This study uses a variational method combined with satellite observations to reconstruct three‐dimensional temperature and salinity profiles for the Northern Indian Ocean. Sensitivity experiments show that sea surface temperature dominantly improve the temperature reconstruction of upper 100 m; sea surface salinity determines salinity estimation in the upper 100 m; sea surface height anomaly dominates the reconstruction of thermocline. The reconstructed temperature fields can be greatly improved in the thermocline by removing barotropic signal from the altimeter sea surface height data through a linear regression method. Ocean reanalysis and in situ temperature and salinity data are used to evaluate the results of reconstruction. Comparing with Simple Ocean Data Assimilation (SODA) in 2016, the spectral correlation between the reconstruction and the SODA density anomalies show that the reconstruction fields can retrieve mesoscale and large‐scale signals better. Moreover, the reconstruction salinity is much more accurate than SODA salinity in the upper ocean over the Bay of Bengal (BoB). Compared with CTD section observations, the reconstruction fields can capture the mesoscale eddy structure in the Arabian Sea and BoB well, respectively. The long time series of reconstruction along Argo trajectory shows that the reconstruction fields can better reproduce the observed intraseasonal oscillations of thermocline/halocline in the BoB. Compared with the World Ocean Atlas 2013 climatology, the reconstruction fields can better characterize upper ocean water mass variability.

Using sea surface data, including sea surface temperature (SST), sea surface height (SSH), and sea surface salinity (SSS), from two reanalysis data sets as input, we explore the potential for retrieving subsurface three‐dimensional (3‐D) structures in the Southwest Indian Ocean (SWIO) based on the “interior+surface quasi‐geostrophy” (isQG) method. We find that the isQG method is particularly successful south of 35°S corresponding to greater SSH anomaly (SSHA) and to higher ratio of the barotropic mode (BT) and the first baroclinic mode (BC1). Meanwhile, higher‐resolution surface data may provide more coherent retrieved results with original reanalysis data in the inactive eddy regions north of 35°S, where coarser data set cannot resolve mesoscale signals. Based on a long time series reconstruction by the Simple Ocean Data Assimilation (SODA) surface data from 2011 to 2015 in the SWIO, our spectrum correlation analysis shows that the isQG solution achieves a better skill near the surface layer and under the thermocline depth with horizontal scale larger than 50 km, but it degrades from ~30‐ to 200‐m depth south of 35°S. In contrast, the spectral correlation north of 35°S is relatively low, with correlation coefficient beyond (below) 0.5 for signals with a wavelength longer (shorter) than 200 km near surface layer and from 250‐ to 500‐m depth. Mechanisms about the regional‐dependent isQG skill are also discussed. This study should have important implications for both operational oceanography and ocean dynamics research in the SWIO, including the greater Agulhas system.

By incorporating the high‐resolution satellite remote sensing sea surface temperature (SST) with low‐resolution sea level anomaly and sea surface salinity (SSS), this study explores the reconstructability of the three‐dimensional (3D) eddy structures via the “interior + surface quasigeostrophic” (isQG) method in the South Indian Ocean. We apply the incorporation of high‐resolution SST to improve the spatial resolution of the reconstruction. We also propose a correction scheme for density reconstruction within the mixed layer to offset the absence of mixed layer dynamics in the SQG framework. Comparison against the in situ observations demonstrates a satisfactory reconstructability for subsurface velocity and density anomalies. Statistically, the zonal velocity reconstructability outperforms its meridional counterpart and the corresponding velocity phase. The reconstructed shallow‐layer velocity exhibits a superior skill in eddy‐active regions, when compared with drogued drifter observations. Reconstructed subsurface velocities reproduce the spatial structures of eddy‐induced velocity anomaly along the GO‐SHIP observation transect, although present smaller magnitudes. Results demonstrate the potential applicability of the isQG method for reconstructing mesoscale eddies, particularly in the ocean at mid‐to‐high latitudes, where subsurface dynamics are strongly influenced by barotropic and the first baroclinic modes. With the upcoming high‐advanced satellite observations, the isQG framework is expected to achieve better subsurface estimations.

This study investigates and evaluates methods for the three-dimensional thermohaline reconstruction of the Arctic Ocean using multi-source observational data. A multivariate statistical regression model based on sea ice seasonal variation is developed, driving by satellite data, and in situ data is used to validate the model output. The study indicates that the multivariate statistical regression model effectively captures the characteristics of the three-dimensional thermohaline structure of the Arctic Ocean. Areas with large reconstruction errors are primarily observed in the salinity values of ice-free regions and the temperature values of ice-covered regions. The statistical regression experiments reveal that salinity errors in ice-free regions are caused by inaccuracies in the satellite salinity data, while temperature errors in ice-covered areas mainly result from the inadequate representation of the under-ice temperature structure of the reanalysis data. The continuous and stable thermohaline data produced using near real-time satellite data as input provide an important foundation for studying Arctic marine environmental characteristics and assessing climate change.

High-resolution satellite observations increasingly enable characterization of mesoscale and submesoscale ocean variability, but their ability to inform reconstruction of subsurface circulation remains uncertain, particularly in regions where intense multi-scale interactions challenge geostrophic constraints. The Surface Quasi-Geostrophic (SQG) framework offers potential in reconstructing three-dimensional upper-ocean dynamics from surface fields, yet its performance across methods and dynamical regimes has not been systematically quantified. Using two parallel physically consistent mesoscale-resolving (1/20°) and submesoscale-permitting (1/60°) model simulations, we investigate four established SQG-based reconstruction methods for their applicability to reconstruct three-dimensional subsurface velocity and density anomalies from surface information in the core Agulhas region. The extended “interior + surface quasigeostrophic” numerical solution-based method (L19), which refines the representation of higher baroclinic modes following the “effective” SQG framework, emerges as the most skillful. L19 effectively reconstructs mesoscale structures (>100 km) and maintains strong spectral agreement with model simulations down to ~50 km near the surface. Its skill varies with seasonal mixed‐layer depth and regional eddy activity, improving under shallow, stable mixed layers and in energetic areas along the Agulhas Retroflection, ring pathways, and the Agulhas Return Current. While density reconstructions remain robust across dynamical regimes, velocity reconstructions deteriorate when submesoscale (