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International audience; This study assesses marine community production based on the diel variability of bio-optical properties monitored by two BioGeoChemical-Argo (BGC-Argo) floats. Experiments were conducted in two distinct Mediterranean systems, the northwestern Ligurian Sea and the central Ionian Sea, during summer months. We derived particulate organic carbon (POC) stock and gross community production integrated within the surface, euphotic and subsurface chlorophyll maximum (SCM) layers, using an existing approach applied to diel cycle measurements of the particulate beam attenuation (cp) and backscattering (bbp) coefficients. The diel cycle of cp provided a robust proxy for quantifying biological production in both systems; that of bbp was comparatively less robust. Derived primary production estimates vary by a factor of 2 depending upon the choice of the bio-optical relationship that converts the measured optical coefficient to POC, which is thus a critical step to constrain. Our results indicate a substantial contribution to the water column production of the SCM layer (16 %-42 %), which varies largely with the considered system. In the Ligurian Sea, the SCM is a seasonal feature that behaves as a subsurface biomass maximum (SBM) with the ability to respond to episodic abiotic forcing by increasing production. In contrast, in the Ionian Sea, the SCM is permanent, primarily induced by phytoplankton photoacclimation, and contributes moderately to water column production. These results clearly demonstrate the strong potential for transmissometers deployed on BGC-Argo profiling floats to quantify non-intrusively in situ biological production of organic carbon in the water column of stratified oligotrophic systems with recurring or permanent SCMs, which are widespread features in the global ocean.

International audience; The emerging availability of Biogeochemical-Argo (BGC-Argo) float data creates new opportunities to combine models and observations in investigations of the interior structures and dynamics of marine ecosystems. An existing variational data assimilation scheme (3DVarBio) has been upgraded and coupled with the Copernicus Marine Environment Monitoring Service biogeochemical model of the Mediterranean Sea to assimilate BGC-Argo chlorophyll profile observations. Our results show that the assimilation of BGC-Argo float data is feasible. Moreover, the proposed data assimilation framework provides significant corrections to the chlorophyll concentrations and is able to consistently readjust the shapes of chlorophyll profiles during surface blooms occurring in winter vertically mixed conditions, and in the case of the summer deep chlorophyll maxima. Sensitivity analysis and diagnostic metrics have been applied to evaluate the impact of assimilation and the relevance of different factors of the 3DVarBio method. A key factor is the observation error that has been tuned on a monthly basis to incorporate the representation error. Different frequencies of the assimilation cycle have been tested: daily or 3-day assimilation fosters the highest skill performances despite the reduced impacts and the increase of computational burden. Considering the present size of the BGC-Argo Mediterranean network (about 15 floats) and the estimated non-homogeneous correlation radius length scale (15.2 km on average), the chlorophyll assimilation can constrain the phytoplankton dynamics along the whole water column over an area up to 10% of the Mediterranean Sea.

The Mediterranean Sea has been identified as a hotspot for climate change. Furthermore, its very diverse trophic regimes, in such a little area, make it an extremely interesting region from a biogeochemical perspective. Numerous studies aim at better understanding and representing the Mediterrenean dynamics and biogeochemistry through modeling. This is a crucial step in order to predict the future anthropogenic impacts on the Mediterranean Sea and their possible effects on its biogeochemistry, and all what depends on it. The number of models that simulate the Mediterranean biogeochemistry, and the data available to compare with are now sufficient to draw an overall picture of the Mediterranean Sea biogeochemical models state of the art.In this study, we gathered 10 biogeochemical simulations of the Mediterranean Sea, including 8 regional and 2 high-resolution global configurations. The simulations are compared with surface chlorophyll estimates derived from satellite observations; chlorophyll, nitrate, oxygen, and particulate organic carbon concentrations derived from BGC-Argo floats, and phytoplankton group-specific primary production estimated from ocean color satellite observations. Our first aim is to describe and compare all known Mediterranean biogeochemical models, and to highlight their specificity. This should give an insight into the current achievements, and expose what biogeochemical model products are hence available for further ecological analysis. Furthermore, a specific attention is given to how well each model performs in selected regions of the Mediterranean Sea, in order to understand which specific process is needed to adequately represent the different trophic regimes of the Mediterranean Sea.

