Vertical distribution of chlorophyll &amp;lt;I&amp;gt;a&amp;lt;/I&amp;gt; concentration and phytoplankton community composition from in situ fluorescence profiles: a first database for the global ocean

Sauzède, Raphaëlle; Lavigne, Héloïse; Claustre, Hervé; Uitz, Julia; Schmechtig, Catherine; D’Ortenzio, Fabrizio; Guinet, Christophe; Pesant, Stéphane

doi:10.5194/essd-7-261-2015

Cited by 31 publications

(22 citation statements)

References 42 publications

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“…9a, b). The observed ranges of Chl, FDOM, b bp (700), K d (λ), and K d (PAR) values derived from BGC-Argo measurements (Table 1) are also in good agreement with previous observations (Morel and Maritorena, 2001;Morel et al, 2007a, b;Cetinić et al, 2012;Dall'Olmo et al, 2012;Peloquin et al, 2013;Sauzède et al, 2015;Valente et al, 2016). As examples of their spatial distribution across the explored regions, K d (412) and K d (PAR) are shown in Fig.…”

Section: Bopad-prof: Spatiotemporal Distribution Of the Biogeochemicasupporting

confidence: 89%

“…It is worth noting that the latter two studies have been pursued by exclusively exploiting BOPAD-surf and BOPAD-prof. The new and systematic way BGC-Argo floats collect data, and their potential in dramatically increasing oceanic observations in a restricted time, also supplement and complement published carbon cycle and optically relevant pan-oceanic data compilations (Peloquin et al, 2013;Sauzède et al, 2015;Bakker et al, 2016;Mouw et al, 2016;Valente et al, 2016).…”

Section: Conclusion and Recommendations For Usementioning

confidence: 86%

“…BGC-Argo can therefore help the scientific community to accumulate observations on biogeochemical properties from the surface to the interior of the ocean in a new and systematic way (Claustre et al, 2010a;Biogeochemical-Argo Planning Group, 2016;Johnson and Claustre, 2016). This, together with several other recent efforts to compile global biologically or biogeochemically relevant datasets (Peloquin et al, 2013;Sauzède et al, 2015;Bakker et al, 2016;Mouw et al, 2016;Valente et al, 2016), may provide new insights on marine ecological and biogeochemical processes and help better understand if oceans and their properties have changed and/or are changing over the decades .…”

Section: Introductionmentioning

confidence: 99%

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Two databases derived from BGC-Argo float measurements for marine biogeochemical and bio-optical applications

Organelli

Barbieux

Claustre

et al. 2017

Earth Syst. Sci. Data

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Abstract. Since 2012, an array of 105 Biogeochemical-Argo (BGC-Argo) floats has been deployed across the world's oceans to assist in filling observational gaps that are required for characterizing open-ocean environments. Profiles of biogeochemical (chlorophyll and dissolved organic matter) and optical (single-wavelength particulate optical backscattering, downward irradiance at three wavelengths, and photosynthetically available radiation) variables are collected in the upper 1000 m every 1 to 10 days. The database of 9837 vertical profiles collected up to January 2016 is presented and its spatial and temporal coverage is discussed. Each variable is quality controlled with specifically developed procedures and its time series is quality-assessed to identify issues related to biofouling and/or instrument drift. A second database of 5748 profile-derived products within the first optical depth (i.e., the layer of interest for satellite remote sensing) is also presented and its spatiotemporal distribution discussed. This database, devoted to field and remote ocean color applications, includes diffuse attenuation coefficients for downward irradiance at three narrow wavebands and one broad waveband (photosynthetically available radiation), calibrated chlorophyll and fluorescent dissolved organic matter concentrations, and singlewavelength particulate optical backscattering. To demonstrate the applicability of these databases, data within the first optical depth are compared with previously established bio-optical models and used to validate remotely derived bio-optical products. The quality-controlled databases are publicly available from the SEANOE (SEA scieNtific Open data Edition) publisher at https://doi.org/10.17882/49388 and https://doi.org/10.17882/47142 for vertical profiles and products within the first optical depth, respectively.

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Section: Bopad-prof: Spatiotemporal Distribution Of the Biogeochemicasupporting

confidence: 89%

Section: Conclusion and Recommendations For Usementioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

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Two databases derived from BGC-Argo float measurements for marine biogeochemical and bio-optical applications

Organelli

Barbieux

Claustre

et al. 2017

Earth Syst. Sci. Data

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“…A substantial fraction of recent fluorescence measurements from Bio-Argo platforms has already been included in a new global Chl a database described and provided by Sauzède et al (2015b). Their quality-controlled data comprise profiles of total Chl a concentration together with some additional estimates of the relative contributions from pico, nano, and micro phytoplankton.…”

Section: Examples Of Recent Advances In Data Availabilitymentioning

confidence: 99%

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

et al. 2017

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Abstract. To describe the underlying processes involved in oceanic plankton dynamics is crucial for the determination of energy and mass flux through an ecosystem and for the estimation of biogeochemical element cycling. Many planktonic ecosystem models were developed to resolve major processes so that flux estimates can be derived from numerical simulations. These results depend on the type and number of parameterizations incorporated as model equations. Furthermore, the values assigned to respective parameters specify a model's solution. Representative model results are those that can explain data; therefore, data assimilation methods are utilized to yield optimal estimates of parameter values while fitting model results to match data. Central difficulties are (1) planktonic ecosystem models are imperfect and (2) data are often too sparse to constrain all model parameters. In this review we explore how problems in parameter identification are approached in marine planktonic ecosystem modelling.We provide background information about model uncertainties and estimation methods, and how these are considered for assessing misfits between observations and model results. We explain differences in evaluating uncertainties in parameter estimation, thereby also discussing issues of parameter identifiability. Aspects of model complexity are addressed and we describe how results from cross-validation studies provide much insight in this respect. Moreover, approaches are discussed that consider time-and spacedependent parameter values. We further discuss the use of dynamical/statistical emulator approaches, and we elucidate issues of parameter identification in global biogeochemical models. Our review discloses many facets of parameter identification, as we found many commonalities between the objectives of different approaches, but scientific insight differed between studies. To learn more from results of planktonic ecosystem models we recommend finding a good balance in the level of sophistication between mechanistic modelling and statistical data assimilation treatment for parameter estimation.

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“…The SPSG is also one of the least known and studied oceanic regions because this area is vast and remote, leading to scarce biogeochemical in situ observations 15 (Sauzède et al, 2015) and large uncertainties on phytoplankton biomass variability. This gyre is also distinguishable by its extremely weak sources of nutrients from deep layers (Raimbault et al, 2008), as well as from atmospheric flux Mahowald et al, 2005;Wagener et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

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

Sauzède¹,

Martínez

Fommervault

et al. 2018

Preprint

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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 20 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 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, 30Hybrid 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 significant supply of nitrate in the ocean upper layer (down to ~100 m) while a zone of weak currents in the southwestern zone behind Tahiti forms an accumulation zone, hence allowing phytoplankton 35 growth up to 20 km away from the coastlines. Moreover, bio-optical measurements suggest that the composition of

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Vertical distribution of chlorophyll <I>a</I> concentration and phytoplankton community composition from in situ fluorescence profiles: a first database for the global ocean

Cited by 31 publications

References 42 publications

Two databases derived from BGC-Argo float measurements for marine biogeochemical and bio-optical applications

Two databases derived from BGC-Argo float measurements for marine biogeochemical and bio-optical applications

Reviews and syntheses: parameter identification in marine planktonic ecosystem modelling

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

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