Ocean Profiles Research Articles

A consistent ocean oxygen profile dataset with new quality control and bias assessment

Abstract. Global ocean oxygen concentrations have declined in the past decades, posing threats to marine life and human society. High-quality and bias-free observations are crucial to understanding ocean oxygen changes and assessing their impact. Here, we propose a new automated quality control (QC) procedure for ocean profile oxygen data. This procedure consists of a suite of 10 quality checks, with outlier rejection thresholds being defined based on underlying statistics of the data. The procedure is applied to three main instrumentation types: bottle casts, CTD (conductivity–temperature–depth) casts, and Argo profiling floats. Application of the quality control procedure to several manually quality-controlled datasets of good quality suggests the ability of the scheme to successfully identify outliers in the data. Collocated quality-controlled oxygen profiles obtained by means of the Winkler titration method are used as unbiased references to estimate possible residual biases in the oxygen sensor data. The residual bias is found to be negligible for electrochemical sensors typically used on CTD casts. We explain this as the consequence of adjusting to the concurrent sample Winkler data. Our analysis finds a prevailing negative residual bias with the magnitude of several µmol kg−1 for the delayed-mode quality-controlled and adjusted profiles from Argo floats varying among the data subsets adjusted by different Argo Data Assembly Centers (DACs). The respective overall DAC- and sensor-specific corrections are suggested. We also find the bias dependence on pressure, a feature common to both AANDERAA optodes and SBE43-series sensors. Applying the new QC procedure and bias adjustments resulted in a new global ocean oxygen dataset from 1920 to 2023 with consistent data quality across bottle samples, CTD casts, and Argo floats. The adjusted Argo profile data are available at the Marine Science Data Center of the Chinese Academy of Sciences (https://doi.org/10.12157/IOCAS.20231208.001, Gouretski et al., 2024).

A neural network algorithm for quantifying seawater pH using Biogeochemical-Argo floats in the open Gulf of Mexico

Within the Gulf of Mexico (GOM), measurements of ocean pH are limited across space and time. This has hindered our ability to robustly monitor and study regional carbon dynamics, inclusive of ocean acidification, over this biogeochemically variable sea. The 2021 launch of Biogeochemical-Argo (BGC-Argo) ocean profiling floats that carry five sensors represented the entry of this particular ocean observing technology into this region. The GOM BGC-Argo floats have vastly increased the number of oxygen, nitrate, pH, chlorophyll-a fluorescence, and particulate backscattering profile observations within the “open GOM” region (>1,000 m water column depth). To circumvent a set of uncertainties associated with the collected sensor pH data, regionally trained neural network algorithms were developed to skillfully predict GOM pH (total scale, in situ temperature and pressure), which served as a secondary QC and sensor performance assessment tool. The GOM neural network pH (GOM-NNpH) algorithms were trained using a selection of climate quality CTD and bottle data (temperature, salinity, oxygen, nitrate, pressure, and location) collected as a part of NOAA’s Gulf of Mexico Ecosystems and Carbon Cruises (GOMECC). Neural network pH estimates were generated using the newly developed GOMNNpH algorithm and two widely used, globally trained neural network algorithms (Empirical Seawater Property Estimation Routines (ESPER) and CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network (CANYON-B)) to compare algorithm performance against validation data. The results demonstrate the advanced skill of the GOM-NNpH in capturing water column variability and robustly reconstructing GOM pH profiles. Using a combination of concurrent float-measured seawater values of pressure, temperature, salinity, and oxygen, a GOM-NNpH algorithm was applied to two years of BGC-Argo float data. Resulting data were used to diagnose the performance of float pH sensors and to generate a time series of neural network estimated pH based on the collected float profiles. These algorithms emphasize the value of regionally-trained neural networks and their utility by the BGC-Argo community. Further, the GOM-NNpH algorithms can also be applied by a variety of users to estimate pH values in the open GOM in the absence of direct pH observations.

