OneArgo is the ongoing extension of the Argo array into seasonal ice zones, marginal seas, deeper waters (Deep-Argo) and biogeochemical parameters (BGC-Argo). The aim of the Argo programme is to achieve the full implementation of this new ambitious global, full-depth and multidisciplinary OneArgo array by the end of the UN Ocean Decade (2030).
The session focuses on the use of Argo data in physical and biogeochemical ocean monitoring, forecasting and reanalysis systems. It covers the use of Argo data for model parameter optimization, model trajectory correction through data assimilation, verification/validation activities, field reconstruction through AI methods, and model/data fusion (e.g., bias correction, AI tools). The session also covers design studies such as results from Observing System Experiments and Observing System Simulation Experiments. Specific focus will be put on highlighting the role of Deep-Argo and BGC-Argo data, and synergies between OneArgo and other observing systems. Contributions are also expected from research teams involved in the development and implementation of the OneArgo array in terms of design, data processing, and novel functionalities.
At the OceanObs19 Conference, the OneArgo design was endorsed as an evolution and extension of the original Argo mission. Some key features of the OneArgo design include a quarter of the floats making temperature and salinity measurements to full ocean depth, and another quarter carrying a suite of BioGeoChemical (BGC) sensors. A further enhancement was greater sampling density in the tropical and western boundary current regions of the open ocean. Since OceanObs19, Argo has also developed a Polar Mission, to reach the marginal ice zones. BGC parameters that can be reliably measured on floats, and delivered as profiles in real-time for numerical models capable of assimilating them, include dissolved oxygen, nitrate and pH, and chlorophyll, backscatter and incoming solar radiation.
Technical advances have enabled some general improvements in Argo sampling and data delivery: latency between measurements and data distribution is reduced, often to less than 12 hours; the quality of measurements distributed in real-time is improved (biases removed); for the latest generation of floats, half of the profiles have the shallowest measurement in the upper 2 metres of the ocean to better serve air-sea and mixed-layer requirements; Argo has taken steps to avoid fixed-time-of-day sampling that could introduce diurnal bias in upper ocean measurements.
Through the UN Decade, Argo is engaged in co-design with complementary observing networks that extend into boundary and coastal regions. While there are competing requirements to optimise for different use cases, Argo seeks interactions on how to improve its new design.
The expansion of the original Argo mission into the OneArgo design requires a substantial increase in resources. Pilot arrays (regional, fixed-duration) for the Deep and BGC Missions have been funded and deployed. These pilots enabled technical difficulties to be identified and overcome, and full capability demonstrated. The G7 Future of the Seas and Ocean Initiative has called for OneArgo to be implemented by 2030. The Argo community is now ready to work towards the implementation of OneArgo, but no Argo partner nation has yet allocated the required national resources. Even with only the pilots in place, Argo floats are already the majority source of subsurface data for the BGC parameters that Argo measures. OneArgo will not become reality unless increased resources are allocated. Data users can assist by demonstrating the impact OneArgo will have on science and services, and by advocating for its value to both users and supporting agencies.
How to cite: King, B., Wijffels, S., and Owens, B.: OneArgo – Evolving and extending Argo’s missions and data delivery. Achievements, status and outlook, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9605, https://doi.org/10.5194/egusphere-egu25-9605, 2025.
Although ocean biogeochemistry plays an important role in the regulation of Earth’s climate and marine habitats, key questions remain about expected changes to global biogeochemical processes associated with anthropogenic impacts on the Earth system. The OneArgo array is a revolutionary ocean observing system that delivers critical observations in four dimensions and in near-real-time. Recent expansion of the Argo program to include floats carrying a suite of biogeochemical sensors (i.e. the BGC-Argo mission) is providing new opportunities to study critical processes such as primary production and carbon export, ocean acidification and deoxygenation, and air–sea gas fluxes. This work describes an approach to leverage those BGC sensor observations, along with shipboard and core Argo float observations, to construct time-varying data products of dissolved oxygen, nitrate, and pH for the upper 2 km of the ocean. The products are constructed by training empirical machine learning (ML) models that take advantage of relationships between ocean biogeochemical and physical parameters, along with the widespread distribution of BGC training data available from the BGC-Argo array and the broad coverage of ocean physical parameters provided by the core Argo array. Improvements relative to earlier approaches for BGC data product creation include an objective clustering step to identify regions of similar variability prior to model training, a comprehensive evaluation of ML model uncertainty using Earth system model testbeds, and a higher resolution grid over both space and time. Potential opportunities to compute net community production, monitor ocean acidification extreme events, and evaluate ocean deoxygenation are explored using these novel data products.
How to cite: Sharp, J., Fassbender, A., Carter, B., Lyman, J., and Johnson, G.: OneArgo Enables New Gap-filled Data Products of Ocean Biogeochemistry, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8177, https://doi.org/10.5194/egusphere-egu25-8177, 2025.
Phytoplankton biomass, the foundation of the oceanic food web, is predominantly estimated from chlorophyll-a (Chla) concentration. In vivo chlorophyll-a fluorescence (fluo), a key proxy for Chla, has become one of the most widely measured biogeochemical parameters in the ocean. This advancement is largely due to the integration of fluorometers onto BioGeoChemical-Argo (BGC-Argo) profiling floats, a key component of the multidisciplinary OneArgo array. By significantly expanding the number of fluo profiles compared to historical ship-based observations, this development has solidified OneArgo's role as a cornerstone of the global biogeochemical observing system.
