Seasonal Prediction of Regional Arctic Sea Ice Using the High-Resolution Climate Prediction System CMA-CPSv3

Sea ice is a central part of the Arctic climate system, and its changes have a significant impact on the Earth's climate. Yet, its state, especially in summer, is not fully understood and correctly predicted in dynamical forecast systems. In this study, the seasonal prediction skill of Arctic sea ice is investigated in a high-resolution dynamical forecast system, the China Meteorological Administration Climate Prediction System (CMA-CPSv3). A 7-month-long retrospective forecast is made every other month from 2001 to 2021. Employing the anomaly correlation coefficient as the metric of the prediction skill, we show that CMA-CPSv3 can predict regional Arctic sea ice extent and sea ice thickness up to 7 lead months. The Bering Sea exhibits the highest prediction skill among the 14 Arctic subregions. CMA-CPSv3 outperforms the anomaly persistence forecast in the Bering Sea, Sea of Okhotsk, Laptev Sea, and East Siberian Sea. The sources of the sea ice prediction skill partly come from the good performance of upper ocean temperature in CMA-CPSv3. This holds true not only for winter sea ice in the Arctic marginal seas but also for summer sea ice in several Arctic central seas. Furthermore, CMA-CPSv3 exhibits a strong relationship between the variability of sea ice and surface heat fluxes. This underscores the importance of enhancing the representation of air-sea heat exchanges in dynamical forecast systems to improve the prediction skill of sea ice. The reduction of Arctic sea ice has a significant impact on the climate and ecosystems, and accurately predicting Arctic sea ice is of broad interest. In this work, we investigate the seasonal prediction skill of sea ice in a high-resolution climate model. Using the anomaly correlation coefficient as the skill metric, we find that the prediction skill of sea ice is good up to 7 months and varies by region and target month. Notably, the Bering Sea shows the highest prediction accuracy among the 14 Arctic subregions. Then, we explore the sources of sea ice prediction skill and find that the skill is closely related to the good performance of upper ocean temperature in the model. Furthermore, we show that the regional Arctic sea ice variability is significantly modulated by surface heat fluxes. These results suggest that improving the representation of air-sea heat exchanges in climate models can enhance the prediction skill of sea ice. Our study contributes to an improved understanding and predicting of the Arctic sea ice variability. The China Meteorological Administration Climate Prediction System (CMA-CPSv3) is used for seasonal predictions of Arctic sea ice CMA-CPSv3 has skill to predict regional Arctic sea ice up to 7 months and shows the highest skill in the Bering Sea Good performance of ocean subsurface temperature provides crucial sources of regional sea ice prediction skills