Estimates of particulate organic carbon over the euphotic depth from in situ measurements. Application to satellite data over the global ocean

Duforêt-Gaurier, Lucile; Loisel, Hubert; Dessailly, David; Nordkvist, Karin; Alvain, Séverine

doi:10.1016/j.dsr.2009.12.007

Cited by 46 publications

(27 citation statements)

References 46 publications

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“…Organic carbon from POM forms a small fraction of total organic carbon in marine environment; almost 97-99% of POM gets remineralized in the photic layers while a very small fraction gets buried into the bottom sediment (Hedges andKiel, 1995, Honjo et al, 2008). Hence, POM controls the efficiency and quality of 'biological pump' linking the surface productivity to deep carbon export (Gordon and Goñi, 2003;Duforet-Gaurier et al, 2010). Chemical characterisation of POM including isotopic analyses of carbon and nitrogen help us to gain insights into various processes that influence the quality of carbon exported to the deep-ocean.…”

Section: Introductionmentioning

confidence: 99%

Latitudinal δ13C and δ15N variations in particulate organic matter (POM) in surface waters from the Indian ocean sector of Southern Ocean and the Tropical Indian Ocean in 2012

Soares

Bhaskar

Naik

et al. 2015

Deep Sea Research Part II: Topical Studies in Oceanography

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Section: Introductionmentioning

confidence: 99%

Latitudinal δ13C and δ15N variations in particulate organic matter (POM) in surface waters from the Indian ocean sector of Southern Ocean and the Tropical Indian Ocean in 2012

Soares

Bhaskar

Naik

et al. 2015

Deep Sea Research Part II: Topical Studies in Oceanography

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“…Furthermore, by limiting our dataset to the top 10 m, we have precluded the possibility of testing any potential impact that the vertical structure in the first optical depth might have on algorithm performance. Examples exist in the literature where the depth variations in particulate organic carbon been taken into account (Duforêt-Gaurier et al, 2010), and this may be an avenue worth exploring also for phytoplankton carbon algorithms.…”

Section: The Picophytoplankton C Match-up Datasetmentioning

confidence: 99%

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

et al. 2017

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The differences among phytoplankton carbon (C phy ) predictions from six ocean color algorithms are investigated by comparison with in situ estimates of phytoplankton carbon. The common satellite data used as input for the algorithms is the Ocean Color Climate Change Initiative merged product. The matching in situ data are derived from flow cytometric cell counts and per-cell carbon estimates for different types of pico-phytoplankton. This combination of satellite and in situ data provides a relatively large matching dataset (N > 500), which is independent from most of the algorithms tested and spans almost two orders of magnitude in C phy . Results show that not a single algorithm outperforms any of the other when using all matching data. Concentrating on the oligotrophic regions (Chlorophyll-a concentration, B, less than 0.15 mg Chl m −3 ), where flow cytometric analysis captures most of the phytoplankton biomass, reveals significant differences in algorithm performance. The bias ranges from −35 to +150% and unbiased root mean squared difference from 5 to 10 mg C m −3 among algorithms, with chlorophyll-based algorithms performing better than the rest. The backscatteringbased algorithms produce different results at the clearest waters and these differences are discussed in terms of the different algorithms used for optical particle backscattering coefficient (b bp ) retrieval.

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“…In oligotrophic waters, this approach may underestimate the POC reservoir where high POC accompanies deep chl a maxima, because it assumes that POC is uniform throughout the MLD and equals the near-surface POC concentration. This work has been improved by the empirical algorithm of Duforêt-Gaurier et al (2010) who derive integrated euphotic zone POC from the entire SeaWiFS satellite data set.…”

Section: Marine Carbon Observations Frommentioning

confidence: 99%

Ocean-Atmosphere Interactions of Gases and Particles

2014

Springer Earth System Sciences

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Why a chapter on Perspectives and Integration in SOLAS Science in this book? SOLAS science by its nature deals with interactions that occur: across a wide spectrum of time and space scales, involve gases and particles, between the ocean and the atmosphere, across many disciplines including chemistry, biology, optics, physics, mathematics, computing, socio-economics and consequently interactions between many different scientists and across scientific generations. This chapter provides a guide through the remarkable diversity of cross-cutting approaches and tools in the gigantic puzzle of the SOLAS realm.Here we overview the existing prime components of atmospheric and oceanic observing systems, with the acquisition of ocean-atmosphere observables either from in situ or from satellites, the rich hierarchy of models to test our knowledge of Earth System functioning, and the tremendous efforts accomplished over the last decade within the COST Action 735 and SOLAS Integration project frameworks to understand, as best we can, the current physical and biogeochemical state of the atmosphere and ocean commons. A few SOLAS integrative studies illustrate the full meaning of interactions, paving the way for even tighter connections between thematic fields. Ultimately, SOLAS research will also develop with an enhanced consideration of societal demand while preserving fundamental research coherency.

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Estimates of particulate organic carbon over the euphotic depth from in situ measurements. Application to satellite data over the global ocean

Cited by 46 publications

References 46 publications

Latitudinal δ13C and δ15N variations in particulate organic matter (POM) in surface waters from the Indian ocean sector of Southern Ocean and the Tropical Indian Ocean in 2012

Latitudinal δ13C and δ15N variations in particulate organic matter (POM) in surface waters from the Indian ocean sector of Southern Ocean and the Tropical Indian Ocean in 2012

Intercomparison of Ocean Color Algorithms for Picophytoplankton Carbon in the Ocean

Ocean-Atmosphere Interactions of Gases and Particles

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