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With so many studies today on the water quality of the sea, one can hardly comprehend the multitude of topics that arise all over the world. This study provides a few graphic syntheses related to the most frequent words (including their clustering and links), trend topics, the spatial distribution of the researched areas, and the thematic evolution of the research directions over the decades. The most frequent authors' keywords have a 50% similitude between the ocean studies and the studies related to the Mediterranean Sea; these keywords are part of a causal chain that dominates the marine studies on water quality: nutrients → eutrophication → phytoplankton → chlorophyll → seagrass. The most frequent words in the titles and abstracts of the selected papers from the Web of Science are "concentration" and "species"; in the Mediterranean studies, "chlorophyll" and "temperature" are the most frequent. In close connection with water quality, Zostera marina (eelgrass) and Crassotrea virginica (eastern oyster) prevail at the global scale, while Posidonia oceanica (Neptune grass) is relevant in the Mediterranean space. Some of the most studied water bodies are the South China Sea, San Francisco Bay, Chesapeake Bay, and, in the Mediterranean Sea, the Adriatic, Ionian, Aegean, and Marmara seas. "Climate change" and "remote sensing" are trend topics that shape the current studies on water quality; the increasing sea surface temperature enhances algal blooms—these need to be monitored using satellite imagery for the sustainable evolution of human activities, including aquaculture. [ABSTRACT FROM AUTHOR]

Changes in the phenology of physical and ecological variables associated with climate change are likely to have significant effect on many aspects of the Baltic ecosystems. We apply a set of phenological indicators to multiple environmental variables measured by satellite sensors for 17-35 years to detect possible changes in the seasonality in the Baltic Sea environment. We detect significant temporal changes such as earlier start of the summer season and prolongation of the productive season in multiple variables ranging from basic physical drivers to ecological status indicators. While increasing trends in the absolute values of variables like sea-surface temperature (SST), diffuse attenuation of light (Ked490) and satellite-detected chlorophyll concentration (CHL) are detectable, the corresponding changes in their seasonal cycles are more dramatic. For example, the cumulative sum of 30 000Wm-2 of surface incoming shortwave irradiance (SIS) was reached 23 days earlier in 2014 compared to the beginning of the time series in 1983. The period of the year with SST of at least 17 °C has almost doubled (from 29 days in 1982 to 56 days in 2014), the period with Ked490 over 0.4m-1 has increased from about 60 days in 1998 to 240 days in 2013, i.e. quadrupled. The period with satellite-detected CHL of at least 3mgm-3 has doubled from approximately 110 days in 1998 to 220 days in 2013. While the timing of both the phytoplankton spring and summer blooms have advanced, the annual CHL maximum that in the 1980s corresponded to the spring diatom bloom in May has now switched to the summer cyanobacteria bloom in July. [ABSTRACT FROM AUTHOR]

There are several factors to account for marine growth including but not limited to temperature, salinity, chlorophyll-a content, existing species in the environment and predating. This paper proposes a model of biological growth for hard species on marine structures, which can be compatible with site-specific and realistic ecology while also being able to translate the results for analyses linked to lifetime hydrodynamic or structural effects via commercial software or computing. The model preserves fundamentals of ecological aspects rather than using heuristics or random sampling to data fitting on sparsely collected information. The coefficients used in the proposed model align to the real world, with location-specific values, and can be adapted to new information. The growth model is demonstrated for Mythulis Edulis (blue mussel) colonisation to assess the lifetime hydrodynamic effects for the West Coast of Ireland and the Gulf of Guinea. The model can be extended to any hard growth approach. [ABSTRACT FROM AUTHOR]

Mesoscale eddies are phenomena that widely exist in the ocean and have a significant impact on the ocean's temperature and salt structure, as well as on acoustic propagation effects. Currently, utilizing the limited data on mesoscale eddy environments for refined acoustic field reconstruction in offshore conditions at mid-to-far-ocean distances is an urgent problem that needs to be addressed. In this paper, we propose a mesoscale eddy reconstruction method (EddyGAN) based on the generative adversarial network (GAN) model which is inspired by the concept of global localization. We adopt a hybrid algorithm for eddy identification using JCOPE2M high-resolution reanalysis data and Archiving, Validation, and Interpretation of Satellite Oceanographic (AVISO) satellite altimeter data to extract mesoscale eddy sound speed profile (SSP) sample data, and then apply the EddyGAN model to train this dataset and perform mesoscale eddy acoustic field reconstruction. We also propose an evaluation method for mesoscale eddy acoustic field reconstruction that uses RMSE, SSIM, and convergence zone (CZ) accuracy based on World Ocean Atlas (WOA) climate state data completion as indicators. The reconstruction result of this model achieves an RMSE of 1.7 m/s, an SSIM of 0.77, and an average CZ accuracy of over 70%. This method better characterizes the mesoscale eddy sound field than the native GAN and other reconstruction methods, improves the accuracy of mesoscale eddy acoustic field reconstruction, and provides superior performance, offering significant reference value for mesoscale eddy reconstruction technology and subsequent ocean acoustic research. [ABSTRACT FROM AUTHOR]

Medium-voltage DC (MVDC) technology has gained increasing attention in recent years. Power electronics devices dominate these grids. Accurate simulation of such a grid, with detailed models of switching semiconductors, can quickly became very time-consuming, according to the number of connected devices to be simulated. A simulation approach based on interactions on a continuous time model can be very interesting, especially for developing a system-level control model of such a modern MVDC distribution grid. The aim of this paper is to present all the steps required for obtaining a continuous time modelling of a +/−10 kV MVDC grid case study, including a solid-state transformer (SST)- and modular multilevel converter (MMC)-based active front end (AFE). An additional aim of this paper is to supply educational content about the use of the continuous time simulation approach, thanks to a detailed description of the various devices modelled into the presented MVDC grid. The results of a certain number of simulation scenarios are eventually presented. [ABSTRACT FROM AUTHOR]

A two-dimensional model of sediment column geophysics and geochemistry has been adapted to the problem of an accretionary wedge formation, patterned after the margin of the Juan de Fuca plate as it subducts under the North American plate. Much of the model description was given in a companion paper about application of the model to a passive margin setting; here we build on that formulation to simulate the deformation of the sediment wedge as it approaches the subduction zone. The active margin configuration of the model shares sensitivities with the passive margin configuration, in that sensitivities to organic carbon deposition and respiration kinetics, and to vertical bubble transport and redissolution in the sediment, are stronger than the sensitivity to ocean temperature. The active margin simulation also shows a sensitivity to plate subduction velocity, with higher plate velocities producing less hydrate per meter of coastline than slower velocities or the passive margin configuration. However, the local hydrate concentrations, as pore volume saturation, are higher in the active setting than the passive, as generally observed in the field. [ABSTRACT FROM AUTHOR]

