Articles | Volume 1-osr7
https://doi.org/10.5194/sp-1-osr7-5-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/sp-1-osr7-5-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
27 Sep 2023
| OSR7 | Chapter 2.3
| 27 Sep 2023 | OSR7 | Chapter 2.3
Satellite monitoring of surface phytoplankton functional types in the Atlantic Ocean over 20 years (2002–2021)
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Marine Bretagnon
ACRI-ST, Sophia Antipolis CEDEX, France
Svetlana N. Losa
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Shirshov Institute of Oceanology, Russian Academy of Sciences, Moscow, Russia
Vanda Brotas
MARE/ARNET – Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, Portugal
Mara Gomes
MARE/ARNET – Marine and Environmental Sciences Centre, Faculdade de Ciências, Universidade de Lisboa, Campo Grande, Lisbon, Portugal
Ilka Peeken
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Leonardo M. A. Alvarado
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Antoine Mangin
ACRI-ST, Sophia Antipolis CEDEX, France
Astrid Bracher
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany
Institute of Environmental Physics, University of Bremen, Bremen, Germany
Related authors
Ehsan Mehdipour, Hongyan Xi, Alexander Barth, Aida Alvera-Azcárate, Adalbert Wilhelm, and Astrid Bracher
EGUsphere, https://doi.org/10.5194/egusphere-2025-112, https://doi.org/10.5194/egusphere-2025-112, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Phytoplankton are vital for marine ecosystems and nutrient cycling, detectable by optical satellites. Data gaps caused by clouds and other non-optimal conditions limit comprehensive analyses like trend monitoring. This study evaluated DINCAE and DINEOF gap-filling methods for reconstructing chlorophyll-a datasets, including total chlorophyll-a and five major phytoplankton groups. Both methods showed robust reconstruction capabilities, aiding pattern detection and long-term ocean colour analysis.
Hongyan Xi, Marine Bretagnon, Ehsan Mehdipour, Julien Demaria, Antoine Mangin, and Astrid Bracher
State Planet Discuss., https://doi.org/10.5194/sp-2024-15, https://doi.org/10.5194/sp-2024-15, 2024
Revised manuscript accepted for SP
Short summary
Short summary
To better understand the marine phytoplankton variability on different scales in both space and time, this study proposed a machine learning based scheme to provide continuous and consistent long-term observations of various phytoplankton groups from space on a global scale, which enables time series analysis for further trend and anomaly investigations. This study provides an essential ocean variable to help assess the ocean health in the biogeochemical aspect.
Ja-Yeon Moon, Jan Streffing, Sun-Seon Lee, Tido Semmler, Miguel Andrés-Martínez, Jiao Chen, Eun-Byeoul Cho, Jung-Eun Chu, Christian L. E. Franzke, Jan P. Gärtner, Rohit Ghosh, Jan Hegewald, Songyee Hong, Dae-Won Kim, Nikolay Koldunov, June-Yi Lee, Zihao Lin, Chao Liu, Svetlana N. Loza, Wonsun Park, Woncheol Roh, Dmitry V. Sein, Sahil Sharma, Dmitry Sidorenko, Jun-Hyeok Son, Malte F. Stuecker, Qiang Wang, Gyuseok Yi, Martina Zapponini, Thomas Jung, and Axel Timmermann
Earth Syst. Dynam., 16, 1103–1134, https://doi.org/10.5194/esd-16-1103-2025, https://doi.org/10.5194/esd-16-1103-2025, 2025
Short summary
Short summary
Based on a series of storm-resolving greenhouse warming simulations conducted with the AWI-CM3 model at 9 km global atmosphere and 4–25 km ocean resolution, we present new projections of regional climate change, modes of climate variability, and extreme events. The 10-year-long high-resolution simulations for the 2000s, 2030s, 2060s, and 2090s were initialized from a coarser-resolution transient run (31 km atmosphere) which follows the SSP5-8.5 greenhouse gas emission scenario from 1950–2100 CE.
Anisbel Leon-Marcos, Moritz Zeising, Manuela van Pinxteren, Sebastian Zeppenfeld, Astrid Bracher, Elena Barbaro, Anja Engel, Matteo Feltracco, Ina Tegen, and Bernd Heinold
Geosci. Model Dev., 18, 4183–4213, https://doi.org/10.5194/gmd-18-4183-2025, https://doi.org/10.5194/gmd-18-4183-2025, 2025
Short summary
Short summary
This study represents the primary marine organic aerosol (PMOA) emissions, focusing on their sea–atmosphere transfer. Using the FESOM2.1–REcoM3 model, concentrations of key organic biomolecules were estimated and integrated into the ECHAM6.3–HAM2.3 aerosol–climate model. Results highlight the influence of marine biological activity and surface winds on PMOA emissions, with reasonably good agreement with observations improving aerosol representation in the southern oceans.
