Twenty years of ocean observations with China Argo.

The international Argo program, a global observational array of nearly 4 000 autonomous profiling floats initiated in the late 1990s, which measures the water temperature and salinity of the upper 2 000 m of the global ocean, has revolutionized oceanography. It has been recognized one of the most successful ocean observation systems in the world. Today, the proposed decade action "OneArgo" for building an integrated global, full-depth, and multidisciplinary ocean observing array for beyond 2020 has been endorsed. In the past two decades since 2002, with more than 500 Argo deployments and 80 operational floats currently, China has become an important partner of the Argo program. Two DACs have been established to process the data reported from all Chinese floats and deliver these data to the GDACs in real time, adhering to the unified quality control procedures proposed by the Argo Data Management Team. Several Argo products have been developed and released, allowing accurate estimations of global ocean warming, sea level change and the hydrological cycle, at interannual to decadal scales. In addition, Deep and BGC-Argo floats have been deployed, and time series observations from these floats have proven to be extremely useful, particularly in the analysis of synoptic-scale to decadal-scale dynamics. The future aim of China Argo is to build and maintain a regional Argo fleet comprising approximately 400 floats in the northwestern Pacific, South China Sea, and Indian Ocean, accounting for 9% of the global fleet, in addition to maintaining 300 Deep Argo floats in the global ocean (25% of the global Deep Argo fleet). A regional BGC-Argo array in the western Pacific also needs to be established and maintained.

Biogeographical Classification of the Global Ocean From BGC-Argo Floats.

Biogeographical classifications of the global ocean generalize spatiotemporal trends in species or biomass distributions across discrete ocean biomes or provinces. These classifications are generally based on a combination of remote-sensed proxies of phytoplankton biomass and global climatologies of biogeochemical or physical parameters. However, these approaches are limited in their capacity to account for subsurface variability in these parameters. The deployment of autonomous profiling floats in the Biogeochemical Argo network over the last decade has greatly increased global coverage of subsurface measurements of bio-optical proxies for phytoplankton biomass and physiology. In this study, we used empirical orthogonal function analysis to identify the main components of variability in a global data set of 422 annual time series of Chlorophyll a fluorescence and optical backscatter profiles. Applying cluster analysis to these results, we identified six biomes within the global ocean: two high-latitude biomes capturing summer bloom dynamics in the North Atlantic and Southern Ocean and four mid- and low-latitude biomes characterized by variability in the depth and frequency of deep chlorophyll maximum formation. We report the distribution of these biomes along with associated trends in biogeochemical and physicochemical environmental parameters. Our results demonstrate light and nutrients to explain most variability in phytoplankton distributions for all biomes, while highlighting a global inverse relationship between particle stocks in the euphotic zone and transfer efficiency into the mesopelagic zone. In addition to partitioning seasonal variability in vertical phytoplankton distributions at the global scale, our results provide a potentially novel biogeographical classification of the global ocean.

Deep Chlorophyll Maxima in the Global Ocean: Occurrences, Drivers and Characteristics.

Stratified oceanic systems are characterized by the presence of a so-called Deep Chlorophyll a Maximum (DCM) not detectable by ocean color satellites. A DCM can either be a phytoplankton (carbon) biomass maximum (Deep Biomass Maximum, DBM), or the consequence of photoacclimation processes (Deep photoAcclimation Maximum, DAM) resulting in the increase of chlorophyll a per phytoplankton carbon. Even though these DCM (further qualified as either DBMs or DAMs) have long been studied, no global-scale assessment has yet been undertaken and large knowledge gaps still remain in relation to the environmental drivers responsible for their formation and maintenance. In order to investigate their spatial and temporal variability in the open ocean, we use a global data set acquired by more than 500 Biogeochemical-Argo floats given that DCMs can be detected from the comparative vertical distribution of chlorophyll a concentrations and particulate backscattering coefficients. Our findings show that the seasonal dynamics of the DCMs are clearly region-dependent. High-latitude environments are characterized by a low occurrence of intense DBMs, restricted to summer. Meanwhile, oligotrophic regions host permanent DAMs, occasionally replaced by DBMs in summer, while subequatorial waters are characterized by permanent DBMs benefiting from favorable conditions in terms of both light and nutrients. Overall, the appearance and depth of DCMs are primarily driven by light attenuation in the upper layer. Our present assessment of DCM occurrence and of environmental conditions prevailing in their development lay the basis for a better understanding and quantification of their role in carbon budgets (primary production and export).