Light limitation can affect the distribution of biota and nutrients in the ocean. Light absorption by colored detrital material (CDM) was included in a fully coupled Earth System Model using a new parameterization for shortwave attenuation. Two model runs were conducted, with and without light attenuation by CDM. In a global average sense, greater light limitation associated with CDM increased surface chlorophyll, biomass and nutrients together. These changes can be attributed to the movement of biological productivity higher up the water column, which increased surface chlorophyll and biomass while simultaneously decreasing total biomass. Meanwhile, the reduction in biomass resulted in greater nutrient availability throughout the water column. Similar results were found on a regional scale in an analysis of the oceans by biome. In coastal regions, surface chlorophyll increased by 35% while total integrated phytoplankton biomass diminished by 18 %. The largest relative increases in modeled surface chlorophyll and biomass in the open ocean were found in the equatorial biomes, while largest decreases in depth-integrated biomass and chlorophyll were found in the subpolar and polar biomes. This mismatch of surface and subsurface trends and their regional dependence was analyzed by comparing the competing factors of diminished light availability and increased nutrient availability on phytoplankton growth in the upper 200 m. Overall, increases in surface biomass were expected to accompany greater nutrient uptake and therefore diminish surface nutrients, but changes in light limitation decoupled trends between these two variables. Understanding changes in biological productivity requires both surface and depth-resolved information. Surface trends may be minimal or of the opposite sign to depth-integrated amounts, depending on the vertical structure of phytoplankton abundance. [ABSTRACT FROM AUTHOR]

Studies the impact of the water currents, temperature and salinity along the coastal areas of northern Japan on the occurrence of Dinophysis species, the causative plankton of diarrhetic shellfish poisoning. Collection of samples of the species and seawater along Kesennuma Bay; Determination and monitoring of inorganic concentration of nitrogen and phosphorus; Influences of salinity and the different environmental conditions to the appearance of the species.

Selecting an optimal subset of sensors that can accurately reconstruct the full state of the ocean can reduce the cost of themonitoring system and improvemonitoring efficiency. Typically, in data-driven sensor selection processes, the use of Euclidean distance to evaluate reconstruction error is susceptible to non-Gaussian noise and outliers present in ocean data. This paper proposes a Robust Sensor Selection (RSS) evaluation model based on the Maximum Correntropy Criterion (MCC) through subspace learning, enabling the selection of robust sensor measurement subsets and comprehensive data reconstruction. To more accurately quantify the impact of varying noise magnitudes, noise weights were incorporated into the model's objective function. Additionally, the local geometric structure of data samples is utilized to further enhance reconstruction accuracy through the selected sensors. Subsequently, the MCC_RSS algorithm is proposed, which employs the Block Coordinate Update (BCU) method to achieve the optimal solution for the proposed model. Experiments conducted using ocean temperature and salinity datasets validate the proposed MCC_RSS algorithm. The results demonstrate that the sensor selection method proposed in this paper exhibits strong robustness, outperforming comparative methods under varying proportions of outliers and non-Gaussian noise. [ABSTRACT FROM AUTHOR]

This review will serve as an entry point for international researchers who would like to tap into the vast scientific potential of Chinese-language literature on oceanic fronts. We focused on observational physical oceanography studies of marine fronts. A thorough bibliographic search netted 95 papers published in 1982–2023, with a sharp increase in the total number of papers from 2006–2010 to 2011–2015, when this number almost tripled. This trend continued unabated through the early 2020s. The sharp increase in Chinese-language publications preceded by several years a rapid increase in English-language publications in the same field. Regionally, the overwhelming majority of papers is focused on the China Seas, particularly the East China Seas and northern South China Sea. Elsewhere, a number of papers were dedicated to the Southern Ocean and North Atlantic. Thematically, papers on remote sensing of ocean fronts dominate the literature, with special attention to the development of front-detection algorithms that can be applied to satellite data on sea surface temperature, salinity, chlorophyll, and sea level anomaly. Numerous papers on marine fronts in the China Seas present important results that have to be considered by international researchers. Overall, this review emphasizes the significant contributions made by Chinese oceanographers, particularly to studies of the frontal oceanography of the China Seas. [ABSTRACT FROM AUTHOR]

Heavy rain events are frequently recorded in the Western Mediterranean causing economic losses and even human casualties. The Western Mediterranean is a deep and almost closed sea surrounded by high mountain ranges and with little exchange of water with the Atlantic ocean. A main factor in the development of torrential rains are ocean-atmosphere exchanges of heat and moisture that can potentially destabilize air masses travelling over the sea. The study of air mass trajectories previous to the rain event permits the identification of sea areas that could probably contribute to the development or intensification of rainfall. From a previous Mediterranean sea surface temperature climatology, its spatio-temporal distribution patterns have been studied showing two main distribution modes in winter and summer and transitional regimes in spring and autumn. Hence, three heavy precipitation events, for such winter and summer sea temperature regimes and for fall transition, affecting the Valencia region have been selected to study the effect of sea surface temperature in torrential rains. Simula tions with perturbed sea surface temperature in different areas along the air mass path were run to compare results with unperturbed simulation. The variation of sea surface temperature in certain areas caused significant changes in model accumulated values and its spatial distribution. Therefore, the existence of recharge areas where air-sea interaction favors the development of torrential rainfall in Valencia region has been shown. This methodology could be extended to the whole Mediterranean basin to look for such potential recharge areas. The identification of sea areas that contribute to the development or intensification of heavy rain events in the Mediterranean countries could be a useful prognosis and/or monitoring tool. [ABSTRACT FROM AUTHOR]

In the process of ocean exploration, highly accurate and sensitive measurements of seawater temperature and pressure significantly impact the study of seawater's physical, chemical, and biological processes. In this paper, three different package structures, V-shape, square-shape, and semicircle-shape, are designed and fabricated, and an optical microfiber coupler combined Sagnac loop (OMCSL) is encapsulated in these structures with polydimethylsiloxane (PDMS). Then, the temperature and pressure response characteristics of the OMCSL, under different package structures, are analyzed by simulation and experiment. The experimental results show that structural change hardly affects temperature sensitivity, and square-shape has the highest pressure sensitivity. In addition, with an input error of 1% F.S., temperature and pressure errors were calculated, which shows that a semicircle-shape structure can increase the angle between lines in the sensitivity matrix method (SMM), and reduce the effect of the input error, thus optimizing the ill-conditioned matrix. Finally, this paper shows that using the machine learning method (MLM) effectively improves demodulation accuracy. In conclusion, this paper proposes to optimize the ill-conditioned matrix problem in SMM demodulation by improving sensitivity with structural optimization, which essentially explains the cause of the large errors for multiparameter cross-sensitivity. In addition, this paper proposes to use the MLM to solve the problem of large errors in the SMM, which provides a new method to solve the problem of the ill-conditioned matrix in SMM demodulation. These have practical implications for engineering an all-optical sensor that can be used for detection in the ocean environment. [ABSTRACT FROM AUTHOR]

The article presents a study which examines the influence climatic changes in land cover from the interactive terrestrial carbon cycle in the HadGEM2 Earth System configuration (HadGEM2-ES). The study used the 1980-2005 sections of the historical coupled model runs and observed the sea surface temperatures (SSTs). The study shows that the response of the dynamic vegetation to biases in regional climate can impact the existing climate simulation and climatic changes scenarios.