However, converting fluo into Chla is not straightforward, as it is influenced by various factors, including the composition and physiological state of phytoplankton communities. Accurate calibration of fluo into Chla is therefore both challenging and essential for fully utilizing the rapidly growing volume of fluo data. The Argo Data Management Team (ADMT) has made significant efforts to calibrate and validate fluo measurements from OneArgo floats, aiming to deliver Chla estimates with the highest possible accuracy. Despite these efforts, the current OneArgo Chla dataset still exhibits substantial regional biases in real-time (RT), particularly in high-latitude regions such as the Southern Ocean.
Recent advances in observation-based products have introduced innovative solutions to address these challenges, including new delayed-mode (DM) correction methods that significantly reduce regional biases in Chla estimates. However, a key issue persists: DM and real-time (RT) datasets often differ considerably depending on the location, resulting in inconsistencies that compromise the homogeneity and interoperability of the OneArgo database. To address this, we propose a new RT correction method, based on observation-based products, to improve Chla accuracy and better align RT data with DM-calibrated values. This advancement is expected to be implemented soon, enabling a more seamless integration of RT and DM datasets and ultimately enhancing the overall quality and utility of the OneArgo Chla dataset.
This study underscores the potential of new observation-based products to enhance the accuracy and coherence of the OneArgo Chla dataset. High-quality OneArgo data are critical for both scientific research and operational oceanography, including the assimilation of data into biogeochemical models.
How to cite: Sauzède, R., Schmechtig, C., Renosh, P. R., Uitz, J., and Claustre, H.: Enhancing OneArgo Chlorophyll-a Data Quality and Uniformity, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17464, https://doi.org/10.5194/egusphere-egu25-17464, 2025.
Studying the state of marine ecosystems, their changes over time, and the influence of human activities requires accurate ocean observations. However, reliable measurements are sparse and unevenly distributed across both time and space, with significant disparities in coverage among different variables. In recent years, autonomous oceanographic instruments such as Argo profiling floats have improved the collection of subsurface data. Despite this advancement, physical variables like temperature and salinity, and oxygen are more affordable to monitor, while sensors for biogeochemical variables—such as nitrate, chlorophyll, and bbp700—remain costly. This disparity results in a gap between the abundance between physical and biogeochemical measurements, confirming the need for methods that estimate biogeochemical variables using high-frequency physical data to fully leverage ocean observing systems like Argo.
Some existing ANN-based techniques, which rely on Multilayer Perceptron (MLP) architectures trained on point-wise ship-based measurements, allow for the prediction of, e.g., nitrate profiles by exploiting intrinsic information contained in the input profiles of T, S, and oxygen. Alternatively, we propose an approach that directly infers the vertical profile in a single step using a spatially aware neural network.
Using a spatial-aware neural network, we propose an approach that directly infers the entire vertical profile in a single step. By leveraging the typical shape of biogeochemical profiles as a learnable constraint, the model can fully exploit the potential of the BGC-Argo dataset.
A regional approach using a spatial-aware neural network has already been proposed in “PPCon 1.0: Biogeochemical Argo Profile Prediction with 1D Convolutional Networks”. However, PPCon was limited to the Mediterranean Sea, while our objective is to develop a global-scale model.
Given PPCon’s promising results—demonstrating smooth and accurate profile predictions with improvements over previous MLP applications—we extend this approach by developing a global 1D CNN to predict nitrate, chlorophyll, and backscattering (bbp700) from date, geolocation, and profiles of temperature, salinity, and oxygen.
PPCon’s promising results demonstrated smooth and accurate profile predictions in the Mediterranean Sea, showing improvements over previous MLP applications, particularly for chlorophyll and bbp700, while nitrate performance remained comparable.
We build on this approach by developing a global 1D CNN using a quality-checked dataset of 101,000 chlorophyll-a and 63,000 nitrate profiles spanning 2012 to 2024.
Additionally, the new approach incorporates transfer learning, enabling a pre-trained model to be fine-tuned on different datasets by replacing and retraining the final layers of the network.
The novel 2-step method and preliminary results will be presented to highlight the potential for the automatic generation of regional models.
How to cite: Pietropolli, G., Carolina, A., Cossarini, G., and Manzoni, L.: GLOBIO: Bridging Global and Local Scales for Biogeochemical Profiles Prediction, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3779, https://doi.org/10.5194/egusphere-egu25-3779, 2025.
To protect marine ecosystems threatened by climate change and anthropic stressors, it is essential to operationally monitor ocean health indicators. These are metrics synthetizing multiple marine processes relevant to the users of operational services. Here we assess if selected ocean indicators simulated by operational models can be controlled (here meaning constrained effectively) by biogeochemical observations, by using a newly proposed methodological framework. The method consists in firstly screening the sensitivities of the indicators with respect to the initial conditions of the observable variables. These initial conditions are perturbed stochastically in Monte Carlo simulations of one-dimensional configurations of a multi-model ensemble. Then, the models are applied in three-dimensional ensemble assimilation experiments, where the reduction of the ensemble variance corroborates the controllability of the indicators by the observations. The method is applied for ten relevant ecosystem indicators (ranging from inorganic chemicals to plankton production), seven observation types (representing data from satellite and underwater platforms), and an ensemble of five biogeochemical models of different complexity, employed operationally by the European Copernicus Marine Service. We demonstrate that all the indicators are controlled by one or more types of observations. In particular, the indicators of phytoplankton phenology are controlled and improved by the merged observations from the surface ocean colour and chlorophyll profiles from biogeochemical-ARGO floats. Similar observations also control and reduce the uncertainty of the plankton community structure and production. However, the uncertainty of the trophic efficiency and POC increases when assimilating chlorophyll-a data, though observations were not available to assess whether that was due to a worsen model skill. We recommend that the assessment of controllability proposed here becomes a standard practice in designing operational monitoring, reanalysis and forecast systems, to ultimately provide the users of operational services with more precise estimates of ocean ecosystem indicators.
