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

  • Xuerong Sun*
  • , Robert J.W. Brewin
  • , Shubha Sathyendranath
  • , Giorgio Dall'Olmo
  • , David Antoine
  • , Ray Barlow
  • , Astrid Bracher
  • , Malika Kheireddine
  • , Mengyu Li
  • , Dionysios E. Raitsos
  • , Fang Shen
  • , Gavin H. Tilstone
  • , Vincenzo Vellucci
  • *Corresponding author for this work
  • University of Exeter
  • Plymouth Marine Laboratory
  • National Institute of Oceanography and Applied Geophysics
  • Curtin University
  • UMR 7093
  • Bayworld
  • Alfred Wegener Institute - Helmholtz Centre for Polar and Marine Research
  • University of Bremen
  • King Abdullah University of Science and Technology
  • East China Normal University
  • National and Kapodistrian University of Athens
  • Sorbonne Université

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish
Article number114487
JournalRemote Sensing of Environment
Volume316
DOIs
StatePublished - 1 Jan 2025

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 13 - Climate Action
    SDG 13 Climate Action

Keywords

  • Climate change
  • Forward modelling
  • Inherent and apparent optical properties
  • Ocean colour modelling framework
  • Ocean-colour remote sensing
  • Phytoplankton size classes

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