Understanding the properties, spatial distribution, and diversity of benthic habitats in a region is a primary goal of many coastal environmental monitoring programs. This information is often represented through classified maps, which depict the seafloor divided into discrete regions of similar biotic and abiotic properties, such as dominant macrofauna, geologic features, and surficial sediment characteristics. Due to advancements in underwater sonar survey technology and machine learning algorithms, machine learning-based classification of side-scan sonar imagery has emerged as a widely used tool for the development of classified benthic maps. These algorithms use textural analysis techniques combined with user-provided training datasets to distinguish between different surficial compositions and arrangements. For these processes to be effective, reliable ground truthing must be employed to verify the validity of classified maps produced. Historically, benthic classification initiatives have combined sonar imagery analysis with in situ ground-truthing techniques, such as grab sampling and ROV or diver observations, to categorize sonar imagery on a local scale. As technologies continue to advance, however, high-confidence benthic classification from sonar imagery without in situ ground truthing has the potential to become a reality. This presents opportunities to develop classified benthic maps on shorter timescales and with a smaller financial commitment. BIVALVE (Benthic Imagery VALidation and Visual Enhancement) is a practical and reliable methodology and supporting Python-based software toolkit for the development of verified classified maps from photographic ground-truthing data intended for use in the validation of classified maps generated from side-scan sonar data by machine-learning algorithms, with the long-term goal of reducing or potentially eliminating the need for in situ ground truthing. Classified maps produced by machinelearning processes can subsequently be overlain on this verified map using GIS software for comparative analysis, allowing for multiple avenues for the quantification of algorithm performance. The methodology herein is comprised of four stages: 1. data acquisition and preprocessing, 2. derivation and correlation of geospatial metadata with images, 3. classification of images by a human analyst, and 4. map generation using GIS software. Images are collected in tandem with side-scan sonar data by an autonomous underwater vehicle (AUV) - mounted, nadir-oriented camera set to time-lapse mode. The methodology outlined was implemented for a dataset captured in La Parguera Nature Reserve, Puerto Rico in April 2024 using a GoPro Hero 7 camera mounted on a REMUS 100 AUV. Of the 911 images captured within the side-scan survey area, 857 could be classified into one of three categories (Dense SAV, Sparse SAV, and Sand) with a confidence level of 75% or above by a human analyst, representing 94.1% of the total dataset. The remaining images we left unclassified due to high localized turbidity or other uncertainty.

As of March 2023, the U.S. Department of Energy announced the nationwide renewable energy goal of 30 gigawatts (GW) of renewable offshore wind energy by 2030 and setting the standard of 110 GW by 2050. The location of these offshore wind energy areas (WEAs) span along the Mid-Atlantic Bight (MAB) and overlap with the seasonally occurring Cold Pool. The Mid-Atlantic Cold Pool is an economically and environmentally important feature of the northeastern seaboard that supports benthic fauna and commercial fisheries. Monitoring and understanding the stratification strength of the Cold Pool in specified areas along the coast is critical for determining possible future implications caused by offshore wind farm construction. Remote satellite images of existing European wind farms have shown turbulent wakes generated by turbine monopiles in tidal flows, yet the effects of these wakes on Mid-Atlantic stratification are still unclear. In this paper, in situ observational data from Slocum gliders provide high-resolution observations of the seasonally occurring thermocline and pycnocline off the southern coast of New Jersey along a commonly sampled transect known as the Rutgers University Glider Endurance Line. Calculations of water column stability using buoyancy frequency are conducted for the summer months of each year between 2016 and 2021 when the Cold Pool is at its peak to evaluate the strength of stratification. Results indicate nearshore regions are more stable than offshore regions based on higher buoyancy frequency values and a higher density gradient.

The issue of microplastics is becoming increasingly severe; meanwhile, there is a surge in the construction of offshore wind farms. The base of wind turbines can act as artificial reefs, playing a positive role in facilitating the proliferation of a variety of marine organisms and the establishment of ecosystems. However, the potential impact of microplastics from estuaries on the ecosystems within wind farms is concerning. In this study, we use the particle tracking model ROMSPath to perform a simulation tracking the trajectories of 3650 particles over a year to reveal the connectivity from Delaware Bay to offshore wind lease areas located in the Mid-Atlantic Bight. The results suggest that 92% of the particles released from Delaware Bay did not remain in any wind lease areas after 180 days. A total of 26 wind lease areas were reached by the particles, with the area hosting the highest percentage capturing up to 41% of the particles. The particles were released from the Delaware River near Wilmington, DE and it took a minimum of 54 days for them to reach the wind lease area nearest to the bay mouth. The average residence time of the particles ranges from 0.14 to 6.5 days, indicating it would be reasonable to assume a particular impact from estuarine outflow on the wind lease area ecosystems. This study provides a reference for understanding estuarine particles' dispersion and migration patterns entering offshore wind development zones.

