Sea surface salinity and temperature are essential climate variables in monitoring and modeling ocean health. Multispectral ocean color satellites allow the estimation of these properties at a resolution of 10 to 300 m, which is required to correctly represent their spatial variability in coastal waters. This paper investigates the effect of pre-applying an unsupervised classification in the performance of both temperature and salinity inversion. Two methodologies were explored: clustering based solely on spectral radiances, and clustering applied directly to satellite images. The former improved model generalization by identifying similar water clusters across different locations, reducing location dependency. It also demonstrated results correlating cluster type with salinity and temperature distributions thereby enhancing regression model performance and improving a global ocean color sea surface temperature regression model RMSE error by 10%. The latter approach, applying clustering directly to satellite images, incorporated spatial information into the models and enabled the identification of front boundaries and gradient information, improving global sea surface temperature models RMSE by 20% and sea surface salinity models by 30%, compared to the initial ocean color model. Beyond improving algorithm performance, optical water classification can be used to monitor and interpret changes to water optics, including algal blooms, sediment disturbance or other climate change or antropogenic disturbances. For example, the clusters have been used to show the impact of a category 4 hurricane landfall on the Mississippi estuarine region.

Machine learning (ML) techniques have emerged as a powerful tool for predicting weather and climate systems. However, much of the progress to date focuses on predicting the short-term evolution of the atmosphere. Here, we look at the potential for ML methodology to predict the evolution of the ocean. The presence of land in the domain is a key difference between ocean modeling and previous work looking at atmospheric modeling. Here, we look to train a convolutional neural network (CNN) to emulate a process-based General Circulation Model (GCM) of the ocean, in a configuration which contains land. We assess performance on predictions over the entire domain and near to the land (coastal points). Our results show that the CNN replicates the underlying GCM well when assessed over the entire domain. RMS errors over the test dataset are low in comparison to the signal being predicted, and the CNN model gives an order of magnitude improvement over a persistence forecast. When we partition the domain into near land and the ocean interior and assess performance over these two regions, we see that the model performs notably worse over the near land region. Near land, RMS scores are comparable to those from a simple persistence forecast. Our results indicate that ocean interaction with land is something the network struggles with and highlight that this is may be an area where advanced ML techniques specifically designed for, or adapted for, the geosciences could bring further benefits.

Plastic pollution in the Arctic marine system is sparsely quantified, and few enforceable policies are in place to ameliorate the issue. With an inflow-outflow budget for the Arctic Ocean, we identify gateways through which plastic enters and exits the Arctic marine system. While estimating the flux of plastic through rivers, sea ice, and ocean, we also quantify marine plastic pollution from Arctic shipping and fishing. Plastic fluxes are calculated using horizontal volume fluxes of water and ice and combining them with plastic waste concentration data; flux from fishing and shipping is generated through combining waste estimates with estimated ship traffic. We estimate that fishing and shipping contribute 105 tonnes of plastic flux per annum, compared to 10−1 tonnes per annum from river inflow. The ocean has a far smaller net outflow, dwarfed by that of ice, at 10−8 to 10−7 and 10−5 to 10−3 tonnes per annum, respectively. We examine how a suite of proposed policy interventions would quantitatively change those concentrations, and how the current governance environment makes each feasible; we find interventions targeting vessel traffic most effective. These interventions include a prohibition on the use of certain plastics in fishing as well as a Polar Code permitting scheme.

