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| Bittig, Henry C.; Maurer, Tanya L.; Plant, Joshua N.; Schmechtig, Catherine; Wong, Annie P. S.; Claustre, Hervé; Trull, Thomas W.; Udaya Bhaskar, T. V. S.; Boss, Emmanuel; Dall’olmo, Giorgio; Organelli, Emanuele; Poteau, Antoine; Johnson, Kenneth S.; Hanstein, Craig; Leymarie, Edouard; Le Reste, Serge; Riser, Stephen C.; Rupan, A. Rick; Taillandier, Vincent; Thierry, Virginie; Xing, Xiaogang. |
| The Biogeochemical-Argo program (BGC-Argo) is a new profiling-float-based, ocean wide, and distributed ocean monitoring program which is tightly linked to, and has benefited significantly from, the Argo program. The community has recommended for BGC-Argo to measure six additional properties in addition to pressure, temperature and salinity measured by Argo, to include oxygen, pH, nitrate, downwelling light, chlorophyll fluorescence and the optical backscattering coefficient. The purpose of this addition is to enable the monitoring of ocean biogeochemistry and health, and in particular, monitor major processes such as ocean deoxygenation, acidification and warming and their effect on phytoplankton, the main source of energy of marine ecosystems. Here we... |
| Tipo: Text |
Palavras-chave: Ocean observation; Ocean biogeochemical cycles; Sensors; Carbon cycle; Ocean optics; Best practices; Argo. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00512/62344/66607.pdf |
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| Bittig, Henry C.; Steinhoff, Tobias; Claustre, Harve; Fiedler, Bjoern; Williams, Nancy L.; Sauzede, Raphaelle; Koertzinger, Arne; Gattuso, Jean-pierre. |
| This work presents two new methods to estimate oceanic alkalinity (A(T)), dissolved inorganic carbon (C-T), pH, and pCO(2) 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... |
| Tipo: Text |
Palavras-chave: Carbon cycle; GLODAP; Marine carbonate system; Surface pCO(2) climatology; Revelle buffer factor increase; Machine learning; Nutrient estimation. |
| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00675/78681/80879.pdf |
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| Olsen, Are; Lange, Nico; Key, Robert M.; Tanhua, Toste; Bittig, Henry C.; Kozyr, Alex; Álvarez, Marta; Azetsu-scott, Kumiko; Becker, Susan; Brown, Peter J.; Carter, Brendan R.; Cotrim Da Cunha, Leticia; Feely, Richard A.; Van Heuven, Steven; Hoppema, Mario; Ishii, Masao; Jeansson, Emil; Jutterström, Sara; Landa, Camilla S.; Lauvset, Siv K.; Michaelis, Patrick; Murata, Akihiko; Pérez, Fiz F; Pfeil, Benjamin; Schirnick, Carsten; Steinfeldt, Reiner; Suzuki, Toru; Tilbrook, Bronte; Velo, Anton; Wanninkhof, Rik; Woosley, Ryan J.. |
| The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface-to-bottom ocean biogeochemical data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of seawater samples. GLODAPv2.2020 is an update of the previous version, GLODAPv2.2019. The major changes are data from 106 new cruises added, extension of time coverage to 2019, and the inclusion of available (also for historical cruises) discrete fugacity of CO2 (fCO2) values in the merged product files. GLODAPv2.2020 now includes measurements from more than 1.2 million water samples from the global oceans collected on 946 cruises. The data for the 12 GLODAP core variables (salinity, oxygen,... |
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| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00668/78015/80254.pdf |
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| Pearlman, Jay; Bushnell, Mark; Coppola, Laurent; Karstensen, Johannes; Buttigieg, Pier Luigi; Pearlman, Francoise; Simpsons, Pauline; Barbier, Michele; Muller-karger, Frank E.; Munoz-mas, Cristian; Pissierssens, Peter; Chandler, Cyndy; Hermes, Juliet; Heslop, Emma; Jenkyns, Reyna; Achterberg, Eric P.; Bensi, Manuel; Bittig, Henry C.; Blandin, Jerome; Bosch, Julie; Bourles, Bernard; Bozzano, Roberto; Buck, Justin J. H.; Burger, Eugene F.; Cano, Daniel; Cardin, Vanessa; Llorens, Miguel Charcos; Cianca, Andres; Chen, Hua; Cusack, Caroline; Delory, Eric; Garello, Rene; Giovanetti, Gabriele; Harscoat, Valerie; Hartman, Susan; Heitsenrether, Robert; Jirka, Simon; Lara-lopez, Ana; Lanteri, Nadine; Leadbetter, Adam; Manzella, Giuseppe; Maso, Joan; Mccurdy, Andrea; Moussat, Eric; Ntoumas, Manolis; Pensieri, Sara; Petihakis, George; Pinardi, Nadia; Pouliquen, Sylvie; Przeslawski, Rachel; Roden, Nicholas P.; Silke, Joe; Tamburri, Mario N.; Tang, Hairong; Tanhua, Toste; Telszewski, Maciej; Testor, Pierre; Thomas, Julie; Waldmann, Christoph; Whoriskey, Fred. |
