The increase of temporal resolution from weekly to daily and spatial resolution from ~1° to ~(1/4)° in merged altimeter sea surface height data along with their over two decades of time series opens an unprecedented opportunity to reveal the “spectrum” of eddy variabilities from days to years in time and from mesoscale to semimesoscale in space. Eddies with a lifetime shorter than 1 month or longer than 1 year are classified here as short lived and long lived, respectively. Contrasting the variabilities of short‐ and long‐lived eddies could be an effective way to explore their geographic origins and dynamic formations. In the time domain, the population fluctuations of short‐ and long‐lived eddies are basically dominated by annual and interannual component, respectively. The magnitude of kinematic and dynamic properties of oceanic eddies is positively correlated with their lifetime in general. Statistically, the properties of short‐lived (long‐lived) eddies experience a symmetric (an asymmetric) growth and decay with a single flat peak during their life cycles. In the space domain, eddy occurrence is observed as a high probability event, which can reach every corner of the ocean. However, homes of short‐ and long‐lived eddies are highly regionalized and are geographically separated. A prominent “young eddy belt” is observed in the tropical oceans for the first time. These findings suggest two fundamental characteristics with regard to short‐ and long‐lived mesoscale eddies: residing in largely separated geographic zones under different mechanisms while following a similar pattern of intrinsic life cycle in terms of property evolution.

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Oceans at a depth ranging from ~100 to ~1000-m (defined as the intermediate water here), though poorly understood compared to the sea surface, is a critical layer of the Earth system where many important oceanographic processes take place. Advances in ocean observation and computer technology have allowed ocean science to enter the era of big data (to be precise, big data for the surface layer, small data for the bottom layer, and the intermediate layer sits in between) and greatly promoted our understanding of near-surface ocean phenomena. During the past few decades, however, the intermediate ocean is also undergoing profound changes because of global warming, the research and prediction of which are of intensive concern. Due to the lack of three-dimensional ocean theories and field observations, how to remotely sense the intermediate ocean from space becomes a very attractive but challenging scientific issue. With the rapid development of the next generation of information technology, artificial intelligence (AI) has built a new bridge from data science to marine science (called Deep Blue AI, DBAI), which acts as a powerful weapon to extend the paradigm of modern oceanography in the era of the metaverse. This review first introduces the basic prior knowledge of water movement in the ~100 m ocean and vertical stratification within the ~1000-m depths as well as the data resources provided by satellite remote sensing, field observation, and model reanalysis for DBAI. Then, three universal DBAI methodologies, namely, associative statistical, physically informed, and mathematically driven neural networks, are elucidated in the context of intermediate ocean remote sensing. Finally, the unique advantages and potentials of DBAI in data mining and knowledge discovery are demonstrated in a top-down way of “surface-to-interior” via several typical examples in physical and biological oceanography.