The Southern Ocean is a major sink of atmospheric carbon dioxide (CO2) and a key component of the global carbon cycle. Phytoplankton primary production modulates air-sea CO2 exchange, yet its response to ongoing climate-driven changes in storm intensity and storm-track position remains poorly constrained. A major challenge is that most primary production estimates rely on satellite observations restricted to the ocean surface, thereby missing subsurface production and limiting interpretation of storm-driven variability and long-term changes. Here we use the Copernicus Marine Service 3D biogeochemical product derived from in situ and satellite observations to reconstruct depth-resolved primary production over 1998–2023. Weekly three-dimensional fields of phytoplankton biomass and light-related variables are used as inputs to a depth- and phytoplankton-group-resolved bio-optical primary production model. Storm occurrence is characterized using the ERA5 atmospheric reanalysis. This approach allows us to examine how storms influence the vertical distribution of primary production and its partitioning among major phytoplankton groups. We evaluate how these effects vary across Southern Ocean regions and seasons.

The majority of particulate organic carbon in the ocean’s mesopelagic zone is contained in particles smaller than 100 µm. However, the challenges of measuring their abundance and carbon content using shipboard techniques has limited our understanding of their role in the ocean carbon cycle. Here we use a 4D Biogeochemical-Argo based data product to investigate the spatial distribution and seasonality in small particles, as well as their loss in the mesopelagic zone. We find that small particle loss in the mesopelagic is greater at high latitudes than at low latitudes. We examine several hypotheses that might explain this observed pattern, although limitations in the extant data prevent us drawing definitive conclusions. Contrary to expectations, we find that temperature-driven remineralisation is unlikely to be the primary driver of the observed patterns, and instead find that particle lability may be a significant factor in explaining this unexpected result. The spatial distribution of small organic mesopelagic particles reveals greater particle loss at high than low latitudes, likely driven by particle lability rather than temperature, based on 4D global BGC-Argo observations combined with machine learning and biogeochemical modeling.

Abstract. Monitoring carbon cycle processes is key to understanding climate system science. As the second largest carbon reservoir on Earth, the ocean regulates carbon balance through Particulate Organic Carbon (POC), which links surface biomass production, the deep ocean, and sedimentation. The degradation of POC in the deep ocean notably impacts atmospheric CO2 levels. POC estimation is achieved by measuring proxies like the Particulate Backscattering Coefficient (bbp), obtained from satellite observations and in situ sensors, such as the BioGeoChemical-Argo (BGC-Argo) floats. These floats provide global- scale profiles of ocean biogeochemical properties. Previous research has combined data from BGC-Argo floats and satellite sensors, demonstrating the potential of machine learning models to infer vertical bio-optical properties in the water column. By bridging the gap between surface optical properties and deep ocean processes, this approach enhances the estimation within the top 250 meters of the water column. This study focuses on such estimations, including remote sensing data from the Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensor. The addition of optical information about absorption and scattering processes has improved the accuracy of the Random Forest models, which show promising results, especially within the first 50 meters in the Subtropical Gyres. However, in dynamic regions like the North Atlantic, results are less consistent, suggesting further research is needed to understand how the complexity of the water column’s physical state modifies the bbp vertical fluxes.

Understanding factors controlling the biological carbon pump (BCP) at the regional scale is of major interest for better characterizing carbon sequestration into the deep ocean and, therefore, the ocean's role in climate regulation. This study focuses on high‐latitude marine regions, which are responsible for the majority of marine CO 2 absorption. Using data from Biogeochemical‐Argo floats, a bioregionalization method was performed on 335 annual time series of chlorophyll a concentration and particulate backscattering coefficient, variables from which particulate organic carbon (POC) could be estimated. This analysis highlighted six regimes characterized by distinct seasonality in productivity, export, and transfer of small POC (<100 μm). Both hemispheres exhibited regimes with strong summer blooms and others with deep chlorophyll maxima. Across these regimes, variations in phytoplankton phenology and particle assemblages drove three distinct systems of BCP strength and efficiency for small particles. Despite these differences, processes such as gravitational sinking, the mixed layer pump, or particle fragmentation facilitated the export of small particles down to ∼1,000 m across all regions. This resulted in an average annual contribution of ∼10% of small particles to total organic carbon fluxes at depth, highlighting the role of small particles in long‐term carbon sequestration. These findings emphasize the need for future investigations into processes driving small‐particle carbon export and transfer in the mesopelagic zone at annual and seasonal scales.

