Numerical models are crucial for quantifying the ocean-atmosphere interactions associated with the El Niño-Southern Oscillation (ENSO) phenomenon in the tropical Pacific. Current coupled models often exhibit significant biases and inter-model differences in simulating ENSO, underscoring the need for alternative modeling approaches. The Regional Ocean Modeling System (ROMS) is a sophisticated ocean model widely used for regional studies and has been coupled with various atmospheric models. However, its application in simulating ENSO processes on a basin scale in the tropical Pacific has not been explored. For the first time, this study presents the development of a basin-scale hybrid coupled model (HCM) for the tropical Pacific, integrating ROMS with a statistical atmospheric model that captures the interannual relationships between sea surface temperature (SST) and wind stress anomalies. The HCM is evaluated for its capability to simulate the annual mean, seasonal, and interannual variations of the oceanic state in the tropical Pacific. Results demonstrate that the model effectively reproduces the ENSO cycle, with a dominant oscillation period of approximately two years. The ROMS-based HCM developed here offers an efficient and robust tool for investigating climate variability in the tropical Pacific.

Salinity difference is a significant characteristic that distinguishes marine and terrestrial aquatic ecosystems. The carbonic anhydrase (CA) plays an important role in responding to the salinity changes in organisms, including regulating ion transport, osmotic balance, and internal environmental homeostasis. However, there is little research on the multiple ca gene family members under salinity stress. Therefore, we analyzed and researched the ca gene family members under different salinity conditions (0 %o and 30 %o) in a euryhaline fish of marine medaka (Oryzias melastigma). In this study, we have screened and identified 18 members of the family in its genome, which are scattered across 10 chromosomes. Although some members had multiple copies, for example, ca16 with 3 copies, there was no homologous pair among the ca genes. These proteins were localized in different cellular compartments (cytoplasm, membrane, mitochondrion, extracellular space, and nucleus) and exhibited alpha helices, beta sheets, and loops, with all containing at least one CA catalytic domain. According to the results of transcriptome and qPCR, we concluded that CA1, CA5A, CA12, CAR12, and CAHZA may respond to low salinity stress. Furthermore, RPF2 may be an important interacting protein with these CA proteins. Our results provide a molecular basis for the salinity domestication of fishes.

Natural aerosols provide large uncertainties regarding the quantification of the climate effects of aerosols,especially those related to aerosol-cloud interactions[1].Advancing research on the interaction of natural aerosols and climate can provide a better assessment of the climate impacts of anthropogenic activities.As one of the most dominant natural aerosols,sea salt has the highest proportion of mass in natural aerosols[2].

In this study, a Tropical Cyclone (TC) Intensity Estimation (TIE) was proposed by comprehensively utilizing the multi-features of infrared (IR) remote sensing images, namely MFTIE model. The new model comprises of three modules: a CNN module based on Residual Blocks to extract the graphical features, an optimized multilayer perceptron (MLP) module with residual structure to process the statistical features, and a fully connected output module to give the final estimation of TC intensity. With the HURSAT-B1 dataset a root-mean-square error of 8.91 kt on test set. Besides, the MF-TIE model was compared with a model that uses only images as inputs, and the results showed that the test accuracy of the proposed model improved by 7.67 %. Furthermore, the comparisons with existing models confirm the performance superiority of the proposed model. The findings suggest that utilizing multi-source features of remote sensing data, such as the dynamic and radiative characteristics of IR images, has great potential in the field of remote sensing inversion.

Oceanic eddies are crucial to global ocean dynamics but challenging to monitor with traditional methods. This paper presents SAREYOLO, a novel deep-learning framework for the real-time detection of oceanic eddies in high-resolution spaceborne Synthetic Aperture Radar (SAR) imagery. We constructed a large-scale dataset of 25,010 annotated SAR sub-images from multiple satellites (Sentinel-1, Gaofen-3, ENVISAT). Through extensive experiments, an optimized YOLOv11 model, integrated with a Transformer attention module and a Wise-IoU loss function, was developed, achieving a mean Average Precision (mAP50) of 0.956 and an F1-score of 0.9. The system demonstrates strong generalization across diverse oceanic regions and unseen sensors, and processes a full SAR scene in under 0.05 seconds. This work establishes a robust, efficient solution for operational, near-real-time monitoring of oceanic eddies on a global scale.

