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Your search keyword '"Sauzède, Raphaëlle"' showing total 93 results
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93 results on '"Sauzède, Raphaëlle"'

2. Gathering users and developers to shape together the next-generation ocean reanalyses: Ocean reanalyses workshop of the European Copernicus Marine Service

Author

Yang, Chunxue, primary, Bourdallé-Badie, Romain, additional, Drevillon, Marie, additional, Amaya, Dillon, additional, Aouf, Lotfi, additional, Aydogdu, Ali, additional, Barton, Benjamin, additional, Bell, Mike, additional, Boyer, Tim, additional, Blauw, Anouk, additional, Carton, James, additional, Candela, Tony, additional, Cossarini, Gianpiero, additional, Dabrowski, Tomasz, additional, de Boisseson, Eric, additional, de Mora, Lee, additional, Fablet, Ronan, additional, Forget, Gaël, additional, Fujii, Yosuke, additional, Garric, Gilles, additional, Giunta, Valentina, additional, Salamon, Peter, additional, Hersbach, Hans, additional, Juza, Mélanie, additional, Sommer, Julien Le, additional, Martin, Matthew, additional, McAdam, Ronan, additional, Garcia, Melisa Menendez, additional, Morim, Joao, additional, Nicolì, Dario, additional, Reppucci, Antonio, additional, Samuelsen, Annette, additional, Sauzède, Raphaëlle, additional, Slivinski, Laura, additional, Specq, Damien, additional, Storto, Andrea, additional, Tuomi, Laura, additional, Vandenbulcke, Luc, additional, Aznar, Roland, additional, Beuvier, Jonathan, additional, Cipollone, Andrea, additional, Clementi, Emanuela, additional, Di Biagio, Valeria, additional, Escudier, Romain, additional, Giesen, Rianne, additional, Greiner, Eric, additional, Guihou, Karen, additional, Korabel, Vasily, additional, Lamouroux, Julien, additional, Chune, Stephane Law, additional, Lellouche, Jean- Michel, additional, Levier, Bruno, additional, Lima, Leonardo, additional, Mangin, Antoine, additional, Mayer, Michael, additional, Melet, Angelique, additional, Miraglio, Pietro, additional, Oikonomou, Charikleia, additional, Pfeffer, Julia, additional, Renshaw, Richard, additional, Ringgaard, Ida, additional, Thual, Sulian, additional, Titaud, Olivier, additional, Tonani, Marina, additional, van Gennip, Simon, additional, von Schuckmann, Karina, additional, Drillet, Yann, additional, and Traon, Pierre-Yves Le, additional

Published
2024
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5. Reconstructing Global Chlorophyll-a Variations Using a Non-linear Statistical Approach.

Author

Martinez, Elodie, Gorgues, Thomas, Lengaigne, Matthieu, Sauzède, Raphaëlle, Menkes, Christophe, Uitz, Julia, Di Lorenzo, Emanuele, and Fablet, Ronan

Subjects
EL Nino, OCEAN color, SPATIO-temporal variation, CHLOROPHYLL in water, COLORIMETRY, MACHINE learning, OCEAN
Abstract

Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32- years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979--2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability. [ABSTRACT FROM AUTHOR]

Published
2024
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6. A census of quality-controlled Biogeochemical-Argo float measurements

Author

Stoer, Adam C., primary, Takeshita, Yuichiro, additional, Maurer, Tanya Lee, additional, Begouen Demeaux, Charlotte, additional, Bittig, Henry C., additional, Boss, Emmanuel, additional, Claustre, Hervé, additional, Dall’Olmo, Giorgio, additional, Gordon, Christopher, additional, Greenan, Blair John William, additional, Johnson, Kenneth S., additional, Organelli, Emanuele, additional, Sauzède, Raphaëlle, additional, Schmechtig, Catherine Marie, additional, and Fennel, Katja, additional

Published
2023
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7. Machine learning in marine ecology: an overview of techniques and applications

Author

Rubbens, Peter, primary, Brodie, Stephanie, additional, Cordier, Tristan, additional, Destro Barcellos, Diogo, additional, Devos, Paul, additional, Fernandes-Salvador, Jose A, additional, Fincham, Jennifer I, additional, Gomes, Alessandra, additional, Handegard, Nils Olav, additional, Howell, Kerry, additional, Jamet, Cédric, additional, Kartveit, Kyrre Heldal, additional, Moustahfid, Hassan, additional, Parcerisas, Clea, additional, Politikos, Dimitris, additional, Sauzède, Raphaëlle, additional, Sokolova, Maria, additional, Uusitalo, Laura, additional, Van den Bulcke, Laure, additional, van Helmond, Aloysius T M, additional, Watson, Jordan T, additional, Welch, Heather, additional, Beltran-Perez, Oscar, additional, Chaffron, Samuel, additional, Greenberg, David S, additional, Kühn, Bernhard, additional, Kiko, Rainer, additional, Lo, Madiop, additional, Lopes, Rubens M, additional, Möller, Klas Ove, additional, Michaels, William, additional, Pala, Ahmet, additional, Romagnan, Jean-Baptiste, additional, Schuchert, Pia, additional, Seydi, Vahid, additional, Villasante, Sebastian, additional, Malde, Ketil, additional, and Irisson, Jean-Olivier, additional

Published
2023
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8. AI for Marine, Ocean and Climate Change Monitoring.

Author

Nieves, Veronica, Ruescas, Ana, and Sauzède, Raphaëlle

Subjects
DEEP learning, ARTIFICIAL neural networks, MACHINE learning, ARTIFICIAL intelligence, CLIMATE change, OCEAN color, OCEAN
Abstract

This article compilation explores the use of artificial intelligence (AI), machine learning (ML), and data analytics in marine, oceanic, and climate change monitoring. The featured applications cover a range of topics, including sea-surface temperature prediction, Sargassum detection, cloud classification, chlorophyll-a concentration modeling, oceanic light models, subsurface ocean temperature prediction, near-surface humidity monitoring, and sea-surface salinity correction. These advancements in AI and ML techniques have the potential to enhance our understanding and management of marine and climate dynamics. The article concludes by emphasizing the need for continued research and advancements in these areas. [Extracted from the article]

Published
2024
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9. Real-time quality control of optical backscattering data from Biogeochemical-Argo floats

Author

Dall'Olmo, Giorgio, primary, Bhaskar TVS, Udaya, additional, Bittig, Henry, additional, Boss, Emmanuel, additional, Brewster, Jodi, additional, Claustre, Hervé, additional, Donnelly, Matt, additional, Maurer, Tanya, additional, Nicholson, David, additional, Paba, Violetta, additional, Plant, Josh, additional, Poteau, Antoine, additional, Sauzède, Raphaëlle, additional, Schallenberg, Christina, additional, Schmechtig, Catherine, additional, Schmid, Claudia, additional, and Xing, Xiaogang, additional

Published
2023
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10. Multi-Observation Thematic Assembly: existing products and future evolutions

Author

Verbrugge, Nathalie, primary, Etienne, Hélène, additional, Buongiorno Nardelli, Bruno, additional, Chau, Tran, additional, Chevallier, Frédéric, additional, Ciani, Daniele, additional, Claustre, Hervé, additional, Dibarboure, Gérald, additional, Gehlen, Marion, additional, Greiner, Eric, additional, Kolodziejczyk, Nicolas, additional, Mulet, Sandrine, additional, Pannimpullath, Renosh, additional, Parracho, Claudia, additional, Sammartino, Michela, additional, Sauzède, Raphaëlle, additional, and Tarot, Stéphane, additional

