Articles | Volume 22, issue 13
https://doi.org/10.5194/bg-22-3253-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-22-3253-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Simulating vertical phytoplankton dynamics in a stratified ocean using a two-layered ecosystem model
Centre for Geography and Environmental Science, Department of Earth and Environmental Sciences, Faculty of Environment, Science and Economy, University of Exeter, Cornwall, UK
Johannes J. Viljoen
Centre for Geography and Environmental Science, Department of Earth and Environmental Sciences, Faculty of Environment, Science and Economy, University of Exeter, Cornwall, UK
Xuerong Sun
Centre for Geography and Environmental Science, Department of Earth and Environmental Sciences, Faculty of Environment, Science and Economy, University of Exeter, Cornwall, UK
Žarko Kovač
Faculty of Science, University of Split, Rudera Boškovića 33, 21000 Split, Croatia
Shubha Sathyendranath
National Centre for Earth Observation, Plymouth Marine Laboratory, Plymouth, UK
Robert J. W. Brewin
Centre for Geography and Environmental Science, Department of Earth and Environmental Sciences, Faculty of Environment, Science and Economy, University of Exeter, Cornwall, UK
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Daniel J. Ford, Gemma Kulk, Shubha Sathyendranath, and Jamie D. Shutler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2025-389, https://doi.org/10.5194/essd-2025-389, 2025
Preprint under review for ESSD
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Chlorophyll-a is routinely monitored using ocean colour satellites, however, these data records have gaps. Here we present a methodology to provide a spatially and temporally complete chlorophyll-a record, using Biogeochemical Argo floats as a constraint on wintertime chlorophyll-a, and a statistical kriging approach to fill cloud gaps. Thereby, providing a complete record at monthly 0.25° resolution between 1997 and 2023, consistent to the underlying climate data record.
Thomas M. Jordan, Giorgio Dall'Olmo, Gavin Tilstone, Robert J. W. Brewin, Francesco Nencioli, Ruth Airs, Crystal S. Thomas, and Louise Schlüter
Earth Syst. Sci. Data, 17, 493–516, https://doi.org/10.5194/essd-17-493-2025, https://doi.org/10.5194/essd-17-493-2025, 2025
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We present a compilation of water optical properties and phytoplankton pigments from the surface of the Atlantic Ocean collected during nine cruises between 2009 and 2019. We derive continuous Chlorophyll a concentrations (a biomass proxy) from water absorption. We then illustrate geographical variations and relationships for water optical properties, Chlorophyll a, and other pigments. The dataset will be useful to researchers in ocean optics, remote sensing, ecology, and biogeochemistry.
Yuan Zhang, Fang Shen, Renhu Li, Mengyu Li, Zhaoxin Li, Songyu Chen, and Xuerong Sun
Earth Syst. Sci. Data, 16, 4793–4816, https://doi.org/10.5194/essd-16-4793-2024, https://doi.org/10.5194/essd-16-4793-2024, 2024
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This work describes AIGD-PFT, the first AI-driven global daily gap-free 4 km phytoplankton functional type (PFT) product from 1998 to 2023. AIGD-PFT enhances the accuracy and spatiotemporal coverage quantification of eight major PFTs (i.e. diatoms, dinoflagellates, haptophytes, pelagophytes, cryptophytes, green algae, prokaryotes, and Prochlorococcus).
Bror F. Jönsson, Christopher L. Follett, Jacob Bien, Stephanie Dutkiewicz, Sangwon Hyun, Gemma Kulk, Gael L. Forget, Christian Müller, Marie-Fanny Racault, Christopher N. Hill, Thomas Jackson, and Shubha Sathyendranath
Geosci. Model Dev., 16, 4639–4657, https://doi.org/10.5194/gmd-16-4639-2023, https://doi.org/10.5194/gmd-16-4639-2023, 2023
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While biogeochemical models and satellite-derived ocean color data provide unprecedented information, it is problematic to compare them. Here, we present a new approach based on comparing probability density distributions of model and satellite properties to assess model skills. We also introduce Earth mover's distances as a novel and powerful metric to quantify the misfit between models and observations. We find that how 3D chlorophyll fields are aggregated can be a significant source of error.
