The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic

Brewin, Robert J. W.; Ciavatta, Stefano; Sathyendranath, Shubha; Skákala, Jozef; Bruggeman, Jorn; Ford, David; Platt, Trevor

doi:10.3390/s19194182

Cited by 16 publications

(21 citation statements)

References 99 publications

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“…We assimilated in this study two satellite products: a PFT chlorophyll (Brewin et al., 2017) and a PFT absorption product (Brewin et al., 2019). Based on a simple, conceptual model (Brewin et al., 2010, 2015, 2017) used the Ocean Colour Climate Change Initiative (OC‐CCI, project of the European Space Agency, Version 3.0, Sathyendranath et al., 2016, 2019) data for total chlorophyll‐a to estimate chlorophyll in the four ERSEM PFTs (diatoms, dinoflagellates, nanophytoplankton, and picophytoplankton).…”

Section: Model Data and Methodologymentioning

confidence: 99%

“…The PFT absorption product has been derived from the unbiased PFT chlorophyll data using the model of Brewin et al. (2019) for the North Atlantic. Based on specific absorption coefficients

a_{i}^{*} false(λ false)

fitted from in situ measurements, the model of Brewin et al.…”

Section: Model Data and Methodologymentioning

confidence: 99%

“…In this study, we (a) assessed the impact of the new bio‐optical module on ERSEM skill in the “free run” (without assimilation), and (b) assimilated satellite PFT chlorophyll (Brewin et al., 2017) and PFT optical absorption (Brewin et al., 2019) products into ERSEM using bio‐optical module. The runs assimilating satellite products into ERSEM coupled to the bio‐optical module will be compared with two ERSEM free runs (with and without the bio‐optical module) and with the currently established assimilation of PFT chlorophyll using the preexisting ERSEM light scheme (Ciavatta et al., 2018; Skákala et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

“…In analyzing the differences between assimilating PFT chlorophyll and PFT absorption, it is important to note that the products are not independent: PFT absorption is derived from PFT chlorophyll using the model of Brewin et al. (2019). However, the absorption model used in ERSEM (based on Gregg & Casey, 2009; and Gregg & Rousseaux, 2016) is independent from the satellite model of Brewin et al.…”

Section: Introductionmentioning

confidence: 99%

“…However, the absorption model used in ERSEM (based on Gregg & Casey, 2009; and Gregg & Rousseaux, 2016) is independent from the satellite model of Brewin et al. (2019) and so the two may not be entirely consistent. The key role played by data assimilation is that it merges information from multiple sources, and by assimilating PFT absorption into the model, we aim to move closer toward the optimal representation of underwater light.…”

Section: Introductionmentioning

confidence: 99%

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Improved Representation of Underwater Light Field and Its Impact on Ecosystem Dynamics: A Study in the North Sea

et al. 2020

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Understanding ecosystem state on the North‐West European (NWE) Shelf is of major importance for both economy and climate research. The purpose of this work is to advance our modeling of in‐water optics on the NWE Shelf, with important implications for how we model primary productivity, as well as for assimilation of water‐leaving radiances. We implement a stand‐alone bio‐optical module into the existing coupled physical‐biogeochemical model configuration. The advantage of the bio‐optical module, when compared to the preexisting light scheme is that it resolves the underwater light spectrally and distinguishes between direct and diffuse downwelling streams. The changed underwater light compares better with both satellite and in situ observations. The module lowered the underwater photosynthetically active radiation, decreasing the simulated primary productivity, but overall, the improved underwater light had relatively limited impact on the phytoplankton seasonal dynamics. We showed that the model skill in representing phytoplankton seasonal cycle (e.g., phytoplankton bloom) can be substantially improved either by assimilation of satellite phytoplankton functional type (PFT) chlorophyll, or by assimilating a novel PFT absorption product. Assimilation of the two PFT products yields similar results, with an important difference in the PFT community structure. Both assimilative runs lead to lower plankton biomass and increase the nutrient concentrations. We discuss some future directions on how to improve our model skill in biogeochemistry without using assimilation, for example, by improving nutrient forcing, retuning the model parameters, and using the bio‐optical module to provide a two‐way physical‐biogeochemical coupling, improving the consistency between model physical and biogeochemical components.