Ocean Reanalyses Workshop of the European Copernicus Marine Service What: Gather together ocean reanalyses users and producers to identify users' needs of ocean reanalyses and design the strategy to improve ocean reanalyses to fulfill users' needs When: 10-12 October 2023 Where: Toulouse, France, and online

Abstract. The predictive accuracy of marine biogeochemical models is fundamentally limited by uncertainty in their parameter values. We present a parameter optimization framework using iterative Importance Sampling (iIS) to constrain the PISCES model by leveraging the rich, multi-variable dataset provided by Biogeochemical-Argo (BGC-Argo) floats. Using data from a BGC-Argo float in the North Atlantic, we assimilate a comprehensive suite of 20 biogeochemical metrics to constrain all 95 parameters of the PISCES model within a 1D vertical configuration. Our global sensitivity analysis (GSA) identifies parameters controlling zooplankton dynamics as the dominant source of model sensitivity for this specific site. We compare three strategies: (1) optimizing a subset of parameters for their strong direct influence (Main effects); (2) optimizing a larger subset that also includes parameters influential through non-linear interactions (Total effects); and (3) simultaneously optimizing all 95 parameters. All three approaches achieve a statistically indistinguishable and significant improvement in model skill, reducing Normalized Root Mean Square Error (NRMSE) by 54–56 %. The rich, multi-variable dataset provides sufficient orthogonal constraints to yield posterior parameter distributions with negligible inter-correlation, shifting the long-standing challenge of correlated equifinality to uncorrelated equifinality, where a range of optimal parameter sets can be found independently. Parameter uncertainty is reduced by 16–41 %, and the optimized ensembles demonstrate strong portability. While all strategies produce a similar, tightly constrained predictive spread for the assimilated variables, they differ significantly in computational cost and in their estimation of uncertainty for unobserved parts of the model. The prerequisite GSA was ~40 times more computationally expensive than the optimization, while the All-parameters strategy, by exploring the full parameter space, provides a more comprehensive and robust quantification of the model's uncertainty in unassimilated variables. We therefore conclude that directly optimizing all model parameters is the recommended strategy. This work delivers a validated, parameter set for the North Atlantic and demonstrates a scalable framework to advance biogeochemical modeling from using static, globally-uniform parametrization to developing a map of regionally-tuned parameters.

This study presents a regionally trained version of the “CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural network” (CANYON) method, named CANYON-PU, for estimating primary macronutrients (phosphates, silicates, and nitrates) in the Peruvian Upwelling System (PUS). Using a neural network approach, the model was trained using extensive biogeochemical data spanning between 2003 and 2021, collected by the Peruvian Institute of Marine Research (IMARPE). Variables representing the low-frequency variability related to ENSO were introduced in the training and significantly improved the performance of the algorithm. The performance of CANYON-PU was validated against independent datasets and demonstrated an improvement in accuracy over the global CANYON model that struggled to represent the nutrient distribution in the PUS mainly due to the lack of samples in its training. Therefore, CANYON-PU successfully captured nutrient variability across different spatial and temporal scales, showcasing its applicability to diverse datasets, including high-frequency data such as profiling floats or gliders. This work highlights the effectiveness of neural networks for representing the nutrient distribution within highly variable ecosystems like the PUS.