Super-resolution (SR) techniques are commonly used to obtain high-resolution (HR) sea surface temperature (SST) data. However, existing SST SR methods require collocated optical and thermal infrared images as inputs, making them completely unusable at night. This letter presented a novel SST SR method that uses two thermal infrared sensors (TIRSs) by the residual channel attention network (RCAN). The process is tested by 1 km-resolution Sentiel-3/SLSTR and Terra/MODIS data to obtain 250 m SST data. Evaluation results indicate that this method outperforms the scheme that uses visible light data as input in terms of better metrics in root mean square error (RMSE) of 0.12 degrees C, structural similarity index measure (SSIM) of 0.784, and peak signal-to-noise ratio (PSNR) of 22.35 dB. The case studies also demonstrate its capability for night-time applications.

East Asian marginal seas (EAMS) circulation is closely configurated by sea level rise during the last deglaciation. Here, we perform simulations to reconstruct the EAMS circulation on the basis of sea levels from -90 to 0 m of the present, using a high-resolution regional ocean circulation model under present-day fixed surface and lateral boundary conditions. Our results show that the EAMS circulation underwent twice abrupt changes: a rapid initiation of its modern structure when sea level rise exceeded -40 m, followed by a temporary overshoot of the Japan-Sea throughflows at -5 m. These nonlinear processes are caused by the opening of the Soya Strait and thus formation of the modern EAMS-circulation structure, and a transient absence of the circulation resembling a Kuroshio Large Meander following around-island integral constraint, respectively. Conceptually, our findings introduce the around-island integral constraint on abrupt shift in the global marginal-sea circulation during the last deglaciation.

Abstract. The El Niño and Southern Oscillation (ENSO) constitutes the most prominent interannual climate variation mode in the climate system, originating from ocean-atmosphere interactions in the tropical Pacific. Accurately modeling ENSO variation has consistently posed a great challenge, exhibiting strong model-dependent representations and simulations of ENSO. This study presents a novel Hybrid Coupled Model (HCM), denoted as HCMROMS, built upon the Regional Ocean Modeling System (ROMS) that has been widely used for regional modeling studies. For basin-wide applications to the tropical Pacific, here, the ROMS is incorporated with a statistical atmospheric model, which is based on singular value decomposition (SVD), capturing interannual relationships of atmospheric perturbations such as wind stress and freshwater flux anomalies with sea surface temperature (SST) anomalies. The model is constructed in a flexible way so that various components representing atmospheric forcing and oceanic biogeochemistry can be easily included as a module in the HCMROMS. Results demonstrate that the HCMROMS can simulate a stable quasi-three-year ENSO cycle when the interannual wind stress coupling coefficient, ατ , is set at 1.5. The HCMROMS reproduces the three-dimensional (3D) evolution of ENSO-related anomalies, revealing that the most pronounced temperature anomalies occur beneath the surface at 150 m. The interannual temperature anomaly budget highlights the dominance of the advection process in the simulated ENSO. Vertical mixing contributes negatively to ENSO anomalies, damping temperature anomalies from the surface due to the turbulent heat flux feedback. This newly developed HCMROMS is poised to serve as an efficient modeling tool for ENSO research in the future.