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Section: Model Data and Methodologymentioning

confidence: 99%

“…The PFT absorption product has been derived from the unbiased PFT chlorophyll data using the model of Brewin et al. (2019) for the North Atlantic. Based on specific absorption coefficients

a_{i}^{*} false(λ false)

fitted from in situ measurements, the model of Brewin et al.…”

Section: Model Data and Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Representation of Underwater Light Field and Its Impact on Ecosystem Dynamics: A Study in the North Sea

et al. 2020

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Light absorption spectra of naturally mixed phytoplankton assemblages for retrieval of phytoplankton group composition in coastal oceans

Sun

Shen

Brewin

et al. 2022

Limnology & Oceanography

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Phytoplankton group composition is complex and highly variable in coastal waters. Given that different taxonomic groups have different pigment signatures, which in turn impact the light absorption spectra of phytoplankton, the absorption spectral-based approach has the potential for distinguishing phytoplankton groups. Using a large dataset of in situ surface observations of concurrent HPLC (high-performance liquid chromatography) pigments and phytoplankton absorption spectra collected from 2015 to 2018 in Chinese coastal oceans, in situ phytoplankton group composition was obtained from chemotaxonomic analysis (CHEMTAX). By using the linear additive principle on phytoplankton absorption spectra and CHEMTAX results, the chlorophyll-specific absorption spectra of eight phytoplankton groups were reconstructed, including prasinophytes, dinoflagellates, cryptophytes, chlorophytes, cyanobacteria, diatoms, chrysophytes, and prymnesiophytes. These chlorophyllspecific absorption spectra were subsequently used as inputs to a spectral-based inversion model for estimating phytoplankton group composition from the phytoplankton absorption coefficient. The optimal band selection and initial guesses of the phytoplankton group composition, derived from correlation and HCA (hierarchical cluster analysis) analyses, were included in the model inversion to improve the accuracy of retrievals. The performance of the proposed model was validated using an independent dataset, showing accurate estimates of chlorophyll a (Chl a) concentrations for seven phytoplankton groups (0.371 ≤ r ≤ 0.721, p < 0.05), apart from chrysophytes. Our results suggest that the absorption spectral-based approach is able to discriminate phytoplankton group composition quantitatively, which has implications for retrieving Chl a concentrations of phytoplankton groups from hyperspectral platforms and satellites.

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Ocean Biology Studied from Space

Sathyendranath,

Brewin,

Ciavatta

et al. 2023

Surv Geophys

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Visible spectral radiometric measurements from space, commonly referred to as ocean-colour measurements, provide a rich stream of information on ocean biota as well as on biological and ecosystem processes. The strength of the ocean-colour technology for observing marine life lies in its global reach, combined with its ability to sample the field at a variety of spatial and temporal scales that match the scales of the processes themselves. Another advantage lies in the growing length of the time series of ocean-colour-derived products, enabiling investigations into any long-term changes, if present. This paper presents an overview of the principles and applications of ocean-colour data. The concentration of chlorophyll-a, the major pigment present in phytoplankton–single-celled, free-floating plants that are present in the sunlit layers of the ocean–was the first, and remains the most common, biological variable derived from ocean-colour data. Over the years, the list of ocean-colour products have grown to encompass many measures of the marine ecosystem and its functions, including primary production, phenology and ecosystem structure. Applications that exploit the data are many and varied, and include ecosystem-based fisheries management, biogeochemical cycles in the ocean, ecosystem health and climate change. An integrated approach, incorporating other modes of ocean observations and models with satellite observations, is needed to investigate the mysteries of the marine ecosystem.

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The Influence of Temperature and Community Structure on Light Absorption by Phytoplankton in the North Atlantic

Cited by 16 publications

References 99 publications

Improved Representation of Underwater Light Field and Its Impact on Ecosystem Dynamics: A Study in the North Sea

Improved Representation of Underwater Light Field and Its Impact on Ecosystem Dynamics: A Study in the North Sea

Light absorption spectra of naturally mixed phytoplankton assemblages for retrieval of phytoplankton group composition in coastal oceans

Ocean Biology Studied from Space

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