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Abstract Circulation in the Gulf of Mexico is dominated by the Loop Current and associated mesoscale eddies. These mesoscale eddies pose a safety risk to offshore energy production and potential dispersal of large-scale pollutants like oil. We use a data-driven, physics-informed, and numerically consistent deep learning–based ocean emulator called OceanNet to generate a 120-day forecast of the sea surface height (SSH) in the eastern Gulf of Mexico. OceanNet uses a new dataset of high-resolution data assimilative ocean reanalysis (1993–2022) as input. This model is trained using years 1993–2018 and evaluated on four eddies during years 2019–21. For comparison, we use a state-of-the-art numerical ocean model to generate a dynamical model prediction initialized every 5 days from 27 April 2019 to 1 April 2020 (during eddies Sverdrup and Thor) using persistent forcing and boundary conditions. The dynamical model takes seven wall-clock days to run, whereas OceanNet runs in minutes. Edges of Loop Current eddies (LCEs) pose the most potent risk to offshore energy operations and pollutant dispersal due to strong water velocities. Therefore, most of the analysis focuses on edge accuracy, quantified by the modified Hausdorff distance. The edge of the LCEs is defined by the 17-cm sea surface height contour, which generally coincides with the strongest water velocity. The OceanNet prediction outperforms both persistence and the dynamical model prediction. Overall, this new ocean emulator provides a promising new approach to generate seasonal forecasts of LCEs and generates large model ensembles efficiently to quantify forecast uncertainty that is long needed by scientists and decision-makers for offshore operations. Significance StatementCirculation in the Gulf of Mexico (GoM) is dominated by the energetic Loop Current and associated mesoscale eddies (typically 150–400 km in diameter). As these eddies propagate westward through the Gulf, they pose a safety risk to offshore energy production and potential large-scale pollutant dispersal. We used ocean model output (1993–2022) to train a data-driven ocean emulator called OceanNet that generates a seasonal (up to 120 day) prediction of sea surface height (SSH) in the eastern GoM. For comparison, a simple dynamical model prediction is also evaluated. OceanNet’s performance is assessed with a focus on edge accuracy, the most potent risk to offshore energy operations and pollutant dispersal. Overall, OceanNet performs well for a seasonal forecast and shows great potential for further development. 

ABSTRACT ObjectiveEstuarine fishes experience significant diel and seasonal variations in their environments, with climate change introducing additional stressors, including altered salinity, temperatures, and water levels. American Eels Anguilla rostrata are present in Atlantic estuaries from Venezuela to Greenland. Despite their wide distribution and shrinking population, American Eels are understudied, in part because of the research challenges posed by their unusual catadromous life history. This study examines the spatial effects of changing estuarine water quality variables (water temperature, dissolved oxygen, and salinity) on the American Eel population in the Hudson River estuary (HRE). MethodsThe Hudson River Biological Monitoring Program, conducted from 1974 to 2017, consists of a suite of surveys recording fish abundance data and water quality variables. As the largest component of the Hudson River Biological Monitoring Program, the Long River Ichthyoplankton Survey contains 44 years of data on American Eels in the HRE. Using LRS catch data and Hudson River Biological Monitoring Program water quality measurements, we developed statistical models of American Eel population centers in the HRE. ResultsThe young-of-year and yearling-or-older population centers shifted downstream over the course of the Long River Ichthyoplankton Survey at average rates of approximately 1.1 and 0.41 km per year, respectively, despite higher temperatures and lower dissolved oxygen conditions closer to the estuary’s mouth. Mean water temperature and dissolved oxygen for the entire estuary have significant relationships with the population centers of both age-classes, although the eels were not apparently tracking stable conductivity or water temperature conditions; nor were the young of year tracking stable dissolved oxygen levels. ConclusionsThe downstream shift in HRE American Eel population centers over several decades and the relationship between this shift and changing environmental conditions indicate the need for improved understanding of the population dynamics of the globally distributed and declining species of the genus Anguilla. This knowledge is critical in the face of rapidly changing ecosystems. 

ABSTRACT Rapid warming could drastically alter host–parasite relationships, which is especially important for fisheries crucial to human nutrition and economic livelihoods, yet we lack a synthetic understanding of how warming influences parasite‐induced mortality in these systems. We conducted a meta‐analysis using 266 effect sizes from 52 empirical papers on harvested aquatic species and determined the relationship between parasite‐induced host mortality and temperature and how this relationship was altered by host, parasite, and study design traits. Overall, higher temperatures increased parasite‐induced host mortality; however, the magnitude of this relationship varied. Hosts from the order Salmoniformes experienced a greater increase in parasite‐induced mortality with temperature than the average response to temperature across fish orders. Opportunistic parasites were associated with a greater increase in infected host mortality with temperature than the average across parasite strategies, while bacterial parasites were associated with lower infected host mortality as temperature increased than the average across parasite types. Thus, parasites will generally increase host mortality as the environment warms; however, this effect will vary among systems. 

Abstract. Many meteorological and oceanographic processes throughout the eastern US and western Atlantic Ocean, such as storm tracks and shelf water transport, are influenced by the position and warm sea surface temperature of the Gulf Stream (GS) – the region's western boundary current. Due to highly nonlinear processes associated with the GS, predicting its meanders and frontal position has been a long-standing challenge within the numerical modeling community. Although the weather and climate modeling communities have begun to turn to data-driven machine learning frameworks to overcome analogous challenges, there has been less exploration of such models in oceanography. Using a new dataset from a high-resolution data-assimilative ocean reanalysis (1993–2022) for the northwestern Atlantic Ocean, OceanNet (a neural-operator-based digital twin for regional oceans) was trained to predict the GS's frontal position over subseasonal to seasonal timescales. Here, we present the architecture of OceanNet and the advantages it holds over other machine learning frameworks explored during development. We also demonstrate that predictions of the GS meander are physically reasonable over at least a 60 d period and remain stable for longer. OceanNet can generate a 120 d forecast of the GS meander within seconds, offering significant computational efficiency. 

In this paper, we numerically optimize broadband pulse shapes that maximize Hahn echo amplitudes. Pulses are parameterized as neural networks (NN), nonlinear amplitude limited Fourier series (FS), and discrete time series (DT). These are compared to an optimized choice of the conventional hyperbolic secant (HS) pulse shape. A power constraint is included, as are realistic shape distortions due to power amplifier nonlinearity and the transfer function of the microwave resonator. We find that the NN, FS, and DT parameterizations perform equivalently, offer improvements over the best HS pulses, and contain a large number of equivalent optimal maxima, implying the flexibility to include further constraints or optimization goals in future designs.