Impact of satellite-based ice skin temperature initialization on Arctic winter forecasts using the Korean integrated model

Abstract Ice skin temperature (IST) is critical for representing surface energy exchange in Arctic, yet its initialization in operational numerical weather prediction (NWP) systems is often oversimplified, typically inherited from background states or prescribed as spatially uniform values, because reliable, spatiotemporally continuous observations are scarce. This study examines the impact of satellite-based IST initialization using the Korean Integrated Model (KIM), a global NWP system, that operationally initializes IST with a fixed 271.5 K over sea ice, making a suitable testbed. To provide a more realistic surface boundary, we generate a gap-free, physically consistent IST dataset with a standalone sea-ice model nudged by satellite-retrieved ISTs, and then use it to initialize KIM. Winter 2021–2022 experiments reveal that the control run, despite initializing IST with warm value, exhibits a slight lower-tropospheric cold bias relative to ERA5, implying an inherent cooling tendency that counterbalances boundary-imposed warming. Switching to the satellite-based IST for initialization reduces random error by up to 5% but introduces a systematic cold shift in the lower troposphere, moving the system farther from ERA5 in a mean-state sense. Thus, while satellite-based IST initialization measurably influences short-range Arctic forecasts, its benefits are limited by pre-existing model biases. Diagnostics indicate that KIM simulates systematically smaller liquid cloud amounts than ERA5, which weakens downward longwave radiation and enhances surface cooling, likely contributing to the colder conditions.