Impact of Satellite-Based Ice Surface Temperature Initialization on Arctic Winter Forecasts Using the Korean Integrated Model

Abstract. Ice surface temperature (IST) is critical for representing surface energy exchange in Arctic forecasts, yet its initialization in operational numerical weather prediction (NWP) systems remains overly simplified—often inherited from background states or prescribed as spatially uniform values—due to the scarcity of reliable, spatiotemporally continuous observations. This study examines the forecast impact of realistic IST initialization using the Korean Integrated Model (KIM), a global NWP system that performs well at mid- and low-latitudes but shows limited forecast skill over the Arctic, particularly during winter. In its operational configuration, KIM uniformly initializes a fixed IST value of 271.35 K (−1.8 °C) over all sea ice-covered regions, making it a suitable testbed for this investigation. We generate a physically consistent, gap-free IST dataset using a standalone sea ice model nudged with satellite-retrieved ISTs and initialize it into KIM. Numerical experiments for the 2021–2022 Arctic winter show that the control run, despite being initialized with unrealistically warm IST, exhibits a slight cold bias in the lower troposphere—indicating an inherent cooling tendency in KIM that counterbalances artificial heating from the surface boundary. The experimental run, initialized with realistic IST, further amplifies this cold bias. Although random errors are reduced by 3–5 % in the lower atmosphere, the intensified bias ultimately degrades overall forecast performance. These results demonstrate that while realistic IST initialization influences short-range Arctic forecasts, its benefits are limited without concurrent improvements in underlying model bias. The findings underscore the need for parallel improvements in internal model processes to fully realize its benefits, thereby offering guidance for achieving meaningful gains in Arctic forecast accuracy within KIM.