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Biogeochemical- (BGC-) Argo aims to deploy and maintain a global array of autonomous profiling floats to monitor ocean biogeochemistry. With over 250,000 profiles collected so far, the BGC-Argo network is rapidly expanding toward the target of a sustained fleet of 1,000 floats. These floats prioritize the measurement of six key properties: oxygen, nitrate, pH, chlorophyll-a, suspended particles, and downwelling light. To assess the current biogeochemical state of the ocean, its variability, and trends with confidence, it is crucial to quality control these measurements. Accordingly, BGC-Argo maintains a quality control system using manual inspection and parameter-specific algorithms for flagging and adjusting data. In this study, we provide a census of the quantity and quality of measurements from BGC-Argo based on their quality flagging system. The purpose of this census is to assess the current status of the array in terms of data quality, how data quality has changed over time, and to provide a better understanding of the quality-controlled data to current and future users. Alongside increasing profile numbers and spatial coverage, we report that for most parameters between 80 and 95% of the profiles collected so far contain high-quality BGC data, with an exception for pH. The quality of pH profiles has seen a large improvement in the last five years and is on track to match the data quality of other BGC parameters. We highlight how BGC-Argo is improving and discuss strategies to increase the quality and quantity of BGC profiles available to users. This census shows that tracking percentages of high-quality data through time is useful for monitoring float sensor technology and helpful for ensuring the long-term success of BGC-Argo.

Deposition of Siberian wildfire aerosols, which contained nitrogen, enhanced phytoplankton growth in the eastern Eurasian Basin of the Arctic Ocean in summer 2014, suggest satellite-based ocean color data and atmospheric transport modeling.

The vertical distribution of light and its spectral composition are critical factors influencing numerous physical, chemical, and biological processes within the oceanic water column. In this study, we present vertically resolved models of downwelling irradiance (ED) at three different wavelengths and photosynthetically available radiation (PAR) on a global scale. These models rely on the SOCA (Satellite Ocean Color merged with Argo data to infer bio-optical properties to depth) methodology, which is based on an artificial neural network (ANN). The new light models are trained with light profiles (ED/PAR) acquired from BioGeoChemical-Argo (BGC-Argo) floats. The model inputs consist of surface ocean color radiometry data (i.e., Rrs, PAR, and kd(490)) derived by satellite and extracted from the GlobColour database, temperature and salinity profiles originating from BGC-Argo, as well as temporal components (day of the year and local time in cyclic transformation). The model outputs correspond to ED profiles at the three wavelengths of the BGC-Argo measurements (i.e., 380, 412, and 490 nm) and PAR profiles. We assessed the retrieval of light profiles by these light models using three different datasets: BGC-Argo profiles that were not used for the training (i.e., 20% of the initial database); data from four independent BGC-Argo floats that were used neither for the training nor for the 20% validation dataset; and the SeaBASS database (in situ data collected from various oceanic cruises). The light models show satisfactory predictions when thus compared with real measurements. From the 20% validation database, the light models retrieve light variables with high accuracies (root mean squared error (RMSE)) of 76.42 μmol quanta m−2 s−1 for PAR and 0.04, 0.08, and 0.09 W m−2 nm−1 for ED380, ED412, and ED490, respectively. This corresponds to a median absolute percent error (MAPE) that ranges from 37% for ED490 and PAR to 39% for ED380 and ED412. The estimated accuracy metrics across these three validation datasets are consistent and demonstrate the robustness and suitability of these light models for diverse global ocean applications.

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.