Under the umbrella of the EOSC ecosystem, the FAIR-EASE project funded under HORIZON-INFRA-2021-EOSC-01-04 aims to facilitate access to interoperable data and services for earth and environmental multi-disciplinary use cases, demonstrating the capabilities to support open science (https://fairease.eu/). Based on three of its pilots more specifically: the Volcano Space Observatory pilot, the Ocean Biogeochemical Observations pilot and the Coastal Dynamic pilot, the FAIR-EASE partners would like to highlight both the synergy and the new emerging interdisciplinary collaborations and progresses that can be achieved in the framework of such a European project promoting FAIR principles. Indeed,* The Volcano Space Observatory Pilot supports the implementation of innovative web services (notably here the open access VOLCPLUME web platform) displaying a broad range of satellite and ground-based data relevant to the characterization of volcanic gas and particle properties for the near real-time monitoring of volcanic activity and atmospheric hazards.* The Ocean Biogeochemical (BGC) Observations aims to provide a common QA/QC (Quality Assessment /Quality Control) platform to the whole BGC community to enhance the BGC data quality and address fundamental scientific questions. * The webODV software, part of the Coastal Water Dynamic pilot tools, allows to display and superimpose very heterogeneous datasets (i.e. satellite surface data vs. in situ profiles data, climatology vs. in situ profiles data, observations vs. model simulations in general).Taking as a starting point, the eruption of the Hunga Tonga-Hunga Ha’apai volcano on January 15, 2022, and the availability of various satellite observations of volcanic plumes and ocean surface properties together with in situ Argo (Argo is an international program that collects information from inside the ocean using a fleet of robotic instruments that drift with the ocean currents) floats measuring BGC variables such as the chlorophyll-a and suspended particles in the eruption area, FAIR-EASE partners aim to investigate the potential impacts of such a major stratospheric eruptions a record breaking eruption in the satellite era, on the marine ecosystem. Volcano and BGC community expertise as well as tools developed and pooled on Galaxy Europe platform (Galaxy is an open-source Virtual Research Environment) during the FAIR-EASE project support scientists in their investigation.

The Peruvian coastal upwelling system (PCUS) is one of the most productive in the world ocean. The Peruvian Marine Research Institute (IMARPE) has been monitoring the PCUS since the 1960’s with an increase in the frequency and spatial distribution of measurements since the early 2000’s focusing on temperature, salinity and oxygen. In recent years, autonomous gliders have started to be routinely deployed by IMARPE, collecting a large amount of profiles. However, there is still a gap for the high-resolution sampling of biogeochemical parameters such as nutrients (nitrate, phosphate and silicate).New methods using machine learning to reconstruct missing data have been developed recently with promising results (Sauzède et al, 2017; Bittig et al., 2018; Fourrier et al., 2020). In particular, a recent global approach using neural networks (NN) named CANYON-B (CArbonate system and Nutrientes concentration from hYdrological properties and Oxygen using a Neural network) was developed in order to fill those gaps and infer nutrients’ concentrations from the more sampled variables of temperature, salinity and oxygen (Bittig et al., 2018).In this work we show the application of this global CANYON-B algorithm to the PCUS using all the historical IMARPE’s CTD casts. Moreover, we trained a new NN and compared its outputs with the ones from CANYON-B, demonstrating the benefits of training the NN with the extensive regional data set collected by IMARPE.

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.

1 LOPS, IUEM, IRD, Ifremer, CNRS, Univ. Brest, Brest, France, 2 EIO, IRD, Ifremer, UPF and ILM, Tahiti, French Polynesia, 3 LOCEAN-IPSL, Sorbonne Universités/UPMC-CNRS-IRD-MNHN, Paris, France, 4 ENTROPIE, IRD, Univ. de la Réunion, CNRS, Univ. de la Nouvelle Calédonie, Ifremer, Noumea, New Caledonia, 5 Laboratoire d’Océanographie de Villefranche, CNRS and Sorbonne Université, Villefranche-sur-Mer, France, Georgia Institute of Technology, Atlanta, GA, United States, 7 IMT Atlantique, Lab-STICC, UMR CNRS 6285, Brest, France