Impact of Mesoscale Eddies on Deep Chlorophyll Maxima.

Deep Chlorophyll Maxima (DCM) are ubiquitous features in stratified oceanic systems. Their establishment and maintenance result from hydrographical stability favoring specific environmental conditions with respect to light and nutrient availability required for phytoplankton growth. This stability can potentially be challenged by mesoscale eddies impacting the water column's vertical structure and thus the environmental parameters that condition the subsistence of DCMs. Here, data from the global BGC-Argo float network are collocated with mesoscale eddies to explore their impact on DCMs. We show that cyclonic eddies, by providing optimal light and nutrient conditions, increase the occurrence of DCMs characterized by Deep Biomass Maxima for phytoplankton. In contrast, DCMs in anticyclonic eddies seem to be driven by photoacclimation as they coincide with Deep Acclimation Maxima without biomass accumulation. These findings suggest that the two types of eddies potentially have different impacts on the role of DCMs in global primary production.

Correction of Biogeochemical-Argo Radiometry for Sensor Temperature-Dependence and Drift: Protocols for a Delayed-Mode 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.

Detection of Coccolithophore Blooms With BioGeoChemical-Argo Floats.

Coccolithophores (calcifying phytoplankton) form extensive blooms in temperate and subpolar oceans as evidenced from ocean-color satellites. This study examines the potential to detect coccolithophore blooms with BioGeoChemical-Argo (BGC-Argo) floats, autonomous ocean profilers equipped with bio-optical and physicochemical sensors. We first matched float data to ocean-color satellite data of calcite concentration to select floats that sampled coccolithophore blooms. We identified two floats in the Southern Ocean, which measured the particulate beam attenuation coefficient (c p) in addition to two core BGC-Argo variables, Chlorophyll-a concentration ([Chl-a]) and the particle backscattering coefficient (b bp). We show that coccolithophore blooms can be identified from floats by distinctively high values of (1) the b bp/c p ratio, a proxy for the refractive index of suspended particles, and (2) the b bp/[Chl-a] ratio, measurable by any BGC-Argo float. The latter thus paves the way to global investigations of environmental control of coccolithophore blooms and their role in carbon export.

Detecting Mesopelagic Organisms Using Biogeochemical-Argo Floats.

During the North Atlantic Aerosols and Marine Ecosystems Study in the western North Atlantic, float-based profiles of fluorescent dissolved organic matter and backscattering exhibited distinct spike layers at ∼  300 m. The locations of the spikes were at depths similar or shallower to where a ship-based scientific echo sounder identified layers of acoustic backscatter, an Underwater Vision Profiler detected elevated concentration of zooplankton, and mesopelagic fish were sampled by a mesopelagic net tow. The collocation of spike layers in bio-optical properties with mesopelagic organisms suggests that some can be detected with float-based bio-optical sensors. This opens the door to the investigation of such aggregations/layers in observations collected by the global biogeochemical-Argo array allowing the detection of mesopelagic organisms in remote locations of the open ocean under-sampled by traditional methods.

Small Phytoplankton Shapes Colored Dissolved Organic Matter Dynamics in the North Atlantic Subtropical Gyre.

The North Atlantic subtropical gyre (NASTG) is a model of the future ocean under climate change. Ocean warming signals are hidden within the blue color of these clear waters and can be tracked by understanding the dynamics among phytoplankton chlorophyll ([Chl]) and colored dissolved organic matter (CDOM). In NASTG, [Chl] and CDOM are strongly correlated. Yet, this unusual correlation for open oceans remains unexplained. Here, we test main hypotheses by analyzing high spatiotemporal resolution data collected by Biogeochemical-Argo floats between 2012 and 2018. The direct production of CDOM via phytoplankton metabolism is the main occurring mechanism. More importantly, CDOM dynamics strongly depend on the abundance of picophytoplankton. Our findings thus highlight the critical role of these small organisms under the ocean warming scenario. Picophytoplankton will enhance the production of colored dissolved compounds and, ultimately, impact on the ocean carbon cycle.

Quantifying the Impact of Linear Regression Model in Deriving Bio-Optical Relationships: The Implications on Ocean Carbon Estimations.

