Toward a Data Assimilation System for Seamless Sea Ice Prediction Based on the AWI Climate Model.

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. Plain Language Summary: Sea ice prediction over seasonal time scale has attracted the focus from both the scientific and socioeconomic communities recently. We develop a system aiming for seamless sea ice prediction using a coupled model that is equipped with an unstructured ocean/sea ice component. The high‐resolution mesh over the polar regions allows us to explore its possible benefits on the prediction across a wide range of time scales. To this end, the sea surface temperature, sea ice concentration, sea ice thickness, and sea ice drift observations are assimilated in the current system to diagnose the performance of the model initialization for future forecasts. Key Points: A data assimilation system is developed for seamless sea ice prediction on a climate model equipped with an unstructured ocean/ice componentThe system performance is examined based on experiments assimilating synthetic (perfect model) and real observationsMultivariate assimilation of sea ice drift and sea surface temperature remarkably improves the ocean/ice model state in the polar regions [ABSTRACT FROM AUTHOR]