Ocean Parameters Research Articles

Physical oceanographic factors controlling the ocean circulation-induced magnetic field.

Oceanic tidal constituents and depth-integrated electrical conductivity (ocean conductivity content, or OCC) extracted from electromagnetic (EM) field data are known to have a strong potential for monitoring ocean heat content, which reflects the Earth's energy imbalance. In comparison to ocean tide models, realistic ocean general circulation models have a greater need to be baroclinic; therefore, both OCC and depth-integrated conductivity-weighted velocity ([Formula: see text]) data are required to calculate the ocean circulation-induced magnetic field (OCIMF). Owing to a lack of [Formula: see text] observations, we calculate the OCIMF using an ocean state estimate. There are significant trends in the OCIMF primarily owing to responses in the velocities to external forcings and the warming influence on OCC between 1993 and 2017, particularly in the Southern Ocean. Despite being depth-integrated quantities, OCC and [Formula: see text] (which primarily determine the OCIMF in an idealized EM model) can provide a strong constraint on the baroclinic velocities and ocean mixing parameters when assimilated into an ocean state estimation framework. A hypothetical fleet of full-depth EM-capable floats would therefore help improve the accuracy of the OCIMF computed with an ocean state estimate, which could potentially provide valuable guidance on how to extract the OCIMF from satellite magnetometry observations.This article is part of the theme issue 'Magnetometric remote sensing of Earth and planetary oceans'.

From “open ocean” to “exposed aquaculture”: why and how we are changing the standard terminology describing “offshore aquaculture”

The term “offshore” with regards to aquaculture has hitherto encompassed various perspectives, including technology, geographic location, legal jurisdiction, and more. To resolve the ambiguity in this term and understand its implications for current and future aquaculture development, “offshore” should be resolved into two separate metrics: distance from shore and energy exposure. The United Nations Convention on the Law of the Sea (UNCLOS) distinguishes between internal waters, territorial sea, contiguous zone, exclusive economic zone (EEZ), and the high seas, but currently has no precise definition for “offshore” in its provisions, and therefore no applicable laws pertaining to “offshore” aquaculture. Regulating a multi-technology aquaculture sector may require integrating new spatial concepts into the law rather than merely adapting and extending current regulatory designs to include new production concepts. The metrics of distance from shore and exposure are seen as a range rather than a specific threshold, allowing for a continuum. Distance from shore is readily quantified as a distance from a baseline. To rigorously quantify the exposure, the influence and interactions of oceanic parameters (water depth, water current, and wave height and period) we utilized to generate six indices. These oceanic parameters are seen as the main contributions which influence the physical and some biological parameters required for site, species, and technology selection. Four shellfish, three seaweed, and three finfish sites along with 20 potential aquaculture sites were examined using the indices in association with the energy index to determine tolerances of the structures and their ability to cultivate their relevant species. Two indices, Specific Exposure Energy (SEE) and Exposure Velocity (EV), were selected for utilization in the analysis of sites based on their ease of use and applicability. The interaction between the energy indices and various aspects of farm operations and performance were explored. The indices developed and used in the case studies presented have been shown to be useful tools in the general assessment of the energy that will influence the species and equipment selection at potential aquaculture sites. The indices do not provide a definitive answer as to the potential financial success of a site as this requires other inputs relating to infrastructure costs, annual production, distance from port, sales strategy, etc. However, the Specific Exposure Energy index creates a useful tool to describe site energy and be comprehensible to a wide range of stakeholders. We recommend the SEE index be adopted as the predominant tool to communicate the exposure level of aquaculture sites.

Performance Characteristics of Newly Developed Real-Time Wave Measurement Buoy Using the Variometric Approach