The article presents a study which examines the power-law behavior in millennium climate simulations using the method of discrete Fourier transform. The study integrates COSMOS network Earth System Model, sensitivity analysis, and oscillatory timescales to determine the global mean temperature of the power-law. Results show that land areas have lower β than the ocean.

In this study, a method is presented to retrieve the surface reflectance using reflectance measured at the top of the atmosphere for the two views provided by the Along-Track Scanning Radiometer (AATSR). In the first step, the aerosol optical depth (AOD) is obtained using the AATSR dual view algorithm (ADV) by eliminating the effect of the surface on the measured radiances. Hence the AOD is independent of surface properties and can thus be used in the second step to provide the aerosol part of the atmospheric correction which is needed for the surface reflectance retrieval. The method is applied to provide monthly maps of both AOD and surface reflectance at two wavelengths (555 and 659 nm) for the whole year of 2007. The results are validated vs. surface reflectance provided by the AERONET-based Surface Reflectance Validation Network (ASRVN). Correlation coefficients are 0.8 and 0.9 for 555 and 659 nm, respectively. The standard deviation is 0.001 for both wavelengths and the absolute error is less than 0.02. Pixel-by-pixel comparison with MODIS (MODerate resolution Imaging Spectrometer) monthly averaged surface reflectances show a good correlation (0.91 and 0.89 for 555 and 659 nm, respectively) with some (up to 0.05) overestimation by ADV over bright surfaces. The difference between the ADV and MODIS retrieved surface reflectance is smaller than ±0.025 for 68.3% of the collocated pixels at 555nm and 79.9% of the collocated pixels at 659 nm. An application of the results over Australia illustrates the variation of the surface reflectances for different land cover types. The validation and comparison results suggest that the algorithm can be successfully used for the both AATSR and ATSR-2 (which has characteristics similar to AATSR) missions, which together cover 17 years period of measurements (1995-2012), as well as a prototype for The Sea and Land Surface Temperature Radiometer (SLSTR) to be launched in 2015 onboard the Sentinel-3 satellite. [ABSTRACT FROM AUTHOR]

We present a new coupled ocean circulation - ice model configuration of the Baltic Sea. The model features, contrary to most existing configurations, a high horizontal resolution of ≈ 1 nautical mile which is eddy resolving over much of the domain. The vertical discretisation comprises a total of 47 vertical levels. Results from a 1987 to 1999 hind cast simulation show that the model's fidelity is competitive. As suggested by a comparison with sea surface temperatures observed from space, this applies especially to near-surface processes. Hence, the configuration is well suited to serve as a nucleus of a full-fledged coupled ocean circulation biogeochemical model (which is yet to be developed). A caveat is that the model fails to reproduce major inflow events. We trace this back to spurious vertical circulation patterns at the sills which may well be endemic to high resolution models based on geopotential coordinates. Further, we present indications that - so far neglected - eddy/wind effects exert significant control on wind-induced up- and downwelling. [ABSTRACT FROM AUTHOR]

Numerical simulations provide a considerable aid in studying past climates. Out of the various approaches taken in designing numerical climate experiments, transient simulations have been found to be the most optimal when it comes to comparison with proxy data. However, multi-millennial or longer simulations using fully coupled general circulation models are computationally very expensive such that acceleration techniques are frequently applied. In this study, we compare the results from transient simulations of the present and the last interglacial with and without acceleration of the orbital forcing, using the comprehensive coupled climate model CCSM3 (Community Climate System Model 3). Our study shows that in most parts of the world, the simulation of long-term variations in interglacial surface climate is not significantly affected by the use of the acceleration technique (with an acceleration factor 10) and, hence, large-scale model-data comparison of surface variables is not hampered. However, in high-latitude regions where the surface climate has a direct connection to the deep ocean, e.g. in the Southern Ocean or the Nordic Seas, acceleration-induced biases in sea-surface temperature evolution may occur with potential influence on the dynamics of the overlying atmosphere. [ABSTRACT FROM AUTHOR]

Sea Surface Temperature is the key variable when tackling seasonal to decadal climate forecast. Dynamical models are unable to properly reproduce tropical climate variability, introducing biases that prevent a skillful predictability. Statistical methodologies emerge as an alternative to improve the predictability and reduce these biases. Recent studies have put forward the non-stationary behavior of the teleconnections between tropical oceans, showing how the same tropical mode has different impacts depending on the considered sequence of decades. To improve the predictability, the Sea Surface Temperature based Statistical Seasonal foreCAST model (S4CAST) introduces the novelty of considering the non-stationary links between the predictor and predictand fields. This paper describes the development of S4CAST model whose operation is focused on the study of the predictability of any variable related to sea surface temperature. An application focused on West African rainfall predictability has been implemented as a benchmark example. [ABSTRACT FROM AUTHOR]

We present a numerical model of the ocean that couples a three-stream radiative transfer component with a marine biogeochemical-ecosystem in a dynamic threedimensional physical framework. The radiative transfer component resolves spectral irradiance as it is absorbed and scattered within the water column. We explicitly include the effect of several optically important water constituents (the phytoplankton community, detrital particles, and coloured dissolved organic matter, CDOM). The model is evaluated against in situ observed and satellite derived products. In particular we compare to concurrently measured biogeochemical, ecosystem and optical data along a north-south transect of the Atlantic Ocean. The simulation captures the patterns and magnitudes of these data, and estimates surface upwelling irradiance analogous to that observed by ocean colour satellite instruments. We conduct a series of sensitivity experiments to demonstrate, globally, the relative importance of each of the water constituents, and the crucial feedbacks between the light field and the relative fitness of phytoplankton types, and the biogeochemistry of the ocean. CDOM has proportionally more importance at short wavelengths and in more productive waters, phytoplankton absorption is especially important at the deep chlorophyll a (Chl a) maximum, and absorption by water molecules is relatively most important in the highly oligotrophic gyres. Sensitivity experiments in which absorption by any of the optical constituents was increased led to a decrease in the size of the oligotrophic regions of the subtropical gyres: lateral nutrient supplies were enhanced as a result of decreasing high latitude productivity. Scattering does not as strongly affect the ecosystem and biogeochemistry fields within the water column but is important for setting the surface upwelling irradiance, and hence sea surface reflectance. Having a model capable of capturing bio-optical feedbacks will be important for improving our understanding of the role of light and optical constituents on ocean biogeochemistry, especially in a changing environment. The potential benefits of capturing surface upwelling irradiance will be important for making closer connections to satellite derived products in the future. [ABSTRACT FROM AUTHOR]

This study explored the potential application of deep learning techniques in sea surface temperature (SST) investigations using a mixed method, bibliometric analysis and methodological approach. CiteSpace software was utilized for a bibliometric study on 137 academic publications from 2018 to 2023. Various databases were employed for methodological analysis, which involved examining publications based on models, methodologies, applications and research areas. The data were manually organized in a relational framework of an SQL database. The analysis underscored China's prominence as a leader in the extensive research devoted to this field. The United States of America and the United Kingdom played pivotal roles in providing the essential data that served as the foundation for these studies. Moreover, the long short‐term memory (LSTM) algorithm was the predominant computational deep learning algorithm extensively used in this specific context. The analysis highlighted significant knowledge gaps in areas such as SST forecasting, modelling, satellite remote sensing, extreme events and data reconstruction. Future scientists need to show more interest in these and related subjects, while Chinese and American scientists should prioritize paper quality over quantity. Additionally, fostering stronger collaborations between universities and institutions is vital for further advancements. Ultimately, this study offers valuable insights into hotspot research areas and development processes, establishing the foundation for research and suggesting possible avenues for future development. The results of this evaluation serve as an essential guide for researchers and modellers involved in prediction initiatives using deep learning. [ABSTRACT FROM AUTHOR]