Satellite-derived observations of ocean colour provide continuous data on chlorophyll-a concentration (Chl-a) at global scales but are limited to the ocean’s surface. So far, biogeochemical models have been the only means of generating continuous vertically resolved Chl-a profiles on a regular grid. MULTIOBS is a multi-observations oceanographic dataset that provides depth-resolved biological data based on merged satellite- and Argo-derived in situ hydrological data. This product is distributed by the European Union’s Copernicus Marine Service and offers global multiyear, gridded Chl-a profiles within the ocean’s productive zone at a weekly temporal resolution. MULTIOBS addresses the scarcity of observation-based vertically resolved Chl-a datasets, particularly in less sampled regions like the Eastern Mediterranean Sea (EMS). Here, we conduct an independent evaluation of the MULTIOBS dataset in the oligotrophic waters of the EMS using in situ Chl-a profiles. Our analysis shows that this product accurately and precisely retrieves Chl-a across depths, with a slight 1% overestimation and an observed 1.5-fold average deviation between in situ data and MULTIOBS estimates. The deep chlorophyll maximum (DCM) is adequately estimated by MULTIOBS both in terms of positioning (root mean square error, RMSE = 13 m) and in terms of Chl-a (RMSE = 0.09 mg m−3). The product accurately reproduces the seasonal variability of Chl-a and it performs reasonably well in reflecting its interannual variability across various depths within the productive layer (0–120 m) of the EMS. We conclude that MULTIOBS is a valuable dataset providing vertically resolved Chl-a data, enabling a holistic understanding of euphotic zone-integrated Chl-a with an unprecedented spatiotemporal resolution spanning 25 years, which is essential for elucidating long-term trends and variability in oceanic primary productivity.

AbstractThere is growing evidence that marine microorganisms may influence cloud cover over the ocean through their impact on sea spray and trace gas emissions, further forming cloud droplets or ice crystals. However, evidence of a robust causal relationship based on observations is still pending. In this study, we use 4 years of multi‐instrument satellite data to segregate low‐level clouds into ice‐containing and liquid‐water clouds to obtain clear relationships between cloud types and ocean biological tracers, especially with nanophytoplankton cell abundances. Results suggest that microorganisms may be involved in compensating effects on cloud properties, increasing the frequency of occurrence of warm‐liquid clouds, and decreasing the occurrence of ice‐containing clouds in most regions during springtime. The relationships observed in most regions do not apply to the South Pacific Ocean in the 40°S–50°S latitude band. These results shed light on overlooked potential compensating effects of ocean microorganisms on cloud cover.

Numerical models of ocean biogeochemistry are becoming the major tools used to detect and predict the impact of climate change on marine resources and to monitor ocean health. However, with the continuous improvement of model structure and spatial resolution, incorporation of these additional degrees of freedom into fidelity assessment has become increasingly challenging. Here, we propose a new method to provide information on the model predictive skill in a concise way. The method is based on the conjoint use of a k-means clustering technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-means algorithm and the assessment metrics reduce the number of model data points to be evaluated. The metrics evaluate either the model state accuracy or the skill of the model with respect to capturing emergent properties, such as the deep chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo observations as the sole evaluation data set ensures the accuracy of the data, as it is a homogenous data set with strict sampling methodologies and data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine Service. The model performance is evaluated using the model efficiency statistical score, which compares the model–observation misfit with the variability in the observations and, thus, objectively quantifies whether the model outperforms the BGC-Argo climatology. We show that, overall, the model surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and the mixed layers as well as silicate in the mesopelagic layer. However, there are still areas for improvement with respect to reducing the model–data misfit for certain variables such as silicate, pH, and the partial pressure of CO2 in the mixed layer as well as chlorophyll-a-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed here can also aid in refining the design of the BGC-Argo network, in particular regarding the regions in which BGC-Argo observations should be enhanced to improve the model accuracy via the assimilation of BGC-Argo data or process-oriented assessment studies. We strongly recommend increasing the number of observations in the Arctic region while maintaining the existing high-density of observations in the Southern Oceans. The model error in these regions is only slightly less than the variability observed in BGC-Argo measurements. Our study illustrates how the synergic use of modeling and BGC-Argo data can both provide information about the performance of models and improve the design of observing systems.

In the ever-evolving landscape of marine, oceanic, and climate change monitoring, the intersection of cutting-edge artificial intelligence (AI), machine learning (ML), and data analytics has emerged as a pivotal catalyst for transformative advancements [...]