Numerical models of ocean biogeochemistry are becoming a major tool to detect and predict the impact of climate change on marine resources and ocean health. Classically, validation of such models relies on comparison with surface quantities from satellite (such as chlorophyll-a concentrations), climatologies, or sparse in situ data (such as cruises observations, and permanent fixed oceanic stations). However, these datasets are not fully suitable to assess how models represent many climate-relevant biogeochemical processes. These limitations now begin to be overcome with the availability of a large number of vertical profiles of light, pH, oxygen, nitrate, chlorophyll-a concentrations and particulate backscattering acquired by the Biogeochemical-Argo (BGC-Argo) floats network. Additionally, other key biogeochemical variables such as dissolved inorganic carbon and alkalinity, not measured by floats, can be predicted by machine learning-based methods applied to float oxygen concentrations. Here, we demonstrate the use of the global array of BGC-Argo floats for the validation of biogeochemical models at the global scale. We first present 18 key metrics of ocean health and biogeochemical functioning to quantify the success of BGC model simulations. These metrics are associated with the air-sea CO2 flux, the biological carbon pump, oceanic pH, oxygen levels and Oxygen Minimum Zones (OMZs). The metrics are either a depth-averaged quantity or correspond to the depth of a particular feature. We also suggest four diagnostic plots for displaying such metrics.

Regional processes play a key role in the global carbon budget. Major ocean CO

As part of the Copernicus Marine Environment Monitoring Service (CMEMS), a physical-biogeochemical coupled model system has been developed to monitor and forecast the ocean dynamics and marine ecosystem of the European waters. The model domain, called Iberia-Biscay-Ireland (IBI), covers the North-East Atlantic Ocean from the Canary Islands to Iceland, including the North Sea and the Western Mediterranean. Based on NEMO-PISCES with a horizontal resolution of 1/36°, the CMEMS IBI coupled model has provided 7-day weekly ocean forecasts for CMEMS since April 2018. But prior to its operational launch, a pre-operational qualification simulation (2010-2016) has allowed assessing the model's capacity to reproduce the main biogeochemical and ecosystem features of the IBI area. The objective is to evaluate the capacity of the coupled model to reproduce the spatial distribution and seasonal dynamics of the main biogeochemical variables, using this 7-year qualification simulation. The model results are compared with available satellite estimates as well as in situ observations, with a focus on BGC-Argo floats. The evaluation confirms that this model system can be a useful tool to better understand the current state and changes in the marine biogeochemistry of European waters and can also provide key variables for developing indicators to monitor the health of marine ecosystems.

The operational production of data-assimilated biogeochemical state of the ocean is one of the challenging core projects of the Copernicus Marine Environment Monitoring Service (hereafter CMEMS). In that framework, Mercator Ocean International is in charge of improving the realism of its global 1⁄4° coupled physical-biogeochemical simulations, analyses and re‐analyses, and to develop an effective capacity to routinely estimate the biogeochemical state of the ocean, including, amongst others, the implementation of biogeochemical data assimilation. Primary objectives are to enhance the time representation of the seasonal cycle in the real time and reanalysis systems, and to provide a better control of the production in the equatorial regions.In that framework, Mercator Ocean International has successfully updated its global biogeochemical analysis and forecasting system with an Ocean Color data assimilation capability. In this system, successfully commissioned in September 2019, the biogeochemical model (NEMO/PISCES) is offline coupled with the dynamical ocean (1/12° coarsened to 1/4° resolution) from the CMEMS global physical analysis and forecasting operational system (PSY4), at a daily frequency, and benefits from the assimilation of satellite (SSH-SST-SIC) and in situ physical data. Nevertheless, a biogeochemical climatological damping is activated in the biogeochemical model in order to mitigate the impact of some misconstrained processes (vertical velocities) from this physical data-assimilated forcing (under investigation). The dedicated assimilation of biogeochemical data relies on a simplified version of the SEEK filter, where the forecast error covariances are built from a fixed-basis - but seasonally variable - ensemble of anomalies computed from a multi-year numerical experiment (without biogeochemical data assimilation) with respect to a running mean. Regarding Ocean Colour observations, the system relies, as a first step, on the CMEMS Global Ocean surface chlorophyll concentration products, delivered in NRT.The objective of this presentation is thus to provide (1) a short description of the implementation of the aforementioned data assimilation methodology in the forecasting system; (2) a synthesis of the assessment of this global biogeochemical forecasting system, by cross-comparing the assimilated solution with various datasets, both spatial (Ocean Colour) and in situ (BGC-Argo, GLODAP), and (3) a synthetic overview of the impact/benefit of the assimilation of the Ocean Colour data.

