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Abstract Methane hydrates on continental margins sequester substantial amounts of methane. Ocean warming often triggers hydrate dissociation, influencing global climate and marine ecosystems. Here we use ocean reanalysis to investigate bottom marine heatwaves affecting methane seeps at the Northwest Atlantic continental margin. These heatwaves are driven by downward displacement of ocean subsurface isopycnals that brings anomalously warm water to the seafloor, which frequently lacked surface expressions. The isopycnal anomalies are generated by the interaction of Gulf Stream mesoscale activity and slope topography, including warm core ring intrusions and impingements at the tail of the Grand Banks. Once generated, the isopycnal anomalies propagate along the slope and affect the seep area via topographic Rossby waves, offering predictability for bottom marine heatwaves. A northward shift and increasing instability of the Gulf Stream has intensified bottom marine heatwaves at the seep area since 2009, and future climate change will likely exacerbate this trend.

Abstract While data-driven approaches demonstrate great potential in atmospheric modeling and weather forecasting, ocean modeling poses distinct challenges due to complex bathymetry, land, vertical structure, and flow non-linearity. This study introduces OceanNet, a principled neural operator-based digital twin for regional sea-suface height emulation. OceanNet uses a Fourier neural operator and predictor-evaluate-corrector integration scheme to mitigate autoregressive error growth and enhance stability over extended time scales. A spectral regularizer counteracts spectral bias at smaller scales. OceanNet is applied to the northwest Atlantic Ocean western boundary current (the Gulf Stream), focusing on the task of seasonal prediction for Loop Current eddies and the Gulf Stream meander. Trained using historical sea surface height (SSH) data, OceanNet demonstrates competitive forecast skill compared to a state-of-the-art dynamical ocean model forecast, reducing computation by 500,000 times. These accomplishments demonstrate initial steps for physics-inspired deep neural operators as cost-effective alternatives to high-resolution numerical ocean models.

Abstract This is the protocol for a Campbell Systematic Review. The objectives are as follows: The aim of this systematic review is to advance our understanding of the key characteristics of effective preschool‐based interventions designed to foster self‐regulation. To accomplish this, the review addresses the following questions: 1. What types of preschool‐based interventions have been developed to promote self‐regulation? 2. What is the average effect of these preschool‐based interventions on self‐regulation, focusing on four key constructs: integrative effortful control, integrative executive function, self‐regulation, and self‐regulated learning? 3. What characteristics—such as Resource Allocation, Activity Type, and Instruction Method—could potentially contribute to the effects of preschool‐based interventions in promoting self‐regulation?

Abstract Background The COVID-19 pandemic has caused an unprecedented need for accessible health care services and significantly accelerated the development processes of telehealth tools for autism spectrum disorder (ASD) early screening and diagnosis. This study aimed to examine the feasibility and utility of a time-efficient telehealth tool combining a structured snack time assessment activity and a novel behaviour coding scheme for identifying ASD. Methods A total of 134 1–6-year-old individuals with ASD (age in months: mean = 51.3, SD = 13.1) and 134 age- and sex-matched typically developing individuals (TD) (age in months: mean = 54, SD = 9.44) completed a 1-min snack time interaction assessment with examiners. The recorded videos were then coded by trained coders for 17 ASD-related behaviours; the beginning and end points and the form and function of each behaviour were recorded, which took 10–15 min. Coded details were transformed into 62 indicators representing the count, duration, rate, and proportion of those behaviours. Results Twenty indicators with good reliability were selected for group difference, univariate and multivariate analyses. Fifteen behaviour indicators differed significantly between the ASD and TD groups and remained significant after Bonferroni correction, including the children’s response to the examiner’s initiation, eye gaze, pointing, facial expressions, vocalization and verbalization, and giving behaviours. Five indicators were included in the final prediction model: total counts of eye gaze, counts of standard pointing divided by the total counts of pointing, counts of appropriate facial expressions, counts of socially oriented vocalizations and verbalizations divided by the total counts of vocalizations and verbalizations, and counts of children using giving behaviours to respond to the examiner's initiations divided by the total counts of the examiner's initiation of snack requisitions. The ROC curve revealed a good prediction performance with an area under the curve (AUC) of 0.955, a sensitivity of 92.5% and a specificity of 84.3%. Conclusion Our results suggest that the snack activity-based ASD telehealth approach shows promise in primary health care settings for early ASD screening.

