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We assess prediction skill in the Arctic and Antarctic in the prototype MiKlip prediction system that is based on the Max Planck Institute Earth System Model (MPI-ESM). The three-dimensional temperature and salinity fields in the ocean in the decadal hindcasts are initialized from GECCO2 and ORA-S4 and the atmosphere is initialized with ERA40 and ERA-Interim, while sea ice is not initialized. We show that the initialization can improve the predictability of sea surface temperatures and air temperature in certain polar regions. In the Arctic these areas include the North Pacific Ocean, the North Atlantic Ocean and the East-Siberian Sea. The skill improvement mainly derives from a better representation of observed variability patterns, since the initialization of oceanic and atmospheric parameters enables the simulations to start from the correct phase of natural variability. We additionally analyse prediction skill in sea-ice variables using different metrics.