Spisula solidissima (Atlantic surfclams) are bottom dwelling bivalves native to the Mid-Atlantic Bight (MAB). They are sensitive to ongoing climate change-induced ocean warming and ocean acidification. Ocean warming has increased bottom temperatures and ocean and coastal acidification has depressed the aragonite saturation state ( $\Omega_{\text{Arag}}$ ), an essential mineral for shell-forming organisms. Ongoing changes in carbonate chemistry may negatively impact the physiology of surfclams which could in turn impact New Jersey surfclam fisheries. A gap in ocean acidification research is access to co-located biological response monitoring. Most literature on organism response is from single-species laboratory studies and may not capture realistic, natural conditions, or variability. Simultaneous measurements of surfclam biological response indicators need to be co-located with carbonate chemistry observations in the field to observe and predict biological impact in situ. The objective of this project was to conduct co-located sampling to determine the correlation between observed carbonate chemistry and biological data from Atlantic surfclams in their natural habitat. A vessel-based survey was performed to collect oceanographic measurements, including carbonate chemistry, and surfclam samples off New Jersey on the Mid-Atlantic shelf. Mean bottom or subsurface oceanographic measurements were used as inputs to $\Omega_{\text{Arag}}$ . A subsample of surfclams at each station were measured for shell length, thickness, and weight. Shell strength was also determined using a tensile strength machine and was defined as the force (kiloNewtons) applied in the middle of the shell at which the shell begins to break. Shell strength was standardized to shell weight for each shell (kN/g). Results showed no significant correlations between shell strength and carbonate chemistry. This could be due to potential acclimation capacity, potential buffering capacity of seawater, and/or potential energy reallocation. The results indicate that shell strength may not be the best metric to determine the impacts on Atlantic surfclams in a highly variable environment. The exposure to low $\text{pH}/\Omega_{\text{Arag}}$ may need to be much longer to see responses in shell strength. The lack of correlation between shell strength and carbonate chemistry does not mean that Atlantic surfclams were not impacted by pH and $\Omega_{\text{Arag}}$ , but other unmeasured physiological metrics may have instead been affected.

Rutgers University's accelerated master's degree program in Operational Oceanography (MOO) was established in 2019 to fulfill the workforce gap of the New Blue Economy (NBE), which includes satisfying renewable energy demands as the global population approaches 9 billion by 2050. The MOO program provides students experiential learning opportunities throughout the entire 12-month curriculum, often intersecting with the various technology and data teams that operate a state-of-art ocean observing network and comprise RU COOL (Rutgers University's Center for Ocean Observing Leadership), an internationally oceanographic center of excellence developing new technologies, research, outreach, and educational paradigms for working in the ocean. Students collaborate as a cohort on hands-on activities and assignments involving operational oceanographic equipment, specifically the large fleet of Slocum gliders and expansive network of High-Frequency Radar, both of which are key data pillars for RU COOL. Students work independently on data analysis, learning to analyze, synthesize, and visualize large datasets of real-time oceanographic data and numerical ocean model output on Rutgers University's High-Performance Computing (HPC) cluster, all using the versatile and transferable Python programming language. These were the tenets with which the program was initialized. In the past 4 years, the MOO program has evolved considerably. The first 2 years saw the students mostly remote due to the COVID-19 global pandemic, with limited experiential learning opportunities either in the lab or in the field. The program was pivoted to a strong focus on data processing during this time, such that the graduates would still be both competitive and capable upon degree completion. As those restrictions lifted in the third year and the program returned to the original intent, focus was redistributed across both tenets. Internalizing both student feedback and performance after each course and year, as well as industry feedback on desired skills, the program curriculum shifted significantly for the fourth year. Students were tasked to collaboratively run two quarterly glider deployments. This included coordination with our glider staff team for preparation, and real-time marine weather-based decision making for the operation and piloting, as well as extensive subsequent data analysis. This unique learning opportunity came with significant student responsibility, but the cohort collaboration and tapered support from the glider staff team ultimately allowed for great student successes. The endeavor realized the student-led glider transect offshore of New Jersey, originally conceptualized at the creation of the program. This element of the ocean observatory of RU COOL now enables applied, operational experience for subsequent cohorts in the MOO program. The program's goal has been to capitalize on the unique ocean observing lab resources and capabilities of RU COOL and Rutgers to meet the NBE workforce needs with the accelerated, experiential learning of a new generation of operational oceanography graduate students. Through the continual evolution of the program towards this goal, all MOO graduates have received employment in an oceanography-related career. The program curriculum continues to refine and adapt with each cohort, both to enhance the applied, experiential learning opportunities and to ensure skill proficiency that continually aligns with industry and government workforce needs. And while these global needs exceed the capacity of the MOO program to solely meet, our program may serve as a model for other universities to begin developing their own NBE pipelines.