Spatial–temporal variability of phytoplankton community and potentially harmful species in the Golden Horn Estuary (Sea of Marmara) was investigated from October 2018 to September 2019 together with some environmental factors. A total of 148 phytoplankton taxa were identified during the study period. Among these, 134 taxa (90.5%) consisted of diatoms (71 taxa, 48%) and dinoflagellates (63 taxa, 42.5%), while 14 taxa (9.5%) were other groups. Seventeen species were recorded for the first time in the study area. Species richness was highest in October, while it was lowest in August. The species diversity (H') varied according to sampling stations. Cell abundances were higher especially in the middle and upper estuary in spring and summer than in autumn and winter. The abundance of diatoms and euglenophyceans was highest in spring, while the abundance of raphidophycean and cryptophycean was highest in summer. Temperature was correlated positively with total abundance (P < 0.01), but negatively with species diversity (H') (P < 0.01). Several dense algal blooms causing discolouration in surface water occurred in spring and summer. A total of 12 microalgae species known as potentially toxic were detected during this study period. Among these, dinoflagellates Alexandrium cf. tamarense and Dinophysis infundibulum were recorded for the first time in the study area. The increase in species diversity and richness in the upper estuary, and the decrease in frequency of bloom events compared with the previous years indicated the changes in environmental conditions in this study period. Findings showed that phytoplankton might be used as an indicator of the changing environmental conditions in such ecosystems.

There has been much recent interest in developing data-driven models for weather and climate predictions. However, there are open questions regarding their generalizability and robustness, highlighting a need to better understand how they make their predictions. In particular, it is important to understand whether data-driven models learn the underlying physics of the system against which they are trained, or simply identify statistical patterns without any clear link to the underlying physics. In this paper, we describe a sensitivity analysis of a regression-based model of ocean temperature, trained against simulations from a 3D ocean model setup in a very simple configuration. We show that the regressor heavily bases its forecasts on, and is dependent on, variables known to be key to the physics such as currents and density. By contrast, the regressor does not make heavy use of inputs such as location, which have limited direct physical impacts. The model requires nonlinear interactions between inputs in order to show any meaningful skill—in line with the highly nonlinear dynamics of the ocean. Further analysis interprets the ways certain variables are used by the regression model. We see that information about the vertical profile of the water column reduces errors in regions of convective activity, and information about the currents reduces errors in regions dominated by advective processes. Our results demonstrate that even a simple regression model is capable of learning much of the physics of the system being modeled. We expect that a similar sensitivity analysis could be usefully applied to more complex ocean configurations.

The importance of studying the radiocarbon content of dissolved inorganic carbon (DI14C) in the oceans has been recognized for decades. Starting with the GEOSECS program in the 1970s, 14C sampling has been a part of most global survey programs. Early results were used to study air-sea gas exchange while the more recent results are critical for helping calibrate ocean general circulation models used to study the effects of climate change. Here we summarize the major programs and discuss some of the important insights the results are starting to provide.

Inertia-gravity waves in the atmosphere and ocean are transported and refracted by geostrophic turbulent currents. Provided that the wave group velocity is much greater than the speed of geostrophic turbulent currents, kinetic theory can be used to obtain a comprehensive statistical description of the resulting interaction (Savva et al., J. Fluid Mech., vol. 916, 2021, A6). The leading-order process is scattering of wave energy along a surface of constant frequency, $\omega$, in wavenumber space. The constant-$\omega$ surface corresponding to the linear dispersion relation of inertia-gravity waves is a cone extending to arbitrarily high wavenumbers. Thus, wave scattering by geostrophic turbulence results in a cascade of wave energy to high wavenumbers on the surface of the constant-$\omega$ cone. Solution of the kinetic equations shows establishment of a wave kinetic energy spectrum $\sim k_h^{-2}$, where $k_h$ is the horizontal wavenumber.

Horizontal and vertical distribution of cephalopod paralarvae (PL) from the Mesoamerican Barrier Reef System (MBRS) in the Western Caribbean was studied during two oceanographic cruises in 2006 and 2007. A total of 1034 PL belonging to 12 families, 22 genera, 24 species, 5 morphotypes and a species complex were identified. Abralia redfieldi, Onychoteuthis banksii and Ornithoteuthis antillarum were the most abundant taxa. The taxonomic identification from these three species was corroborated with DNA barcoding (99.8–100% of similarity). Paralarvae of Octopus insularis were reported for the first time in the wild. Most PL occupied the Caribbean Surface Water mass in the 0–25 m depth stratum. Largest paralarval abundances were related to local oceanographic features favouring retention such as the Honduras Gyre and Cozumel eddy. No day-night differences were found in PL abundance, although Abralia redfieldi showed evidence of diel vertical migration. Distribution of PL in epipelagic waters of the MBRS was probably related to ontogenetic migration, hydrographic features of meso and subscale, and to the circulation regimes dominated by the Yucatan Current. The MBRS represents an important dispersion area for PL, potentially connecting a species-rich Caribbean community with the Gulf of Mexico and Florida waters.