&lt;div&gt; &lt;p&gt;&lt;span data-contrast=&quot;none&quot;&gt;Producing comprehensive information about the ocean has become a top priority to monitor and predict the ocean and climate change.&lt;/span&gt;&lt;span data-contrast=&quot;none&quot;&gt; Complementary to ocean state estimate provided by modelling/assimilation systems, a multi observations-based approach is developed thought the Copernicus Marine Service MultiOBservation Thematic Assembly (&lt;/span&gt;&lt;span data-contrast=&quot;auto&quot;&gt;MOB TAC). Recent advances in data fusion techniques and use of machine-learning approach open the possibility of producing estimators of ocean physic and biogeochemistry (BGC) operationally, using input data from diverse sensors, satellites and in-situ programs.&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:160,&amp;quot;335559740&amp;quot;:259}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt; &lt;/div&gt; &lt;div&gt; &lt;p&gt;&lt;span data-contrast=&quot;auto&quot;&gt;MOB TAC provides the following multi observations products at global scale:&amp;#160;&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:60,&amp;quot;335559740&amp;quot;:259}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt; &lt;/div&gt; &lt;div&gt; &lt;p&gt;&lt;span data-contrast=&quot;auto&quot;&gt;Blue ocean&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:60,&amp;quot;335559740&amp;quot;:259}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt; &lt;/div&gt; &lt;div&gt; &lt;div&gt; &lt;ul&gt; &lt;li data-leveltext=&quot;&amp;#61607;&quot; data-font=&quot;Wingdings&quot; data-listid=&quot;1&quot; data-list-defn-props=&quot;{&amp;quot;335552541&amp;quot;:1,&amp;quot;335559684&amp;quot;:-2,&amp;quot;335559685&amp;quot;:720,&amp;quot;335559991&amp;quot;:360,&amp;quot;469769226&amp;quot;:&amp;quot;Wingdings&amp;quot;,&amp;quot;469769242&amp;quot;:[9642],&amp;quot;469777803&amp;quot;:&amp;quot;left&amp;quot;,&amp;quot;469777804&amp;quot;:&amp;quot;&amp;#61607;&amp;quot;,&amp;quot;469777815&amp;quot;:&amp;quot;hybridMultilevel&amp;quot;}&quot; aria-setsize=&quot;-1&quot; data-aria-posinset=&quot;1&quot; data-aria-level=&quot;1&quot;&gt;&lt;span data-contrast=&quot;auto&quot;&gt;3D temperature, salinity, geopotential height and geostrophic current fields, both in near-real-time (NRT) and as long time series (REP=Reprocessing) in delayed-mode;&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;li data-leveltext=&quot;&amp;#61607;&quot; data-font=&quot;Wingdings&quot; data-listid=&quot;1&quot; data-list-defn-props=&quot;{&amp;quot;335552541&amp;quot;:1,&amp;quot;335559684&amp;quot;:-2,&amp;quot;335559685&amp;quot;:720,&amp;quot;335559991&amp;quot;:360,&amp;quot;469769226&amp;quot;:&amp;quot;Wingdings&amp;quot;,&amp;quot;469769242&amp;quot;:[9642],&amp;quot;469777803&amp;quot;:&amp;quot;left&amp;quot;,&amp;quot;469777804&amp;quot;:&amp;quot;&amp;#61607;&amp;quot;,&amp;quot;469777815&amp;quot;:&amp;quot;hybridMultilevel&amp;quot;}&quot; aria-setsize=&quot;-1&quot; data-aria-posinset=&quot;2&quot; data-aria-level=&quot;1&quot;&gt;&lt;span data-contrast=&quot;auto&quot;&gt;2D sea surface salinity and sea surface density fields, both in NRT and as REP;&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;li