Ocean Features Research Articles

A Refined Method for Altimetry SSH Data Fusion to Enhance the Characterization of Kuroshio Extension: A Case Study

Abstract High-quality merged altimetry sea level products have been instrumental in analyzing oceanic dynamics, spanning global and regional sea level trends, and capturing interannual signals, but effectively characterizing mesoscale features remains a persistent issue. The essence of addressing this challenge lies in the selection of highly correlated data sources for fusion, within the confines of limited discrete observations from sea surface altimeters. To tackle this challenge, we propose a refined method focused on reducing the suboptimal interpolation window size and optimizing the mapping process, particularly emphasizing the enhancement of Kuroshio Extension (KE) characterization. By scrutinizing the spatial and temporal evolution of Sea Surface Temperature (SST) fields, we have devised a modified strategy for selecting altimetry along-track data. This strategic approach facilitates the acquisition of more precise observations on a finer time scale, thereby circumventing the inclusion of altimetry observations with low correlation. To validate the efficacy of this approach, we performed a case study of Sea Surface Height (SSH) data fusion in the KE region. As a result, the portrayal of KE intricacies, encompassing its axis and meandering, has markedly improved in the refined SSH gridded maps. Comparative analysis against drifter observations underscores the efficacy of the refined method. Velocity biases have been substantially reduced from −0.21 m/s to 0.05 m/s, while the correlation coefficient has surged from 0.64 to 0.90. Concurrently, the root mean square error has plummeted from 0.42 m/s to 0.21 m/s. These improvements signify significant strides toward more accurate and reliable characterizations of oceanic mesoscale features, particularly evident in the nuanced depiction of the KE.

Reconstruction of Typhoon-Induced Ocean Thermal Structures Using Deep Learning and Multi-Source Satellite Data with News Impact Analysis

Reconstructing the three-dimensional thermal structure of the ocean under typhoon conditions presents significant challenges due to the scarcity of observational data, particularly in subsurface regions, and the limitations of existing observation methods in terms of spatial and temporal resolution. Accurate reconstruction of these structures is crucial for understanding the energy exchange between the ocean and typhoons, as this exchange directly influences typhoon intensity and trajectory. To address these challenges, this study introduces a fully connected transformer network (FCT), which integrates fully connected layers with a transformer model. The FCT model leverages the attention mechanisms inherent in the transformer architecture to effectively extract and integrate multi-scale ocean dynamical features. Using data from Typhoon Lekima in 2019, this study reconstructs ocean thermal structures at various depths and achieves an RMSE of 1.03 °C and an MAE of 0.83 °C when validated against Argo data. Furthermore, the model’s robustness was demonstrated through five-fold cross-validation, with the validation loss exhibiting minor fluctuations across folds but remaining stable overall, with an average validation loss of 0.986 °C, indicating the model’s generalizability. Sensitivity analysis also revealed the model’s resilience to variations in key input variables, showing minimal impact on output even with perturbations of up to 10% in input data. In addition, the study incorporates content analysis of typhoon-related news reports from 2011 to 2020, revealing a predominance of political topics, which underscores the central role of government in disaster response, with economic and ecological topics following. This integrated approach not only enhances our understanding of the interactions between ocean thermal structures and typhoon dynamics but also provides valuable insights into the societal impacts of typhoons, as reflected in media coverage, contributing to improved disaster management strategies.

A two-stage deep learning architecture for detection global coastal and offshore submesoscale ocean eddy using SDGSAT-1 multispectral imagery

