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

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

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

This dataset contains high-resolution WRF downscaling from 1990-12-01 to 2021-02-28 (hourly resolution). The horizontal domain has 1.5 km grid spacing covering the entire states of California and Nevada in the United States. Variables included are air temperature and relative humidity at 2 m and wind speed at 10 m above the ground. Bias correction has not been applied to Version 1.

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.

The file “GCOS_EHI_1960-2020_Atmosphere_Heat_Content_data.nc” presents an updated estimate of the atmospheric heat content (AHC) from 1960-2020 calculated using observational data and reanalyses. The estimate is given for the AHC relative to 1960. This represents an update to the record described in von Schuckmann et al. (2020) with the ENSO signal removed. The data are used in von Schuckmann et al. (2022).

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/

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.

Much of what was summarized about the North Sea dataset (Loewe, 2022) carries over to the Baltic Sea setting. To make the current text a stand-alone resource that summary is reproduced here mutatis mutandis. Despite all equivalence, there is an important difference as to gale classification arising from relocating the analysis grid that is addressed in the following as well. 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 Baltic 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 for the North Sea can be found in Loewe et al. (2005), while the variant scheme has been detailed in Loewe et al. (2006). The original 16-point analysis grid devised for the North Sea was shifted 5 degrees to the North and 15 degrees to the East to accommodate the Baltic Sea. Though formally valid at its central point (60°N, 20°E), results are representative of the Baltic Sea region between 55°N-65°N and 15°E-25°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 Coriolis-adjusted thresholds for the gale index G* of 29.9, 38.7, and 47.2 hPa for gale (G), severe gale (SG), and very severe gale (VSG), respectively. These thresholds are elevated by the Coriolis frequency ratio f(60N)/f(55N) (i.e. sin60°/sin55°) over those used with the North Sea dataset (Loewe, 2022) to ensure that gales are identified at an identical geostrophic wind and vorticity scale in either region. G* is a composite measure of gradient and Laplacian of the pressure field at each grid’s central point. Coriolis-adjustment accounts for the fact that the strength of geostrophic flow and vorticity of which G* is indicative also depends on latitude according to Coriolis frequency. Note also that previously given exceedance probabilities of 10, 2, and 1/3.65 % apply to the North Sea thresholds for the period 1971-2000, only. For the same period of reference empirical exceedance probabilities for the Baltic Sea are at 6.4, 1.0, and 0.5/3.65 %. Technically, the set of weather-typing and gale-classification rules is implemented as a lean FORTRAN code (lwtbssim.f), internally known as "Simple Lamb weather-typing scheme for the Baltic 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/DE/DATEN/Klima-und-Meer/Wetterlagen-Stuerme/wetterlagen-und-stuerme_node.html. (A corresponding English page is currently being devised 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 Baltic 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.

This data contains results of the mesoscale transport and stream model METRAS (Schlünzen et al., 2018) for 20 summer situations described in detail in Boettcher et al. (in prep.). Combined they characterise the Hamburg urban summer 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). The data cover an area of approximately 60 x 60 km² with a spatial resolution of 250 m in horizontal. 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.