Anisbel Leon-Marcos, Manuela van Pinxteren, Sebastian Zeppenfeld, Moritz Zeising, Astrid Bracher, Laurent Oziel, Ina Tegen, and Bernd Heinold
EGUsphere, https://doi.org/10.5194/egusphere-2025-2829, https://doi.org/10.5194/egusphere-2025-2829, 2025
Short summary
Short summary
This study links modelled ocean surface concentrations of key marine organic groups with the aerosol-climate model ECHAM-HAM to quantify species-resolved primary marine organic aerosol emissions from 1990 to 2019. Results show strong seasonality, driven by productivity and summer sea ice loss. Emissions and burdens increased over time with more frequent positive anomalies in the last decade, revealing an overall upward trend with regional differences across the Arctic and aerosol species.
Ehsan Mehdipour, Hongyan Xi, Alexander Barth, Aida Alvera-Azcárate, Adalbert Wilhelm, and Astrid Bracher
EGUsphere, https://doi.org/10.5194/egusphere-2025-112, https://doi.org/10.5194/egusphere-2025-112, 2025
This preprint is open for discussion and under review for Geoscientific Model Development (GMD).
Short summary
Short summary
Phytoplankton are vital for marine ecosystems and nutrient cycling, detectable by optical satellites. Data gaps caused by clouds and other non-optimal conditions limit comprehensive analyses like trend monitoring. This study evaluated DINCAE and DINEOF gap-filling methods for reconstructing chlorophyll-a datasets, including total chlorophyll-a and five major phytoplankton groups. Both methods showed robust reconstruction capabilities, aiding pattern detection and long-term ocean colour analysis.
Mathieu Ratynski, Sergey Khaykin, Alain Hauchecorne, Joan Alexander, Alexis Mariaccia, Philippe Keckhut, and Antoine Mangin
EGUsphere, https://doi.org/10.5194/egusphere-2025-394, https://doi.org/10.5194/egusphere-2025-394, 2025
Short summary
Short summary
This study investigates how tropical convection generates gravity waves, which play a key role in transporting energy across the atmosphere. By combining Aeolus satellite data with ERA5 reanalysis data and radio-occultation measurements, we identified significant wave activity overlooked by ERA5, particularly over the Indian Ocean. Aeolus fills major gaps in wind data, offering a clearer picture of wave dynamics and challenging assumptions about their behavior, improving climate models.
Hongyan Xi, Marine Bretagnon, Ehsan Mehdipour, Julien Demaria, Antoine Mangin, and Astrid Bracher
State Planet Discuss., https://doi.org/10.5194/sp-2024-15, https://doi.org/10.5194/sp-2024-15, 2024
Revised manuscript accepted for SP
Short summary
Short summary
To better understand the marine phytoplankton variability on different scales in both space and time, this study proposed a machine learning based scheme to provide continuous and consistent long-term observations of various phytoplankton groups from space on a global scale, which enables time series analysis for further trend and anomaly investigations. This study provides an essential ocean variable to help assess the ocean health in the biogeochemical aspect.
Matías Osorio, Alejandro Agesta, Tim Bösch, Nicolás Casaballe, Andreas Richter, Leonardo M. A. Alvarado, and Erna Frins
Atmos. Chem. Phys., 24, 7447–7465, https://doi.org/10.5194/acp-24-7447-2024, https://doi.org/10.5194/acp-24-7447-2024, 2024
Short summary
Short summary
This study concerns the detection and quantification of long-transport emissions of a biomass burning event, which represents a major source of air pollutants, due to the release of large amounts of aerosols and chemical species into the atmosphere. The quantification was done using ground-based observations (which play an important role in assessing the abundance of trace gases and aerosols) over Montevideo (Uruguay) and using satellite observations.
Sebastian Zeppenfeld, Manuela van Pinxteren, Markus Hartmann, Moritz Zeising, Astrid Bracher, and Hartmut Herrmann
Atmos. Chem. Phys., 23, 15561–15587, https://doi.org/10.5194/acp-23-15561-2023, https://doi.org/10.5194/acp-23-15561-2023, 2023
Short summary
Short summary
Marine carbohydrates are produced in the surface of the ocean, enter the atmophere as part of sea spray aerosol particles, and potentially contribute to the formation of fog and clouds. Here, we present the results of a sea–air transfer study of marine carbohydrates conducted in the high Arctic. Besides a chemo-selective transfer, we observed a quick atmospheric aging of carbohydrates, possibly as a result of both biotic and abiotic processes.