The South Pacific Ocean contributes to the global carbon cycle by exchanging CO2 between the atmosphere and intermediate to deep water masses. The path of the Antarctic Intermediate Water (AAIW) in the South Pacific gyre has been inferred from salinity, oxygen, and nutrient measurements, but radiocarbon (14C) measurements—a direct tracer of the carbon cycle—remain sparse. Here, we present the first radiocarbon profiles in the western Coral Sea and compare our measurements with South Pacific stations from GLODAPv2, a database of ocean hydrochemistry. Surface and subsurface waters in the Coral Sea cannot be attributed to a single source based on their Δ14C signatures, and we observe a penetration of bomb-produced 14C. AAIW in the western Coral Sea shows Δ14C values comparable to those in the South Pacific gyre, consistent with circulation of AAIW in the lower part of the southern equatorial current. The deep waters of the western Coral Sea have significantly higher 14C than the South Pacific at the same isopycnal, consistent with a northward intrusion of Circumpolar Deep Water from the Tasman Sea, along with a westward influx of deep waters from the Central Pacific. In accordance with silicate concentrations published previously, this shows the dual origin of deep waters in the Coral Sea.

Genetic connectivity directly shapes the demographic profile of marine species, and has become one of the most intensely researched areas in marine ecology. More importantly, it has changed the way we design and describe Marine Protected Areas across the world. Population genetics is the preferred tool when measuring connectivity patterns, however, these methods often assume that dispersal patterns are (1) natural and (2) follow traditional metapopulation models. In this short review, we formally introduce the phenomenon of cryptic dispersal, where multiple introductory events can undermine these assumptions, resulting in grossly inaccurate connectivity estimates. We also discuss the evolutionary consequences of cryptic dispersal and advocate for a cross-disciplinary approach that incorporates larval transport models into population genetic studies to provide a level of oceanographic realism that will result in more accurate estimates of dispersal. As globalized trade continues to expand, the rate of anthropogenic movement of marine organisms is also expected to increase and as such, integrated methods will be required to meet the inevitable conservation challenges that will arise from it.

In order to obtain a better knowledge of past oceanographic variability offshore southern Chile, this study reappraises the changes in the sources of nutrients over the last 25 ka based on a detailed comparison of previously published nitrogen isotope and microfossil records (dinoflagellate cysts, coccoliths and diatoms) from ODP Site 1233 (41°S). Our findings support the main conclusions of Martinez et al. (2006) in the sense that both the Subantarctic Surface Water and the Gunther Undercurrent are potential sources for the recorded late Quaternary sedimentary δ15N signatures at Site 1233, with variable contributions of both sources during different time periods. This study indicates that Subantarctic Surface Water forms the main source for nutrients during the last glacial maximum (25–18.6 cal ka BP), the first part of the deglaciation (18.6–15.7 cal ka BP) and the Holocene (9.8 cal ka BP until present). An increased contribution of Equatorial Subsurface Water as a source of nutrients to the photic zone offshore southern Chile is observed between 14.4 and 9.8 cal ka BP, which is indicative for upwelling conditions at least after 13.2 cal ka BP as indicated by the microfossil data.

