Accurate initialization of ocean states is essential for skillful prediction of Earth system variability across seasonal to decadal timescales. In this study, we evaluate the impact of a newly developed four-dimensional ensemble variational (4DEnVar)-based weakly coupled ocean data assimilation (WCODA) system within the DOE Energy Exascale Earth System Model version 2 (E3SMv2) on global and regional climate variability. By assimilating monthly ocean temperature and salinity from the EN4.2.1 reanalysis into the fully coupled model, we demonstrate substantial improvements in simulating both interannual and decadal climate variability. Compared to the free-running coupled simulation, the assimilation experiment exhibits markedly enhanced interannual correlations with observations for global mean surface air temperature and precipitation anomalies. The representation of key climate modes, including ENSO, the Indian Ocean Dipole, and multidecadal variability in the Pacific and Atlantic Oceans, also improves significantly. Regional evaluation over the contiguous United States further shows enhanced skill in simulating winter surface air temperature and precipitation variability, particularly in the northern and southern regions, respectively, linked to improved ENSO representation. These findings underscore the critical role of coupled forecasts in the data assimilation cycle for propagating observational information across Earth system components. By integrating ocean observations within a coupled framework, the WCODA system enables cross-component information exchange among the ocean, atmosphere, and land, thereby generating physically consistent initial states. These improvements contribute to more accurate simulations of Earth system variability across multiple timescales and advance the development of more reliable prediction systems in support of societal resilience.