Articles | Volume 15, issue 2
https://doi.org/10.5194/bg-15-613-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-15-613-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
30 Jan 2018
Research article | Highlight paper |
| 30 Jan 2018
| 30 Jan 2018
Modelling ocean-colour-derived chlorophyll a
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Center for Climate Change Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
Anna E. Hickman
Ocean and Earth Sciences, University of Southampton, National Oceanography Centre Southampton, Southampton, SO14 3ZH, UK
Oliver Jahn
Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
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While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
Paul J. Tréguer, Jill N. Sutton, Mark Brzezinski, Matthew A. Charette, Timothy Devries, Stephanie Dutkiewicz, Claudia Ehlert, Jon Hawkings, Aude Leynaert, Su Mei Liu, Natalia Llopis Monferrer, María López-Acosta, Manuel Maldonado, Shaily Rahman, Lihua Ran, and Olivier Rouxel
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This study highlights recent advances and challenges of applying coupled physical-biogeochemical modeling for investigating the distribution of the key phytoplankton groups in the Southern Ocean. By leveraging satellite and in situ observations we define numerical ecological model requirements in the phytoplankton trait specification and level of physiological and morphological differentiation for capturing and explaining the observed biogeography of diatoms, coccolithophores and Phaeocystis.
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Silicon is the second most abundant element of the Earth's crust. In this review, we show that silicon inputs and outputs, to and from the world ocean, are 57 % and 37 % higher, respectively, than previous estimates. These changes are significant, modifying factors such as the geochemical residence time of silicon, which is now about 8000 years and 2 times faster than previously assumed. We also update the total biogenic silica pelagic production and provide an estimate for sponge production.
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Phytoplankton are an essential component of the marine food web and earth's carbon cycle. We use observations, ecological theory and a unique trait-based ecosystem model to explain controls on patterns of marine phytoplankton biodiversity. We find that different dimensions of diversity (size classes, biogeochemical functional groups, thermal norms) are controlled by a disparate combination of mechanisms. This may explain why previous studies of phytoplankton diversity had conflicting results.
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Biogeosciences, 16, 1249–1264, https://doi.org/10.5194/bg-16-1249-2019, https://doi.org/10.5194/bg-16-1249-2019, 2019
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Ishan D. Joshi and Eurico J. D'Sa
Biogeosciences, 15, 4065–4086, https://doi.org/10.5194/bg-15-4065-2018, https://doi.org/10.5194/bg-15-4065-2018, 2018
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The standard quasi-analytical algorithm (QAA) was tuned for various ocean color sensors as QAA-V and optimized for and evaluated in a variety of waters from highly absorbing and turbid to relatively clear shelf waters. The QAA-V-derived optical properties of total absorption and backscattering coefficients showed an obvious improvement when compared to the standard QAA and were used to examine suspended particulate matter dynamics in Galveston Bay following flooding due to Hurricane Harvey.
Yasmina Loozen, Karin T. Rebel, Derek Karssenberg, Martin J. Wassen, Jordi Sardans, Josep Peñuelas, and Steven M. De Jong
Biogeosciences, 15, 2723–2742, https://doi.org/10.5194/bg-15-2723-2018, https://doi.org/10.5194/bg-15-2723-2018, 2018
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Nitrogen (N) is an essential nutrient for plant growth. It would be interesting to detect it using satellite data. The goal was to investigate if it is possible to remotely sense the canopy nitrogen concentration and content of Mediterranean trees using a product calculated from satellite reflectance data, the MERIS Terrestrial Chlorophyll Index (MTCI). The tree plots were located in Catalonia, NE Spain. The relationship between MTCI and canopy N was present but dependent on the type of trees.
Gholamreza Mohammadpour, Jean-Pierre Gagné, Pierre Larouche, and Martin A. Montes-Hugo
Biogeosciences, 14, 5297–5312, https://doi.org/10.5194/bg-14-5297-2017, https://doi.org/10.5194/bg-14-5297-2017, 2017
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The mass-specific absorption coefficients of total suspended particulate matter (aSPM*) had relatively low (high) values in areas of of the St. Lawrence Estuary influenced by marine (freshwater) waters and dominated by large-sized (small-sized) and organic-rich (mineral-rich) particulates.
The inorganic content of particulates was correlated with size-fractionated aSPM* values at a wavelength of 440 nm and the spectral slope of aSPM* as computed within the spectral range 400–710 nm.
Albert-Miquel Sánchez and Jaume Piera
Biogeosciences, 13, 4081–4098, https://doi.org/10.5194/bg-13-4081-2016, https://doi.org/10.5194/bg-13-4081-2016, 2016
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In this paper, several methods for the retrieval of the refractive indices are used in three different examples modeling different shapes and particle size distributions. The error associated with each method is discussed and analyzed. It is finally demonstrated that those inverse methods using a genetic algorithm provide optimal estimations relative to other techniques that, although faster, are less accurate.
