MODES applies three-dimensional linear wave theory for the decomposition of global circulation in terms of normal-mode functions (NMFs). NMFs used by MODES are eigensolutions of the linearized primitive equations in the terrain-following sigma coordinates and were derived by Kasahara and Puri (1981, Mon. Wea. Rev). The available data are three data sets (40 years), calculated from ERA5 reanalyses by modal filtering of certain wave components, here Kelvin waves (KW), Mixed Rossby-gravity waves (MRG) and Rossby wave n=1 (Rosn1). Near-realtime modal decompositions of ECMWF deterministic forecasts, using the same tool (MODES) as has been used for the generation of the dataset are under this URL: https://modes.cen.uni-hamburg.de/

The spectral longwave feedback parameter quantifies the change in Earth's spectrally resolved outgoing longwave radiation (OLR) in response to warming. It contains the radiative signature of all longwave feedbacks making it a key quantity influencing Earth's climate sensitivity. By spectrally resolving these changes in OLR, one can gain important information about the underlying feedback processes. This experiment contains spectrally resolved radiative quantities that can be used for the calculation of the global mean all-sky spectral longwave feedback parameter based on seasonal and interannual variability, using both satellite observations and simulations. This dataset was updated to provide more information on the sensitivity of the spectral longwave feedback parameter on relative humidity changes as well as on the impact of the surface feedback at different surface temperatures.

This data contains results of the mesoscale transport and stream model METRAS (Schlünzen et al., 2018) for 43 winter situations described in detail in Boettcher et al. (in prep.). Combined they characterise the Hamburg urban winter climate based on statistical-dynamical downscaling (Boettcher et al., in prep.). They are used as reference climate for quantifying the impact of climate adaptation measures. The situation selection is based on analysed in-situ data for years 1981 to 2010 (Boettcher et al., in prep.). The data include wind speed, wind direction, wind components, real air temperature, relative humidity, total air pressure and total air density at lowest model level (about 10 m above ground). A non-uniform grid is used. The data cover an area of approximately 240 x 240 km² with a horizontal spatial solution of 6000 m at the lateral boundaries and down to 250 m resolution in the inner domain. Forcing data are ECMWF analysis data at lateral and upper model boundaries. Each situation covers 3 days of model time. Data have a resolution of 30 minutes.

An operational, single-polarized X-band weather radar (WRX) provides measurements in Hamburg’s city center since 2013. This local area weather radar (LAWR) is located on the rooftop of the high-rise building "Geomatikum" in Hamburg (HHG), which is the location of the Meteorological Institute of the Universität Hamburg. The radar operates at one beam elevation angle with a high temporal 30 s, range 60 m, and sampling 1° resolution refining observations of the German nationwide C-band radars within a 20 km scan radius. Several sources of radar-based errors were adjusted gradually improving the measurement variables, e.g. the radar calibration, alignment, attenuation, noise, non-meteorologial echoes. This experiment includes data sets of the equivalent radar reflectivity factor (dbz) in level 1 (without attenuation correction) and the rainfall rate (rr) in level 2 (applied attenuation correction). The WRX/LAWR HHG measurements were calibrated and evaluated with measurements of micro rain radars (MRR). With this high-quality and -resolution weather radar product, refined studies on the spatial and temporal scale of urban precipitation will be possible. For example, the data sets will be used for further hydrological research in an urban area within the project Sustainable Adaption Scenarios for Urban Areas – Water from Four Sides of the Cluster of Excellence Climate Climatic Change, and Society (CliCCS). This work was partly funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany‘s Excellence Strategy – EXC 2037 'CLICCS - Climate, Climatic Change, and Society' – Project Number: 390683824, contribution to the Center for Earth System Research and Sustainability (CEN) of Universität Hamburg. Now a more recent version (Version 2) exists with the following changes: - We provide daily instead of hourly files to reduce the number of files for better data handling. For the days 23.09.2014, 12.03.2015, 09.06.2015, 05.07.2017, and 01.02.2018 there are two files to avoid additional time dependencies of variables because of changes in calibration or alignment parameters. - We changed the data type (double to int64) and the unit days since 1970-01-01 to seconds since 1970-01-01 of the time coordinate. - We changed the standard names / long names of the variables azimuth, range and ele. - We added the integer variable grid_mapping with the attributes grid_mapping_name ("radar_lidar_radial_scan"), latitude_of_projection_origin, longitude_of_projection_origin and height_of_projection_origin, as suggested by the CfRadial conventions. Since the grid_mapping variable provides the same information as the variables lat_center, lon_center and zsl_center, we removed them. We added the attribute grid_mapping to the variable rr and dbz.

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.