Published
2023
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11. Machine learning in marine ecology: an overview of techniques and applications

Author

Rubbens, Peter, Brodie, Stephanie, Cordier, Tristan, Destro Barcellos, Diogo, Devos, Paul, Fernandes-Salvador, Jose A, Fincham, Jennifer I, Gomes, Alessandra, Handegard, Nils Olav, Howell, Kerry, Jamet, Cédric, Kartveit, Kyrre Heldal, Moustahfid, Hassan, Parcerisas, Clea, Politikos, Dimitris, Sauzède, Raphaëlle, Sokolova, Maria, Uusitalo, Laura, Van den Bulcke, Laure, van Helmond, Aloysius T M, Watson, Jordan T, Welch, Heather, Beltran-Perez, Oscar, Chaffron, Samuel, Greenberg, David S, Kühn, Bernhard, Kiko, Rainer, Lo, Madiop, Lopes, Rubens M, Möller, Klas Ove, Michaels, William, Pala, Ahmet, Romagnan, Jean-Baptiste, Schuchert, Pia, Seydi, Vahid, Villasante, Sebastian, Malde, Ketil, Irisson, Jean-Olivier, Whidden, Christopher, Rubbens, Peter, Brodie, Stephanie, Cordier, Tristan, Destro Barcellos, Diogo, Devos, Paul, Fernandes-Salvador, Jose A, Fincham, Jennifer I, Gomes, Alessandra, Handegard, Nils Olav, Howell, Kerry, Jamet, Cédric, Kartveit, Kyrre Heldal, Moustahfid, Hassan, Parcerisas, Clea, Politikos, Dimitris, Sauzède, Raphaëlle, Sokolova, Maria, Uusitalo, Laura, Van den Bulcke, Laure, van Helmond, Aloysius T M, Watson, Jordan T, Welch, Heather, Beltran-Perez, Oscar, Chaffron, Samuel, Greenberg, David S, Kühn, Bernhard, Kiko, Rainer, Lo, Madiop, Lopes, Rubens M, Möller, Klas Ove, Michaels, William, Pala, Ahmet, Romagnan, Jean-Baptiste, Schuchert, Pia, Seydi, Vahid, Villasante, Sebastian, Malde, Ketil, Irisson, Jean-Olivier, and Whidden, Christopher

Abstract

Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of & SIM;1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.

Published
2023
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12. BGC Argo quality control manual for particles backscattering

Author

Dall'Olmo, Giorgio, Bhaskar Tvs, Udaya, Bittig, Henry, Boss, Emmanuel, Brewster, Jodi, Claustre, Hervé, Donnelly, Matt, Maurer, Tanya L., Nicholson, David, Paba, Violetta, Plant, Joshua N., Poteau, Antoine, Sauzède, Raphaëlle, Schallenberg, Christina, Schmechtig, Catherine, Schmid, Claudia, Xing, Xiaogang, Dall'Olmo, Giorgio, Bhaskar Tvs, Udaya, Bittig, Henry, Boss, Emmanuel, Brewster, Jodi, Claustre, Hervé, Donnelly, Matt, Maurer, Tanya L., Nicholson, David, Paba, Violetta, Plant, Joshua N., Poteau, Antoine, Sauzède, Raphaëlle, Schallenberg, Christina, Schmechtig, Catherine, Schmid, Claudia, and Xing, Xiaogang

Abstract

This document is the BGC-Argo quality control manual for particles backscattering. It describes the method used in real-time to apply quality control flags to particles backscattering calculated from specific sensors mounted on Argo profiling floats.

Published
2023
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14. Real-time quality control of optical backscattering data from Biogeochemical-Argo floats

Author

Dall'Olmo, Giorgio, primary, Bhaskar TVS, Udaya, additional, Bittig, Henry, additional, Boss, Emmanuel, additional, Brewster, Jodi, additional, Claustre, Hervé, additional, Donnelly, Matt, additional, Maurer, Tanya, additional, Nicholson, David, additional, Paba, Violetta, additional, Plant, Josh, additional, Poteau, Antoine, additional, Sauzède, Raphaëlle, additional, Schallenberg, Christina, additional, Schmechtig, Catherine, additional, Schmid, Claudia, additional, and Xing, Xiaogang, additional

Published
2022
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15. Using machine learning and Biogeochemical-Argo (BGC-Argo) floats to assess biogeochemical models and optimize observing system design.

Author

Mignot, Alexandre, Claustre, Hervé, Cossarini, Gianpiero, D'Ortenzio, Fabrizio, Gutknecht, Elodie, Lamouroux, Julien, Lazzari, Paolo, Perruche, Coralie, Salon, Stefano, Sauzède, Raphaëlle, Taillandier, Vincent, and Teruzzi, Anna

Subjects
SYSTEMS design, MACHINE learning, MARINE resources, MIXING height (Atmospheric chemistry), MARINE ecosystem health, MARINE service, BIOGEOCHEMISTRY
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 CO 2 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. [ABSTRACT FROM AUTHOR]

Published
2023
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16. NOUVELLE DISTRIBUTION VERTICALE GLOBALE DE LA CONCENTRATION DE CARBONE ORGANIQUE PARTICULAIRE ET DE CHLOROPHYLLE-A MAILLEE, UTILISANT L'APPRENTISSAGE AUTOMATIQUE POUR LE CMEMS

Author

Sauzède, Raphaëlle, Claustre, Hervé, Pannimpullath, Remanan, Uitz, Julia, Guinehut, Stéphanie, Collecte Localisation Satellites (CLS), Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Shom, Ifremer, EuroGOOS AISBL, and Institut de la Mer de Villefranche, CNRS-INSU, Sorbonne Université, Villefranche-Sur-Mer, France

Subjects
Multi-observations, Chlorophyll-a concentration, Machine learning, [SDE]Environmental Sciences, CMEMS, Particulate organic carbon
Abstract

International audience; As part of Copernicus Marine Environmental Monitoring Service (CMEMS), the multi-observations thematic assembly center aims to provide products based on observations and data fusion techniques (Guinehut et al., 2021). Sauzede et al., (2016) have demonstrated the potential of using hydrological measurements and ocean color satellite observations to infer the vertical distribution of backscattering coeffi cient, a proxy for the stock of particulate organic carbon (POC). The 'Satellite Ocean-Color merged with Argo data to infer bio-optical properties to depth' (SOCA) method is a neural-network-based method trained using the Biogeochemical-Argo database. SOCA has been upgraded to improve the POC retrieval and additionally retrieve the chlorophyll-a concentration (Chl). Using this method with CMEMS hydrological and satellite products, weekly 3-dimensional fi elds of POC and associated uncertainty were retrieved for the 1998-2018 period and made available from the CMEMS online portal since July 2020. The 3-dimensional products of SOCA-retrieved Chl will be made available by the end of 2021. Both of these products will be updated yearly as new input data become available. These new CMEMS products represent a most valuable source of data useful not only for supporting the quality control of Biogeochemical-Argo fl oat observations but also for data assimilation and initialization/validation of biogeochemical models.

