Ocean Dynamics Research Articles

Pacific‐Arctic Connections: Assessing Flow Through Bering Strait in Context With Dynamic Ocean Topography and Surface Stress

AbstractBering Strait is the only ocean gateway connecting the Pacific and Arctic oceans. The ∼1 Sv northward flow of Pacific water through the strait to the Arctic Ocean has been increasing by ∼0.01 Sv/yr since 1990. Monthly dynamic ocean topography (DOT), wind, and sea‐ice data at Bering Strait are analyzed in context with the long‐term record of flow through the strait to investigate local drivers. Ocean transport is found to be proportional to the across‐strait slope in DOT, suggesting some component of the flow is in geostrophic balance. Along‐strait ocean surface stresses, which modulate the across‐strait DOT slope via Ekman transport, are analyzed in the presence of a seasonally varying ice cover. It is shown that northward interior ocean flow under sea ice in winter results in southward surface stresses, and westward Ekman transport that slows the geostrophic component of the northward ocean flow. As the number of open water days local to Bering Strait increase each year, we find no trend in the annual mean surface stress, that is, the loss of sea ice is not leading to increased northward wind stress input that would enhance northward ocean flow. This analysis is consistent with the theory that changes in both the atmosphere and ocean non‐local to Bering Strait are likely driving the increased transport from the Pacific into the Arctic via Bering Strait.

Quantifying the Role of Ocean Dynamics in SST Variability across GCMs and Observations

Abstract Midlatitude SSTs forced by mesoscale oceanic processes can affect the large-scale atmosphere, pointing to the ocean's crucial role outside the tropics. Previous studies have shown oceanic mesoscale processes' effect on global and regional climate variability. This study quantifies the local contribution of ocean dynamics to mixed-layer temperature across the globe by directly estimating the ocean heat flux divergence resolved by state-of-the-art ocean reanalysis, eddy-resolving, and eddy-parameterized versions of two US national climate models and indirectly from air-sea flux satellite-based estimates. Our results show that the eddy-resolving climate simulations resolve mixed-layer temperature variances that are larger and closer to those inferred from observations than both their eddy-parameterized counterparts and ECCO over much of the extratropics. The observations and the eddy-resolving models indicate a more significant role of ocean dynamics in the mixed layer temperature variability than the surface fluxes over most extratropics compared to their eddy-parameterized versions. A frequency domain analysis shows that the better-resolved ocean mesoscale and thermal gradients enhance the variance over timescale from two months to thirty years. Results show agreement in the ocean's contribution among satellite-based estimates, ocean reanalysis products, and ocean eddy-resolving simulations. At the same time, differences emerge for ECCO and the eddy-parameterized models, suggesting that surface fluxes account for a larger fraction of the mixed layer temperature variability in most of the extratropics.

Closing the Loops on Southern Ocean Dynamics: From the Circumpolar Current to Ice Shelves and From Bottom Mixing to Surface Waves

AbstractA holistic review is given of the Southern Ocean dynamic system, in the context of the crucial role it plays in the global climate and the profound changes it is experiencing. The review focuses on connections between different components of the Southern Ocean dynamic system, drawing together contemporary perspectives from different research communities, with the objective of closing loops in our understanding of the complex network of feedbacks in the overall system. The review is targeted at researchers in Southern Ocean physical science with the ambition of broadening their knowledge beyond their specific field, and aims at facilitating better‐informed interdisciplinary collaborations. For the purposes of this review, the Southern Ocean dynamic system is divided into four main components: large‐scale circulation; cryosphere; turbulence; and gravity waves. Overviews are given of the key dynamical phenomena for each component, before describing the linkages between the components. The reviews are complemented by an overview of observed Southern Ocean trends and future climate projections. Priority research areas are identified to close remaining loops in our understanding of the Southern Ocean system.

Salinity Stress Acclimation Strategies in Chlamydomonas sp. Revealed by Physiological, Morphological and Transcriptomic Approaches.

