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