Accurate measurement of ocean wave parameters is critical for applications including ocean modeling, coastal engineering, and disaster management. This article introduces a novel global navigation satellite system (GNSS) drifting buoy for surface wave measurements that addresses the challenges of performing real-time, high-precision measurements and realizing cost-effective large-scale deployment. Unlike traditional approaches, this buoy uses the kinematic extension of the variometric approach for displacement analysis stand-alone engine (Kin-VADASE) velocity measurement method, thus eliminating the need for additional high-precision measurement units and an expensive complement of satellite orbital products. Through testing in the South China Sea and Laoshan Bay, the results showed good consistency in significant wave height and main wave direction between the novel buoy and a Datawell DWR-G4, even under mild wind and wave conditions. However, wave mean period disparities were observed partially because of sampling frequency differences. To validate this idea, we used Joint North Sea Wave Project (Jonswap) spectral waves as input signals, the bias characteristics of the mean periods of the spectral calculations were compared under conditions of identical input signals and gradient-distributed wind speeds. Results showed an average difference of 0.28 s between the sampling frequencies of 1.28 Hz and 5 Hz. The consequence that high-frequency signals have considerable effects on the mean wave period calculations indicates the necessity of the buoy’s high-frequency operation mode. This GNSS drifting buoy offers a cost-effective, globally deployable solution for ocean wave measurement. Its potential for large-scale networked ocean wave observation makes it a valuable oceanic research and monitoring instrument.

Machine learning reveals biological activities as the dominant factor in controlling deoxygenation in the South Yellow Sea

Dissolved oxygen (DO) is a crucial element for both biotic and abiotic processes in marine ecosystems, but has declined globally in recent decades. Therefore, there is an urgent need for solid large-scale and continuous estimation of DO concentration in vital ecosystems, such as coastal areas. A random forest (RF) model for DO in South Yellow Sea (SYS) was developed by integrating satellite data and simulation data during 2011–2019. The root mean squared error (RMSE) for the training and test sets were 0.514 mg/L and 0.732 mg/L, respectively. Spatiotemporal distributions of DO of multiple layers in the study area during 2011–2019 were very well reproduced by the RF model and showed a slight decline trend in most SYS areas, while more intense decline occurred in the deep central SYS. The analysis of the mechanisms of DO decline in the South Yellow Sea cold water mass (SYSCWM), located in the deep central SYS, indicates that the deoxygenation here is largely due to biological activities. This finding may have implications for studies on drivers of deoxygenation in coastal areas. Furthermore, integrating satellite data with machine learning models can offer a powerful approach to capturing the continuous spatiotemporal characteristics of ocean parameters over large spatial scales.

An Empirical Algorithm for Estimating the Absorption of Colored Dissolved Organic Matter from Sentinel-2 (MSI) and Landsat-8 (OLI) Observations of Coastal Waters

Sentinel-2/MSI and Landsat-8/OLI sensors enable the mapping of ocean color-related bio-optical parameters of surface coastal and inland waters. While many algorithms have been developed to estimate the Chlorophyll-a concentration, Chl-a, and the suspended particulate matter, SPM, from OLI and MSI data, the absorption by colored dissolved organic matter, acdom, a key parameter to monitor the concentration of dissolved organic matter, has received less attention. Herein we present an inverse model (hereafter referred to as AquaCDOM) for estimating acdom at the wavelength 412 nm (acdom (412)), within the surface layer of coastal waters, from measurements of ocean remote sensing reflectance, Rrs (λ), for these two high spatial resolution (around 20 m) sensors. Combined with a water class-based approach, several empirical algorithms were tested on a mixed dataset of synthetic and in situ data collected from global coastal waters. The selection of the final algorithms was performed with an independent validation dataset, using in situ, synthetic, and satellite Rrs (λ) measurements, but also by testing their respective sensitivity to typical noise introduced by atmospheric correction algorithms. It was found that the proposed algorithms could estimate acdom (412) with a median absolute percentage difference of ~30% and a median bias of 0.002 m−1 from the in situ and synthetic datasets. While similar performances have been shown with two other algorithms based on different methodological developments, we have shown that AquaCDOM is much less sensitive to atmospheric correction uncertainties, mainly due to the use of band ratios in its formulation. After the application of the top-of-atmosphere gains and of the same atmospheric correction algorithm, excellent agreement has been found between the OLI- and MSI-derived acdom (412) values for various coastal areas, enabling the application of these algorithms for time series analysis. An example application of our algorithms for the time series analysis of acdom (412) is provided for a coastal transect in the south of Vietnam.