How to cite: Ciavatta, S., Lazzari, P., Alvarez, E., Bertino, L., Bolding, K., Bruggeman, J., Capet, A., Cossarini, G., Daryabor, F., Nerger, L., Popov, M., Skakala, J., Spada, S., Teruzzi, A., Wakamatsu, T., Yumruktepe, Ç., and Brasseur, P.: Control of simulated ocean ecosystem indicators by biogeochemical observations. , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11637, https://doi.org/10.5194/egusphere-egu25-11637, 2025.
Numerical models of ocean biogeochemistry serve as critical tools for detecting and predicting the impacts of climate change on marine resources, and for monitoring ocean health. Recent research by Rodgers et al. (2023) has identified significant limitations in current CMIP6 class models, particularly in their representation of seasonal partial pressure of CO2 (pCO2) temporal phasing and magnitude in key ocean regions such as the North Atlantic. These limitations arise primarily from parameter uncertainty, as model parameters are typically derived from laboratory experiments using a limited range of species that do not represent the diversity of marine organisms. In addition, certain parameters remain experimentally indeterminate, resulting in wide plausible ranges that introduce considerable uncertainty into model predictions.
Our research addresses these challenges through a comprehensive approach to parameter optimisation using ensemble-based data assimilation techniques. In particular, we focus on reducing the systematic bias in the PISCES marine biogeochemical model distributed with NEMO v4.2, simulation of the North Atlantic seasonal pCO2 cycle, while generating robust uncertainty estimates through ensemble methods.
The optimization process began with an extensive sensitivity analysis using SOBOL indices to identify the parameters most influential in controlling seasonal pCO2 dynamics. We then implemented a particle filter algorithm to optimize these key parameters in a NEMO-PISCES 1D configuration using data from a North Atlantic BGC Argo float. The particle filter generated an ensemble of thousands of state variable solutions, each representing a different PISCES parameterization with reference values varying between 0.01 and 2 times their nominal range. From this ensemble, we identified the ten parameter combinations that most effectively minimized the model-data discrepancy. These optimized parameter sets were then used to generate a 3D regional NEMO-PISCES ensemble in the North Atlantic - the ensemble approach providing a robust framework for uncertainty quantification.
Our results show significant improvements in model performance, with the optimized PISCES parameter set in the 1D configuration achieving a 40% reduction in RMSE for seasonal cycle predictions of surface nutrients, chlorophyll, and carbon components compared to the standard PISCES configuration. Most importantly, all ensemble members successfully reproduce seasonal pCO2 phasing and magnitude in agreement with observation-based data, addressing a critical limitation of the reference model while providing uncertainty estimates consistent with observational uncertainties.
This research demonstrates the effectiveness of ensemble-based data assimilation techniques in optimizing biogeochemical model parameters, thereby enhancing the accuracy and reliability of ocean simulations. These improvements significantly strengthen our capacity to monitor ocean health, forecast climate change impacts on marine ecosystems, and provide robust scientific guidance for marine resource management decisions.
How to cite: Hyvernat, Q., Mignot, A., Gutknecht, E., Ruggiero, G., Claustre, H., and D'Ortenzio, F.: Optimizing PISCES Parameters for North Atlantic Seasonal pCO2 Predictions: An Ensemble-Based Approach Using BGC-Argo Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4770, https://doi.org/10.5194/egusphere-egu25-4770, 2025.
The biogeochemical (BGC) Argos are providing high-resolution vertical profiles of the upper ocean in the Bay of Bengal since 2012. This work demonstrates the effective applications of BGC-Argos in understanding the biophysical interactions in the Bay of Bengal with four different studies carried out by our group. First, a study (Das & Sil, 2024; DSR-II) using a single BGC-Argo (WMO ID: 2902161), which measured temperature, salinity, chlorophyll-a, and dissolved oxygen at a high-frequency (∼5 h) cycle down to 80 m depth, showed temperature and chlorophyll-a are strongly linked to solar insolation. The mean chlorophyll-a in the upper layer increased from 0600 h and peaked around 1800 h local time; however, surface chlorophyll-a increased only after 1100 h. The similarity between dissolved oxygen and the difference between the surface and mean chlorophyll-a further indicated photoacclimation variations on a diurnal scale. In a follow-up study (Gupta et al., 2024; RSMA), the comparison of Bio-Argos (WMO ID: 2902158, 2902160, 2902114, and 2902161) with the CMEMS data (which does not include any data assimilation) showed a statistically significant correlation coefficient of more than 0.60 in the Bay of Bengal. Bio-Argo measurement of the Chl-a concentration can inform the model about the phytoplankton biomass, which affects light attenuation and absorption lengths in the water column. A Bio-Argo (WMO ID: 2902217) was then utilized for a regional biophysical model validation, which analyzed the influence of different types of eddies on biological productivity (Shee et al., 2024, DAO). Bio-Argo (WMO ID: 2902093) was also very useful in revealing the subsurface extent of increased productivity after the passage of a cyclone in another study (Pramanik and Sil, 2021; JGR-Ocean). The Bio-Argo (WMO ID:2902156) showed the development of the Sri Lankan Dome and was used for validation of a bio-physical regional model used for its interannual variation (Pramanik et al., 2020; IJRS). The high-resolution, in-situ measurements provided by BGC-Argo floats are instrumental in capturing temporal and spatial variations, thereby supporting the development of more accurate oceanographic models and assessments.