Climate changes may include variations in salinity concentrations at sea by changing ocean dynamics. These variations may be especially challenging for marine photosynthetic organisms, affecting their growth and distribution. Chlamydomonas spp. are ubiquitous and are often found in extreme salinity conditions. For this reason, they are considered good model species to study salinity adaptation strategies. In the current study, we used an integrated approach to study the Chlamydomonas sp. CCMP225 response to salinities of 20‱ and 70‱, by combining physiological, morphological, and transcriptomic analyses, and comparing differentially expressed genes in the exponential and stationary growth phases under the two salinity conditions. The results showed that the strain is able to grow under all tested salinity conditions and maintains a surprisingly high photosynthetic efficiency even under high salinities. However, at the highest salinity condition, the cells lose their flagella. The transcriptomic analysis highlighted the up- or down-regulation of specific gene categories, helping to identify key genes responding to salinity stress. Overall, the findings may be of interest to the marine biology, ecology, and biotechnology communities, to better understand species adaptation mechanisms under possible global change scenarios and the potential activation of enzymes involved in the synthesis of bioactive molecules.

Challenges, Advances and Opportunities in Regional Sea Level Projections: The Role of Ocean‐Shelf Dynamics

AbstractFuture sea level rise and changes in extreme weather will increase the frequency of flooding and intensify the risks for the millions of people living in low‐lying coastal areas. Concerns about coastal adaptation have been broadened due to societal awareness of the threat from rising seas, leading to a large set of potential adaptation users with diverse needs for adequate sea level projections in coastal areas beyond the current state of the art regional projections. In this paper, we provide an overview of the potential steps for improvement of regional sea level projections along the global coastline, with specific focus on the contribution from ocean dynamics to seasonal‐decadal variability of coastal sea level, and its implications for changes in frequency and magnitude of extreme sea levels. We discuss the key gaps in our knowledge and predictive capability of these dynamics as they relate to sea level variability on seasonal to decadal timescales, and conclude by suggesting ways in which these knowledge gaps could be addressed.

Effects of wave breaking on moisture flux

Wave breaking is a ubiquitous phenomenon in ocean dynamics, serving as a critical conduit for the exchange of momentum, heat, and energy between the atmosphere and the ocean. Although its vital role in air-sea moisture exchange is widely acknowledged, quantifying its exact impact on moisture flux remained quite challenging due to data limitations and the inherently turbulent nature of the process. To overcome these challenges, we construct a comprehensive 10-year global dataset that incorporates multiple breaking wave variables, informed by statistical breaking theories, to capture the intricacies of the breaking process and its consequential effects. We then employ a stacked machine learning model to elucidate the complex relationships between wave breaking and moisture flux. The performance of our stacked model, which is enriched with breaking wave variables, is validated against the fifth-generation European Centre for Medium-Range Weather Forecasts reanalysis data (ERA5). The results offer excellent predictions and highlight the importance of breaking-wave-related variables in regulating moisture flux, thereby substantiating the integral role of wave breaking in modulating air-sea interactions and moisture transport.

Patagonian dust, Agulhas Current, and Antarctic ice-rafted debris contributions to the South Atlantic Ocean over the past 150,000 years

Disentangling inputs of aeolian dust, ice-rafted debris (IRD), and eroded continental detritus delivered by ocean currents to marine sediments provide important insights into Earth System processes and climate. This study uses Sr-Nd-Pb isotope ratios of the continent-derived (lithogenic) fraction in deep-sea core TN057-6 from the subantarctic Southern Ocean southwest of Africa over the past 150,000 y to identify source regions and quantify their relative contributions and fluxes utilizing a mixing model set in a Bayesian framework. The data are compared with proxies from parallel core Ocean Drilling Program Site 1090 and newly presented data from potential South America aeolian dust source areas (PSAs), allowing for an integrated investigation into atmospheric, oceanic, and cryospheric dynamics. PSA inputs varied on glacial/interglacial timescales, with southern South American sources dominating up to 88% of the lithogenic fraction (mainly Patagonia, which provided up to 68%) during cold periods, while southern African sources were more important during interglacials. During the warmer Marine Isotope Stage (MIS) 3 of the last glacial period, lithogenic fluxes were twice that of colder MIS2 and MIS4 at times, and showed unique isotope ratios best explained by Antarctic-derived IRD, likely from the Weddell Sea. The IRD intrusions contributed up to 41% at times and followed Antarctic millennial warming events that raised temperatures, causing instability of icesheet margins. High IRD was synchronous with increased bioavailable iron, nutrient utilization, high biological productivity, and decreased atmospheric CO2. Overall, TN057-6 sediments record systematic Southern Hemisphere climate shifts and cryospheric changes that impacted biogeochemical cycling on both glacial/interglacial and subglacial timescales.