Sea surface temperature (SST) prediction plays an important role in scientific research, environmental protection, and other marine-related fields. However, most of the current prediction methods are not effective enough to utilize the spatial correlation of SSTs, which limits the improvement of SST prediction accuracy. Therefore, this paper first explores spatial correlation mining methods, including regular boundary division, convolutional sliding translation, and clustering neural networks. Then, spatial correlation mining through a graph convolutional neural network (GCN) is proposed, which solves the problem of the dependency on regular Euclidian space and the lack of spatial correlation around the boundary of groups for the above three methods. Based on that, this paper combines the spatial advantages of the GCN and the temporal advantages of the long short-term memory network (LSTM) and proposes a spatiotemporal fusion model (GCN-LSTM) for SST prediction. The proposed model can capture SST features in both the spatial and temporal dimensions more effectively and complete the SST prediction by spatiotemporal fusion. The experiments prove that the proposed model greatly improves the prediction accuracy and is an effective model for SST prediction. [ABSTRACT FROM AUTHOR]

The main goal of the project DUNE was to estimate the impact of atmospheric deposition on an oligotrophic ecosystem based on mesocosm experiments simulating strong atmospheric inputs of Aeolian dust. Atmospheric deposition is now recognized as a significant source of macro- and micro-nutrients for the surface ocean, but the quantification of its role on the biological carbon pump is still poorly determined. We proposed in DUNE to investigate the role of atmospheric inputs on the functioning of an oligotrophic system particularly well adapted to this kind of study: the Mediterranean Sea. The Mediterranean Sea - etymologically, sea surrounded by land - is submitted to atmospheric inputs that are very variable both in frequency and intensity. During the thermal stratification period, only atmospheric deposition is prone to fertilize Mediterranean surface waters which has become very oligotrophic due to the nutrient depletion (after the spring bloom). This paper describes the objectives of DUNE and the implementation plan of a series of mesocosms experiments during which either wet or dry and a succession of two wet deposition fluxes of 10 gm-2 of Saharan dust have been simulated. After the presentation of the main biogeochemical initial conditions of the site at the time of each experiment, a general overview of the papers published in this special issue is presented, including laboratory results on the solubility of trace elements in erodible soils in addition to results from the mesocosm experiments. Our mesocosm experiments aimed at being representative of real atmospheric deposition events onto the surface of oligotrophic marine waters and were an original attempt to consider the vertical dimension in the study of the fate of atmospheric deposition within surface waters. Results obtained can be more easily extrapolated to quantify budgets and parameterize processes such as particle migration through a "captured water column". The strong simulated dust deposition events were found to impact the dissolved concentrations of inorganic dissolved phosphorus, nitrogen, iron and other trace elements. In the case of Fe, adsorption on sinking particles yields a decrease in dissolved concentration unless binding ligands were produced following a former deposition input and associated fertilization. For the first time, a quantification of the C export induced by the aerosol addition was possible. Description and parameterization of biotic (heterotrophs and autotrophs, including diazotrophs) and abiotic processes (ballast effect due to lithogenic particles) after dust addition in sea surface water, result in a net particulate organic carbon export in part controlled by the "lithogenic carbon pump". [ABSTRACT FROM AUTHOR]

On interannual-to-multidecadal time scales variability in sea surface temperature appears to be organized in large-scale spatiotemporal patterns. In this paper, we investigate these patterns by studying the community structure of interaction networks con- structed from sea surface temperature observations. Much of the community structure as well as the first neighbour maps can be interpreted using known dominant patterns of variability, such as the El Niño/Southern Oscillation and the Atlantic Multidecadal Oscillation and teleconnections. The community detection method allows to overcome some shortcomings of Empirical Orthogonal Function analysis or composite analysis and hence provides additional information with respect to these classical analysis tools. The community analysis provides also new insight into the relationship between patterns of sea surface temperature and the global mean surface temperature (GMST). On the decadal-to-multidecadal time scale, we show that only two communities (Indian Ocean and North Atlantic) determine most of the GMST variability. [ABSTRACT FROM AUTHOR]

Accurately predicting the spatio-temporal evolution trends and long-term dynamics of three-dimensional ocean temperature and salinity plays a crucial role in monitoring climate system changes and conducting fundamental oceanographic research. Numerical models are the most prevalent of the traditional approaches, which are often too complex and lack of generality. Recently, with the rise of AI, many data-driven methods are proposed. However, most of them take no consideration of natural physical laws that may cause issues of physical inconsistency among different variables. In this paper, we proposed PGTransNet, a novel physics-guided transformer network for 3D Ocean temperature and salinity forecasting. This model is based on Vision Transformer, and to enhance the performance we have three aspects of improvements. Firstly, we design a loss function that deliveries the physical relationship among temperature, salinity and density by fusing the Thermodynamic Equation. Secondly, to capture global and long-term dependencies effectively, we add the Pacific Decadal Oscillation (PDO) and North Pacific Gyre Oscillation (NPGO) in the embedding layer. Thirdly, we adopted the Laplacian sparse positional encodings to alleviate the artifacts caused by high-norm tokens. The former two are the core components to leverage the physical information. Finally, to comprehensively evaluate PGTransnet, we conduct rich experiments in metrics RMSE, Anomoly Correlation Coefficients, Bias and physical consistency. Our proposal demonstrates higher prediction accuracy with fast convergence, and the metrics and visualizations show that our model is insensitive to hyperparameter tuning, ensuring better generalization and adherence to physical consistency. Moreover, as observed from the spatial distribution of the anomaly correlation coefficient, the model exhibits higher forecasting accuracy for coastal and marginal sea regions. [ABSTRACT FROM AUTHOR]

