Coupling ecological concepts with an ocean-colour model: Parameterisation and forward modelling

In the first part of this paper series (Sun et al., 2023), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. We also discuss applications of the OCMF, such as its potential for inverse modelling and phytoplankton climate trend detection, which will be explored further in subsequent papers.

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Publication Details

Journal
Remote Sensing of Environment
Published
2024-11-15
DOI
https://doi.org/10.1016/j.rse.2024.114487
Citations
3
Primary Topic
Marine and coastal ecosystems
Type
article
Field-Weighted Citation Impact
1.44

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article

Coupling ecological concepts with an ocean-colour model: Parameterisation and forward modelling

Giorgio Dall’Olmo, Gavin H. Tilstone, Malika Kheireddine, Vincenzo Vellucci et al.
3 citations
Remote Sensing of Environment
Marine and coastal ecosystems
1.44
article

Coupling ecological concepts with an ocean-colour model: Parameterisation and forward modelling

Giorgio Dall’Olmo, Gavin H. Tilstone, Malika Kheireddine, Vincenzo Vellucci, Shubha Sathyendranath, Fang Shen, Astrid Bracher, David Antoine, Xuerong Sun, Mengyu Li, Robert J. W. Brewin, ‪Dionysios E. Raitsos, Ray Barlow
article en
3 citations

Abstract

In the first part of this paper series (Sun et al., 2023), we developed an ecological model that partitions the total chlorophyll-a concentration (Chl-a) into three phytoplankton size classes (PSCs), pico-, nano-, and microplankton. The parameters of this model are controlled by sea surface temperature (SST), intended to capture shifts in phytoplankton size structure independently of variations in total Chl-a. In this second part of the series, we present an Ocean Colour Modelling Framework (OCMF), building on the classical Case-1 assumption, that explicitly incorporates our ecological model. The OCMF assumes the presence of the three PSCs and the existence of an independent background of non-algal particles. The framework assumes each phytoplankton group resides in a distinct optical environment, assigning chlorophyll-specific inherent optical properties to each group, both directly (phytoplankton) and indirectly (non-algal particulate and dissolved substances). The OCMF is parameterised, validated, and assessed using a large global dataset of inherent and apparent optical properties. We use the OCMF to explore the influence of variations in temperature and Chl-a on phytoplankton size structure and its resulting effects on ocean colour. We also discuss applications of the OCMF, such as its potential for inverse modelling and phytoplankton climate trend detection, which will be explored further in subsequent papers.

Remote Sensing of EnvironmentVol. 316
Centre National de la Recherche Scientifique (FR), National and Kapodistrian University of Athens (GR), Curtin University (AU), Plymouth Marine Laboratory (GB), Sorbonne Université (FR), Laboratoire de Biologie du Développement de Villefranche-sur-Mer (FR), Laboratoire d’Océanographie de Villefranche (FR), Bayworld Centre for Research and Education (ZA), National Institute of Oceanography and Applied Geophysics (IT), Institut de la Mer de Villefranche (FR), East China Normal University (CN), King Abdullah University of Science and Technology (SA)
National Aeronautics and Space Administration, Simons Foundation, UK Research and Innovation, Royal Society, Sight Research UK, Deutsche Forschungsgemeinschaft, National Natural Science Foundation of China, National Oceanic and Atmospheric Administration, Nutrition Obesity Research Center, University of North Carolina, Natural Environment Research Council, National Centre for Earth Observation
Life below water
Openalex Percentile: Top 19%
Marine and coastal ecosystems
1.44
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