Submesoscale ocean eddies are essential oceanic phenomenon that control and influence the ocean energy cascade. Most existing eddy detection methods rely on low-resolution satellite altimeter data, which fail to capture submesoscale ocean features and oceanographic phenomena in shallow water. Introducing high-resolution multispectral data can alleviate these problems, yet it has been largely overlooked. A generalized and efficient deep learning architecture that combines developments in deep learning with Sustainable Development Goals Science Satellite 1 (SDGSAT-1) multispectral data from earth observations offers a potential pathway for more fine detection of ocean eddies. Considering that oceanic eddy exhibits spatially sparse characteristics on high-resolution remote sensing scenes, the oceanic eddy detection (OED) model suitable for global coastal and offshore regions is divided into two stages: eddy information judgment and eddy position determination. Correspondingly, SDGSAT-1 multispectral data from November 2021 to December 2022 were carried out to construct two submesoscale eddy datasets for training and testing each stage model. The union validation of multiple metrics demonstrates that the proposed OED model and its stage models achieve state-of-the-art (SOTA) performance, especially in optically complex coastal and offshore waters. We applied the model to real-world scenes captured by SDGSAT-1 in 2023, and found that the detected results were mainly located at the water depth below 200 m. The authenticity of the recognition results is validated using sea surface chlorophyll concentration, temperature, and topography data, indicating that the OED model has achieved remarkable effectiveness under various sea conditions. In addition, the temporal distributions and statistical characteristics of detected submesoscale eddies are analyzed over an extended period (November 2021 to November 2023). Finally, HISEA-2, Landsat-9, and Sentinel-2 served as testing grounds to validate the generalization of the proposed methodology, with experimental results demonstrating that the OED model possesses significant developmental potential for multi-source remote sensing data. This paper presents a comprehensive deep learning framework for the global-scale detection of submesoscale eddies and underscores the pivotal role of high-resolution multispectral imagery as an innovative data source for global coastal and offshore eddy identification.

Oceanic eddy with submesoscale edge drives intense air-sea exchanges and beyond

Oceanic mesoscale eddies influence air-sea interaction and atmosphere dynamics through ventilating heat and moisture upward. However, whether the sea surface temperature (SST) gradient on the eddy edge could affect the heat and moisture release is still unknown because of the limited observations and coarse-resolution climate models. Using high-resolution atmospheric simulations, this study compares the atmospheric response to the mesoscale (~ 40 km) and submesoscale (~ 4 km) SST gradients at the edge of an eddy. Results show that submesoscale SST gradient drives stronger surface heat and moisture fluxes, enhancing the vertical mixing intensity by 2–3 times within and above the marine atmospheric boundary layer. As a result, one local precipitation event is found to be an order of magnitude larger overlying the eddy. Our findings highlight the importance of resolving oceanic submesoscale features for accurately predicting atmosphere dynamics and precipitation over the ocean.

Impact of the Fujiwhara effect from tropical cyclones Seroja and Odette on ocean dynamic in Southern Indonesia: Insights from argo data and model analysis

Tropical cyclone (TC) disturbances can cause changes in ocean dynamics before, during, and afterwards. We employed an Argo data and ocean model product of Copernicus Marine Environment Monitoring Service (CMEMS) to address the gap in observations of the ocean response from the severe TC Seroja and its interactions with the tropical low TC Odette, known as the rare Fujiwhara effect phenomenon. Our study in southern Indonesia seawater was conducted between March and April 2021, beginning with reconciling the deviation of the CMEMS ocean model product by assessing the model accuracy to the Argo float data to build confidence in using the model for further analysis. Comparison revealed that the temperature model datasets have a higher correlation coefficient (∼0.95) with the Argo observation than the salinity model datasets. Further, we investigated the temporal evolution of vertical profiles and upper ocean responses both inside and beyond the TCs route zone. We discovered that the upper ocean response results in a colder and saltier surface with a recovery time of more than 15 days. Furthermore, as a result of the strong surface cooling, mixed layers inside the Seroja track were rapidly thickening following the passage of TC. Our findings provide insights into how the TCs Seroja and Odette affect the ocean layers, especially the primary ocean features and behaviours (temperature and salinity), using the Argo and model datasets.

Emergent ecology in a microscale model of the surface ocean.