Linear regression is widely used in applied sciences and, in particular, in satellite optical oceanography, to relate dependent to independent variables. It is often adopted to establish empirical algorithms based on a finite set of measurements, which are later applied to observations on a larger scale from platforms such as autonomous profiling floats equipped with optical instruments (e.g., Biogeochemical Argo floats; BGC-Argo floats) and satellite ocean colour sensors (e.g., SeaWiFS, VIIRS, OLCI). However, different methods can be applied to a given pair of variables to determine the coefficients of the linear equation fitting the data, which are therefore not unique. In this work, we quantify the impact of the choice of "regression method" (i.e., either type-I or type-II) to derive bio-optical relationships, both from theoretical perspectives and by using specific examples. We have applied usual regression methods to an in situ data set of particulate organic carbon (POC), total chlorophyll-a (TChla), optical particulate backscattering coefficient (bbp), and 19 years of monthly TChla and bbp ocean colour data. Results of the regression analysis have been used to calculate phytoplankton carbon biomass (Cphyto) and POC from: i) BGC-Argo float observations; ii) oceanographic cruises, and iii) satellite data. These applications enable highlighting the differences in Cphyto and POC estimates relative to the choice of the method. An analysis of the statistical properties of the dataset and a detailed description of the hypothesis of the work drive the selection of the linear regression method.

Change in water column total chlorophyll-a in the Mediterranean revealed by satellite observation.

Chlorophyll-a (Chl-a) is a crucial pigment in algae and macrophytes, which makes the concentration of total Chl-a in the water column (total Chl-a) an essential indicator for estimating the primary productivity and carbon cycle of the ocean. Integrating the Chl-a concentration at different depths (Chl-a profile) is an important way to obtain the total Chl-a. However, due to limited cost and technology, it is difficult to measure Chl-a profiles directly in a spatially continuous and high-resolution way. In this study, we proposed an integrated strategy model that combines three different machine learning methods (PSO-BP, random forest and gradient boosting) to predict the Chl-a profile in the Mediterranean by using several sea surface variables (photosynthetically active radiation, spectral irradiance, sea surface temperature, wind speed, euphotic depth and KD490) and subsurface variables (mixed layer depth) observed by or estimated from satellite and BGC-Argo float observations. After accuracy estimation, the integrated model was utilized to generate the time series total Chl-a in the Mediterranean from 2003 to 2021. By analysing the time series results, it was found that seasonal fluctuation contributed the most to the variation in total Chl-a. In addition, there was an overall decreasing trend in the Mediterranean phytoplankton biomass, with the total Chl- decreasing at a rate of 0.048 mg/m2 per year, which was inferred to be related to global warming and precipitation reduction based on comprehensive analysis with sea surface temperature and precipitation data.

A Conceptual Approach to Partitioning a Vertical Profile of Phytoplankton Biomass Into Contributions From Two Communities.

We describe an approach to partition a vertical profile of chlorophyll-a concentration into contributions from two communities of phytoplankton: one (community 1) that resides principally in the turbulent mixed-layer of the upper ocean and is observable through satellite visible radiometry; the other (community 2) residing below the mixed-layer, in a stably stratified environment, hidden from the eyes of the satellite. The approach is tuned to a time-series of profiles from a Biogeochemical-Argo float in the northern Red Sea, selected as its location transitions from a deep mixed layer in winter (characteristic of vertically well-mixed systems) to a shallow mixed layer in the summer with a deep chlorophyll-a maximum (characteristic of vertically stratified systems). The approach is extended to reproduce profiles of particle backscattering, by deriving the chlorophyll-specific backscattering coefficients of the two communities and a background coefficient assumed to be dominated by non-algal particles in the region. Analysis of the float data reveals contrasting phenology of the two communities, with community 1 blooming in winter and 2 in summer, community 1 negatively correlated with epipelagic stratification, and 2 positively correlated. We observe a dynamic chlorophyll-specific backscattering coefficient for community 1 (stable for community 2), positively correlated with light in the mixed-layer, suggesting seasonal changes in photoacclimation and/or taxonomic composition within community 1. The approach has the potential for monitoring vertical changes in epipelagic biogeography and for combining satellite and ocean robotic data to yield a three-dimensional view of phytoplankton distribution.

Real-time quality control of optical backscattering data from Biogeochemical-Argo floats.

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.