This study proposes a deep-learning model to map underwater topography from spaceborne synthetic aperture radar (SAR) images. For the model development, 18 high-resolution Sentinel-1 dual-polarization SAR images covering the southern Cape Cod area from 2014 to 2022 are collected, among which twelve images were used for model training, while the rest were used for testing. In order to facilitate the model learning complex and nonlinear relations among various parameters, the model input is designed to consist of 8 variables, which involve radar backscatter coefficient, radar observation geometry, geography information and marine dynamic environmental parameters. The deep learning network consists of a convolutional layer, two residual blocks, and a fully connected layer, facilitating feature extraction and regression prediction. The final model performances in retrieving shallow water depth are shown as follows: For the training set, the model achieved a root mean square error (RMSE) of 1.02 m and a relative error of 4.32%, with the maximum detectable water depth reaching 48.17 m. For testing, the best performance of the model presented with an RMSE of 1.59 m and a relative error of 5.29%, while the worst performance corresponds to an RMSE of 4.53 m with a relative error of 15.61%. Thus, the proposed model has the capacity of detecting the shallow water depth up to 50 meters with high precision.

Climate model simulations tend to drift away from the real world because of model errors induced by an incomplete understanding and implementation of dynamics and physics. Parameter estimation uses data assimilation methods to optimize model parameters, which minimizes model errors by incorporating observations into the model through state-parameter covariance. However, traditional parameter estimation schemes that simultaneously estimate multiple parameters using observations could fail to reduce model errors because of the low signal-to-noise ratio in the covariance. Here, based on the saturation time scales of model sensitivity that depend on different parameters and model components, we design a new multicycle parameter estimation scheme, where each cycle is determined by the saturation time scale of sensitivity of the model state associated with observations in each climate system component. The new scheme is evaluated using two low-order models. The results show that due to high signal-to-noise ratios sustained during the parameter estimation process, the new scheme consistently reduces model errors as the number of estimated parameters increases. The new scheme may improve comprehensive coupled climate models by optimizing multiple parameters with multisource observations, thereby addressing the multiscale nature of component motions in the Earth system.

Phytoplankton are a crucial component of aquatic ecosystems, and effective monitoring of them can provide valuable insights into ocean environments and ecosystem changes. Traditional phytoplankton monitoring methods are often complex and lack timely analysis. Therefore, deep learning algorithms offer a promising approach for automated phytoplankton monitoring. However, the lack of large-scale, high-quality training samples has become a major bottleneck in advancing phytoplankton tracking. In this paper, we propose a challenging benchmark dataset, Multiple Phytoplankton Tracking (MPT), which covers diverse background information and variations in motion during observation. The dataset includes 27 species of phytoplankton and zooplankton, 14 different backgrounds to simulate diverse and complex underwater environments, and a total of 140 videos. To enable accurate real-time observation of phytoplankton, we introduce a multi-object tracking method, Deviation-Corrected Multi-Scale Feature Fusion Tracker(DSFT), which addresses issues such as focus shifts during tracking and the loss of small target information when computing frame-to-frame similarity. Specifically, we introduce an additional feature extractor to predict the residuals of the standard feature extractor's output, and compute multi-scale frame-to-frame similarity based on features from different layers of the extractor. Extensive experiments on the MPT have demonstrated the validity of the dataset and the superiority of DSFT in tracking phytoplankton, providing an effective solution for phytoplankton monitoring.

Incident shortwave radiation can penetrate and heat the upper ocean water column, acting to modulate the stratification, vertical mixing and sea surface temperature. As a light-absorbing constituent, ocean chlorophyll (CHL) plays an important role in regulating these processes; however, its heating effect on the ocean state remains controversial and exhibits strong model dependence on ways the solar radiation transmission and the related CHL-induced heating are represented. In this study, we implement a chlorophyll-based two-way coupling between physical and ecological processes within the Regional Ocean Modeling System (ROMS). The bio-physics coupled model performs well in simulating the structure and variability of oceanic physical and ecological fields in the tropical Indo-Pacific region. Three CHL-related heating terms are analyzed based on the model output to diagnose the ocean biology-induced heating effects, namely the shortwave radiation part penetrating out of the base of the mixed layer (ML; Qpen), the portion absorbed within the ML (Qabs), and the rate of temperature change of the ML resulting from the Qabs effects (Rsr). Results show that the spatio-temporal distributions of the three heating terms are mainly determined by the ML depth (MLD). However, Qpen can also be regulated by the euphotic depth (ED), especially in the western-central equatorial Pacific. This moderating effect is particularly evident during El Niño when the ED tends to be greater than the MLD; positive ED anomalies act to enhance the positive Qpen anomalies caused by negative MLD anomalies. For the first time, the bio-heating effects are quantified within the ROMS-based two-way coupling context between the physical submodel and ecological submodel over the tropical Indo-Pacific Ocean, providing a basis for further understanding of the bio-effects and mechanisms. It is expected that the methodology and understanding developed in this study can help explore the chlorophyll-related processes in the ocean and the interactions with the atmosphere.