| The oceans play a key role in global issues such as climate change, food security, and human health. Given their vast dimensions and internal complexity, efficient monitoring and predicting of the planet's ocean must be a collaborative effort of both regional and global scale. A first and foremost requirement for such collaborative ocean observing is the need to follow well-defined and reproducible methods across activities: from strategies for structuring observing systems, sensor deployment and usage, and the generation of data and information products, to ethical and governance aspects when executing ocean observing. To meet the urgent, planet-wide challenges we face, methods across all aspects of ocean observing should be broadly adopted by the ocean... |
| Tipo: Text |
Palavras-chave: Best practices; Sustainability; Interoperability; Digital repository; Peer review; Ocean observing; Ontologies; Methodologies. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00503/61423/65111.pdf |
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| Friedlingstein, Pierre; O'Sullivan, Michael; Jones, Matthew W.; Andrew, Robbie M.; Hauck, Judith; Olsen, Are; Peters, Glen P.; Peters, Wouter; Pongratz, Julia; Sitch, Stephen; Le Quere, Corinne; Canadell, Josep G.; Ciais, Philippe; Jackson, Robert B.; Alin, Simone; Aragao, Luiz E. O. C.; Arneth, Almut; Arora, Vivek; Bates, Nicholas R.; Becker, Meike; Benoit-cattin, Alice; Bittig, Henry C.; Bopp, Laurent; Bultan, Selma; Chandra, Naveen; Chevallier, Frederic; Chini, Louise P.; Evans, Wiley; Florentie, Liesbeth; Forster, Piers M.; Gasser, Thomas; Gehlen, Marion; Gilfillan, Dennis; Gkritzalis, Thanos; Gregor, Luke; Gruber, Nicolas; Harris, Ian; Hartung, Kerstin; Haverd, Vanessa; Houghton, Richard A.; Ilyina, Tatiana; Jain, Atul K.; Joetzjer, Emilie; Kadono, Koji; Kato, Etsushi; Kitidis, Vassilis; Korsbakken, Jan Ivar; Landschutzer, Peter; Lefevre, Nathalie; Lenton, Andrew; Lienert, Sebastian; Liu, Zhu; Lombardozzi, Danica; Marland, Gregg; Metzl, Nicolas; Munro, David R.; Nabel, Julia E. M. S.; Nakaoka, Shin-ichiro; Niwa, Yosuke; O'Brien, Kevin; Ono, Tsuneo; Palmer, Paul I.; Pierrot, Denis; Poulter, Benjamin; Resplandy, Laure; Robertson, Eddy; Rodenbeck, Christian; Schwinger, Jorg; Seferian, Roland; Skjelvan, Ingunn; Smith, Adam J. P.; Sutton, Adrienne J.; Tanhua, Toste; Tans, Pieter P.; Tian, Hanqin; Tilbrook, Bronte; Van Der Werf, Guido; Vuichard, Nicolas; Walker, Anthony P.; Wanninkhof, Rik; Watson, Andrew J.; Willis, David; Wiltshire, Andrew J.; Yuan, Wenping; Yue, Xu; Zaehle, Sonke. |
| Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere in a changing climate - the "global carbon budget" - is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe and synthesize data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. Fossil CO2 emissions ( EFOS) are based on energy statistics and cement production data, while emissions from land-use change ( ELUC), mainly deforestation, are based on land use and land-use change data and bookkeeping models. Atmospheric CO2 concentration is measured directly... |
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| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00677/78860/81159.pdf |
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| Olsen, Are; Lange, Nico; Key, Robert M.; Tanhua, Toste; Alvarez, Marta; Becker, Susan; Bittig, Henry C.; Carter, Brendan R.; Da Cunha, Leticia Cotrim; Feely, Richard A.; Van Heuven, Steven; Hoppema, Mario; Ishii, Masao; Jeansson, Emil; Jones, Steve D.; Jutterstrom, Sara; Karlsen, Maren K.; Kozyr, Alex; Lauvset, Siv K.; Lo Monaco, Claire; Murata, Akihiko; Perez, Fiz F; Pfeil, Benjamin; Schirnick, Carsten; Steinfeldt, Reiner; Suzuki, Toru; Telszewski, Maciej; Tilbrook, Bronte; Velo, Anton; Wanninkhof, Rik. |