Aleksandra Cherkasheva, Rustam Manurov, Piotr Kowalczuk, Alexandra N. Loginova, Monika Zabłocka, and Astrid Bracher
EGUsphere, https://doi.org/10.5194/egusphere-2023-2495, https://doi.org/10.5194/egusphere-2023-2495, 2023
Preprint archived
Short summary
Short summary
We aimed to improve the quality of regional Greenland Sea primary production estimates. Seventy two versions of primary production model setups were tested against field data. Best performing models had local biomass and light absorption profiles. Thus by using local parametrizations for these parameters we can improve Arctic primary production model performance. Annual Greenland Sea basin estimates are larger than previously reported.
Hubert Loisel, Lucile Duforêt-Gaurier, Trung Kien Tran, Daniel Schaffer Ferreira Jorge, François Steinmetz, Antoine Mangin, Marine Bretagnon, and Odile Hembise Fanton d'Andon
State Planet, 1-osr7, 11, https://doi.org/10.5194/sp-1-osr7-11-2023, https://doi.org/10.5194/sp-1-osr7-11-2023, 2023
Short summary
Short summary
In this paper, we will show how a proxy for particulate composition (PPC), classifying the suspended particulate matter into its organic, mineral, or mixed fractions, can be estimated from remote-sensing observations. The selected algorithm will then be applied to MERIS observations (2002–2012) over global coastal waters to discuss the significance of this new product. A specific focus will be on the English Channel and the southern North Sea.
Valérie Gros, Bernard Bonsang, Roland Sarda-Estève, Anna Nikolopoulos, Katja Metfies, Matthias Wietz, and Ilka Peeken
Biogeosciences, 20, 851–867, https://doi.org/10.5194/bg-20-851-2023, https://doi.org/10.5194/bg-20-851-2023, 2023
Short summary
Short summary
The oceans are both sources and sinks for trace gases important for atmospheric chemistry and marine ecology. Here, we quantified selected trace gases (including the biological metabolites dissolved dimethyl sulfide, methanethiol and isoprene) along a 2500 km transect from the North Atlantic to the Arctic Ocean. In the context of phytoplankton and bacterial communities, our study suggests that methanethiol (rarely measured before) might substantially influence ocean–atmosphere cycling.
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
Short summary
Short summary
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.
Hanna M. Kauko, Philipp Assmy, Ilka Peeken, Magdalena Różańska-Pluta, Józef M. Wiktor, Gunnar Bratbak, Asmita Singh, Thomas J. Ryan-Keogh, and Sebastien Moreau
Biogeosciences, 19, 5449–5482, https://doi.org/10.5194/bg-19-5449-2022, https://doi.org/10.5194/bg-19-5449-2022, 2022
Short summary
Short summary
This article studies phytoplankton (microscopic
plantsin the ocean capable of photosynthesis) in Kong Håkon VII Hav in the Southern Ocean. Different species play different roles in the ecosystem, and it is therefore important to assess the species composition. We observed that phytoplankton blooms in this area are formed by large diatoms with strong silica armors, which can lead to high silica (and sometimes carbon) export to depth and be important prey for krill.
M. A. Soppa, D. A. Dinh, B. Silva, F. Steinmetz, L. Alvarado, and A. Bracher
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVI-1-W1-2021, 69–72, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-69-2022, https://doi.org/10.5194/isprs-archives-XLVI-1-W1-2021-69-2022, 2022
Yanan Zhao, Dennis Booge, Christa A. Marandino, Cathleen Schlundt, Astrid Bracher, Elliot L. Atlas, Jonathan Williams, and Hermann W. Bange
Biogeosciences, 19, 701–714, https://doi.org/10.5194/bg-19-701-2022, https://doi.org/10.5194/bg-19-701-2022, 2022
Short summary
Short summary
We present here, for the first time, simultaneously measured dimethylsulfide (DMS) seawater concentrations and DMS atmospheric mole fractions from the Peruvian upwelling region during two cruises in December 2012 and October 2015. Our results indicate low oceanic DMS concentrations and atmospheric DMS molar fractions in surface waters and the atmosphere, respectively. In addition, the Peruvian upwelling region was identified as an insignificant source of DMS emissions during both periods.
