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1. Dynamics of absorption properties of CDOM and its composition in Likas estuary, North Borneo, Malaysia

Author

Saiyidah Munirah Mohd-Shazali, Jafar-Sidik Madihah, Nurzaliah Ali, Chen Cheng-Ann, Robert J.W. Brewin, Md. Suffian Idris, and P. Purba Noir

Subjects
Coloured dissolved organic matter (CDOM), The absorption coefficient of CDOM (aCDOM), Molecular weight and degradation of CDOM, High molecular weight (HMW) CDOM, Low molecular weight (LMW) CDOM, Oceanography, GC1-1581
Abstract

Chromophoric Dissolved Organic Matter (CDOM) is a vital water constituent in aquatic ecosystems that contributes to water colour, affects light penetration, and impacts primary production. This study aims to determine the spatial and monsoonal variability of CDOM absorption properties in the Likas estuary, characterise the source of CDOM, and investigate the correlations between CDOM absorption properties and salinity. Likas estuary is a small estuary located in Kota Kinabalu city on the west coast of Sabah, facing the South China Sea. A mangrove ecosystem surrounds it with manufactured structures such as residential areas and public facilities. Surface water samples were collected at 19 stations: upstream of rivers to the river mouth and coastal area during spring tides every month, from June 2018 to July 2019, for 14-months. The distribution of aCDOM(440) in the study area is predictable as a signature in a coastal area with a decreasing gradient from the upstream towards coastal water (0.29 ± 0.19 m−1 to 1.05 ± 0.39 m−1). There are increasing spatial patterns of spectral slopes S275-295 and SR. However, S350-400 and S300-600 declined spatial gradients from the upstream to coastal water. Thus, S300-600 indicates a linear relationship between aCDOM(440), which unconventional results in coastal water. We suspect this is due to a small coverage of the study site with a distance of 0.5 m intervals of each station. This could be why the S300-600 had constant values throughout the study area (with no statistical difference between stations). In addition, S300-600 was merely varied in the stations located at the river mouth and coastal water. Based on the spectral slope ratio (SR), most of the stations located in the Darau, Inanam, and Bangka-Bangka rivers had SR values less than 1. Hence, CDOM in these stations is a terrestrial-dominated source. Therefore, from our observations during the study period, monsoonal variation could alter the source of CDOM in the study area.

Published
2022
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2. An Ocean-Colour Time Series for Use in Climate Studies: The Experience of the Ocean-Colour Climate Change Initiative (OC-CCI)

Author

Shubha Sathyendranath, Robert J.W. Brewin, Carsten Brockmann, Vanda Brotas, Ben Calton, Andrei Chuprin, Paolo Cipollini, André B. Couto, James Dingle, Roland Doerffer, Craig Donlon, Mark Dowell, Alex Farman, Mike Grant, Steve Groom, Andrew Horseman, Thomas Jackson, Hajo Krasemann, Samantha Lavender, Victor Martinez-Vicente, Constant Mazeran, Frédéric Mélin, Timothy S. Moore, Dagmar Müller, Peter Regner, Shovonlal Roy, Chris J. Steele, François Steinmetz, John Swinton, Malcolm Taberner, Adam Thompson, André Valente, Marco Zühlke, Vittorio E. Brando, Hui Feng, Gene Feldman, Bryan A. Franz, Robert Frouin, Richard W. Gould, Stanford B. Hooker, Mati Kahru, Susanne Kratzer, B. Greg Mitchell, Frank E. Muller-Karger, Heidi M. Sosik, Kenneth J. Voss, Jeremy Werdell, and Trevor Platt

Subjects
ocean colour, water-leaving radiance, remote-sensing reflectance, phytoplankton, chlorophyll-a, inherent optical properties, climate change initiative, optical water classes, essential climate variable, uncertainty characterisation, Chemical technology, TP1-1185
Abstract

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Published
2019
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4. Coupling ecological concepts with an ocean-colour model: Phytoplankton size structure

Author

Xuerong Sun, Robert J.W. Brewin, Shubha Sathyendranath, Giorgio Dall’Olmo, Ruth Airs, Ray Barlow, Astrid Bracher, Vanda Brotas, Malika Kheireddine, Tarron Lamont, Emilio Marañón, Xosé Anxelu G. Morán, Dionysios E. Raitsos, Fang Shen, Gavin H. Tilstone, and Morán, Xosé Anxelu G.