The ocean’s meso- and submeso-scales (1-100 km, days to weeks) host features like filaments and eddies that have a key structuring effect on phytoplankton distribution, but that due to their ephemeral nature, are challenging to observe. This problem is exacerbated in regions with heavy cloud coverage and/or difficult access like the Southern Ocean, where observations of phytoplankton distribution by satellite are sparse, manned campaigns costly, and automated devices limited by power consumption. Here, we address this issue by considering high-resolution in-situ data from 18 bio-logging devices deployed on southern elephant seals (Mirounga leonina) in the Kerguelen Islands between 2018 and 2020. These devices have submesoscale-resolving capabilities of light profiles due to the high spatio-temporal frequency of the animals’ dives (on average 1.1 +-0.6 km between consecutive dives, up to 60 dives per day), but observations of fluorescence are much coarser due to power constraints. Furthermore, the chlorophyll a concentrations derived from the (uncalibrated) bio-logging devices’ fluorescence sensors lack a common benchmark to properly qualify the data and allow comparisons of observations. By proposing a method based on functional data analysis, we show that a reliable predictor of chlorophyll a concentration can be constructed from light profiles (14 686 in our study). The combined use of light profiles and matchups with satellite ocean-color data enable effective (1) homogenization then calibration of the bio-logging devices’ fluorescence data and (2) filling of the spatial gaps in coarse-grained fluorescence sampling. The developed method improves the spatial resolution of the chlorophyll a field description from ~30 km to ~12 km. These results open the way to empirical study of the coupling between physical forcing and biological response at submesoscale in the Southern Ocean, especially useful in the context of upcoming high-resolution ocean-circulation satellite missions.

In-situ fluorescence is a widely used method to estimate the chlorophyll-a (Chla) concentration, a proxy of the phytoplankton biomass. With the emergence of autonomous platforms such as BioGeoChemical-Argo (BGC-Argo) profiling floats, its use has expanded to global scale observations. However, the relationship between in-situ fluorescence and Chla may vary significantly, leading to major discrepancies between oceanic regions. This study aims to investigate the main sources of the natural variability in the in-situ fluorescence signal in the global open ocean, specifically the influence of the phytoplankton community composition. In this view, we analyzed a combination of three datasets comprising concomitant measurements of in-situ fluorescence, pigment concentrations and phytoplankton absorption spectra. Two datasets cover several contrasted bioregions of the global ocean whereas the third one consists of a regional time series in the northwestern Mediterranean Sea, which allows to examine the effect of phytoplankton community composition on the fluorescence signal on the global, seasonal and vertical scales. We studied the variability of the two major drivers of the natural variability of the fluorescence process, i.e. the light absorption and the fluorescence quantum yield of phytoplankton, in regards of the variability of the pigment composition of the communities. The community composition correlates substantially with the Chla-to-fluorescence ratio, with high fluorescence values associated with phytoplankton communities dominated by large cells. This trend may be explained by the combined effects of the community composition on the phytoplankton absorption coefficient and the fluorescence quantum yield, and is consistently observed globally, seasonally and vertically. Non-photosynthetic pigments also appear to play a critical role in oligotrophic surface waters, leading to a reduction of the quantum yield of fluorescence. The results indicate that the phytoplankton community composition plays a key role in the relationship between the in-situ fluorescence signal and Chla concentration. Therefore, we suggest that taking into account the composition of phytoplankton communities in the retrieval of the Chla concentration from current in-situ fluorometers, those mounted on BGC-Argo floats in particular, would lead to a better estimation of the phytoplankton biomass on a wide range of spatial and temporal scales.

A recent paradigm explains that the downward pumping of biogenic carbon in the ocean is performed by the combined action of six different biological carbon pumps (BCPs): the biological gravitational pump, the physically driven pumps (Mixed Layer Pump, Eddy Subduction Pump and Large-scale Subduction Pump), and the animal-driven pumps (diurnal and seasonal vertical migrations of zooplankton and larger animals). Here, we propose a research community approach to implement the new paradigm through the integrated study of these BCPs in the World Ocean. The framework to investigate the BCPs combines measurements from different observational platforms, i.e., oceanographic ships, satellites, moorings, and robots (gliders, floats, and robotic surface vehicles such as wavegliders and saildrones). We describe the following aspects of the proposed research framework: variables and processes to be measured in both the euphotic and twilight zones for the different BCPs; spatial and temporal scales of occurrence of the various BCPs; selection of key regions for integrated studies of the BCPs; multi-platform observational strategies; and upscaling of results from regional observations to the global ocean using deterministic models combined with data assimilation and machine learning to make the most of the wealth of unique measurements. The proposed approach has the potential not only to bring together a large multidisciplinary community of researchers, but also to usher the community toward a new era of discoveries in ocean sciences.

Hydrothermal activity is recognized to be significant in regulating the dynamics of trace elements in the ocean. Here the authors report the first observational evidence of upwelled hydrothermally influenced deep waters stimulating massive phytoplankton blooms in the Southern Ocean.