Controls on Early Cretaceous South Atlantic Ocean circulation and carbon burial – a climate model–proxy synthesis

Abstract. Black shale sediments from the Barremian to Aptian South Atlantic document the intense and widespread burial of marine organic carbon during the initial stages of seafloor spreading between Africa and South America. The enhanced sequestration of atmospheric CO2 makes these young ocean basins potential drivers of the Early Cretaceous carbon cycle and climate perturbations. The opening of marine gateways between initially restricted basins and related circulation and ventilation changes are a commonly invoked explanation for the transient formation and disappearance of these regional carbon sinks. However, large uncertainties in palaeogeographic reconstructions limit the interpretation of available palaeoceanographic data and prevent any robust model-based quantifications of the proposed circulation and carbon burial changes. Here, we present a new approach to assess the principal controls on the Early Cretaceous South Atlantic and Southern Ocean circulation changes under full consideration of the uncertainties in available boundary conditions. Specifically, we use a large ensemble of 36 climate model experiments to simulate the Barremian to Albian progressive opening of the Falkland Plateau and Georgia Basin gateways with different configurations of the proto-Drake Passage, the Walvis Ridge, and atmospheric CO2 concentrations. The experiments are designed to complement available geochemical data across the regions and to test circulation scenarios derived from them. All simulations show increased evaporation and intermediate water formation at subtropical latitudes that drive a meridional overturning circulation whose vertical extent is determined by the sill depth of the Falkland Plateau. The densest water masses formed in the southern Angola Basin and potentially reached the deep Cape Basin as Walvis Ridge Overflow Water. Palaeogeographic uncertainties are as important as the lack of precise knowledge of atmospheric CO2 levels for the simulated temperature and salinity spread in large parts of the South Atlantic. Overall temperature uncertainties reach up to 15 °C and increase significantly with water depth. The ensemble approach reveals temporal changes in the relative importance of geographic and radiative forcings for the simulated oceanographic conditions and, importantly, nonlinear interactions between them. The progressive northward opening of the highly restricted Angola Basin increased the sensitivity of local overturning and upper-ocean stratification to atmospheric CO2 concentrations due to large-scale changes in the hydrological cycle, while the chosen proto-Drake Passage depth is critical for the ocean dynamics and CO2 response in the southern South Atlantic. Finally, the simulated processes are integrated into a recent carbon burial framework to document the principal control of the regional gateway evolution on the progressive shift from the prevailing saline and oxygen-depleted subtropical water masses to the dominance of ventilated high-latitude deep waters.

The Zonal Seasonal Cycle of Tropical Precipitation: Introducing the Indo-Pacific Monsoonal Mode

