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Marine plastic pollution poses a potential threat to the ecosystem, but the sources and their magnitudes remain largely unclear. Existing bottom-up emission inventories vary among studies for two to three orders of magnitudes (OMs). Here, we adopt a top-down approach that uses observed dataset of sea surface plastic concentrations and an ensemble of ocean transport models to reduce the uncertainty of global plastic discharge. The optimal estimation of plastic emissions in this study varies about 1.5 OMs: 0.70 (0.13-3.8 as a 95% confidence interval) million metric tons yr-1 at the present day. We find that the variability of surface plastic abundance caused by different emission inventories is higher than that caused by model parameters. We suggest that more accurate emission inventories, more data for the abundance in the seawater and other compartments, and more accurate model parameters are required to further reduce the uncertainty of our estimate.

In 2018 we celebrated 25 years of development of radar altimetry, and the progress achieved by this methodology in the fields of global and coastal oceanography, hydrology, geodesy and cryospheric sciences. Many symbolic major events have celebrated these developments, e.g., in Venice, Italy, the 15th (2006) and 20th (2012) years of progress and more recently, in 2018, in Ponta Delgada, Portugal, 25 Years of Progress in Radar Altimetry. On this latter occasion it was decided to collect contributions of scientists, engineers and managers involved in the worldwide altimetry community to depict the state of altimetry and propose recommendations for the altimetry of the future. This paper summarizes contributions and recommendations that were collected and provides guidance for future mission design, research activities, and sustainable operational radar altimetry data exploitation. Recommendations provided are fundamental for optimizing further scientific and operational advances of oceanographic observations by altimetry, including requirements for spatial and temporal resolution of altimetric measurements, their accuracy and continuity. There are also new challenges and new openings mentioned in the paper that are particularly crucial for observations at higher latitudes, for coastal oceanography, for cryospheric studies and for hydrology. The paper starts with a general introduction followed by a section on Earth System Science including Ocean Dynamics, Sea Level, the Coastal Ocean, Hydrology, the Cryosphere and Polar Oceans and the “Green” Ocean, extending the frontier from biogeochemistry to marine ecology. Applications are described in a subsequent section, which covers Operational Oceanography, Weather, Hurricane Wave and Wind Forecasting, Climate projection. Instruments’ development and satellite missions’ evolutions are described in a fourth section. A fifth section covers the key observations that altimeters provide and their potential complements, from other Earth observation measurements to in situ data. Section 6 identifies the data and methods and provides some accuracy and resolution requirements for the wet tropospheric correction, the orbit and other geodetic requirements, the Mean Sea Surface, Geoid and Mean Dynamic Topography, Calibration and Validation, data accuracy, data access and handling (including the DUACS system). Section 7 brings a transversal view on scales, integration, artificial intelligence, and capacity building (education and training). Section 8 reviews the programmatic issues followed by a conclusion.

In this paper, the importance of model horizontal resolution in mixing and dispersion is investigated by comparing two data‐assimilative high‐resolution simulations (4 and 1 km), one of which is submesoscale‐permitting. By employing both Eulerian and Lagrangian metrics, upper‐ocean differences between the mesoscale‐resolving and submesoscale‐permitting simulations are examined in the northeastern Gulf of Mexico, a region of high mesoscale and submesoscale activity. Mixing in both simulations is explored by conducting Lagrangian experiments to track the generation of Lagrangian coherent structures (LCSs) and their associated transport barriers. Finite‐time Lyapunov exponent (FTLE) fields show higher separation rates of fluid particles in the submesoscale‐permitting case, which indicate more vigorous mixing with differences being more pronounced in the shelf regions (depths ≤ 500 m). The extent of the mixing homogeneity is examined using probability density functions (PDFs) of FTLEs with results suggesting that mixing is heterogeneous in both simulations, but some homogeneity is exhibited in the submesoscale‐permitting case. The FTLE fields also indicate that chaotic advection dominates turbulent mixing in both simulations regardless of the horizontal resolution. In the submesoscale‐permitting experiment, however, smaller scale LCSs emerge as noise‐like filaments that suggest a larger turbulent mixing component than in the mesoscale‐resolving experiment. The impact of resolution is then explored by investigating the spread of oil particles at the location of the Deepwater Horizon oil spill. Plain Language Summary: Small‐scale processes (0.1–50 km) play a critical role in upper‐ocean water transport and mixing. Thus, the added value from resolving these finer scales in numerical models is evaluated by comparing two high‐resolution numerical simulations in the northeastern Gulf of Mexico: one at 1 km horizontal resolution that truly resolves features on the order of 10 km and one at 4 km that only resolves features greater than 50 km. Passive fluid particle experiments are conducted to document the mixing and identify structures that act as barriers to transport between fluids with different properties and determine the fate of dispersants in the ocean. In both simulations, the particles' distribution is spatially heterogeneous, which indicates that mixing is largely controlled by these structures. Results also show that neighboring fluid particles separate faster from one another in the 1 km simulation than in the 4 km simulation, meaning that mixing of fluids is more intense. Lastly, we find that the 1 km simulation is more realistic when applied to oil particle dispersion at the location of the Deepwater Horizon oil spill. Key Points: Resolving submesoscale motions leads to increased Lagrangian transport and mixing as well as the generation of more intricate LCSsChaotic advection dominates turbulent mixing regardless of the horizontal model resolution (either 4 km or 1 km)Submesoscale‐permitting simulation yields reduced error against drifter observations compared to mesoscale‐resolving counterpart [ABSTRACT FROM AUTHOR]