The international array of profiling floats known as Argo is a major component of the global ocean- and climate-observing system. In 2010, the NAOS (Novel Argo Observing System) project was selected as part of France’s Equipex “Investissement d’Avenir” program. The objectives of NAOS were to consolidate the French contribution to the Argo core mission (global temperature and salinity measurements down to 2,000 m) as well as to develop the future generation of French Argo profiling floats and prepare the next phase of the Argo program with an extension to the deep ocean (Deep-Argo), biogeochemistry (BGC-Argo) and polar seas. This paper summarizes the main technological advances and at-sea validations carried out as part of NAOS: development of a deep (4,000 m) float, a new BGC float for Research & Development (R&D) applications, and a BGC float for deployments in Arctic areas, assessment of a new density and Absolute Salinity optical sensor, improvement of the reliability of the standard Argo float, and upgraded satellite-transmission performance. French profiling floats developed in this way are now operational and among the most deployed worldwide, and the density sensor is the most promising of its kind for profiling floats applications.

Argo, the international array of profiling floats, is a major component of the global ocean and climate observing system. In 2010, the NAOS (Novel Argo Observing System) project was selected as part of the French “Investissements d’Avenir” Equipex program. The objectives of NAOS were to consolidate the French contribution to Argo’s core mission (global temperature and salinity measurements down to 2000 m), and also to develop the future generation of French Argo profiling floats and prepare the next phase of the Argo program with an extension to the deep ocean (Deep Argo), biogeochemistry (BGC-Argo) and polar seas. This paper summarizes how NAOS has met its objectives. The project significantly boosted France’s contribution to Argo’s core mission by deploying more than 100 NAOS standard Argo profiling floats. In addition, NAOS deployed new-generation floats as part of three scientific experiments: biogeochemical floats in the Mediterranean Sea, biogeochemical floats in the Arctic Ocean, and deep floats with oxygen sensors in the North Atlantic. The experiment in the Mediterranean Sea, launched in 2012, implemented and maintained a network of BGC-Argo floats at basin scale for the first time. The 32 BGC-Argo floats deployed and about 4000 BGC profiles collected have vastly improved characterization of the biogeochemical and ecosystem dynamics of the Mediterranean. Meanwhile, experiments in the Arctic and in the North Atlantic, starting in 2015 and deploying 20 Arctic BGC floats and 23 deep floats, have provided unique observations on biogeochemical cycles in the Arctic and deep-water masses, as well as ocean circulation variability in the North Atlantic. NAOS has therefore paved the way to the new operational phase of the Argo program in France that includes BGC and Deep Argo extensions. The objectives and characteristics of this new phase of Argo-France are discussed in the conclusion.

In the past two decades, the Argo Program has collected, processed, and distributed over two million vertical profiles of temperature and salinity from the upper two kilometers of the global ocean. A similar number of subsurface velocity observations near 1,000 dbar have also been collected. This paper recounts the history of the global Argo Program, from its aspiration arising out of the World Ocean Circulation Experiment, to the development and implementation of its instrumentation and telecommunication systems, and the various technical problems encountered. We describe the Argo data system and its quality control procedures, and the gradual changes in the vertical resolution and spatial coverage of Argo data from 1999 to 2019. The accuracies of the float data have been assessed by comparison with high-quality shipboard measurements, and are concluded to be 0.002°C for temperature, 2.4 dbar for pressure, and 0.01 PSS-78 for salinity, after delayed-mode adjustments. Finally, the challenges faced by the vision of an expanding Argo Program beyond 2020 are discussed.