As part of the Copernicus Marine Environment Monitoring Service (CMEMS), an operational ocean forecasting system was developed to monitor the ocean dynamics and marine ecosystems of the European waters; and more specifically on the IBI (Iberia-Biscay-Ireland) area. The CMEMS IBI physical-biogeochemical coupled system covers the North-East Atlantic Ocean from the Canary Islands to Iceland, including the North Sea and the Western Mediterranean, with a NEMO-PISCES 1/36° model application. Since its operational launch in April 2018, this CMEMS IBI system has provided a weekly short-term (7-days) forecast of the ocean dynamics and key biogeochemical variables of the marine ecosystem. The main goal of this paper is to assess the performances of the IBI biogeochemical system, using a 7-year retrospective simulation that spans from 2010 to 2016. The model results are validated with available satellite and in situ observations from the International Council for the Exploration of the Sea (ICES), the European Marine Observation and Data Network (EMODnet) and the Biogeochemical-Argo float network. The simulation successfully reproduces the spatial distribution and seasonal cycles of oxygen, nutrients, chlorophyll-a, and net primary production. This simulation can be a useful tool to better understand the current state and changes in the marine biogeochemistry of European waters. It can also provide key variables for developing indicators to monitor the health of European marine ecosystems. These indicators may be of interest to scientists, policy makers, environmental agencies and the general public.

The North Atlantic bloom corresponds to a strong seasonal increase in phytoplankton that produces organic carbon through photosynthesis. It is still debated what physical and biological conditions trigger the bloom, because comprehensive time series of the vertical distribution of phytoplankton biomass are lacking. Vertical profiles from nine floats that sampled the waters of the North Atlantic every few days for a couple of years reveal that phytoplankton populations start growing in early winter at very weak rates. A proper bloom with rapidly accelerating population growth rates instead starts only in spring when atmospheric cooling subsides and the mixed layer rapidly shoals. While the weak accumulation of phytoplankton in winter is crucial to maintaining a viable population, the spring bloom dominates the overall seasonal production of organic carbon., The drivers of North Atlantic phytoplankton bloom have been debated for decades, partially owing to incomplete sub-surface observations. Here, Mignot et al. use robotic sensors to provide detailed observations of developing blooms and to explore the drivers of different phases of plankton growth.

International audience; A neural network-based method is developed to assess the vertical distribution of (1) chlorophyll a concentration ([Chl]) and (2) phytoplankton community size indices (i.e., microphytoplankton, nanophytoplankton, and picophytoplankton) from in situ vertical profiles of chlorophyll fluorescence. This method (FLAVOR for Fluorescence to Algal communities Vertical distribution in the Oceanic Realm) uses as input only the shape of the fluorescence profile associated with its acquisition date and geo-location. The neural network is trained and validated using a large database including 896 concomitant in situ vertical profiles of High-Performance Liquid Chromatography (HPLC) pigments and fluorescence. These profiles were collected during 22 oceanographic cruises representative of the global ocean in terms of trophic and oceanographic conditions, making our method applicable to most oceanic waters. FLAVOR is validated with respect to the retrieval of both [Chl] and phytoplankton size indices using an independent in situ data set and appears to be relatively robust spatially and temporally. To illustrate the potential of the method, we applied it to in situ measurements of the BATS (Bermuda Atlantic Time Series Study) site and produce monthly climatologies of [Chl] and associated phytoplankton size indices. The resulting climatologies appear very promising compared to climatologies based on available in situ HPLC data. With the increasing availability of spatially and temporally well-resolved data sets of chlorophyll fluorescence, one possible global-scale application of FLAVOR could be to develop 3-D and even 4-D climatologies of [Chl] and associated composition of phytoplankton communities. The Matlab and R codes of the proposed algorithm are provided as supporting information.