Luisa Galgani and Anja Engel
Biogeosciences, 13, 2453–2473, https://doi.org/10.5194/bg-13-2453-2016, https://doi.org/10.5194/bg-13-2453-2016, 2016
G. E. Kim, M.-A. Pradal, and A. Gnanadesikan
Biogeosciences, 12, 5119–5132, https://doi.org/10.5194/bg-12-5119-2015, https://doi.org/10.5194/bg-12-5119-2015, 2015
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Light absorption by colored detrital material (CDM) was included in a fully coupled Earth system model. Chlorophyll and biomass increased near the surface but decreased at greater depths when CDM was included. Concurrently, total biomass decreased leaving more nutrients in the water. Regional changes were analyzed by comparing the competing factors of diminished light availability and increased nutrient availability on phytoplankton growth.
J. A. Gamon, O. Kovalchuck, C. Y. S. Wong, A. Harris, and S. R. Garrity
Biogeosciences, 12, 4149–4159, https://doi.org/10.5194/bg-12-4149-2015, https://doi.org/10.5194/bg-12-4149-2015, 2015
Short summary
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NDVI and PRI sensors (SRS, Decagon Inc.) exhibited complementary responses during spring photosynthetic activation in evergreen and deciduous stands. In evergreens, PRI was most strongly influenced by changing chlorophyll:carotenoid pool sizes over the several weeks of the study, while it was most affected by xanthophyll cycle pigment activity at the diurnal timescale. These automated PRI and NDVI sensors offer new ways to explore environmental and physiological constraints on photosynthesis.
M. Grenier, A. Della Penna, and T. W. Trull
Biogeosciences, 12, 2707–2735, https://doi.org/10.5194/bg-12-2707-2015, https://doi.org/10.5194/bg-12-2707-2015, 2015
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Four bio-profilers were deployed in the high-biomass plume downstream of the Kerguelen Plateau (KP; Southern Ocean) to examine the conditions favouring phytoplankton accumulation. Regions of very high Chla accumulation were mainly associated with surface waters from the northern KP. Light limitation seems to have a limited influence on production. A cyclonic eddy was associated with a significant export of organic matter and a subsequent dissolved inorganic carbon storage in the ocean interior.
I. Cetinić, M. J. Perry, E. D'Asaro, N. Briggs, N. Poulton, M. E. Sieracki, and C. M. Lee
Biogeosciences, 12, 2179–2194, https://doi.org/10.5194/bg-12-2179-2015, https://doi.org/10.5194/bg-12-2179-2015, 2015
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The ratio of simple optical properties measured from underwater autonomous platforms, such as floats and gliders, is used as a new tool for studying phytoplankton distribution in the North Atlantic Ocean. The resolution that optical instruments carried by autonomous platforms provide allows us to study phytoplankton patchiness and its drivers in the oceanic systems.
B. Heim, E. Abramova, R. Doerffer, F. Günther, J. Hölemann, A. Kraberg, H. Lantuit, A. Loginova, F. Martynov, P. P. Overduin, and C. Wegner
Biogeosciences, 11, 4191–4210, https://doi.org/10.5194/bg-11-4191-2014, https://doi.org/10.5194/bg-11-4191-2014, 2014
M. Kahru and R. Elmgren
Biogeosciences, 11, 3619–3633, https://doi.org/10.5194/bg-11-3619-2014, https://doi.org/10.5194/bg-11-3619-2014, 2014
E. J. D'Sa, J. I. Goes, H. Gomes, and C. Mouw
Biogeosciences, 11, 3225–3244, https://doi.org/10.5194/bg-11-3225-2014, https://doi.org/10.5194/bg-11-3225-2014, 2014
A. Matsuoka, M. Babin, D. Doxaran, S. B. Hooker, B. G. Mitchell, S. Bélanger, and A. Bricaud
Biogeosciences, 11, 3131–3147, https://doi.org/10.5194/bg-11-3131-2014, https://doi.org/10.5194/bg-11-3131-2014, 2014
S. Q. Wang, J. Ishizaka, H. Yamaguchi, S. C. Tripathy, M. Hayashi, Y. J. Xu, Y. Mino, T. Matsuno, Y. Watanabe, and S. J. Yoo
Biogeosciences, 11, 1759–1773, https://doi.org/10.5194/bg-11-1759-2014, https://doi.org/10.5194/bg-11-1759-2014, 2014
S. L. Shang, Q. Dong, C. M. Hu, G. Lin, Y. H. Li, and S. P. Shang
Biogeosciences, 11, 269–280, https://doi.org/10.5194/bg-11-269-2014, https://doi.org/10.5194/bg-11-269-2014, 2014
