The concurrent simulation of ocean circulations and ocean tides is carried out with the Max-Planck Institute Ocean Model (MPIOM/TP6M L40 mpiom-1.6.3.) forced by the full luni-solar tidal potential as an additional body force and by the surface fluxes of momentum, heat and freshwater derived the NCEP/NCAR reanalysis for the period 1981-2012. A tripolar grid with a horizontal resolution of about 0.1 degrees is used. There are total 40 vertical levels in z-coordinates. More details can be find in Li and von Storch (2020). The file name of the data sets is composed as follows. STORMTIDE2_TP6ML40_<variable_acronym>_3d_1hr_<date>_<run>.nc There are five 3-dimensional hourly variables: sea_water_potential_temperature (tho), sea_water_salinity (sao), sea_water_x_velocity (u), sea_water_y_velocity (v) and upward_sea_water_velocity (w), for January, April, July and October of 2012. 2012 is the last year of the simulation. Storing four months hourly was doable at the time when the simulation is produced. One run is provided.

Reflectances measured in the visible frequency range at three channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observation Satellite (EOS) TERRA were used to derive the melt pond fraction on Arctic sea ice using an artificial neural network. This analysis was done on reflectances gridded onto a polar-stereographic grid tangent to the Earths' surface at 70 deg N with 500 m grid resolution. The reflectances used originate from the 8-day composite reflectances provided via https://wist.echo.nasa.gov/api/ as product: "MODIS surface Reflectance 8-Day L3 Global 500m SIN Grid V005". After gridding and flagging for clouds and other disturbances the artificial neural network was applied, providing fractions of three surface classes: 1) melt ponds, 2) sea ice and snow, and 3) open water at 500 m grid resolution. This data has been interpolated onto a similar polar-stereographic grid but with 12.5 km grid resolution. The data set offered here comprises several data layers: the melt pond fraction, its standard deviation, the open water fraction, and the number of individual valid grid cells with 500 m grid resolution included in each 12.5 km grid cell. In addition, in three separate data layers melt pond fraction, its standard deviation, and the open water fraction are given only for those grid cells (with 12.5 km grid resolution) where more than 90 % of the native 500 m grid resolution data indicate clear sky conditions. Grid cells with an open water fraction larger than 85 % have been generally flagged as invalid. The data set is updated annually.

Reflectances measured in the visible frequency range at three channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observation Satellite (EOS) TERRA were used to derive the melt pond fraction on Arctic sea ice using an artificial neural network. This analysis was done on reflectances gridded onto a polar-stereographic grid tangent to the Earths' surface at 70 deg N with 500 m grid resolution. The reflectances used originate from the 8-day composite reflectances provided via https://wist.echo.nasa.gov/api/ as product: "MODIS surface Reflectance 8-Day L3 Global 500m SIN Grid V005". After gridding and flagging for clouds and other disturbances the artificial neural network was applied, providing fractions of three surface classes: 1) melt ponds, 2) sea ice and snow, and 3) open water at 500 m grid resolution. This data has been interpolated onto a similar polar-stereographic grid but with 12.5 km grid resolution. The data set offered here comprises several data layers: the melt pond fraction, its standard deviation, the open water fraction, and the number of individual valid grid cells with 500 m grid resolution included in each 12.5 km grid cell. In addition, in three separate data layers melt pond fraction, its standard deviation, and the open water fraction are given with those grid cells (with 12.5 km grid resolution) flagged as invalid where less than 90 % of the native 500 m grid resolution data indicate clear sky conditions. Valid for all these layers is, that grid cells with an open water fraction larger than 85 % have been flagged as invalid as well. The data set offered here is version 02 of the melt pond data set. The main difference to version 01 is a bias correction carried out to remove a positive bias in the melt pond fraction and in the open water fraction.