Christophe Lerot, François Hendrick, Michel Van Roozendael, Leonardo M. A. Alvarado, Andreas Richter, Isabelle De Smedt, Nicolas Theys, Jonas Vlietinck, Huan Yu, Jeroen Van Gent, Trissevgeni Stavrakou, Jean-François Müller, Pieter Valks, Diego Loyola, Hitoshi Irie, Vinod Kumar, Thomas Wagner, Stefan F. Schreier, Vinayak Sinha, Ting Wang, Pucai Wang, and Christian Retscher
Atmos. Meas. Tech., 14, 7775–7807, https://doi.org/10.5194/amt-14-7775-2021, https://doi.org/10.5194/amt-14-7775-2021, 2021
Short summary
Short summary
Global measurements of glyoxal tropospheric columns from the satellite instrument TROPOMI are presented. Such measurements can contribute to the estimation of atmospheric emissions of volatile organic compounds. This new glyoxal product has been fully characterized with a comprehensive error budget, with comparison with other satellite data sets as well as with validation based on independent ground-based remote sensing glyoxal observations.
Stefanie Arndt, Christian Haas, Hanno Meyer, Ilka Peeken, and Thomas Krumpen
The Cryosphere, 15, 4165–4178, https://doi.org/10.5194/tc-15-4165-2021, https://doi.org/10.5194/tc-15-4165-2021, 2021
Short summary
Short summary
We present here snow and ice core data from the northwestern Weddell Sea in late austral summer 2019, which allow insights into possible reasons for the recent low summer sea ice extent in the Weddell Sea. We suggest that the fraction of superimposed ice and snow ice can be used here as a sensitive indicator. However, snow and ice properties were not exceptional, suggesting that the summer surface energy balance and related seasonal transition of snow properties have changed little in the past.
Cited articles
Aiken, J., Pradhan, Y., Barlow, R., Lavender, S., Poulton, A., Holligan, P.,
and Hardman-Mountford, N.: Phytoplankton pigments and functional types in
the Atlantic Ocean: A decadal assessment, 1995–2005, Deep-Sea Res. Pt. II, 56, 899–917, https://doi.org/10.1016/j.dsr2.2008.09.017, 2009.
Alvarado, L. M. A., Soppa, M. A., Gege, P., Losa, S. N., Dröscher, I., Xi, H., and Bracher, A.: Retrievals of the main phytoplankton groups at Lake
Constance using OLCI, DESIS, and evaluated with field observations, 12th
EARSeL Workshop on Imaging Spectroscopy, Potsdam, Germany, 22–24 June 2022,
https://elib.dlr.de/189789, 2022.
Amante, C. and Eakins, B. W.: ETOPO1 1 Arc-Minute Global Relief Model:
Procedures, data sources and analysis, NOAA technical memorandum NESDIS
NGDC-24, National Geophysical Data Center, NOAA [data set],
https://doi.org/10.7289/V5C8276M, 2009.
Antoine, D., Morel, A., Gordon, H. R., Banzon, V. F., and Evans, R. H.:
Bridging ocean color observations of the 1980s and 2000s in search of
long-term trends, J. Geophys. Res.-Oceans, 110, C06009,
https://doi.org/10.1029/2004JC002620, 2005.
Behrenfeld, M. J., O'Malley R. T., Boss, E. S., Westberry, T. K., Graff, J.
R., Halsey, K. H., Milligan, A. J., Siegel, D. A., and Brown, M. B.:
Revaluating ocean warming impacts on global phytoplankton, Nat. Clim.
Change, 6, 3223–3330, https://doi.org/10.1038/nclimate2838, 2016.
Bindoff, N. L., Cheung, W. W. L., Kairo, J. G., Arístegui, J., Guinder,
V. A., Hallberg, R., Hilmi, N., Jiao, N., Karim, M. S., Levin, L., O'Donoghue, S., Purca Cuicapusa, S. R., Rinkevich, B., Suga, T., Tagliabue,
A., and Williamson, P.: Changing Ocean, Marine Ecosystems, and Dependent
Communities, in: IPCC Special Report on the Ocean and Cryosphere in a
Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P, Tignor, M., Poloczanska, E., Mintenbeck, K.,
Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N.
M., Cambridge University Press, Cambridge, UK and New York, NY, USA,
447–587, https://doi.org/10.1017/9781009157964.007, 2019.
Bolaños, L. M., Karp-Boss, L., Choi, C. J., Worden, A. Z., Graff., J.
R., Haëntjens, N., Chase, A. P., Della Penna, A., Gaube, P., Morison,
F., Menden-Deuer, S., Westberry, T. K., O'Malley, R. T., Boss, E.,
Behrenfeld, M. J., and Glovannoni, S. J.: Small phytoplankton dominate
western North Atlantic biomass, The ISME Journal, 14, 1663–1674,
https://doi.org/10.1038/s41396-020-0636-0, 2020.