Abstract The intertropical convergence zone (ITCZ) is associated with a zonal band of strong precipitation that migrates meridionally over the seasonal cycle. Tropical precipitation also migrates zonally, such as from the South Asian monsoon in Northern Hemisphere summer (JJA) to the precipitation maximum of the west Pacific in Northern Hemisphere winter (DJF). To explore this zonal movement in the Indo-Pacific sector, we analyze the seasonal cycle of tropical precipitation using a 2D energetic framework and study idealized atmosphere–ocean simulations with and without ocean dynamics. In the observed seasonal cycle, an atmospheric energy and precipitation anomaly forms over South Asia in northern spring and summer due to heating over land. It is then advected eastward into the west Pacific in northern autumn and remains there due to interactions with the Pacific cold tongue and equatorial easterlies. We interpret this phenomenon as a “monsoonal mode,” a zonally propagating moist energy anomaly of continental and seasonal scale. To understand the behavior of the monsoonal mode, we develop and explore an analytical model in which the monsoonal mode is advected by low-level winds, is sustained by interaction with the ocean, and decays due to the free tropospheric mixing of energy. Significance Statement Regional concentrations of tropical precipitation, such as the South Asian monsoon, provide water to billions of people. These features have strong seasonal cycles that have typically been framed in terms of meridional shifts of precipitation following the sun’s movement. Here, we study zonal shifts of tropical precipitation over the seasonal cycle in observations and idealized simulations. We find that land–ocean contrasts trigger a monsoon with concentrated precipitation over Asia in northern summer and near-surface eastward winds carry this precipitation into the west Pacific during northern autumn in what we call a “monsoonal mode.” This concentrated precipitation remains over the west Pacific during northern winter, as further migration is impeded by the cold sea surface temperatures (SSTs) and easterly winds of the east Pacific.

Applications of deep learning in physical oceanography: a comprehensive review

Deep learning, a data-driven technology, has attracted widespread attention from various disciplines due to the rapid advancements in the Internet of Things (IoT) big data, machine learning algorithms and computational hardware in recent years. It proves to achieve comparable or even more accurate results than traditional methods in a more flexible manner in existing applications in various fields. In the field of physical oceanography, an important scientific field of oceanography, the abundance of ocean surface data and high dynamic complexity pave the way for an extensive application of deep learning. Moreover, researchers have already conducted a great deal of work to innovate traditional approaches in ocean circulation, ocean dynamics, ocean climate, ocean remote sensing and ocean geophysics, leading oceanographic studies into the “AI ocean era”. In our study, we categorize numerous research topics in physical oceanography into four aspects: surface elements, subsurface elements, typical ocean phenomena, and typical weather and climate phenomena. We review the cutting-edge applications of deep learning in physical oceanography over the past three years to provide comprehensive insights into its development. From the perspective of three application scenarios, namely spatial data, temporal data and data generation, three corresponding deep learning model types are introduced, which are convolutional neural networks (CNNs), recurrent neural networks (RNNs) and generative adversarial networks (GANs), and also their principal application tasks. Furthermore, this study discusses the current bottlenecks and future innovative prospects of deep learning in oceanography. Through summarizing and analyzing the existing research, our aim is to delve into the potential and challenges of deep learning in physical oceanography, providing reference and inspiration for researchers in future oceanographic studies.

Applications of Finite-Time Lyapunov Exponent in detecting Lagrangian Coherent Structures for coastal ocean processes: a review

Addressing the threats of climate change, pollution, and overfishing to marine ecosystems necessitates a deeper understanding of coastal and oceanic fluid dynamics. Within this context, Lagrangian Coherent Structures (LCS) emerge as essential tools for elucidating the complexities of marine fluid dynamics. Methods used to detect LCS include geometric, probabilistic, cluster-based and braid-based approaches. Advancements have been made to employ Finite-time Lyapunov Exponents (FTLE) to detect LCS due to its high efficacy, reliability and simplicity. It has been proven that the FTLE approach has provided invaluable insights into complex oceanic phenomena like shear, confluence, eddy formations, and oceanic fronts, which also enhanced the understanding of tidal-/wind-driven processes. Additionally, FTLE-based LCS were crucial in identifying barriers to contaminant dispersion and assessing pollutant distribution, aiding environmental protection and marine pollution management. FTLE-based LCS has also contributed significantly to understanding ecological interactions and biodiversity in response to environmental issues. This review identifies pressing challenges and future directions of FTLE-based LCS. Among these are the influences of external factors such as river discharges, ice formations, and human activities on ocean currents, which complicate the analysis of ocean fluid dynamics. While 2D FTLE methods have proven effective, their limitations in capturing the full scope of oceanic phenomena, especially in 3D environments, are evident. The advent of 3D LCS analysis has marked progress, yet computational demands and data quality requirements pose significant hurdles. Moreover, LCS extracted from FTLE fields involves establishing an empirical threshold that introduces considerable variability due to human judgement. Future efforts should focus on enhancing computational techniques for 3D analyses, integrating FTLE and LCS into broader environmental models, and leveraging machine learning to standardize LCS detection.