How to cite: Sil, S., Gangopadhyay, A., Das, S., Gupta, H., Shee, A., and Pramanik, S.: Exploring Upper Layer Bio-Physical Processes in the Bay of Bengal using BGC-Argos, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17322, https://doi.org/10.5194/egusphere-egu25-17322, 2025.
Oceanic submesoscale processes are ubiquitous in the North Pacific Subtropical Gyre (NPSG), where the biological carbon pump is generally ineffective. Due to difficulties in collecting continuous observations, however, it remains uncertain whether episodic submesoscale processes can drive significant changes in particulate organic carbon (POC) export into the mesopelagic ocean. Here we present observations from high-frequency Biogeochemical-Argo floats in the NPSG, which captured the enhanced POC export fluxes during the intensifying stages of a submesoscale front and a cyclonic eddy compared to their other life stages. A higher percentage of POC export flux was found to be transferred to the base of mesopelagic layer at the front compared to that at the intensifying eddy and the mean of previous studies (37% vs. ~10%), suggesting that the POC export efficiency was significantly strengthened by submesoscale dynamics. Such findings highlight the importance of submesoscale fronts for carbon export and sequestration in subtropical gyres.
How to cite: Xing, X., Guo, M., Xiu, P., Dall’Olmo, G., Chen, W., and Chai, F.: Efficient biological carbon export to themesopelagic ocean induced bysubmesoscale fronts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5085, https://doi.org/10.5194/egusphere-egu25-5085, 2025.
Simultaneous detection and sizing of plankton and marine particles is now possible at global scale with the Underwater Vision Profiler 6 (UVP6) mounted on BGC-Argo floats. Combined with other biogeochemical sensors, the UVP6 delivers Essential Ocean Variables (EOVs), from nutrients to plankton and detritus, critical for monitoring and modeling. To date, over a hundred of UVP6 have been deployed by different laboratories across all oceans. When deployed on BGC-Argo floats, particle size distribution or taxa counts -obtained through embedded recognition, are typically the only available data, as the floats are generally not recovered. Here we report multi-year patterns of plankton and particles obtained from four successful deployments and recoveries at different latitudes, ranging from the equator to 50° South and depths down to 2000 m. Objects larger than 0.6 mm were classified using machine learning recognition (for plankton and particle) and k-means clustering (only for particles) methods. To date, five morphological categories of marine snow (particles > 500µm) were defined, based on shape, darkness, and structural heterogeneity, while plankton images were validated by experts in 20 broad categories. We show how these results can be used to assess plankton diversity, detritus composition, carbon vertical flux, and attenuation down to the bathypelagic layers in a wide range of environmental conditions. In cases of low mesoscale activity, results show that different phytoplankton blooms produce different marine snow morphotypes having different fates. Dense marine snow is found to be the most exported and also the deepest (down to 2000 m depth). Other morphotypes, such as filaments or porous marine snow, were generally not exported below the surface layer. Size and morphology were important to determine marine snow sinking speed. In high mesoscale activity, the steady marine snowfall is disrupted by ocean horizontal and vertical circulations and intermittent export events are observed down to 600m depth. When fully integrated in a global network of BGC-Argo floats, underwater cameras will complement existing global observations of biogeochemical variables and small planktonic organisms, detected by optical sensors, by also capturing data on larger organisms and particles.
How to cite: Stemmann, L., Rainer, K., Léo, L., Alexandre, A., Marin, A., Alberto, B., Philip, B., Camille, C., Hervé, C., Lionel, G., Joelle, H., Jean Olivier, I., Edouard, L., Fabien, L., Juliette, M., Ariadna, N., Marc, P., Antoine, P., and Dodji, S. Y.: Unraveling planktonic ecosystems dynamics using imaging sensors on BGC-Argo floats, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11373, https://doi.org/10.5194/egusphere-egu25-11373, 2025.
Oxygen, measured using optical sensors, was the first biogeochemical parameter recorded by Argo floats. The Argo program, now renamed OneArgo to take account of the ocean's biogeochemical (BGC) and deep components, now counts over 300,000 dissolved oxygen concentration profiles. This number of profiles makes the OneArgo program a key player in characterizing the biogeochemical state of the ocean.
Among the biogeochemical parameters on floats, endorsed by the International Oceanographic Commission (IOC), oxygen can be used to quantify, for example, the Net Community Production and when monitored at large scales, it helps in understanding the extent of the Oxygen Minimum Zone (OMZ) and the deoxygenation of the ocean. Moreover, it is widely used in the calibration processes of other key parameters (Maurer et al., 2021) measured on floats such as the nitrate concentration and pH.
OneArgo is built around autonomous robots, which implies specific constraints on data processing (Qualification, Validation... ) despite efforts to recover floats, post-deployment calibration to enhance sensor characterization and data quality remains rare. We would like to present the work lead in the European Data Assembly Center (DAC) Coriolis, in order to provide an operationally and consistent dissolved oxygen concentration profiles dataset, addressing Real Time (RT) and Delayed Mode (DM) processing for a wide range of sensors and float technologies.
How to cite: Racape, V., Schmechtig, C., Thierry, V., Bittig, H., Carval, T., Bernard, V., Rannou, J.-P., and Fontaine, L.: Building an operational and consistent oxygen dataset at the Coriolis DAC, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19854, https://doi.org/10.5194/egusphere-egu25-19854, 2025.