In vivo fluorescence of Chlorophyll-a (Chl-a) is a potentially useful property to study the vertical distribution of phytoplankton biomass. However the technique is presently not fully exploited as it should be, essentially because of the difficulties in converting the fluorescence signal into an accurate Chl-a concentration. These difficulties arise noticeably from natural variations in the Chl-a fluorescence relationship, which is under the control of community composition as well as of their nutrient and light status. As a consequence, although vertical profiles of fluorescence are likely the most recorded biological property in the open ocean, the corresponding large databases are underexploited. Here with the aim to convert a fluorescence profile into a Chl-a concentration profile, we test the hypothesis that the Chl-a concentration can be gathered from the sole knowledge of the shape of the fluorescence profile. We analyze a large dataset from 18 oceanographic cruises conducted in case-1 waters from the highly stratified hyperoligotrophic waters (surface Chl-a = 0.02 mg m−3) of the South Pacific Gyre to the eutrophic waters of the Benguela upwelling (surface Chl-a = 32 mg m−3) and including the very deep mixed waters in the North Atlantic (Mixed Layer Depth = 690 m). This dataset encompasses more than 700 vertical profiles of Chl-a fluorescence as well as accurate estimations of Chl-a by High Performance Liquid Chromatography (HPLC). Two typical fluorescence profiles are identified, the uniform profile, characterized by a homogeneous layer roughly corresponding to the mixed layer, and the non-uniform profile, characterized by the presence of a Deep Chlorophyll Maximum. Using appropriate mathematical parameterizations, a fluorescence profile is subsequently represented by 3 or 5 shape parameters for uniform or non-uniform profiles, respectively. For both situations, an empirical model is developed to predict the "true" Chl-a concentration from these shape parameters. This model is then used to calibrate a fluorescence profile in Chl-a units. The validation of the approach provides satisfactory results with a median absolute percent deviation of 33 % when comparing the HPLC Chl-a profiles to the Chl-a-calibrated fluorescence. The proposed approach thus opens the possibility to produce Chl-a climatologies from uncalibrated fluorescence profile databases that have been acquired in the past and to which numerous new profiles will be added, thanks to the recent availability of autonomous platforms (profiling floats, gliders and animals) instrumented with miniature fluorometers.

The North Atlantic spring bloom is a massive annual growth event of marine phytoplankton, tiny free-floating algae that form the base of the ocean's food web and generates a large fraction of the global primary production of organic matter. The conditions that trigger the onset of the spring bloom in the Nordic Seas, at the northern edge of the North Atlantic, are studied using in situ data from six bio-optical floats released north of the Arctic Circle. It is often assumed that spring blooms start as soon as phytoplankton cells daily irradiance is sufficiently abundant that division rates exceed losses. The bio-optical float data instead suggest the tantalizing hypothesis that Nordic Seas blooms start when the photoperiod, the number of daily light hours experienced by phytoplankton, exceeds a critical value, independently of division rates. The photoperiod trigger may have developed at high latitudes where photosynthesis is impossible during polar nights and phytoplankton enters into a dormant stage in winter. While the first accumulation of biomass recorded by the bio-optical floats is consistent with the photoperiod hypothesis, it is possible that some biomass accumulation started before the critical photoperiod but at levels too low to be detected by the fluorometers. More precise observations are needed to test the photoperiod hypothesis., National Science Foundation (U.S.) (NSF award OCE-1155205)