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Validation and intercomparison of ocean colour algorithms for estimating particulate organic carbon in the oceans

Validation and intercomparison of ocean colour algorithms for estimating particulate organic carbon in the oceans
Validation and intercomparison of ocean colour algorithms for estimating particulate organic carbon in the oceans
Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-colour signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean colour data requires algorithms that are well validated, with uncertainties characterised. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean colour data provided by the Ocean Colour Climate Change Initiative (OC-CCI) and validated against the largest database of $\textit{in situ}$ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of \cite{stramski2008} and \cite{loisel2002}) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.
2296-7745
Evers-King, Hayley L.
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Martinez-Vicente, Victor
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Brewin, Robert J.
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Dall'Olmo, Georgio
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Hickman, Anna
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Jackson, Thomas
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Kostadinov, Tihomir
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Krasemann, Hajo
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Loisel, Hubert
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Rottgers, Rüdiger
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Roy, Shovonlal
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Stramski, Dariusz
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Thomalla, Sandy
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Platt, Trevor
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Sathyendranath, Shubha
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Evers-King, Hayley L.
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Martinez-Vicente, Victor
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Brewin, Robert J.
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Dall'Olmo, Georgio
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Hickman, Anna
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Jackson, Thomas
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Kostadinov, Tihomir
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Krasemann, Hajo
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Loisel, Hubert
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Rottgers, Rüdiger
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Roy, Shovonlal
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Stramski, Dariusz
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Thomalla, Sandy
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Platt, Trevor
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Sathyendranath, Shubha
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Evers-King, Hayley L., Martinez-Vicente, Victor, Brewin, Robert J., Dall'Olmo, Georgio, Hickman, Anna, Jackson, Thomas, Kostadinov, Tihomir, Krasemann, Hajo, Loisel, Hubert, Rottgers, Rüdiger, Roy, Shovonlal, Stramski, Dariusz, Thomalla, Sandy, Platt, Trevor and Sathyendranath, Shubha (2017) Validation and intercomparison of ocean colour algorithms for estimating particulate organic carbon in the oceans. Frontiers in Marine Science, 4. (doi:10.3389/fmars.2017.00251).

Record type: Article

Abstract

Particulate Organic Carbon (POC) plays a vital role in the ocean carbon cycle. Though relatively small compared with other carbon pools, the POC pool is responsible for large fluxes and is linked to many important ocean biogeochemical processes. The satellite ocean-colour signal is influenced by particle composition, size, and concentration and provides a way to observe variability in the POC pool at a range of temporal and spatial scales. To provide accurate estimates of POC concentration from satellite ocean colour data requires algorithms that are well validated, with uncertainties characterised. Here, a number of algorithms to derive POC using different optical variables are applied to merged satellite ocean colour data provided by the Ocean Colour Climate Change Initiative (OC-CCI) and validated against the largest database of $\textit{in situ}$ POC measurements currently available. The results of this validation exercise indicate satisfactory levels of performance from several algorithms (highest performance was observed from the algorithms of \cite{stramski2008} and \cite{loisel2002}) and uncertainties that are within the requirements of the user community. Estimates of the standing stock of the POC can be made by applying these algorithms, and yield an estimated mixed-layer integrated global stock of POC between 0.77 and 1.3 Pg C of carbon. Performance of the algorithms vary regionally, suggesting that blending of region-specific algorithms may provide the best way forward for generating global POC products.

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Accepted/In Press date: 21 July 2017
e-pub ahead of print date: 9 August 2017
Published date: 9 August 2017

Identifiers

Local EPrints ID: 412806
URI: https://eprints.soton.ac.uk/id/eprint/412806
ISSN: 2296-7745
PURE UUID: 5eaaa221-bea3-4a8f-b00e-12a4534c9c1b
ORCID for Anna Hickman: ORCID iD orcid.org/0000-0002-2774-3934

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Date deposited: 02 Aug 2017 16:30
Last modified: 10 Dec 2019 01:37

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