Abstract The surface-intensified, poleward-flowing Gulf Stream (GS) encounters the equatorward-flowing Deep Western Boundary Current (DWBC) at 36° N off Cape Hatteras. In this study, daily output from a data-assimilative, high-resolution (800 m), regional ocean reanalysis was examined to quantify variability in the velocity structure of the GS and DWBC during 2017–2018. The validity of this reanalysis was confirmed with independent observations of ocean velocity and density that demonstrate a high level of realism in the model’s representation of the regional circulation. The model’s daily velocity time series across a transect off Cape Hatteras was examined using rotated Empirical Orthogonal Function analysis, and analysis suggests three leading modes that characterize the variability of the western boundary currents throughout the water column. The first mode, related to meandering of the GS current, accounts for 55.3% of the variance, followed by a “wind-forced mode”, which accounts for 12.5% of the variance. The third mode, influenced by the DWBC and upper-ocean eddies, accounts for 7.1% of the variance.

Changes in marine carbon cycling due to hurricanes with different intensity and translation speeds have not been systematically investigated. This study uses an idealized coupled physical-biogeochemical model and a suite of model sensitivity analyses to better quantify the relationship between hurricane characteristics and marine property changes, including variations in air-sea carbon flux and partial pressure of carbon dioxide in water (pCO2w). We find that strong (category 4–5), mid-speed (5–8 m/s) storms cause the most carbon flux from the atmosphere to the ocean, and that the relationship between air-sea carbon flux and hurricane properties is non-linear. Climate models that do not consider synoptic-scale, storm-induced physical-biogeochemical coupling may underestimate regional carbon sinks.

Abstract Hurricane‐induced compound flooding is a combined result of multiple processes, including overland runoff, precipitation, and storm surge. This study presents a dynamical coupling method applied at the boundary of a processes‐based hydrological model (the hydrological modeling extension package of the Weather Research and Forecasting model) and the two‐dimensional Regional Ocean Modeling System on the platform of the Coupled‐Ocean‐Atmosphere‐Wave‐Sediment Transport Modeling System. The coupled model was adapted to the Cape Fear River Basin and adjacent coastal ocean in North Carolina, United States, which suffered severe losses due to the compound flood induced by Hurricane Florence in 2018. The model's robustness was evaluated via comparison against observed water levels in the watershed, estuary, and along the coast. With a series of sensitivity experiments, the contributions from different processes to the water level variations in the estuary were untangled and quantified. Based on the temporal evolution of wind, water flux, water level, and water‐level gradient, compound flooding in the estuary was categorized into four stages: (I) swelling, (II) local‐wind‐dominated, (III) transition, and (IV) overland‐runoff‐dominated. A nonlinear effect was identified between overland runoff and water level in the estuary, which indicated the estuary could serve as a buffer for surges from the ocean side by reducing the maximum surge height. Water budget analysis indicated that water in the estuary was flushed 10 times by overland runoff within 23 days after Florence's landfall.

