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1
Ford, David. "Assimilating synthetic Biogeochemical-Argo and ocean colour observations into a global ocean model to inform observing system design." Biogeosciences 18, no. 2 (January 21, 2021): 509–34. http://dx.doi.org/10.5194/bg-18-509-2021.
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Abstract. A set of observing system simulation experiments was performed. This assessed the impact on global ocean biogeochemical reanalyses of assimilating chlorophyll from remotely sensed ocean colour and in situ observations of chlorophyll, nitrate, oxygen, and pH from a proposed array of Biogeochemical-Argo (BGC-Argo) floats. Two potential BGC-Argo array distributions were tested: one for which biogeochemical sensors are placed on all current Argo floats and one for which biogeochemical sensors are placed on a quarter of current Argo floats. Assimilating BGC-Argo data greatly improved model results throughout the water column. This included surface partial pressure of carbon dioxide (pCO2), which is an important output of reanalyses. In terms of surface chlorophyll, assimilating ocean colour effectively constrained the model, with BGC-Argo providing no added benefit at the global scale. The vertical distribution of chlorophyll was improved by assimilating BGC-Argo data. Both BGC-Argo array distributions gave benefits, with greater improvements seen with more observations. From the point of view of ocean reanalysis, it is recommended to proceed with development of BGC-Argo as a priority. The proposed array of 1000 floats will lead to clear improvements in reanalyses, with a larger array likely to bring further benefits. The ocean colour satellite observing system should also be maintained, as ocean colour and BGC-Argo will provide complementary benefits.
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Yumruktepe, Veli Çağlar, Erik Askov Mousing, Jerry Tjiputra, and Annette Samuelsen. "An along-track Biogeochemical Argo modelling framework: a case study of model improvements for the Nordic seas." Geoscientific Model Development 16, no. 22 (November 28, 2023): 6875–97. http://dx.doi.org/10.5194/gmd-16-6875-2023.
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Abstract. We present a framework that links in situ observations from the Biogeochemical Argo (BGC-Argo) array to biogeochemical models. The framework minimizes the technical effort required to construct a Lagrangian-type 1D modelling experiment along BGC-Argo tracks. We utilize the Argo data in two ways: (1) to drive the model physics and (2) to evaluate the model biogeochemistry. BGC-Argo physics data are used to nudge the model physics closer to observations to reduce the errors in the biogeochemistry stemming from physics errors. This allows us to target the model biogeochemistry and, by using the Argo biogeochemical dataset, we identify potential sources of model errors, introduce changes to the model formulation, and validate model configurations. We present experiments for the Nordic seas and showcase how we identify potential BGC-Argo buoys to model, prepare forcing, design experiments, and approach model improvement and validation. We use the ECOSMO II(CHL) model as the biogeochemical component and focus on chlorophyll a. The experiments reveal that ECOSMO II(CHL) requires improvements during low-light conditions, as the comparison to BGC-Argo reveals that ECOSMO II(CHL) simulates a late spring bloom and does not represent the deep chlorophyll maximum layer formation in summer periods. We modified the productivity and chlorophyll a relationship and statistically documented decreased bias and error in the revised model when using BGC-Argo data. Our results reveal that nudging the model temperature and salinity closer to BGC-Argo data reduces errors in biogeochemistry, and we suggest a relaxation time period of 1–10 d. The BGC-Argo data coverage is ever-growing and the framework is a valuable asset, as it improves biogeochemical models by performing efficient 1D model configurations and evaluation and then transferring the configurations to a 3D model with a wide range of use cases at the operational, regional/global and climate scales.
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Mignot, Alexandre, Hervé Claustre, Gianpiero Cossarini, Fabrizio D'Ortenzio, Elodie Gutknecht, Julien Lamouroux, Paolo Lazzari, et al. "Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design." Biogeosciences 20, no. 7 (April 12, 2023): 1405–22. http://dx.doi.org/10.5194/bg-20-1405-2023.
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Abstract. Numerical models of ocean biogeochemistry are becoming the major tools used to detect and predict the impact of climate change on marine resources and to monitor ocean health. However, with the continuous improvement of model structure and spatial resolution, incorporation of these additional degrees of freedom into fidelity assessment has become increasingly challenging. Here, we propose a new method to provide information on the model predictive skill in a concise way. The method is based on the conjoint use of a k-means clustering technique, assessment metrics, and Biogeochemical-Argo (BGC-Argo) observations. The k-means algorithm and the assessment metrics reduce the number of model data points to be evaluated. The metrics evaluate either the model state accuracy or the skill of the model with respect to capturing emergent properties, such as the deep chlorophyll maximums and oxygen minimum zones. The use of BGC-Argo observations as the sole evaluation data set ensures the accuracy of the data, as it is a homogenous data set with strict sampling methodologies and data quality control procedures. The method is applied to the Global Ocean Biogeochemistry Analysis and Forecast system of the Copernicus Marine Service. The model performance is evaluated using the model efficiency statistical score, which compares the model–observation misfit with the variability in the observations and, thus, objectively quantifies whether the model outperforms the BGC-Argo climatology. We show that, overall, the model surpasses the BGC-Argo climatology in predicting pH, dissolved inorganic carbon, alkalinity, oxygen, nitrate, and phosphate in the mesopelagic and the mixed layers as well as silicate in the mesopelagic layer. However, there are still areas for improvement with respect to reducing the model–data misfit for certain variables such as silicate, pH, and the partial pressure of CO2 in the mixed layer as well as chlorophyll-a-related, oxygen-minimum-zone-related, and particulate-organic-carbon-related metrics. The method proposed here can also aid in refining the design of the BGC-Argo network, in particular regarding the regions in which BGC-Argo observations should be enhanced to improve the model accuracy via the assimilation of BGC-Argo data or process-oriented assessment studies. We strongly recommend increasing the number of observations in the Arctic region while maintaining the existing high-density of observations in the Southern Oceans. The model error in these regions is only slightly less than the variability observed in BGC-Argo measurements. Our study illustrates how the synergic use of modeling and BGC-Argo data can both provide information about the performance of models and improve the design of observing systems.
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Teruzzi, Anna, Giorgio Bolzon, Laura Feudale, and Gianpiero Cossarini. "Deep chlorophyll maximum and nutricline in the Mediterranean Sea: emerging properties from a multi-platform assimilated biogeochemical model experiment." Biogeosciences 18, no. 23 (November 30, 2021): 6147–66. http://dx.doi.org/10.5194/bg-18-6147-2021.
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Abstract. Data assimilation has led to advancements in biogeochemical modelling and scientific understanding of the ocean. The recent operational availability of data from BGC-Argo (biogeochemical Argo) floats, which provide valuable insights into key vertical biogeochemical processes, stands to further improve biogeochemical modelling through assimilation schemes that include float observations in addition to traditionally assimilated satellite data. In the present work, we demonstrate the feasibility of joint multi-platform assimilation in realistic biogeochemical applications by presenting the results of 1-year simulations of Mediterranean Sea biogeochemistry. Different combinations of satellite chlorophyll data and BGC-Argo nitrate and chlorophyll data have been tested, and validation with respect to available independent non-assimilated and assimilated (before the assimilation) observations showed that assimilation of both satellite and float observations outperformed the assimilation of platforms considered individually. Moreover, the assimilation of BGC-Argo data impacted the vertical structure of nutrients and phytoplankton in terms of deep chlorophyll maximum depth, intensity, and nutricline depth. The outcomes of the model simulation assimilating both satellite data and BGC-Argo data provide a consistent picture of the basin-wide differences in vertical features associated with summer stratified conditions, describing 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.
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Renosh, Pannimpullath Remanan, Jie Zhang, Raphaëlle Sauzède, and Hervé Claustre. "Vertically Resolved Global Ocean Light Models Using Machine Learning." Remote Sensing 15, no. 24 (December 7, 2023): 5663. http://dx.doi.org/10.3390/rs15245663.
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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.
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Wang, Bin, Katja Fennel, and Liuqian Yu. "Can assimilation of satellite observations improve subsurface biological properties in a numerical model? A case study for the Gulf of Mexico." Ocean Science 17, no. 4 (August 26, 2021): 1141–56. http://dx.doi.org/10.5194/os-17-1141-2021.
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Abstract. Given current threats to ocean ecosystem health, there is a growing demand for accurate biogeochemical hindcasts, nowcasts, and predictions. Provision of such products requires data assimilation, i.e., a comprehensive strategy for incorporating observations into biogeochemical models, but current data streams of biogeochemical observations are generally considered insufficient for the operational provision of such products. This study investigates to what degree the assimilation of satellite observations in combination with a priori model calibration by sparse BGC-Argo profiles can improve subsurface biogeochemical properties. The multivariate deterministic ensemble Kalman filter (DEnKF) has been implemented to assimilate physical and biological observations into a three-dimensional coupled physical–biogeochemical model, the biogeochemical component of which has been calibrated by BGC-Argo float data for the Gulf of Mexico. Specifically, observations of sea surface height, sea surface temperature, and surface chlorophyll were assimilated, and profiles of both physical and biological variables were updated based on the surface information. We assessed whether this leads to improved subsurface distributions, especially of biological properties, using observations from five BGC-Argo floats that were not assimilated. An alternative light parameterization that was tuned a priori using BGC-Argo observations was also applied to test the sensitivity of data assimilation impact on subsurface biological properties. Results show that assimilation of the satellite data improves model representation of major circulation features, which translate into improved three-dimensional distributions of temperature and salinity. The multivariate assimilation also improves the agreement of subsurface nitrate through its tight correlation with temperature, but the improvements in subsurface chlorophyll were modest initially due to suboptimal choices of the model's optical module. Repeating the assimilation run by using the alternative light parameterization greatly improved the subsurface distribution of chlorophyll. Therefore, even sparse BGC-Argo observations can provide substantial benefits for biogeochemical prediction by enabling a priori model tuning. Given that, so far, the abundance of BGC-Argo profiles in the Gulf of Mexico and elsewhere has been insufficient for sequential assimilation, updating 3D biological properties in a model that has been well calibrated is an intermediate step toward full assimilation of the new data types.