The large yellow croaker, an endemic migratory species in China's coastal waters, holds considerable economic importance. It is very necessary and significant to study the spatio-temporal evolution of large yellow croaker fishery based on environmental factors. In this paper, the HY-1C/D Coastal Zone Imager was applied to reveal distributional details of suspended sediment concentration and chlorophyll-a concentration in large yellow croaker fishery. Furthermore, combined with multi-source data, the characteristics of other environmental parameters such as temperature, salinity, current, wind and fronts were analyzed to reveal the changing of large yellow croaker fishery location. The results show that: (1) The environmental factors of large yellow croaker fishery exhibit pronounced spatial and temporal characteristics. Generally, the temperature (9-28°C), salinity (20-34 ‰) and other water environmental factors of the fishing ground are conducive to the survival and growth of large yellow croaker. The formation and change of fishery are closely related to environmental factors in the water. (2) The change of environmental factors in the water may induce the change of large yellow croaker fishery location. Winter sea surface temperature has significantly risen over the past two decades (2004-2023). With the sea surface temperature rising by approximately 2°C west of 125°E, the overwintering ground has expanded westward, increasing by nearly 29%. In addition, the scale and number of offshore windmills increased by 836% and 456% respectively over the decade (2013-2023). In 2023, offshore wind farms cover an area of 145.47 km2 and have 200 windmills. Wind turbine piles resembling artificial reefs. It can not only promote the waters vertical exchange to enhance the nutrients in upper waters but also provide a protective base for fish spawning. Thus, effectively attracting fish species. The construction of offshore wind farms has extended spawning ground north-westward to the mouth of the Yangtze River Estuary, expanding by nearly 21%. This study improved the cognition level of resources, habitat environment and change trend of large yellow croaker. It also provided technical support for the construction and protection of the large yellow croaker fishery. [ABSTRACT FROM AUTHOR]

In 2023, the rapid increase in global temperature of around 0.25 °C caught the scientific community by surprise. Its cause has been investigated largely by exploring variations on a long-term trend, with little success. Building on previous work, this paper proposes an alternative explanation—on decadal timescales, observed temperature shows a complex, nonlinear response to forcing, stepping through a series of steady-state regimes. The 2023 event is nominated as the latest in the sequence. Step changes in historical and modeled global mean surface temperatures (GMSTs) were detected using the bivariate test. Each time series was then separated into gradual (trends) and rapid components (shifts) and tested using probative criteria. For sea surface, global and land surface temperatures from the NOAA Global Surface Temperature Dataset V6.0 1880–2022, the rapid component of total warming was 94% of 0.72 °C, 78% of 1.16 °C and 74% of 1.93 °C, respectively. These changes are too large to support the gradual warming hypothesis. The recent warming was initiated in March 2023 by sea surface temperatures (SSTs) in the southern hemisphere, followed by an El Niño signal further north. Global temperatures followed, then land. A preceding regime shift in 2014 and subsequent steady-state 2015–2022 was also initiated and sustained by SSTs. Analysis of the top 100 m annual average ocean temperature from 1955 shows that it forms distinct regimes, providing a substantial 'heat bank' that sustains the changes overhead. Regime shifts are also produced by climate models. Archived data show these shifts emerged with coupling of the ocean and atmosphere. Comparing shifts and trends with equilibrium climate sensitivity (ECS) in an ensemble of 94 CMIP5 RCP4.5 models 2006–2095 showed that shifts had 2.9 times the influence on ECS than trends. Factors affecting this relationship include ocean structure, initialization times, physical parameters and model skill. Single model runs with skill ≥75 showed that shifts were 6.0 times more influential than trends. These findings show that the dominant warming mechanism is the sudden release of heat from the ocean rather than gradual warming in the atmosphere. The model ensemble predicted all regime changes since the 1970s within ±1 year, including 2023. The next shift is projected for 2036, but current emissions are tracking higher than projected by RCP4.5. Understanding what these changes mean for the estimation of current and future climate risks is an urgent task. [ABSTRACT FROM AUTHOR]

Gas hydrates are globally acknowledged as a significant strategic alternative energy source, and there is a consensus on the necessity to enhance their exploration. However, gas hydrates are highly prone to decomposition under variations in external environmental conditions, which can result in subsea methane seepage activities. Consequently, investigating subsea methane seepage activities holds substantial theoretical and practical significance for exploring gas hydrates. This paper evaluates the history of methane seepage activities in the Qiongdongnan Basin (QDNB) by analyzing the carbon and oxygen isotopic characteristics of benthic foraminifera and the geochemical properties of pore water from gravity sediment cores at sites QH-CL4 and QH-CL40. The results indicate that since the Marine isotope stage2 (MIS2), continuous micro-methane seepage activity has been present in the QDNB, characterized by a slight negative deviation in the carbon isotopes of benthic foraminifera. Methane seepage activity intensified during 14.6 ka BP and between 19.64–23.22 ka BP. This increase is thought to be associated with rising seawater temperature during the Bølling–Allerød interstadial and declining sea level during the Last Glacial Maximum, respectively. Moreover, current geochemical characteristics of pore water reveal strong methane seepage activity, with flux as high as 28.968 mmol·m-²·a-¹. This ongoing activity has led to gas hydrate formation within shallow layers while also causing negative deviations in pore water salinity. [ABSTRACT FROM AUTHOR]

Mg / Ca ratios in foraminiferal tests are routinely used as paleo temperature proxy, but on long timescales, also hold the potential to reconstruct past seawater Mg / Ca. Impact of both temperature and seawater Mg/Ca on Mg incorporation in foraminifera have been quantified by a number of studies. The underlying mechanism responsible for Mg incorporation in foraminiferal calcite and its sensitivity to environmental conditions, however, is not fully identified. A recently published biomineralization model (Nehrke et al., 2013) proposes a combination of transmembrane transport and seawater leakage or vacuolization to link calcite Mg / Ca to seawater Mg /Ca and explains inter-species variability in Mg / Ca ratios. To test the assumptions of this model, we conducted a culture study in which seawater Mg / Ca was manipulated by varying [Ca2+] and keeping [Mg2+] constant. Foraminiferal growth rates, test thickness and calcite Mg / Ca of newly formed chambers were analyzed. Results showed optimum growth rates and test thickness at Mg / Ca closest to that of ambient seawater. Calcite Mg / Ca is positively correlated to seawater Mg / Ca, indicating that not absolute seawater [Ca2+] and [Mg2+], but the telative ratio controls Mg / Ca in tests. These results demonstrate that the calcification process cannot be based only on seawater vacuolization, supporting the mixing model proposed by Nehrke et al. (2013). Here we, however, suggest a transmembrane transport fractionation that is not as strong as suggested by Nehrke et al. (2013). [ABSTRACT FROM AUTHOR]

We implement a new stomatal conductance model, based on the optimality approach, within the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. Coupled land-atmosphere simulations are then performed using CABLE within the Australian Community Climate and Earth Systems Simulator (ACCESS) with prescribed sea surface temperatures. As in most land surface models, the default stomatal conductance scheme only accounts for differences in model parameters in relation to the photosynthetic pathway, but not in relation to plant functional types. The new scheme allows model parameters to vary by plant functional type, based on a global synthesis of observations of stomatal conductance under different climate regimes over a wide range of species. We show that the new scheme reduces the latent heat flux from the land surface over the boreal forests during the Northern Hemisphere summer by 0.5 to 1.0 mm day-1. This leads to warmer daily maximum and minimum temperatures by up to 1.0 °C and warmer extreme maximum temperatures by up to 1.5 °C. These changes generally improve the climate model's climatology and improve existing biases by 10-20 %. The change in the surface energy balance also affects net primary productivity and the terrestrial carbon balance. We conclude that the improvements in the global climate model which result from the new stomatal scheme, constrained by a global synthesis of experimental data, provide a valuable advance in the long-term development of the ACCESS modelling system. [ABSTRACT FROM AUTHOR]

