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| Bellacicco, M.; Cornec, M.; Organelli, E.; Brewin, R. J. W.; Neukermans, G.; Volpe, G.; Barbieux, M.; Poteau, A.; Schmechtig, C.; D'Ortenzio, F.; Marullo, S.; Claustre, H.; Pitarch, J.. |
| Understanding spatial and temporal dynamics of non-algal particles in open ocean is of the utmost importance to improve estimations of carbon export and sequestration. These particles covary with phytoplankton abundance but also accumulate independently of algal dynamics. The latter likely represents an important fraction of organic carbon, but it is largely overlooked. A possible way to study these particles is via their optical backscattering properties (b(bp)) and relationship with chlorophyll-a (Chi). To this aim, we estimate the fraction of b(bp) associated with the non-algal particle portion (b(bp)(k)) that does not covary with Chl by using a global Biogeochemical-Argo data set. We quantify the spatial, temporal, and vertical variability of b(bp)(k).... |
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| Ano: 2019 |
URL: https://archimer.ifremer.fr/doc/00676/78803/81047.pdf |
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| Palacz, A. P.; St John, M. A.; Brewin, R. J. W.; Hirata, Toshio; Gregg, W. W.. |
| Modeling and monitoring plankton functional types (PFTs) is challenged by the insufficient amount of field measurements of ground truths in both plankton models and bio-optical algorithms. In this study, we combine remote sensing data and a dynamic plankton model to simulate an ecologically sound spatial and temporal distribution of phyto-PFTs. We apply an innovative ecological indicator approach to modeling PFTs and focus on resolving the question of diatom-coccolithophore coexistence in the subpolar high-nitrate and low-chlorophyll regions. We choose an artificial neural network as our modeling framework because it has the potential to interpret complex nonlinear interactions governing complex adaptive systems, of which marine ecosystems are a prime... |
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| Ano: 2013 |
URL: https://archimer.ifremer.fr/doc/00170/28126/26337.pdf |
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| Ciavatta, S.; Brewin, R. J. W.; Skakala, J.; Polimene, L.; De Mora, L.; Artioli, Y.; Allen, J. I.. |
| We assimilated phytoplankton functional types (PFTs) derived from ocean color into a marine ecosystem model, to improve the simulation of biogeochemical indicators and emerging properties in a shelf sea. Error-characterized chlorophyll concentrations of four PFTs (diatoms, dinoflagellates, nanoplankton, and picoplankton), as well as total chlorophyll for comparison, were assimilated into a physical-biogeochemical model of the North East Atlantic, applying a localized Ensemble Kalman filter. The reanalysis simulations spanned the years 1998-2003. The skill of the reference and reanalysis simulations in estimating ocean color and in situ biogeochemical data were compared by using robust statistics. The reanalysis outperformed both the reference and the... |
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| Ano: 2018 |
URL: https://archimer.ifremer.fr/doc/00673/78494/80766.pdf |
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