Bracher, A., Bouman, H. A., Brewin, R. J. W., Bricaud, A., Brotas, V.,
Ciotti, A. M., Clementson, L., Devred, E., Di Cicco, A., Dutkiewicz, S.,
Hardman-Mountford, N. J., Hickman, A. E., Hieronymi, M., Hirata, T., Losa,
S. N., Mouw, C. B., Organelli, E., Raitsos, D. E., Uitz, J., Vogt, M., and
Wolanin, A.: Obtaining phytoplankton diversity from ocean color: a
scientific roadmap for future development, Front. Mar. Sci., 4, 1–15,
https://doi.org/10.3389/fmars.2017.00055, 2017.
Bracher, A., Xi, H., Dinter, T., Mangin, A., Strass, V. H., von Appen, W.-J., and Wiegmann, S.: High resolution water column phytoplankton composition across the Atlantic Ocean from ship-towed vertical undulating radiometry, Front. Mar. Sci., 7, 235, https://doi.org/10.3389/fmars.2020.00235, 2020.
Bracher, A., Brewin, R. J. W., Ciotti, A. M., Clementson, L. A., Hirata, T., Kostadinov, T., Mouw, C. B., and Organelli, E.: Applications of satellite
remote sensing technology to the analysis of phytoplankton community
structure on large scales, in: Advances in Phytoplankton Ecology, edited by: Clementson, L. A., Eriksen, R. S., and Willis, A., Elsevier, 217–244,
https://doi.org/10.1016/B978-0-12-822861-6.00015-7, 2022.
Brewin, R. J. W., Sathyendranath, S., Jackson, T., Barlow, R., Brotas, V.,
Airs, R., and Lamont, T.: Influence of light in the mixed-layer on the
parameters of a three-component model of phytoplankton size class, Remote
Sens. Environ., 168, 437–450, https://doi.org/10.1016/j.rse.2015.07.004, 2015.
Brewin, R. J. W., Tilstone, G. H., Jackson, T., Cain, T., Miller, P. I.,
Lange, P. K., Misra, A., and Airs, R. L.: Modelling size-fractionated
primary production in the Atlantic Ocean from remote sensing, Prog.
Oceanogr., 158, 130–149, https://doi.org/10.1016/j.pocean.2017.02.002, 2017.
Brotas, V., Brewin, R. J. W., Sa, C., Brito, A. C., Silva, A., Mendes, C. R., Diniz, T., Kaufmann, M., Tarran, G., Groom, S. B., Platt, T., and Sathyendranath, S.: Deriving phytoplankton size classes from satellite data:
Validation along a trophic gradient in the eastern Atlantic Ocean, Remote
Sens. Environ., 134, 66–77, https://doi.org/10.1016/j.rse.2013.02.013, 2013.
Brotas, V., Tarran, G. A., Veloso, V., Brewin, R. J. W., Woodward, E. M. S.,
Airs, R., Beltran, C., Ferreira, A., and Groom, S. B.: Complementary Approaches to Assess Phytoplankton Groups and Size Classes on a Long
Transect in the Atlantic Ocean, Front. Mar. Sci., 8, 682621,
https://doi.org/10.3389/fmars.2021.682621, 2022.
Colella, S., Böhm, E., Cesarini, C., Garnesson, P., Netting, J., and Calton, B.: EU Copernicus Marine Service Product User Manual for Ocean Colour Products, Issue 3.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/PUM/CMEMS-OC-PUM.pdf (last access: 22 March 2023), 2022.
Deppeler, S. L. and Davidson, A. T.: Southern Ocean Phytoplankton in a
Changing Climate, Front. Mar. Sci., 4, 40, https://doi.org/10.3389/fmars.2017.00040, 2017.
EU Copernicus Marine Service Product: Global Ocean Colour (Copernicus-GlobColour), Bio-Geo-Chemical, L4 (monthly and interpolated) from Satellite Observations (1997-ongoing), Mercator Ocean International [data set], https://doi.org/10.48670/moi-00281, 2022.
Flanders Marine Institute: Longhurst Provinces, Marine Regions, Flanders Marine Institute [data set],
https://www.marineregions.org/sources.php#longhurst (last access: 5 May 2022), 2009.
Flombaum, P., Gallegos, J. L., Gordillo, R. A., Rincon, J., Zabala, L. L.,
Jiao, N., Karl, D. M., Li, W. K. W., Lomas, M. W., Veneziano, D., Vera, C.
S., Vrugt, J. A., and Martiny, A. C.: Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus, P. Natl. Acad. Sci. USA, 110, 9824–9829, https://doi.org/10.1073/pnas.1307701110, 2013.
Garnesson, P., Mangin, A., Bretagnon, M., and Jutard, Q.: EU Copernicus Marine Service Quality Information Document (QUID) for OC TAC Products OCEANCOLOUR OBSERVATIONS GlobColour, Issue 3.0, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-101to104-111-113-116-118.pdf (last access: 22 March 2023), 2022.
