Recent studies have started to explore coupled data assimilation (CDA) in coupled ocean–atmosphere models because of the great potential of CDA to improve climate analysis and seamless weather–climate prediction on weekly-to-decadal time scales in advanced high-resolution coupled models. In this review article, we briefly introduce the concept of CDA before outlining its potential for producing balanced and coherent weather–climate reanalysis and minimizing initial coupling shocks. We then describe approaches to the implementation of CDA and review progress in the development of various CDA methods, notably weakly and strongly coupled data assimilation. We introduce the method of coupled model parameter estimation (PE) within the CDA framework and summarize recent progress. After summarizing the current status of the research and applications of CDA-PE, we discuss the challenges and opportunities in high-resolution CDA-PE and nonlinear CDA-PE methods. Finally, potential solutions are laid out.

最近的研究已经开始探索海洋-大气耦合模型中的耦合数据同化（CDA），这是因为CDA在改进高分辨率高分辨率耦合模型中每周到十年的时间尺度上改善气候分析和无缝的天气-气候预测的巨大潜力。在这篇综述文章中，我们先简要介绍CDA的概念，然后概述CDA产生平衡和连贯的天气-气候再分析和最小化初始耦合冲击的潜力。然后，我们描述实现CDA的方法，并回顾各种CDA方法（特别是弱耦合和强耦合数据同化）的开发进展。我们在CDA框架内介绍了耦合模型参数估计（PE）方法，并总结了最新进展。在总结了CDA-PE研究和应用的现状之后，我们讨论了高分辨率CDA-PE和非线性CDA-PE方法的挑战和机遇。最后，提出了潜在的解决方案。