Typhoon Early Warning and Monitoring Based on the Comprehensive Characteristics of Oceanic and Ionospheric Echoes from HFSWR: The Case of Typhoon Muifa

As devastating natural disasters, typhoons pose a tremendous threat to human society, making effective typhoon early warning and monitoring crucial. To address this challenge, High Frequency Surface Wave Radar (HFSWR), which can observe oceanic parameters such as typhoon wind fields in real time and even capture the dynamic changes in the ionosphere, has become an effective tool for typhoon monitoring. This paper investigates the interaction mechanisms about Typhoon-Acoustic Gravity Waves (AGWs)-Ionosphere, as well as Typhoon-Ocean Waves for HFSWR, and simulates these interaction processes within HFSWR. Then a typhoon early warning and monitoring scheme for HFSWR has been proposed: In the first stage, the S-shaped ionospheric disturbances observed by HFSWR are utilized as precursor signals for early typhoon warnings. In addition, the second stage involves analyzing changes in first-order oceanic echo spectral peak ratio to pinpoint when the typhoon eye enters the radar detection range, thus initiating the typhoon monitoring phase. Subsequently, the measured data from HFSWR collected during Typhoon “Muifa” (2212) in conjunction with the proposed scheme are evaluated in detail. The results indicate that AGWs generated by typhoons can propagate into non-typhoon areas within the detection range, causing S- shaped ionospheric disturbances and providing approximately 6 h of early warning. At around 8:05 (UTC+8), an increasing trend in the first-order spectral peak ratio was noted, indicating the entry of the typhoon eye into the detection range, which closely aligns with the official typhoon path and marks the transition to the monitoring phase. The proposed scheme is expected to enhance the capability for typhoon early warning and real-time monitoring in specific sea areas and mitigate the risks associated with typhoon-related disasters.

Composited sea surface responses to tropical cyclones in the Bay of Bengal during 2003–2020

Tropical cyclones (TCs) in the Bay of Bengal (BoB) caused significant changes in the sea surface as they passed through the sea. To quantitatively study the differences in oceanic responses caused by TCs during pre-monsoon and post-monsoon seasons in the BoB from 2003 to 2020 and the varying importance of TC intensity and translation speed in modulating oceanic responses, the oceanic environmental parameters affected by TCs were composited and analyzed. The spatial distributions show that the responses of sea surface temperature (SST) rather than chlorophyll-a (Chl-a) concentrations and sea surface salinity (SSS) coincided with the maximum response centers of the wind power index (WPi). All the temporal changes in SST, Chl-a, and SSS indicated that the changes induced by TCs began approximately 2–3 days before TC passage, continued to increase during cyclone passage, and then slowly returned to the initial state after at least 8 days. Compared with TC intensity, TC translation speed generally played a less important role in influencing the ocean responses of SST, Chl-a and SSS. This result is somewhat different from previous results in which the response of Chl-a was correlated with TC translation speed rather than TC intensity in the northern Indian Ocean. In addition, the SSS response was positively proportional (R = 0.886) to the TC translation speed in the BoB, which was different from that in the Northwest Pacific. The differences in the responses during pre-monsoon and post-monsoon seasons were mainly in terms of quantity, as all the composited results were similar. In addition, they were more pronounced in their responses but less significant in their correlations during pre-monsoon.

Genesis and Propagation of Low‐Frequency Abyssal T‐Waves

AbstractAbyssal T‐waves are seismo‐acoustic waves originating from abyssal oceans. Unlike subduction‐zone‐generated slope T‐waves which are generated through multiple reflections between the sea surface and the gently dipping seafloor, the genesis of abyssal T‐waves cannot be explained by the same theory. Several hypotheses, including seafloor scattering, sea surface scattering, and internal‐wave‐induced volumetric scattering, have been proposed to elucidate their genesis and propagation. The elusive mechanism of abyssal T‐waves, particularly at low‐frequencies, hinders their use to quantify ocean temperatures through seismic ocean thermometry (SOT) and estimate oceanic earthquake parameters. Here, using realistic geophysical and oceanographic data, we first conduct numerical simulations to compare synthetic low‐frequency abyssal T‐waves under different hypotheses. Our simulations for the Romanche and Blanco transform faults suggest seafloor scattering as the dominant mechanism, with sea surface and internal waves contributing marginally. Short‐scale bathymetry can significantly enhance abyssal T‐waves across a broad frequency range. Also, observed T‐waves from repeating earthquakes in the Romanche, Chain, and Blanco transform faults exhibit remarkably high repeatability. Given the dynamic nature of sea surface roughness and internal waves, the highly repeatable T‐wave arrivals further support the seafloor scattering as the primary mechanism. The dominance of seafloor scattering makes abyssal T‐waves useable for constraining ocean temperature changes, thereby greatly expanding the data spectrum of SOT. Our observations of repeating abyssal T‐waves in the Romanche and Chain transform faults could provide a valuable data set for understanding Equatorial Atlantic warming. Still, further investigations incorporating high‐resolution bathymetry are warranted to better model abyssal T‐waves for earthquake parameter estimation.