Published
2021

17. Biogeochemical Argo Cheat Sheets: Data distribution; Quality control and GDAC; Chlorophyll-a; Optical backscatter; pH; Irradiance; Oxygen; Nitrate

Author

Baldry, Kimberlee, Sauzède, Raphaëlle, Cornec, Marin, and Baldry, Kimberlee

Subjects
Data quality control [Data Management Practices], radiometers [Instrument Type Vocabulary], Biogeochemical water sampling, Chlorophyll-a, optical backscatter sensors [Instrument Type Vocabulary], ocean colour radiometers [Instrument Type Vocabulary], Irradiance, Biological oceanography::Phytoplankton [Parameter Discipline], Chemical oceanography::Other inorganic chemical measurements [Parameter Discipline], Chemical oceanography::Nutrients [Parameter Discipline], Nitrate, pH sensors [Instrument Type Vocabulary], fluorometers [Instrument Type Vocabulary]
Abstract

Eight cheat sheets for users of Biogeochemical Argo data. The sheets describe data distribution, quality control in the Global Data Acquisition Center and the six core Biogeochemical Argo variables (chlorophyll-a, optical backscatter, pH, Irradiance, oxygen and nitrate). The cheat sheets aim to guide users by displaying information on data processing, quality control and sensor performance for education purposes. This research was supported by the Scientific Committee on Antarctic Research in the form of a Fellowship award and the Australian Research Council's Special Research Initiative for Antarctic Gateway Partnership (Project ID SR140300001). Published Refereed Current 14.A Phytoplankton Biomass And Diversity Particulate Matter Oxygen Nutrients Inorganic Carbon Manual (incl. handbook, guide, cookbook etc) Standard Operating Procedure

Published
2021

18. NEW GLOBAL VERTICAL DISTRIBUTION OF GRIDDED PARTICULATE ORGANIC CARBON AND CHLOROPHYLL-A CONCENTRATION USING MACHINE LEARNING FOR CMEMS

Author

Sauzède, Raphaëlle, Claustre, Hervé, Pannimpullath, Remanan, Uitz, Julia, Guinehut, Stéphanie, and MORVAN, Gaël

Subjects
Multi-observations, [SDE] Environmental Sciences, Chlorophyll-a concentration, Machine learning, CMEMS, Particulate organic carbon
Abstract

As part of Copernicus Marine Environmental Monitoring Service (CMEMS), the multi-observations thematic assembly center aims to provide products based on observations and data fusion techniques (Guinehut et al., 2021). Sauzede et al., (2016) have demonstrated the potential of using hydrological measurements and ocean color satellite observations to infer the vertical distribution of backscattering coeffi cient, a proxy for the stock of particulate organic carbon (POC). The 'Satellite Ocean-Color merged with Argo data to infer bio-optical properties to depth' (SOCA) method is a neural-network-based method trained using the Biogeochemical-Argo database. SOCA has been upgraded to improve the POC retrieval and additionally retrieve the chlorophyll-a concentration (Chl). Using this method with CMEMS hydrological and satellite products, weekly 3-dimensional fi elds of POC and associated uncertainty were retrieved for the 1998-2018 period and made available from the CMEMS online portal since July 2020. The 3-dimensional products of SOCA-retrieved Chl will be made available by the end of 2021. Both of these products will be updated yearly as new input data become available. These new CMEMS products represent a most valuable source of data useful not only for supporting the quality control of Biogeochemical-Argo fl oat observations but also for data assimilation and initialization/validation of biogeochemical models.

Published
2021

20. Reconstructing global chlorophyll-a variations using a non-linear statistical approach

Author

Martinez, Elodie, Gorgues, Thomas, Lengaigne, Matthieu, Fontana, Clément, Sauzède, Raphaëlle, Menkès, Christophe E., Uitz, Julia, Di Lorenzo, Emanuele, Fablet, Ronan, Laboratoire d'Océanographie Physique et Spatiale (LOPS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Ecosystèmes Insulaires Océaniens (UMR 241) (EIO), Université de la Polynésie Française (UPF)-Institut Louis Malardé [Papeete] (ILM), Institut de Recherche pour le Développement (IRD)-Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), Ecologie marine tropicale des océans Pacifique et Indien (ENTROPIE [Nouvelle-Calédonie]), Ifremer - Nouvelle-Calédonie, Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Université de la Nouvelle-Calédonie (UNC), Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Georgia Institute of Technology [Atlanta], Lab-STICC_IMTA_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de la Polynésie Française (UPF)-Institut Louis Malardé [Papeete] (ILM), Institut de Recherche pour le Développement (IRD), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité)-École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Cité (UPCité), Institut de Recherche pour le Développement (IRD [Nouvelle-Calédonie])-Ifremer - Nouvelle-Calédonie, Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de la Nouvelle-Calédonie (UNC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique (IMT Atlantique), and IMT Atlantique (IMT Atlantique)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, machine learning, [STAT.ML]Statistics [stat]/Machine Learning [stat.ML], decadel variability, global scale, phytoplankton variability, satellite ocean color, decadal variability
Abstract

International audience; Monitoring the spatio-temporal variations of surface chlorophyll-a concentration (Chl, a proxy of phytoplankton biomass) greatly benefited from the availability of continuous and global ocean color satellite measurements from 1997 onward. These two decades of satellite observations are however still too short to provide a comprehensive description of Chl variations at decadal to multi-decadal timescales. This paper investigates the ability of a machine learning approach (a non-linear statistical approach based on Support Vector Regression, hereafter SVR) to reconstruct global spatio-temporal Chl variations from selected surface oceanic and atmospheric physical parameters. With a limited training period (13 years), we first demonstrate that Chl variability from a 32-years global physical-biogeochemical simulation can generally be skillfully reproduced with a SVR using the model surface variables as input parameters. We then apply the SVR to reconstruct satellite Chl observations using the physical predictors from the above numerical model and show that the Chl reconstructed by this SVR more accurately reproduces some aspects of observed Chl variability and trends compared to the model simulation. This SVR is able to reproduce the main modes of interannual Chl variations depicted by satellite observations in most regions, including El Niño signature in the tropical Pacific and Indian Oceans. In stark contrast with the trends simulated by the biogeochemical model, it also accurately captures spatial patterns of Chl trends estimated by satellite data, with a Chl increase in most extratropical regions and a Chl decrease in the center of the subtropical gyres, although the amplitude of these trends are underestimated by half. Results from our SVR reconstruction over the entire period (1979–2010) also suggest that the Interdecadal Pacific Oscillation drives a significant part of decadal Chl variations in both the tropical Pacific and Indian Oceans. Overall, this study demonstrates that non-linear statistical reconstructions can be complementary tools to in situ and satellite observations as well as conventional physical-biogeochemical numerical simulations to reconstruct and investigate Chl decadal variability.

Published
2020

21. Enhancement of phytoplankton biomass leeward of Tahiti as observed by Biogeochemical-Argo floats

Author

Sauzède, Raphaëlle, Martinez, Elodie, Maes, Christophe, De Fommervault, Orens Pasqueron, Poteau, Antoine, Mignot, Alexandre, Claustre, Hervé, Uitz, Julia, Oziel, Laurent, Maamaatuaiahutapu, Keitapu, Rodier, Martine, Schmechtig, Catherine, Laurent, Victoire, Sauzède, Raphaëlle, Martinez, Elodie, Maes, Christophe, De Fommervault, Orens Pasqueron, Poteau, Antoine, Mignot, Alexandre, Claustre, Hervé, Uitz, Julia, Oziel, Laurent, Maamaatuaiahutapu, Keitapu, Rodier, Martine, Schmechtig, Catherine, and Laurent, Victoire