Corals of the Hawaiian Archipelago are well situated in the North Pacific Gyre (NPG) to record how bomb-produced radiocarbon has been sequestered and transported by the sea. While this signal can be traced accurately through time in reef-building corals and used to infer oceanographic processes and determine the ages of marine organisms, a comprehensive and validated record has been lacking for the Hawaiian Archipelago. In this study, a coral core from Kure Atoll in the northwestern Hawaiian Islands was used to create a high-resolution bomb 14C record for the years 1939–2002, and was then used with other 14C measurements in fish otoliths and seawater to explore differences and similarities in the bomb 14C signal throughout the Hawaiian Archipelago. The Kure Atoll sample series produced a well-defined bomb 14C curve that, with some exceptions, was similar to other coral 14C records from the Hawaiian Archipelago. Subtle differences in the coral 14C records across the region may be explained by the large-scale ocean circulation patterns and decadal cycles of the NPG. The most rapid increase of 14C, in the 1950s and 1960s, showed similar timing across the Hawaiian Archipelago and provides a robust basis for use of bomb 14C dating to obtain high-precision age determinations of marine organisms. Reference otoliths of juvenile fish demonstrated the use of the post-peak 14C decline period as a viable reference in the age validation of younger and more recently collected fishes, and effectively extended the utility of bomb 14C dating to the latest 30 yr.

This paper describes the hydrography and the larval fish assemblage of Guinea Bissau waters, and analyses the spatial distribution of the main families in relation to the oceanographic features of the area. Data were obtained during an oceanographic survey, undertaken between October and November 2008. In addition to 98 demersal fishing hauls, a total of 33 stations, located between 20 and 1000 m depth, were sampled for hydrography and ichthyoplankton. Data showed that Guinea-Bissauan surface waters are characterized by a strong thermohaline front that flows parallel to the bathymetry of the area. Warm surface waters (SST > 29°C) occupy the inner shelf, and colder (SST < 26°C), chlorophyll-a-rich waters take over the shelf break. Continental runoff seems responsible for the low salinity of the inner-shelf waters whereas the colder types bear thermohaline features typical of tropical Atlantic waters. These features define a scenario which favours the development of fish early life stages, reflected in the high abundance and diversity of fish larvae recorded. A total of 84 taxa of fish larvae were identified. Only the family Clupeidae accounted for 54.8% of the sampled larvae. Other important families were Carangidae (8.8%), Sparidae (8.4%) and Myctophidae (5.9%).

The recently published Bedmap2 datasets mark the culmination of several decades of subice and subocean Antarctic topographic surveying by many nations, but maps of the topographic data distribution show that in the global context, the Antarctic bed remains very poorly sampled. Most of the remaining large unmapped areas on Earth lie under Antarctic ice and polar surveying continues to be difficult and expensive, thus it is important to identify where future efforts should be concentrated. A survey of 75 experts in various aspects of polar science shows that a lack of adequate topographic data is an important constraint in several themes, but the data gaps and the data needs do not tend to coincide. There is strong demand for higher resolution surveying in previously visited areas, particularly in the most dynamic and most rapidly changing regions as identified by glaciologists, oceanographers, hydrologists, biologists and geomorphologists, while geologists and ice core scientists focus on the most important areas for understanding Antarctica over deeper time. The data requirements identified here could be addressed for most areas given sufficient time and funding, but the technology needed to survey the interiors of the large ice shelf cavities has only just been developed.

At-sea behaviour of central-place foraging fur seals and penguins in the Southern Ocean is understudied during the latter stages of parental care and the subsequent pre-moulting period. This biologically important period is costly to investigate due to the risk (or certainty) of losing tracking instruments when the animals moult. Early in this period, parents must meet the increasing demands of larger, more mobile offspring that are still nutritionally dependent and then the parents must recover lost body condition prior to the onset of their annual moult. This study reports late-season, at-sea movement patterns of macaroni penguins, chinstrap penguins and adult female Antarctic fur seals from the subantarctic island Bouvetøya, in relation to remotely-sensed oceanographic features. Foraging trips differing significantly in direction and distance travelled compared to those performed earlier in the breeding season, coincide with the time when offspring would be expected to become independent. On these trips, macaroni penguins moved towards the Polar Front while chinstrap penguins and Antarctic fur seals moved southward. Individuals from all three species appeared to target submesoscale ocean features once they were presumed to have been released from the constraints of feeding their young and were able to travel greater distances from the colony.