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Abstract

The rate of warming in the Arctic has been much faster than the global average and the decline of sea ice decline has been accelerated. The “low-ice regime” in the future is likely to happen and impacts the Arctic environment, especially around the Arctic coasts. Hence, it is becoming increasingly critical to foresee changes in Arctic sea ice and climate states as well as their potential impacts to guide human activities from natural resource management to risk assessment decisions. While climate models project a continuous decline of sea ice on a decadal time scale, the advancement of reliable predictive skills in seasonal sea ice forecasts remains challenging. Here, we use a state-of-the-art numerical model, Regional Arctic System Model (RASM), which forecasts Arctic sea ice at time scales from weeks up to six months. RASM is a fully-coupled regional climate system model, consisting of the atmosphere, ocean, sea ice, and land components, coupled through the Community Earth System Model flux coupler. The ocean and sea ice configurations include the horizontal resolution of 1/12 degree with 45-vertical levels and 5-thickness categories, respectively. The atmosphere is configured on a 50-km grid with 40 vertical levels and dynamically downscaled using the Climate Forecasting System (CFS) Reanalysis (CFSR) and its version 2 (CFSv2). This presentation summarizes our results of summer sea ice predictions from 2011 to 2022. In particular, the effects of lead time and initial conditions are investigated on the quantitative skill of seasonal predictability of Arctic sea ice.