Abstract

The South Pacific Subtropical Gyre (SPSG) is a vast and remote oceanic system where the variability in phytoplankton biomass and production is still largely uncertain due to the lack of in situ biogeochemical observations. The SPSG is an oligotrophic environment where the ecosystem is controlled predominantly by nutrient depletion in surface waters. However, this dynamic is altered in the vicinity of islands where increased biological activity occurs (i.e. the island mass effect, IME). This study mainly focuses on in situ observations which show evidence of an IME leeward of Tahiti (17.7°S - 149.5°W), French Polynesia. Physical and biogeochemical observations collected with two Biogeochemical-Argo profiling floats are used to investigate the dynamics of phytoplankton biomass. Data from the first float, drifting from April 2015 to November 2016 over >1000 km westward of Tahiti, describe the open ocean conditions. The second float, deployed leeward of Tahiti in October 2015, stayed within 45 km off Tahiti for three months before it stopped communicating. In the oligotrophic central SPSG, our observations show that the deepening of the deep chlorophyll maximum (DCM) from winter to summer is light-driven and that the wintertime increase in chlorophyll a concentration in the upper layer is likely to be due to the process of photoacclimation, consistent with previous observations in oligotrophic environments. In contrast, leeward of Tahiti, the DCM widens toward the surface during late spring in association with a biological enhancement in the upper layer. Using Biogeochemical-Argo data, meteorological data from Tahiti, Hybrid Coordinate Ocean Model outputs and satellite-derived products (i.e., horizontal currents and associated fronts), the physical mechanisms involved in producing this biological enhancement leeward of Tahiti have been investigated. The IME occurs during a period of strong precipitation and in a zone of weak currents downstream of the island. We conject

Published
2020
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22. Introduction to the French GEOTRACES North Atlantic Transect (GA01): GEOVIDE cruise

Author

Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences, Sarthou, Géraldine, Lherminier, Pascale, Achterberg, Eric P., Alonso-Pérez, Fernando, Bucciarelli, Eva, Boutorh, Julia, Bouvier, Vincent, Boyle, Edward A, Branellec, Pierre, Carracedo, Lidia I., Casacuberta, Nuria, Castrillejo, Maxi, Cheize, Marie, Contreira Pereira, Leonardo, Cossa, Daniel, Daniault, Nathalie, De Saint-Léger, Emmanuel, Dehairs, Frank, Deng, Feifei, Desprez de Gésincourt, Floriane, Devesa, Jérémy, Foliot, Lorna, Fonseca-Batista, Debany, Gallinari, Morgane, García-Ibáñez, Maribel I., Gourain, Arthur, Grossteffan, Emilie, Hamon, Michel, Heimbürger, Lars Eric, Henderson, Gideon M., Jeandel, Catherine, Kermabon, Catherine, Lacan, François, Le Bot, Philippe, Le Goff, Manon, Le Roy, Emilie, Lefèbvre, Alison, Leizour, Stéphane, Lemaitre, Nolwenn, Masqué, Pere, Ménage, Olivier, Menzel Barraqueta, Jan-Lukas, Mercier, Herlé, Perault, Fabien, Pérez, Fiz F., Planquette, Hélène F., Planchon, Frédéric, Roukaerts, Arnout, Sanial, Virginie, Sauzède, Raphaëlle, Schmechtig, Catherine, Shelley, Rachel U., Stewart, Gillian, Sutton, Jill N., Tang, Yi, Tisnérat-Laborde, Nadine, Tonnard, Manon, Tréguer, Paul, van Beek, Pieter, Zurbrick, Cheryl M, Zunino, Patricia, Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences, Sarthou, Géraldine, Lherminier, Pascale, Achterberg, Eric P., Alonso-Pérez, Fernando, Bucciarelli, Eva, Boutorh, Julia, Bouvier, Vincent, Boyle, Edward A, Branellec, Pierre, Carracedo, Lidia I., Casacuberta, Nuria, Castrillejo, Maxi, Cheize, Marie, Contreira Pereira, Leonardo, Cossa, Daniel, Daniault, Nathalie, De Saint-Léger, Emmanuel, Dehairs, Frank, Deng, Feifei, Desprez de Gésincourt, Floriane, Devesa, Jérémy, Foliot, Lorna, Fonseca-Batista, Debany, Gallinari, Morgane, García-Ibáñez, Maribel I., Gourain, Arthur, Grossteffan, Emilie, Hamon, Michel, Heimbürger, Lars Eric, Henderson, Gideon M., Jeandel, Catherine, Kermabon, Catherine, Lacan, François, Le Bot, Philippe, Le Goff, Manon, Le Roy, Emilie, Lefèbvre, Alison, Leizour, Stéphane, Lemaitre, Nolwenn, Masqué, Pere, Ménage, Olivier, Menzel Barraqueta, Jan-Lukas, Mercier, Herlé, Perault, Fabien, Pérez, Fiz F., Planquette, Hélène F., Planchon, Frédéric, Roukaerts, Arnout, Sanial, Virginie, Sauzède, Raphaëlle, Schmechtig, Catherine, Shelley, Rachel U., Stewart, Gillian, Sutton, Jill N., Tang, Yi, Tisnérat-Laborde, Nadine, Tonnard, Manon, Tréguer, Paul, van Beek, Pieter, Zurbrick, Cheryl M, and Zunino, Patricia

Abstract

The GEOVIDE cruise, a collaborative project within the framework of the international GEOTRACES programme, was conducted along the French-led section in the North Atlantic Ocean (Section GA01), between 15 May and 30 June 2014. In this special issue (https://www.biogeosciences.net/special-issue900.html), results from GEOVIDE, including physical oceanography and trace element and isotope cyclings, are presented among 18 articles. Here, the scientific context, project objectives, and scientific strategy of GEOVIDE are provided, along with an overview of the main results from the articles published in the special issue.

Published
2020

27. Enhancement of phytoplankton biomass leeward of Tahiti as observed by Biogeochemical-Argo floats

Author

Sauzède, Raphaëlle, primary, Martinez, Elodie, additional, Maes, Christophe, additional, Pasqueron de Fommervault, Orens, additional, Poteau, Antoine, additional, Mignot, Alexandre, additional, Claustre, Hervé, additional, Uitz, Julia, additional, Oziel, Laurent, additional, Maamaatuaiahutapu, Keitapu, additional, Rodier, Martine, additional, Schmechtig, Catherine, additional, and Laurent, Victoire, additional

Published
2020
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28. A neural network approach to estimate water-column nutrient concentrations and carbonate system variables in the Mediterranean Sea: CANYON-MED

Author

Fourrier, Marine, Coppola, Laurent, D'Ortenzio, Fabrizio, Claustre, Hervé, Sauzède, Raphaëlle, Bittig, Henry C., Álvarez, Marta, Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Collecte Localisation Satellites (CLS), Leibniz Institute for Baltic Sea Research Warnemünde (IOW), Instituto Español de Oceanografía, A Coruña (IEO), and Fourrier, Marine

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, ComputingMilieux_MISCELLANEOUS
Abstract

International audience

Published
2019

30. Poster_EGU_2019_FOURRIER_VF.pdf

Author

Fourrier, Marine, Coppola, Laurent, D'Ortenzio, Fabrizio, Claustre, Hervé, Sauzède, Raphaëlle, Bittig, Henry C., and Álvarez, Marta