Large offshore wind turbines are planned for construction off the New Jersey coastline to aid in the state's transition towards more renewable energy and decrease their dependence on fossil fuels. To facilitate this transition, it is important to have accurate estimates of how much power these turbines will generate. From an oceanographic perspective, New Jersey is a relatively unique wind farm location in that it frequently experiences the phenomenon known as coastal upwelling. Upwelling, which brings colder water from depth to the surface, is a result of persistent southwesterly winds along New Jersey's coastline in the summer months. Coastal upwelling can occur along any coast with an alongshore wind, however large temperature shifts (2 to 3°C) are a defining characteristic of upwelling in the Mid-Atlantic Bight due to the presence of the subsurface cold pool. To investigate the effects of upwelling, wind speed data at various altitudes from a floating LiDAR buoy (Atlantic Shores Offshore Wind Buoy 4) were examined across the upwelling season to understand the atmospheric and oceanic conditions within the Atlantic Shores Offshore Wind Lease Area. Daily images of sea surface temperature from the A VHRR satellite were used to determine which days in the chosen timeframe experienced large sea surface temperature shifts associated with coastal upwelling. In total, this methodology yielded a total of 39 upwelling days (43.8 % ) and 50 non-upwelling days (56.2 %). To investigate the relationship between upwelling and power generation, this study compared the power production estimates between the upwelling and non-upwelling days. Power estimates were made using the power curve for a 15 MW wind turbine. The results of the independent sample t-tests revealed that wind speeds and power production estimations were significantly greater at all times of day during upwelling conditions (P < 0.05 for all comparisons). There is a need to examine the effects of the oceanic conditions on wind characteristics because the ocean and atmosphere interact dynamically. The results of this analysis are novel in the aspect that few (if any) studies have looked at the effects of upwelling on wind characteristics in the context of power generation.

The habitat of North Atlantic right whales, like that of countless other species, is changing as a result of a changing climate. As scientists, proactive policy makers and innovative industries look for climate change solutions, the ocean is seen as providing myriad opportunities in the new blue economy. Offshore wind offers a promising alternative to fossil fuels, but impacts of turbine surveying, construction and operation on right whales are still largely unknown. Over the past decade, researchers at the Anderson Cabot Center for Ocean Life at the New England Aquarium have documented changes in right whale distribution in a historic whaling area overlapping with planned offshore wind development on the southern New England shelf. During a decade of aerial surveys, right whales began using the area year-round, with increasing abundance trends found in winters and springs. This study examined changes in oceanic conditions on the southern New England shelf and Nantucket Shoals with a potential relationship to the right whale abundance shifts between 2013 and 2019. Maps of coastal water masses and the fronts between them provided by satellite data are the basis for this comparative study. We identified water masses and their gradients during each season prior to 2016, and each season after 2016. The number of unique water masses and associated statistics were calculated for each season, which suggested a trend of convergence of seasonal averages of unique daily water masses in the survey area. We also compared which specific water masses were present prior to 2016 with those afterward, and analyzed the characteristics of those that exhibited substantial changes. Results suggest an overall cooling of water in the study area during winters and falls, and an overall warming during springs. Gradient values increased during spring, summer and fall months from the first time frame to the second. The percentage of area in which strong gradients were found increased meaningfully in summer and fall on the Nantucket Shoals. The goal of this study was to further understand the dynamic habitat of an imperiled species to contribute to the creation of tools for responsible wind energy development. While a transition away from fossil fuels will mitigate climate change impacts, strong preservation policies must be simultaneously enacted to protect right whales as new energy infrastructure is added to their environment.

