To increase our capability to predict Arctic sea ice and climate, we have developed a coupled atmosphere‐sea ice‐ocean model configured for the pan‐Arctic with sufficient flexibility. The Los Alamos Sea Ice Model is coupled with the Weather Research and Forecasting Model and the Regional Ocean Modeling System in the Coupled Ocean‐Atmosphere‐Wave‐Sediment Transport modeling system. It is well known that dynamic models used to predict Arctic sea ice at short‐term periods strongly depend on model initial conditions. Parallel Data Assimilation Framework is implemented into the new modeling system to assimilate sea ice observations and generate skillful model initialization, which aid in the prediction procedures. The Special Sensor Microwave Imager/Sounder sea ice concentration, the CyroSat‐2, and Soil Moisture and Ocean Salinity sea ice thickness are assimilated with the localized error subspace transform ensemble Kalman filter. We conduct Arctic sea ice prediction for the melting seasons of 2017 and 2018. Predictions with improved initial sea ice conditions show reasonable sea ice evolution and small biases in the minimum sea ice extent, although the ice refreezing is delayed. Our prediction experiments suggest that the use of appropriate uncertainty for the observed sea ice thickness can lead to improved spatial distribution of the initial ice thickness and thus the predicted sea ice distribution. Our new modeling system initialized by the output of the National Centers for Environmental Prediction Climate Forecast System seasonal forecasts with data assimilation can significantly increase the sea ice prediction skills in sea ice extent for the entire Arctic as well as in the Northern Sea Route compared with the predictions by the National Centers for Environmental Prediction Climate Forecast System.

中文翻译：

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利用新开发的全耦合区域模型和卫星海冰观测资料进行季节性北极海冰预报

为了提高我们预测北极海冰和气候的能力，我们开发了一种为泛北极配置的大气-海冰-海洋耦合模型，具有足够的灵活性。洛斯阿拉莫斯海冰模型与天气研究和预报模型以及区域-海洋-大气-波浪-泥沙耦合模拟系统中的区域海洋模拟系统相结合。众所周知，用于短期预测北极海冰的动力学模型在很大程度上取决于模型的初始条件。新的建模系统中实施了并行数据同化框架，以同化海冰观测并生成熟练的模型初始化，从而有助于进行预测程序。特殊传感器微波成像仪/海底冰浓度，CyroSat-2，用局部误差子空间变换集成卡尔曼滤波器将土壤水分和海洋盐分的海冰厚度同化。我们对2017年和2018年的融化季节进行北极海冰预测。尽管初始冰冻条件有所延迟，但初始海冰条件有所改善的预测显示合理的海冰演化和最小海冰范围的较小偏差。我们的预测实验表明，对观测到的海冰厚度使用适当的不确定性可以改善初始冰层厚度的空间分布，从而改善预测的海冰分布。