Subjects
HPLC pigments, Ocean-colour remote sensing, Ocean-colour model, Soil Science, Climate change, Geology, Computers in Earth Sciences, Phytoplankton size classes, Size-fractionated fluorometric
Abstract

Phytoplankton play a central role in the planetary cycling of important elements and compounds. Understanding how phytoplankton are responding to climate change is consequently a major question in Earth Sciences. Monitoring phytoplankton is key to answering this question. Satellite remote sensing of ocean colour is our only means of monitoring phytoplankton in the entire surface ocean at high temporal and large spatial scales, and the continuous ocean-colour data record is now approaching a length suitable for addressing questions around climate change, at least in some regions. Yet, developing ocean-colour algorithms for climate change studies requires addressing issues of ambiguity in the ocean-colour signal. For example, for the same chlorophyll-a concentration (Chl-a) of phytoplankton, the colour of the ocean can be different depending on the type of phytoplankton present. One route to tackle the issue of ambiguity is by enriching the ocean-colour data with information on sea surface temperature (SST), a good proxy of changes in three phytoplankton size classes (PSCs) independent of changes in total Chl-a, a measure of phytoplankton biomass. Using a global surface in-situ dataset of HPLC (high performance liquid chromatography) pigments, size-fractionated filtration data, and concurrent satellite SST spanning from 1991 to 2021, we re-tuned, validated and advanced an SST-dependent three-component model that quantifies the relationship between total Chl-a and Chl-a associated with the three PSCs (pico-, nano- and microplankton). Similar to previous studies, striking dependencies between model parameters and SST were captured, which were found to improve model performance significantly. These relationships were applied to 40 years of monthly composites of satellite SST, and significant trends in model parameters were observed globally, in response to climate warming. Changes in these parameters highlight issues in estimating long-term trends in phytoplankton biomass (Chl-a) from ocean colour using standard empirical algorithms, which implicitly assume a fixed relationship between total Chl-a and Chl-a of the three size classes. The proposed ecological model will be at the centre of a new ocean-colour modelling framework, designed for investigating the response of phytoplankton to climate change, described in subsequent parts of this series of papers., This work is supported primarily by a UKRI Future Leader Fellowship (MR/V022792/1). Additional supports from the UK National Centre for Earth Observation (NCEO), the Simons Foundation Project Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems (CBIOMES, 549947, Shubha Sathyendranath), and Royal Society International Exchanges 2021 Cost Share (NSFC) grant (IEC NSFC 211058) are acknowledged. Astrid Bracher and Vanda Brotas are funded by the European Union’s Horizon 2020 Research and Innovation Programme (N810139): Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation (PORTWIMS). Astrid Bracher is also funded by the ESA 656 S5P+Innovation Theme 7 Ocean Colour (S5POC) project (No. 4000127533/19/I-NS). The AWI in-situ data are supported by the Helmholtz Infrastructure Initiative FRAM. Tarron Lamont and Ray Barlow acknowledge funding, logistical, and administrative support from the South African National Department of Forestry, Fisheries and the Environment (DFFE), Bayworld Centre for Research and Education (BCRE), and the South African National Research Foundation (NRF grants: 129229 and 132073). Fang Shen is funded by National Natural Science Foundation of China (No. 42076187 and No. 41771378) for the sampling of in-situ data in eastern China seas. The Atlantic Meridional Transect (AMT) is funded by the UK Natural Environment Research Council (NERC) through its National Capability Long-term Single Centre Science Programme, Climate Linked Atlantic Sector Science (grant number NE/R015953/1) to Plymouth Marine Laboratory. This work contributes to the international IMBeR project and is contribution number 386 of the AMT programme. The authors would like to acknowledge all the contributors who have shared in-situ data to the public domains, including the Western Channel Observatory, TARA Ocean, Rothera Research Station, NASA SeaBASS, ADON, Government of Canada, DATAONE, BCODMO, EDI Data Portal, PANGAEA, and BODC, and all the scientists and crew who were involved in the collection of in-situ data are sincerely appreciated. We thank NOAA for providing daily and monthly OISST (version 2) SST data; ESA for providing monthly SST-CCI (version 2.1) SST data; ESA for providing monthly OC-CCI (version 5.0) Chl-a climatology data; GEBCO for providing bathymetric data (GEBCO2021 Grid). The authors also thank Hongyan Xi for providing comments and suggestions on the early version of the manuscript.