A regional neural network-based method, “CANYON-MED” is developed to estimate nutrients and carbonate system variables specifically in the Mediterranean Sea over the water column from pressure, temperature, salinity, and oxygen together with geolocation and date of sampling. Six neural network ensembles were developed, one for each variable (i.e., three macronutrients: nitrates (NO3-), phosphates (PO43-) and silicates (SiOH4), and three carbonate system variables: pH on the total scale (pHT), total alkalinity (AT), and dissolved inorganic carbon or total carbon (CT), trained using a specific quality-controlled dataset of reference “bottle” data in the Mediterranean Sea. This dataset is representative of the peculiar conditions of this semi-enclosed sea, as opposed to the global ocean. For each variable, the neural networks were trained on 80% of the data chosen randomly and validated using the remaining 20%. CANYON-MED retrieved the variables with good accuracies (Root Mean Squared Error): 0.73 μmol.kg–1 for NO3-, 0.045 μmol.kg–1 for PO43- and 0.70 μmol.kg–1 for Si(OH)4, 0.016 units for pHT, 11 μmol.kg–1 for AT and 10 μmol.kg–1 for CT. A second validation on the ANTARES independent time series confirmed the method’s applicability in the Mediterranean Sea. After comparison to other existing methods to estimate nutrients and carbonate system variables, CANYON-MED stood out as the most robust, using the aforementioned inputs. The application of CANYON-MED on the Mediterranean Sea data from autonomous observing systems (integrated network of Biogeochemical-Argo floats, Eulerian moorings and ocean gliders measuring hydrological properties together with oxygen concentration) could have a wide range of applications. These include data quality control or filling gaps in time series, as well as biogeochemical data assimilation and/or the initialization and validation of regional biogeochemical models still lacking crucial reference data. Matlab and R code are available at https:// github.com/MarineFou/CANYON-MED/.

The decline of sea-ice thickness, area, and volume due to the transition from multi-year to first-year sea ice has improved the under-ice light environment for pelagic Arctic ecosystems. One unexpected and direct consequence of this transition, the proliferation of under-ice phytoplankton blooms (UIBs), challenges the paradigm that waters beneath the ice pack harbor little planktonic life. Little is known about the diversity and spatial distribution of UIBs in the Arctic Ocean, or the environmental drivers behind their timing, magnitude, and taxonomic composition. Here, we compiled a unique and comprehensive dataset from seven major research projects in the Arctic Ocean (11 expeditions, covering the spring sea-ice-covered period to summer ice-free conditions) to identify the environmental drivers responsible for initiating and shaping the magnitude and assemblage structure of UIBs. The temporal dynamics behind UIB formation are related to the ways that snow and sea-ice conditions impact the under-ice light field. In particular, the onset of snowmelt significantly increased under-ice light availability (>0.1–0.2 mol photons m–2 d–1), marking the concomitant termination of the sea-ice algal bloom and initiation of UIBs. At the pan-Arctic scale, bloom magnitude (expressed as maximum chlorophyll a concentration) was predicted best by winter water Si(OH)4 and PO43– concentrations, as well as Si(OH)4:NO3– and PO43–:NO3– drawdown ratios, but not NO3– concentration. Two main phytoplankton assemblages dominated UIBs (diatoms or Phaeocystis), driven primarily by the winter nitrate:silicate (NO3–:Si(OH)4) ratio and the under-ice light climate. Phaeocystis co-dominated in low Si(OH)4 (i.e., NO3:Si(OH)4 molar ratios >1) waters, while diatoms contributed the bulk of UIB biomass when Si(OH)4 was high (i.e., NO3:Si(OH)4 molar ratios

The necessity of wide, global-scale observing systems for marine biogeochemistry emerged dramatically in the last decade. A global network based on Biogeochemical (BGC) Argo floats is considered to be one of the most promising approaches for reaching this goal. As a first step, pilot studies were encouraged to test the feasibility of a global BGC-Argo array, to consolidate the methods and practices under development, and to set up the array’s characteristics. A pilot study in The Mediterranean Sea—deemed a suitable candidate for a test case because it combines a relatively large diversity of oceanic BGC conditions in a reduced open-ocean basin—was consequently approved as a part of the “Novel Argo ocean Observing System” (NAOS) project, a French national initiative to promote, consolidate, and develop the Argo network. We present here a first assessment of the NAOS Mediterranean array, in view of scientific choices on observing-system strategy, on implementation and statistics on network performances, and on data-quality control.