Abstract

Poster for EGU 2019. Session OS 2.5.A neural network approach to estimate water-column nutrient concentrations and carbonate system parameters in the Mediterranean Sea: CANYON-MEDThe semi-enclosed nature of the Mediterranean Sea, together with its small inertia which is due to the relatively short residence time of its water masses, make it highly reactive to external forcings and anthropogenic pressure.In this context, several rapid changes have been observed in physical and biogeochemical processes in recent decades, partly masked by episodic events and high regional variability. To better understand the underlying processes driving the Mediterranean evolution and in order to anticipate changes, the measurement and integration of many climatic and biogeochemical variables are mandatory.In the context of a critically undersampled ocean, the development and intensive use of instrumented in situ autonomous platforms will allow, in the medium term, to densify the measurements of some biogeochemical variables. However, the measurements carried out by in situ autonomous platforms (e.g. profiling floats, gliders, moorings) are not exhaustive.Recently, deep learning techniques and in particular neural networks have been developed for the global ocean. The CANYON (for Carbonate system and Nutrients concentration from hYdrological properties and Oxygen using a Neural-network) neural network-based method provides estimations of nutrients (i.e. nitrates, phosphates and silicates) and carbonate system parameters (i.e. total alkalinity, dissolved inorganic carbon, pHT, pCO2) from systematically measured oceanographic variables such as in situ measurements of pressure, temperature, salinity, and oxygen together with geolocation and date of sampling.However, while providing satisfactory results for the global ocean, the CANYON approach produces limited results in the Mediterranean Sea stemming from the Mediterranean Sea’s specific characteristics (such as its elevated salinity). The CANYON approach has therefore been adapted to this region considered as a "miniature ocean" and a "hot-spot" of climate change. In situ measurements from 33 cruises from 1976 to 2017 have been assembled to constitute a new quality-controlled database for the training of a regional neural network.The updated method, CANYON-MED, constitutes an improvement of the CANYON method, and satisfactory results are obtained: accuracies of 0.73, 0.043, and 0.63 µmol.kg-1 for the nitrates, phosphates and silicates concentrations respectively, and 0.014, 17 µmol.kg-1 and 12 µmol.kg-1 for pHT, total alkalinity and dissolved organic carbon respectively.CANYON-MED will generate “virtual” data of parameters not yet measured by autonomous platforms.Applied to the large and growing network of autonomous platforms, CANYON-MED could be used to increase the amount of biogeochemical data in the Mediterranean Sea and fill the gaps in time-series, dramatically improving our understanding of nutrients, pH and pCO2 variability of the basin.

Published
2019
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31. Role of Iron in the Marquesas Island Mass Effect

Author

Raapoto, Hirohiti, Martinez, Elodie, Petrenko, Anne, Doglioli, Andrea, Gorgues, Thomas, Sauzède, Raphaëlle, Maamaatuaiahutapu, Keitapu, Maes, Christophe, Menkes, Christophe, Lefèvre, Jérôme, Raapoto, Hirohiti, Martinez, Elodie, Petrenko, Anne, Doglioli, Andrea, Gorgues, Thomas, Sauzède, Raphaëlle, Maamaatuaiahutapu, Keitapu, Maes, Christophe, Menkes, Christophe, and Lefèvre, Jérôme

Abstract

A remarkable chlorophyll‐a concentration (Chl, a proxy of phytoplankton biomass) plume can be noticed on remotely sensed ocean color observations at the boundary separating the equatorial mesotrophic from the subtropical oligotrophic waters in the central South Pacific Ocean. This prominent biological feature is known as the island mass effect of the Marquesas archipelago. Waters surrounding these islands present high macronutrient concentrations but an iron depletion. In this study, the origin of Chl enhancement is investigated using a modeling approach. Four simulations based on identical physical and biogeochemical forcings but with different iron sources are conducted and analyzed. Only simulations considering an iron input from the island sediments present similar patterns (despite being too weak) of vertical and horizontal Chl distributions as compared to biogeochemical‐Argo profiling float and satellite observations. In addition, simulations with no other iron input than the boundary forcings reveal the relative importance of remote processes in modulating the seasonal pattern of Chl around the archipelago through horizontal advection of nutrient‐rich waters from the equator toward the archipelago and vertical mixing uplifting deep nutrient‐rich waters toward the upper lit layer.

Published
2019
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32. Role of Iron in the Marquesas Island Mass Effect

Author

Raapoto, Hirohiti, primary, Martinez, Elodie, additional, Petrenko, Anne, additional, Doglioli, Andrea, additional, Gorgues, Thomas, additional, Sauzède, Raphaëlle, additional, Maamaatuaiahutapu, Keitapu, additional, Maes, Christophe, additional, Menkes, Christophe, additional, and Lefèvre, Jérôme, additional

Published
2019
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33. Seasonal dynamics and disturbance of phytoplankton biomass in the wake of Tahiti as observed by Biogeochemical-Argo floats

Author

Sauzède, Raphaëlle, Martinez, Elodie, Pasqueron De Fommervault, Orens, Poteau, Antoine, Mignot, Alexandre, Maes, Christophe, Claustre, Hervé, Uitz, Julia, Maamaatuaiahutapu, Keitapu, Rodier, Martine, Schmechtig, Catherine, Laurent, Victoire, Sauzède, Raphaëlle, Martinez, Elodie, Pasqueron De Fommervault, Orens, Poteau, Antoine, Mignot, Alexandre, Maes, Christophe, Claustre, Hervé, Uitz, Julia, Maamaatuaiahutapu, Keitapu, Rodier, Martine, Schmechtig, Catherine, and Laurent, Victoire

Abstract

The South Pacific Subtropical Gyre (SPSG) is a vast and remote area where large uncertainties on variability in phytoplankton biomass and production remain due to the lack of biogeochemical in situ observations. In such oligotrophic environments, ecosystems are predominantly controlled by nutrients depletion in surface waters. However, this oligotrophic character can be disturbed in the vicinity of islands where enhancement of biological activity is known to occur (i.e. the island mass effect, IME). This study mainly focuses on in situ observations showing that an IME can be evidenced leeward of Tahiti (17.7° S–149.5° W), French Polynesia. Concomitant physical and biogeochemical observations collected with two Biogeochemical-Argo (BGC-Argo) profiling floats from April 2015 to November 2016 are used to investigate the dynamics of phytoplankton biomass. The first float has a transit of more than 1000 km westward of Tahiti (open ocean conditions) while the second one remained in the Tahitian wake (around 45 km from the island coasts). In the oligotrophic central SPSG, the wintertime increase in upper layer chlorophyll a concentration is likely due to photoacclimation process. Vertical observations show a light-driven deepening of the deep chlorophyll maximum (DCM) from winter to summer, consistently with previous descriptions. At the opposite, within the Tahitian wake, the DCM temporary widens during late spring in association with a biological enhancement in the upper layer. Combining in situ measurements with meteorological data along the Tahiti coasts, Hybrid Coordinate Ocean Model outputs and satellite-derived products (i.e., horizontal currents and associated fronts), the physical mechanisms involved in the disturbance of phytoplankton seasonal cycle in the Tahitian wake have been investigated. This disturbance results from the concomitant occurrence of strong precipitations and a zone of weak currents leeward Tahiti. We conjecture that the land drainage induces a

Published
2018
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34. Introduction to the French GEOTRACES North Atlantic transect (GA01): GEOVIDE cruise