Selecting HyperNav deployment sites for calibrating and validating PACE ocean color observations

A novel ocean profiling float system for calibrating and validating satellite-based ocean color observations has been developed and tested. The float-based radiometric sampling system, herein referred to as HyperNav, is complementary to traditional moored in-situ observing systems and provides additional capability due to the relatively small platform size and high radiometric accuracy that allows for opportunistic deployments at locations during seasons and conditions that are best for ocean color observations. The purpose of this study is to optimize the deployment locations of an array of HyperNav systems to support the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission by performing System Vicarious Calibration (SVC) observations. Specifically, we present the development of logistical and scientific criteria for selecting suitable sites for developing an SVC network of profiling-float-based radiometric systems capable of calibrating and validating ocean color observations. As part of the analyses described in this paper, we have synthetically deployed HyperNav at potential US-based and international sites, including: north of Crete island; south-east of Bermuda island; south of Puerto Rico island; southwest of Port Hueneme, CA; west of Monterey, CA; west of Kona, HI; and south-west of Tahiti island. The synthetic analyses identified Kona, Puerto Rico, Crete, and Tahiti as promising SVC sites. All sites considered are suitable for generating a significant number of validation match-ups. Optimally deploying HyperNav systems at these sites during the PACE post-launch SVC campaign is expected to cost-effectively provide a large number of SVC match-ups to fulfill the PACE calibration requirements.

One-third of Southern Ocean productivity is supported by dust deposition.

Natural iron fertilization of the Southern Ocean by windblown dust has been suggested to enhance biological productivity and modulate the climate1-3. Yet, this process has never been quantified across the Southern Ocean and at annual timescales4,5. Here we combined 11 years of nitrate observations from autonomous biogeochemical ocean profiling floats with a Southern Hemisphere dust simulation to empirically derive the relationship between dust-iron deposition and annual net community production (ANCP) in the iron-limited Southern Ocean. Using this relationship, we determined the biological response to dust-iron in the pelagic perennially ice-free Southern Ocean at present and during the last glacial maximum (LGM). We estimate that dust-iron now supports 33% ± 15% of Southern Ocean ANCP. During the LGM, when dust deposition was 5-40-fold higher than today, the contribution of dust to Southern Ocean ANCP was much greater, estimated at 64% ± 13%. We provide quantitative evidence of basin-wide dust-iron fertilization of the Southern Ocean and the potential magnitude of its impact on glacial-interglacial timescales, supporting the idea of the important role of dust in the global carbon cycle and climate6-8.

Upper Ocean variations at IODP Hole U1505C in the northern South China Sea and their response to the East Asian Monsoon during the middle Miocene

We used the samples from International Ocean Discovery Program (IODP) Hole U1505C in the northern South China Sea (SCS) to reconstruct the upper ocean profiles and discussed the East Asian Monsoon (EAM) variations during 15.0–12.5 Ma. 15 genera and 41 species were identified, and 7 planktonic foraminifer datums were recognized, then a reliable chronostratigraphic framework was updated. A principal component analysis (PCA) was used to identify the suitable assemblages for reconstructing upper water profiles, based on planktonic foraminifera relative abundance and isotope records. The sea surface temperature (SST), paleo-productivity, and the depth of thermocline (DOT) were reconstructed by the content of warm water species, high productivity species, and the ratio of mixed layer to thermocline species, respectively. Three main phases were identified: (1) during 15.0–14.7 Ma, the SST and paleo-productivity were high, while the thermocline was shallow, indicating a warm and rainy climate influenced by East Asian Summer Monsoon (EASM); (2) during 14.7–13.8 Ma, the SST was lower, the paleo-productivity increased significantly, and the thermocline deepened, indicating an enhanced East Asian Winter Monsoon (EAWM); (3) during 13.8–12.5 Ma, the SST increased slightly, the paleo-productivity deceased, while the DOT showed neglectable change, indicating a stable EAWM and a waning EASM. The results of the Morlet wavelet spectrum revealed that EAM and the upper ocean profiles in the northern SCS were predominantly regulated by the eccentricity cycles, emphasizing the significant impact of low-latitude processes on climate variations.