Tihomir S. Kostadinov, Lisl Robertson Lain, Christina Eunjin Kong, Xiaodong Zhang, Stéphane Maritorena, Stewart Bernard, Hubert Loisel, Daniel S. F. Jorge, Ekaterina Kochetkova, Shovonlal Roy, Bror Jonsson, Victor Martinez-Vicente, and Shubha Sathyendranath
Ocean Sci., 19, 703–727, https://doi.org/10.5194/os-19-703-2023, https://doi.org/10.5194/os-19-703-2023, 2023
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We present a remote sensing algorithm to estimate the size distribution of particles suspended in natural near-surface ocean water using ocean color data. The algorithm can be used to estimate the abundance and carbon content of phytoplankton, photosynthesizing microorganisms that are at the basis of the marine food web and play an important role in Earth’s carbon cycle and climate. A merged, multi-sensor satellite data set and the model scientific code are provided.
André Valente, Shubha Sathyendranath, Vanda Brotas, Steve Groom, Michael Grant, Thomas Jackson, Andrei Chuprin, Malcolm Taberner, Ruth Airs, David Antoine, Robert Arnone, William M. Balch, Kathryn Barker, Ray Barlow, Simon Bélanger, Jean-François Berthon, Şükrü Beşiktepe, Yngve Borsheim, Astrid Bracher, Vittorio Brando, Robert J. W. Brewin, Elisabetta Canuti, Francisco P. Chavez, Andrés Cianca, Hervé Claustre, Lesley Clementson, Richard Crout, Afonso Ferreira, Scott Freeman, Robert Frouin, Carlos García-Soto, Stuart W. Gibb, Ralf Goericke, Richard Gould, Nathalie Guillocheau, Stanford B. Hooker, Chuamin Hu, Mati Kahru, Milton Kampel, Holger Klein, Susanne Kratzer, Raphael Kudela, Jesus Ledesma, Steven Lohrenz, Hubert Loisel, Antonio Mannino, Victor Martinez-Vicente, Patricia Matrai, David McKee, Brian G. Mitchell, Tiffany Moisan, Enrique Montes, Frank Muller-Karger, Aimee Neeley, Michael Novak, Leonie O'Dowd, Michael Ondrusek, Trevor Platt, Alex J. Poulton, Michel Repecaud, Rüdiger Röttgers, Thomas Schroeder, Timothy Smyth, Denise Smythe-Wright, Heidi M. Sosik, Crystal Thomas, Rob Thomas, Gavin Tilstone, Andreia Tracana, Michael Twardowski, Vincenzo Vellucci, Kenneth Voss, Jeremy Werdell, Marcel Wernand, Bozena Wojtasiewicz, Simon Wright, and Giuseppe Zibordi
Earth Syst. Sci. Data, 14, 5737–5770, https://doi.org/10.5194/essd-14-5737-2022, https://doi.org/10.5194/essd-14-5737-2022, 2022
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A compiled set of in situ data is vital to evaluate the quality of ocean-colour satellite data records. Here we describe the global compilation of bio-optical in situ data (spanning from 1997 to 2021) used for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The compilation merges and harmonizes several in situ data sources into a simple format that could be used directly for the evaluation of satellite-derived ocean-colour data.
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Co-editor-in-chief
Recent findings by Viljoen et al. (2024, Nature Climate Change) reveal contrasting phytoplankton trends above and below the mixed layer depth in the Sargasso Sea between 2011 and 2022, linked to ongoing ocean warming. Building on these observations, the current study by Zheng et al. seeks to replicate the detected patterns and unravel the drivers of these decadal changes. To this end, the authors develop a two-layer ecosystem model that conceptualizes stratified ocean systems as comprising two distinct ecological regimes: one within the surface mixed layer and another beneath it. Their results offer valuable insights into how phytoplankton communities may respond to future climate-driven stratification and highlight the critical need for improved monitoring efforts capable of capturing subsurface biological dynamics beyond the reach of satellite observations.
Recent findings by Viljoen et al. (2024, Nature Climate Change) reveal contrasting phytoplankton...
Short summary
Phytoplankton contribute to half of Earth’s primary production, but not a lot is known about subsurface phytoplankton, living at the base of the sunlit ocean. We develop a two-layered box model to simulate phytoplankton seasonal and interannual variations in different depth layers of the ocean. Our model captures seasonal and long-term trends of the two layers, explaining how they respond to a warming ocean, furthering our understanding of how phytoplankton are responding to climate change.
Phytoplankton contribute to half of Earth’s primary production, but not a lot is known about...
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