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Modelling ocean colour derived Chlorophyll-a

Modelling ocean colour derived Chlorophyll-a
Modelling ocean colour derived Chlorophyll-a
This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a) product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model) water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called derived Chl a in this paper). We compare the derived Chl a to the actual model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that region-specific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter). The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation and peak of the spring bloom in the derived Chl a lags the actual Chl a by days and sometimes weeks. These results indicate that care should also be taken when studying phenology through satellite-derived products of Chl a. This study also reemphasizes that ocean-colour-derived Chl a is not the same as the real in situ Chl a. In fact the model derived Chl a compares better to real-world satellite-derived Chl a than the model actual Chl a. Modellers should keep this is mind when evaluating model output with ocean colour Chl a and in particular when assimilating this product. Our goal is to illustrate the use of a numerical laboratory that (a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use and (b) helps the ocean colour community to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development.
1726-4170
613-630
Dutkiewicz, Stephanie
a704ddd3-bd6c-4f4a-ba0c-f6420c9c3b3b
Hickman, Anna E.
a99786c6-65e6-48c8-8b58-0d3b5608be92
Jahn, Oliver
a9d6758c-c0a0-45d4-ac90-1b6726957fe5
Dutkiewicz, Stephanie
a704ddd3-bd6c-4f4a-ba0c-f6420c9c3b3b
Hickman, Anna E.
a99786c6-65e6-48c8-8b58-0d3b5608be92
Jahn, Oliver
a9d6758c-c0a0-45d4-ac90-1b6726957fe5

Dutkiewicz, Stephanie, Hickman, Anna E. and Jahn, Oliver (2018) Modelling ocean colour derived Chlorophyll-a. Biogeosciences, 613-630. (doi:10.5194/bg-15-613-2018).

Record type: Article

Abstract

This article provides a proof of concept for using a biogeochemical/ecosystem/optical model with a radiative transfer component as a laboratory to explore aspects of ocean colour. We focus here on the satellite ocean colour chlorophyll a (Chl a) product provided by the often-used blue/green reflectance ratio algorithm. The model produces output that can be compared directly to the real-world ocean colour remotely sensed reflectance. This model output can then be used to produce an ocean colour satellite-like Chl a product using an algorithm linking the blue versus green reflectance similar to that used for the real world. Given that the model includes complete knowledge of the (model) water constituents, optics and reflectance, we can explore uncertainties and their causes in this proxy for Chl a (called derived Chl a in this paper). We compare the derived Chl a to the actual model Chl a field. In the model we find that the mean absolute bias due to the algorithm is 22 % between derived and actual Chl a. The real-world algorithm is found using concurrent in situ measurement of Chl a and radiometry. We ask whether increased in situ measurements to train the algorithm would improve the algorithm, and find a mixed result. There is a global overall improvement, but at the expense of some regions, especially in lower latitudes where the biases increase. Not surprisingly, we find that region-specific algorithms provide a significant improvement, at least in the annual mean. However, in the model, we find that no matter how the algorithm coefficients are found there can be a temporal mismatch between the derived Chl a and the actual Chl a. These mismatches stem from temporal decoupling between Chl a and other optically important water constituents (such as coloured dissolved organic matter and detrital matter). The degree of decoupling differs regionally and over time. For example, in many highly seasonal regions, the timing of initiation and peak of the spring bloom in the derived Chl a lags the actual Chl a by days and sometimes weeks. These results indicate that care should also be taken when studying phenology through satellite-derived products of Chl a. This study also reemphasizes that ocean-colour-derived Chl a is not the same as the real in situ Chl a. In fact the model derived Chl a compares better to real-world satellite-derived Chl a than the model actual Chl a. Modellers should keep this is mind when evaluating model output with ocean colour Chl a and in particular when assimilating this product. Our goal is to illustrate the use of a numerical laboratory that (a) helps users of ocean colour, particularly modellers, gain further understanding of the products they use and (b) helps the ocean colour community to explore other ocean colour products, their biases and uncertainties, as well as to aid in future algorithm development.

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More information

Submitted date: 18 August 2017
Accepted/In Press date: 21 August 2017
e-pub ahead of print date: 21 August 2017
Published date: 30 January 2018

Identifiers

Local EPrints ID: 416868
URI: https://eprints.soton.ac.uk/id/eprint/416868
ISSN: 1726-4170
PURE UUID: cd5af031-b0da-46f2-968a-922a33c11125
ORCID for Anna E. Hickman: ORCID iD orcid.org/0000-0002-2774-3934

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Date deposited: 12 Jan 2018 17:30
Last modified: 06 Oct 2018 00:31

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