Gregg, W. W. and Rousseaux, C. S.: Decadal trends in global pelagic ocean
chlorophyll: A new assessment integrating multiple satellites, in situ data,
and models, J. Geophys. Res.-Oceans, 119, 5921–5933,
https://doi.org/10.1002/2014JC010158, 2014.
Gruber, N.: Warming up, turning sour, losing breath: Ocean biogeochemistry
under global change, Philos. T. Roy. Soc. A, 369, 1980–1996, https://doi.org/10.1098/rsta.2011.0003, 2011.
Gruber, N., Boyd, P. W., Frölicher T. L., and Vogt, M.: Biogeochemical
extremes and compound events in the ocean, Nature, 600, 395–407,
https://doi.org/10.1038/s41586-021-03981-7, 2021.
Head, E. J. H. and Pepin, P.: Monitoring changes in phytoplankton abundance
and composition in the Northwest Atlantic: a comparison of results obtained
by continuous plankton recorder sampling and colour satellite imagery, J.
Plankton Res., 32, 1649–1660, https://doi.org/10.1093/plankt/fbq120, 2010.
Henson, S. A., Sarmiento, J. L., Dunne, J. P., Bopp, L., Lima, I., Doney, S. C., John, J., and Beaulieu, C.: Detection of anthropogenic climate change in satellite records of ocean chlorophyll and productivity, Biogeosciences, 7, 621–640, https://doi.org/10.5194/bg-7-621-2010, 2010.
Henson, S. A., Beaulieu, C., and Lampitt, R.: Observing climate change
trends in ocean biogeochemistry: when and where, Glob. Change Biol., 22,
1561–1571, https://doi.org/10.1111/gcb.13152, 2016.
Hirata, T., Hardman-Mountford, N. J., Brewin, R. J. W., Aiken, J., Barlow, R., Suzuki, K., Isada, T., Howell, E., Hashioka, T., Noguchi-Aita, M., and Yamanaka, Y.: Synoptic relationships between surface Chlorophyll-a and diagnostic pigments specific to phytoplankton functional types, Biogeosciences, 8, 311–327, https://doi.org/10.5194/bg-8-311-2011, 2011.
IOCCG: Phytoplankton Functional Types from Space, in: Reports of International Ocean Colour Coordinating Group (IOCCG), Report Number 15, edited by: Sathyendranath, S., IOCCG, Dartmouth, Nova Scotia, Canada, 154 pp., https://doi.org/10.25607/OBP-106, 2014.
Jackson, T.: ESA Ocean Colour Climate Change Initiative – Phase 3 Product, User Guide for v5.0 Dataset, Issue 1.0, ESA/ESRIN, https://docs.pml.space/share/s/okB2fOuPT7Cj2r4C5sppDg (last access: 30 March 2023), 2020.
Käse, L. and Geuer, J. K.: Phytoplankton Responses to Marine Climate
Change – An Introduction, in: YOUMARES 8 – Oceans Across Boundaries:
Learning from each other, edited by: Jungblut, S., Liebich, V., and Bode B.,
SpringerOpen, Cham, Switzerland, 55–71, https://doi.org/10.1007/978-3-319-93284-2, 2018.
Kulk, G., Platt, T., Dingle, J., Jackson, T., Jönsson, B. F., Bouman, H.
A., Babin, M., Brewin, R. J. W., Doblin, M., Estrada, M., Figueiras, F. G.,
Furuya, K., González-Benítez, N., Gudfinnsson, H. G., Gudmundsson, K., Huang, B., Isada, T., Kovac, Z., Lutz, V. A., Maranon, E., Raman, M., Richardson, K., Rozema, P. D., van de Poll, W. H., Segura, V., Tilstone, G. H., Uitz, J., van Dongen-Vogels, V., Yoshikawa, T., and Sathyendranath, S.: Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades, Remote Sens.-Basel, 12, 826,
https://doi.org/10.3390/rs12050826, 2020.
Lange, P. K., Werdell, P. J., Erickson, Z. K., Dall'Olmo, G., Brewin, R. J. W., Zubkov, M. V., Tarran, G. A., Bouman, H. A., Slade, W. H., Craig, S. E.,
Poulton, N. J., Bracher, A., Lomas, M. W., and Cetinić, I.: Radiometric
approach for the detection of picophytoplankton assemblages across oceanic
fronts, Opt. Express, 28, 25682–25705, https://doi.org/10.1364/OE.398127, 2020.
Longhurst, A. R.: Ecological Geography of the Sea, Academic Press,
Cambridge, Massachusetts, U.S.A., 542 pp.,
https://doi.org/10.1016/B978-0-12-455521-1.X5000-1, 2007.