Dissolved oxygen (DO) is an important passive tracer of the ocean that plays an essential role in structuring marine ecosystems. The thickest and most intense oxygen minimum zone (OMZ) at intermediate depths is found in the Arabian Sea, which is located in the northwestern part of the tropical Indian Ocean. In this work, changes in DO concentration in the water column of the Arabian Sea are extensively examined over the recent decade utilizing BGC-Argo profile records. Here, the upper layer experiences deoxygenation, which is attributed to improved stratification. In contrast, below the top ocean, the DO concentration shows significant increasing trend throughout the depths. The oxygenation at intermediate levels of this region is caused by enhanced isopycnal mixings in the presence of salt finger instabilities. This research reveals the role of salinity in regulating DO variations in the water column. This investigation also demonstrates that the OMZ in this region has shrunk over the period by roughly 200 m from the bottom. Furthermore, DO concentrations in the OMZ have increased by around 140%, which is associated with the North Indian Intermediate Water masses over the study region.
How to cite: Shee, A.: Recent Changes in Dissolved Oxygen Concentrations in the Arabian Sea using BGC-Argo Data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18090, https://doi.org/10.5194/egusphere-egu25-18090, 2025.
The steady-state buoyancy budget underpinning the Atlantic Meridional Overturning Circulation (AMOC) implies a balance between the surface-forced and mixing-driven transformation of North Atlantic Deep Water (NADW) and its meridional export. Here, we employ a climatology of ocean temperature and salinity data, a contemporary Deep-Argo array, and an atmospheric reanalysis to assess this balance in the subpolar North Atlantic and Nordic Seas over interannual, decadal, and bidecadal timescales. We quantify the residual of this balance - the rate of water mass volume change - and its role in the water mass transformation budget. The analysis reveals that the magnitude and density range of local volume trends decrease with longer timescales. On decadal and bidecadal scales, trends are confined to upper NADW with minimal changes in AMOC limb volumes, suggesting that water mass transformation and AMOC may be interchangeable. On interannual scales, trends are larger and span lighter and denser density ranges in the eastern basins, aligning with surface-forced transformation patterns. Here, the volume of the AMOC limbs is impacted and AMOC intensity will lag transformation rates by the southward export timescales of transformed water masses.
How to cite: Desbruyères, D., Mercier, H., Johnson, G. C., Thierry, V., and Mork, K. A.: Buoyancy redistribution within the lower limb of the Atlantic Meridional Overturning Circulation revealed by Deep Argo, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21641, https://doi.org/10.5194/egusphere-egu25-21641, 2025.
Turbulent mixing is the dominant driver in the exchange of oceanic properties across vertical layers and lateral fronts. It shapes the ocean’s stratification dynamics that regulate deep convective processes and water mass exchanges. It also plays an important role in the rate of atmosphere-ocean interactions, and thus is a key factor in our understanding of the dynamics that govern the earth’s climate. Direct measurement of turbulent mixing and associated fluxes of energy, oxygen and nutrients requires measurement of small-scale velocity and/or scalar fluctuations at fast rates. These observations have traditionally been resource-intensive and, consequentially, mostly local and intermittent. Progress is being made on the integration of microstructure turbulence sensors on Argo-class profiling floats, as a novel parameter to measure and from which to derive eddy-diffusivity values for assimilation into ocean and climate models. This is achieved through the integration of rudimentary sensor packages and low-power data loggers. The development includes onboard processing of the otherwise voluminous microstructure data, to make it suitable for satellite transmission. Experiments with two types of such integrations have been done by progressive adopters of the technology. In this presentation results are presented from a turbulence float deployment in the Iceland basin of the North Atlantic, where freshwater-driven deep convection takes place, as well as from deployment of a float in a turbulent eddy of the Kuroshio current.
Targeted deployment of arrays of turbulence floats will result in improved monitoring of regions of the ocean over larger spatiotemporal scales. Results will contribute to the understanding of the mechanistic state and variability in regions of intense mixing, like the Atlantic sub-polar gyre. In addition to presenting scientific results, this presentation will discuss some of the technical requirements for integration of microstructure sensor packages on floats, and proposed methods for data quality, assessment and control.
How to cite: ten Doeschate, A., Fernandez-Castro, B., Aranos, L., Inoue, R., and Juteau, J.-P.: Ocean turbulence observations from autonomous profiling floats , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15310, https://doi.org/10.5194/egusphere-egu25-15310, 2025.
Spectral clustering method is a powerful tool for identifying Lagrangian coherent clusters from lagrangian trajectories. These coherent clusters group trajectories that are most similar to each other within the same cluster while being most dissimilar from trajectories in other clusters. Traditional spectral clustering defines similarity based on the physical distance between particles. Here, we generalize the spectral clustering technique to incorporate other physically significant properties, such as water density, temperature, or salinity into the similarity definition between trajectories.
We apply the generalized spectral clustering method to the global ARGO float dataset and compare the resulting coherent clusters to those identified using other dynamical systems techniques for Lagrangian coherent structures identification, including FTLEs, LAVDs, and encounter volume. Different methods may identify different clusters because they use different definitions of coherence, making them most effective when used together. Also, we investigate the temporal evolution of these clusters and assess their consistency over time to understand changes or stability in water masses and ocean properties over the past decade.
How to cite: Curbelo, J. and Rypina, I.: ARGO Float Data Analysis Using a Generalized Spectral Clustering Method, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7732, https://doi.org/10.5194/egusphere-egu25-7732, 2025.
Accurate reconstruction of subsurface temperature profiles is essential for advancing our understanding of ocean dynamics and climate variability. In the Mediterranean Sea, Argo float temperature observations between 10 dbar and 500 dbar are often sparse or uncertain, limiting their utility for operational oceanography and climate studies. To address this challenge, we propose a physics-informed deep learning framework that leverages spatial-temporal dependencies and integrates auxiliary data from remote sensing and simulation products. The model incorporates wind stress, absolute dynamic topography, sea surface temperature, and simulated temperature and salinity fields from Copernicus datasets to reconstruct and correct uncertainties in Argo float data.