Published
2023

5. The paralarval stage as key to predicting squid catch: Hints from a process-based model

Author

Jorn Bruggeman, Zoe L. Jacobs, Ekaterina Popova, Warwick H.H. Sauer, Jessica M. Gornall, Robert J.W. Brewin, and Michael J. Roberts

Subjects
Oceanography
Abstract

Squid species show pronounced interannual variability in population size. While this may partially reflect changes in fisheries pressure, it is thought to be primarily the result of environmental variability. Most squid have an annual life cycle with only a short period dedicated to reproduction. With little overlap between generations, the environment can exert a major influence on stock size. In this study we explore, through a combination of process-based modelling and statistical analysis, whether environmental variability explains variability in catch of the chokka squid, Loligo reynaudii, over the Agulhas Bank off South Africa. We focus on growth and survival during the first two months spent as “paralarva” in the pelagic. This period has been suggested to be a key bottleneck and a potential predictor of catch. To describe prey availability and predation pressure, we develop a dynamic model of the size spectrum (1 mg–1000 kg) of the ecosystem over the Agulhas Bank, with trophic in- teractions governed by size. In tandem, we develop a model for the growth of individual L. reynaudii, which specifies where in the size spectrum individual squid can be found at each stage of their development. We find a correlation of 0.74 between modelled biomass representative for L. reynaudii at the end of its paralarval stage and catch per unit effort (CPUE) in the subsequent season in the period 1995–2015. This suggests that the paralarval stage is indeed a bottleneck: modelled food availability and predation pressure experienced by paralarvae ex- plains 55% of the variability in CPUE, which is a proxy for spawning stock biomass. As the paralarval stage ends approximately nine months before the time of spawning and maximum catch, this work could be used to develop catch predictor with a nine-month lag.

Published
2022
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7. List of contributors

Author

Manal Al-Kandari, Andrew E. Allen, Catharina Alves-de-Souza, Mark Baird, John A. Berges, Levente Bodrossy, Christopher J.S. Bolch, Chris Bowler, Astrid Bracher, Robert J.W. Brewin, Michele A. Burford, Lisa Campbell, Aurea M. Ciotti, Alejandro Clément, Lesley A. Clementson, Nicole Correa, Peter D. Countway, Kathryn J. Coyne, Pascal Craw, Stephanie Dutkiewicz, Chetan C. Gaonkar, Laurence Garczarek, Catherine Gérikas Ribeiro, Sydney M. Greenlee, Andres Gutiérrez-Rodríguez, Matthew J. Harke, Darren W. Henrichs, Anna Hickman, Andrea Highfield, Takafumi Hirata, Sharon E. Hook, Federico M. Ibarbalz, Bethany C. Kolody, Tihomir S. Kostadinov, Bernd Krock, Adriana Lopes dos Santos, Lara Marcus, Jorge I. Mardones, Glenn B. McGregor, Mathieu Mongin, Colleen B. Mouw, Satoshi Nagai, Brett A. Neilan, Scott D. Nodder, Denise Ong, Emanuele Organelli, Javier Paredes-Mella, Anthony Richardson, Declan Schroeder, Barbara C. Sendall, Xiao Li Shi, Sebastian Silva, Jennifer Skerratt, Matthew C. Smith, Monika Soja-Wozniak, Rowena Stern, Andrew D.L. Steven, Alison R. Taylor, Kimberlee Thamatrakoln, Daniel Vaulot, Luigi Vezzulli, Flora Vincent, Peter Von Dassow, Yanfei Wang, Karen Wild-Allen, Anusuya Willis, Susanna A. Wood, Jason N. Woodhouse, Kyoko Yarimizu, and Erica B. Young