Insightful inspection of the nonlinear instability of an azimuthal disturbance separating two rotating magnetic liquid columns

The nonlinear stability examination of two revolving magnetized liquid columns connecting two completely submerged fluids in a permeable region is the aim of the existing paper. Two endless vertical cylinders occupied with two magnetic fluids make up the present structure. Significantly, the disturbance at the border displays an azimuthal behavior. The entire structure is activated by an azimuthal unchanging magnetic field (MF). The increasing interest in the atmospheric and oceanic dynamics is the primary motivation in exploring this problem. To relax the complication of the mathematical processes, the viscous potential theory (VPT) is established. The motion is assessed using three basic coexistent field formulations: Maxwell's formula, Brinkman's formula, and the continuity condition, in the construction of the Coriolis force and centrifugal implications. The explanations of the linearized formula of motion produce a nonlinear categorizing diffusion structure because of the implications of the nonlinear boundary conditions (BCs). The non-perturbative approach (NPA) based on the He's frequency formulation (HFF) is employed to transform the nonlinear characteristic ordinary differential equation (ODE) into a linear one. A short description of the NPA is also presented. The nonlinear ODE with real and imaginary coefficients is exposed by the stability analysis. The stability requirements are implemented using only a nonlinear analysis. As demonstrated, as an unusual state, it is exposed that ignoring the Weber number removes all complex items of the nonlinear formulation. Physically, this means the absence of the angular velocities from the physical model. For both the real and complex situations of the original equation, the stability remains unchanged. It is found that the azimuthal MF, rotating parameter, and Darcy’s numeral have a maintenance impact. On the other hand, the azimuthal wave numeral has a destabilizing one. Several polar designs are drawn to agreement the stability situations.

Sea Surface Wind Speed Estimation From the Combination of Satellite Scatterometer and Radiometer Parameters

AbstractSatellite research on global sea surface winds is crucial for understanding and monitoring the dynamics of earth's oceans, providing valuable insights into weather patterns, climate changes, and oceanographic processes. This study investigates the fusion of active (microwave scatterometer) and passive (microwave radiometer) satellite data using Machine learning (ML) for sea surface wind speed estimation. Employing Random Forest Regression (RF), Convolutional Neural Network Regression (CNN), and Multiple Linear Regression (MLR). Evaluation against reference data sets (Advanced Scatterometer, ERA5, Cross‐Calibrated Multi‐Platform (CCMP), buoy wind speeds) highlights the robustness of the proposed models. The research findings indicate that both RF and CNN exhibit superior accuracy in the active, passive, and joint active‐passive models compared to the simplistic MLR model. The joint models of the three regression methods outperform the individual active or passive models. The root mean square deviation (RMSD) accuracy of RF and CNN joint models, when compared with ASCAT, is in the order of 0.7 m/s, and when compared with buoys, the RMSD accuracy is around 1.1 m/s. The ML models, especially RF and CNN, demonstrate superior performance, providing accurate and reliable estimations crucial for meteorological and oceanographic applications. These findings underscore the potential operational use of ML techniques in enhancing remote sensing applications.

The Bonney Coast upwelling: How physical processes shape the feeding behaviour of blue whales

This study employs a fully coupled physical-biological model to explore the oceanic dynamics and phytoplankton production in one of Australia’s most prominent coastal upwelling systems, the Bonney Coast Upwelling, that has barely been studied before. The study focusses on how physical processes provide two different food sources for blue whales (Balaenoptera musculus), namely, krill (treated as nonbuoyant particles) and zooplankton, both feeding on phytoplankton. While plankton grows in the euphotic zone in response to nutrient enrichment on time scales of weeks, krill can only be transported into the region via ambient currents. Findings of this study suggest that phytoplankton blooms appear slowly in the main upwelling plume on timescales of 4-8 weeks. Dynamical influences from incoming coastal Kelvin waves significantly weaken or strengthen this classical upwelling plume and its plankton productivity. On the other hand, the upwelling-favourable wind induces a continuous coastal current that also extends eastward past the Bonney Coast. This current operates to transport and distribute krill (that cannot swim horizontally) westward along the shelf, which explains the apparent conundrum why blue whales also feed on the upstream side of the upwelling plume. The author postulates that the variability of both plankton production and the intensity of the upwelling flow (passing krill swarms along the shelf) control the feeding locations of blue whales and other baleen whales on Australia’s southern shelves.