Losa, S. N., Soppa, M. A., Dinter, T., Wolanin, A., Brewin, R. J. W., Bricaud, A., Oelker, J., Peeken, I., Gentili, B., Rozanov, V., and Bracher, A.: Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel
Measurements to Determine Phytoplankton Functional Types (SynSenPFT), Front.
Mar. Sci., 4, 1–22, https://doi.org/10.3389/fmars.2017.00203, 2017.
McClain, C. R.: A decade of satellite ocean color observations, Annu. Rev.
Mar. Sci., 1, 19–42, https://doi.org/10.1146/annurev.marine.010908.163650, 2009.
Mélin, F. and Franz, B. A.: Chapter 6.1 – Assessment of satellite ocean colour radiometry and derived geophysical products, Experimental Methods in the Physical Sciences, 47, 609–638, https://doi.org/10.1016/B978-0-12-417011-7.00020-9, 2014.
Moisan, T. A., Rufty, K. M., Moisan, J. R., and Linkswiler, M. A.: Satellite
Observations of Phytoplankton Functional Type Spatial Distributions,
Phenology, Diversity, and Ecotones, Front. Mar. Sci., 4, 189,
https://doi.org/10.3389/fmars.2017.00189, 2017.
Neukermans, G., Oziel, L., and Babin, M.: Increased intrusion of warming
Atlantic water leads to rapid expansion of temperate phytoplankton in the
Arctic, Glob. Change Biol., 24, 2545–2553, https://doi.org/10.1111/gcb.14075, 2018.
Nöthig, E. M., Bracher, A., Engel, A., Metfies, K., Niehoff, B., Peeken,
I., Bauerfeind, E., Cherkasheva, A., Gäbler-Schwarz, S., Hardge, K.,
Kilias, E., Kraft, A., Kidane, Y. M., Lalande, C., Piontek, J., Thomisch, K.,
and Wurst, M.: Summertime plankton ecology in Fram Strait – a compilation
of long- and short-term observations, Polar Res., 34, 23349,
https://doi.org/10.3402/polar.v34.23349, 2015.
Nöthig, E. M., Ramondenc, S., Haas, A., Hehemann, L., Walter, A., Bracher, A., Lalande, C., Metfies, K., Peeken, I., Bauerfeind, E., and Boetius, A.: Summertime Chlorophyll a and Particulate Organic Carbon Standing Stocks in Surface Waters of the Fram Strait and the Arctic Ocean (1991–2015), Front. Mar. Sci., 7, 350, https://doi.org/10.3389/fmars.2020.00350, 2020.
Oziel, L., Baudena, A., Ardyna, M., Massicotte, P., Randelhoff, A., Sallée, J.-B., Ingvaldsen, R. B., Devred, E., and Babin, M.: Faster Atlantic currents drive poleward expansion of temperate phytoplankton in the Arctic Ocean, Nat. Commun., 11, 1705, https://doi.org/10.1038/s41467-020-15485-5, 2020.
Pardo, S., Jackson, T., Taylor, B., Netting, J., Calton, B., and Howey, B.: EU Copernicus Marine Service Quality Information Document (QUID) for OC TAC Products – Atlantic and Arctic Observation Products, Issue 2.2, Mercator Ocean International, https://catalogue.marine.copernicus.eu/documents/QUID/CMEMS-OC-QUID-009-066-067-068-069-088-091.pdf (last access: 22 March 2022), 2020.
Quéré, C. L., Harrison, S. P., Colin Prentice, I., Buitenhuis, E. T., Aumont, O., Bopp, L., Claustre, H., Cotrim Da Cunha, L., Geider, R., Giraud, X., Klaas, C., Kohfeld, K. E., Legendre, L., Manizza, M., Platt, T., Rivkin, R. B., Sathyendranath, S., Uitz, J., Watson, A. J., and Wolf-Gladrow, D.: Ecosystem dynamics based on plankton functional types for global ocean biogeochemistry models, Glob. Change Biol., 11, 2016–2040, https://doi.org/10.1111/j.1365-2486.2005.1004.x, 2005.
Reyes-Prieto, A., Yoon, H. S., and Bhattacharya, D.: Marine Algal Genomics
and Evolution, in: Encyclopedia of Ocean Sciences, 2nd edn., edited by: Steele, J. H., Academic Press, 552–559, https://doi.org/10.1016/B978-012374473-9.00779-7, 2009.