Reflectances measured in the visible frequency range at three channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observation Satellite (EOS) TERRA were used to derive the melt pond fraction on Arctic sea ice using an artificial neural network. This analysis was done on reflectances gridded onto a polar-stereographic grid tangent to the Earths' surface at 70 deg N with 500 m grid resolution. The reflectances used originate from the 8-day composite reflectances provided via https://wist.echo.nasa.gov/api/ as product: "MODIS surface Reflectance 8-Day L3 Global 500m SIN Grid V005". After gridding and flagging for clouds and other disturbances the artificial neural network was applied, providing fractions of three surface classes: 1) melt ponds, 2) sea ice and snow, and 3) open water at 500 m grid resolution. This data has been interpolated onto a similar polar-stereographic grid but with 12.5 km grid resolution. The data set offered here comprises several data layers: the melt pond fraction, its standard deviation, the open water fraction, and the number of individual valid grid cells with 500 m grid resolution included in each 12.5 km grid cell. In addition, in three separate data layers melt pond fraction, its standard deviation, and the open water fraction are given with those grid cells (with 12.5 km grid resolution) flagged as invalid where less than 90 % of the native 500 m grid resolution data indicate clear sky conditions. Valid for all these layers is, that grid cells with an open water fraction larger than 85 % have been flagged as invalid as well. The data set offered here is version 02 of the melt pond data set. The main difference to version 01 is a bias correction carried out to remove a positive bias in the melt pond fraction and in the open water fraction.

Reflectances measured in the visible frequency range at three channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Earth Observation Satellite (EOS) TERRA were used to derive the melt pond fraction on Arctic sea ice using an artificial neural network. This analysis was done on reflectances gridded onto a polar-stereographic grid tangent to the Earths' surface at 70 deg N with 500 m grid resolution. The reflectances used originate from the 8-day composite reflectances provided via https://wist.echo.nasa.gov/api/ as product: "MODIS surface Reflectance 8-Day L3 Global 500m SIN Grid V005". After gridding and flagging for clouds and other disturbances the artificial neural network was applied, providing fractions of three surface classes: 1) melt ponds, 2) sea ice and snow, and 3) open water at 500 m grid resolution. This data has been interpolated onto a similar polar-stereographic grid but with 12.5 km grid resolution. The data set offered here comprises several data layers: the melt pond fraction, its standard deviation, the open water fraction, and the number of individual valid grid cells with 500 m grid resolution included in each 12.5 km grid cell. In addition, in three separate data layers melt pond fraction, its standard deviation, and the open water fraction are given only for those grid cells (with 12.5 km grid resolution) where more than 90 % of the native 500 m grid resolution data indicate clear sky conditions. Grid cells with an open water fraction larger than 85 % have been generally flagged as invalid. The data set is updated annually.

In work package 6 of the nextGEMS project, several ocean-only model runs were performed with FESOM (Version 2.0) and ICON-O (Version 2.6.6), to test the sensitivity of the upper tropical Atlantic to different settings of the vertical mixing scheme. Two different mixing schemes were tested: TKE and KPP. For TKE, we tested different settings of the c_k parameter (0.1, 0.2 and 0.3), and for KPP different settings of the critical bulk Richardson number (0.3 and 0.27). These runs were done with both ICON-O and FESOM, to enable a comparison of the effects of the vertical mixing settings across different models. From ICON-O only, there are some additional TKE runs available, where we increased the interior ocean background mixing, and switched on the Langmuir turbulence parameterisation. There is also an ICON-O run which uses the FESOM default forcing bulk formulae, to check how much of the differences between the models originates from their different default bulk formulae. All model runs are ocean only, forced with hourly ERA5 reanalysis data. The horizontal resolution is 10km (for FESOM, the extratropical regions have a coarser grid). The output from the tropical Atlantic from these model runs is provided here, with a high temporal resolution of 3 hours, and interpolated to a 0.1°x0.1° latitude-longitude grid. Please read the readme before using the data: https://www.wdc-climate.de/ui/entry?acronym=nextGEMSWp6OceanREADME nextGEMS is funded through the European Union’s Horizon 2020 research and innovation program under the grant agreement number 101003470.

