Abstract/Summary

We establish a methodology for assimilating satellite observations of ice surface temperature (IST) into a coupled ocean and sea-ice model. The method corrects the 2 meter air temperature based on the difference between the modelled and the observed IST. Thus the correction includes biases in the surface forcing and the ability of the model to convert incoming parameters at the surface to a net heat flux. A multi-sensor, daily, gap-free surface temperature analysis has been constructed over the Arctic region. This study revealed challenges estimating the ground truth based on buoys measuring IST, as the quality of the measurement varied from buoy to buoy. With these precautions we find a cold temperature bias in the remotely sensed data, and a warm bias in the modelled data relative to ice mounted buoy temperatures, prior to assimilation. As a consequence, this study weighted the modelled IST and the observed IST equally in the correction. The impact of IST was determined for experiments with and without the assimilation of IST and sea-ice concentration. We find that assimilation of remotely sensed data results in a cooling of IST, which improves the timing of the snow melt onset. The improved snow cover in spring is only based on observations from one buoy, thus additional good quality observations could strengthen the conclusions. The ice cover and the sea-ice thickness are increased, primarily in the experiment without sea-ice concentration assimilation.