Microbial processes operate at the microscale, which is not resolved by existing ecosystem models. Here, we present a novel model that simulates a 1 mL three-dimensional cube using a hybrid Lagrangian-Eulerian approach, at ecologically relevant timescales. The model simulates individual microbes, including three phytoplankton size classes with healthy, senescent, and dead lifecycle stages; copiotrophic and oligotrophic heterotrophic bacteria; and dissolved organic matter at 50 µm resolution. Diffusion, shear, sedimentation, chemotaxis, and attachment processes are explicitly resolved. The emerging quantitative representation of the ecosystem shows that (1) copiotrophs grow mostly attached to eukaryotic phytoplankters and get almost all of their carbon from them vs. oligotrophs that grow on exudates and lysates of cyanobacteria; (2) contrasting diel patterns in substrate appearance in the phycosphere vs. ambient water and growth of particle-associated copiotrophs vs. free-living oligotrophs; (3) attached bacteria reduce carbon flux from the phycosphere, lowering chemotactic efficiency toward eukaryotes below that toward cyanobacteria; (4) shear reduces chemotactic efficiency and fitness of the copiotroph; and (5) the main benefit of chemotaxis is to locate attachment partners. These patterns are consistent with available observations. Our study provides insights into the microscale ecology of marine bacteria, and the open-source code is a tool for further research in this area.IMPORTANCEA large amount of global CO2 fixation is performed by marine phytoplankton, and a substantial fraction of that is released as dissolved organic carbon and further processed by heterotrophic bacteria. The interaction between phytoplankton and bacteria, i.e., the carbon flux between them, is therefore an important process in the global carbon and climate system. Some bacteria have developed specialized behavioral traits, like swimming and attachment, to increase their carbon acquisition. These interactions occur at the micrometer scale, for example, the immediate vicinity of phytoplankters (the phycosphere), but existing biogeochemical models typically only simulate down to the 1 meter vertical or ~100 kilometer horizontal scale. We present a new microscale model and use it to predict fluxes and other features in the surface ocean. The model makes important predictions about the fluxes between various types of phytoplankton and bacteria and the role of behavioral traits, and it provides a basis and tool for further research in this area.

Air–Sea Coupling Feedbacks over Tropical Instability Waves

Abstract Tropical instability waves (TIWs) are oceanic features that form around the equatorial Pacific cold tongue and influence the large-scale circulation and coupled climate variability including El Niño–Southern Oscillation. Local air–sea coupling over TIWs is thought to play an important role in the atmosphere and ocean’s energy and tracer budgets but is not well captured in coarse-resolution models. In this study, we isolate the impacts of TIW thermal (sea surface temperature–driven) and current (surface current–driven) feedbacks by removing TIW signatures in air–sea coupling fields in a high-resolution regional coupled model. The thermal feedback is found to damp TIW temperature variance by a factor of 2, associated both with the direct dependence of surface heat fluxes on SST (∼74%) and indirect impacts on surface winds (∼35%) and air temperature and humidity (∼−9%). These changes lead to cooling of the cold tongue SST by up to 0.1°C through reduced TIW-driven meridional heat fluxes and associated small changes in atmospheric circulation. The current feedback is decomposed into TIW (i.e., mesoscale) and mean (i.e., large-scale) components using separate experiments, with both having distinct impacts on TIWs and the mean state. The mesoscale current feedback reduces TIW eddy kinetic energy (EKE) by 22% through the eddy wind work, while the mean current feedback induces a further reduction of 8% by extracting energy from the mean currents and thus reducing barotropic EKE shear production. An improved understanding of small-scale tropical Pacific processes is needed to address biases in coarse-resolution models that impact their predictions and projections of Pacific climate variability and change. Significance Statement Tropical instability waves (TIWs) are oceanic features with ∼1000-km wavelengths that propagate westward on either side of the eastern equatorial Pacific cold tongue. TIWs drive lateral and vertical heat fluxes that impact several aspects of El Niño–Southern Oscillation. While climate models with a moderate, 1/4° ocean resolution can capture some TIW variability, they fail to properly represent many associated processes such as the impact of TIWs on the overlying atmosphere. Using sensitivity studies performed using a high-resolution regional coupled model, we study the impact of TIW air–sea coupling on the eastern Pacific climate system. Increased understanding of small-scale processes from studies such as this is essential to understand and address biases in models used for seasonal climate predictions and projections in the Pacific region.