Author

Sarthou, Geraldine, Lherminier, Pascale, Achterberg, Eric P., Alonso-Pérez, Fernando, Bucciarelli, Eva, Boutorh, Julia, Bouvier, Vincent, Boyle, Edward A., Branellec, Pierre, Carracedo, Lidia I., Casacuberta, Nuria, Castrillejo, Maxi, Cheize, Marie, Contreira Pereira, Leonardo, Cossa, Daniel, Daniault, Nathalie, De Saint-Léger, Emmanuel, Dehairs, Frank, Deng, Feifei, Desprez de Gésincourt, Floriane, Devesa, Jérémy, Foliot, Lorna, Fonseca-Batista, Debany, Gallinari, Morgane, García-Ibáñez, Maribel I., Gourain, Arthur, Grossteffan, Emilie, Hamon, Michel, Heimbürger, Lars Eric, Henderson, Gideon M., Jeandel, Catherine, Kermabon, Catherine, Lacan, François, Le Bot, Phillippe, Le Goff, Manon, Le Roy, Emilie, Lefèbvre, Alison, Leizour, Stéphane, Lemaitre, Nolwenn, Masqué, Pere, Ménage, Olivier, Barraqueta, Jan-Lukas Menzel, Mercier, Herlé, Perault, Fabien, Pérez, Fiz F., Planquette, Hélène F., Planchon, Frédéric, Roukaerts, Arnout, Sanial, Virginie, Sauzède, Raphaëlle, Schmechtig, Catherine, Shelley, Rachel U., Stewart, Gillian, Sutton, Jill N., Tang, Yi, Tisnérat-Laborde, Nadine, Tonnard, Manon, Tréguer, Paul, van Beek, Pieter, Zurbrick, Cheryl M., Zunino, Patricia, Sarthou, Geraldine, Lherminier, Pascale, Achterberg, Eric P., Alonso-Pérez, Fernando, Bucciarelli, Eva, Boutorh, Julia, Bouvier, Vincent, Boyle, Edward A., Branellec, Pierre, Carracedo, Lidia I., Casacuberta, Nuria, Castrillejo, Maxi, Cheize, Marie, Contreira Pereira, Leonardo, Cossa, Daniel, Daniault, Nathalie, De Saint-Léger, Emmanuel, Dehairs, Frank, Deng, Feifei, Desprez de Gésincourt, Floriane, Devesa, Jérémy, Foliot, Lorna, Fonseca-Batista, Debany, Gallinari, Morgane, García-Ibáñez, Maribel I., Gourain, Arthur, Grossteffan, Emilie, Hamon, Michel, Heimbürger, Lars Eric, Henderson, Gideon M., Jeandel, Catherine, Kermabon, Catherine, Lacan, François, Le Bot, Phillippe, Le Goff, Manon, Le Roy, Emilie, Lefèbvre, Alison, Leizour, Stéphane, Lemaitre, Nolwenn, Masqué, Pere, Ménage, Olivier, Barraqueta, Jan-Lukas Menzel, Mercier, Herlé, Perault, Fabien, Pérez, Fiz F., Planquette, Hélène F., Planchon, Frédéric, Roukaerts, Arnout, Sanial, Virginie, Sauzède, Raphaëlle, Schmechtig, Catherine, Shelley, Rachel U., Stewart, Gillian, Sutton, Jill N., Tang, Yi, Tisnérat-Laborde, Nadine, Tonnard, Manon, Tréguer, Paul, van Beek, Pieter, Zurbrick, Cheryl M., and Zunino, Patricia

Abstract

The GEOVIDE cruise, a collaborative project within the framework of the international GEOTRACES programme, was conducted along the French-led section in the North Atlantic Ocean (Section GA01), between 15 May and 30 June 2014. In this special issue (https://www.biogeosciences.net/special_issue900.html), results from GEOVIDE, including physical oceanography and trace element and isotope cyclings, are presented among 18 articles. Here, the scientific context, project objectives, and scientific strategy of GEOVIDE are provided, along with an overview of the main results from the articles published in the special issue.

Published
2018

35. An Alternative to Static Climatologies: Robust Estimation of Open Ocean CO2 Variables and Nutrient Concentrations From T, S, and O2 Data Using Bayesian Neural Networks

Author

Bittig, Henry C., Steinhoff, Tobias, Claustre, Hervé, Fiedler, Björn, Williams, Nancy L., Sauzède, Raphaëlle, Körtzinger, Arne, Gattuso, Jean-Pierre, Bittig, Henry C., Steinhoff, Tobias, Claustre, Hervé, Fiedler, Björn, Williams, Nancy L., Sauzède, Raphaëlle, Körtzinger, Arne, and Gattuso, Jean-Pierre

Abstract

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

Published
2018
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36. Introduction to the French GEOTRACES North Atlantic transect (GA01): GEOVIDE cruise

Author

LabexMER, Institut Français de Recherche pour l'Exploitation de la Mer, Agence Nationale de la Recherche (France), Centre National de la Recherche Scientifique (France), Sarthou, Géraldine, Lherminier, Pascale, Achterberg, Eric P., Alonso Pérez, Fernando, Bucciarelli, Eva, Boutorh, Julia, Bouvier, Vincent, Boyle, Edward A., Branellec, Pierre, Carracedo, L., Casacuberta, Nuria, Castrillejo, Maxi, Cheize, Marie, Contreira Pereira, Leonardo, Cossa, Daniel, Daniault, Nathalie, De Saint-Léger, Emmanuel, Dehairs, Frank, Deng, Feifei, Desprez de Gésincourt, Floriane, Devesa, Jérémy, Foliot, Lorna, Fonseca-Batista, Debany, Gallinari, Morgane, García-Ibáñez, Maribel I., Gourain, Arthur, Grossteffan, Emilie, Hamon, Michel, Heimbürger-Boavida, Lars-Eric, Henderson, Gideon M., Jeandel, Catherine, Kermabon, Catherine, Lacan, François, Le Bot, Philippe, Le Goff, Manon, Le Roy, Emilie, Lefèbvre, Alison, Leizour, Stéphane, Lemaitre, Nolwenn, Masqué, Pere, Ménage, Olivier, Menzel Barraqueta, Jan-Lukas, Mercier, Herlé, Perault, Fabien, Pérez, Fiz F., Planquette, Hélène, Planchon, Frédéric, Roukaerts, Arnout, Sanial, Virginie, Sauzède, Raphaëlle, Schmechtig, Catherine, Shelley, Rachel U., Stewart, Gillian, Sutton, Jill N., Tang, Yi, Tisnérat-Laborde, Nadine, Tonnard, Manon, Tréguer, Paul, van Beek, Pieter, Zurbrick, Cheryl M., Zunino, P., LabexMER, Institut Français de Recherche pour l'Exploitation de la Mer, Agence Nationale de la Recherche (France), Centre National de la Recherche Scientifique (France), Sarthou, Géraldine, Lherminier, Pascale, Achterberg, Eric P., Alonso Pérez, Fernando, Bucciarelli, Eva, Boutorh, Julia, Bouvier, Vincent, Boyle, Edward A., Branellec, Pierre, Carracedo, L., Casacuberta, Nuria, Castrillejo, Maxi, Cheize, Marie, Contreira Pereira, Leonardo, Cossa, Daniel, Daniault, Nathalie, De Saint-Léger, Emmanuel, Dehairs, Frank, Deng, Feifei, Desprez de Gésincourt, Floriane, Devesa, Jérémy, Foliot, Lorna, Fonseca-Batista, Debany, Gallinari, Morgane, García-Ibáñez, Maribel I., Gourain, Arthur, Grossteffan, Emilie, Hamon, Michel, Heimbürger-Boavida, Lars-Eric, Henderson, Gideon M., Jeandel, Catherine, Kermabon, Catherine, Lacan, François, Le Bot, Philippe, Le Goff, Manon, Le Roy, Emilie, Lefèbvre, Alison, Leizour, Stéphane, Lemaitre, Nolwenn, Masqué, Pere, Ménage, Olivier, Menzel Barraqueta, Jan-Lukas, Mercier, Herlé, Perault, Fabien, Pérez, Fiz F., Planquette, Hélène, Planchon, Frédéric, Roukaerts, Arnout, Sanial, Virginie, Sauzède, Raphaëlle, Schmechtig, Catherine, Shelley, Rachel U., Stewart, Gillian, Sutton, Jill N., Tang, Yi, Tisnérat-Laborde, Nadine, Tonnard, Manon, Tréguer, Paul, van Beek, Pieter, Zurbrick, Cheryl M., and Zunino, P.