data-leveltext=&quot;&amp;#61607;&quot; data-font=&quot;Wingdings&quot; data-listid=&quot;1&quot; data-list-defn-props=&quot;{&amp;quot;335552541&amp;quot;:1,&amp;quot;335559684&amp;quot;:-2,&amp;quot;335559685&amp;quot;:720,&amp;quot;335559991&amp;quot;:360,&amp;quot;469769226&amp;quot;:&amp;quot;Wingdings&amp;quot;,&amp;quot;469769242&amp;quot;:[9642],&amp;quot;469777803&amp;quot;:&amp;quot;left&amp;quot;,&amp;quot;469777804&amp;quot;:&amp;quot;&amp;#61607;&amp;quot;,&amp;quot;469777815&amp;quot;:&amp;quot;hybridMultilevel&amp;quot;}&quot; aria-setsize=&quot;-1&quot; data-aria-posinset=&quot;3&quot; data-aria-level=&quot;1&quot;&gt;&lt;span data-contrast=&quot;auto&quot;&gt;2D total surface and near-surface currents, both in NRT and as REP;&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;li data-leveltext=&quot;&amp;#61607;&quot; data-font=&quot;Wingdings&quot; data-listid=&quot;1&quot; data-list-defn-props=&quot;{&amp;quot;335552541&amp;quot;:1,&amp;quot;335559684&amp;quot;:-2,&amp;quot;335559685&amp;quot;:720,&amp;quot;335559991&amp;quot;:360,&amp;quot;469769226&amp;quot;:&amp;quot;Wingdings&amp;quot;,&amp;quot;469769242&amp;quot;:[9642],&amp;quot;469777803&amp;quot;:&amp;quot;left&amp;quot;,&amp;quot;469777804&amp;quot;:&amp;quot;&amp;#61607;&amp;quot;,&amp;quot;469777815&amp;quot;:&amp;quot;hybridMultilevel&amp;quot;}&quot; aria-setsize=&quot;-1&quot; data-aria-posinset=&quot;4&quot; data-aria-level=&quot;1&quot;&gt;&lt;span data-contrast=&quot;auto&quot;&gt;3D Vertical velocity fields as REP;&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;li data-leveltext=&quot;&amp;#61607;&quot; data-font=&quot;Wingdings&quot; data-listid=&quot;1&quot; data-list-defn-props=&quot;{&amp;quot;335552541&amp;quot;:1,&amp;quot;335559684&amp;quot;:-2,&amp;quot;335559685&amp;quot;:720,&amp;quot;335559991&amp;quot;:360,&amp;quot;469769226&amp;quot;:&amp;quot;Wingdings&amp;quot;,&amp;quot;469769242&amp;quot;:[9642],&amp;quot;469777803&amp;quot;:&amp;quot;left&amp;quot;,&amp;quot;469777804&amp;quot;:&amp;quot;&amp;#61607;&amp;quot;,&amp;quot;469777815&amp;quot;:&amp;quot;hybridMultilevel&amp;quot;}&quot; aria-setsize=&quot;-1&quot; data-aria-posinset=&quot;5&quot; data-aria-level=&quot;1&quot;&gt;&lt;span data-contrast=&quot;auto&quot;&gt;L2Q and L4 sea surface salinity from SMOS in REP and NRT (only L2Q)&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;/ul&gt; &lt;/div&gt; &lt;/div&gt; &lt;div&gt; &lt;div&gt; &lt;p&gt;&lt;span data-contrast=&quot;auto&quot;&gt;Green ocean&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559685&amp;quot;:0,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/p&gt; &lt;/div&gt; &lt;div&gt; &lt;ul&gt; &lt;li data-leveltext=&quot;&amp;#61607;&quot; data-font=&quot;Wingdings&quot; data-listid=&quot;1&quot; data-list-defn-props=&quot;{&amp;quot;335552541&amp;quot;:1,&amp;quot;335559684&amp;quot;:-2,&amp;quot;335559685&amp;quot;:720,&amp;quot;335559991&amp;quot;:360,&amp;quot;469769226&amp;quot;:&amp;quot;Wingdings&amp;quot;,&amp;quot;469769242&amp;quot;:[9642],&amp;quot;469777803&amp;quot;:&amp;quot;left&amp;quot;,&amp;quot;469777804&amp;quot;:&amp;quot;&amp;#61607;&amp;quot;,&amp;quot;469777815&amp;quot;:&amp;quot;hybridMultilevel&amp;quot;}&quot; aria-setsize=&quot;-1&quot; data-aria-posinset=&quot;1&quot; data-aria-level=&quot;1&quot;&gt;&lt;span data-contrast=&quot;auto&quot;&gt;2D surface carbon data sets of FCO2, pCO2, DIC, Alkalinity, saturation states of surface waters with respect to calcite and aragonite as REP;&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;li