We present a new mechanistic model, Stochastic Lagrangian Aggregate Model of Sinking particles (SLAMS) for the biological pump in the ocean, which tracks the evolution of individual particles as they aggregate, disaggregate, sink, and are altered by chemical and biological processes. SLAMS considers the impacts of ballasting by mineral phases, binding of aggregates by transparent exopolymer particles (TEP), zooplankton grazing, and the fractal geometry (porosity) of the aggregates. Parameterizations for age-dependent organic carbon (orgC) degradation kinetics, and disaggregation driven by zooplankton grazing and TEP degradation, are motivated by observed particle fluxes and size spectra throughout the water column. The model is able to explain observed variations in orgC export efficiency and rain ratio from the euphotic zone and to the sea floor as driven by sea surface temperature and the primary production rate and sea-sonality of primary production. The model provides a new mechanistic framework with which to predict future changes on the flux attenuation of orgC in response to climate change forcing. [ABSTRACT FROM AUTHOR]

A well-defined surface wind divergence (SWD) belt with distinct cloud properties forms over the equatorial Atlantic during the boreal summer months. This belt separates the deep convective clouds of the intertropical convergence zone (ITCZ) from the shallow marine stratocumulus cloud decks forming over the cold-water subtropical region of the southern Hadley cell. Using the QuikSCAT-SeaWinds and Aqua-MODIS instruments, we examined the large-scale spatiotemporal variability of the SWD belt during a 6 year period (2003-2008) and the related links to cloud properties over the Atlantic Ocean. The Atlantic SWD belt was found to be most pronounced from May to August, between the equator and 2° N latitude. A positive correlation and a strong link were observed between formation of the SWD belt and a sharp sea-surface temperature gradient on the northern border of the cold tongue, supporting Wallace's vertical-mixing mechanism. The dominant cloud type over this region was shallow cumulus. Cloud properties were shown to be strongly linked to the formation and strength of the SWD zone. Our findings help understand the link between ocean-atmosphere dynamics and cloud properties over this region, and suggest that the SWD zone be considered a unique cloud belt of the southern branch of the Atlantic Hadley cell. [ABSTRACT FROM AUTHOR]

Complex oceanic circulation and air--sea interaction make the eastern tropical Pacific Ocean (ETPO) a highly variable source of CO2 to the atmosphere. Although the scientific community have amassed 70 000 surface partial-pressure of carbon dioxide (pCO2) datapoints within the ETPO region over the past 25 years, the spatial and temporal resolution of this dataset is insufficient to fully quantify the seasonal to inter-annual variability of the region, a region where pCO2 has been observed to fluctuate by >300 µatm. Upwelling and rainfall events dominate the surface physical and chemical characteristics of the ETPO, with both yielding unique signatures in sea surface temperature and salinity. Thus, we explore the potential of using a statistical description of pCO2 within sea-surface salinity-temperature space. These SSS/SST relationships are based on in-situ SOCAT data collected within the ETPO. This statistical description is then applied to high resolution (0.25°) SMOS sea surface salinity and OSTIA sea surface temperature in order to compute regional pCO2. As a result, we are able to resolve pCO2 at sufficiently high resolution to elucidate the influence various physical processes have on the pCO2 of the surface ETPO. Normalised (to 2014) oceanic pCO2 between July 2010 and June 2014 within the entire ETPO was 41 µatm supersaturated with respect to 2014 atmospheric partial pressures. Values of pCO2 within the ETPO were found to be broadly split between southeast and a northwest regions. The north west, central and South Equatorial Current regions were supersaturated, with wintertime wind jet driven upwelling found to be the first order control on pCO2 values. This contrasts with the southeastern/Gulf of Panama region, where heavy rainfall combined with rapid stratification of the upper water-column act to dilute dissolved inorganic carbon, and yield pCO2 values under-saturated with respect to atmospheric partial pressures of CO2. [ABSTRACT FROM AUTHOR]

A Climate Pattern-Scaling Model (CPSM) that simulates global patterns of climate change, for a prescribed emissions scenario, is described. A CPSM works by quantitatively establishing the statistical relationship between a climate variable at a specific location (e.g. daily maximum surface temperature, Tmax) and one or more predictor time series (e.g. global mean surface temperature, Tglobal) - referred to as the "training" of the CPSM. This training uses a regression model to derive fit-coefficients that describe the statistical relationship between the predictor time series and the target climate variable time series. Once that relationship has been determined, and given the predictor time series for any greenhouse gas (GHG) emissions scenario, the change in the climate variable of interest can be reconstructed - referred to as the "application" of the CPSM. The advantage of using a CPSM rather than a typical atmosphere-ocean global climate model (AOGCM) is that the predictor time series required by the CPSM can usually be generated quickly using a simple climate model (SCM) for any prescribed GHG emissions scenario and then applied to generate global fields of the climate variable of interest. The training can be performed either on historical measurements or on output from an AOGCM. Using model output from 21st century simulations has the advantage that the climate change signal is more pronounced than in historical data and therefore a more robust statistical relationship is obtained. The disadvantage of us20 ing AOGCM output is that the CPSM training might be compromised by any AOGCM inadequacies. For the purposes of exploring the various methodological aspects of the CPSM approach, AOGCM output was used in this study to train the CPSM. These investigations of the CPSM methodology focus on monthly mean fields of daily temperature extremes (Tmax and Tmin). Key conclusions are: (1) overall, the CPSM trained on simulations based on the Representative Concentration Pathway (RCP) 8.5 emissions scenario is able to reproduce AOGCM simulations of Tmax and Tmin based on predictor time series from an RCP 4.5 emissions scenario; (2) access to hemisphere average land and ocean temperatures as predictors improves the variance that can be explained, particularly over the oceans; (3) regression model fit-coefficients derived from individual simulations based on the RCP 2.6, 4.5 and 8.5 emissions scenarios agree well over most regions of the globe (the Arctic is the exception); (4) training the CPSM on concatenated time series from an ensemble of simulations does not result in fit-coefficients that explain significantly more of the variance than an approach that weights results based on single simulation fits; and (5) the inclusion of a linear time dependence in the regression model fit-coefficients improves the variance explained, primarily over the oceans. [ABSTRACT FROM AUTHOR]

It is widely recognized that the variation of average surface chlorophyll a concentration (Chl) in the South China Sea (SCS) is closely related to wind forcing, especially during the intense winter monsoon. In this study we demonstrate that, after removal of the seasonal cycles, the variation of Chl showed strong asymmetric responses to wind speed under El Ni ñ;o or La Ni ña conditions. The analysis was based on a time-series of Chl in the study area (115-117 °E, 17-19 °N) around the SEATS (South-East Asian Timeseries Study) station located in the central northern SCS from September 1997 to the end of 2011, which was constructed by merging the SeaWiFS data (1997-2006) and MODIS data (2003-2011). The merged daily data were validated by shipboard observations at the SEATS station. The non-seasonal variations of monthly mean Chl, wind speed, sea surface height (SSH) and sea surface temperature (SST) were examined against the multivariate ENSO index (MEI). The analysis reveals strongly asymmetric correlations of Chl and SST with positive MEI (El Ni ño) or negative MEI (La Ni ña). Under El Ni ño conditions, both showed significant correlations with MEI or wind speed; under La Niña conditions, both showed weak or insignificant correlations. The contrast was more pronounced for Chl than for SST. The subdued responses of Chl to wind forcing under La Ni ña conditions were probably attributed to a deepened thermocline, for which wind driven nutrient pumping is less efficient. A deeper thermocline, which was observed during the 1999-000 La Ni ña event and inferred by positive SSH anomalies during other La Ni ña events, was probably caused by reduced SCS throughflow under La Ni ña conditions. Intrusion of the nutrient-depleted Kuroshio water in the surface layer as observed during the 1999-2000 La Ni ña could be partially responsible for the suppressed Chl response. [ABSTRACT FROM AUTHOR]

