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.

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.

The field campaign LOFZY 2005 (LOFoten ZYklonen, engl.: Cyclones) was carried out in the frame of Collaborative Research Centre 512, which deals with low-pressure systems (cyclones) and the climate system of the North Atlantic. Cyclones are of special interest due to their influence on the interaction between atmosphere and ocean. Cyclone activity in the northern part of the Atlantic Ocean is notably high and is of particular importance for the entire Atlantic Ocean. An area of maximum precipitation exists in front of the Norwegian Lofoten islands. One aim of the LOFZY field campaign was to clarify the role cyclones play in the interaction of ocean and atmosphere. In order to obtain a comprehensive dataset of cyclone activity and ocean-atmosphere interaction a field experiment was carried out in the Lofoten region during March and April 2005. Employed platforms were the Irish research vessel RV Celtic Explorer which conducted a meteorological (radiosondes, standard parameters, observations) and an oceanographic (CTD) program. The German research aircraft Falcon accomplished eight flight missions (between 4-21 March) to observe synoptic conditions with high spatial and temporal resolution. In addition 23 autonomous marine buoys were deployed in advance of the campaign in the observed area to measure drift, air-temperature and -pressure and water-temperature. In addition to the published datasets several other measurements were performed during the experiment. Corresonding datasets will be published in the near future and are available on request. Details about all used platforms and sensors and all performed measurements are listed in the fieldreport. The following datasets are available on request: ground data at RV Celtic Explorer

The drift buoys experiment FRAMZY 2007 consisted of the deployment and tracking of an array of 29 drifting autonomous buoys (16 ice, 13 water) in the Fram Strait region. The buoys were deployed in March 2007 and sampled data until end of April 2007. The aim of the experiment was to study the Atmosphere-Ocean interaction and the Atmosphere-Ice-Ocean interaction, especially the impact of cyclones on the energy budget of sea ice and ocean surface. FRAMZY 2007 was the third one in a series of five field experiments (1999,2002,2008,2009) carried out in the frame of the Collaborative Research Centre 512 (Cyclones and the North Atlantic Climate System) funded by the German Science Foundation.

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.

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.

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.

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.