data-leveltext=&quot;&amp;#61607;&quot; data-font=&quot;Wingdings&quot; data-listid=&quot;1&quot; data-list-defn-props=&quot;{&amp;quot;335552541&amp;quot;:1,&amp;quot;335559684&amp;quot;:-2,&amp;quot;335559685&amp;quot;:720,&amp;quot;335559991&amp;quot;:360,&amp;quot;469769226&amp;quot;:&amp;quot;Wingdings&amp;quot;,&amp;quot;469769242&amp;quot;:[9642],&amp;quot;469777803&amp;quot;:&amp;quot;left&amp;quot;,&amp;quot;469777804&amp;quot;:&amp;quot;&amp;#61607;&amp;quot;,&amp;quot;469777815&amp;quot;:&amp;quot;hybridMultilevel&amp;quot;}&quot; aria-setsize=&quot;-1&quot; data-aria-posinset=&quot;2&quot; data-aria-level=&quot;1&quot;&gt;&lt;span data-contrast=&quot;auto&quot;&gt;Nutrient and Carbon vertical distribution (including Nitrates, Phosphates, Silicates, pH, pCO2, Alkalinity, DIC) profiles as REP and NRT;&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;li data-leveltext=&quot;&amp;#61607;&quot; data-font=&quot;Wingdings&quot; data-listid=&quot;1&quot; data-list-defn-props=&quot;{&amp;quot;335552541&amp;quot;:1,&amp;quot;335559684&amp;quot;:-2,&amp;quot;335559685&amp;quot;:720,&amp;quot;335559991&amp;quot;:360,&amp;quot;469769226&amp;quot;:&amp;quot;Wingdings&amp;quot;,&amp;quot;469769242&amp;quot;:[9642],&amp;quot;469777803&amp;quot;:&amp;quot;left&amp;quot;,&amp;quot;469777804&amp;quot;:&amp;quot;&amp;#61607;&amp;quot;,&amp;quot;469777815&amp;quot;:&amp;quot;hybridMultilevel&amp;quot;}&quot; aria-setsize=&quot;-1&quot; data-aria-posinset=&quot;3&quot; data-aria-level=&quot;1&quot;&gt;&lt;span data-contrast=&quot;auto&quot;&gt;3D Particulate Organic Carbon (POC), particulate backscattering coefficient (bbp) and Chlorophyll a (Chl-a) fields as REP.&lt;/span&gt;&lt;span data-ccp-props=&quot;{&amp;quot;201341983&amp;quot;:0,&amp;quot;335551550&amp;quot;:6,&amp;quot;335551620&amp;quot;:6,&amp;quot;335559739&amp;quot;:200,&amp;quot;335559740&amp;quot;:276}&quot;&gt;&amp;#160;&lt;/span&gt;&lt;/li&gt; &lt;/ul&gt; &lt;/div&gt; &lt;div&gt; &lt;p&gt;&lt;span data-contrast=&quot;auto&quot;&gt;Parallel to its portfolio, MOB TAC has and will further develop specific expertise about the integration of multiple satellites and in-situ based observations coming from the other CMEMS TACs and projects. &lt;/span&gt;&lt;span data-contrast=&quot;none&quot;&gt;Furthermore, MOB TAC provides specific Ocean Monitoring Indicators (OMIs), based on the above products, to monitor and the global ocean carbon sink.&amp;#8239;&lt;/span&gt;&lt;/p&gt; &lt;/div&gt; &lt;/div&gt;

BACKGROUND:Biogeochemical-Argo floats are collecting an unprecedented number of profiles of optical backscattering measurements in the global ocean. Backscattering (BBP) data are crucial to understanding ocean particle dynamics and the biological carbon pump. Yet, so far, no procedures have been agreed upon to quality control BBP data in real time.METHODS:Here, we present a new suite of real-time quality-control tests and apply them to the current global BBP Argo dataset. The tests were developed by expert BBP users and Argo data managers and have been implemented on a snapshot of the entire Argo dataset.RESULTS:The new tests are able to automatically flag most of the "bad" BBP profiles from the raw dataset.CONCLUSIONS:The proposed tests have been approved by the Biogeochemical-Argo Data Management Team and will be implemented by the Argo Data Assembly Centres to deliver real-time quality-controlled profiles of optical backscattering. Provided they reach a pressure of about 1000 dbar, these tests could also be applied to BBP profiles collected by other platforms.

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of & SIM;1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.