Methyl iodide (CH3I) is a volatile organic halogen compound that contributes significantly to the transport of iodine from the ocean to the atmosphere, where it plays an important role in tropospheric chemistry. CH3I is naturally produced and occurs in the global ocean. The processes involved in the formation of CH3I, however, are not fully understood. In fact, there is an ongoing debate whether production by phytoplankton or photochemical degradation of organic matter is the main source term. Here, both the biological and photochemical production mechanisms are considered in a biogeochemical module that is coupled to a one-dimensional water column model for the Eastern Tropical Atlantic. The model is able to reproduce observed subsurface maxima of CH3I concentrations. But, the dominating source process cannot be clearly identified as sub- surface maxima can occur due to both, direct biological and photochemical production. However, good agreement between the observed and simulated difference between surface and subsurface methyl iodide concentrations is achieved only when direct biological production is taken into account. Published production rates for the biological CH3I source that were derived from laboratory studies are shown to be inappropriate for explaining CH3I concentrations in the Eastern Tropical Atlantic. [ABSTRACT FROM AUTHOR]

The article presents a study which aimed to address the issue on estimating height-dependent refractivity's range-varying parameters on the sea surface from radar sea clutter. The study made use of particle filter (PF) for tracking atmospheric duct structure's horizontal variations from radar sea clutter. Results revealed the important role being played by refractivity from clutter (RFC) in radar systems' full usage.

The article focuses on a study which seeks to identify cloud-type resolved cloud radiative budgets and cloud radiative effects from surface measurements of broadband radiative fluxes across the Atlantic Ocean. It performs a radiative closure study using the ECHAM5 single column model in identifying the models ability to reproduce the effects of clouds on the climate system. It creates an extensive data base of radiative and atmospheric measurements.

Marine heatwave (MHW) events have significant consequences for marine ecosystems and human society. This paper investigates a MHW's spatial–temporal characteristics in the Changjiang River Estuary and its surrounding coastal regions (CRESs), as well as analyzes the drivers, using satellite and reanalysis data spanning from 1982–2021. The findings show that, during the last 40 years, all four of the MHW indicators have increased. The summer MHW is more severe than other seasons, showing a rising pattern from southeast to northwest. The rise of MHWs can be attributed to the increase in sea surface heat flux, weak wind speed, and powerful El Niño events. Additionally, two special MHW events were detected during the entire study period: Event A lasted for 191 days from 9 October 2006 to 17 April 2007; Event B had an average intensity of 4.93 °C from 5 July 1994 to 1 August 1994. For locations so close to each other, the characteristics of MHWs can also vary, and the mechanisms behind them are highly complex. [ABSTRACT FROM AUTHOR]

Nitrogen fixation in temperate oceans is a potentially important, but poorly understood process that may influence the marine nitrogen budget. This study determined seasonal variations in nitrogen fixation and nifH gene diversity within the euphotic zone in the temperate coastal region of the northwestern North Pacific. Nitrogen fixation as high as 13.6 nmolN L-1 d-1 was measured from early summer to fall when the surface temperature exceeded 14.2 °C and the surface nitrate concentration was low (≤ 0.30 µM), although we also detected nitrogen fixation in subsurface layers (42-62 m) where nitrate concentrations were high (> 1 µM). During periods with high nitrogen fixation, the nifH sequences of UCYN-A were recovered, suggesting that these groups played a key role in nitrogen fixation. The nifH genes were also recovered in spring and winter when nitrogen fixation was undetectable. These genes consisted of many sequences affiliated with Cluster III diazotrophs (putative anaerobic bacteria), which hitherto have rarely been reported to be abundant in surface diazotroph communities in marine environments. [ABSTRACT FROM AUTHOR]

N2 fixation rates were measured daily in large (~ 50 m³) mesocosms deployed in the tropical South West Pacific coastal ocean (New Caledonia) to investigate the spatial and temporal dynamics of diazotrophy and the fate of diazotroph-derived nitrogen (DDN) in a low nutrient, low chlorophyll ecosystem. The mesocosms were intentionally fertilized with ~ 0.8 μM dissolved inorganic phosphorus (DIP) to stimulate diazotrophy. Bulk N2 fixation rates were replicable between the three mesocosms, averaged 18.5 ± 1.1 nmol N L-1 d-1 over the 23 days, and increased by a factor of two during the second half of the experiment (days 15 to 23) to reach 27.3 ± 1.0 nmol N L-1 d-1. These rates are higher than the upper range reported for the global ocean, indicating that the waters surrounding New Caledonia are particularly favourable for N2 fixation. During the 23 days of the experiment, N2 fixation rates were positively correlated with seawater temperature, primary production, bacterial production, standing stocks of particulate organic carbon, nitrogen and phosphorus, and alkaline phosphatase activity, and negatively correlated with DIP concentrations, DIP turnover time, nitrate, and dissolved organic nitrogen and phosphorus concentrations. The fate of DDN was investigated during the bloom of the unicellular diazotroph, UCYN-C, that occurred during the second half of the experiment. Quantification of diazotrophs in the sediment traps indicates that ~ 10% of UCYN-C from the water column were exported daily to the traps, representing as much as 22.4 ± 5.5% of the total POC exported at the height of the UCYN-C bloom. This export was mainly due to the aggregation of small (5.7 ± 0.8 μm) UCYN-C cells into large (100-500 μm) aggregates. During the same time period, a DDN transfer experiment based on high-resolution nanometer scale secondary ion mass spectrometry (nanoSIMS) coupled with 15N2 isotopic labelling revealed that 16 ± 6% of the DDN was released to the dissolved pool and 21 ± 4% was transferred to non-diazotrophic plankton, mainly picoplankton (18 ± 4%) followed by diatoms (3 ± 2%) within 24 h of incubation. This is consistent with the observed dramatic increase in picoplankton and diatom abundances, primary production, bacterial production and standing stocks of particulate organic carbon, nitrogen and phosphorus during the second half of the experiment in the mesocosms. These results offer insights into the fate of DDN during a bloom of UCYN-C in low nutrient, low chlorophyll ecosystems. [ABSTRACT FROM AUTHOR]