Mesoscale cyclonic eddies born in an eastern boundary upwelling system enhance microbial eukaryote diversity in oligotrophic offshore waters

Mesoscale eddies which originate in Eastern Boundary Upwelling Systems (EBUS) such as the Canary Current System, entrap nutrient-rich coastal water and travel offshore while aging. We have analyzed the protistan plankton community structures in the deep chlorophyll maximum (DCM), sub-DCM, and oxygen minimum zone (OMZ) of three differently aged cyclonic EBUS eddies off Northwest Africa, as well as of non-eddy affected reference sites, using DNA metabarcoding. Throughout all water depths, we found that the investigated eddies generated local dispersal-driven hotspots of protistan plankton diversity in the naturally oligotrophic subtropical offshore waters off Northwest Africa. Based on the taxonomic composition of protistan plankton communities, these diversity hotspots are likely to play an important role in carbon sequestration and for regional food webs up to top predatory levels. Thereby, the life-span of an eddy emerged as an important criterion, how local offshore protistan plankton diversity is transformed quantitatively and qualitatively: each of the three eddies was characterized by notably distinct protistan plankton communities. This could be linked to the physicochemical water properties (predominantly macronutrients, temperature, and salinity) of the eddies' cores and rings, which experience pronounced changes during the eddies’ westward trajectories. Furthermore, we found evidence that eddy-specific deep-water protistan communities are relatively short-lived compared to the ones in the sunlit DCM. However, our results do not only witness from the importance of fine-scale physical ocean features for regional ecosystem processes, but they also show the complexity of these ocean features and that we are still far from understanding the biological processes and their driving forces in such features.

Modon solutions in an N-layer quasi-geostrophic model

Modons, or dipolar vortices, are common and long-lived features of the upper ocean, consisting of a pair of counter-rotating monopolar vortices moving through self-advection. Such structures remain stable over long times and may be important for fluid transport over large distances. Here, we present a semi-analytical method for finding fully nonlinear modon solutions in a multi-layer quasi-geostrophic model with arbitrarily many layers. Our approach is to reduce the problem to a multi-parameter linear eigenvalue problem which can be solved using numerical techniques from linear algebra. The method is shown to replicate previous results for one- and two-layer models and is applied to a three-layer model to find a solution describing a mid-depth propagating, topographic vortex.

Presenting a Long-Term, Reprocessed Dataset of Global Sea Surface Temperature Produced Using the OSTIA System

Over the past few decades, the oceans have stored the majority of the excess heat in the climate system resulting from anthropogenic emissions. An accurate, long-term sea surface temperature (SST) dataset is essential for monitoring and researching the changes to the global oceans. A variety of SST datasets have been produced by various institutes over the years, and here, we present a new SST data record produced originally within the Copernicus Marine Environment Monitoring Service (which is therefore named CMEMS v2.0) and assess: (1) its accuracy compared to independent observations; (2) how it compares with the previous version (named CMEMS v1.2); and (3) its performance during two major volcanic eruptions. By comparing both versions of the CMEMS datasets using independent in situ observations, we show that both datasets are within the target accuracy of 0.1 K, but that CMEMS v2.0 is closer to the ground truth. The uncertainty fields generated by the two analyses were also compared, and CMEMS v2.0 was found to provide a more accurate estimate of its own uncertainties. Frequency and vector analysis of the SST fields determined that CMEMS v2.0 feature resolution and horizontal gradients were also superior, indicating that it resolved oceanic features with greater clarity. The behavior of the two analyses during two volcanic eruption events (Mt. Pinatubo and El Chichón) was examined. A comparison with the HadSST4 gridded in situ dataset suggested a cool bias in the CMEMS v2.0 dataset versus the v1.2 dataset following the Pinatubo eruption, although a comparison with sparser buoy-only observations yielded less clear results. No clear impact of the El Chichón eruption (which was a smaller event than Mt. Pinatubo) on CMEMS v2.0 was found. Overall, with the exception of a few specific and extreme events early in the time series, CMEMS v2.0 possesses high accuracy, resolution, and stability and is recommended to users.

