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| Von Schuckmann, Karina; Le Traon, Pierre-yves; Smith, Neville; Pascual, Ananda; Djavidnia, Samuel; Gattuso, Jean-pierre; Grégoire, Marilaure; Nolan, Glenn; Aaboe, Signe; Aguiar, Eva; Álvarez Fanjul, Enrique; Alvera-azcárate, Aida; Aouf, Lotfi; Barciela, Rosa; Behrens, Arno; Belmonte Rivas, Maria; Ben Ismail, Sana; Bentamy, Abderrahim; Borgini, Mireno; Brando, Vittorio E.; Bensoussan, Nathaniel; Blauw, Anouk; Bryère, Philippe; Buongiorno Nardelli, Bruno; Caballero, Ainhoa; Çağlar Yumruktepe, Veli; Cebrian, Emma; Chiggiato, Jacopo; Clementi, Emanuela; Corgnati, Lorenzo; De Alfonso, Marta; De Pascual Collar, Álvaro; Deshayes, Julie; Di Lorenzo, Emanuele; Dominici, Jean-marie; Dupouy, Cécile; Drévillon, Marie; Echevin, Vincent; Eleveld, Marieke; Enserink, Lisette; García Sotillo, Marcos; Garnesson, Philippe; Garrabou, Joaquim; Garric, Gilles; Gasparin, Florent; Gayer, Gerhard; Gohin, Francis; Grandi, Alessandro; Griffa, Annalisa; Gourrion, Jerome; Hendricks, Stefan; Heuzé, Céline; Holland, Elisabeth; Iovino, Doroteaciro; Juza, Mélanie; Kurt Kersting, Diego; Kipson, Silvija; Kizilkaya, Zafer; Korres, Gerasimos; Kõuts, Mariliis; Lagemaa, Priidik; Lavergne, Thomas; Lavigne, Heloise; Ledoux, Jean-baptiste; Legeais, Jean Francois; Lehodey, Patrick; Linares, Cristina; Liu, Ye; Mader, Julien; Maljutenko, Ilja; Mangin, Antoine; Manso-narvarte, Ivan; Mantovani, Carlo; Markager, Stiig; Mason, Evan; Mignot, Alexandre; Menna, Milena; Monier, Maeva; Mourre, Baptiste; Müller, Malte; Nielsen, Jacob Woge; Notarstefano, Giulio; Ocaña, Oscar; Pascual, Ananda; Patti, Bernardo; Payne, Mark R.; Peirache, Marion; Pardo, Silvia; Pérez Gómez, Begoña; Pisano, Andrea; Perruche, Coralie; Peterson, K. Andrew; Pujol, Marie-isabelle; Raudsepp, Urmas; Ravdas, Michalis; Raj, Roshin P.; Renshaw, Richard; Reyes, Emma; Ricker, Robert; Rubio, Anna; Sammartino, Michela; Santoleri, Rosalia; Sathyendranath, Shubha; Schroeder, Katrin; She, Jun; Sparnocchia, Stefania; Staneva, Joanna; Stoffelen, Ad; Szekely, Tanguy; Tilstone, Gavin H.; Tinker, Jonathan; Tintoré, Joaquín; Tranchant, Benoît; Uiboupin, Rivo; Van Der Zande, Dimitry; Wood, Richard; Woge Nielsen, Jacob; Zabala, Mikel; Zacharioudaki, Anna; Zuberer, Frédéric; Zuo, Hao. |
| Tipo: Text |
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| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00515/62637/67004.pdf |
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| Franks, Peter J. S.; Di Lorenzo, Emanuele; Goebel, Nicole L.; Chenillat, Fanny; Riviere, Pascal; Edward, Christopher A.; Miller, Arthur J.. |
| Understanding the effects of climate change on planktonic ecosystems requires the synthesis of large, diverse data sets of variables that often interact in nonlinear ways. One fruitful approach to this synthesis is the use of numerical models. Here, we describe how models have been used to gain understanding of the physical-biological couplings leading to decadal changes in the southern California Current ecosystem. Moving from basin scales to local scales, we show how atmospheric, physical oceanographic, and biological dynamics interact to create long-term fluctuations in the dynamics of the California Current ecosystem. |
| Tipo: Text |
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| Ano: 2013 |
URL: http://archimer.ifremer.fr/doc/00156/26735/24829.pdf |
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| Martinez, Elodie; Gorgues, Thomas; Lengaigne, Matthieu; Fontana, Clement; Sauzède, Raphaëlle; Menkes, Christophe; Uitz, Julia; Di Lorenzo, Emanuele; Fablet, Ronan. |
| 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... |
| Tipo: Text |
Palavras-chave: Machine learning; Phytoplankton variability; Satellite ocean color; Decadel variability; Global scale. |
| Ano: 2020 |
URL: https://archimer.ifremer.fr/doc/00641/75314/75810.pdf |
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