Probabilistic acoustic predictions leveraging NESBA and ARGO measurements

Ocean acoustic propagation models used for sonar performance prediction often rely on global or regional ocean modeling and data assimilation, or monthly climatologic averages, for estimating the 4D ocean temperature and salinity (T/S) field. Ocean models rely heavily on satellite-based ocean surface measurements, e.g., sea surface temperature (SST) and height (SSH), plus typically vastly less resolved in space and time vertical water column measurements derived using ship-based and/or ARGO float instruments. Systems also exist that create 3D synthetic T/S fields from satellite SST/SSH measurements, e.g., the Modular Ocean Data Assimilation System (MODAS) and the more recent Improved Synthetic Ocean Profile (ISOP) system. It is unclear, however, how well these synthetic T/S fields capture significant ocean ducting conditions. Similarly, climatologic averages combine historical vertical T/S profile data in spatial cells by month; but the averaging process tends to blur out important features. This talk will address the use of 2021 New England Shelf Break Acoustics (NESBA) experiment and ARGO vertical profile data for: (1) testing the degree to which ocean models or climatology capture important acoustic features under a range of conditions; and (2) providing more realistic sonar performance prediction means and uncertainties. [Work supported by the Office of Naval Research].

OPO-CVI: design and implementation of an ocean profiling observation system for wave-powered vertical profiler following an ISO standard

This paper presents the design and implementation of an ISO-compliant ocean profiling observation system for wave-powered vertical profiler. This system aims to provide a comprehensive, scalable, and interoperable solution for high-resolution, real-time oceanic observation. As a part of this system, we introduce a wave-powered vertical profiler, known as “Wave Master,” designed to offer enhanced stability and reliability for long-term oceanic data collection. The core of the paper focuses on the Ocean Profiling Observation Complex Virtual Instrument (OPO-CVI), a comprehensive system developed in alignment with ISO 21851 standard. OPO-CVI seamlessly integrates data collection, transmission, storage, and visualization. Specifically, OPO-CVI addresses the challenges of information isolation, system rigidity, and lack of modularity in traditional ocean profiling methods by standardizing data formats and transmission protocols, allowing for seamless integration of new observation elements, and employing a modular architecture for enhanced scalability and reusability. By offering detailed technical insights into the OPO-CVI architecture and its compliance with ISO 21851 standard, this paper aims to contribute significantly to the advancement of standardized, efficient, and reliable oceanic observation systems.

Research on multi-element detection technology and system for spaceborne ocean profile

Research on multi-element detection technology and system for spaceborne ocean profile

The Effect of Advection on the Three Dimensional Distribution of Turbulent Kinetic Energy and Its Generation in Idealized Tropical Cyclone Simulations

AbstractThe distribution of turbulent kinetic energy (TKE) and its budget terms is estimated in simulated tropical cyclones (TCs) of various intensities. Each simulated TC is subject to storm motion, wind shear, and oceanic coupling. Different storm intensities are achieved through different ocean profiles in the model initialization. For each oceanic profile, the atmospheric simulations are performed with and without TKE advection. In all simulations, the TKE is maximized at low levels (i.e., below 1 km) and ∼0.5 km radially inward of the azimuthal‐mean radius of maximum wind speed at 1‐km height. As in a previous study, the axisymmetric TKE decreases with height in the eyewall, but more abruptly in simulations without TKE advection. The largest TKE budget terms are shear generation and dissipation, though variability in vertical turbulent transport and buoyancy production affect the change in the azimuthal‐mean TKE distribution. The general relationships between the TKE budget terms are consistent across different radii, regardless of storm intensity. In terms of the asymmetric distribution in the eyewall, TKE is maximized in the front‐left quadrant where the sea surface temperature (SST) is highest and is minimized in the rear‐right quadrant where the SST is the lowest. In the category‐5 simulation, the height of the TKE maximum varies significantly in the eyewall between quadrants and is between ∼400 m in the rear‐right quadrant and ∼1,000 m in the front‐left quadrant. When TKE advection is included in the simulations, the maximum eyewall TKE values are downwind compared to the simulations without TKE advection.