Commercial cooking has higher intensity and more severe instantaneous cooking pollution from volatile organic chemicals compared to home cooking, making health risk assessment of occupational exposure for chefs a priority. In this study, chefs from three cooking styles of kitchens, including steaming, frying, and grilling, were selected to investigate the external and internal exposures, health risks and effects of several typical aromatic hydrocarbons (benzene, toluene and naphthalene). Naphthalene was found to be the most concentrated contaminant in air samples among the different kitchens, while benzene had the lowest concentration. The concentration of toluene in frying kitchens was significantly higher than that in steaming kitchens. Air concentrations of toluene in frying kitchens, as well as benzene concentrations in grilling kitchens exceeded the standard level according to indoor air quality standard (GB/T18883-2002). Regarding the metabolites of pollutants in urine, the content of S-benzylmercapturic acids (S-BMA) for frying chefs was significantly higher than that for other cooking styles of chefs, which was consistent with the relatively higher air concentrations of toluene. There was a good correlation between internal and external exposure of the pollutants. The level of oxidative stress was influenced by 2-hydroxynaphthalene (2-OHN) and S-BMA, indicating the potential health risks of these occupational exposed chefs. This study indicates the need to improve the monitoring of typical aromatic hydrocarbons, as well as to investigate their potential health effects in large-scale groups, and improve the ventilation in kitchens.

Global remote sensing of solar-induced fluorescence (SIF), a proxy for plant photosynthetic activity, represents a breakthrough in the systematic observation of global-scale gross primary production and other ecosystem functions. Here, we hypothesize that all earth ecosystem variabilities, including SIF, are affected by climate variations. The main contribution of this study is to apply a global empirical orthogonal function (EOF) analysis of SIF to quantify the relations between the large-scale GPP variability and climate variations. We used 2007–2019 SIF data derived from the Global Ozone Monitoring Experiment-2 (GOME-2) satellite sensor observations and a rotated empirical orthogonal function (EOF) analysis to explore global SIF variability over years and decades. The first leading EOF mode captures the well-known ENSO pattern, with most of the variance over continents in the tropical Pacific and Indian Oceans. The second and third leading EOF modes in SIF variability are significantly related to the NAO and PDO climate indices, respectively. Our analysis also shows that the 2011 La Niña (2015 El Niño) elevated (decreased) global SIF.

Best practices and international standards for determining n-year return period extreme wave (sea states) conditions allow wave energy converter designers and project developers the option to apply simple univariate or more complex bivariate extreme value analysis methods. The present study compares extreme sea state estimates derived from univariate and bivariate methods and investigates the performance of spectral wave models for predicting extreme sea states at buoy locations within several regional wave climates along the US East and West Coasts. Two common third-generation spectral wave models are evaluated, a WAVEWATCH III® model with a grid resolution of 4 arc-minutes (6–7 km), and a Simulating WAves Nearshore model, with a coastal resolution of 200–300 m. Both models are used to generate multi-year hindcasts, from which extreme sea state statistics used for wave conditions characterization can be derived and compared to those based on in-situ observations at National Data Buoy Center stations. Comparison of results using different univariate and bivariate methods from the same data source indicates reasonable agreement on average. Discrepancies are predominantly random. Large discrepancies are common and increase with return period. There is a systematic underbias for extreme significant wave heights derived from model hindcasts compared to those derived from buoy measurements. This underbias is dependent on model spatial resolution. However, simple linear corrections can effectively compensate for this bias. A similar approach is not possible for correcting model-derived environmental contours, but other methods, e.g., machine learning, should be explored.

During the past five to ten years, a broad partnership of institutions under NOPP sponsorship has collaborated in developing and demonstrating the performance and application of eddy-resolving, real-time global- and basin-scale ocean prediction systems using the HYbrid Coordinate Ocean Model (HYCOM). The partnership represents a broad spectrum of the oceanographic community, bringing together academia, federal agencies, and industry/commercial entities, and spanning modeling, data assimilation, data management and serving, observational capabilities, and application of HYCOM prediction system outputs. In addition to providing real-time, eddy-resolving global- and basin-scale ocean prediction systems for the US Navy and NOAA, this project also offered an outstanding opportunity for NOAA-Navy collaboration and cooperation, ranging from research to the operational level. This paper provides an overview of the global HYCOM ocean prediction system and highlights some of its achievements. An important outcome of this effort is the capability of the global system to provide boundary conditions to even higher-resolution regional and coastal models.