Fish-inspired tracking of underwater turbulent plumes

Autonomous ocean-exploring vehicles have begun to take advantage of onboard sensor measurements of water properties such as salinity and temperature to locate oceanic features in real time. Such targeted sampling strategies enable more rapid study of ocean environments by actively steering towards areas of high scientific value. Inspired by the ability of aquatic animals to navigate via flow sensing, this work investigates hydrodynamic cues for accomplishing targeted sampling using a palm-sized robotic swimmer. As proof-of-concept analogy for tracking hydrothermal vent plumes in the ocean, the robot is tasked with locating the center of turbulent jet flows in a 13,000-liter water tank using data from onboard pressure sensors. To learn a navigation strategy, we first implemented RL on a simulated version of the robot navigating in proximity to turbulent jets. After training, the RL algorithm discovered an effective strategy for locating the jets by following transverse velocity gradients sensed by pressure sensors located on opposite sides of the robot. When implemented on the physical robot, this gradient following strategy enabled the robot to successfully locate the turbulent plumes at more than twice the rate of random searching. Additionally, we found that navigation performance improved as the distance between the pressure sensors increased, which can inform the design of distributed flow sensors in ocean robots. Our results demonstrate the effectiveness and limits of flow-based navigation for autonomously locating hydrodynamic features of interest.

Enhancing the Resolution of Satellite Ocean Data Using Discretized Satellite Gridding Neural Networks

Ocean satellite data are often impeded by intrinsic limitations in resolution and accuracy. However, conventional data reconstruction approaches encounter substantial challenges when facing the nonlinear oceanic system and high-resolution fusion of variables. This research presents a Discrete Satellite Gridding Neural Network (DSGNN), a new machine learning method that processes satellite data within a discrete grid framework. By transforming the positional information of grid elements into a standardized vector format, the DSGNN significantly elevates the accuracy and resolution of data fusion through a neural network model. This method’s innovative aspect lies in its discretization and fusion technique, which not only enhances the spatial resolution of oceanic data but also, through the integration of multi-element datasets, better reflects the true physical state of the ocean. A comprehensive analysis of the reconstructed datasets indicates the DSGNN’s consistency and reliability across different seasons and oceanic regions, especially in its adept handling of complex nonlinear interactions and small-scale oceanic features. The DSGNN method has demonstrated exceptional competence in reconstructing global ocean datasets, maintaining small error variance, and achieving high congruence with in situ observations, which is almost equivalent to 1/12° hybrid coordinate ocean model (HYCOM) data. This study offers a novel and potent strategy for the high-resolution reconstruction and fusion of ocean satellite datasets.

Transoceanic Memory (Studies): Oceans as Mode, Method and Material

Abstract There is a renewed interest in scholarship exploring the connection between memory and the environment, remembering and ecocriticism, and between memory cultures and the Anthropocene. Oceanic spaces feature as frameworks to conceptualise this environmental turn across the disciplines, but have not been used to theorise an approach to memory. Taking cue from literary examples from the Anglophone sphere, and building on the work of Paul Gilroy, Isabel Hofmeyr, Michael North and Ananya Kabir, this essay conceptualises transoceanic memory (studies) as a transdisciplinary approach to memory; as mode, method and material. It is mode in its capacity to frame oceans as providing ideational, aesthetic, material and imaginary space to the many processes of remembering, and to reveal relational and exclusionary dynamics inherent to the formation of collective memory. It is method in the manner in which a transoceanic lens offers a comparative framework for transdisciplinary memory studies, allowing for different methods and models of memory to coalesce and communicate. Transoceanic memory is material in the sense in which it relates to oceans as ecosystems, as environmental formations in their own right, and with their own mnemonic agency.

Ocean component of the first operational version of Hurricane Analysis and Forecast System: Evaluation of HYbrid Coordinate Ocean Model and hurricane feedback forecasts