| The Global Ocean Data Analysis Project (GLODAP) is a synthesis effort providing regular compilations of surface to bottom ocean biogeochemical data, with an emphasis on seawater inorganic carbon chemistry and related variables determined through chemical analysis of water samples. This update of GLODAPv2, v2.2019, adds data from 116 cruises to the previous version, extending its coverage in time from 2013 to 2017, while also adding some data from prior years. GLODAPv2.2019 includes measurements from more than 1.1 million water samples from the global oceans collected on 840 cruises. The data for the 12 GLODAP core variables (salinity, oxygen, nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity, pH, CFC-11, CFC-12, CFC-113, and CCl4)... |
| Tipo: Text |
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| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00675/78722/80999.pdf |
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| Roemmich, Dean; Alford, Matthew H.; Claustre, Hervé; Johnson, Kenneth; King, Brian; Moum, James; Oke, Peter; Owens, W. Brechner; Pouliquen, Sylvie; Purkey, Sarah; Scanderbeg, Megan; Suga, Toshio; Wijffels, Susan; Zilberman, Nathalie; Bakker, Dorothee; Baringer, Molly; Belbeoch, Mathieu; Bittig, Henry C.; Boss, Emmanuel; Calil, Paulo; Carse, Fiona; Carval, Thierry; Chai, Fei; Conchubhair, Diarmuid Ó.; D’ortenzio, Fabrizio; Dall’olmo, Giorgio; Desbruyeres, Damien; Fennel, Katja; Fer, Ilker; Ferrari, Raffaele; Forget, Gael; Freeland, Howard; Fujiki, Tetsuichi; Gehlen, Marion; Greenan, Blair; Hallberg, Robert; Hibiya, Toshiyuki; Hosoda, Shigeki; Jayne, Steven; Jochum, Markus; Johnson, Gregory C.; Kang, Kiryong; Kolodziejczyk, Nicolas; Körtzinger, Arne; Traon, Pierre-yves Le; Lenn, Yueng-djern; Maze, Guillaume; Mork, Kjell Arne; Morris, Tamaryn; Nagai, Takeyoshi; Nash, Jonathan; Garabato, Alberto Naveira; Olsen, Are; Pattabhi, Rama Rao; Prakash, Satya; Riser, Stephen; Schmechtig, Catherine; Schmid, Claudia; Shroyer, Emily; Sterl, Andreas; Sutton, Philip; Talley, Lynne; Tanhua, Toste; Thierry, Virginie; Thomalla, Sandy; Toole, John; Troisi, Ariel; Trull, Thomas W.; Turton, Jon; Velez-belchi, Pedro Joaquin; Walczowski, Waldemar; Wang, Haili; Wanninkhof, Rik; Waterhouse, Amy F.; Waterman, Stephanie; Watson, Andrew; Wilson, Cara; Wong, Annie P. S.; Xu, Jianping; Yasuda, Ichiro. |
| The Argo Program has been implemented and sustained for almost two decades, as a global array of about 4000 profiling floats. Argo provides continuous observations of ocean temperature and salinity versus pressure, from the sea surface to 2000 dbar. The successful installation of the Argo array and its innovative data management system arose opportunistically from the combination of great scientific need and technological innovation. Through the data system, Argo provides fundamental physical observations with broad societally-valuable applications, built on the cost-efficient and robust technologies of autonomous profiling floats. Following recent advances in platform and sensor technologies, even greater opportunity exists now than 20 years ago to (i)... |
| Tipo: Text |
Palavras-chave: Argo; Floats; Global; Ocean; Warming; Circulation; Temperature; Salinity. |
| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00509/62043/66192.pdf |
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| Bittig, Henry C.; Schmechtig, Catherine; Rannou, Jean-philippe; Poteau, Antoine. |
| Thermal lag or sensor time response corrections (e.g., [1]) require knowledge of the time of each individual sensor observation. In the Argo data system, the float's trajectory file is the natural place to put measurement timing information. Historically, only few levels of a float's profile have been timed, and DACs stored these sparse timed levels of the float profile in the trajectory already. Depending on the DAC, all measured paramters are stored with the timing information, or just the PRES variable is stored together with the time. With the need to have all observations timed, this approach leads to the following problems: a) With all parameters stored together with the timing information and put into the trajectory file, the trajectory file in... |
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| Ano: 2017 |
URL: http://archimer.ifremer.fr/doc/00369/47998/48031.pdf |
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