Measuring the underwater light field is a key mission of the international Biogeochemical-Argo program. Since 2012, 0–250 dbar profiles of downwelling irradiance at 380, 412 and 490 nm besides photosynthetically available radiation (PAR) have been acquired across the globe every 1 to 10 days. The resulting unprecedented amount of radiometric data has been previously quality-controlled for real-time distribution and ocean optics applications, yet some issues affecting the accuracy of measurements at depth have been identified such as changes in sensor dark responsiveness to ambient temperature, with time and according to the material used to build the instrument components. Here, we propose a quality-control procedure to solve these sensor issues to make Argo radiometry data available for delayed-mode distribution, with associated error estimation. The presented protocol requires the acquisition of ancillary radiometric measurements at the 1000 dbar parking depth and night-time profiles. A test on >10,000 profiles from across the world revealed a quality-control success rate >90% for each band. The procedure shows similar performance in re-qualifying low radiometry values across diverse oceanic regions. We finally recommend, for future deployments, acquiring daily 1000 dbar measurements and one night profile per year, preferably during moonless nights and when the temperature range between the surface and 1000 dbar is the largest.

The Argo Program has been implemented and sustained for almost two decades, as a global array of about 4000 profiling floats. Argo provides continuous observations of ocean temperature and salinity versus pressure, from the sea surface to 2000 dbar. The successful installation of the Argo array and its innovative data management system arose opportunistically from the combination of great scientific need and technological innovation. Through the data system, Argo provides fundamental physical observations with broad societally-valuable applications, built on the cost-efficient and robust technologies of autonomous profiling floats. Following recent advances in platform and sensor technologies, even greater opportunity exists now than 20 years ago to (i) improve Argo’s global coverage and value beyond the original design, (ii) extend Argo to span the full ocean depth, (iii) add biogeochemical sensors for improved understanding of oceanic cycles of carbon, nutrients, and ecosystems, and (iv) consider experimental sensors that might be included in the future, for example to document the spatial and temporal patterns of ocean mixing. For Core Argo and each of these enhancements, the past, present, and future progression along a path from experimental deployments to regional pilot arrays to global implementation is described. The objective is to create a fully global, top-to-bottom, dynamically complete, and multidisciplinary Argo Program that will integrate seamlessly with satellite and with other in situ elements of the Global Ocean Observing System (Legler et al., 2015). The integrated system will deliver operational reanalysis and forecasting capability, and assessment of the state and variability of the climate system with respect to physical, biogeochemical, and ecosystems parameters. It will enable basic research of unprecedented breadth and magnitude, and a wealth of ocean-education and outreach opportunities.

The Biogeochemical-Argo program (BGC-Argo) is a new profiling-float-based, ocean wide, and distributed ocean monitoring program which is tightly linked to, and has benefited significantly from, the Argo program. The community has recommended for BGC-Argo to measure six additional properties in addition to pressure, temperature and salinity measured by Argo, to include oxygen, pH, nitrate, downwelling light, chlorophyll fluorescence and the optical backscattering coefficient. The purpose of this addition is to enable the monitoring of ocean biogeochemistry and health, and in particular, monitor major processes such as ocean deoxygenation, acidification and warming and their effect on phytoplankton, the main source of energy of marine ecosystems. Here we describe the salient issues associated with the operation of the BGC-Argo network, with information useful for those interested in deploying floats and using the data they produce. The topics include float testing, deployment and increasingly, recovery. Aspects of data management, processing and quality control are covered as well as specific issues associated with each of the six BGC-Argo sensors. In particular, it is recommended that water samples be collected during float deployment to be used for validation of sensor output.

The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical and biogeochemical ocean and sea-ice state for the global ocean and the European regional seas. CMEMS serves a wide range of users (more than 15,000 users are now registered to the service) and applications. Observations are a fundamental pillar of the CMEMS value-added chain that goes from observation to information and users. Observations are used by CMEMS Thematic Assembly Centres (TACs) to derive high-level data products and by CMEMS Monitoring and Forecasting Centres (MFCs) to validate and constrain their global and regional ocean analysis and forecasting systems. This paper presents an overview of CMEMS, its evolution, and how the value of in situ and satellite observations is increased through the generation of high-level products ready to be used by downstream applications and services. The complementary nature of satellite and in situ observations is highlighted. Long-term perspectives for the development of CMEMS are described and implications for the evolution of the in situ and satellite observing systems are outlined. Results from Observing System Evaluations (OSEs) and Observing System Simulation Experiments (OSSEs) illustrate the high dependencies of CMEMS systems on observations. Finally future CMEMS requirements for both satellite and in situ observations are detailed.