Sathyendranath, S., Brewin, R. J. W., Brockmann, C., Brotas, V., Calton, B.,
Chuprin, A., Cipollini, P., Couto, A. B., Dingle, J., Doerffer, R., Donlon,
C., Dowell, M., Farman, A., Grant, M., Groom, S., Horseman, A., Jackson, T.,
Krasemann, H., Lavender, S., Martinez-Vicente, V., Mazeran, C., Mélin,
F., Moore, T. S., Müller, D., Regner, P., Roy, S., Steele, C., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zühlke, M., Brando, V. E., Feng, H., Feldman, G., Franz, B. A., Frouin, R., Gould, R. W., Hooker, S. B., Kahru, M., Kratzer, S., Mitchell, B. G., Muller-Karger, F. E., Sosik, H. M., Voss, K. J., Werdell, J., and Platt, T.: An ocean-colour time series for use in climate studies: The experience of the Ocean-Colour Climate Change Initiative (OC-CCI), Sensors, 19, 4285, https://doi.org/10.3390/s19194285, 2019.
Soppa, M. A., Völker, C., and Bracher, A.: Diatom Phenology in the
Southern Ocean: Mean Patterns, Trends and the Role of Climate Oscillations,
Remote Sens.-Basel, 8, 420, https://doi.org/10.3390/rs8050420, 2016.
Uitz, J., Claustre, H., Morel, A., and Hooker, S. B.: Vertical distribution
of phytoplankton communities in open ocean: An assessment based on surface
chlorophyll, J. Geophys. Res.-Oceans, 111, C08005,
https://doi.org/10.1029/2005JC003207, 2006.
van Oostende, M., Hieronymi, M., Krasemann, H., Baschek, B., and
Röttgers, R.: Correction of inter-mission inconsistencies in merged
ocean colour satellite data, Front. Remote Sens., 3, 1–17,
https://doi.org/10.3389/frsen.2022.882418, 2022.
Vidussi, F., Claustre, H., Manca, B. B., Luchetta, A., and Marty, J.-C.:
Phytoplankton pigment distribution in relation to upper thermocline circulation in the eastern Mediterranean Sea during winter, J. Geophys. Res.-Oceans, 106, 19939–19956, https://doi.org/10.1029/1999JC000308, 2001.
von Schuckmann, K., Le Traon, P.., Smith, N., Pascual, N., Samuel Djavidnia,
S., Gattuso, J., and Grégoire, M. (Eds.): Copernicus Marine Service
Ocean State Report, Issue 5 Supplement, J. Oper. Oceanogr., 14, 1–185,
https://doi.org/10.1080/1755876X.2021.1946240, 2021.
Xi, H., Losa, S. N., Mangin, A., Soppa, M. A., Garnesson, P., Demaria, J.,
Liu, Y., d'Andon, O. H. F., and Bracher, A.: A global retrieval algorithm of
phytoplankton functional types: Towards the applications to CMEMS GlobColour
merged products and OLCI data, Remote Sens. Environ., 240, 111704,
https://doi.org/10.1016/j.rse.2020.111704?, 2020.
Xi, H., Losa, S. N., Mangin, A., Garnesson, P., Bretagnon, M., Demaria, J.,
Soppa, M. A., d'Andon, O. H. F., and Bracher, A.: Global chlorophyll a
concentrations of phytoplankton functional types with detailed uncertainty
assessment using multi-sensor ocean color and sea surface temperature
satellite products, J. Geophys. Res.-Oceans, 126, e2020JC017127,
https://doi.org/10.1029/2020JC017127, 2021.
Xi, H., Peeken, I., Gomes, M., Brotas, V., Tilstone, G., Brewin, R. J. W.,
Dall'Olmo, G., Tracana, A., Alvarado, L. M. A., Murawski, S., Wiegmann, S.,
and Bracher, A.: Phytoplankton pigment concentrations and phytoplankton
groups measured on water samples collected from various expeditions in the
Atlantic Ocean from 71∘ S to 84∘ N, PANGAEA [data set],
https://doi.org/10.1594/PANGAEA.954738, 2023.
Yang, B., Boss, E. S., Haëntjens, N., Long, M. C., Behrenfeld, M. J.,
Eveleth, R., and Doney, S. C.: Phytoplankton phenology in the North Atlantic: Insights from profiling float measurements, Front. Mar. Sci., 7, 139, https://doi.org/10.3389/fmars.2020.00139, 2020.
Short summary
Continuous monitoring of phytoplankton groups using satellite data is crucial for understanding global ocean phytoplankton variability on different scales in both space and time. This study focuses on four important phytoplankton groups in the Atlantic Ocean to investigate their trend, anomaly and phenological characteristics both over the whole region and at subscales. This study paves the way to promote potentially important ocean monitoring indicators to help sustain the ocean health.
Continuous monitoring of phytoplankton groups using satellite data is crucial for understanding...
Altmetrics
Final-revised paper
Preprint