The formation of cold, dense waters south of the Antarctic Circumpolar Current (ACC) is one of the main drivers of the global overturning circulation, with major effects on the earth's climate. A key region where dense waters are formed is the Ross Sea, which is separated from the ACC by the Ross Gyre. The strength and variability of the Ross Gyre circulation impacts the formation and export of dense water, but observations of the Ross Gyre circulation are limited because of its remote location, severe weather conditions, and ice cover that has limited the application of remote sensing techniques. Quantitative estimates of the gyre's total strength are difficult to obtain from hydrographic observations alone due to the limited sampling and the relatively weak stratification. In this paper, we use a combination of observations and modeling studies to estimate the strength and variability of the Ross Gyre transport and investigate the relative contributions of the wind, buoyancy forcing, eddy fluxes, and the influence of ACC to the Ross Gyre circulation. We find that the mean transport of the Ross Gyre can be as high as about 45 Sv, more than twice the typical estimate of about 20 Sv. Sensitivity experiments to wind and buoyancy forcing, nonlinear terms, and the ACC were performed with a regional configuration of the Hybrid Coordinate Ocean Model (HYCOM). The numerical experiments show that the total Ross Gyre circulation, and its variability, are primarily wind-driven. The ACC is responsible for a small recirculation. Buoyancy and nonlinearity or eddy fluxes play a smaller role in the gyre dynamics, though they are regionally important. [ABSTRACT FROM AUTHOR]

Diapycnal mixing plays a primary role in the thermodynamic balance of the ocean and, consequently, in oceanic heat and carbon uptake and storage. Though observed mixing rates are on average consistent with values required by inverse models, recent attention has focused on the dramatic spatial variability, spanning several orders of magnitude, of mixing rates in both the upper and deep ocean. Away from ocean boundaries, the spatio-temporal patterns of mixing are largely driven by the geography of generation, propagation and dissipation of internal waves, which supply much of the power for turbulent mixing. Over the last five years and under the auspices of US CLIVAR, a NSF- and NOAA-supported Climate Process Team has been engaged in developing, implementing and testing dynamics-based parameterizations for internal-wave driven turbulent mixing in global ocean models. The work has primarily focused on turbulence 1) near sites of internal tide generation, 2) in the upper ocean related to wind-generated near inertial motions, 3) due to internal lee waves generated by low-frequency mesoscale flows over topography, and 4) at ocean margins. Here we review recent progress, describe the tools developed, and discuss future directions.