Our framework employs a deep neural network architecture augmented with physics-informed loss functions (PINNs), ensuring consistency with oceanographic principles such as temperature-salinity relationships and geostrophic balance. Evaluation metrics, including root mean-squared error (RMSE), structural similarity index (SSIM), and PINN-based loss terms, are utilized to quantify the model's accuracy and adherence to physical laws. During testing, the model is validated by reconstructing observed Argo temperature profiles and comparing them against independent datasets.
Preliminary results demonstrate that the proposed approach significantly improves the reconstruction of missing temperature profiles, achieving reduced RMSE, high SSIM values, and strong alignment with physical constraints. This study highlights the potential of combining physics-informed deep learning with remote sensing to enhance the reliability and accuracy of observational datasets in complex marine environments like the Mediterranean Sea.
How to cite: Gasana Elysee, M., Pirro, A., Mauri, E., Martellucci, R., and Menna, M.: Reconstruction of Argo Float Temperature Data in the Mediterranean Sea Using Physics-Informed Deep Learning and Remote Sensing , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15771, https://doi.org/10.5194/egusphere-egu25-15771, 2025.
Accurate circulation modeling in the Gulf of Mexico (GoM) is hampered by the limited availability of in-situ subsurface data, leading to inaccuracies in subsurface representations. These inaccuracies diminish the reliability of ocean models and constrain the duration of dependable forecasts. To address this, we present the latest version of the Neural Synthetic Profiles from Remote Sensing and Observations (NeSPReSO), a data-driven approach designed to efficiently and accurately estimate subsurface temperature and salinity profiles using satellite-derived surface data as input. This method provides a robust alternative to conventional synthetic data generation techniques.
NeSPReSO applies Principal Component Analysis (PCA) to extract dominant features from temperature and salinity profiles in an Argo dataset. A neural network is then trained to predict these features using inputs such as time, location, and satellite-derived variables, including absolute dynamic topography, sea surface temperature, and sea surface salinity. The model's performance was rigorously evaluated using independent Argo profiles and glider data collected in the GoM, demonstrating better performance compared to traditional methods such as Gravest Empirical Modes (GEM), Multiple Linear Regression (MLR), and Improved Synthetic Ocean Profile (ISOP). Results show reductions in root mean square error and bias, indicating that NeSPReSO effectively captures the primary variability of subsurface fields. Furthermore, the synthetic profiles generated by NeSPReSO align well with observed data, accurately representing key oceanographic features such as thermoclines, haloclines, and the region's temperature-salinity structure.
To facilitate widespread application, we have developed an API that allows users to generate synthetic profiles for any location in the Gulf of Mexico at varying spatial and temporal resolutions. This resource offers the broader scientific community a valuable tool for estimating quantities such as the region's heat content and enhancing oceanographic research and forecasting capabilities.
How to cite: Zavala-Romero, O., Miranda, J., Hiron, L., Chassignet, E., Subrahmanyam, B., Meunier, T., Pallas-Sanz, E., and Tenreiro, M.: NeSPReSO: A Neural Approach for Generating Synthetic Ocean Profiles Using ARGO Data, with an Accessible API for the Gulf of Mexico, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13321, https://doi.org/10.5194/egusphere-egu25-13321, 2025.
The variability of the Loop Current has been extensively studied due to its influence on the energy and heat distribution in the Gulf of Mexico, which in turn impacts both the oil industry and hurricane forecasting. Improving the predictability of the Loop Current's path and the detachment of its eddies requires a better understanding of the thermohaline structure, which has driven the deployment of numerous Argo profiling buoys in the eastern Gulf over the past decade. Despite these efforts, significant gaps remain in both the temporal and spatial coverage of in situ observations. Several methods have been developed to address these gaps by generating vertical projections from surface data, combining remote sensing information with available hydrographic profiles. In this study, the satGEM (satellite-Gravest Empirical Mode, Meijers et al., 2011) method is applied to project profile data onto geostrophic stream-function space, the latter being the absolute dynamic topography derived from satellite data. The performance of this method in reproducing thermohaline profiles under various dynamic conditions of the Loop Current and its eddies is compared with other vertical projection techniques currently used in the region, including both empirical methods (such as those based on Machine Learning) as well as hydrodynamical models with data assimilation.
How to cite: Perez-Brunius, P., García-Carrillo, P., Miranda, J., Zavala, O., Meunier, T., and Bower, A.: Reconstructing the 4D Thermohaline Field in the Gulf of Mexico from Argo Float Data Using Geostrophic Stream-Function Projections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14753, https://doi.org/10.5194/egusphere-egu25-14753, 2025.
The Antarctic and Subantarctic regions of the oceans, situated mainly in the Southern Ocean, play a crucial role in connecting all oceans through the Antarctic Circumpolar Current (ACC). It is essential to better understand the processes that occur in these regions, such as water mass formation, deep convection, and hence their contribution to the Meridional Overturning Circulation (MOC). Particularly noteworthy areas are those with abrupt bathymetric changes, such as the Campbell Plateau in southwestern New Zealand. In the present study, Argo floats data from 2003 to 2024 are used to identify the main water masses along the sectors from the eastern and western edges of the Campbell Plateau to the Antarctic continental shelf. These sectors, located between subtropical and subantarctic fronts, are characterized by the formation of Sub-Antarctic Mode Water (SAMW) and Antarctic Intermediate Water (AAIW), which contribute to shaping the broader oceanic circulation patterns. Recent results reveal the presence of eight distinct water masses in the study region and emphasize their peculiar seasonal variability. Also, a decadal analysis identifies colder waters in the period 2003-2013 compared to 2014-2024, while significant changes in the trends of salinity and temperatures are observed in the different sectors. Preliminary results of this study highlight a unique 'dual mode' in temperature dynamics, where rising temperatures in one sector are accompanied by declining temperatures in the other. Similar patterns were also found in the salinity results. Finally, the use of Argo float data provides an unprecedented level of detail in examining the spatial and temporal resolution of an area located between these two different sectors of the ACC, whose changes most likely influence the global and Southern Ocean circulation patterns, with consequent implications on climate.