An efficient system to produce in situ high quality radiometric measurements is compulsory to rigorously perform the vicarious calibration of satellite sensors dedicated to Ocean Color Radiometry (OCR) and to validate their derived products. This requirement is especially needed during the early stages of an OCR satellite activity or for remote areas poorly covered by oceanographic cruises with possible bio-optical anomalies. Taking advantage of Argo's profiling float technology, we present a new autonomous profiling float dedicated to in situ radiometric measurements. The float is based on the Provor CTS5 (manufacturer NKE) with an added novel two protruding arm design allowing for sensor redundancies, shading mitigation and near-surface data. Equipped with two identical radiometers on each arm that measure downward irradiance and upwelling radiance at seven wavelengths, the ProVal float generates both redundant radiometric profiles as well as an estimate of Remote Sensing Reflectance. Results from 449 profiles obtained in the NW Mediterranean Sea and in the Indian sector of the Southern Ocean are presented to illustrate the ProVal float technical maturity. Analysis of the behavior of the profiling float, including tilting and ascent speeds is presented. The vertical stability of the ProVal exhibits 85% of surface data of the Mediterranean Sea with a tilt smaller than 10 degrees. This percentage is 40% in the Southern Ocean due to rougher seas. Redundant sensors provide a characterization of the relative drift between sensors over the deployment which is found to be

This work presents two new methods to estimate oceanic alkalinity (AT), dissolved inorganic carbon (CT), pH, and pCO2 from temperature, salinity, oxygen, and geolocation data. “CANYON-B” is a Bayesian neural network mapping that accurately reproduces GLODAPv2 bottle data and the biogeochemical relations contained therein. “CONTENT” combines and refines the four carbonate system variables to be consistent with carbonate chemistry. Both methods come with a robust uncertainty estimate that incorporates information from the local conditions. They are validated against independent GO-SHIP bottle and sensor data, and compare favorably to other state-of-the-art mapping methods. As “dynamic climatologies” they show comparable performance to classical climatologies on large scales but a much better representation on smaller scales (40–120 d, 500–1,500 km) compared to in situ data. The limits of these mappings are explored with pCO2 estimation in surface waters, i.e., at the edge of the domain with high intrinsic variability. In highly productive areas, there is a tendency for pCO2 overestimation due to decoupling of the O2 and C cycles by air-sea gas exchange, but global surface pCO2 estimates are unbiased compared to a monthly climatology. CANYON-B and CONTENT are highly useful as transfer functions between components of the ocean observing system (GO-SHIP repeat hydrography, BGC-Argo, underway observations) and permit the synergistic use of these highly complementary systems, both in spatial/temporal coverage and number of observations. Through easily and robotically-accessible observations they allow densification of more difficult-to-observe variables (e.g., 15 times denser AT and CT compared to direct measurements). At the same time, they give access to the complete carbonate system. This potential is demonstrated by an observation-based global analysis of the Revelle buffer factor, which shows a significant, high latitude-intensified increase between +0.1 and +0.4 units per decade. This shows the utility that such transfer functions with realistic uncertainty estimates provide to ocean biogeochemistry and global climate change research. In addition, CANYON-B provides robust and accurate estimates of nitrate, phosphate, and silicate. Matlab and R code are available at https://github.com/HCBScienceProducts/.