Abstract

The GEOVIDE cruise, a collaborative project within the framework of the international GEOTRACES programme, was conducted along the French-led section in the North Atlantic Ocean (Section GA01), between 15 May and 30 June 2014. In this special issue (https://www.biogeosciences.net/special_issue900.html), results from GEOVIDE, including physical oceanography and trace element and isotope cyclings, are presented among 18 articles. Here, the scientific context, project objectives, and scientific strategy of GEOVIDE are provided, along with an overview of the main results from the articles published in the special issue.

Published
2018

38. A global neural network-based parameterization of biogeochemical water mass properties and processes based on GLODAP data

Author

Bittig, Henry, Sauzède, Raphaëlle, Claustre, Hervé, Fommervault, Orens, Gattuso, Jean-Pierre, Legendre, Louis, Johnson, Kenneth S, Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de Recherche pour le Développement (IRD [Polynésie]), Centro de Investigacion Cientifica y de Education Superior de Ensenada [Mexico] (CICESE), Monterey Bay Aquarium Research Institute (MBARI), and Monterey Bay Aquarium Research Institute

Subjects
[SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography
Abstract

International audience

Published
2017

39. Introduction to the French GEOTRACES North Atlantic Transect (GA01): GEOVIDE cruise

Author

Sarthou, Géraldine, primary, Lherminier, Pascale, additional, Achterberg, Eric P., additional, Alonso-Pérez, Fernando, additional, Bucciarelli, Eva, additional, Boutorh, Julia, additional, Bouvier, Vincent, additional, Boyle, Edward A., additional, Branellec, Pierre, additional, Carracedo, Lidia I., additional, Casacuberta, Nuria, additional, Castrillejo, Maxi, additional, Cheize, Marie, additional, Contreira Pereira, Leonardo, additional, Cossa, Daniel, additional, Daniault, Nathalie, additional, De Saint-Léger, Emmanuel, additional, Dehairs, Frank, additional, Deng, Feifei, additional, Desprez de Gésincourt, Floriane, additional, Devesa, Jérémy, additional, Foliot, Lorna, additional, Fonseca-Batista, Debany, additional, Gallinari, Morgane, additional, García-Ibáñez, Maribel I., additional, Gourain, Arthur, additional, Grossteffan, Emilie, additional, Hamon, Michel, additional, Heimbürger, Lars Eric, additional, Henderson, Gideon M., additional, Jeandel, Catherine, additional, Kermabon, Catherine, additional, Lacan, François, additional, Le Bot, Philippe, additional, Le Goff, Manon, additional, Le Roy, Emilie, additional, Lefèbvre, Alison, additional, Leizour, Stéphane, additional, Lemaitre, Nolwenn, additional, Masqué, Pere, additional, Ménage, Olivier, additional, Menzel Barraqueta, Jan-Lukas, additional, Mercier, Herlé, additional, Perault, Fabien, additional, Pérez, Fiz F., additional, Planquette, Hélène F., additional, Planchon, Frédéric, additional, Roukaerts, Arnout, additional, Sanial, Virginie, additional, Sauzède, Raphaëlle, additional, Schmechtig, Catherine, additional, Shelley, Rachel U., additional, Stewart, Gillian, additional, Sutton, Jill N., additional, Tang, Yi, additional, Tisnérat-Laborde, Nadine, additional, Tonnard, Manon, additional, Tréguer, Paul, additional, van Beek, Pieter, additional, Zurbrick, Cheryl M., additional, and Zunino, Patricia, additional

Published
2018
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41. Supplementary material to "Introduction to the French GEOTRACES North Atlantic Transect (GA01): GEOVIDE cruise"

Author

Sarthou, Géraldine, primary, Lherminier, Pascale, additional, Achterberg, Eric P., additional, Alonso-Pérez, Fernando, additional, Bucciarelli, Eva, additional, Boutorh, Julia, additional, Bouvier, Vincent, additional, Boyle, Edward A., additional, Branellec, Pierre, additional, Carracedo, Lidia I., additional, Casacuberta, Nuria, additional, Castrillejo, Maxi, additional, Cheize, Marie, additional, Contreira Pereira, Leonardo, additional, Cossa, Daniel, additional, Daniault, Nathalie, additional, De Saint-Léger, Emmanuel, additional, Dehairs, Frank, additional, Deng, Feifei, additional, Desprez de Gésincourt, Floriane, additional, Devesa, Jérémy, additional, Foliot, Lorna, additional, Fonseca-Batista, Debany, additional, Gallinari, Morgane, additional, García-Ibáñez, Maribel I., additional, Gourain, Arthur, additional, Grossteffan, Emilie, additional, Hamon, Michel, additional, Heimbürger, Lars Eric, additional, Henderson, Gideon M., additional, Jeandel, Catherine, additional, Kermabon, Catherine, additional, Lacan, François, additional, Le Bot, Philippe, additional, Le Goff, Manon, additional, Le Roy, Emilie, additional, Lefèbvre, Alison, additional, Leizour, Stéphane, additional, Lemaitre, Nolwenn, additional, Masqué, Pere, additional, Ménage, Olivier, additional, Menzel Barraqueta, Jan-Lukas, additional, Mercier, Herlé, additional, Perault, Fabien, additional, Pérez, Fiz F., additional, Planquette, Hélène F., additional, Planchon, Frédéric, additional, Roukaerts, Arnout, additional, Sanial, Virginie, additional, Sauzède, Raphaëlle, additional, Shelley, Rachel U., additional, Stewart, Gillian, additional, Sutton, Jill N., additional, Tang, Yi, additional, Tisnérat-Laborde, Nadine, additional, Tonnard, Manon, additional, Tréguer, Paul, additional, van Beek, Pieter, additional, Zurbrick, Cheryl M., additional, and Zunino, Patricia, additional

Published
2018
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43. Seasonal dynamics and disturbance of phytoplankton biomass in the wake of Tahiti as observed by Biogeochemical-Argo floats

Author

Sauzède, Raphaëlle, primary, Martinez, Elodie, additional, Pasqueron de Fommervault, Orens, additional, Poteau, Antoine, additional, Mignot, Alexandre, additional, Maes, Christophe, additional, Claustre, Hervé, additional, Uitz, Julia, additional, Maamaatuaiahutapu, Keitapu, additional, Rodier, Martine, additional, Schmechtig, Catherine, additional, and Laurent, Victoire, additional

Published
2018
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44. Supplementary material to "Seasonal dynamics and disturbance of phytoplankton biomass in the wake of Tahiti as observed by Biogeochemical-Argo floats"

Author

Sauzède, Raphaëlle, primary, Martinez, Elodie, additional, Pasqueron de Fommervault, Orens, additional, Poteau, Antoine, additional, Mignot, Alexandre, additional, Maes, Christophe, additional, Claustre, Hervé, additional, Uitz, Julia, additional, Maamaatuaiahutapu, Keitapu, additional, Rodier, Martine, additional, Schmechtig, Catherine, additional, and Laurent, Victoire, additional

Published
2018
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45. Study and parameterization of the vertical distribution of phytoplankton biomass in the global ocean

Author

Sauzède, Raphaëlle, Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Université Pierre et Marie Curie - Paris VI, Hervé Claustre, Julia Uitz, and STAR, ABES