The first operational version of the coupled Hurricane Analysis and Forecast System (HAFSv1) launched in 2023 consists of the HYbrid Coordinate Ocean Model (HYCOM) and finite-volume cubed-sphere (FV3) dynamic atmosphere model. This system is a product of efforts involving improvements and updates over a 4-year period (2019–2022) through extensive collaborations between the Environmental Modeling Center at the US National Centers for Environmental Prediction (NCEP) and NOAA Atlantic Oceanography and Meteorology Laboratory. To provide two sets of numerical guidance, the initial operational capability of HAFSv1 was configured to two systems—HFSA and HFSB. In this study, we present in-depth analysis of the forecast skills of the upper ocean that was co-evolved by the HFSA and HFSB. We chose hurricane Laura (2020) as an example to demonstrate the interactions between the storm and oceanic mesoscale features. Comparisons performed with the available in situ observations from gliders as well as Argos and National Data Buoy Center moorings show that the HYCOM simulations have better agreement for weak winds than high winds (greater than Category 2). The skill metrics indicate that the model sea-surface temperature (SST) and mixed layer depth (MLD) have a relatively low correlation. The SST, MLD, mixed layer temperature (MLT), and ocean heat content (OHC) are negatively biased. For high winds, SST and MLT are more negative, while MLD is closer to the observations with improvements of about 8%–19%. The OHC discrepancy is proportional to predicted wind intensity. Contrarily, the mixed layer salinity (MLS) uncertainties are smaller and positive for higher winds, probably owing to the higher MLD. The less-negative bias of MLD for high winds implies that the wind-force mixing is less effective owing to the higher MLD and high buoyancy stability (approx. 1.5–1.7 times) than the observations. The heat budget analysis suggests that the maximum heat loss by hurricane Laura was O(< 3°C per day). The main contributor here is advection, followed by entrainment, which act against or with each other depending on the storm quadrant. We also found relatively large unaccountable heat residuals for the in-storm period, and the residuals notably led the heat tendency, meaning that further improvements of the subscale simulations are warranted. In summary, HYCOM simulations showed no systematic differences forced by either HFSA or HFSB.

Dispersal and connectivity modelling simulations for invertebrate larvae passing through the Strait of Gibraltar

Abstract The link between the northeastern Atlantic and Mediterranean Sea created by the Strait of Gibraltar and the adjacent Iberian and Moroccan coasts marks remarkable transition areas between distinct environments that harbour a diverse mixture of species. The area is interesting regarding marine connectivity and the transport of pelagic invertebrate larvae, crucial knowledge to manage over-exploited populations, and minimize the impacts of climate change and anthropogenic activities. Biophysical models were developed, combining oceanic and particle-tracking Lagrangian simulations with in situ zooplankton distribution data. The conditions driving the larval exchange between the sub-basins and the connectivity throughout the region were explored, using crustacean decapod larvae as biological references. The potential exchange between both sub-basins was confirmed, although specific larval traits revealed contrasting scenarios. The simulations showed that slope-dwelling and mesopelagic larvae have advantage when crossing from the Alboran into the Atlantic, in comparison with shelf-dwelling and epipelagic larvae. Transport pathways and retention areas were identified, and passive drifts were shown to increase the dispersal range of the simulations. The spatial origin of the larval release, larval duration, vertical distribution, and the interaction of larvae with the oceanic features are presented as the main factors impacting the effective larval input into the Atlantic or Mediterranean basins.

From nutrients to fish: Impacts of mesoscale processes in a global CESM-FEISTY eddying ocean model framework

The ocean sustains ecosystems that are essential for human livelihood and habitability of the planet. The ocean holds an enormous amount of carbon, and serves as a critical source of nutrition for human societies worldwide. Climate variability and change impact marine biogeochemistry and ecosystems. Thus, having state-of-the-art simulations of the ocean, which include marine biogeochemistry and ecosystems, is critical for understanding the role of climate variability and change on the ocean biosphere. Here we present a novel global eddy-resolving (0.1° horizontal resolution) simulation of the ocean and sea ice, including ocean biogeochemistry, performed with the Community Earth System Model (CESM). The simulation is forced by the atmospheric dataset based on the Japanese Reanalysis (JRA-55) product over the 1958–2021 period. We present a novel configuration of the CESM marine ecosystem model in this simulation which includes two zooplankton classes: microzooplankton and mesozooplankton. This novel planktonic food web structure facilitates “offline” coupling with the Fisheries Size and Functional Type (FEISTY) model. FEISTY is a size- and trait-based model of fish functional types contributing to fisheries. We present an evaluation of the ocean biogeochemistry, marine ecosystem (including fish types), and sea ice in this high resolution simulation compared to available observations and a corresponding low resolution (nominal 1°) simulation. Our analysis offers insights into environmental controls on trophodynamics within the ocean. We find that this high resolution simulation provides a realistic reconstruction of nutrients, oxygen, sea ice, plankton and fish distributions over the global ocean. On global and large regional scales the high resolution simulation is comparable to the standard 1° simulation, but on smaller scales, explicitly resolving the mesoscale dynamics is shown to be important for accurately capturing trophodynamic structuring, especially in coastal ecosystems. We show that fine-scale ocean features leave imprints on ocean ecosystems, from plankton to fish, from the tropics to polar regions. This simulation also offers insights on ocean acidification over the past 64 years, as well as how large-scale climate variations may impact upper trophic levels. The data generated by the simulations are publicly available and will be a fruitful community resource for a large variety of oceanographic science questions.