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Claustre, Hervé, Kenneth S. Johnson, and Yuichiro Takeshita. "Observing the Global Ocean with Biogeochemical-Argo." Annual Review of Marine Science 12, no. 1 (January 3, 2020): 23–48. http://dx.doi.org/10.1146/annurev-marine-010419-010956.
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Biogeochemical-Argo (BGC-Argo) is a network of profiling floats carrying sensors that enable observation of as many as six essential biogeochemical and bio-optical variables: oxygen, nitrate, pH, chlorophyll a, suspended particles, and downwelling irradiance. This sensor network represents today's most promising strategy for collecting temporally and vertically resolved observations of biogeochemical properties throughout the ocean. All data are freely available within 24 hours of transmission. These data fill large gaps in ocean-observing systems and support three ambitions: gaining a better understanding of biogeochemical processes (e.g., the biological carbon pump and air–sea CO2 exchanges) and evaluating ongoing changes resulting from increasing anthropogenic pressure (e.g., acidification and deoxygenation); managing the ocean (e.g., improving the global carbon budget and developing sustainable fisheries); and carrying out exploration for potential discoveries. The BGC-Argo network has already delivered extensive high-quality global data sets that have resulted in unique scientific outcomes from regional to global scales. With the proposed expansion of BGC-Argo in the near future, this network has the potential to become a pivotal observation system that links satellite and ship-based observations in a transformative manner.
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Shu, Chan, Peng Xiu, Xiaogang Xing, Guoqiang Qiu, Wentao Ma, Robert J. W. Brewin, and Stefano Ciavatta. "Biogeochemical Model Optimization by Using Satellite-Derived Phytoplankton Functional Type Data and BGC-Argo Observations in the Northern South China Sea." Remote Sensing 14, no. 5 (March 7, 2022): 1297. http://dx.doi.org/10.3390/rs14051297.
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Marine biogeochemical models have been widely used to understand ecosystem dynamics and biogeochemical cycles. To resolve more processes, models typically increase in complexity, and require optimization of more parameters. Data assimilation is an essential tool for parameter optimization, which can reduce model uncertainty and improve model predictability. At present, model parameters are often adjusted using sporadic in-situ measurements or satellite-derived total chlorophyll-a concentration at sea surface. However, new ocean datasets and satellite products have become available, providing a unique opportunity to further constrain ecosystem models. Biogeochemical-Argo (BGC-Argo) floats are able to observe the ocean interior continuously and satellite phytoplankton functional type (PFT) data has the potential to optimize biogeochemical models with multiple phytoplankton species. In this study, we assess the value of assimilating BGC-Argo measurements and satellite-derived PFT data in a biogeochemical model in the northern South China Sea (SCS) by using a genetic algorithm. The assimilation of the satellite-derived PFT data was found to improve not only the modeled total chlorophyll-a concentration, but also the individual phytoplankton groups at surface. The improvement of simulated surface diatom provided a better representation of subsurface particulate organic carbon (POC). However, using satellite data alone did not improve vertical distributions of chlorophyll-a and POC. Instead, these distributions were improved by combining the satellite data with BGC-Argo data. As the dominant variability of phytoplankton in the northern SCS is at the seasonal timescale, we find that utilizing monthly-averaged BGC-Argo profiles provides an optimal fit between model outputs and measurements in the region, better than using high-frequency measurements.
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Izett, Robert W., Katja Fennel, Adam C. Stoer, and David P. Nicholson. "Reviews and syntheses: expanding the global coverage of gross primary production and net community production measurements using Biogeochemical-Argo floats." Biogeosciences 21, no. 1 (January 2, 2024): 13–47. http://dx.doi.org/10.5194/bg-21-13-2024.
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Abstract. This paper provides an overview and demonstration of emerging float-based methods for quantifying gross primary production (GPP) and net community production (NCP) using Biogeochemical-Argo (BGC-Argo) float data. Recent publications have described GPP methods that are based on the detection of diurnal oscillations in upper-ocean oxygen or particulate organic carbon concentrations using single profilers or a composite of BGC-Argo floats. NCP methods rely on budget calculations to partition observed tracer variations into physical or biological processes occurring over timescales greater than 1 d. Presently, multi-year NCP time series are feasible at near-weekly resolution, using consecutive or simultaneous float deployments at local scales. Results, however, are sensitive to the choice of tracer used in the budget calculations and uncertainties in the budget parameterizations employed across different NCP approaches. Decadal, basin-wide GPP calculations are currently achievable using data compiled from the entire BGC-Argo array, but finer spatial and temporal resolution requires more float deployments to construct diurnal tracer curves. A projected, global BGC-Argo array of 1000 floats should be sufficient to attain annual GPP estimates at 10∘ latitudinal resolution if floats profile at off-integer intervals (e.g., 5.2 or 10.2 d). Addressing the current limitations of float-based methods should enable enhanced spatial and temporal coverage of marine GPP and NCP measurements, facilitating global-scale determinations of the carbon export potential, training of satellite primary production algorithms, and evaluations of biogeochemical numerical models. This paper aims to facilitate broader uptake of float GPP and NCP methods, as singular or combined tools, by the oceanographic community and to promote their continued development.
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Terzić, Elena, Paolo Lazzari, Emanuele Organelli, Cosimo Solidoro, Stefano Salon, Fabrizio D'Ortenzio, and Pascal Conan. "Merging bio-optical data from Biogeochemical-Argo floats and models in marine biogeochemistry." Biogeosciences 16, no. 12 (July 1, 2019): 2527–42. http://dx.doi.org/10.5194/bg-16-2527-2019.
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Abstract. New autonomous robotic platforms for observing the ocean, i.e. Biogeochemical-Argo (BGC-Argo) floats, have drastically increased the number of vertical profiles of irradiance, photosynthetically available radiation (PAR), and algal chlorophyll concentrations around the globe independent of the season. Such data may therefore be a fruitful resource to improve performances of numerical models for marine biogeochemistry. Here we present a work that integrates 1314 vertical profiles of PAR acquired by 31 BGC-Argo floats operated in the Mediterranean Sea between 2012 and 2016 into a one-dimensional model to simulate the vertical and temporal variability of algal chlorophyll concentrations. The model was initially forced with PAR measurements to assess its skill when using quality-controlled light profiles, and subsequently with a number of alternative bio-optical models to analyse the model capability when light observations are not available. Model outputs were evaluated against co-located chlorophyll profiles measured by BGC-Argo floats. Results highlight that the data-driven model is able to reproduce the spatial and temporal variability of deep chlorophyll maxima depth observed at a number of Mediterranean sites well. Further, we illustrate the key role of PAR and vertical mixing in shaping the vertical dynamics of primary producers in the Mediterranean Sea. The comparison of alternative bio-optical models identifies the best simple one to be used, and suggests that model simulations benefit from considering the diel cycle.
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Germineaud, Cyril, Jean-Michel Brankart, and Pierre Brasseur. "An Ensemble-Based Probabilistic Score Approach to Compare Observation Scenarios: An Application to Biogeochemical-Argo Deployments." Journal of Atmospheric and Oceanic Technology 36, no. 12 (December 2019): 2307–26. http://dx.doi.org/10.1175/jtech-d-19-0002.1.
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AbstractA cross-validation algorithm is developed to perform probabilistic observing system simulation experiments (OSSEs). The use of a probability distribution of “true” states is considered rather than a single “truth” using a cross-validation algorithm in which each member of an ensemble simulation is alternatively used as the “truth” and to simulate synthetic observation data that reflect the observing system to be evaluated. The other available members are used to produce an updated ensemble by assimilating the specific data, while a probabilistic evaluation of the observation impacts is obtained using a comprehensive set of verification skill scores. To showcase this new type of OSSE studies with tractable numerical costs, a simple biogeochemical application under the Horizon 2020 AtlantOS project is presented for a single assimilation time step, in order to investigate the value of adding biogeochemical (BGC)-Argo floats to the existing satellite ocean color observations. Further experiments must be performed in time as well for a rigorous and effective evaluation of the BGC-Argo network design, though some evidence from this preliminary work suggests that assimilating chlorophyll data from a BGC-Argo array of 1000 floats can provide additional error reduction at the surface, where the use of spatial ocean color data is limited (due to cloudy conditions), as well at depths ranging from 50 to 150 m.
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Sauzède, R., J. E. Johnson, H. Claustre, G. Camps-Valls, and A. B. Ruescas. "ESTIMATION OF OCEANIC PARTICULATE ORGANIC CARBON WITH MACHINE LEARNING." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 949–56. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-949-2020.
