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An updated global bathymetry and topography grid is presented using a spatial sampling interval of 15 arc sec. The bathymetry is produced using a combination of shipboard soundings and depths predicted using satellite altimetry. New data consists of >33.6 million multibeam and singlebeam measurements collated by several institutions, namely, the National Geospatial‐Intelligence Agency, Japan Agency for Marine‐Earth Science and Technology, Geoscience Australia, Center for Coastal and Ocean Mapping, and Scripps Institution of Oceanography. New altimetry data consists of 48, 14, and 12 months of retracked range measurements from Cryosat‐2, SARAL/AltiKa, and Jason‐2, respectively. With respect to SRTM15_PLUS (Olson et al.,), the inclusion of these new data results in a ∼1.4‐km improvement in the minimum wavelength recovered for sea surface free‐air gravity anomalies, a small increase in the accuracy of altimetrically derived predicted depths, and a 1.24% increase, from 9.60% to 10.84%, in the total area of ocean floor that is constrained by shipboard soundings at 15‐arc sec resolution. Bathymetric grid cells constrained by satellite altimetry have estimated uncertainties of ±150 m in the deep oceans and ±180 m between coastlines and the continental rise. Onshore, topography data are sourced from previously published digital elevation models, predominately SRTM‐CGIAR V4.1 between 60°N and 60°S. ArcticDEM is used above 60°N, while Reference Elevation Model of Antarctica is used below 62°S. Auxiliary grids illustrating shipboard data coverage, marine free‐air gravity anomalies, and vertical gradient gradients are also provided in common data formats. Key Points An updated global elevation grid is presented using a spatial sampling interval of 15 arc sec New bathymetry data include more than 33.6 million ship soundings and more than 6 years of non‐repeat altimetry measurements The percentage of seafloor mapped by echo soundings remains low; our current compilation covers only 10.84% at 15‐arc sec resolution

Traditional bathymetry inversion methods that rely on an altimetry-derived gravity anomaly (GA) and/or a vertical gravity gradient anomaly (VGG) have been widely used for bathymetry prediction in the South China Sea. However, few studies attempt new methods to combine multisource gravity data to improve the accuracy of the bathymetry. In this study, we introduce a fully connected deep neural network (FC-DNN) to merge GA, VGG, and the deflection of vertical (DOV) to predict the bathymetry in the South China Sea. Single beam sounding depths were used as sample data for neural network training. Independent shipboard depths and GEBCO2023, topo_25.1, and ETOPO2022 models were applied as validation data. The assessment results showed that the FC-DNN model reached a high precision level with an STD of 49.20 m. More than 70% of the differences between the FC-DNN bathymetric model and other depth models were less than 100 m. Furthermore, the spectral analysis results showed that the FC-DNN bathymetry model has stronger energy in medium and short wavelengths than other models, which indicates that additional gravity field element DOVs can recover richer topographic signals in those particular bands.

High-precision and high-resolution gravity fields can be derived from multi-source satellite altimeter data. A gravity anomaly model around the South China Sea (SCSGA) V1.0 on a 1′ × 1′ grid is established from sea surface heights (SSHs) of several geodetic missions (GMs) and exact repeat missions. Gridded deflections of the vertical are first calculated from SSHs by the least squares collocation (LSC) method and then used to derive gravity anomalies by the inverse Vening Meinesz formula. In gravity derivation processing, we establish an approximate relationship among the precision of altimetric gravity, precision of geoid gradients, and density of geoid gradients (the average number of geoid gradients per 1′ × 1′ region). The weights of geoid gradients from the Ka-band altimeter for the LSC are innovatively determined by an iterative method. Finally, SCSGA V1.0 is assessed by ship-borne gravity anomalies and marine gravity models. The performance of GMs in gravity derivation is evaluated. In general, the altimetric gravity precision in regions with many islands and reefs increases more obviously than those in other regions when the geoid gradient density increases. The standard deviation of SCSGA V1.0 is 2.78 mGal, which is slightly better than those of four recognized global marine gravity models around the SCS. CryoSat-2 is the most important dataset for SCSGA V1.0. Ka-band SARAL/AltiKa plays a major role in gravity derivation, and the contribution of Haiyang-2A is greater than those of other Ku-band satellites, except CryoSat-2. SCSGA V1.0 is concluded to reach an international advanced level for marine gravity from altimeter data around the SCS.