Unlocking the geochemical features of the Paleocene southern Pacific Ocean using carbon isotopes and biolipids

Little is known about the Paleocene in the southern Pacific Ocean due to scarce marine records. Here, we present a systematic geochemical investigation using biomarkers, carbonate content, and carbon isotopes of a set of early Paleocene deep-sea cores from International Ocean Discovery Program Expedition 378 Site U1553. The results provide a new Paleocene biomarker profile for the Campbell Plateau, New Zealand. The occurrence and distribution of a series of hopenes, ββ hopanes, sterenes and biomarker-based maturity parameters indicate that these organically-lean sediments are in an early diagenetic stage, with an equivalent vitrinite reflectance of approximately 0.4%. Biomarker data (e.g., gammacerane index and homohopane index), carbonate carbon isotopes (−27.9 ‰ to +2.0 ‰) combined with onboard data (sulphur and methane contents) indicate that the redox properties of the Paleocene southern Pacific Ocean changed from an oxidising-anoxic transition zone to a more reducing marine environment (water depth > 1000 m). The sources of the organic matter are diverse, including algae, bacteria, diatoms, dinoflagellates, and higher plants, but are dominated by aquatic organisms. Our new biomarker record bridges a Paleocene biosphere knowledge gap in the high latitude South Pacific.

Intensified Currents Associated With Benthic Storms Underneath an Eddying Jet

AbstractBenthic storms are episodes of intensified near‐bottom currents capable of sediment resuspension in the deep ocean. They typically occur under regions of high surface eddy kinetic energy (EKE), such as the Gulf Stream. Although they have long been observed, the mechanism(s) responsible for their formation and their relationships with salient features of the deep ocean, such as bottom mixed layers (BMLs) and benthic nepheloid layers (BNLs), remain poorly understood. Here we conduct idealized experiments with a primitive‐equation model to explore the impacts of the unforced instability of a surface‐intensified jet on near‐bottom flows of a deep zonal channel. Vertical resolution is increased near the bottom to represent the bottom boundary layer. We find that the unstable near‐surface jet develops meanders and evolves into alternating, deep‐reaching cyclones and anticyclones. Simultaneously, EKE increases near the bottom due to the convergence of vertical eddy pressure fluxes, leading to near‐bottom currents comparable to those observed during benthic storms. These currents in turn form BMLs with thickness of O(100 m) from enhanced velocity shears and turbulence production near the bottom. The deep cyclonic eddies transport fluid particles both laterally and vertically, from near the bottom through the entire BML and may contribute to the formation of the lower part of BNLs. A sloping bottom reduces both the intensity of the near‐bottom currents and the extent of vertical transport. Overall, our study highlights a significant response of the abyssal environment to near‐surface current instability, with potential implications for sediment transport in the deep ocean.