This study utilizes Deep Neural Networks (DNN) to improve the K‐Profile Parameterization (KPP) for the vertical mixing effects in the ocean's surface boundary layer turbulence. The deep neural networks were trained using 11‐year turbulence‐resolving solutions, obtained by running a large eddy simulation model for Ocean Station Papa, to predict the turbulence velocity scale coefficient and unresolved shear coefficient in the KPP. The DNN‐augmented KPP schemes (KPP_DNN) have been implemented in the General Ocean Turbulence Model (GOTM). The KPP_DNN is stable for long‐term integration and more efficient than existing variants of KPP schemes with wave effects. Three different KPP_DNN schemes, each differing in their input and output variables, have been developed and trained. The performance of models utilizing the KPP_DNN schemes is compared to those employing traditional deterministic first‐order and second‐moment closure turbulent mixing parameterizations. Solution comparisons indicate that the simulated mixed layer becomes cooler and deeper when wave effects are included in parameterizations, aligning closer with observations. In the KPP framework, the velocity scale of unresolved shear, which is used to calculate ocean surface boundary layer depth, has a greater impact on the simulated mixed layer than the magnitude of diffusivity does. In the KPP_DNN, unresolved shear depends not only on wave forcing, but also on the mixed layer depth and buoyancy forcing. Plain Language Summary: The uppermost tens of meters of the ocean, known as the ocean surface boundary layer, are rich in intricate and chaotic fine‐scale (cm to 100s m) ocean currents referred to as turbulence. These currents, spanning from centimeters to hundreds of meters, play pivotal roles in shaping the oceanic environment and influencing Earth's climate dynamics. Despite their significance, simulating these fine‐scale ocean currents remains beyond the capabilities of current and foreseeable supercomputing resources. Consequently, simplified formulas derived from fundamental principles are commonly employed to approximate these currents in ocean and climate models. However, these approximations still cannot cover all types of choppy currents and uncertainties in these approximations represent a substantial source of bias in contemporary ocean and climate modeling endeavors. In this study, we enhance one of the prevalent physics‐based approximations of fine‐scale turbulent currents using machine learning techniques. Our tests show that integrating machine learning in physics‐based approximation is stable and efficient and is suitable for use in ocean and climate models. Key Points: New versions of K‐Profile Parameterization (KPP), augmented by deep neural networks (DNNs) (KPP_DNN), were developedThe KPP_DNN schemes were trained using Large Eddy Simulations and implemented in the General Ocean Turbulence ModelThe KPP_DNN schemes are stable for long‐term integration and as efficient as the physics‐based KPP schemes [ABSTRACT FROM AUTHOR]

In recent years, efforts have been made to include tides in both operational ocean models as well as climate and earth system models. The accuracy of the barotropic tides is often limited by the model topography, which is in turn limited by model horizontal resolution. In this work, we explore the reduction of barotropic tidal errors in an ocean general circulation model (Modular Ocean Model version 6; MOM6) using sub‐grid scale topography representation. We follow the methodology from Adcroft (2013, https://doi.org/10.1016/j.ocemod.2013.03.002), which utilizes statistics from finer resolution topographic data sets to represent sub‐grid scale features with a light computational cost in a structured finite volume formulation. The geometric effect from sub‐grid scale topography can be introduced to the model with only a few parameters at each grid cell. The porous barriers, which are implemented at the walls of the grid cells, are used to modify transport between grid cells. Our results show that the globally averaged tidal error in lower‐resolution simulations is significantly reduced with the use of porous barriers. We argue this method is a potentially useful tool to improve simulations of tides (and other flows) in low‐resolution simulations. Plain Language Summary: In recent years, significant attention has been given to simulating tides in diverse ocean and climate models, because of the roles of tides in impacting sea levels. In order to better predict future sea level changes, it is therefore essential to improve the accuracy of surface tides simulated in numerical ocean models. One known source of errors that could impact the accuracy of modeled tides stems from the accuracy of ocean topography in the models, which relies on how many grid boxes (i.e., model resolution) are used to represent the complicated seafloor elevations. The model's resolution, however, is often constrained by the efficiency of computations. Our objective in this study is to enhance the portrayal of ocean topography at a given resolution to minimize tidal errors. To achieve this, we employ a technique known as "porous barriers," which uses approximations to mimic the effects of ocean topography that would be omitted in the models, without imposing significant computational burdens. Our findings demonstrate that this technique of porous barriers can substantially reduce tidal errors in our ocean model. We also expect a wider application of this technique to numerous other physical processes within the ocean. Key Points: The accuracy of tides in ocean models is affected by the representation of topography, which is constrained by model horizontal resolutionThe geometric effect of the sub‐grid scale topographic features can be approximated in the model with the porous barriersThe porous barriers can significantly reduce globally averaged errors for barotropic tides [ABSTRACT FROM AUTHOR]