Multi-station collaborative wave height prediction based on multi-feature identification and interpretable analysis

This study proposes a novel deep learning model, the graph convolutional gated recurrent unit (GC-GRU), to address the critical challenge of accurate forecasting of ocean wave heights due to the complex nonlinear spatiotemporal variability of wave dynamics. The proposed model, which integrates the strengths of graph convolutional networks (GCNs) for spatial feature extraction and gated recurrent units (GRUs) for temporal feature extraction, allows for effective capture of complex spatiotemporal patterns in wave height data and is evaluated on a dataset of 666 observation stations in the Gulf of Mexico, forecasting wave heights up to 36 h in advance. Comparative experiments with traditional CNN and GRU models demonstrate the superior predictive performance of the GC-GRU approach. Additionally, we introduce the shapley additive explanation (SHAP) values to provide physical insights into the key physical variables and historical patterns driving the model's predictions. The results show that wind speed and mean wave period are the most influential factors related to wave height variations. It is expected that this work presents a significant advancement in wave height forecasting by introducing the innovative GC-GRU architecture and leveraging SHAP analysis to interpret the model's inner workings. The findings are expected to have important implications for enhancing coastal and maritime operations as well as informing our understanding of complex ocean wave dynamics.

Formation Mechanisms of the Decadal Indian Ocean Dipole Driven by Remote Forcing from the Tropical Pacific

Abstract On decadal time scales, a zonal SST dipole dominates the tropical Indian Ocean in boreal late summer and fall, called the decadal Indian Ocean dipole (D-IOD). The D-IOD has a spatial pattern different from the traditional interannual IOD, with its eastern pole located off Java, rather than the whole Sumatra–Java coasts as the latter. Here, we show that the D-IOD is generated by both the remote tropical Pacific decadal variability (TPDV) forcing and the decadal modulation of interannual IODs, but with its distinctive spatial pattern and seasonality mainly shaped by the former. In August–September (AS), due to the seasonal strengthening of trade winds, the descending branch of TPDV-induced Walker circulation moves westward into the eastern Indian Ocean relative to June–July, which stimulates equatorial easterly anomalies and oceanic upwelling Kelvin waves, causing subsurface cooling off Java. The subsurface cooling just occurs within the time window of climatological coastal upwelling so that subsurface cold anomalies are brought into the surface by mean upwelling and further transported offshore by mean flows, forming the D-IOD eastern pole. The subsurface cooling is only generated near Java but not Sumatra, because the former is closer to the exit of the Indonesian Throughflow (ITF). Weakened ITF during positive TPDV inhibits the growth of subsurface warming off Java prior to the establishment of AS equatorial easterly anomalies, whereas this ITF effect is not observed off Sumatra. Moreover, warming of the D-IOD western pole might be associated with off-equatorial Rossby waves induced by TPDV-related wind stress curls. Significance Statement The decadal Indian Ocean dipole (D-IOD) is one of the leading decadal modes of the tropical Indian Ocean SST variations. Its formation mechanisms, especially those related to its spatiotemporal characteristics, are not well understood. Based on observations and reanalysis, we show that the tropical Pacific decadal variability (TPDV) mainly accounts for the distinctive spatial pattern and seasonality of the D-IOD via the seasonal migration of the anomalous Walker circulation and ensuing oceanic subsurface dynamics. Our results highlight that the TPDV is an important source of D-IOD’s predictability, and it might be beneficial for operational decadal predictions for the tropical Indian Ocean.