Published
2022
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8. A high-tech, low-cost, Internet of Things surfboard fin for coastal citizen science, outreach, and education

Author

Philip Bresnahan, Tyler Cyronak, Robert J.W. Brewin, Andreas Andersson, Taylor Wirth, Todd Martz, Travis Courtney, Nathan Hui, Ryan Kastner, Andrew Stern, Todd McGrain, Danica Reinicke, Jon Richard, Katherine Hammond, and Shannon Waters

Subjects
Outreach, Coastal oceanography, Sea surface temperature, Surfing, Earth Sciences, Geology, Citizen science, Biological Sciences, Aquatic Science, Oceanography, Life Below Water
Published
2022
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9. Copernicus Marine Service Ocean State Report, Issue 4

Author

Karina von Schuckmann, Pierre-Yves Le Traon, Neville Smith, Ananda Pascual, Samuel Djavidnia, Jean-Pierre Gattuso, Marilaure Grégoire, Glenn Nolan, Signe Aaboe, Enrique Álvarez Fanjul, Lotfi Aouf, Roland Aznar, T. H. Badewien, Arno Behrens, Maristella Berta, Laurent Bertino, Jeremy Blackford, Giorgio Bolzon, Federica Borile, Marine Bretagnon, Robert J.W. Brewin, Donata Canu, Paola Cessi, Stefano Ciavatta, Bertrand Chapron, Thi Tuyet Trang Chau, Frédéric Chevallier, Boriana Chtirkova, Stefania Ciliberti, James R. Clark, Emanuela Clementi, Clément Combot, Eric Comerma, Anna Conchon, Giovanni Coppini, Lorenzo Corgnati, Gianpiero Cossarini, Sophie Cravatte, Marta de Alfonso, Clément de Boyer Montégut, Christian De Lera Fernández, Francisco Javier de los Santos, Anna Denvil-Sommer, Álvaro de Pascual Collar, Paulo Alonso Lourenco Dias Nunes, Valeria Di Biagio, Massimiliano Drudi, Owen Embury, Pierpaolo Falco, Odile Fanton d’Andon, Luis Ferrer, David Ford, H. Freund, Manuel García León, Marcos García Sotillo, José María García-Valdecasas, Philippe Garnesson, Gilles Garric, Florent Gasparin, Marion Gehlen, Ana Genua-Olmedo, Gerhard Geyer, Andrea Ghermandi, Simon A. Good, Jérôme Gourrion, Eric Greiner, Annalisa Griffa, Manuel González, Ismael Hernández-Carrasco, Stéphane Isoard, John J. Kennedy, Susan Kay, Anton Korosov, Kaari Laanemäe, Peter E. Land, Thomas Lavergne, Paolo Lazzari, Jean-François Legeais, Benedicte Lemieux, Bruno Levier, William Llovel, Vladyslav Lyubartsev, Vidar S. Lien, Leonardo Lima, Pablo Lorente, Julien Mader, Marcello G. Magaldi, Ilja Maljutenko, Antoine Mangin, Carlo Mantovani, Veselka Marinova, Simona Masina, Elena Mauri, J. Meyerjürgens, Alexandre Mignot, Robert McEwan, Carlos Mejia, Angélique Melet, Milena Menna, Benoît Meyssignac, Alexis Mouche, Baptiste Mourre, Malte Müller, Giulio Notarstefano, Alejandro Orfila, Silvia Pardo, Elisaveta Peneva, Begoña Pérez-Gómez, Coralie Perruche, Monika Peterlin, Pierre-Marie Poulain, Nadia Pinardi, Yves Quilfen, Urmas Raudsepp, Richard Renshaw, Adèle Révelard, Emma Reyes-Reyes, M. Ricker, Pablo Rodríguez-Rubio, Paz Rotllán, Eva Royo Gelabert, Anna Rubio, Inmaculada Ruiz-Parrado, Shubha Sathyendranath, Jun She, Cosimo Solidoro, Emil V. Stanev, Joanna Staneva, Andrea Storto, Jian Su, Tayebeh Tajalli Bakhsh, Gavin H. Tilstone, Joaquín Tintoré, Cristina Toledano, Jean Tournadre, Benoit Tranchant, Rivo Uiboupin, Arnaud Valcarcel, Nadezhda Valcheva, Nathalie Verbrugge, Mathieu Vrac, J.