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Abstract. Understanding and quantifying ocean carbon sinks of the planet is of paramount relevance in the current scenario of global change. Particulate organic carbon (POC) is a key biogeochemical parameter that helps us characterize export processes of the ocean. Ocean color observations enable the estimation of bio-optical proxies of POC (i.e. particulate backscattering coefficient, bbp) in the surface layer of the ocean quasi-synoptically. In parallel, the Argo program distributes vertical profiles of the physical properties with a global coverage and a high spatio-temporal resolution. Merging satellite ocean color and Argo data using a neural networkbased method has already shown strong potential to infer the vertical distribution of bio-optical properties at global scale with high space-time resolution. This method is trained and validated using a database of concurrent vertical profiles of temperature, salinity, and bio-optical properties, i.e. bbp, collected by Biogeochemical-Argo (BGC-Argo) floats, matched up with satellite ocean color products. The present study aims at improving this method by 1) using a larger dataset from BGC-Argo network since 2016 for training, 2) using additional inputs such as altimetry data, which provide significant information on mesoscale processes impacting the vertical distribution of bbp, 3) improving the vertical resolution of estimation, and 4) examining the potential of alternative machine learning-based techniques. As a first attempt with the new data, we used some feature-specific preprocessing routines followed by a Multi-Output Random Forest algorithm on two regions with different ocean dynamics: North Atlantic and Subtropical Gyres. The statistics and the bbp profiles obtained from the validation floats show promising results and suggest this direction is worth investigating even further at global scale.
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Fumenia, Alain, Anne Petrenko, Hubert Loisel, Kahina Djaoudi, Alain deVerneil, and Thierry Moutin. "Optical proxy for particulate organic nitrogen from BGC-Argo floats." Optics Express 28, no. 15 (July 7, 2020): 21391. http://dx.doi.org/10.1364/oe.395648.
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Begouen Demeaux, Charlotte, and Emmanuel Boss. "Validation of Remote-Sensing Algorithms for Diffuse Attenuation of Downward Irradiance Using BGC-Argo Floats." Remote Sensing 14, no. 18 (September 9, 2022): 4500. http://dx.doi.org/10.3390/rs14184500.
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Estimates of the diffuse attenuation coefficient (Kd) at two different wavelengths and band-integrated (PAR) were obtained using different published algorithms developed for open ocean waters spanning in type from explicit-empirical, semi-analytical and implicit-empirical and applied to data from spectral radiometers on board six different satellites (MODIS-Aqua, MODIS-Terra, VIIRS–SNPP, VIIRS-JPSS, OLCI-Sentinel 3A and OLCI-Sentinel 3B). The resultant Kds were compared to those inferred from measurements of radiometry from sensors on board autonomous profiling floats (BGC-Argo). Advantages of BGC-Argo measurements compared to ship-based ones include: 1. uniform sampling in time throughout the year, 2. large spatial coverage, and 3. lack of shading by platform. Over 5000 quality-controlled matchups between Kds derived from float and from satellite sensors were found with values ranging from 0.01 to 0.67 m−1. Our results show that although all three algorithm types provided similarly ranging values of Kd to those of the floats, for most sensors, a given algorithm produced statistically different Kd distributions from the two others. Algorithm results diverged the most for low Kd (clearest waters). Algorithm biases were traced to the limitations of the datasets the algorithms were developed and trained with, as well as the neglect of sun angle in some algorithms. This study highlights: 1. the importance of using comprehensive field-based datasets (such as BGC-Argo) for algorithm development, 2. the limitation of using radiative-transfer model simulations only for algorithm development, and 3. the potential for improvement if sun angle is taken into account explicitly to improve empirical Kd algorithms. Recent augmentation of profiling floats with hyper-spectral radiometers should be encouraged as they will provide additional constraints to develop algorithms for upcoming missions such as NASA’s PACE and SBG and ESA’s CHIME, all of which will include a hyper-spectral radiometer.
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O’Brien, Terence, and Emmanuel Boss. "Correction of Radiometry Data for Temperature Effect on Dark Current, with Application to Radiometers on Profiling Floats." Sensors 22, no. 18 (September 7, 2022): 6771. http://dx.doi.org/10.3390/s22186771.
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Measurements of daytime radiometry in the ocean are necessary to constrain processes such as photosynthesis, photo-chemistry and radiative heating. Profiles of downwelling irradiance provide a means to compute the concentration of a variety of in-water constituents. However, radiometers record a non-negligible signal when no light is available, and this signal is temperature dependent (called the dark current). Here, we devise and evaluate two consistent methods for correction of BGC-Argo radiometry measurements for dark current: one based on measurements during the day, the other based on night measurements. A daytime data correction is needed because some floats never measure at night. The corrections are based on modeling the temperature of the radiometer and show an average bias in the measured value of nearly 0.01 W m−2 nm−1, 3 orders of magnitude larger than the reported uncertainty of 2.5×10−5 W m−2 nm−1 for the sensors deployed on BGC-Argo floats (SeaBird scientific OCR504 radiometers). The methods are designed to be simple and robust, requiring pressure, temperature and irradiance data. The correction based on nighttime profiles is recommended as the primary method as it captures dark measurements with the largest dynamic range of temperature. Surprisingly, more than 28% of daytime profiles (130,674 in total) were found to record significant downwelling irradiance at 240–250 dbar. The correction is shown to be small relative to near-surface radiance and thus most useful for studies investigating light fields in the twilight zone and the impacts of radiance on deep organisms. Based on these findings, we recommend that BGC-Argo floats profile occasionally at night and to depths greater than 250 dbar. We provide codes to perform the dark corrections.
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Lazzari, Paolo, Stefano Salon, Elena Terzić, Watson W. Gregg, Fabrizio D'Ortenzio, Vincenzo Vellucci, Emanuele Organelli, and David Antoine. "Assessment of the spectral downward irradiance at the surface of the Mediterranean Sea using the radiative Ocean-Atmosphere Spectral Irradiance Model (OASIM)." Ocean Science 17, no. 3 (May 25, 2021): 675–97. http://dx.doi.org/10.5194/os-17-675-2021.
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Abstract. A multiplatform assessment of the Ocean–Atmosphere Spectral Irradiance Model (OASIM) radiative model focussed on the Mediterranean Sea for the period 2004–2017 is presented. The BOUée pour l'acquiSition d'une Série Optique à Long termE (BOUSSOLE) mooring and biogeochemical Argo (BGC-Argo) float optical sensor observations are combined with model outputs to analyse the spatial and temporal variabilities in the downward planar irradiance at the ocean–atmosphere interface. The correlations between the data and model are always higher than 0.6. With the exception of downward photosynthetic active radiation and the 670 nm channel, correlation values are always higher than 0.8 and, when removing the inter-daily variability, they are higher than 0.9. At the scale of the BOUSSOLE sampling (15 min temporal resolution), the root mean square difference oscillates at approximately 30 %–40 % of the averaged model output and is reduced to approximately 10 % when the variability between days is filtered out. Both BOUSSOLE and BGC-Argo indicate that bias is up to 20 % for the irradiance at 380 and 412 nm and for wavelengths above 670 nm, whereas it decreases to less than 5 % at the other wavelengths. Analysis of atmospheric input data indicates that the model skill is strongly affected by cloud dynamics. High skills are observed during summer when the cloud cover is low.
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Bellacicco, Vellucci, Scardi, Barbieux, Marullo, and D’Ortenzio. "Quantifying the Impact of Linear Regression Model in Deriving Bio-Optical Relationships: The Implications on Ocean Carbon Estimations." Sensors 19, no. 13 (July 9, 2019): 3032. http://dx.doi.org/10.3390/s19133032.
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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
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Barbieux, Marie, Julia Uitz, Bernard Gentili, Orens Pasqueron de Fommervault, Alexandre Mignot, Antoine Poteau, Catherine Schmechtig, et al. "Bio-optical characterization of subsurface chlorophyll maxima in the Mediterranean Sea from a Biogeochemical-Argo float database." Biogeosciences 16, no. 6 (April 1, 2019): 1321–42. http://dx.doi.org/10.5194/bg-16-1321-2019.
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Abstract. As commonly observed in oligotrophic stratified waters, a subsurface (or deep) chlorophyll maximum (SCM) frequently characterizes the vertical distribution of phytoplankton chlorophyll in the Mediterranean Sea. Occurring far from the surface layer “seen” by ocean colour satellites, SCMs are difficult to observe with adequate spatio-temporal resolution and their biogeochemical impact remains unknown. Biogeochemical-Argo (BGC-Argo) profiling floats represent appropriate tools for studying the dynamics of SCMs. Based on data collected from 36 BGC-Argo floats deployed in the Mediterranean Sea, our study aims to address two main questions. (1) What are the different types of SCMs in the Mediterranean Sea? (2) Which environmental factors control their occurrence and dynamics? First, we analysed the seasonal and regional variations in the chlorophyll concentration (Chl a), particulate backscattering coefficient (bbp), a proxy of the particulate organic carbon (POC) and environmental parameters (photosynthetically active radiation and nitrates) within the SCM layer over the Mediterranean Basin. The vertical profiles of Chl a and bbp were then statistically classified and the seasonal occurrence of each of the different types of SCMs quantified. Finally, a case study was performed on two contrasted regions and the environmental conditions at depth were further investigated to understand the main controls on the SCMs. In the eastern basin, SCMs result, at a first order, from a photoacclimation process. Conversely, SCMs in the western basin reflect a biomass increase at depth benefiting from both light and nitrate resources. Our results also suggest that a variety of intermediate types of SCMs are encountered between these two endmember situations.
