The reconstruction of the ocean’s 3D thermal structure is essential to the study of ocean interior processes and global climate change. Satellite remote sensing technology can collect large-scale, high-resolution ocean observation data, but only at the surface layer. Based on empirical statistical and artificial intelligence models, deep ocean remote sensing techniques allow us to retrieve and reconstruct the 3D ocean temperature structure by combining surface remote sensing observations with in situ float observations. This study proposed a new deep learning method, Convolutional Long Short-Term Memory (ConvLSTM) neural networks, which combines multisource remote sensing observations and Argo gridded data to reconstruct and produce a new long-time-series global ocean subsurface temperature (ST) dataset for the upper 2000 m from 1993 to 2020, which is named the Deep Ocean Remote Sensing (DORS) product. The data-driven ConvLSTM model can learn the spatiotemporal features of ocean observation data, significantly improves the model’s robustness and generalization ability, and outperforms the LighGBM model for the data reconstruction. The validation results show our DORS dataset has high accuracy with an average R² and RMSE of 0.99/0.34 °C compared to the Argo gridded dataset, and the average R² and NRMSE validated by the EN4-Profile dataset over the time series are 0.94/0.05 °C. Furthermore, the ST structure between DORS and Argo has good consistency in the 3D spatial morphology and distribution pattern, indicating that the DORS dataset has high quality and strong reliability, and well fills the pre-Argo data gaps. We effectively track the global ocean warming in the upper 2000 m from 1993 to 2020 based on the DORS dataset, and we further examine and understand the spatial patterns, evolution trends, and vertical characteristics of global ST changes. From 1993 to 2020, the average global ocean temperature warming trend is 0.063 °C/decade for the upper 2000 m. The 3D temperature trends revealed significant spatial heterogeneity across different ocean basins. Since 2005, the warming signal has become more significant in the subsurface and deeper ocean. From a remote sensing standpoint, the DORS product can provide new and robust data support for ocean interior process and climate change studies.

Observation of the ocean is crucial to the studies of ocean dynamics, climate change, and biogeochemical cycle. However, current oceanic observations are patently insufficient, because the in situ observations are of difficulty and high cost while the satellite remote‐sensed measurements are mainly the sea surface data. To make up for the shortage of ocean interior data and make full use of the abundant satellite data, here we develop a data‐driven deep learning model to estimate ocean subsurface and interior variables from satellite‐observed sea surface data. Exclusively and simply using satellite data, three‐dimensional ocean temperature and salinity fields are successfully reconstructed, which are at 26 level depths from 0 to 2,000 m. We further design a scheme to increase the horizontal resolution from 1° to 1/4°, which is higher than the Argo gridded data. Estimations from our model are accurate, reliable, and stable for a wide range of research areas and periods. Dynamic height fields that are derived from the estimated temperature and salinity, as well as the associated ocean geostrophic flows, are also calculated and analyzed, which indicates the potentials of our model for reconstructing the ocean circulation fields as well. This study enriches oceanic observations with respect to vertical dimension and horizontal resolution, which can largely make up for the paucity of the subsurface and deep ocean observation, both before and during Argo era. This work also provides some new foundations for and insights into geoscience and climate change fields.

Biological productivity in the summer Vietnam boundary upwelling system in the western South China Sea, as in many coastal upwelling systems, is strongly modulated by wind. However, the role of ocean circulation and mesoscale eddies has not been elucidated. Here, we show a close spatiotemporal covariability between primary production and kinetic energy. High productivity is associated with high kinetic energy, which accounts for ∼15 % of the production variability. Results from a physical–biological coupled model reveal that the elevated kinetic energy is linked to the strength of the current separation from the coast. In the low production scenario, the circulation is not only weaker but also shows weak separation. In the higher production case, the separated current forms an eastward jet into the interior South China Sea, and the associated southern recirculation traps nutrients and favors productivity. When separation is absent, the model shows weakened circulation and eddy activity, with ∼21 % less nitrate inventory and ∼16 % weaker primary productivity.

Accurately retrieving ocean interior parameters from remote sensing observations is essential for ocean and climate studies because direct observations are sparse and costly. Furthermore, high-resolution structure of seawater properties is critical for understanding the oceanic processes and changes on multiple scales. Here, we designed a new method based on a deep neural network to retrieve subsurface temperature anomaly (STA) and subsurface salinity anomaly (SSA) in the Pacific Ocean at high (1/4°) and super (1/12°) horizontal resolution. We utilized multisource satellite-observed sea surface data (e.g., sea level, temperature, salinity, and wind vector) as inputs. The results revealed that our model retrieved the high- and super-resolution STA/SSA with high accuracy, and the model was reliable in a wide range of depths (near surface to 4000 m) and times (all months in 2014). Regarding the high-resolution STA (SSA) estimation, the average coefficient of determination ( $R^{2}$ ) was 0.984 (0.966), and the average root-mean-squared error (RMSE) was 0.068 °C (0.016 psu). For the super-resolution STA, the average $R^{2}$ was 0.988 and RMSE was 0.093 °C. Here, we established an effective technique that improved the resolution and accuracy of estimating the ocean interior parameters from satellite observation. The new technique provides some new insights into oceanic observation and dynamics.

Oceanic absorption of heat generated by greenhouse gas emissions has resulted in warming of the dark ocean (>200 m) that contains a large refractory DOM (RDOM) pool. However, changes in microbially mediated production and degradation dynamics for RDOM components in response to warming of dark marginal basins remain unclear. Herein, we integrated data from the dark South China, Mediterranean and Japan Seas (temperature range: 0.09–15.1°C) to demonstrate that in situ production efficiency of two ubiquitous humic‐like fluorophores increased at higher dark ocean temperature, whereas chromophores at a wavelength of 325 nm (a_CDOM(325)) changed from production to degradation at higher temperature. Increased oxygen consumption and a reduction of labile substrates in future warmer dark ocean environments may initiate the final degradation of some semi‐refractory components, thereby increasing the recalcitrance of RDOM, but decreasing the DOM inventory in dark basins and creating a positive feedback to rising atmospheric CO₂.

Variability of the Indian Ocean (IO) shallow overturning circulation during 1958–2017 was investigated, which consisted of a cross‐equator cell (CEC) and a southern subtropical cell (SSTC). Structures of these shallow cells were examined through the velocity sections and the transport steamfunction patterns based on the newly released Ocean Reanalysis System 5 data set, and the results showed that the returning flows, at the subsurface, of the shallow cells mainly presented at the western boundary while absented in the ocean interior. Strengths of the IO shallow cells were then determined according to the annual‐mean meridional streamfunction profiles. Both the CEC and the SSTC exhibited significant variability on interannual to decadal timescales during 1958–2017, and this variability mainly resulted from changes in both meridional Ekman transports and meridional geostrophic flows in the upper layer, and each component could dominate the shallow cells' variations in certain years. Contributions of the geostrophic currents to the shallow cells were likely opposite to that of the Ekman transport in the South Indian Ocean during years when the latter reached the extremes. The CEC and the SSTC were negatively correlated during the research period. The CEC exhibited a decadal weakening during 2000–2015, contributing to the decadal increasing of the IO heat content, while the SSTC only experienced very little strengthening. Here this paper presents a comprehensively study of the interannual to decadal variability of the IO shallow cells and the corresponding reasons and influences and tried to link these variations with other variations in the IO.