A neural network-based method (CANYON: CArbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) was developed to estimate water-column (i.e., from surface to 8,000 m depth) biogeochemically relevant variables in the Global Ocean. These are the concentrations of three nutrients [nitrate (NO3−), phosphate (PO43−), and silicate (Si(OH)4)] and four carbonate system parameters [total alkalinity (AT), dissolved inorganic carbon (CT), pH (pHT), and partial pressure of CO2 (pCO2)], which are estimated from concurrent in situ measurements of temperature, salinity, hydrostatic pressure, and oxygen (O2) together with sampling latitude, longitude, and date. Seven neural-networks were developed using the GLODAPv2 database, which is largely representative of the diversity of open-ocean conditions, hence making CANYON potentially applicable to most oceanic environments. For each variable, CANYON was trained using 80 % randomly chosen data from the whole database (after eight 10° × 10° zones removed providing an “independent data-set” for additional validation), the remaining 20 % data were used for the neural-network test of validation. Overall, CANYON retrieved the variables with high accuracies (RMSE): 1.04 μmol kg−1 (NO3−), 0.074 μmol kg−1 (PO43−), 3.2 μmol kg−1 (Si(OH)4), 0.020 (pHT), 9 μmol kg−1 (AT), 11 μmol kg−1 (CT) and 7.6 % (pCO2) (30 μatm at 400 μatm). This was confirmed for the eight independent zones not included in the training process. CANYON was also applied to the Hawaiian Time Series site to produce a 22 years long simulated time series for the above seven variables. Comparison of modeled and measured data was also very satisfactory (RMSE in the order of magnitude of RMSE from validation test). CANYON is thus a promising method to derive distributions of key biogeochemical variables. It could be used for a variety of global and regional applications ranging from data quality control to the production of datasets of variables required for initialization and validation of biogeochemical models that are difficult to obtain. In particular, combining the increased coverage of the global Biogeochemical-Argo program, where O2 is one of the core variables now very accurately measured, with the CANYON approach offers the fascinating perspective of obtaining large-scale estimates of key biogeochemical variables with unprecedented spatial and temporal resolutions. The Matlab and R codes of the proposed algorithms are provided as Supplementary Material.

Pigment-based phytoplankton community composition and primary production were investigated for the first time in the Red Sea in February-April 2015 to demonstrate how the strong south to north environmental gradients determine phytoplankton community structure in Red Sea offshore regions (along the central axis). Taxonomic pigments were used as size group markers of pico, nano-, and microphytoplankton. Phytoplankton primary production rates associated with the three phytoplankton groups (pico-, nano-, and microphytoplankton) were estimated using a bio-optical model. Pico- (Synechococcus and Prochlorococcus sp.) and Nanophytoplankton (Prymnesiophytes and Pelagophytes) were the dominant size groups and contributed to 49 and 38%, respectively, of the phytoplankton biomass. Microphytoplankton (diatoms) contributed to 13% of the phytoplankton biomass within the productive layer (1.5 Zeu). Sub-basin and mesoscale structures (cyclonic eddy and mixing) were exceptions to this general trend. In the southern Red Sea, diatoms and picophytoplankton contributed to 27 and 31% of the phytoplankton biomass, respectively. This result induced higher primary production rates (430 ± 50 mgC m−2 d−1) in this region (opposed to CRS and NRS). The cyclonic eddy contained the highest microphytoplankton proportion (45% of TChla) and the lowest picophytoplankton contribution (17% of TChla) while adjacent areas were dominated by pico- and nano-phytoplankton. We estimated that the cyclonic eddy is an area of enhanced primary production, which is up to twice those of the central part of the basin. During the mixing of the water column in the extreme north of the basin, we observed the highest TChla integrated (40 mg m−2) and total primary production rate (640 mgC m−2 d−1) associated with the highest nanophytoplankton contribution (57% of TChla). Microphytoplankton were a major contributor to total primary production (54%) in the cyclonic eddy. The contribution of picophytoplankton (Synechococcus and Prochlorococcus sp.) reached maximum values (49%) in the central Red Sea. Nanophytoplankton seem to provide a ubiquitous substantial contribution (30–56%). Our results contribute to providing new insights on the spatial distribution and structure of phytoplankton groups. An understanding and quantification of the carbon cycle in the Red Sea was made based on estimates of primary production associated with pico-, nano-, and microphytoplankton.

Chemical and biological sensor technologies have advanced rapidly in the past five years. Sensors that require low power and operate for multiple years are now available for oxygen, nitrate, and a variety of bio-optical properties that serve as proxies for important components of the carbon cycle (e.g., particulate organic carbon). These sensors have all been deployed successfully for long periods, in some cases more than three years, on platforms such as profiling floats or gliders. Technologies for pH, pCO2, and particulate inorganic carbon are maturing rapidly as well. These sensors could serve as the enabling technology for a global biogeochemical observing system that might operate on a scale comparable to the current Argo array. Here, we review the scientific motivation and the prospects for a global observing system for ocean biogeochemistry.