The diffuse attenuation coefficient of downward irradiance Kd (λ) is an important parameter in regulating physical and biogeochemical processes in the upper oceanic layer. Remote sensing of Kd(λ) allows for repetitive temporal and spatial measurements at the global scale. Accurate retrieval of Kd (λ) from ocean color satellite sensors is therefore of interest in constraining many oceanic processes.

Estimates of Kd (λ) from 3 different published algorithms spanning from explicit-empirical (NASA), semi-analytical (Lee et al., 2005), and implicity-empirical (Jamet et al., 2012) over 5 satellites sensors (MODIS-aqua, MODIS-terra, VIIRS–SNPP, VIIRS-JN, and Sentinel-3) were compared to autonomous profiling floats (BGC-Argo) measurements of Kd (λ) and Ed (λ, 0-) at visible wavelengths shared by the floats’ and sensors’. Photosynthetically Available Radiation (PAR) was also retrieved from both the floats and the sensors and Kd (PAR) was computed. Advantages of BGC-Argo measurements compared to ship-born ones include 1. uniform sampling in time throughout the year, 2. Large spatial coverage and 3. lack of shading by platform.

Before using them, downwelling irradiances (Ed (λ)) of the ~37000 retrieved profiles from floats were quality-controlled (QC) and extrapolated to the surface to calculate Ed (0-). The QC removed any dark signal along the Ed(λ)/PAR profile and identified profiles with clouds, wave-focusing, and spike occurrences based on the shape (Organelli et. al, 2016). Matched-up satellite observations of Remote Sensing Reflectance (Rrs) were used to compute satellite-derived Kd (λ) for comparison with in situ float measurements.

Over 2,000 matchups between Float and Satellite sensors retrieved Kd (λ) values ranging from ~ 0.01 to 0.53 m-1. Our results show that although all 3 algorithms are overall good predictors of Kd (λ), for a given sensor’s matchups, each algorithm produced statistically different Kd (λ) distributions from the others. Algorithms results diverged the most for low Kd values, i.e., for the clearest waters.

This study shows the value of using BGC-Argo floats as validation for remote sensing products. The recent development of instrumenting the fleet with hyperspectral radiometers should be encouraged, as it will provide even better constraints on algorithms associated with the upcoming NASA PACE mission, which will have a Hyperspectral radiometer.

Ocean colour data are defined as an Essential Climate Variable by the CEOS (Committee on Earth Observation Satellites). The information retrieved from ocean colour is primarily the spectrum of marine reflectance, from which other products are derived. Especially, it is possible to derive the chlorophyll concentration, an important variable reflecting the quantity of phytoplankton which is at the basis of the marine food web and plays a major role in the global carbon cycle.

However, satellite observations require regular calibration and validation, to check for potential sensor drift, local algorithm mismatch, etc. To perform a robust validation of the chlorophyll concentration, reference in situ measurements should cover a large fraction of the global ocean. However, the survey performed by the Copernicus Cal/Val Solution (CCVS) H2020 project revealed a gap of available reference measurements especially in polar regions (Ligi et al., 2021). Other issues identified concern data timeliness (which is critical for current operational missions such as Sentinel-3) and restrictive data policies on some measurements.

Thanks to their progressively increasing network, the BGC-Argo floats could address some of these limitations. Today, 218 BGC-Argo floats are equipped with fluorimeter to document the chlorophyll concentration. For ocean colour validation, these autonomous platforms present the advantage to deliver data in near real time (after a first step of quality control), and cover most of the global ocean.
BGC-Argo data are currently used operationally for the validation of ocean colour data, and specially, chlorophyll concentration in the frame of the Copernicus Marine Service (OC-TAC – Quality Control and Validation http://octac.acri.fr/).

Use of the BGC-Argo data for the validation of chlorophyll product from Sentinel-3 appears to be especially relevant to palliate the relatively few numbers of traditional HPLC in situ data for the last years. Thanks to their additional sensors (temperature, salinity, radiometer, etc…), BGC-Argo would allow to explain potential deviations between in situ and remote sensing measurements (e.g., wind induced mixing, particles origins). In addition, BGC-Argo data can be cited for the validation for other ocean colour products, as the particulate backscattering (bbp) or the diffuse attenuation coefficient (Kd).

Note that BGC-Argo are profiling floats which supply data within the water column from the surface down to 2000 m, while satellite observe the surface of the ocean. It is therefore first required to define a protocol to process BGC-Argo data and make them comparable with remote sensing data. Details about the different inter-comparison procedures and illustrations of results will be presented.