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Ryan-Keogh, Thomas J., Sandy J. Thomalla, Pedro M. S. Monteiro, and Alessandro Tagliabue. "Multidecadal trend of increasing iron stress in Southern Ocean phytoplankton." Science 379, no. 6634 (February 24, 2023): 834–40. http://dx.doi.org/10.1126/science.abl5237.
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Southern Ocean primary productivity is principally controlled by adjustments in light and iron limitation, but the spatial and temporal determinants of iron availability, accessibility, and demand are poorly constrained, which hinders accurate long-term projections. We present a multidecadal record of phytoplankton photophysiology between 1996 and 2022 from historical in situ datasets collected by Biogeochemical Argo (BGC-Argo) floats and ship-based platforms. We find a significant multidecadal trend in irradiance-normalized nonphotochemical quenching due to increasing iron stress, with concomitant declines in regional net primary production. The observed trend of increasing iron stress results from changing Southern Ocean mixed-layer physics as well as complex biological and chemical feedback that is indicative of important ongoing changes to the Southern Ocean carbon cycle.
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Barbieux, Marie, Julia Uitz, Alexandre Mignot, Collin Roesler, Hervé Claustre, Bernard Gentili, Vincent Taillandier, et al. "Biological production in two contrasted regions of the Mediterranean Sea during the oligotrophic period: an estimate based on the diel cycle of optical properties measured by BioGeoChemical-Argo profiling floats." Biogeosciences 19, no. 4 (February 24, 2022): 1165–94. http://dx.doi.org/10.5194/bg-19-1165-2022.
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Abstract. 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.
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Ricour, Florian, Arthur Capet, Fabrizio D'Ortenzio, Bruno Delille, and Marilaure Grégoire. "Dynamics of the deep chlorophyll maximum in the Black Sea as depicted by BGC-Argo floats." Biogeosciences 18, no. 2 (February 1, 2021): 755–74. http://dx.doi.org/10.5194/bg-18-755-2021.
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Abstract. The deep chlorophyll maximum (DCM) is a well-known feature of the global ocean. However, its description and the study of its formation are a challenge, especially in the peculiar environment that is the Black Sea. The retrieval of chlorophyll a (chl a) from fluorescence (Fluo) profiles recorded by Biogeochemical Argo (BGC-Argo) floats is not trivial in the Black Sea, due to the very high content of coloured dissolved organic matter (CDOM) which contributes to the fluorescence signal and produces an apparent increase in the chl a concentration with depth. Here, we revised Fluo correction protocols for the Black Sea context using co-located in situ high-performance liquid chromatography (HPLC) and BGC-Argo measurements. The processed set of chl a data (2014–2019) is then used to provide a systematic description of the seasonal DCM dynamics in the Black Sea and to explore different hypotheses concerning the mechanisms underlying its development. Our results show that the corrections applied to the chl a profiles are consistent with HPLC data. In the Black Sea, the DCM begins to form in March, throughout the basin, at a density level set by the previous winter mixed layer. During a first phase (April–May), the DCM remains attached to this particular layer. The spatial homogeneity of this feature suggests a hysteresis mechanism, i.e. that the DCM structure locally influences environmental conditions rather than adapting instantaneously to external factors. In a second phase (July–September), the DCM migrates upward, where there is higher irradiance, which suggests the interplay of biotic factors. Overall, the DCM concentrates around 45 % to 65 % of the total chlorophyll content within a 10 m layer centred around a depth of 30 to 40 m, which stresses the importance of considering DCM dynamics when evaluating phytoplankton productivity at basin scale.
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Galí, Martí, Marcus Falls, Hervé Claustre, Olivier Aumont, and Raffaele Bernardello. "Bridging the gaps between particulate backscattering measurements and modeled particulate organic carbon in the ocean." Biogeosciences 19, no. 4 (March 1, 2022): 1245–75. http://dx.doi.org/10.5194/bg-19-1245-2022.
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Abstract. Oceanic particulate organic carbon (POC) is a small but dynamic component of the global carbon cycle. Biogeochemical models historically focused on reproducing the sinking flux of POC driven by large fast-sinking particles (LPOC). However, suspended and slow-sinking particles (SPOC, here < 100 µm) dominate the total POC (TPOC) stock, support a large fraction of microbial respiration, and can make sizable contributions to vertical fluxes. Recent developments in the parameterization of POC reactivity in PISCES (Pelagic Interactions Scheme for Carbon and Ecosystem Studies model; PISCESv2_RC) have improved its ability to capture POC dynamics. Here we evaluated this model by matching a global 3D simulation and 1D simulations at 50 different locations with observations made from biogeochemical (BGC-) Argo floats and satellites. Our evaluation covers globally representative biomes between 0 and 1000 m depth and relies on (1) a refined scheme for converting particulate backscattering at 700 nm (bbp700) to POC, based on biome-dependent POC / bbp700 ratios in the surface layer that decrease to an asymptotic value at depth; (2) a novel approach for matching annual time series of BGC-Argo vertical profiles to PISCES 1D simulations forced by pre-computed vertical mixing fields; and (3) a critical evaluation of the correspondence between in situ measurements of POC fractions, PISCES model tracers, and SPOC and LPOC estimated from high vertical resolution bbp700 profiles through a separation of the baseline and spike signals. We show that PISCES captures the major features of SPOC and LPOC across a range of spatiotemporal scales, from highly resolved profile time series to biome-aggregated climatological profiles. Model–observation agreement is usually better in the epipelagic (0–200 m) than in the mesopelagic (200–1000 m), with SPOC showing overall higher spatiotemporal correlation and smaller deviation (typically within a factor of 1.5). Still, annual mean LPOC stocks estimated from PISCES and BGC-Argo are highly correlated across biomes, especially in the epipelagic (r=0.78; n=50). Estimates of the SPOC / TPOC fraction converge around a median of 85 % (range 66 %–92 %) globally. Distinct patterns of model–observations misfits are found in subpolar and subtropical gyres, pointing to the need to better resolve the interplay between sinking, remineralization, and SPOC–LPOC interconversion in PISCES. Our analysis also indicates that a widely used satellite algorithm overestimates POC severalfold at high latitudes during the winter. The approaches proposed here can help constrain the stocks, and ultimately budgets, of oceanic POC.
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Su, Jiaoyang, Peter G. Strutton, and Christina Schallenberg. "The subsurface biological structure of Southern Ocean eddies revealed by BGC-Argo floats." Journal of Marine Systems 220 (August 2021): 103569. http://dx.doi.org/10.1016/j.jmarsys.2021.103569.
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Matsumoto, George I., Kenneth S. Johnson, Steve Riser, Lynne Talley, Susan Wijffels, and Roberta Hotinski. "The Global Ocean Biogeochemistry (GO-BGC) Array of Profiling Floats to Observe Changing Ocean Chemistry and Biology." Marine Technology Society Journal 56, no. 3 (June 8, 2022): 122–23. http://dx.doi.org/10.4031/mtsj.56.3.25.
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Abstract The Global Ocean Biogeochemistry (GO-BGC) Array is a project funded by the US National Science Foundation to build a global network of chemical and biological sensors on Argo profiling floats. The network will monitor biogeochemical cycles and ocean health. The floats will collect from a depth of 2,000 meters to the surface, augmenting the existing <ext-link ext-link-type="uri" xlink:href="https://argo.ucsd.edu/">Argo array</ext-link> that monitors ocean temperature and salinity. Data will be made freely available within a day of being collected via the Argo data system. These data will allow scientists to pursue fundamental questions concerning ocean ecosystems, monitor ocean health and productivity, and observe the elemental cycles of carbon, oxygen, and nitrogen through all seasons of the year. Such essential data are needed to improve computer models of ocean fisheries and climate, to monitor and forecast the effects of ocean warming and ocean acidification on sea life, and to address key questions identified in “Sea Change: 2015‐2025 Decadal Survey of Ocean Sciences” such as: What is the ocean's role in regulating the carbon cycle? What are the natural and anthropogenic drivers of open ocean deoxygenation? What are the consequences of ocean acidification? How do physical changes in mixing and circulation affect nutrient availability and ocean productivity?
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Freilich, Mara, Alexandre Mignot, Glenn Flierl, and Raffaele Ferrari. "Grazing behavior and winter phytoplankton accumulation." Biogeosciences 18, no. 20 (October 18, 2021): 5595–607. http://dx.doi.org/10.5194/bg-18-5595-2021.
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Abstract. Recent observations have shown that phytoplankton biomass increases in the North Atlantic during winter, even when the mixed layer is deepening and light is limited. Current theories suggest that this is due to a release from grazing pressure. Here we demonstrate that the often-used grazing models that are linear at low phytoplankton concentration do not allow for a wintertime increase in phytoplankton biomass. However, mathematical formulations of grazing as a function of phytoplankton concentration that are quadratic at low concentrations (or more generally decrease faster than linearly as phytoplankton concentration decreases) can reproduce the fall to spring transition in phytoplankton, including wintertime biomass accumulation. We illustrate this point with a minimal model for the annual cycle of North Atlantic phytoplankton designed to simulate phytoplankton concentration as observed by BioGeoChemical-Argo (BGC-Argo) floats in the North Atlantic. This analysis provides a mathematical framework for assessing hypotheses of phytoplankton bloom formation.