Coati optimization algorithm based Deep Convolutional Forest method for prediction of atmospheric and oceanic parameters

Droughts and floods are examples of extreme weather events that can result from changes in ocean temperature. Ocean temperature is a key component of the global open sea system. Currently, real-time sea surface temperature (SST) forecasts are generated by numerical models based on physics principles and influenced by boundary and initial conditions. These models generally perform better over large areas than at specific locations. To address this and improve prediction accuracy, particularly in high-precision areas, the Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method is proposed. This optimization approach is utilized to train the Deep Convolutional Forest (DCF) classifier, which then applies the prediction strategy. The COA-DCF method forecasts ocean surface temperature anomalies by considering key variables such as SST, Sea Surface Height (SSH), soil moisture, and wind speed, using historical data ranging from 1 to 10 days across six different locations. The proposed method achieves improved accuracy with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, and a high Pearson’s correlation coefficient (r) of 0.493, 0.487, and 0.4733, respectively, thereby enhancing the overall performance of the deep learning model.

Relative contribution of the ocean-air heat exchange and advective heat transport to the increase of the Barents Sea water temperature in the early 21st century

The annual water temperature in the major water masses of the Barents Sea (BS) has significantly increased since the early 2000s. Advective heat transport from the neighboring water areas and heat exchange through the sea surface are the major factors, which shape the hydrological conditions in the BS. The paper estimates the contributions of heat exchange at the sea-atmosphere boundary and advective heat transport to changes in the average water temperature of the BS for the entire sea area. The average annual heat balance of the BS is calculated using atmospheric and oceanic reanalysis data. The change in the average temperature of the BS water is estimated taking into account the heat consumption for ice melting. The average surface heat balance from 1993 to 2018 was negative throughout the entire sea area: –70…–100 W/m2 in the south and –10…–20 W/m2 in the north. The advective heat supply was calculated for 9 straits with neighboring water areas. The determining source of advective heat is the influx of Atlantic waters from the Norwegian Sea between Cape Nordkapp and Bear Island. An average of 40.8 TW of advective heat is supplied through this margin. The calculations showed the predominance of annual heat influx due to advection over heat loss from the sea surface. This excess heat influx resulted in an estimated increase in the water temperature of the BS from 1993 to 2018 at a rate of 0.28 °C per year (taking into account the heat consumption for ice melting). In conclusion, it can be argued that the analysis has validated the hypothesis proposed in the article about compensation of heat losses from the surface of the BS by advective heat flow. The hypothesis is quantitatively confirmed by calculations on a simple box model (with an accuracy of up to an order of magnitude) based on atmospheric and oceanic reanalysis data. The ERA5 and GLORYS12V1 reanalysis data reliably describe the basic patterns of observed variability of ocean, sea ice and atmospheric parameters in the Barents Sea.

Distributed salinity sensor based on optical frequency domain reflectometry with polyimide coated single mode fiber

A distributed optical fiber sensor for salinity sensing is proposed and analyzed. The sensor uses a single mode fiber coated with polyimide and is based on optical frequency domain reflectometry. By applying the polyimide solution to the surface of the fiber, a layer of polyimide film is formed by heating. As the polyimide coating is very sensitive to changes in the salinity of the external solution, the polyimide film will expand or shrink as the external concentration changes. This converts the value of salt concentration into the frequency shift in the spectrum through cross-correlation. To characterize the sensor, four polyimide-coated sensors with different average coating thicknesses of 126 µm, 170 µm, 192 µm and 249 µm were fabricated. The sensitivities of -21.71 GHz/(mol/L), -28.55 GHz/(mol/L), -29.97 GHz/(mol/L) and -42.78 GHz/(mol/L) were achieved when the salinity was measured from 0 mol/L to 3.09 mol/L, respectively. The sensitivity and response time of polyimide fibers with different diameters were measured. The minimum salinity measurement uncertainty of the sensor is 0.05 mol/L. The sensor has high sensitivity and has promising applications in observing ocean parameters and ion chemical sensing.