The WOCE/ARGO Global Hydrographic Climatology (WAGHC) is concieved as the update of the previous WOCE Global Hydrographic Climatology (WGHC) (Gouretski and Koltermann, 2004). The following improvements have been made compared to the WGHC: 2) finer spatial resolution (0.25 degrees Lat/Lon compared to 0.5 degrees for WGHC); 3) finer vertical resolution (65 compared to 45 WGHC standard levels); 4) monthly temporal resolution compared to the all-data-mean WGHC parameters; 5) narrower overall time period; 6) calculation of the mean year corresponding to the optimally interpolated temperature and salinity values; 7) depth of the upper mixed layer. Similar to the WGHC the optimal spatial interpolation is performed on the local isopycnal surfaces. This approach diminishes the production of the artificial water masses. In addition to the isopycnally interpolated parameters parameter values interpolated on the isobaric levels are also provided. The monthly gridded vertical profiles extend to the depth of 1898 m, below only annual mean parameter values are available. Additionally, there is a dataset and a map available providing indexes for selected regions of the world ocean. Finally, the comparison with the last update of the NOAA World Ocean Atlas (Locarnini et al, 2013) was done.

The WOCE/ARGO Global Hydrographic Climatology (WAGHC) is concieved as the update of the previous WOCE Global Hydrographic Climatology (WGHC) (Gouretski and Koltermann, 2004). The following improvements have been made compared to the WGHC: 2) finer spatial resolution (0.25 degrees Lat/Lon compared to 0.5 degrees for WGHC); 3) finer vertical resolution (65 compared to 45 WGHC standard levels); 4) monthly temporal resolution compared to the all-data-mean WGHC parameters; 5) narrower overall time period; 6) calculation of the mean year corresponding to the optimally interpolated temperature and salinity values; 7) depth of the upper mixed layer. Similar to the WGHC the optimal spatial interpolation is performed on the local isopycnal surfaces. This approach diminishes the production of the artificial water masses. In addition to the isopycnally interpolated parameters parameter values interpolated on the isobaric levels are also provided. The monthly gridded vertical profiles extend to the depth of 1898 m, below only annual mean parameter values are available. Additionally, there is a dataset and a map available providing indexes for selected regions of the world ocean. Finally, the comparison with the last update of the NOAA World Ocean Atlas (Locarnini et al, 2013) was done.

This dataset includes all variables of the model experiment “simA historical” conducted with the global ocean-sea ice-biogeochemistry model FESOM1.4-REcoM2 with ice-shelf cavities and eddy-permitting resolution on Antarctic shelves. For this experiment, both atmospheric CO2 concentrations and all other atmospheric forcing variables (e.g., air temperature, winds, humidity, precipitation) vary throughout the simulation. Output is provided from 1980-2014 (monthly and annual output frequency) and 1990-2009 (daily output frequency). The years 1950-1979 are interpreted as spin-up and not provided here. The output is sorted by output frequency (monthly or daily) and model component (FESOM or REcoM). The data is sorted as follows: - annual_FESOM - annual_REcoM - monthly_FESOM - monthly_REcoM - daily_FESOM - daily_REcoM Filname convention: Variable_outputFreq_FESOM1.4-REcoM2_experimentName_experimentTime_year.nc Computing resources were provided by the North-German Supercomputing Alliance (HLRN) project hbk00079.

This dataset includes all variables of the model experiment “simA ssp126” conducted with the global ocean-sea ice-biogeochemistry model FESOM1.4-REcoM2 with ice-shelf cavities and eddy-permitting resolution on Antarctic shelves. For this experiment, both atmospheric CO2 concentrations and all other atmospheric forcing variables (e.g., air temperature, winds, humidity, precipitation) vary throughout the simulation. Output is provided from 2015-2100. The output is sorted by output frequency (monthly or daily) and model component (FESOM or REcoM). The data is sorted as follows: - annual_FESOM - annual_REcoM - monthly_FESOM - monthly_REcoM Filname convention: Variable_outputFreq_FESOM1.4-REcoM2_experimentName_experimentTime_year.nc Computing resources were provided by the North-German Supercomputing Alliance (HLRN) project hbk00079.