This study , led by Lorenzo Zampieri, and co-authored by Marika Holland, illustrates a novel method based on machine learning for reducing the systematic surface temperature errors that characterize multiple atmospheric reanalyses in sea-ice-covered regions of the Arctic under clear-sky conditions. The corrected reanalysis temperature can be employed to support polar research activities, and in particular to better simulate the evolution of the interacting sea ice and ocean system within numerical models.