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  • 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.

  • This experiment aims to investigate the impact of anthropogenic climate change to the stratification of the Brazilian shelf waters under a strong warming scenario (RCP8.5). Earth System Models (ESMs) predict a stronger increase in stratification over the equatorial regions, due to the increased surface warming. However, they cannot account for all relevant regional processes due to their coarse horizontal resolution. One of the more relevant local processes unresolved by ESMs is the upwelling of South Atlantic Central Water along subtropical Brazil. It brings cold and nutrient-rich waters towards the coast to the most productive shelf regions along the Brazilian coastal waters and is known to play an important role in the stratification of the South Brazil Bight. By including this process in our analysis through our downscaling experiment, we can provide a more complete assessment of the effects of increased greenhouse gas emissions in the stratification of the Brazilian continental shelf.

  • 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.

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