Argo floats constitute a reliable observation system that supports global and full-depth ocean data sampling programs. The addition of bio-optical sensors facilitates the multi-decadal observation of ocean phenomena with respect to biogeochemistry (BGC-Argo). Satellite observations remain a vital source to check the scientific quality of Argo data products, delayed mode (e.g. chlorophyll-a, radiometry). By construction, Argo data is now the main global source of in situ observation for Marine Copernicus Service.
BGC-Argo has recently been extended to marginal seas and optically complex waters, e.g., the Baltic Sea. Estimates of water quality indicators such as chlorophyll-a (chl-a), colored dissolved organic matter (CDOM), and total suspended matter (TSM) from satellite observations are of main interest for the routine monitoring program in the area. However, robust estimates of these parameters using the standard ocean color algorithms have always been a demanding task because of the complex optical behavior of the water. To date, validation of the existing approaches (and/or developing new approaches) that specifically to deal with such optical state still not been well investigated due to the sparsity of in situ observations, as well as the importance of seasonal variability.
Over 1600 BGC-Argo floats have been deployed by November 2021, providing an on-demand capability for observing regions like the Baltic Sea. However, observations are limited by the spectral capabilities of the sensors. Current field exercises in the Baltic Sea are evaluating the addition of hyperspectral radiometers (type RAMSES) to BGC-Argo, which measure downward irradiance from the ultraviolet (280 nm) to the end of the visible range (720 nm). We exploit the novel BGC-Argo dataset to refine empirical and semi-analytical approaches by regional tuning. The core feature here is to investigate standardized, continuous, and synchronized bio-optical profiles of chl-a, CDOM, and particulate backscattering (which vary across short spatio-temporal scales) using one sensor, in conjugation with hyperspectral radiometric observations.
The plausibility of these approaches will be related to its performance in retrieving the end- estimated product (CDOM, chl-a), comparing them to those measured by the floats, and additionally to satellite matchup. We envisage to present an example that exploits timely and consistent information (coupling in situ observations of advanced autonomous platform with satellite information) to device local approaches that constrain the spatial and seasonal patterns of bio-optical properties, in order to observe optically complex regions.

Phytoplankton modulate the planetary cycling of major elements and compounds, channel solar energy into the marine ecosystem and help keep the Earth's climate stable. Understanding how our planet is changing requires monitoring essential climate variables, like phytoplankton, at synoptic scales. Satellite remote-sensing of ocean colour is a useful tool for this, but only monitors the surface layer. Ocean robotic platforms lack the synoptic coverage of satellites, but can monitor the subsurface. Combining satellite and ocean robotic monitoring offers huge potential for understanding and predicting changes in phytoplankton biomass. Historically, empirical functions have been used to describe the vertical structure of chlorophyll-a (a measure of phytoplankton biomass) and to extrapolate the surface measurements from satellite to depth, for applications like quantifying primary production. These include Gaussian functions, sigmoid functions, and statistical methods. Additionally, empirical approaches have been proposed to derive the vertical structure of phytoplankton size classes and taxonomic groups, but few methods have considered this in the context of what communities of phytoplankton a satellite can and cannot see, and in the context of vertical changes in epipelagic biogeography. Here, we describe an approach to partition a vertical profile of chlorophyll-a concentration into contributions from two communities of phytoplankton: one (1) that resides principally in the turbulent mixed-layer of the upper ocean and is observable through satellite visible radiometry; the other (2) living below the mixed-layer, in a stable 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, and 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 inversely correlated with epipelagic stratification and 2 positively correlated. We observe a dynamic and variable 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 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.

Mesoscale vortices, or eddies, are ubiquitous energetic features whose potential to alter the biogeochemical regimes of the oceans arise, among others, from their capacity to blend large-scale gradients (eddy stirring), to isolate and transport water masses over large distances (eddy trapping) and to locally shallow or deepen isopycnals (eddy pumping) [1]. While many studies have been dedicated to highlighting and deciphering these mesoscale biogeochemical mechanisms in the open ocean, the difficulties affecting altimetry products in the nearshore area, constitute a strong barrier to the observation-based characterization of biogeochemical eddy dynamics near the shelf-slope.

Because of their transitional nature, capturing observational snapshots of eddies with a satisfactory degree of horizontal and vertical coverage is challenging. To overcome this difficulty, the method of composite analysis consists of gathering a large number of near-eddy data instances (from observation or model results) and exploring the variability of their local anomalies according to their relative position to eddies. The method thus aims at characterizing average eddy-induced perturbations and provided the basis for many of the recent advances in eddy biogeochemical studies [2].

The BGC-Argo program obviously provides a powerful asset for eddy composite studies, which derives from 1) the large availability of data provided under the hood of common technical protocols, 2) the richness of characterized biogeochemical variables, and 3) the continuity of data acquisition which facilitates the characterization of local anomalies.

The first necessary step to any eddy composite analysis lies in the identification and mapping of mesoscale eddies from remote sensing altimetry data products, in order to provide the required information to express in-situ data in eddy-relative coordinates. Typically, this constitutes a critical step in the near-shore domain, where altimetric products are challenged and may finally limit the outcome of downstream composite analysis attempts.