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Wang, Bin, Katja Fennel, Liuqian Yu, and Christopher Gordon. "Assessing the value of biogeochemical Argo profiles versus ocean color observations for biogeochemical model optimization in the Gulf of Mexico." Biogeosciences 17, no. 15 (August 11, 2020): 4059–74. http://dx.doi.org/10.5194/bg-17-4059-2020.
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Abstract. Biogeochemical ocean models are useful tools but subject to uncertainties arising from simplifications, inaccurate parameterization of processes, and poorly known model parameters. Parameter optimization is a standard method for addressing the latter but typically cannot constrain all biogeochemical parameters because of insufficient observations. Here we assess the trade-offs between satellite observations of ocean color and biogeochemical (BGC) Argo profiles and the benefits of combining both observation types for optimizing biogeochemical parameters in a model of the Gulf of Mexico. A suite of optimization experiments is carried out using different combinations of satellite chlorophyll and profile measurements of chlorophyll, phytoplankton biomass, and particulate organic carbon (POC) from autonomous floats. As parameter optimization in 3D models is computationally expensive, we optimize the parameters in a 1D model version and then perform 3D simulations using these parameters. We show first that the use of optimal 1D parameters, with a few modifications, improves the skill of the 3D model. Parameters that are only optimized with respect to surface chlorophyll cannot reproduce subsurface distributions of biological fields. Adding profiles of chlorophyll in the parameter optimization yields significant improvements for surface and subsurface chlorophyll but does not accurately capture subsurface phytoplankton and POC distributions because the parameter for the maximum ratio of chlorophyll to phytoplankton carbon is not well constrained in that case. Using all available observations leads to significant improvements of both observed (chlorophyll, phytoplankton, and POC) and unobserved (e.g., primary production) variables. Our results highlight the significant benefits of BGC-Argo measurements for biogeochemical parameter optimization and model calibration.
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Chen, Shuangling, Mark L. Wells, Rui Xin Huang, Huijie Xue, Jingyuan Xi, and Fei Chai. "Episodic subduction patches in the western North Pacific identified from BGC-Argo float data." Biogeosciences 18, no. 19 (October 13, 2021): 5539–54. http://dx.doi.org/10.5194/bg-18-5539-2021.
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Abstract. Subduction associated with mesoscale eddies is an important but difficult-to-observe process that can efficiently export carbon and oxygen to the mesopelagic zone (100–1000 dbar). Using a novel BGC-Argo dataset covering the western North Pacific (20–50∘ N, 120–180∘ E), we identified imprints of episodic subduction using anomalies in dissolved oxygen and spicity, a water mass marker. These subduction patches were present in 4.0 % (288) of the total profiles (7120) between 2008 and 2019, situated mainly in the Kuroshio Extension region between March and August (70.6 %). Roughly 31 % and 42 % of the subduction patches were identified below the annual permanent pycnocline depth (300 m vs. 450 m) in the subpolar and subtropical regions, respectively. Around half (52 %) of these episodic events injected oxygen-enriched waters below the maximum annual permanent thermocline depth (450 dbar), with >20 % occurring deeper than 600 dbar. Subduction patches were detected during winter and spring when mixed layers are deep. The oxygen inventory within these subductions is estimated to be on the order of 64 to 152 g O2/m2. These mesoscale events would markedly increase oxygen ventilation as well as carbon removal in the region, both processes helping to support the nutritional and metabolic demands of mesopelagic organisms. Climate-driven patterns of increasing eddy kinetic energies in this region imply that the magnitude of these processes will grow in the future, meaning that these unexpectedly effective small-scale subduction processes need to be better constrained in global climate and biogeochemical models.
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Rasse, Rafael, Hervé Claustre, and Antoine Poteau. "The suspended small-particle layer in the oxygen-poor Black Sea: a proxy for delineating the effective N<sub>2</sub>-yielding section." Biogeosciences 17, no. 24 (December 23, 2020): 6491–505. http://dx.doi.org/10.5194/bg-17-6491-2020.
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Abstract. The shallower oxygen-poor water masses of the ocean confine a majority of the microbial communities that can produce up to 90 % of oceanic N2. This effective N2-yielding section encloses a suspended small-particle layer, inferred from particle backscattering (bbp) measurements. It is thus hypothesized that this layer (hereafter, the bbp-layer) is linked to microbial communities involved in N2 yielding such as nitrate-reducing SAR11 as well as sulfur-oxidizing, anammox, and denitrifying bacteria – a hypothesis yet to be evaluated. Here, data collected by three BGC-Argo floats deployed in the Black Sea are used to investigate the origin of this bbp-layer. To this end, we evaluate how the key drivers of N2-yielding bacteria dynamics impact the vertical distribution of bbp and the thickness of the bbp-layer. In conjunction with published data on N2 excess, our results suggest that the bbp-layer is at least partially composed of the bacteria driving N2 yielding for three main reasons: (1) strong correlations are recorded between bbp and nitrate; (2) the top location of the bbp-layer is driven by the ventilation of oxygen-rich subsurface waters, while its thickness is modulated by the amount of nitrate available to produce N2; and (3) the maxima of both bbp and N2 excess coincide at the same isopycnals where bacteria involved in N2 yielding coexist. We thus advance that bbp and O2 can be exploited as a combined proxy to delineate the N2-yielding section of the Black Sea. This proxy can potentially contribute to refining delineation of the effective N2-yielding section of oxygen-deficient zones via data from the growing BGC-Argo float network.
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Organelli, Emanuele, Marie Barbieux, Hervé Claustre, Catherine Schmechtig, Antoine Poteau, Annick Bricaud, Emmanuel Boss, et al. "Two databases derived from BGC-Argo float measurements for marine biogeochemical and bio-optical applications." Earth System Science Data 9, no. 2 (November 22, 2017): 861–80. http://dx.doi.org/10.5194/essd-9-861-2017.
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Abstract. Since 2012, an array of 105 Biogeochemical-Argo (BGC-Argo) floats has been deployed across the world's oceans to assist in filling observational gaps that are required for characterizing open-ocean environments. Profiles of biogeochemical (chlorophyll and dissolved organic matter) and optical (single-wavelength particulate optical backscattering, downward irradiance at three wavelengths, and photosynthetically available radiation) variables are collected in the upper 1000 m every 1 to 10 days. The database of 9837 vertical profiles collected up to January 2016 is presented and its spatial and temporal coverage is discussed. Each variable is quality controlled with specifically developed procedures and its time series is quality-assessed to identify issues related to biofouling and/or instrument drift. A second database of 5748 profile-derived products within the first optical depth (i.e., the layer of interest for satellite remote sensing) is also presented and its spatiotemporal distribution discussed. This database, devoted to field and remote ocean color applications, includes diffuse attenuation coefficients for downward irradiance at three narrow wavebands and one broad waveband (photosynthetically available radiation), calibrated chlorophyll and fluorescent dissolved organic matter concentrations, and single-wavelength particulate optical backscattering. To demonstrate the applicability of these databases, data within the first optical depth are compared with previously established bio-optical models and used to validate remotely derived bio-optical products. The quality-controlled databases are publicly available from the SEANOE (SEA scieNtific Open data Edition) publisher at https://doi.org/10.17882/49388 and https://doi.org/10.17882/47142 for vertical profiles and products within the first optical depth, respectively.
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Petihakis, George, Leonidas Perivoliotis, Gerasimos Korres, Dionysios Ballas, Constantin Frangoulis, Paris Pagonis, Manolis Ntoumas, et al. "An integrated open-coastal biogeochemistry, ecosystem and biodiversity observatory of the eastern Mediterranean – the Cretan Sea component of the POSEIDON system." Ocean Science 14, no. 5 (October 12, 2018): 1223–45. http://dx.doi.org/10.5194/os-14-1223-2018.
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Abstract. There is a general scarcity of oceanic observations that concurrently examine air–sea interactions, coastal–open-ocean processes and physical–biogeochemical processes, in appropriate spatiotemporal scales and under continuous, long-term data acquisition schemes. In the Mediterranean Sea, the resulting knowledge gaps and observing challenges increase due to its oligotrophic character, especially in the eastern part of the basin. The oligotrophic open Cretan Sea's biogeochemistry is considered to be representative of a greater Mediterranean area up to 106 km2, and understanding its features may be useful on even larger oceanic scales, since the Mediterranean Sea has been considered a miniature model of the global ocean. The spatiotemporal coverage of biogeochemical (BGC) observations in the Cretan Sea has progressively increased over the last decades, especially since the creation of the POSEIDON observing system, which has adopted a multiplatform, multivariable approach, supporting BGC data acquisition. The current POSEIDON system's status includes open and coastal sea fixed platforms, a Ferrybox (FB) system and Bio-Argo autonomous floats that remotely deliver fluorescence as a proxy of chlorophyll-a (Chl-a), O2, pH and pCO2 data, as well as BGC-related physical variables. Since 2010, the list has been further expanded to other BGC (nutrients, vertical particulate matter fluxes), ecosystem and biodiversity (from viruses up to zooplankton) variables, thanks to the addition of sediment traps, frequent research vessel (R/V) visits for seawater–plankton sampling and an acoustic Doppler current profiler (ADCP) delivering information on macrozooplankton–micronekton vertical migration (in the epipelagic to mesopelagic layer). Gliders and drifters are the new (currently under integration to the existing system) platforms, supporting BGC monitoring. Land-based facilities, such as data centres, technical support infrastructure, calibration laboratory and mesocosms, support and give added value to the observatory. The data gathered from these platforms are used to improve the quality of the BGC-ecosystem model predictions, which have recently incorporated atmospheric nutrient deposition processes and assimilation of satellite Chl-a data. Besides addressing open scientific questions at regional and international levels, examples of which are presented, the observatory provides user-oriented services to marine policy makers and the society, and is a technological test bed for new and/or cost-efficient BGC sensor technology and marine equipment. It is part of European and international observing programs, playing a key role in regional data handling and participating in harmonization and best practices procedures. Future expansion plans consider the evolving scientific and society priorities, balanced with sustainable management.