Vortex rings are critical for thrust production underwater. In the ocean, self-propelled mesozooplankton generate vortices while swimming within a weakly stratified fluid. While large-scale biogenic transport has been observed during vertical migration in the wild and lab experiments, little focus has been given to the evolution of induced vortex rings as a function of their propagation direction relative to the density gradient. In this study, the evolution of an isolated vortex ring crossing the interface of a stable two-layer system is examined as a function of its translation direction with respect to gravity. The vortex ring size and position are visualized using planar laser-induced fluorescence (PLIF) and the induced vorticity field derived from particle image velocimetry (PIV) is examined. It is found that the production of baroclinic vorticity significantly affects the propagation of vortex rings crossing the density interface. As a result, any expected symmetry between vortex rings travelling from dense to light fluids and from light to dense fluids breaks down. In turn, the maximum penetration depth of the vortex ring occurs in the case in which the vortex propagates against the density gradient due to the misalignment of the pressure and density gradients. Our results have far-reaching implications for the characterization of local ecosystems in marine environments.

The Surface Water and Ocean Topography (SWOT) satellite delivers an unprecedented spatial resolution, offering new opportunities for advanced marine gravity field modeling. This study investigates the application of SWOT observational data by computing deflections of the vertical (DOVs) using the eight-directional geoid gradient method, followed by gravity field inversion through the inverse Vening–Meinesz (IVM) formula. Experimental results in the South China Sea region demonstrate that SWOT DOVs, based on 19 observation cycles, achieved accuracies of 0.86 arcseconds for the east–west component η and 0.77 arcseconds for the north–south component ξ. The marine gravity field inversion accuracy reached 4.97 mGal, comparable to the multi-source altimetry-derived model SIO_v32.1. Further analysis reveals that the primary contributions of SWOT DOVs are observed within the 3.5–20 km wavelength band, with cross-track systematic errors identified as the key factor influencing both DOV calculations and gravity anomaly inversion. Additionally, extending the SWOT observation period enhances DOV accuracy, particularly for the η. These findings highlight the potential of SWOT data in advancing high-resolution marine gravity field modeling.

Advancements in satellite altimetry have significantly enhanced high-resolution mean sea surface (MSS) models, enabling the computation of high-resolution vertical gravity anomaly gradient (VGAG) models. This study focused on the methodology for computing VGAG models using MSS models, introducing innovative improvements to established techniques. Using the SDUST2020 MSS model within the Arabian Sea research area, the DTU22 and CNES-CLS22 mean dynamic topography (MDT) models, and the XGM2019e_2159 Earth gravity field model for the remove–restore process, the short-wavelength geoid was derived. To harness the extensive marine gravity field information within the MSS model, the study considered the complex marine environment and calculated the second-order derivatives of the geoid in multiple directions. These derivatives were then used to determine their north–south and east–west components through the least squares method, resulting in the computation of the short-wavelength VGAG. By restoring the long-wavelength VGAG, a VGAG model for the study area was established. Finally, the results were analyzed using the SIO V32.1 VGAG model (named curv). Experimental results demonstrated that this approach effectively extracted marine gravity field information from the MSS model using multidirectional data, mitigating the amplification of geoid uncertainties caused by second-order derivatives. Graphical Abstract

Recent studies indicate that the influence of midlatitude SST fronts extends through the marine atmospheric boundary layer (MABL) into the free atmosphere, with implications for climate variability. To better understand the mechanisms of this ocean-to-atmosphere influence, SST-induced MABL convergence is explored here with the Weather Research and Forecasting mesoscale model in an idealized, dry, two-dimensional configuration, for winds crossing from cold to warm SST and from warm to cold SST. For strong cross-front winds,O(10 m s−1), changes in the turbulent mixing and MABL depth across the SST front lead to MABL depth-integrated convergence in the cold-to-warm case and depth-integrated divergence in the warm-to-cold case. The turbulent stress divergence term changes over a shorter length scale than the pressure gradient and Coriolis terms, such that the MABL response directly above the SST front is governed by nonrotating, internal boundary layer–like physics, which are consistent with the vertical mixing mechanism. An important consequence is that the increment in the cross-front surface stress diagnoses the vertical motion at the top of the MABL. These physics are at variance with some previously proposed SST frontal MABL models in which pressure adjustments determine the MABL convergence. The SST-induced MABL convergence results in vertical motion that excites a stationary internal gravity wave in the free atmosphere, analogous to a mountain wave. For a 15 m s−1cross-front wind, the gravity wave forced by an SST increase of 3°C over 200 km is comparable to that forced by an 80-m change in topography.