Here, we evaluate different altimetry data sets derived for the Black Sea (2011-2019) and compare their adequacy to characterize eddy-induced subsurface oxygen and salinity signatures by applying a common composite analysis framework exploiting in-situ data acquired by BGC-Argo profilers.

The identification of eddies locations, contours, and properties was obtained by applying the same py-eddy-tracker procedure [3] to three altimetric sets, that differ in terms of along-track preprocessing, optimal interpolation procedure (gridding), and spatial resolution. To complement the comparison, the same procedure was applied to equivalent model products issued from the CMEMS BS-MFC framework [4]. Oxygen and salinity subsurface anomalies were obtained from BGC-Argo profiles, by applying a temporal high-pass filter to the original time-series, and relocated in eddy-centric coordinates specifically for each altimetric product.

The most recent altimetric data set, prepared with a coastal concern in the frame of the ESA EO4SIBS project, provides statistics of eddy properties that, in comparison with earlier products, are closer to those obtained from model simulations, in particular for coastal anticyclones.

More importantly, the eddies subsurface signatures reconstructed from BGC-Argo are more consistent when the EO4SIBS data set is used to relocate the profiles into the eddy-centric framework, in sense of the spatial structure and statistical significance of the obtained subsurface mean anomaly.

We propose that the estimated error on the reconstructed mean anomaly may serve as an argument to qualify the accuracy of gridded altimetry products and that Argo and BGC-Argo data provide a strong asset in that regard.

Besides, the method allowed us to reveal intense subsurface oxygen anomalies associated with the Black Sea near-shore anticyclones, whose structure supports the hypothesis that the contribution of mesoscale circulation to the Black Sea oxygen cycles extends beyond oxygen transport processes and involves net catalytic effects on biogeochemical processes.

[1] D. J. McGillicuddy, (2016) Mechanisms of Physical-Biological-Biogeochemical interaction at the oceanic mesoscale, Ann. Rev. Mar. Sci., 8, 125–159.

[2] P. Gaube, D. J. McGillicuddy, Jr, D. B. Chelton, M. J. Behrenfeld, P. G. Strutton, (2014) Regional variations in the influence of mesoscale eddies on near-surface chlorophyll, J. Geophys. Res. C: Oceans, 119, 8195–8220.

[3] E. Mason, A. Pascual, J. C. McWilliams, (2014) A new sea surface Height–Based code for oceanic mesoscale eddy tracking, J. Atmos. Ocean. Technol., 31, 1181–1188.

[4] Ciliberti, S. A., et al. (2021) Monitoring and Forecasting the Ocean State and Biogeochemical Processes in the Black Sea: Recent Developments in the Copernicus Marine Service. Journal of Marine Science and Engineering, 9(10), 1146.

We present the results of joint satellite and BGC-Argo assimilation in an operational modelling application of the Mediterranean Sea biogeochemistry. Taking advantage of the frequent, large-scale satellite observations related to the microbial biology of the upper ocean, the assimilation of satellite ocean-colour observations in marine biogeochemical modelling has been successfully applied in recent years at both global and regional scales, with chlorophyll being the most commonly assimilated variable. On the other hand, a novel operational framework of biogeochemical observations has been recently introduced by BGC-Argo floats, which provide valuable insights into key ocean vertical biogeochemical processes. In the present work, we updated an existing 3D variational assimilation scheme to assimilated both satellite and BGC-Argo observations, and the assimilation of different combinations of satellite chlorophyll data and BGC-Argo nitrate and chlorophyll data has been tested in the framework of operational modelling of the Mediterranean Sea biogeochemistry. The simulations have been validated with respect to available independent non-assimilated and assimilated (before the assimilation) observations, showing that assimilation of both satellite and float observations outperformed the assimilation of the two platforms considered individually. Moreover, the joint multi-platform assimilation demonstrated to have impacts on the vertical structure of nutrients and phytoplankton, e.g., on the depth of the deep chlorophyll maximum and of the nutricline, with impacts of the assimilation directly linked to the sampling frequency and dimension of the BGC-Argo network. Thus, considering the perspective of the BGC-Argo network matching the consolidated importance and relevance of satellite observation assimilation, the multi-platform assimilation can improve model representation of both large-scale to mesoscale features and be beneficial for robust reconstruction in global and regional reanalysis. At the Mediterranean Sea scale, the outcomes of the model simulation assimilating both satellite data and BGC-Argo data provided a consistent three-dimensional picture of the basin-wide differences in vertical features associated with summer stratified conditions. Indeed, the assimilated model results described a relatively high variability between the western and eastern Mediterranean, with thinner and shallower but intense deep chlorophyll maxima associated with steeper and narrower nutriclines in the western Mediterranean.