Subjects
Chlorophyll, Coefficient de rétrodiffusion particulaire, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Phytoplancton, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, Phytoplankton, Bio-Optique, Chlorophylle-A, Ocean color, Flotteurs profileurs Bio, Couleur de l'eau, Fluorescence
Abstract

This PhD work focuses on the parameterization of the vertical distribution of phytoplankton biomass and community structure in the global open ocean. First we have developed a neural network-based method for the calibration of the fluorescence in chlorophyll a concentration [Chl] associated with the total phytoplankton biomass and with three phytoplankton size classes. This method, (FLAVOR for Fluorescence to Algal communities Vertical distribution in the Oceanic Realm), was trained and validated using a database of ~900 concomitant fluorescence and HPLC-determined pigment profiles. A global database comprising ~49 000 fluorescence profiles was assembled and calibrated with FLAVOR. The resulting database represents a first step towards a global three-dimensional view of phytoplankton biomass and community composition. Second, two neural networks (SOCA for Satellite Ocean Color and Argo data to infer vertical distribution of bio-optical properties) were developed to infer the vertical distribution of two bio-optical proxies of the phytoplankton biomass, [Chl] and the particulate backscattering coefficient, using as input satellite-derived products matched up with a hydrological Argo profile. The SOCA methods were trained and validated using a global database of ~5 000 profiles of bio-optical and hydrological properties collected from Bio-Argo floats with concomitant satellite products. The database used to develop FLAVOR and SOCA originates from various oceanic regions largely representative of the global ocean, making the methods applicable to most oceanic waters. Finally, we proposed a study dedicated to the North Atlantic where the tools developed in this thesis are used in conjunction with a bio-optical primary production model. This allows us to characterize the seasonal cycle of the vertical distribution of the phytoplankton biomass and primary production in various bio-regions of the North Atlantic., Les travaux présentés dans cette thèse concernent la paramétrisation de la distribution verticale de la biomasse et de la structure des communautés phytoplanctoniques dans l’océan global. Nous avons d’abord développé une méthode neuronale de calibration de la fluorescence en concentration en chlorophylle a ([Chl]) associée à la biomasse phytoplanctonique totale et à trois classes de taille de phytoplancton. Cette méthode, FLAVOR, a été entrainée et validée à l’aide une base de données de ~900 profils de fluorescence et de pigments mesurés pat HPLC. Une base de données globale de ~49000 profils de fluorescence a ensuite été assemblée et calibrée en termes de biomasse chlorophyllienne et composition du phytoplancton. Ce travail représente une première étape vers une vision tridimensionnelle de la biomasse phytoplanctonique. Nous avons ensuite développé deux réseaux de neurones (SOCA) pour estimer la distribution verticale de deux paramètres bio-optiques, [Chl] et le coefficient de rétrodiffusion. Ces réseaux de neurones requièrent comme données d’entrée des données satellites de couleur de l’eau co-localisées avec un profil hydrologique collecté par un flotteur Argo. Ils ont été entrainés et validés avec une base de données globale composée de ~5 000 profils de propriétés bio-optiques et hydrologiques acquises par des flotteurs Bio-Argo. Les bases de données utilisées pour développer les méthodes FLAVOR et SOCA proviennent de régions océaniques représentatives de l’océan global, permettant ainsi l’application de ces méthodes à la majorité des eaux océaniques. Finalement, nous avons mené une étude focalisée sur l’Atlantique Nord qui exploite les outils développés. Les champs tridimensionnels de biomasse obtenus, couplés à un modèle bio-optique de production primaire, permettent d’étudier les cycles saisonniers de la distribution verticale de la biomasse phytoplanctonique et de la production primaire dans différentes bio-régions de l’Atlantique Nord.

Published
2015

47. Voyage of a profiling float: A scientific adventure told to young people

Author

Fommervault, Orens, Sauzède, Raphaëlle, Scheurle, Carolyn, Claustre, Hervé, d'Ortenzio, Fabrizio, Alseamar France, Laboratoire d'océanographie de Villefranche (LOV), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
[SHS.EDU]Humanities and Social Sciences/Education
Abstract

International audience

Published
2014

48. 'Adopt a float': An initiative designed for middle school students to follow the voyage of a Bio-Argo profiling float and share experiences with oceanographers

Author

Uitz, Julia, Scheurle, Carolyn, Lavigne, Héloïse, Fommervault, Orens, Sauzède, Raphaëlle, Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), and Alseamar France

Subjects
[SHS.EDU]Humanities and Social Sciences/Education
Abstract

International audience

Published
2014

49. Introduction to the French GEOTRACES North Atlantic Transect (GA01): GEOVIDE cruise

Author

Sarthou, Géraldine, Lherminier, Pascale, Achterberg, Eric P., Alonso-Pérez, Fernando, Bucciarelli, Eva, Boutorh, Julia, Bouvier, Vincent, Boyle, Edward A., Branellec, Pierre, Carracedo, Lidia I., Casacuberta, Núria, Castrillejo, Maxi, Cheize, Marie, Pereira, Leonardo C., Cossa, Daniel, Daniault, Nathalie, De Saint-Leger, Emmanuel, Dehairs, Frank, Deng, Feifei, De Gésincourt, Floriane D., Devesa, Jérémy, Foliot, Lorna, Fonseca-Batista, Debany, Gallinari, Morgane, García-Ibáñez, Maribel I., Gourain, Arthur, Grossteffan, Emilie, Hamon, Michel, Heimbürger, Lars E., Henderson, Gideon M., Jeandel, Catherine, Kermabon, Catherine, Lacan, François, Le Bot, Philippe, Le Goff, Manon, Le Roy, Emilie, Lefèbvre, Alison, Leizour, Stéphane, Lemaitre, Nolwenn, Masqué, Pere, Ménage, Olivier, Menzel Barraqueta, Jan-Lukas, Mercier, Herlé, Perault, Fabien, Pérez, Fíz F., Planquette, Hélène F., Planchon, Frédéric, Roukaerts, Arnout, Sanial, Virginie, Sauzède, Raphaëlle, Schmechtig, Catherine, Shelley, Rachel U., Stewart, Gillian, Sutton, Jill N., Tang, Yi, Tisnérat-Laborde, Nadine, Tonnard, Manon, Tréguer, Paul, Van Beek, Pieter, Zurbrick, Cheryl M., and Zunino, Patricia

Subjects
13. Climate action, 14. Life underwater
Abstract

The GEOVIDE cruise, a collaborative project within the framework of the international GEOTRACES programme, was conducted along the French-led section in the North Atlantic Ocean (Section GA01), between 15 May and 30 June 2014. In this special issue (https://www.biogeosciences.net/special_issue900.html), results from GEOVIDE, including physical oceanography and trace element and isotope cyclings, are presented among 18 articles. Here, the scientific context, project objectives, and scientific strategy of GEOVIDE are provided, along with an overview of the main results from the articles published in the special issue., Biogeosciences, 15 (23), ISSN:1726-4170

50. Estimates of Water-Column Nutrient Concentrations and Carbonate System Parameters in the Global Ocean: A Novel Approach Based on Neural Networks

Author

Sauzède, Raphaëlle, Bittig, Henry C., Claustre, Hervé, Pasqueron de Fommervault, Orens, Gattuso, Jean-Pierre, Legendre, Louis, Johnson, Kenneth S., Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), and Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
global ocean, neural network, nutrients, profiling floats, [SDE]Environmental Sciences, GLODAPv2 database, Marine Science, carbonate system, ComputingMilieux_MISCELLANEOUS
Abstract

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

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