Trends in satellite-based ocean parameters through integrated time series decomposition and spectral analysis: 1. chlorophyll, sea surface temperature, and sea level anomaly

Abstract This study investigated trends in satellite-based chlorophyll-a (Chl-a; 1998–2022), sea surface temperature (SST; 1982–2022), and sea level anomaly (SLA; 1993–2021) from the European Space Agency’s Climate Change Initiative records, integrating time series decomposition and spectral analysis. Trends in parameters signify prolonged increases, decreases, or no changes over time. These are time series in the same space as original parameters, excluding seasonalities and noise, and can exhibit nonlinearity. Trend rates approximate the pace of change per time unit. We quantified trends using conventional linear-fit and three incrementally advancing methods for time series decomposition: simple moving average (SMA), seasonal-trend decomposition using locally estimated scatterplot smoothing (STL), and multiple STL (MSTL), across the global ocean, the Bay of Bengal, and the Chesapeake Bay. Challenges in decomposition include specifying accurate seasonal periods that are derived here by combining Fourier and Wavelet Transforms. Globally, SST and SLA trend upwards, and Chl-a has no significant change, yet regional variations are notable. We highlight the advantage of extracting multiple periods with MSTL and, more broadly, decomposition’s role in disentangling time-series components (seasonality, trend, noise) without resorting to monotonic functions, thereby preventing overlooking episodic events. Illustrations include extreme events temporarily counteracting background trends, e.g., the 2010–2011 SLA drop due to La Niña-induced rainfall over land. The continuous analysis clarifies the warming hiatus debate, affirming sustained warming. Decadal trend rates per grid cell are also mapped. These are ubiquitously significant for SST and SLA, whereas Chl-a trend rates are globally low but extreme across coasts and boundary currents.

An Overlooked Component of the Meridional Overturning Circulation

Abstract Upwelling along the western boundary of the major ocean basin subtropical gyres has been diagnosed in a wide range of ocean models and state estimates. This vertical transport is O(5 × 106) m3 s−1, which is on the same order of magnitude as the downward Ekman pumping across the subtropical gyres and zonally integrated meridional overturning circulation. Two approaches are used here to understand the reason for this upwelling and how it depends on oceanic parameters. First, a kinematic model that imposes a density gradient along the western boundary demonstrates that there must be upwelling with a maximum vertical transport at middepths in order to maintain geostrophic balance in the western boundary current. The second approach considers the vorticity budget near the western boundary in an idealized primitive equation model of the wind- and buoyancy-forced subtropical and subpolar gyres. It is shown that a pressure gradient along the western boundary results in bottom pressure torque that injects vorticity into the fluid. This is balanced on the boundary by lateral viscous fluxes that redistribute this vorticity across the boundary current. The viscous fluxes in the interior are balanced primarily by the vertical stretching of planetary vorticity, giving rise to upwelling within the boundary current. This process is found to be nearly adiabatic. Nonlinear terms and advection of planetary vorticity are also important locally but are not the ultimate drivers of the upwelling. Additional numerical model calculations demonstrate that the upwelling is a nonlocal consequence of buoyancy loss at high latitudes and thus represents an integral component of the meridional overturning circulation in depth space but not in density space. Significance Statement The purpose of this study is to better understand what is forcing water to upwell along the western boundary at midlatitudes of the major ocean basins. This is a potentially important process since upwelling can bring heat and nutrients closer to the surface, where they can be exchanged with the atmosphere. Also, since ocean currents vary with depth, pathways followed in the upper ocean are different from those found for the deeper ocean, so the amount and location of upwelling influence where these waters go. Idealized numerical models and theory are used to demonstrate that the upwelling is ultimately driven by density changes along the western boundary of the basin that result from heat loss at high latitudes.

Wave‐Coupled Effects on Oceanic Biogeochemistry: Insights From a Global Ocean Biogeochemical Model in the Southern Ocean