This study investigated the community characteristics and environmental influencing factors of ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) in the surface sediments of the East China Sea. The research found no consistent pattern in the richness and diversity of AOA and AOB with respect to the distance from the shore, indicating a complex interplay of factors. The expression levels of AOA amoA gene and AOB amoA gene in the surface sediments of the East China Sea ranged from 4.49 × 102 to 2.17 × 106 copies per gram of sediment and from 6.6 × 101 to 7.65 × 104 copies per gram of sediment, respectively. Salinity (31.77 to 34.53 PSU) and nitrate concentration (1.51 to 10.12 μmol/L) were identified as key environmental factors significantly affecting the AOA community, while salinity and temperature (13.71 to 19.50 °C) were crucial for the AOB community. The study also found that AOA, dominated by the Nitrosopumilaceae family, exhibited higher gene expression levels than AOB, suggesting a more significant role in ammonia oxidation. The expression of AOB was sensitive to multiple environmental factors, indicating a responsive role in nitrogen cycles and ecosystem health. The findings contribute to a better understanding of the biogeochemical processes and ecological roles of ammonia-oxidizing microorganisms in marine sediments.

The Gram-negative marine bacterium GXY010T, which has been isolated from the surface seawater of the western Pacific Ocean, is aerobic, non-motile and non-flagellated. Strain GXY010T exhibits growth across a temperature range of 10–42 °C (optimal at 37 °C), pH tolerance from 7.0 to 11.0 (optimal at 7.5) and a NaCl concentration ranging from 1.0 to 15.0% (w/v, optimal at 5.0%). Ubiquinone-8 (Q-8) was the predominant isoprenoid quinone in strain GXY010T. The dominant fatty acids (>10%) of strain GXY010T were iso-C15:0 (14.65%), summed feature 9 (iso-C17:1 ω9c and/or 10-methyl C16:0) (12.41%), iso-C17:0 (10.85%) and summed feature 3 (C16:1 ω7c and/or C16:1 ω6c) (10.41%). Phosphatidylethanolamine (PE), phosphatidylglycerol (PG), diphosphatidylglycerol (DPG), unidentifiable glycolipid (GL) and four non-identifiable aminolipids (AL1-AL4) were the predominant polar lipids of strain GXY010T. The genomic DNA G+C content was identified as a result of 48.0% for strain GXY010T. The strain GXY010T genome consisted of 2,766,857 bp, with 2664 Open Reading Frames (ORFs), including 2586 Coding sequences (CDSs) and 78 RNAs. Strain GXY010T showed Average Nucleotide Identity (ANI) values of 73.4% and 70.6% and DNA–DNA hybridization (DDH) values of 19.2% and 14.5% with reference species Pseudidiomarina tainanensis MCCC 1A02633T (=PIN1T) and Pseudidiomarina taiwanensis MCCC 1A00163T (=PIT1T). From the results of the polyphasic analysis, a newly named species, Pseudidiomarina fusca sp. nov. within the genus Pseudidiomarina, was proposed. The type strain of Pseudidiomarina fusca is GXY010T (=JCM 35760T = MCCC M28199T = KCTC 92693T).