Remote Sensing Image Harmonization Method for Fine-Grained Ship Classification

Target recognition and fine-grained ship classification in remote sensing face challenges of high inter-class similarity and sample scarcity. A transfer fusion-based ship image harmonization algorithm is proposed to overcome these challenges. This algorithm designs a feature transfer fusion strategy based on the combination of a region-aware instantiation and attention mechanism. Adversarial learning is implemented through an image harmony generator and discriminator module to generate realistic remote sensing ship harmony images. Furthermore, the domain encoder and domain discriminator modules are responsible for extracting feature representations of the foreground and background, and further align the ship foreground with remote sensing ocean background features through feature discrimination. Compared with other advanced image conversion techniques, our algorithm delivers more realistic visuals, improving classification accuracy for six ship types by 3% and twelve types by 2.94%, outperforming Sim2RealNet. Finally, a mixed dataset containing data augmentation and harmonizing samples and real data was proposed for the fine-grained classification task of remote sensing ships. Evaluation experiments were conducted on eight typical fine-grained classification algorithms, and the accuracy of the fine-grained classification for all categories of ships was analyzed. The experimental results show that the mixed dataset proposed in this paper effectively alleviates the long-tail problem in real datasets, and the proposed remote sensing ship data augmentation framework performs better than state-of-the-art data augmentation methods in fine-grained ship classification tasks.

Middle Jurassic to Early Cretaceous orogenesis in the Klamath Mountains Province (Northern California–southern Oregon, USA) occurred by tectonic switching: Insights from detrital zircon U-Pb geochronology of the Condrey Mountain schist

The Klamath Mountains Province of Northern California and southern Oregon, USA, consists of generally east-dipping terranes assembled via Paleozoic to Mesozoic subduction along the western margin of North America. The Klamath Mountains Province more than doubled in mass from Middle Jurassic to Early Cretaceous time, due to alternating episodes of extension (e.g., rifting and formation of the Josephine ophiolite) and shortening (e.g., Siskiyou and Nevadan events). However, the tectonic mechanisms driving this profound Mesozoic growth of the Klamath Mountains Province are poorly understood. In this paper, we show that formation of the Condrey Mountain schist (CMS) of the central Klamath Mountains Province spanned this critical time period and use the archive contained within the CMS as a key to deciphering the Mesozoic tectonics of the Klamath Mountains Province. Igneous samples from the outer CMS subunit yield U-Pb zircon ages of ca. 175–170 Ma, which reflect volcanic protolith eruptive timing. One detrital sample from the same subunit contains abundant (~54% of zircon grains analyzed) Middle Jurassic ages with Paleozoic and Proterozoic grains comprising the remainder and yields a maximum depositional age (MDA) of ca. 170 Ma. These ages, in the context of lithologic and thermochronologic relations, suggest that outer CMS protoliths accumulated in an outboard rift basin and subsequently underthrust the Klamath Mountains Province during the Late Jurassic Nevadan orogeny. Five samples of the chiefly metasedimentary inner CMS yield MDAs ranging from 160 Ma to 130 Ma, with younger ages corresponding to deeper structural levels. Such inverted age zonation is common in subduction complexes and, considering existing K-Ar ages, suggests that the inner CMS was assembled by progressive underplating over a >10 m.y. timespan. Despite this age zonation, age spectra derived from structurally shallow and deep portions of the inner CMS closely overlap those derived from the oldest section of the Franciscan subduction complex (South Fork Mountain schist). These relations suggest that the inner CMS is a composite of South Fork Mountain schist slices that were sequentially underplated beneath the Klamath Mountains Province. The age, inboard position, and structural position (i.e., the CMS resides directly beneath Jurassic arc assemblages with no intervening mantle) of the CMS suggest that these rocks were emplaced during one or more previously unrecognized episodes of shallow-angle subduction restricted to the Klamath Mountains Province. Furthermore, emplacement of the deepest portions of the CMS corresponds with the ca. 136 Ma termination of magmatism in the Klamath Mountains Province, which we relate to the disruption of asthenospheric flow during slab shallowing. The timing of shallow-angle subduction shortly precedes that of the westward translation of the Klamath Mountains Province relative to correlative rocks in the northern Sierra Nevada Range, which suggests that subduction dynamics were responsible for relocating the Klamath Mountains Province from the arc to the forearc. In aggregate, the above relations require at least three distinct phases of extension and/or rifting, each followed by an episode of shallow-angle underthrusting. The dynamic upper-plate deformation envisioned here is best interpreted in the context of tectonic switching, whereby slab steepening and trench retreat alternate with slab shallowing due to recurrent subduction of buoyant oceanic features.