AbstractOceanic biogeochemistry plays a pivotal role in regulating Earth's climate system by governing the cycling of key elements such as carbon, oxygen, and nutrients. Various metocean processes including wind, tides, currents, waves, and eddies significantly influence the dynamics of this system. In particular, ocean surface waves contribute to this intricate interplay by facilitating the exchange of heat, gas, and momentum between the atmosphere and the ocean. Although wave‐coupled effects are substantial, studies on their impacts on oceanic biogeochemistry, particularly on phytoplankton abundance are missing in present‐day research. Additionally, wave‐coupled effects cannot be disregarded in regions like the Southern Ocean (SO), where wind and waves activities are prominent. Addressing this gap, we incorporated a parameterization of surface wave mixing into a global ocean biogeochemical model to investigate its effects on upper ocean and biogeochemical parameters. Our results show that surface wave mixing has significant impacts on sea surface temperature (SST), mixed layer depth (MLD), and nutrient distribution—key factors that influence phytoplankton growth. Additionally, we observed significant improvements in model biases against the observations. During austral summer, additional mixing from surface waves can significantly lower SST by 0.5°C, deepen MLD by 13 m, and enhance Chlorophyll‐a (Chl‐a) concentration, an index of phytoplankton population, by 8% in the SO. This observed increase in Chl‐a concentration is mainly driven by enhanced dissolved iron levels resulting from wave‐induced mixing. Our findings underscore the significance of incorporating surface wave mixing in ocean biogeochemistry studies, an aspect that is currently overlooked.

Ocean wave energy harvesting with high energy density and self-powered monitoring system

Constructing a ocean Internet of Things requires an essential ocean environment monitoring system. However, the widely distributed existing ocean monitoring sensors make it impractical to provide power and transmit monitored information through cables. Therefore, ocean environment monitoring systems particularly need a continuous power supply and wireless transmission capability for monitoring information. Consequently, a high-strength, environmentally multi-compatible, floatable metamaterial energy harvesting device has been designed through integrated dynamic matching optimization of materials, structures, and signal transmission. The self-powered monitoring system breaks through the limitations of cables and batteries in the ultra-low-frequency wave environment (1 to 2 Hz), enabling real-time monitoring of various ocean parameters and wirelessly transmitting the data to the cloud for post-processing. Compared with solar and wind energy in the ocean environment, the energy harvesting device based on the defective state characteristics of metamaterials achieves a high-energy density (99 W/m3). For the first time, a stable power supply for the monitoring system has been realized in various weather conditions (24 h).

Exploring Long-Term Persistence in Sea Surface Temperature and Ocean Parameters via Detrended Cross-Correlation Approach

Long-term cross-correlational structures are examined for pairs of sea surface temperature anomalies (SSTAs) and advective forcing parameters and sea surface height anomalies (SSHAs) and current velocity anomalies (CVAs) in the East/Japan Sea (EJS); all these satellite datasets were collected between 1993 and 2023. By utilizing newly modified detrended cross-correlation analysis algorithms, incorporating local linear trend and local fluctuation level of an SSTA, the analyses were performed on timescales of 400–3000 days. Long-term cross-correlations between SSTAs and SSHAs are strongly persistent over nearly the entire EJS; the strength of persistence is stronger during rising trends and low fluctuations of SSTAs, while anti-persistent behavior appears during high fluctuations of SSTAs. SSTA-CVA pairs show high long-term persistence only along main current pathways: the zonal currents for the Subpolar Front and the meridional currents for the east coast of Korea. SSTA-CVA pairs also show negative long-term persistent behaviors in some spots located near the coasts of Korea and Japan: the zonal currents for the eastern coast of Korea and the meridional currents for the western coast of Japan; these behaviors seem to be related to the coastal upwelling phenomena. Further, these persistent characteristics are more conspicuous in the recent decades (2008~2023) rather than in the past (1993~2008).

Two decades of coral bleaching in selected islands of Pacific Ocean: A holistic impact assessment

Coral reefs are regarded as one of the most diverse, productive as well as sensitive ecosystems existing across the world. Major coral reefs across the Pacific Ocean exhibit a vast variety of species, the majority of which are under threat, largely due to changes in past conducive climatic and oceanic parameters. The present study involves the mapping of coral reefs at select islands of Great Barrier Reefs (Heron Island), Fiji (Namena Island), and Cook Island (Aitutaki Island) using Landsat 7 (ETM+) and Landsat 8 (OLI/ TIRS) satellite data for the years 2002 and 2020. Depth invariant bottom index (DII) is applied for water column correction and identification of bottom type submerged and heterogeneous coral reef classes. Maximum likelihood classifier (supervised classification) is performed to characterize various classes of the coral community. Turbidity values (10.4 – 53 Nephelometric Turbidity unit (NTU)), (4.74–26.1NTU), and (14.56–73.56 NTU) for Heron, Namena, and Aitutaki Islands indicate a pronounced effect of sedimentation due to an increase in Sea level rise and anthropogenic activities. Increasing trend of oceanic parameters viz. SST, PAR, salinity, and sea level rise indicated an adverse impact on coral health and triggered coral bleaching along these Islands especially due to El Niño events in the years 2015–2016. We identified the coral mortality for 18 years of period as 16.1 %, 20.2 % and 11. 2 % in Heron, Namena, and Aitutaki Island respectively. The study provides a holistic impact assessment of various anthropogenic activities and climate change on the health of the coral reef ecosystem.