Insights into oceanographic environmental conditions such as paleoproductivity, sea-surface temperatures, deep-water temperatures, salinity, ice volumes, circulation patterns, and nutrient cycling have all been obtained from geochemical analyses of biomineralized carbonate of marine organisms. However, we cannot fully understand geochemical proxy incorporation and the fidelity of such in species until we better understand fundamental aspects of their ecology such as where and when these (micro)organisms calcify. Here, we present an innovative method using osmotic pumps and the fluorescent marker calcein to help identify where and when calcareous meiofauna calcify in situ. Method development initially involved juvenile quahogs (Mercenaria mercenaria); subsequent method refinement involved a neritic benthic foraminiferal community. Future applications of this method will allow determinations of in situ growth rate in calcareous organisms and provide insights about microhabitats where paleoceanographically relevant benthic foraminifera actually calcify. [ABSTRACT FROM AUTHOR]

Regime shifts have been reported in many marine ecosystems, and are often expressed as an abrupt change occurring in multiple physical and biological components of the system. In the Gulf of Alaska, a regime shift in the late 1970s was observed, indicated by an abrupt increase in sea surface temperature and major shifts in the catch of many fish species. This late 1970s regime shift in the Gulf of Alaska was followed by another shift in the late 1980s, not as pervasive as the 1977 shift, but which nevertheless did not return to the prior state. A thorough understanding of the extent and mechanisms leading to such regime shifts is challenged by data paucity in time and space. We investigate the ability of a suite of ocean biogeochemistry models of varying complexity to simulate regime shifts in the Gulf of Alaska by examining the presence of abrupt changes in time series of physical variables (sea surface temperature and mixed layer depth), nutrients and biological variables (chlorophyll, primary productivity and plankton biomass) using change-point analysis. Our study demonstrates that ocean biogeochemical models are capable of simulating the late 1970s shift, indicating an abrupt increase in sea surface temperature forcing followed by an abrupt decrease in nutrients and biological productivity. This predicted shift is consistent among all the models, although some of them exhibit an abrupt transition (i.e. a significant shift from one year to the next), whereas others simulate a smoother transition. Some models further suggest that the late 1980s shift was constrained by changes in mixed layer depth. Our study demonstrates that ocean biogeochemical can successfully simulate regime shifts in the Gulf of Alaska region, thereby providing better understanding of how changes in physical conditions are propagated from lower to upper trophic levels through bottom-up controls. [ABSTRACT FROM AUTHOR]

This study was designed to determine the effects of flooding on plankton community respiration (CR) in the East China Sea (ECS). In July 2010, a devastating flood occurred in the Changjiang River; the mean monthly discharge was 60 527m³ s-1. To compare, the variables were also examined in the low riverine flow of July 2009 (33 955 m³ s-1). During the flooding, the Changjiang diluted water (CDW) zone, the sea surface salinity (SSS) was ≤ 31 psu, covering almost two thirds of the ECS, which was approximately six times that in the non-flooding period. The mean nitrate concentration was higher in 2010 (6.2 µM) than in 2009 (2.0 µM). However, in the 2010 flood, the mean values of Chl a and the bacterial biomass were only slightly higher or even lower than in 2009. Surprisingly, however, the CR was still higher in the flood period than in the non-flood period, with mean values of 105.6 and 73.2mg C m-3 d-1, respectively. The higher CR in 2010 could be attributed to vigorous plankton activities, especially phytoplankton, at stations in the CDW zone, which were not mostly covered by low SSS in 2009. There was a huge amount of fCO2 drawdown in the 2010 flood. These results suggested that the devastating flood in 2010 had a significant effect on the carbon balance in the ECS. This effect might become more pronounced as extreme rainfall events and flooding magnitudes increase dramatically throughout the world. [ABSTRACT FROM AUTHOR]

The accurate temperature background field plays a vital role in the numerical prediction of sea surface temperature (SST). At present, the SST background field is mainly derived from multi-source data fusion, including satellite SST data and in situ data from marine stations, buoys, and voluntary observing ships. The characteristics of satellite SST data are wide coverage but low accuracy, whereas the in situ data have high accuracy but sparse distribution. For obtaining a more accurate temperature background field and realizing the fusion of measured data with satellite data as much as possible, we propose a sparse data-extended fusion method to predict SST in this paper. By using this method, the actual observed sites and buoys data in the East China Sea area are fused with Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Version 5.0 SST data. Furthermore, the temperature field in the study area were predicted by using Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) deep learning methods, respectively. Finally, we obtained the results by traditional prediction methods to verify them. The experimental results show that the method we proposed in this paper can obtain more accurate prediction results, and effectively compensate for the uncertainty caused by the parameterization of ocean dynamic process, the discrete method, and the error of initial conditions. [ABSTRACT FROM AUTHOR]

The accurate prediction of sea surface temperature (SST) is the basis for our understanding of local and global climate characteristics. At present, the existing sea temperature prediction methods fail to take full advantage of the potential spatial dependence between variables. Among them, graph neural networks (GNNs) modeled on the relationships between variables can better deal with space–time dependency issues. However, most of the current graph neural networks are applied to data that already have a good graph structure, while in SST data, the dependency relationship between spatial points needs to be excavated rather than existing as prior knowledge. In order to predict SST more accurately and break through the bottleneck of existing SST prediction methods, we urgently need to develop an adaptive SST prediction method that is independent of predefined graph structures and can take full advantage of the real temporal and spatial correlations hidden indata sets. Therefore, this paper presents a graph neural network model designed specifically for space–time sequence prediction that can automatically learn the relationships between variables and model them. The model automatically extracts the dependencies between sea temperature multi-variates by embedding the nodes of the adaptive graph learning module, so that the fine-grained spatial correlations hidden in the sequence data can be accurately captured. Figure learning modules, graph convolution modules, and time convolution modules are integrated into a unified end-to-end framework for learning. Experiments were carried out on the Bohai Sea surface temperature data set and the South China Sea surface temperature data set, and the results show that the model presented in this paper is significantly better than other sea temperature model predictions in two remote-sensing sea temperature data sets and the surface temperature of the South China Sea is easier to predict than the surface temperature of the Bohai Sea. [ABSTRACT FROM AUTHOR]

Fish spawning phenology is a major concern for conservation and fisheries management. New intensive data sources, such as GPS-based tracking data and high-resolution catch declaration data, are becoming increasingly available in the field of marine ecology. These data benefit from high spatiotemporal resolution and open new research avenues for investigating the interannual variability in fish phenology. In this paper, we demonstrate how an integrated species distribution model informed by commercial catch data combined with spatiotemporal dimension reduction methods known as empirical orthogonal functions (EOFs) can be used to synthesize spatiotemporal signals in fish reproduction phenology. Specifically, we address the following questions: (1) Can we identify seasonal spatial patterns that can be interpreted in terms of reproductive phenology and essential habitats? (2) Can we identify changes in reproductive phenology over time? (3) Are these changes related to environmental drivers? The analysis illustrates the reproductive phenology of three key commercial species in the Bay of Biscay (sole, hake, and sea bass). The EOF analysis emphasized strong seasonal spatiotemporal patterns that correspond to reproduction patterns and feeding patterns. Based on this methodology, we identified seasonal variations in the timing of reproduction, and we related these variations to sea surface temperature, a key driver of fish reproduction. [ABSTRACT FROM AUTHOR]

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