A new automatic quality control system for ocean profile observations and impact on ocean warming estimate

A new automatic quality control system for ocean profile observations and impact on ocean warming estimate

K nearest neighbors classification of water masses in the western Alboran Sea using the sigma-pi diagram

Different classification techniques of water masses have been developped using the potential temperature-salinity (θ-S) diagram and its volumetric analysis. In this study, we propose a new method to automatically classify water masses via a supervised machine learning algorithm based on the K nearest neighbors (Knn), in the potential density and potential spicity (σ-π) coordinates. This method is applied to temperature and salinity data collected in the western side of the Alboran Sea during a glider mission, dedicated to sample the Western Alboran Gyre (WAG) in late winter 2021. The water masses in the studied region were classified into five different categories following a supervised learning process, based on ocean profile databases available on the region of interest. The results corroborate previous studies of the spatial distribution of water masses in the Alboran Sea, inferred from traditional method based on the expert analysis of the (θ-S) diagram, and suggest that this methodology is efficient and reliable for water masses classification. Compared to a classical clustering computation (herein k-means), this method is more appropriate in a region where the characteristics of the water masses change considerably in both space and time.

Fusion of ocean data from multiple sources using deep learning: Utilizing sea temperature as an example

For investigating ocean activities and comprehending the role of the oceans in global climate change, it is essential to gather high-quality ocean data. However, existing ocean observation data have deficiencies such as inconsistent spatial and temporal distribution, severe fragmentation, and restricted observation depth layers. Data assimilation is computationally intensive, and other conventional data fusion techniques offer poor fusion precision. This research proposes a novel multi-source ocean data fusion network (ODF-Net) based on deep learning as a solution for these issues. The ODF-Net comprises a number of one-dimensional residual blocks that can rapidly fuse conventional observations, satellite observations, and three-dimensional model output and reanalysis data. The model utilizes vertical ocean profile data as target constraints, integrating physics-based prior knowledge to improve the precision of the fusion. The network structure contains channel and spatial attention mechanisms that guide the network model’s attention to the most crucial features, hence enhancing model performance and interpretability. Comparing multiple global sea temperature datasets reveals that the ODF-Net achieves the highest accuracy and correlation with observations. To evaluate the feasibility of the proposed method, a global monthly three-dimensional sea temperature dataset with a spatial resolution of 0.25°×0.25° is produced by fusing ocean data from multiple sources from 1994 to 2017. The rationality tests on the fusion dataset show that ODF-Net is reliable for integrating ocean data from various sources.

Influence of wind-roughed sea surface on detection performance of spaceborne oceanic lidar

The wind-roughed sea surface influences the propagation properties of the laser beam and consequently affects the detection accuracy of spaceborne oceanic lidar. A comprehensive analysis of this problem is lacking to date. Herein, the Cox and Munk surface model is applied to the spaceborne oceanic lidar radiative transfer emulator to solve this problem. The effects of wind-roughed sea surface on oceanic lidar laser pulse propagation properties, such as the deflection angle of the direction, the inaccuracy of the signal depth, and the recommended incident angles, are studied using this model. The relationships among the wind speed, the incident angle and the propagation properties of the laser beam when passing through the sea surface are established. The maximum possible error of detection depth is within 8.2% when the wind speed is below 15 m/s and the incident zenith angle is less than 30°. And it can be reduced to 1.8% by profile averaging, which reveals the spaceborne oceanic lidar is capable to detect the global ocean profile information effectively in windy conditions. Moreover, the recommended incident angles for an oceanic lidar system are 37° and 35°, when the effective reflectance of the whitecaps are 0.2 and 0.6, respectively. The conclusions of this paper have reference value for oceanic lidar remote sensing in practice.