A better understanding of oil droplet formation, degradation, and dispersal in deep waters is needed to enhance prediction of the fate and transport of subsurface oil spills. This research evaluates the influence of initial droplet size and rates of biodegradation on the subsurface transport of oil droplets, specifically those from the Deepwater Horizon oil spill. A three-dimensional coupled model was employed with components that included analytical multiphase plume, hydrodynamic and Lagrangian models. Oil droplet biodegradation was simulated based on first order decay rates of alkanes. The initial diameter of droplets (10–300 μ m) spanned a range of sizes expected from dispersant-treated oil. Results indicate that model predictions are sensitive to biodegradation processes, with depth distributions deepening by hundreds of meters, horizontal distributions decreasing by hundreds to thousands of kilometers, and mass decreasing by 92–99% when biodegradation is applied compared to simulations without biodegradation. In addition, there are two- to four-fold changes in the area of the seafloor contacted by oil droplets among scenarios with different biodegradation rates. The spatial distributions of hydrocarbons predicted by the model with biodegradation are similar to those observed in the sediment and water column, although the model predicts hydrocarbons to the northeast and east of the well where no observations were made. This study indicates that improvement in knowledge of droplet sizes and biodegradation processes is important for accurate prediction of subsurface oil spills.

This document is an editorial published in the journal Frontiers in Marine Science. It discusses the importance of understanding and predicting the ocean dynamics in the Gulf of Mexico. The Gulf of Mexico has been a focus of research due to its significance in maritime transportation, the petroleum industry, and its impact on regional ecosystems, weather, and climate. The editorial highlights recent research efforts to improve understanding and forecasting capabilities of the Gulf of Mexico circulation, particularly the Loop Current and its associated eddies. The document also mentions collaborative research programs and initiatives aimed at reducing risks to offshore energy exploration, forecasting hurricane intensification, and managing fisheries. The editorial concludes by summarizing the articles included in the research topic, which provide new insights into the Gulf of Mexico circulation and its impacts on ecosystems, climate, and weather. The authors express their hope that these articles will contribute to a greater understanding and skillful prediction of the Gulf of Mexico system. [Extracted from the article]

The Processes driving Exchange At Cape Hatteras (PEACH) program seeks to better understand seawater exchanges between the continental shelf and the open ocean near Cape Hatteras, North Carolina. This location is where the Gulf Stream transitions from a boundary-trapped current to a free jet, and where robust along-shelf convergence brings cool, relatively fresh Middle Atlantic Bight and warm, salty South Atlantic Bight shelf waters together, forming an important and dynamic biogeographic boundary. The magnitude of this convergence implies large export of shelf water to the open ocean here. Background on the oceanography of the region provides motivation for the study and gives context for the measurements that were made. Science questions focus on the roles that wind forcing, Gulf Stream forcing, and lateral density gradients play in driving exchange. PEACH observational efforts include a variety of fixed and mobile observing platforms, and PEACH modeling included two different resolutions and data assimilation schemes. Findings to date on mean circulation, the nature of export from the southern Middle Atlantic Bight shelf, Gulf Stream variability, and position variability of the Hatteras Front are summarized, together with a look ahead to forthcoming analyses. [ABSTRACT FROM AUTHOR]

We introduce the National Science Foundation (NSF) AI Institute for Research on Trustworthy AI in Weather, Climate, and Coastal Oceanography (AI2ES). This AI institute was funded in 2020 as part of a new initiative from the NSF to advance foundational AI research across a wide variety of domains. To date AI2ES is the only NSF AI institute focusing on environmental science applications. Our institute focuses on developing trustworthy AI methods for weather, climate, and coastal hazards. The AI methods will revolutionize our understanding and prediction of high-impact atmospheric and ocean science phenomena and will be utilized by diverse, professional user groups to reduce risks to society. In addition, we are creating novel educational paths, including a new degree program at a community college serving underrepresented minorities, to improve workforce diversity for both AI and environmental science. [ABSTRACT FROM AUTHOR]