Precise Orbit Determination for Maneuvering HY2D Using Onboard GNSS Data

The Haiyang-2D (HY2D) satellite is the fourth ocean dynamics environment monitoring satellite launched by China. The satellite operates on a re-entry frozen orbit, which necessitates orbital maneuvers to maintain its designated path once the satellite’s sub-satellite point deviates beyond a certain threshold. However, the execution of orbit maneuvers presents a significant challenge to the field of Precise Orbit Determination (POD). The thesis selects the on-board GPS data of HY2D satellite in December 2023 and five maneuvering days of that year. Employing a multifaceted approach that includes the assessment of observational data quality, orbit overlap, external orbit validation, and SLR (Satellite Laser Ranging) verification, the research delves into precise orbit determination during both maneuver and non-maneuver periods. The results indicate that: (1) The average number of satellites tracked by the receiver is 6.4; (2) During the non-maneuver periods, the average RMS (Root Mean Square) value of the radial difference in the 6-h overlapping arc segment is 0.66 cm, and the three-dimensional position difference is about 1.16 cm; (3) When compared with the precision science orbits (PSO) provided by CNES (Centre National d’Études Spatiales), the average values of the RMS values of the differences in the radial (R), transverse (T), and normal (N) directions during the non-maneuver and maneuver periods are respectively 1.32 cm, 2.31 cm, 1.92 cm and 3.04 cm, 8.78 cm, 2.72 cm. (4) The SLR verification of the orbit revealed a residual RMS of 2.24 cm. This suggests that by incorporating the modeling of maneuver forces during the maneuver periods, the impact of orbital maneuvers on orbit determination can be mitigated.

Ocean dynamics contribute to the amplifying Atlantic–Pacific salinity contrast

Ocean dynamics contribute to the amplifying Atlantic–Pacific salinity contrast

Predicting Ocean Current Temperature Off the East Coast of America with XGBoost and Random Forest Algorithms Using Rstudio

This research investigates the comparative predictive efficacy of two leading machine learning methodologies, specifically the XGBoost and Random Forest models, in estimating ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions along the east coast of North America. Using annual temperature datasets and relevant oceanographic parameters, the data is carefully processed, cleaned and sorted into training and test subsets via the RStudio Platform. The performance evaluation model is carried out using predetermined machine learning assessment criteria, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared. The results show the superiority of the XGBoost model compared to Random Forest in terms of prediction accuracy and minimizing prediction errors. The XGBoost model shows lower MSE values and higher R-squared values than the Random Forest model, indicating its better capacity in explaining data variations. XGBoost consistently provides more accurate predictions and shows higher sensitivity in identifying important factors influencing ocean temperature fluctuations than Random Forest. This research significantly improves understanding and prognostic capabilities regarding ocean temperature dynamics in the TS Gulf Stream and Labrador Current regions. Empirical evidence underlines the efficacy of the XGBoost model in predicting ocean temperatures in the studied region. Continuous model evaluation and parameter refinement for both methodologies is critical to establishing standards for optimal prediction performance. The findings of this research have implications for the fields of oceanography and climate science, and offer potential pathways to comprehensively understand and mitigate the impacts of climate change on marine ecosystems.

Combining barotropic and baroclinic simplified models for drift trajectory predictions

ABSTRACT The shallow-water equations are often used as a classical simplified ocean model for barotropic ocean dynamics. The same equations can also be used to model simplified baroclinic dynamics through the reduced-gravity model. Herein, we propose to utilise a GPU-accelerated shallow-water simulation framework for representing two decoupled simplified ocean models for each of the barotropic and baroclinic dynamics, and use these models for ensemble prediction of short-term drift trajectories in coastal domains. This system can be used complementary to current operational systems as a lightweight tool for uncertainty quantification and in the future for ensemble-based data assimilation of in-situ observations. We show the relevance of our approach by demonstrating how the barotropic and baroclinic dynamical components are of varying importance at different geographical locations, and that one of the models can be used alone or in combination with the other. We benchmark our approach by showing the resulting ensemble trajectories in reference to deterministic trajectories produced by operational models for three Norwegian coastal regions.