Ocean depth–temperature profiles for operational oceanography from a shark-borne transmitter

BackgroundMany regions of the ocean are under-sampled in terms of their biology and physical structure. Increasingly sophisticated animal-borne electronic tags are capable of measuring and transmitting in situ environmental data such as ocean temperature–depth profiles. This has the potential to significantly augment the volume of data acquired from under-sampled regions of the ocean. These data would enhance interpretation of animal behavior and distribution and could be used to inform oceanographic and meteorological models. Building on results obtained from marine mammals and turtles, we present a case study of depth–temperature profiles obtained from a tagged tiger shark.ResultsDuring a 102-day deployment, 1350 geolocations were obtained from a shark from waters around Oahu, Hawaii. Of these, 520 were associated with depth–temperature profiles—some of which were from depths exceeding 500 m. Delay between profile creation and transmission to satellite or land-based receiver averaged 8.9 h (range: 35 s–43 h, median 6.32 h). The profiles were in close agreement with profiles extracted from nearby locations in an operational ROMS model. Land-based receivers played a significant role in augmenting data throughput obtained via satellites.ConclusionsShark-borne transmitters offer a viable option for collecting ocean profiles with reporting latencies that make them suitable for operational oceanography. They can significantly increase sampling frequency (especially subsurface) and sample geographic areas that are otherwise difficult to monitor with Lagrangian methods such as Argo floats. They sample locations and depths that are important to the animal and which in some cases may be biological hotspots. The resolution of the data is comparable with that derived from traditional platforms. By targeting appropriate species of shark, different areas of the ocean could be monitored at significantly higher rates than is currently the case.

An ecotoxicological risk model for the microplastics in arctic waters

The risk posed to Arctic marine life by microplastics, a Contaminants of Emerging Arctic Concern (CEAC), is poorly known. The reason is the limited understanding of the dose-response relationship due to the region's peculiar environmental and geophysical properties and the unique physiological properties of the species living there. The properties of microplastics in the region and their distribution across the oceanic profile further complicate the problem. This paper addresses the knowledge gap by proposing a novel comprehensive ecotoxicity model. The model uses oxidative stress caused by the Reactive Oxygen Species (ROS) to assess cell mortality. Cell mortality has been used as an indicator of ecological risk. The model is implemented in the Bayesian Network (BN) framework to evaluate the cytotoxicity, measured as the probability of causing mortality. The work enhances the understanding and assessment of the cytotoxicity of microplastics in polar cod and associated risks.

Performance study of energy conversion system for ocean thermal profiler

With the increasing scarcity of energy in the world, energy has become an important part of restricting the development and application of traditional ocean profilers. The method of converting ocean thermal energy (OTE) into electrical energy through an energy conversion system is a solution. The model establishment and performance analysis of the energy conversion system are the basis of the ocean thermal profiler (OTP) design. The model and performance are affected by the coupling of multiple parameters, especially rotational speed and pressure. In this study, a universal parameterized model for multi-parameter coupling was proposed. System performance analysis based on experiments including load current, speed, mechanical efficiency and total efficiency was presented. After model parameter identification, the error of mechanical efficiency was within 5%; the total efficiency error was less than 12.8%, and the maximum efficiency point error didn’t exceed 2.21%. The results indicated that the parameterized model was satisfactory for the engineering applications and could guide the design of OTP.

Characterizing vertical upper ocean temperature structures in the European Arctic through unsupervised machine learning

In-situ observations of subsurface ocean temperatures are, in many regions, inconsistently distributed in time and space. These spatio-temporal inconsistencies in the observational network lead to difficulties in utilizing those observations effectively for ocean model evaluation or understanding larger-scale ocean characteristics. Model accuracy of subsurface ocean characteristics is especially important within regions that contain complex ocean structures. One such region is the European Arctic which not only contains several types of water masses with unique characteristics, but also wintertime sea ice coverage and complex bathymetry. This study presents an unsupervised neural networking technique that can be used in combination with traditional ocean model evaluation techniques to provide additional information on the accuracy of modeled vertical ocean temperature profiles. Self-organizing maps is an unsupervised machine learning technique that we apply to approximately twenty thousand Argo and CTD temperature profiles from 2012 to 2020 in the European Arctic to categorize the observed vertical ocean temperature structures in the top 150 m. The observed ocean profile categories, or neurons, defined by the self-organizing map show strong spatial and temporal dependencies. We then use the neuron weights, or the learned temperature profile structure of each neuron, to validate the spatial and temporal variability of modeled vertical temperature structures. This analysis gives us new insights about the model’s capabilities to reproduce specific vertical structures of the top-most ocean layer within different regions and seasons. Mapping modeled ocean temperature profiles onto the neuron-space of the observationally-defined self organized map highlights the potential of this method to advance our understanding of model deficiencies in that region.