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Jayaram, Chiranjivi, T. V. S. Udaya Bhaskar, J. Pavan Kumar, and Debadatta Swain. "Cyclone Enhanced Chlorophyll in the Bay of Bengal as Evidenced from Satellite and BGC-Argo Float Observations." Journal of the Indian Society of Remote Sensing 47, no. 11 (August 31, 2019): 1875–82. http://dx.doi.org/10.1007/s12524-019-01034-1.
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Chen, Jianqiang, Xun Gong, Xinyu Guo, Xiaogang Xing, Keyu Lu, Huiwang Gao, and Xiang Gong. "Improved Perceptron of Subsurface Chlorophyll Maxima by a Deep Neural Network: A Case Study with BGC-Argo Float Data in the Northwestern Pacific Ocean." Remote Sensing 14, no. 3 (January 28, 2022): 632. http://dx.doi.org/10.3390/rs14030632.
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Subsurface chlorophyll maxima (SCMs), commonly occurring beneath the surface mixed layer in coastal seas and open oceans, account for main changes in depth-integrated primary production and hence significantly contribute to the global carbon cycle. To fill the gap of previous methods (in situ measurement, remote sensing, and the extrapolating function based on surface-ocean data) for obtaining SCM characteristics (intensity, depth, and thickness), we developed an improved deep neural network (IDNN) model using a Gaussian radial basis activation function to retrieve the vertical profile of chlorophyll a concentration (Chl a) and associated SCM characteristics from surface-ocean data. The annually averaged SCM depth was further incorporated into the bias term and the Gaussian activation function to improve the estimation accuracy of the IDNN model. Based on the Biogeochemical-Argo (BGC-Argo) data acquired for three regions in the northwestern Pacific Ocean, vertical Chl a profiles produced by our improved DNN model using sea surface Chl a and sea surface temperature (SST) were in good agreement with the observations, especially in regions with low surface Chl a. Compared to other neural-network-based models with one hidden layer and a sigmoid activation function, the IDNN model retrieved vertical Chl a profiles well in more eutrophic subpolar regions. Furthermore, the application of the IDNN model to infer vertical Chl a profiles from remote-sensing information was validated in the northwestern Pacific Ocean.
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Taillandier, Vincent, Thibaut Wagener, Fabrizio D'Ortenzio, Nicolas Mayot, Hervé Legoff, Joséphine Ras, Laurent Coppola, et al. "Hydrography and biogeochemistry dedicated to the Mediterranean BGC-Argo network during a cruise with RV <i>Tethys 2</i> in May 2015." Earth System Science Data 10, no. 1 (March 28, 2018): 627–41. http://dx.doi.org/10.5194/essd-10-627-2018.
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Abstract. We report on data from an oceanographic cruise, covering western, central and eastern parts of the Mediterranean Sea, on the French research vessel Tethys 2 in May 2015. This cruise was fully dedicated to the maintenance and the metrological verification of a biogeochemical observing system based on a fleet of BGC-Argo floats. During the cruise, a comprehensive data set of parameters sensed by the autonomous network was collected. The measurements include ocean currents, seawater salinity and temperature, and concentrations of inorganic nutrients, dissolved oxygen and chlorophyll pigments. The analytical protocols and data processing methods are detailed, together with a first assessment of the calibration state for all the sensors deployed during the cruise. Data collected at stations are available at https://doi.org/10.17882/51678 and data collected along the ship track are available at https://doi.org/10.17882/51691.
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Rembauville, Mathieu, Nathan Briggs, Mathieu Ardyna, Julia Uitz, Philippe Catala, Cristophe Penkerc'h, Antoine Poteau, Hervé Claustre, and Stéphane Blain. "Plankton Assemblage Estimated with BGC-Argo Floats in the Southern Ocean: Implications for Seasonal Successions and Particle Export." Journal of Geophysical Research: Oceans 122, no. 10 (October 2017): 8278–92. http://dx.doi.org/10.1002/2017jc013067.
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Metzl, Nicolas, Jonathan Fin, Claire Lo Monaco, Claude Mignon, Samir Alliouane, David Antoine, Guillaume Bourdin, et al. "A synthesis of ocean total alkalinity and dissolved inorganic carbon measurements from 1993 to 2022: the SNAPO-CO2-v1 dataset." Earth System Science Data 16, no. 1 (January 9, 2024): 89–120. http://dx.doi.org/10.5194/essd-16-89-2024.
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Abstract. Total alkalinity (AT) and dissolved inorganic carbon (CT) in the oceans are important properties with respect to understanding the ocean carbon cycle and its link to global change (ocean carbon sinks and sources, ocean acidification) and ultimately finding carbon-based solutions or mitigation procedures (marine carbon removal). We present a database of more than 44 400 AT and CT observations along with basic ancillary data (spatiotemporal location, depth, temperature and salinity) from various ocean regions obtained, mainly in the framework of French projects, since 1993. This includes both surface and water column data acquired in the open ocean, coastal zones and in the Mediterranean Sea and either from time series or dedicated one-off cruises. Most AT and CT data in this synthesis were measured from discrete samples using the same closed-cell potentiometric titration calibrated with Certified Reference Material, with an overall accuracy of ±4 µmol kg−1 for both AT and CT. The data are provided in two separate datasets – for the Global Ocean and the Mediterranean Sea (https://doi.org/10.17882/95414, Metzl et al., 2023), respectively – that offer a direct use for regional or global purposes, e.g., AT–salinity relationships, long-term CT estimates, and constraint and validation of diagnostic CT and AT reconstructed fields or ocean carbon and coupled climate–carbon models simulations as well as data derived from Biogeochemical-Argo (BGC-Argo) floats. When associated with other properties, these data can also be used to calculate pH, the fugacity of CO2 (fCO2) and other carbon system properties to derive ocean acidification rates or air–sea CO2 fluxes.
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Hague, Mark, and Marcello Vichi. "Southern Ocean Biogeochemical Argo detect under-ice phytoplankton growth before sea ice retreat." Biogeosciences 18, no. 1 (January 4, 2021): 25–38. http://dx.doi.org/10.5194/bg-18-25-2021.
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Abstract. The seasonality of sea ice in the Southern Ocean has profound effects on the life cycle (phenology) of phytoplankton residing under the ice. The current literature investigating this relationship is primarily based on remote sensing, which often lacks data for half of the year or more. One prominent hypothesis holds that, following ice retreat in spring, buoyant meltwaters enhance available irradiance, triggering a bloom which follows the ice edge. However, an analysis of Biogeochemical Argo (BGC-Argo) data sampling under Antarctic sea ice suggests that this is not necessarily the case. Rather than precipitating rapid accumulation, we show that meltwaters enhance growth in an already highly active phytoplankton population. Blooms observed in the wake of the receding ice edge can then be understood as the emergence of a growth process that started earlier under sea ice. Indeed, we estimate that growth initiation occurs, on average, 4–5 weeks before ice retreat, typically starting in August and September. Novel techniques using on-board data to detect the timing of ice melt were used. Furthermore, such growth is shown to occur under conditions of substantial ice cover (>90 % satellite ice concentration) and deep mixed layers (>100 m), conditions previously thought to be inimical to growth. This led to the development of several box model experiments (with varying vertical depth) in which we sought to investigate the mechanisms responsible for such early growth. The results of these experiments suggest that a combination of higher light transfer (penetration) through sea ice cover and extreme low light adaptation by phytoplankton can account for the observed phenology.
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Xing, Xiaogang, Emmanuel Boss, Jie Zhang, and Fei Chai. "Evaluation of Ocean Color Remote Sensing Algorithms for Diffuse Attenuation Coefficients and Optical Depths with Data Collected on BGC-Argo Floats." Remote Sensing 12, no. 15 (July 23, 2020): 2367. http://dx.doi.org/10.3390/rs12152367.
