An ensemble-based data assimilation framework for a coupled ocean–atmosphere model is applied to investigate the influence of assimilating different types of ocean observations on the ocean and atmosphere simulation. The data assimilation is performed with the parallel data assimilation framework (PDAF) for the climate model AWI-CM. Observations of the ocean, namely satellite sea-surface temperature (SST) and temperature and salinity profiles, are assimilated into the ocean component. The atmospheric state is only influenced by the model dynamics. Different assimilation scenarios were carried out with different combinations of observations to investigate to what extent the assimilation into the coupled model leads to a better estimation of the state of the ocean as well as the atmosphere. The influence of the data assimilation is assessed by comparing the ocean prediction with dependent and independent ocean observations. For the atmosphere, the assimilation result is compared with the ERA-Interim atmospheric reanalysis data. The ocean temperature and salinity are improved by all the assimilation scenarios in the coupled system. The assimilation leads to a response of the atmosphere throughout the troposphere and impacts the global atmospheric circulation. Globally the temperature and wind speed are improved in the atmosphere on average.