-O. Wolff, Enrico Zambianchi, O. Zielinski, Ann-Sofie Zinck, Serena Zunino, Fundação para a Ciência e a Tecnologia (Portugal), Ministério da Ciência, Tecnologia e Ensino Superior (Portugal), Institut Cartogràfic i Geològic de Catalunya, Laboratoire d'océanographie de Villefranche (LOV), Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut de la Mer de Villefranche (IMEV), Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Universitat Politècnica de Catalunya. Laboratori d'Enginyeria Marítima, Universitat Politècnica de Catalunya. LIM/UPC - Laboratori d'Enginyeria Marítima, Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] (LSCE), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Extrèmes : Statistiques, Impacts et Régionalisation (ESTIMR), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ), von Schuckmann K., Le Traon P.-Y., Smith N., Pascual A., Djavidnia S., Gattuso J.-P., Gregoire M., Nolan G., Aaboe S., Fanjul E.A., Aouf L., Aznar R., Badewien T.H., Behrens A., Berta M., Bertino L., Blackford J., Bolzon G., Borile F., Bretagnon M., Brewin R.J.W., Canu D., Cessi P., Ciavatta S., Chapron B., Trang Chau T.T., Chevallier F., Chtirkova B., Ciliberti S., Clark J.R., Clementi E., Combot C., Comerma E., Conchon A., Coppini G., Corgnati L., Cossarini G., Cravatte S., de Alfonso M., de Boyer Montegut C., De Lera Fernandez C., de los Santos F.J., Denvil-Sommer A., de Pascual Collar A., Dias Nunes P.A.L., Di Biagio V., Drudi M., Embury O., Falco P., d'Andon O.F., Ferrer L., Ford D., Freund H., Leon M.G., Sotillo M.G., Garcia-Valdecasas J.M., Garnesson P., Garric G., Gasparin F., Gehlen M., Genua-Olmedo A., Geyer G., Ghermandi A., Good S.A., Gourrion J., Greiner E., Griffa A., Gonzalez M., Hernandez-Carrasco I., Isoard S., Kennedy J.J., Kay S., Korosov A., Laanemae K., Land P.E., Lavergne T., Lazzari P., Legeais J.-F., Lemieux B., Levier B., Llovel W., Lyubartsev V., Lien V.S., Lima L., Lorente P., Mader J., Magaldi M.G., Mangin A., Maljutenko I., Mantovani C., Marinova V., Masina S., Mauri E., Meyerjurgens J., Mignot A., McEwan R., Mejia C., Melet A., Menna M., Meyssignac B., Mouche A., Mourre B., Muller M., Notarstefano G., Pardo S., Orfila A., Peneva E., Perez-Gomez B., Perruche C., Peterlin M., Poulain P.-M., Pinardi N., Quilfen Y., Raudsepp U., Renshaw R., Revelard A., Reyes-Reyes E., Ricker M., Rodriguez-Rubio P., Rotllan P., Gelabert E.R., Rubio A., Ruiz-Parrado I., Sathyendranath S., She J., Solidoro C., Stanev E.V., Staneva J., Storto A., Su J., Bakhsh T.T., Tilstone G.H., Tintore J., Toledano C., Tournadre J., Tranchant B., Uiboupin R., Valcarcel A., Valcheva N., Verbrugge N., Vrac M., Wolff J.-O., Zambianchi E., Zielinski O., and Zunino S.

