled Modular System for Shelves and Coasts (MOSSCO), an application layer of the Earth System Modeling Framework (ESMF). Here, we couple (1) the General Estuarine Transport Model (GETM) hydrodynamics and local waves with (2) the Model for Adaptive ECoSystems (MAECS) in the pelagic through the Framework for Aquatic Biogeochemical Models (FABM), and (3) the Ocean Margin Experiment Diagenesis (OMExDia) with added phosphorus cycle in the benthic through FABM. This experiment considers the increase of chlorophyll-a from the shelf towards the coast as a biologically enhanced gradient. It builds on the capability of MAECS (Wirtz and Kerimoglu 2016) to resolve adaptive traits; it tests the trophic effect of carnivory – grazing on zooplankton by juvenile fish and benthic filter feeders – and the effect of viral control on shaping this gradient by performing three sub-experiments (1) a reference including both coastal carnivory and viral control (2); a simulation with uniform carnivory, but including viral control; (3) a simulation including a carnivory gradient but no viral control. This experiment is described and evaluated by Wirtz (2019). Forcing and boundary conditions are provided by (1) zero-gradient open boundary dissolved and particulate nutrients from a North Atlantic shelf simulation with the Ecosystem Model Hamburg (ECOHAM), (2) astronomical forcing of tides as boundary sea surface elevation, (3) surface winds from the CoastDat2 Climate Limited Area (CLM) hindcast, (4) sediment porosity from the North Sea Observation and Assessment of Habitats (NOAH) atlas, and (5) river fluxes and nutrient loads from the Hereon River database. All three simulations cover the period 1 Jan 2000 to 31 Dec 2011; the longer reference simulation ends 31 Dec 2014. The experiment is performed on a curvilinear grid of the Southern North Sea, represented by a 98 x 139 logically rectangular grid, with varying spatial resolution of 3.7-66 sqkm per grid cell, and highest resolution in the Elbe Estuary. Vertical resolution is 20 layers in the pelagic on terrain-following sigma coordinates, and 15 z-levels resolving the ocean floor down to 20 cm. The output format is netCDF in the Climate and Forecast (CF) convention as much as possible. Complete three-dimensional data are available at 36-hour intervals. The experiment was extensively validated with observational data, including the spatially resolved ESA CCI surface chlorophyll product, the time series stations data for dissolved nutrients and chlorophyll from the Noordwijk and Terschelling transects, the stations at List, Norderney and Norderelbe, zooplankton time series data from Helgoland Roads as well as climatological zooplankton from the Continuous Plankton Recorder. The coupled model system and the model setup are described in detail in Lemmen et al. (2018). Validations of the ecosystem coupling were performed, amongst others Slavik et al. (2019) using the same setup. Coupling to sediment processes is described by Nasermoaddeli et al. (2018) and to bentho-pelagic filtration by Lemmen (2018). Model physics has been evaluated by Xu et al. (2022). The model system and all of its components are available as free and open source and available from https://codebase.helmholtz.cloud/mossco/code.