How to cite: Amaral Wasielesky, A., Mauri, E., Rubino, A., Martellucci, R., and Menna, M.: Thermohaline Properties and Trends in the Antarctic and Subantarctic Regions of the Pacific Ocean using 20 years of Argo float data, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11606, https://doi.org/10.5194/egusphere-egu25-11606, 2025.
The global Argo array plays a pivotal role in ocean observing system by providing nearly uniform global coverage of temperature and salinity profiles to measure the upper 2000 meters of the ocean at approximately a 10-day interval. Argo float data (and other ocean in-situ observation types) are assimilated in the ECMWF ocean and sea-ice analysis system, to provide essential ocean and sea-ice initial states for the coupled forecasting system of ECMWF. In this study, we focus on the impact of ocean observations on medium-range forecasts by taking the global Argo array as an example. Similar studies assessing the impact of atmospheric observations are abundant, while there are few studies examining the impact of ocean observation system. Observation impact on ocean reanalysis has first been evaluated using observation system experiments (OSEs), in which different ocean observation types (including Argo) have been denied in the data assimilation system. Assessment of ocean observation impact on the coupled forecasting system of ECMWF has then been carried out, by initializing the ocean and sea-ice states from different OSE reanalyses. Results suggest that removing Argo data degrades the SST forecasts up to day-10. Impact of removing Argo data is comparable to atmospheric model changes in a typical ECMWF IFS Cycle upgrade and leads to a small but significant degradation of forecasted atmospheric fields.
How to cite: Zuo, H., Mogensen, K., de Boisseson, E., Balmaseda, M. A., Browne, P., Chrust, M., Johnson, S., Keeley, S., and Roberts, C.: Effects of Argo floats data on the ECMWF Ocean DA system and coupled forecasts , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11966, https://doi.org/10.5194/egusphere-egu25-11966, 2025.
OSEAN, in collaboration with the Geoazur laboratory, has developed a series of profiling floats capable of operating at depths ranging from 0 to 2000 meters. These floats are equipped with the MERMAID seismic signal acquisition application. Since 2014, we have produced over 100 of these floats. They come in many configurations, including seismic models with MERMAID hydrophones and ARGO models with CTD sensors. Over the past three years, we have adapted the design, allowing it to be used at depths of up to 4,000 meters, primarily for the ARGO program.
These profilers are distinguished by their exceptional reliability and autonomy. The initial units deployed over six years ago have demonstrated remarkable resilience, as evidenced by their continued operational status, thereby attesting to their durability in field conditions.
OSEAN is engineering a state-of-the-art profiler capable of descending to depths of 6000 meters. This enhancement will enable the profilers to access and monitor 97% of the world's oceans, considerably increasing their scientific usefulness.
This advanced instrument draws upon the expertise accumulated through the development of the 2000 and 4000-meter profilers.
The profiler has been designed to accommodate a wide range of payloads to accommodate the many sensors used in the bio-Argo program. It is also equipped with two separate acoustic channels, low-frequency for seismology and high-frequency for meteorological applications and marine mammal tracking. It can also land on the seabed to deliberately avoid moving away from a point of scientific interest.
In any case, equipped with a deep CTD sensor, it has been specially adapted for Deep Argo applications. This adaptation has now been validated, and the first Deep Argo tests are due to start in early 2025, culminating in a final test this summer.
How to cite: Philippe, O., Rebour, C., Hieramente, F., Nolet, G., Hello, Y., Sigloch, K., and Bonnieux, S.: DEEP MOBY : The New 6000-Meter Profiling Float, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12629, https://doi.org/10.5194/egusphere-egu25-12629, 2025.
The UK Met Office produces operational forecasts for the Northwest European Shelf region, for which we routinely assimilate temperature and salinity from Argo but not yet biogeochemical (BGC)-Argo variables. To explore the effect, we conduct a set of data assimilation experiments with measured and machine learning-derived BGC-Argo data. We use the NEMOVAR assimilation scheme in its 3DVar configuration with first guess at appropriate time. Biogeochemical variables are assimilated univariately, meaning that in the assimilation step each variable is updated individually and the model dynamics distribute the changes to other model variables.
The first attempt of assimilating nitrate revealed a bias which we traced to the lateral boundary conditions, which will shortly be fixed in the operational system. A question for assessing the benefit of assimilating BGC-Argo data is how widespread the effect is of assimilating the relatively sparsely distributed BGC-Argo profiles, which only cover the off-shelf area. We find that the spread of the assimilated information depends on the interior circulation, which is affected by the assimilation of physics variables, including how much of the signal is advected onto the shelf. Assimilating nitrate from BGC-Argo also had effects on non-assimilated variables such as the distribution of chlorophyll within and below the mixed layer, and it revealed mismatches in the vertical structure of observed nitrate and the model’s mixed layer depth. The results suggest that our forecasting system can benefit from assimilating BGC-Argo data, directly through the assimilation as well as indirectly by highlighting issues in the physics-BGC interactions. Future work should investigate how to better match the assimilated physics, and BGC and also explore balancing the assimilated information from each variable across the ecosystem to increase the impact of the observations.
