An ensemble optimal interpolation (EnOI) data assimilation system for a high‐resolution (0.1° horizontal) version of the Community Earth System Model Version 2 (CESM2) ocean component is presented. For this purpose, a new version of the Data Assimilation Research Testbed (DART Manhattan) that enables large‐state data assimilation by distributing state vector information across multiple processors at high resolution is used. The EnOI scheme uses a static (but seasonally varying) 84‐member ensemble of precomputed perturbations to approximate samples from the forecast error covariance and utilizes a single model integration to estimate the forecast mean. Satellite altimetry and sea surface temperature observations along with in situ temperature and salinity observations are assimilated. This new data assimilation framework is then used to produce a global high‐resolution retrospective analysis for the 2005–2016 period. Not surprisingly, the assimilation is shown to generally improve the time‐mean ocean state estimate relative to an identically forced ocean model simulation where no observations are ingested. However, diminished improvements are found in undersampled regions. Lack of adequate salinity observations in the upper ocean actually results in deterioration of salinity there. The EnOI scheme is found to provide a practical and cost‐effective alternative to the use of an ensemble of forecasts. Plain Language Summary Decadal climate prediction focuses on climate changes on time scales from a year to a decade or more and is a combination of forced boundary condition and initial value problems. A well‐established source of predictability on decadal time scales comes from the initialization of the ocean state. To exploit the capabilities of the next generation of high‐resolution climate prediction systems, proper initialization of their ocean components is required. This work represents our first attempt at data assimilation in a high‐resolution version of the Community Earth System Model (CESM2). We use a new version of the Data Assimilation Research Testbed (DART) that enables large‐state data assimilation. However, the integration of an ensemble of high‐resolution models remains computationally prohibitive. For this reason, we introduce an ensemble optimal interpolation (EnOI) scheme to assimilate observations much more efficiently. The EnOI scheme uses a static, but seasonally varying, ensemble of precomputed perturbations to approximate samples from the forecast error covariance and eliminates the need for running an ensemble. While our prototype retrospective analysis for the 2005–2016 period shows some limitations, the EnOI scheme is found to provide a practical and cost‐effective alternative to the use of an ensemble of forecasts.
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