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The vertical distribution of irradiance in the ocean is a key input to quantify processes spanning from radiative warming, photosynthesis to photo-oxidation. Here we use a novel dataset of thousands local-noon downwelling irradiance at 490 nm (Ed(490)) and photosynthetically available radiation (PAR) profiles captured by 103 BGC-Argo floats spanning three years (from October 2012 to January 2016) in the world’s ocean, to evaluate several published algorithms and satellite products related to diffuse attenuation coefficient (Kd). Our results show: (1) MODIS-Aqua Kd(490) products derived from a blue-to-green algorithm and two semi-analytical algorithms show good consistency with the float-observed values, but the Chla-based one has overestimation in oligotrophic waters; (2) The Kd(PAR) model based on the Inherent Optical Properties (IOPs) performs well not only at sea-surface but also at depth, except for the oligotrophic waters where Kd(PAR) is underestimated below two penetration depth (2zpd), due to the model’s assumption of a homogeneous distribution of IOPs in the water column which is not true in most oligotrophic waters with deep chlorophyll-a maxima; (3) In addition, published algorithms for the 1% euphotic-layer depth and the depth of 0.415 mol photons m−2 d−1 isolume are evaluated. Algorithms based on Chla generally work well while IOPs-based ones exhibit an overestimation issue in stratified and oligotrophic waters, due to the underestimation of Kd(PAR) at depth.
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Zheng, Huiying, Yue Ma, Jue Huang, Jian Yang, Dianpeng Su, Fanlin Yang, and Xiao Hua Wang. "Deriving vertical profiles of chlorophyll-a concentration in the upper layer of seawaters using ICESat-2 photon-counting lidar." Optics Express 30, no. 18 (August 26, 2022): 33320. http://dx.doi.org/10.1364/oe.463622.
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Chlorophyll-a concentration (chl-a) is a great indicator for estimating phytoplankton biomass and productivity levels and is also particularly useful for monitoring the water quality, biodiversity and species distribution, and harmful algal blooms. A great deal of studies investigated to estimate chl-a concentrations using ocean color remotely sensed data. With the development of photon-counting sensors, spaceborne photon-counting lidar can compensate for the shortcomings of passive optical remote sensing by enabling ocean vertical profiling in low-light conditions (e.g., at night). Using geolocated photons captured by the first spaceborne photon-counting lidar borne on ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2), this research reported methods for deriving vertical profiles of chl-a concentration in the upper layer of ocean waters. This study first calculates the average numbers of backscattered subaqueous photons of ICESat-2 at different water depths, and then estimates the optical parameters in water column based on a discrete theoretical model of the expected number of received signal photons. With the estimated optical parameters, vertical profiles of chl-a concentration are calculated by two different empirical algorithms. In two study areas (mostly with Type I open ocean waters and small part of Type II coastal ocean waters), the derived chl-a concentrations are generally consistent when validated by BGC-Argo (Biogeochemical Argo) data in the vertical direction (MAPEs<15%) and compared with MODIS (Moderate Resolution Imaging Spectroradiometer) data in the along-track direction (average R2>0.86). Using globally covered ICESat-2 data, this approach can be used to obtain vertical profiles of chl-a concentration and optical parameters at a larger scale, which will be helpful to analyze impact factors of climate change and human activities on subsurface phytoplankton species and their growth state.
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Di Biagio, Valeria, Stefano Salon, Laura Feudale, and Gianpiero Cossarini. "Subsurface oxygen maximum in oligotrophic marine ecosystems: mapping the interaction between physical and biogeochemical processes." Biogeosciences 19, no. 23 (December 8, 2022): 5553–74. http://dx.doi.org/10.5194/bg-19-5553-2022.
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Abstract. The subsurface oxygen maximum (SOM) is observed in oligotrophic oceans and is associated with different physical and biological processes. This study characterizes the SOM in the Mediterranean Sea at the basin scale and investigates its driving mechanisms by analysing the output of the 1/24∘ resolution biogeochemical reanalysis provided by the Copernicus Marine Service for the 1999–2019 time period. We validated the model-derived oxygen concentration in the epipelagic layer at different spatial and temporal scales, including novel process comparisons with estimates from in situ observations. Moreover, using Biogeochemical Argo (BGC-Argo) float observations, we estimated the model uncertainty in reproducing the SOM concentration and depth in summer (13 mmol O2 m−3 and 13 m, respectively). The western and eastern Mediterranean Sea depicts different SOM signatures in summer, with higher oxygen values and shallower depths in the western Mediterranean. The concentrations and depths (in the ranges of 230–250 mmol O2 m−3 and 30–100 m, respectively) are in agreement with the estimations from the literature and show mesoscale variability patterns. The western Mediterranean also shows a stronger biological activity, specifically oxygen production and consumption, along the whole epipelagic layer and higher oxygen concentrations at the surface throughout the year, but heavy undersaturated waters are associated with winter deep convection in the northwestern Mediterranean Sea. A 1-year analysis conducted on selected areas that are representative of the heterogeneity of summer SOM highlighted that the SOM can actually be sustained by biological production (as in northwestern Mediterranean areas), or it can be a residual of the confinement of spring production (as in the central Ionian area) and vertical motions influence its depth (as in the Levantine subduction area).
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Wang, Tao, Fei Chai, Xiaogang Xing, Jue Ning, Wensheng Jiang, and Stephen C. Riser. "Influence of multi-scale dynamics on the vertical nitrate distribution around the Kuroshio Extension: An investigation based on BGC-Argo and satellite data." Progress in Oceanography 193 (April 2021): 102543. http://dx.doi.org/10.1016/j.pocean.2021.102543.
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Chai, Fei, Yuntao Wang, Xiaogang Xing, Yunwei Yan, Huijie Xue, Mark Wells, and Emmanuel Boss. "A limited effect of sub-tropical typhoons on phytoplankton dynamics." Biogeosciences 18, no. 3 (February 5, 2021): 849–59. http://dx.doi.org/10.5194/bg-18-849-2021.
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Abstract. Typhoons are assumed to stimulate primary ocean production through the upward mixing of nutrients into the ocean surface. This assumption is based largely on observations of increased surface chlorophyll concentrations following the passage of typhoons. This surface chlorophyll enhancement, occasionally detected by satellites, is often undetected due to intense cloud coverage. Daily data from a BGC-Argo profiling float revealed the upper-ocean response to Typhoon Trami in the northwest Pacific Ocean. Temperature and chlorophyll changed rapidly, with a significant drop in sea surface temperature and a surge in surface chlorophyll associated with strong vertical mixing, which was only partially captured by satellite observations. However, no net increase in vertically integrated chlorophyll was observed during Typhoon Trami or in its wake. In contrast to the prevailing dogma, the result shows that typhoons likely have a limited effect on net primary ocean production. Observed surface chlorophyll enhancements during and immediately following typhoons in tropical and subtropical waters are more likely to be associated with surface entrainment of deep chlorophyll maxima. Moreover, the findings demonstrate that remote sensing data alone can overestimate the impact of storms on primary production in all oceans. Full understanding of the impact of storms on upper-ocean productivity can only be achieved with ocean-observing robots dedicated to high-resolution temporal sampling in the path of storms.
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Bruyant, Flavienne, Rémi Amiraux, Marie-Pier Amyot, Philippe Archambault, Lise Artigue, Lucas Barbedo de Freitas, Guislain Bécu, et al. "The Green Edge cruise: investigating the marginal ice zone processes during late spring and early summer to understand the fate of the Arctic phytoplankton bloom." Earth System Science Data 14, no. 10 (October 20, 2022): 4607–42. http://dx.doi.org/10.5194/essd-14-4607-2022.
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Abstract. The Green Edge project was designed to investigate the onset, life, and fate of a phytoplankton spring bloom (PSB) in the Arctic Ocean. The lengthening of the ice-free period and the warming of seawater, amongst other factors, have induced major changes in Arctic Ocean biology over the last decades. Because the PSB is at the base of the Arctic Ocean food chain, it is crucial to understand how changes in the Arctic environment will affect it. Green Edge was a large multidisciplinary, collaborative project bringing researchers and technicians from 28 different institutions in seven countries together, aiming at understanding these changes and their impacts on the future. The fieldwork for the Green Edge project took place over two years (2015 and 2016) and was carried out from both an ice camp and a research vessel in Baffin Bay, in the Canadian Arctic. This paper describes the sampling strategy and the dataset obtained from the research cruise, which took place aboard the Canadian Coast Guard ship (CCGS) Amundsen in late spring and early summer 2016. The sampling strategy was designed around the repetitive, perpendicular crossing of the marginal ice zone (MIZ), using not only ship-based station discrete sampling but also high-resolution measurements from autonomous platforms (Gliders, BGC-Argo floats …) and under-way monitoring systems. The dataset is available at https://doi.org/10.17882/86417 (Bruyant et al., 2022).
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Begouen Demeaux, Charlotte, and Emmanuel Boss. "Correction: Begouen Demeaux, C.; Boss, E. Validation of Remote-Sensing Algorithms for Diffuse Attenuation of Downward Irradiance Using BGC-Argo Floats. Remote Sens. 2022, 14, 4500." Remote Sensing 16, no. 2 (January 12, 2024): 313. http://dx.doi.org/10.3390/rs16020313.
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Gutknecht, Elodie, Guillaume Reffray, Alexandre Mignot, Tomasz Dabrowski, and Marcos G. Sotillo. "Modelling the marine ecosystem of Iberia–Biscay–Ireland (IBI) European waters for CMEMS operational applications." Ocean Science 15, no. 6 (November 15, 2019): 1489–516. http://dx.doi.org/10.5194/os-15-1489-2019.