Subjects
010504 meteorology & atmospheric sciences, media_common.quotation_subject, [SDE.MCG]Environmental Sciences/Global Changes, Public administration, Oceanography, 01 natural sciences, State (polity), Political science, 14. Life underwater, CMEMS, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, ComputingMilieux_MISCELLANEOUS, [SDU.STU.OC]Sciences of the Universe [physics]/Earth Sciences/Oceanography, 0105 earth and related environmental sciences, Copernicus, media_common, Service (business), [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, Enginyeria agroalimentària::Ciències de la terra i de la vida::Climatologia i meteorologia [Àrees temàtiques de la UPC], 010505 oceanography, Meteorologia marítima, Marine meteorology--Europe, [SDE.ES]Environmental Sciences/Environmental and Society, 13. Climate action, Enginyeria civil::Enginyeria hidràulica, marítima i sanitària::Ports i costes [Àrees temàtiques de la UPC], [SDE]Environmental Sciences, Ocean state report, Copernicus Marine Service, Environment policy
Abstract

Editors: Karina von Schuckmann; Pierre-Yves Le Traon.-- Review Editors: Neville Smith (Chair); Ananda Pascual; Samuel Djavidnia; Jean-Pierre Gattuso; Marilaure Grégoire; Glenn Nolan., The authors would like to thank the Institut Cartogràfic i Geològic de Catalunya (ICGC) for providing data. Thanks are due to FCT/MCTES for the financial support to CESAM (UID/AMB/50017/2019), through national funds., Chapter 1: Introduction and the European Environment policy framework.-- CMEMS OSR4, Chapter 2: State, variability and change in the ocean.-- CMEMS OSR4, Chapter 3: Case studies.-- CMEMS OSR4, Chapter 4: Specific events 2018.

Published
2020
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10. Copernicus Marine Service Ocean State Report, Issue 3

