This paper describes and evaluates the assimilation component of a seamless sea ice prediction system, which is developed based on the fully coupled Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research Climate Model (AWI‐CM, v1.1). Its ocean/ice component with unstructured‐mesh discretization and smoothly varying spatial resolution enables seamless sea ice prediction across a wide range of space and time scales. The model is complemented with the Parallel Data Assimilation Framework to assimilate observations in the ocean/ice component with an Ensemble Kalman Filter. The focus here is on the data assimilation of the prediction system. First, the performance of the system is tested in a perfect‐model setting with synthetic observations. The system exhibits no drift for multivariate assimilation, which is a prerequisite for the robustness of the system. Second, real observational data for sea ice concentration, thickness, drift, and sea surface temperature are assimilated. The analysis results are evaluated against independent in situ observations and reanalysis data. Further experiments that assimilate different combinations of variables are conducted to understand their individual impacts on the model state. In particular, assimilating sea ice drift improves the sea ice thickness estimate, and assimilating sea surface temperature is able to avert a circulation bias of the free‐running model in the Arctic Ocean at middepth. Finally, we present preliminary results obtained with an extended system where the atmosphere is constrained by nudging toward reanalysis data, revealing challenges that still need to be overcome to adapt the ocean/ice assimilation. We consider this system a prototype on the way toward strongly coupled data assimilation across all model components.

中文翻译：

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基于AWI气候模型的无缝海冰预测数据同化系统

本文描述并评估了无缝海冰预测系统的同化组件，该系统是基于完全耦合的阿尔弗雷德·韦格纳研究所，亥姆霍兹极地和海洋研究气候模型中心（AWI-CM，v1.1）开发的。它的海洋/冰成分具有非结构化网格离散和平滑变化的空间分辨率，可以在广泛的时空范围内进行无缝的海冰预测。该模型与“并行数据同化框架”进行了补充，以使用“集合卡尔曼滤波器”对海洋/冰层中的观测进行同化。这里的重点是预测系统的数据同化。首先，在综合模型观察的完美模型环境下测试系统的性能。该系统对于多元同化没有任何漂移，这是系统健壮性的前提。其次，对海冰浓度，厚度，漂移和海面温度的真实观测数据进行了吸收。针对独立的原位观测和重新分析数据评估分析结果。进行进一步的实验来吸收变量的不同组合，以了解它们对模型状态的单独影响。特别是，吸收海冰漂流可以改善海冰厚度估算值，吸收海面温度可以避免中深度北冰洋自由运行模型的环流偏差。最后，我们介绍了通过扩展系统获得的初步结果，该系统通过轻推重新分析数据来限制了大气，揭示仍需克服以适应海洋/冰同化的挑战。我们认为该系统是在所有模型组件之间实现强耦合数据同化的原型。