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Abstract. 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 and more specifically on the Iberia–Biscay–Ireland (IBI) area. The CMEMS IBI coupled model 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. The coupled system has been providing 7 d weekly ocean forecasts for CMEMS since April 2018. 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 of this paper is then to describe the consistency and skill assessment of the PISCES biogeochemical model using this 7-year qualification simulation. The model results are compared with available satellite estimates as well as in situ observations (ICES, EMODnet and BGC-Argo). The simulation successfully reproduces the spatial distribution and seasonal cycles of oxygen, nutrients, chlorophyll a and net primary production, and confirms that PISCES is suitable at such a resolution and can be used for operational analysis and forecast applications. 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. These indicators may be of interest to scientists, policy makers, environmental agencies and the general public.
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Chamberlain, Paul, Lynne D. Talley, Bruce Cornuelle, Matthew Mazloff, and Sarah T. Gille. "Optimizing The Biogeochemical Argo Float Distribution." Journal of Atmospheric and Oceanic Technology, July 12, 2023. http://dx.doi.org/10.1175/jtech-d-22-0093.1.
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Abstract The core Argo array has operated with the design goal of uniform spatial distribution of 3° in latitude and longitude. Recent studies have acknowledged that spatial and temporal scales of variability in some parts of the ocean are not resolved by 3° sampling and have recommended increased core Argo density in the equatorial region, boundary currents, and marginal seas with an integrated vision of other Argo variants. Biogeochemical (BGC) Argo floats currently observe the ocean from a collection of pilot arrays, but recently funded proposals will transition these pilot arrays to a global array. The current BGC Argo implementation plan recommends uniform spatial distribution of BGC Argo floats. For the first time, we estimate the effectiveness of the existing BGC Argo array to resolve the anomaly from the mean using a subset of modeled, full-depth BGC fields. We also study the effectiveness of uniformly-distributed BGC Argo arrays with varying float densities at observing the ocean. Then, using previous Argo trajectories, we estimate the Argo array’s future distribution and quantify how well it observes the ocean. Finally, using a novel technique for sequentially identifying the best deployment locations, we suggest the optimal array distribution for BGC Argo floats to minimize objective mapping uncertainty in a subset of BGC fields and to best constrain BGC temporal variability.
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Stoer, Adam C., Yuichiro Takeshita, Tanya Lee Maurer, Charlotte Begouen Demeaux, Henry C. Bittig, Emmanuel Boss, Hervé Claustre, et al. "A census of quality-controlled Biogeochemical-Argo float measurements." Frontiers in Marine Science 10 (October 27, 2023). http://dx.doi.org/10.3389/fmars.2023.1233289.
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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.
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Johnson, Gregory, and Andrea Fassbender. "After Two Decades, Argo at PMEL, Looks to the Future." Oceanography, 2023. http://dx.doi.org/10.5670/oceanog.2023.223.
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The NOAA Pacific Environmental Laboratory (PMEL) has contributed to the revolutionary Argo ocean observing system since its inception, developing CTD calibration algorithms and software that have been adopted by the international Argo community. PMEL has provided over 1,440 Argo floats—~13% of the global array—with ~500 currently active. PMEL scientific contributions using Argo data have ranged from regional to global analyses of ocean circulation and water-mass variability, to ocean warming and its contributions to sea level rise and Earth’s energy imbalance, to estimates of global ocean deoxygenation. In recent years, PMEL has initiated both Deep Argo (with a regional pilot array of full-ocean-depth profiling floats in the rapidly changing and dynamic western South Atlantic) and Biogeochemical (BGC) Argo (with a pilot array in the biogeochemically diverse and economically important California Current Large Marine Ecosystem). PMEL is also developing innovative near-global maps of ocean physical and biogeochemical parameters using machine learning algorithms that enable investigations of societally important oceanographic phenomena, and an Adopt-A-Float program. Future challenges include growing the financial, infrastructure, and human resources necessary to take the Deep and BGC Argo missions global and to fulfill the One Argo mission of a global, full-depth, multidisciplinary ocean observing array.
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Koestner, Daniel, Dariusz Stramski, and Rick A. Reynolds. "A Multivariable Empirical Algorithm for Estimating Particulate Organic Carbon Concentration in Marine Environments From Optical Backscattering and Chlorophyll-a Measurements." Frontiers in Marine Science 9 (August 12, 2022). http://dx.doi.org/10.3389/fmars.2022.941950.
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Accurate estimates of the oceanic particulate organic carbon concentration (POC) from optical measurements have remained challenging because interactions between light and natural assemblages of marine particles are complex, depending on particle concentration, composition, and size distribution. In particular, the applicability of a single relationship between POC and the spectral particulate backscattering coefficient bbp(λ) across diverse oceanic environments is subject to high uncertainties because of the variable nature of particulate assemblages. These relationships have nevertheless been widely used to estimate oceanic POC using, for example, in situ measurements of bbp from Biogeochemical (BGC)-Argo floats. Despite these challenges, such an in situbased approach to estimate POC remains scientifically attractive in view of the expanding global-scale observations with the BGC-Argo array of profiling floats equipped with optical sensors. In the current study, we describe an improved empirical approach to estimate POC which takes advantage of simultaneous measurements of bbp and chlorophyll-a fluorescence to better account for the effects of variable particle composition on the relationship between POC and bbp. We formulated multivariable regression models using a dataset of field measurements of POC, bbp, and chlorophyll-a concentration (Chla), including surface and subsurface water samples from the Atlantic, Pacific, Arctic, and Southern Oceans. The analysis of this dataset of diverse seawater samples demonstrates that the use of bbp and an additional independent variable related to particle composition involving both bbp and Chla leads to notable improvements in POC estimations compared with a typical univariate regression model based on bbp alone. These multivariable algorithms are expected to be particularly useful for estimating POC with measurements from autonomous BGC-Argo floats operating in diverse oceanic environments. We demonstrate example results from the multivariable algorithm applied to depth-resolved vertical measurements from BGC-Argo floats surveying the Labrador Sea.
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Jemai, Ahlem, Jochen Wollschläger, Daniela Voß, and Oliver Zielinski. "Radiometry on Argo Floats: From the Multispectral State-of-the-Art on the Step to Hyperspectral Technology." Frontiers in Marine Science 8 (July 22, 2021). http://dx.doi.org/10.3389/fmars.2021.676537.
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Over the past two decades, robotic technology such as Argo floats have revolutionized operational autonomous measurement of the oceans. Recently, Biogeochemical Argo floats (BGC-Argo floats) have measured optical and biogeochemical quantities down to a depth of 2,000 m. Among these parameters, are measurements of the underwater light field from which apparent optical properties (AOPs), such as the diffuse attenuation coefficient for downwelling irradiance Kd(λ), can be derived. Presently, multispectral observations are available on this platform at three wavelengths (with 10–20 nm bandwidths) in the ultraviolet and visible part of the spectrum plus the Photosynthetically Available Radiation (PAR; integrated radiation between 400 and 700 nm). This article reviews studies dealing with these radiometric observations and presents the current state-of-the-art in Argo radiometry. It focus on the successful portability of radiometers onboard Argo float platforms and covers applications of the obtained data for bio-optical modeling and ocean color remote sensing. Generating already high-quality datasets in the existing configuration, the BGC-Argo program must now investigate the potential to incorporate hyperspectral technology. The possibility to acquire hyperspectral information and the subsequent development of new algorithms that exploit these data will open new opportunities for bio-optical long-term studies of global ocean processes, but also present new challenges to handle and process increased amounts of data.
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Uitz, J., C. Roesler, E. Organelli, H. Claustre, C. Penkerc'h, S. Drapeau, E. Leymarie, et al. "Characterization of Bio‐Optical Anomalies in the Kerguelen Region, Southern Indian Ocean: A Study Based on Shipborne Sampling and BioGeoChemical‐Argo Profiling Floats." Journal of Geophysical Research: Oceans 128, no. 12 (December 2023). http://dx.doi.org/10.1029/2023jc019671.
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AbstractThe Southern Ocean (SO) is known for its atypical bio‐optical regime. This complicates the interpretation of proxies measured from satellite and in situ platforms equipped with optical sensors, which occupy an important niche for monitoring the vast and remote SO. A ship‐based field study in concert with time series observations from BioGeoChemical‐Argo (BGC‐Argo) profiling floats were used to investigate spatial and temporal variations in bio‐optical relationships in the open ocean waters surrounding the Kerguelen Plateau in the Indian sector of the SO. Compared to other regions with similar chlorophyll concentrations, chlorophyll‐specific phytoplankton absorption in the blue waveband presented a consistent negative anomaly. The anomaly was uniform over deep mixed layers and correlated with phytoplankton size, photoacclimation and atypically high concentrations of fucoxanthin. The BGC‐Argo observation‐based proxies revealed that the blue absorption anomaly increased with chlorophyll concentration both spatially and temporally and, while particularly pronounced in the naturally iron‐fertilized waters, was also found in the High Nutrient Low Chlorophyll region. While phytoplankton size was an important driver of the anomaly, photoacclimation associated with self‐shading of phytoplankton cells was also involved during intense booms. The backscattering coefficient exhibited negative and positive anomalies in the low and high biomass regimes, respectively. The large positive anomaly in high biomass regimes was attributed to the variable non‐algal particles characteristics associated with a relatively high production of bloom by‐products. With clear understanding of the bio‐optical anomalies, BGC‐Argo floats stand as unique tools for monitoring the bio‐optical spatio‐temporal complexity of the SO.