Observational studies suggest that the stratospheric quasi-biennial oscillation (QBO) can modulate tropical deep convection. The authors use a cloud-resolving model with a limited domain, representing a convective column in the tropics, to study the mechanisms of this modulation. The large-scale circulation is parameterized using the weak temperature gradient (WTG) approximation, under which the parameterized large-scale vertical motion acts to relax the horizontal-mean temperature toward a specified reference profile. Temperature variations typically seen in easterly and westerly phases are imposed in the upper troposphere and lower stratosphere of this reference profile. The responses of convection are studied over different sea surface temperatures, holding the reference temperature profile fixed. This can be thought of as studying the response of convection to the QBO over different “relative SSTs” and also corresponds to different equilibrium precipitation rates in the control simulation. The equilibrium precipitation rate shows slight increases in response to a QBO easterly phase temperature perturbation over small SST anomalies and strong decreases over large SST anomalies, and vice versa for the QBO westerly phase perturbation. A column moist static energy budget analysis reveals that the QBO modulates the convective precipitation through two pathways: it changes the high-cloud properties and thus the column radiative cooling, and it alters the shape of the large-scale vertical motion and thus the efficiency of energy transport by the large-scale flow. The nonmonotonicity of the precipitation response with respect to relative SST results from the competition of these two effects.

Deepening of estuarine channels is a common practice to ensure navigation. Here, we investigate whether such deepening impacts physical processes such as the strength of the estuarine exchange flow, the horizontal salinity gradient, and tidal dynamics. We analyze recent and historical hydrodynamic observations in Newark Bay, New Jersey, to assess the effect of channel deepening on tides, circulation, and salinity. The Bay's navigational channel has undergone significant deepening, from 3 to 10 m in the nineteenth century to ~16 m today. Observations presented here include sea-level data from the nineteenth, twentieth, and twenty-first century, and moored Doppler current data and bottom salinity measurements made over the past 20 years. Results show a doubling of the estuarine exchange flow, a slight increase in salinity and in the horizontal salinity gradient, a decrease in tidal current amplitude, and a spatially variable change in the tidal range. The doubling of the exchange flow is consistent with the Hansen and Rattray scaling provided that the horizontal salinity gradient is unable to fully adjust landward because the dredging is limited to a short reach of the estuary. However, uncertainty in channel depth leaves open the possibility that the exchange flow is also augmented by an increase in the horizontal salinity gradient and/or a reduction in vertical mixing. Nevertheless, results demonstrate that a relatively small (15%) increase in depth appears to have doubled the exchange flow. We believe that this result is relevant to other systems where dredging is limited to a short reach of an estuary.

This study aims to enhance the spatial resolution and accuracy of bathymetric prediction by integrating Gravity Anomaly (GA) and Vertical Gravity Gradient Anomaly (VGG) data with a dual-channel Backpropagation Neural Network (BPNN). The seafloor topography of the Izu-Ogasawara Trench in the Western Pacific will be constructed and evaluated using depth models and single-beam data. The BPNN improved the accuracy of seafloor topography prediction by 0.17% and 0.35% using the 1 arc-minute SIO and GEBCO depth models, respectively, in areas without in-situ data. When single-beam data was utilized, the BPNN improved prediction accuracy by 64.93%, 70.29%, and 68.78% compared to the Gravity Geological Method (GGM), SIO v25.1, and GEBCO 2023, respectively. When single-beam, GA, and VGG data were all combined, the root mean square error (RMSE) was reduced to 19.12 m, representing an improvement of 60.92% and 61.13% compared to using only GA or VGG data, respectively. Comparing bathymetric predictions at different depths, the BPNN achieved a mean relative error (MRE) as low as 0.5%. Across various terrains—such as trench areas, seamounts, and deep-sea plains—the accuracy of seafloor topography predicted by the BPNN improved by 88.36%, 87.42%, and 84.39% compared to GGM, SIO and GEBCO depth models, respectively. These findings demonstrate that BPNN can integrate GA and VGG data to enhance both the accuracy and spatial resolution of seafloor topography in regions with and without in-situ data, and across various depths and terrains. This study provides new data and methodological support for constructing high-precision global seafloor topography.