A 15-year reanalysis (2007-2021) of circulation in the coastal ocean and adjacent deep sea of the northeast U.S. continental shelf is described. The analysis uses the Regional Ocean Modeling System (ROMS) and fourdimensional variational (4D-Var) data assimilation (DA) of observations from in situ platforms, coastal radars, and satellites. The reanalysis downscales open boundary information from the Copernicus Marine Environmental Monitoring Service (CMEMS) global analysis. The dynamic model is forced by regional meteorological analyses, observed daily river discharges, and harmonic tides that augment the open boundary conditions. A complementary analysis of the mean seasonal cycle of regional circulation, also computed using ROMS 4DVar but with climatological mean observations and forcing, is used to reduce biases in the CMEMS boundary data and to provide a dynamically and kinematically constrained Mean Dynamic Topography to use in conjunction with the assimilation of satellite altimeter sea level anomaly observations. The configuration of ROMS 4D-Var used is described, presenting details of the comprehensive suite of observations assembled, data pre-processing and quality control procedures, and background and observation error hypotheses. Control variables of the DA are the initial conditions, surface forcing, and boundary conditions of a sequence of non-overlapping 3-day analysis cycles. Comparisons to a non-assimilative version of the same ROMS model configuration show the added skill brought by assimilation of local observations. The improvement that downscaling with assimilation achieves over ocean state estimates from CMEMS and the U.S. Naval Research Laboratory Global Ocean Forecast System (GOFS) is demonstrated by the reduction in residuals of the DA, and by comparison to independent (unassimilated) observations. Wherever data volumes allow, skill assessments are made with the respect to anomalies from the mean seasonal cycle to emphasize performance at the ocean mesoscale. To highlight the utility of the analysis to inform studies related to coastal sea level variability and marine ecosystems, comparisons are made to unassimilated coastal sea level gauges and novel observations from sensors on fishing gear. The assimilation of coastal satellite altimetry data produces coastal sea level results that are coherent with observations across all time scales from interannual to tidal, while bias and correlation metrics show that bottom temperatures in regions of commercial fishing activity in the Mid-Atlantic Bight and the Gulf of Maine are modeled well.

Public awareness of microplastics and their widespread presence throughout most bodies of water are increasingly documented. The accumulation of microplastics in the ocean, however, appears to be far less than their riverine inputs, suggesting that there is a “missing sink” of plastics in the ocean. Estuaries have long been recognized as filters for riverine material in marine biogeochemical budgets. Here we use a model of estuarine microplastic transport to test the hypothesis that the Chesapeake Bay, a large coastal-plain estuary in eastern North America, is a potentially large filter, or “sink,” of riverine microplastics. The 1-year composite simulation, which tracks an equal number of buoyant and sinking 5-mm diameter particles, shows that 94% of riverine microplastics are beached, with only 5% exported from the Bay, and 1% remaining in the water column. We evaluate the robustness of this finding by conducting additional simulations in a tributary of the Bay for different years, particle densities, particle sizes, turbulent dissipation rates, and shoreline characteristics. The resulting microplastic transport and fate were sensitive to interannual variability over a decadal (2010–2019) analysis, with greater export out of the Bay during high streamflow years. Particle size was found to be unimportant while particle density – specifically if a particle was buoyant or not – was found to significantly influence overall fate and mean duration in the water column. Positively buoyant microplastics are more mobile due to being in the seaward branch of the residual estuarine circulation while negatively buoyant microplastics are transported a lesser distance due to being in the landward branch, and therefore tend to deposit on coastlines close to their river sources, which may help guide sampling campaigns. Half of all riverine microplastics that beach do so within 7–13 days, while those that leave the bay do so within 26 days. Despite microplastic distributions being sensitive to some modeling choices (e.g., particle density and shoreline hardening), in all scenarios most of riverine plastics do not make it to the ocean, suggesting that estuaries may serve as a filter for riverine microplastics.

We describe “Doppio”, a ROMS-based (Regional Ocean Modeling System) model of the Mid-Atlantic Bight and Gulf of Maine regions of the northwestern North Atlantic developed in anticipation of future applications to biogeochemical cycling, ecosystems, estuarine downscaling, and near-real-time forecasting. This free-running regional model is introduced with circulation simulations covering 2007– 2017. The ROMS configuration choices for the model are detailed, and the forcing and boundary data choices are described and explained. A comprehensive observational data set is compiled for skill assessment from satellites and in situ observations from regional associations of the U.S. Integrated Ocean Observing Systems, including moorings, autonomous gliders, profiling floats, surface-current-measuring coastal radar, and fishing fleet sensors. Doppio’s performance is evaluated with respect to these observations by representation of subregional temperature and salinity error statistics, as well as velocity and sea level coherence spectra. Model circulation for the Mid-Atlantic Bight and Gulf of Maine is visualized alongside the mean dynamic topography to convey the model’s capabilities.

Coastal ocean models that downscale global operational models are widely used to study regional circulation at enhanced resolutions.When operated as nowcast/forecast systems, these models offer predictions that can provide actionable guidance for maritime applications.A nowcast/forecast system for the northeast U.S. coastal ocean is described in this chapter to illustrate, by example, the many practical issues to be considered when configuring such a model for operational oceanography applications.The system uses the Regional Ocean Modeling System (ROMS) and four-dimensional variational data assimilation of observations from a comprehensive network of in situ platforms, coastal radars, and satellites.The emergence of open access web data services that adhere to community conventions for metadata descriptions for coordinate systems and geo-scientific data types, and support geospatial search and sub-setting, are shown to foster inter-operability of data and model usage, accelerate the test, validate and acceptance cycle for modeling system enhancements, streamline the addition of new data streams, facilitate operational monitoring of the system, and enable novice users to view and download model outputs to underpin the generation of higher level ocean information products.