SOLS: An Open-Source Spaceborne Oceanic Lidar Simulator

In recent years, oceanic lidar has seen a wide range of oceanic applications, such as optical profiling and detecting bathymetry. Furthermore, spaceborne lidars, CALIOP and ICESat-2, designed for atmospheric and ice science applications, have been used for ocean backscattering retrievals, but, until now, there has been no spaceborne lidar specifically designed for ocean detection. There is a demand for an effective lidar simulator to study the detection potential capability of spaceborne oceanic lidar. In this study, an open-source spaceborne oceanic lidar simulator named SOLS was developed, which is available freely. Moreover, the maximum detectable depth and corresponding optimal wavelength for spaceborne lidar were analyzed at a global scale by using SOLS. The factors controlling detection limits of a spaceborne ocean profiling lidar in different cases were discussed. Then, the maximum detectable depths with different relative measurement errors and the influence of solar background radiance were estimated. Subsequently, the effects of laser and detector parameters on maximum detectable depths were studied. The relationship between the lidar detectable depth and the ocean mixed layer depth was also discussed. Preliminary results show that the maximum detectable depth could reach deeper than 120 m in the oligotrophic sea at low latitudes. We found that 490 nm is the optimal wavelength for most of the open seawater. For coastal water, 532 nm is a more suitable choice considering both the technical maturity and geophysical parameters. If possible, a lidar equipped with 440 nm could achieve the greatest depth in oligotrophic seawater in subtropical gyres north and south of the equator. The upper mixed layer vertical structure in most of the global open ocean is within the lidar maximum detectable depth. These results show that SOLS can help the design of future spaceborne oceanic lidar systems a lot.

The Air-Launched Autonomous Micro Observer

Abstract The Air-Launched Autonomous Micro Observer (ALAMO) is a versatile profiling float that can be launched from an aircraft to make temperature and salinity observations of the upper ocean for over a year with high temporal sampling. Similar in dimensions and weight to an airborne expendable bathythermograph (AXBT), but with the same capability as Argo profiling floats, ALAMOs can be deployed from an A-sized (sonobuoy) launch tube, the stern ramp of a cargo plane, or the door of a small aircraft. Unlike an AXBT, however, the ALAMO float directly measures pressure, can incorporate additional sensors, and is capable of performing hundreds of ocean profiles compared to the single temperature profile provided by an AXBT. Upon deployment, the float parachutes to the ocean, releases the air-deployment package, and immediately begins profiling. Ocean profile data along with position and engineering information are transmitted via the Iridium satellite network, automatically processed, and then distributed by the Global Telecommunications System for use by the operational forecasting community. The ALAMO profiling mission can be modified using the two-way Iridium communications to change the profiling frequency and depth. Example observations are included to demonstrate the ALAMO’s utility.

Machine learning approach to prediction of real-time ocean sound speed profile

Sound speed profile (SSP) determines sound propagation in the ocean. Variation of sound speed profiles with time and location heavily impacts underwater sonar systems used in ocean science and engineering. However, mapping sound speed profile over a vast area of the oceans in real-time is impossible by the traditional way, where sound speed is calculated from insitu profiling measurements of temperature, salinity, and pressure, which is expensive and, therefore, limited in sparse locations and time. Here, we aim to develop a machine learning model to predict real-time sound speed profiles anywhere in the global oceans. Our model takes advantage of the big data of long-term global temperature, salinity, and pressure measurements and further accounts for the variability of the surface ocean by inputting sea surface temperature and sea surface salinity data, which can be updated by satellite remote sensing in real-time. Here, SSPs are predicted for Pacific, Atlantic, and Indian ocean regions. The results show that the estimated SSPs had a root-mean-square error of 0.26 m/s and a coefficient of determination of 0.99. About 99% of the estimates lie within ±0.4 m/s of the SSPs obtained from in situ temperature and salinity profiles.