OceanRAIN version 1.0, OceanRAIN-W - Water cycle components, 73 along-track parameters for 8 ships, 6.83 million minutes in total, temporally continuous data for each ship, 1-minute-resolution

The spectral longwave feedback parameter quantifies the change in Earth's spectrally resolved outgoing longwave radiation (OLR) in response to warming. It contains the radiative signature of all longwave feedbacks making it a key quantity influencing Earth's climate sensitivity. By spectrally resolving these changes in OLR, one can gain important information about the underlying feedback processes. This experiment contains spectrally resolved radiative quantities that can be used for the calculation and interpretation of the global mean all-sky spectral longwave feedback parameter based on seasonal and interannual variability, using both satellite observations and simulations. This is an updated version of the experiment. Compared to the first version, this version contains more data on the sensitivity of the spectral longwave feedback parameter on relative humidity changes. Additionally, this version also contains data on the contribution of the surface feedback for different surface temperatures.

This dataset contains the results of nine simulations performed for the validation of the snow cover and precipitation scheme used in the microscale, obstacle-resolving model MITRAS v3.0 (Salim et al., 2013; Schluenzen et al., 2018), v3.1 (Ferner et al. 2023), and v3.3 (Samsel et al. 2025, in review). The model domain extends 240 m x 210 m horizontally and includes orography, slanted roofs, obstacle corners and different surface cover classes. The simulations were performed using different model versions, initial temperatures, precipitation and processing modes. The simulations cover 62 minutes model time, starting at 7:30 am model time, with a temporal resolution of 5 seconds or 5 minutes. This dataset contains a selection of output variables, control variables are not included. The file names of the data sets are composed as follows. {temperature}_{intensity}_{model version}_{comment}_{case ID}.nc There are 3 intensities for temperature: low, medium, high Associated values are: T_low = 272 K, T_medium = 280 K, T_high = 288 K Three versions are considered: v3p0 = MITRAS v3.0 (initial version), v3p1 = MITRAS v3.1 (inclusion of warm rain scheme), v3p3 = MITRAS v3.3 (inclusion of winter parameterisation scheme) Special cases are 'noparallel', where the simulation was performed with the parallelisation mode switched off, and 'noprecip', where no precipitation is initialised. Example: T_high_v3p1_Hwr.nc

ETCCDI indices calculated from two km-scale global models developed within the nextGEMS project (https://nextgems-h2020.eu/): ICON-Sapphire (Hohenegger et al. 2023) and IFS-FESOM (Rackow et al. 2025). The indices are based on the 30-year production simulations of nextGEMS, cycle 4 with a spatial resolution of about 10km (Segura et al. 2025). Here, we provide them in the 29-year period 2021-2049 (as the first year, 2020, is incomplete for IFS), driven by the high-emission pathway SSP3-7.0. The original data and the derived indices are available on the unstructured HEALPix grid (Górski et al. 2005). HEALPix organises data at discrete resolutions or zoom levels. Here, the highest resolved zoom level 9 (about 13km grid spacing corresponding to about 3 million grid cells globally) and the intermediate (“CMIP6-like”) zoom level 6 (about 102km, 50’000 grid cells) are provided. The data were processed by Lukas Brunner (https://orcid.org/0000-0001-5760-4524), using a Climate Data Operators (https://code.mpimet.mpg.de/projects/cdo/embedded/index.html) implementation of the ETCCID indices: code on GitHub (https://doi.org/10.5281/zenodo.15582463). Time-mean plots of all indices are available on Zenodo: https://doi.org/10.5281/zenodo.15613611 If you use the indices, please cite this dataset and the accompanying publication: Brunner L., B. Poschlod, E. Dutra, E. M. Fischer, O. Martius, and J. Sillmann (2025): A global perspective on the spatial representation of climate extremes from km-scale models. Environmental Research Letters, https://doi.org/10.1088/1748-9326/ade1ef

This experiment contains sensitivity test results (Ferner et al. 2023) of 11 simulations with the microscale, obstacle-resolving model MITRAS v 3.1 (Salim et al., 2018; Schluenzen et al., 2018) for a domain of 1.6 x 1.8 km² around Hamburg City Hall in Hamburg. The domain contains various street configurations, open spaces, water surfaces, orography and building heights. The simulations were performed with different initial wind speeds, rain amounts, wind directions, and domain configurations. The simulations cover 1:40 hours, starting at 7:30 LST (LST refers to Local Solar Time), with a temporal resolution of 10, 1 or 5 minutes. This experiment contains a selection of output variables, control variables are not included. The file names of the data sets are composed as follows. {precipitation}_{intensity}_{windspeed}_ {intensity}_{winddirection}_{value}_{case ID}.nc There are 3 intensities: low, medium, high Associated values are these: pr_low = 0.5 mm, pr_medium =0.9 mm, pr_high = 1.7 mm (after 10 minutes) ff_low = 2 m/s, ff_medium = 4 m/s, ff_high = 4 m/s Example: pr_medium_ff_low_dd_270_ML27.nc