How to cite: Rochner, A., Ford, D., and Kay, S.: Exploring the effect of assimilating BGC-Argo observations on the Met Office's marine biogeochemical forecasting system, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14292, https://doi.org/10.5194/egusphere-egu25-14292, 2025.
The Ocean currently takes up ca. 25 % of anthropogenic CO2 emissions and hosts the largest carbon pool between atmospheric, terrestrial and oceanic biospheres. To properly observe and document changes in ocean CO2, a combined approach between observation and modelling, but also the combination of different observation approaches is needed.
The OneArgo programme and its autonomous float measurements can provide the link between research vessel-based observations of the whole water column such as organized in the GO-SHIP programme and surface CO2 observations by Ships of Opportunity (SOOP), organized in the SOCONET (Surface Ocean CO2 Reference Observing Network) programme or its European pillar ICOS (Integrated Carbon Observation System). Autonomous float measurements have the additional benefit to outperform these observation programmes in timeliness for operational applications thanks to its fully near real-time data availability. While the required raise in funding for a global OneArgo implementation slowly helps to build up the array, we present work from the Baltic Sea that can be seen as a regional pilot.
Here, (1) surface carbon measurements by a SOOP, (2) vertical profiling float data of pCO2, and (3) research-vessel based water sampling of the water column build the foundation for a comprehensive CO2 observation network. By cross-validating data across the different research infrastructures, we ensure that data are interoperable. Next steps are to integrate data from the different sources into a comprehensive 4D BGC product of Baltic Sea CO2. While its global counterpart will be based on profiling float pH observations instead of pCO2, the approach and procedures can be mimicked, e.g., in the subpolar North Atlantic, a key region of the oceanic carbon cycle.
Combined, the three different research infrastructures provide highly complementary information to quantify CO2 uptake, on the cycling and fate of CO2 in the water column, and to inform on timescales of CO2 sequestration. Future float deployments in the Baltic Sea will involve sensors for nitrate, oxygen debt, and hyperspectral radiometry to expand OneArgo’s bridging role and scope, e.g., to link up with satellite remote sensing products.
How to cite: Bittig, H. C., Steinhoff, T., Klein, B., Körtzinger, A., Rehder, G., and Zielinski, O.: OneArgo’s bridging role in ocean CO2 observations – The Baltic Sea pilot case, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16427, https://doi.org/10.5194/egusphere-egu25-16427, 2025.
The ocean plays a key role in the climate system, and therefore in the climate change threat. About 90% of the heat excess absorbed since the 1970’s is stored in the ocean and changes in the hydrological cycle related to climate change are also strongly manifested in the ocean. In addition, the ocean acts as a net anthropogenic carbon sink, presently assessed as one fourth of the global uptake, and a moderator of climate change. It is therefore of paramount importance to monitor key ocean properties over long periods, with a global coverage.
Argo has transformed the way of ocean observing in the last decades and is the most important source of in situ marine data. As a major component of both the Global Ocean Observing System and the Global Climate Observing System, it provides near-real time data for forecasting and reanalysis services and high-quality data for climate research. Its implementation began in 1999, reaching a global coverage since 2007 (Roemmich et al. 2009). Originally designed to provide temperature and salinity profiles in the upper 2 000 m of the ice-free ocean (Core-Argo mission), the array has been expanded into seasonal ice zones (Polar-Argo mission), as well as in marginal seas. Successful pilot studies have shown the scientific added-value and the technology readiness to extend its mission towards greater depths (Deep-Argo mission) and biogeochemistry (BGC-Argo mission), hence the new “Global, full depth, multidisciplinary” OneArgo design defined after the OceanObs’19 Conference (Roemmich et al. 2019), aiming for a full implementation by 2030.
Euro-Argo ERIC (European Research Infrastructure Consortium) coordinates the European contribution to the Argo international programme, as the sum of European national contributions from 13 countries plus project-based contributions from the European Commission. This joint effort enables Euro-Argo to aim at maintaining ¼ of the array, with a regional perspective leant towards marginal seas (Mediterranean, Black and Baltic seas) and the European part of the Arctic seas. It has thus become a major source of information for European operational centres such as the Copernicus Marine and Climate Services and the European Centre for Medium-Range Weather Forecasts (ECMWF). In addition, it provides important in situ information for calibration and validation of satellites, and the technological advances in biogeochemical instrumentation have greatly improved the ability to collect data that support marine policies set up by the European Union.
Within this context, Euro-Argo is currently revising its deployment strategy for the next decade, considering specific European needs, while integrating within the European Ocean Observing System and contributing to the international OneArgo new ambitious design. The new strategy will consider feedbacks received from Copernicus in the frame of the COINS and GEORGE HE and European In situ Alliance projects. It will also include first results of studies undertaken within the EA-ONE and TRICUSO HE projects to optimise Argo network efficiency, sustainability, and global impact through refined sampling strategies and regional collaboration, e.g. for float recoveries.
Key elements of this comprehensive design of OneArgo for its European implementation will then be presented.
How to cite: De Roeck, Y.-H., Gourcuff, C., Berry, A., Carse, F., Kassis, D., Klein, B., Mork, K. A., Notarstefano, G., Siiriä, S.-M., Slabakova, V., Stedmon, C., Sterl, A., Thierry, V., Vélez Belchí, P., and Walczowski, W.: Tailor a comprehensive design of OneArgo for its European implementation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19623, https://doi.org/10.5194/egusphere-egu25-19623, 2025.