H. Örek, R. Doerffer, R. Röttgers, M. Boersma, and K. H. Wiltshire
Biogeosciences, 10, 7081–7094, https://doi.org/10.5194/bg-10-7081-2013, https://doi.org/10.5194/bg-10-7081-2013, 2013
S. Bélanger, S. A. Cizmeli, J. Ehn, A. Matsuoka, D. Doxaran, S. Hooker, and M. Babin
Biogeosciences, 10, 6433–6452, https://doi.org/10.5194/bg-10-6433-2013, https://doi.org/10.5194/bg-10-6433-2013, 2013
X. Zhang, Y. Huot, D. J. Gray, A. Weidemann, and W. J. Rhea
Biogeosciences, 10, 6029–6043, https://doi.org/10.5194/bg-10-6029-2013, https://doi.org/10.5194/bg-10-6029-2013, 2013
D. Antoine, S. B. Hooker, S. Bélanger, A. Matsuoka, and M. Babin
Biogeosciences, 10, 4493–4509, https://doi.org/10.5194/bg-10-4493-2013, https://doi.org/10.5194/bg-10-4493-2013, 2013
S. B. Hooker, J. H. Morrow, and A. Matsuoka
Biogeosciences, 10, 4511–4527, https://doi.org/10.5194/bg-10-4511-2013, https://doi.org/10.5194/bg-10-4511-2013, 2013
S. Bélanger, M. Babin, and J.-É. Tremblay
Biogeosciences, 10, 4087–4101, https://doi.org/10.5194/bg-10-4087-2013, https://doi.org/10.5194/bg-10-4087-2013, 2013
A. Matsuoka, S. B. Hooker, A. Bricaud, B. Gentili, and M. Babin
Biogeosciences, 10, 917–927, https://doi.org/10.5194/bg-10-917-2013, https://doi.org/10.5194/bg-10-917-2013, 2013
S. Takao, T. Hirawake, S. W. Wright, and K. Suzuki
Biogeosciences, 9, 3875–3890, https://doi.org/10.5194/bg-9-3875-2012, https://doi.org/10.5194/bg-9-3875-2012, 2012
R. Röttgers and B. P. Koch
Biogeosciences, 9, 2585–2596, https://doi.org/10.5194/bg-9-2585-2012, https://doi.org/10.5194/bg-9-2585-2012, 2012
A. Sadeghi, T. Dinter, M. Vountas, B. Taylor, M. Altenburg-Soppa, and A. Bracher
Biogeosciences, 9, 2127–2143, https://doi.org/10.5194/bg-9-2127-2012, https://doi.org/10.5194/bg-9-2127-2012, 2012
A. Matsuoka, A. Bricaud, R. Benner, J. Para, R. Sempéré, L. Prieur, S. Bélanger, and M. Babin
Biogeosciences, 9, 925–940, https://doi.org/10.5194/bg-9-925-2012, https://doi.org/10.5194/bg-9-925-2012, 2012
B. B. Taylor, E. Torrecilla, A. Bernhardt, M. H. Taylor, I. Peeken, R. Röttgers, J. Piera, and A. Bracher
Biogeosciences, 8, 3609–3629, https://doi.org/10.5194/bg-8-3609-2011, https://doi.org/10.5194/bg-8-3609-2011, 2011
G. Dall'Olmo, E. Boss, M. J. Behrenfeld, T. K. Westberry, C. Courties, L. Prieur, M. Pujo-Pay, N. Hardman-Mountford, and T. Moutin
Biogeosciences, 8, 3423–3439, https://doi.org/10.5194/bg-8-3423-2011, https://doi.org/10.5194/bg-8-3423-2011, 2011
H. Loisel, V. Vantrepotte, K. Norkvist, X. Mériaux, M. Kheireddine, J. Ras, M. Pujo-Pay, Y. Combet, K. Leblanc, G. Dall'Olmo, R. Mauriac, D. Dessailly, and T. Moutin
Biogeosciences, 8, 3295–3317, https://doi.org/10.5194/bg-8-3295-2011, https://doi.org/10.5194/bg-8-3295-2011, 2011
S. Shang, Q. Dong, Z. Lee, Y. Li, Y. Xie, and M. Behrenfeld
Biogeosciences, 8, 841–850, https://doi.org/10.5194/bg-8-841-2011, https://doi.org/10.5194/bg-8-841-2011, 2011
T. S. Kostadinov, D. A. Siegel, and S. Maritorena
Biogeosciences, 7, 3239–3257, https://doi.org/10.5194/bg-7-3239-2010, https://doi.org/10.5194/bg-7-3239-2010, 2010
F. Nencioli, G. Chang, M. Twardowski, and T. D. Dickey
Biogeosciences, 7, 151–162, https://doi.org/10.5194/bg-7-151-2010, https://doi.org/10.5194/bg-7-151-2010, 2010
A. Morel and B. Gentili
Biogeosciences, 6, 2625–2636, https://doi.org/10.5194/bg-6-2625-2009, https://doi.org/10.5194/bg-6-2625-2009, 2009
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Short summary
This study provides a demonstration that a biogeochemical/ecosystem/optical computer model which explicitly captures how light is radiated at the surface of the ocean and can be used as a laboratory to explore products (such as Chl a) that are derived from satellite measurements of ocean colour. It explores uncertainties that arise from data input used to derive the algorithms for the products, and issues arising from the interplay between optically important constituents in the ocean.
This study provides a demonstration that a biogeochemical/ecosystem/optical computer model which...
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