A novel bacterial strain, designated DW002T, was isolated from the sea ice of Cape Evans, McMurdo Sound, Antarctica. Cells of the strain were Gram-negative, obligate anaerobic, motile, non-flagellated, and short rod-shaped. The strain DW002T grew at 4–32 ℃ (optimum at 22–28 ℃) and thrived best at pH 7.0, NaCl concentration of 2.5

Nutrients play a fundamental role in maintaining coastal ecosystem stability. Based on two cruise observations in the winter of 2020 and the summer of 2021, the spatiotemporal variations of dissolved inorganic nitrogen (DIN) and orthophosphate (PO43−) and their influencing factors in Sanya Bay were analyzed. Results show that the mean DIN concentrations in the bay are 2.36 μmol/L in winter and 1.73 μmol/L in summer, and the mean PO43− concentrations are 0.08 μmol/L in winter and 0.04 μmol/L in summer. The nutrient concentrations and composition are significantly affected by the Sanya River. The surface DIN concentrations at the Sanya River estuary are 15.80 and 5.25 times than those inside the bay in winter and summer, respectively. Meanwhile, a high proportion of NO3− (74

The complex interaction between cold and warm ocean currents in the Arctic Ocean cre-ates favorable conditions for the formation and growth of eddies.In the marginal ice zone of the Arctic,some of the upper ice floes,resulting in the formation of distinctive rotational features.These features,which contain traces of ice floes,are referred to as"ice eddies"in this paper.Ice eddies accelerate the melting of the upper ice floes through vertical heat transfer,which affects the development of the marginal ice zone and indirectly regulates the global climate.In this paper,a study is conducted on the detection,identification,and spatial and temporal characterization of Arctic ice eddies using high-resolu-tion Synthetic Aperture Radar(SAR)satellite images of 2022.Firstly,a training dataset is constructed using preprocessed SAR images,and the YOLOv7 target detection model is used to train the model.Then,the process of human-supervised visual identification is conducted based on the results of detec-tion and identification,resulting in the identification of a total of 3615 cyclonic ice eddies and 1482 anti-cyclonic ice eddies.Finally,the ice eddies are characterized using the visual identification results men-tioned above.The statistical results show that ice eddies in the Arctic Ocean are primarily generated from July to November.Their spatial distribution is concentrated along the eastern coast of Greenland and in the north-central Greenland Sea.The diameters of the ice eddies range from 3.85 to 114.9 km.99%of the eddies are smaller than 60 km,with an average diameter of 21.2 km.97%of the eddies have sea-ice coverage ranging from 20%to 70%,with an average sea-ice coverage of 41.76%.The results of the ice eddies detection,identification,and spatial and temporal characterization in this paper provide valu-able methodological references and remote sensing analysis information for analyzing marine phenomena and conducting climate research in the Arctic region.

This study investigated long-term interannual changes in summer circulation and hydrology in the East China Sea (ECS) by performing 35-year high-resolution ocean model simulation from 1981 to 2015. The sea surface temperature (SST) warming trend was considerably weaker in summer than in winter. To the east of the Yangtze Estuary, the interannual variation of SST in summer was mainly dominated by horizontal advection associated with variations in the Taiwan warm current and heat flux in the offshore region north of the Yangtze Estuary. Baroclinic circulation during summer played a crucial role in subsurface mixing. Near the surface, the significant atmospheric wind mode (EOF1) and Kuroshio mode (EOF2) dominate interannual variations in ocean circulation. In the subsurface, local wind around the Tsushima Strait dominated the interannual ocean variation. Anomalous northeasterly winds induced a southwestward pressure gradient due to topographical confinement. These anomalies propagated to the south along the continental shelf through topographic Rossby waves. This study identified two types of anomalous features based on combinations of surface and subsurface EOFs. The combination represents an in-phase contribution between wind and Kuroshio forcings and between the surface and subsurface circulation that enhances the hydrological variability in the ECS. The implication on the relevant biogeochemical and ecological studies in the East China Sea is also very crucial at the interannual time scale.