Performance-based design of environmental parameters for offshore wind turbine foundations

Taking into account the coupling effects of wind, wave, and current on offshore wind turbines (OWTs), a combined approach utilizing non-linear finite element analysis (FEA), back propagation neural network (BPNN), and probabilistic statistical methodology (PSM) is proposed for accurate environmental parameter estimation in OWT foundation design. The structural bearing performances of OWT foundations are precisely predicted through a combination of FEA and BPNN models, and then serve as constraints for probabilistic statistical analysis. Furthermore, for PSM, a robust multivariate joint probability distribution is constructed leveraging copula functions, which not only maximizes marginal information but also effectively captures the intricate dependencies among ocean environmental parameters. Subsequently, the proposed models, along with traditional univariate analysis, are utilized to estimate the return values of diverse load combinations. The results indicate that, for a given return period, the conditional probability model yields lower wave heights and current velocities compared to univariate analysis, while the joint probability model results in lower values for all ocean parameters, making both approaches more suitable for OWT foundation design. This coupled approach presents a more rational and cost-effective solution compared to univariate analysis, potentially resulting in decreased investment costs.

Retrieval of subsurface dissolved oxygen from surface oceanic parameters based on machine learning

Oceanic dissolved oxygen (DO) is crucial for oceanic material cycles and marine biological activities. However, obtaining subsurface DO values directly from satellite observations is limited due to the restricted observed depth. Therefore, it is essential to develop a connection between surface oceanic parameters and subsurface DO values. Machine learning (ML) methods can effectively grasp the complex relationship between input attributes and target variables, making them a valuable approach for estimating subsurface DO values based on surface oceanic parameters. In this study, the potential of ML methods for subsurface DO retrieval is analyzed. Among the selected ML methods, namely support vector regression (SVR), random forest (RF) regression, and extreme gradient boosting (XGBoosting) regression, the RF method generally demonstrates superior performance. As the depth increases, the accuracy of DO estimates tends to initially decrease, then gradually improve, with the poorest performance occurring at the depth of 600 dbar. The range of determination coefficients (R2) and root mean square error (RMSE) values based on the test dataset at different depths lies between 0.53 and 47.59 μmol/kg to 0.99 and 4.01 μmol/kg. In addition, compared to sea surface salinity (SSS) and sea surface chlorophyll-a (SCHL), sea surface temperature (SST) plays a more significant role in DO retrieval. Finally, compared to the pelagic interactions scheme for carbon and ecosystem studies (PISCES) model, the RF method achieves higher retrieval accuracies at depths above 700 dbar. In the deep ocean, the primary differences in DO values obtained from the RF method and the PISCES model-based method are noticeable in the vicinity of the equatorial region.

Transmission characteristics of partially coherent pin-like optical vortex beams in oceanic turbulence

Based on the Rytov approximation theory, the analytical formulae for the mode detection probability and channel capacity of the partially coherent pin-like optical (PCPO) vortex beams propagating in oceanic turbulence are obtained. The effects of light source parameters and oceanic turbulence parameters on the transmission characteristics of the PCPO vortex beams are analyzed in detail by numerical simulations. According to numerical results, a larger spatial coherence length of the partially coherent source endows the beams with a superior channel capacity performance while accompanied by a decrease in transmission robustness. Meanwhile, PCPO vortex beam with greater phase modulation power parameter and longer wavelength is conducive to enhancing the transmission quality through oceanic turbulence. In addition, the channel capacity of the system can be effectively augmented with the increase of the dissipation rate of kinetic energy per unit mass of fluid, the anisotropy factor, the inner scale radius and the decrease of the mean square temperature dissipation rate, the temperature-salinity contribution ratio. The results also indicate that PCPO vortex beam is a better candidate than Gaussian vortex beam for long-distance transmission. This paper provides a theoretical reference for studying an underwater communication link using PCPO vortex beams as the transmission carrier.