Author

Karina von Schuckmann, Pierre-Yves Le Traon, Neville Smith (Chair), Ananda Pascual, Samuel Djavidnia, Jean-Pierre Gattuso, Marilaure Grégoire, Glenn Nolan, Signe Aaboe, Enrique Álvarez Fanjul, Lotfi Aouf, Roland Aznar, T. H. Badewien, Arno Behrens, Maristella Berta, Laurent Bertino, Jeremy Blackford, Giorgio Bolzon, Federica Borile, Marine Bretagnon, Robert J.W. Brewin, Donata Canu, Paola Cessi, Stefano Ciavatta, Bertrand Chapron, Thi Tuyet Trang Chau, Frédéric Chevallier, Boriana Chtirkova, Stefania Ciliberti, James R. Clark, Emanuela Clementi, Clément Combot, Eric Comerma, Anna Conchon, Giovanni Coppini, Lorenzo Corgnati, Gianpiero Cossarini, Sophie Cravatte, Marta de Alfonso, Clément de Boyer Montégut, Christian De Lera Fernández, Francisco Javier de los Santos, Anna Denvil-Sommer, Álvaro de Pascual Collar, Paulo Alonso Lourenco Dias Nunes, Valeria Di Biagio, Massimiliano Drudi, Owen Embury, Pierpaolo Falco, Odile Fanton d'Andon, Luis Ferrer, David Ford, H. Freund, Manuel García León, Marcos García Sotillo, José María García-Valdecasas, Philippe Garnesson, Gilles Garric, Florent Gasparin, Marion Gehlen, Ana Genua-Olmedo, Gerhard Geyer, Andrea Ghermandi, Simon A. Good, Jérôme Gourrion, Eric Greiner, Annalisa Griffa, Manuel González, Ismael Hernández-Carrasco, Stéphane Isoard, John J. Kennedy, Susan Kay, Anton Korosov, Kaari Laanemäe, Peter E. Land, Thomas Lavergne, Paolo Lazzari, Jean-François Legeais, Benedicte Lemieux, Bruno Levier, William Llovel, Vladyslav Lyubartsev, Vidar S. Lien, Leonardo Lima, Pablo Lorente, Julien Mader, Marcello G. Magaldi, Ilja Maljutenko, Antoine Mangin, Carlo Mantovani, Veselka Marinova, Simona Masina, Elena Mauri, J. Meyerjürgens, Alexandre Mignot, Robert McEwan, Carlos Mejia, Angélique Melet, Milena Menna, Benoît Meyssignac, Alexis Mouche, Baptiste Mourre, Malte Müller, Giulio Notarstefano, Alejandro Orfila, Silvia Pardo, Elisaveta Peneva, Begoña Pérez-Gómez, Coralie Perruche, Monika Peterlin, Pierre-Marie Poulain, Nadia Pinardi, Yves Quilfen, Urmas Raudsepp, Richard Renshaw, Adèle Révelard, Emma Reyes-Reyes, M. Ricker, Pablo Rodríguez-Rubio, Paz Rotllán, Eva Royo Gelabert, Anna Rubio, Inmaculada Ruiz-Parrado, Shubha Sathyendranath, Jun She, Cosimo Solidoro, Emil V. Stanev, Joanna Staneva, Andrea Storto, Jian Su, Tayebeh Tajalli Bakhsh, Gavin H. Tilstone, Joaquín Tintoré, Cristina Toledano, Jean Tournadre, Benoit Tranchant, Rivo Uiboupin, Arnaud Valcarcel, Nadezhda Valcheva, Nathalie Verbrugge, Mathieu Vrac, J.-O. Wolff, Enrico Zambianchi, O. Zielinski, Ann-Sofie Zinck, Serena Zunino, Mercator Océan, Société Civile CNRS Ifremer IRD Météo-France SHOM, Laboratoire d’Oéanographie Spatiale [Plouzané] (LOS), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER), Laboratoire d'océanographie de Villefranche (LOV), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire océanologique de Villefranche-sur-mer (OOVM), Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), Norwegian Meteorological Institute, SOCIB Balearic Islands Coastal Ocean Observing and Forecasting System, Organismo publico puertos del estado, Université de Liège, Météo France, Met Office Climate Research Division, United Kingdom Met Office [Exeter], Helmholtz-Zentrum Geesthacht (GKSS), Royal Netherlands Meteorological Institute (KNMI), Institut National des Sciences et Techniques de la Mer (INSTM), Institut Français de Recherche pour l'Exploitation de la Mer - Brest (IFREMER), Consiglio Nazionale delle Ricerche (CNR), Istituto di Science Marine (ISMAR ), Consejo Superior de Investigaciones Científicas [Spain] (CSIC), Deltares system (NETHERLANDS), Deltares system, ARGANS, Consiglio Nazionale delle Ricerche [Napoli] (CNR), AZTI - Tecnalia, Nansen Environmental and Remote Sensing Center [Bergen] (NERSC), Universitat de Girona [Girona], Universitat de Girona (UdG), Centro Euro-Mediterraneo per i Cambiamenti Climatici [Bologna] (CMCC), Istituto di Scienze Marine [Napoli] (ISMAR-CNR), Consiglio Nazionale delle Ricerche (CNR)-Consiglio Nazionale delle Ricerche (CNR), Nucleus for European Modeling of the Ocean (NEMO R&D ), Laboratoire d'Océanographie et du Climat : Expérimentations et Approches Numériques (LOCEAN), Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Muséum national d'Histoire naturelle (MNHN)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), School of Earth and Atmospheric Sciences [Atlanta], Georgia Institute of Technology [Atlanta], Parc Naturel Régional de Corse, Institut méditerranéen d'océanologie (MIO), Centre National de la Recherche Scientifique (CNRS)-Université de Toulon (UTLN)-Aix Marseille Université (AMU)-Institut de Recherche pour le Développement (IRD), Processus et interactions de fine échelle océanique (PROTEO), Rijkswaterstaat [Delft], ACRI, acri, Institute of Marine Sciences / Institut de Ciències del Mar [Barcelona] (ICM), Science et Technologie du Lait et de l'Oeuf (STLO), Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST, Laboratoire d'études en Géophysique et océanographie spatiales (LEGOS), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Centre National d'Études Spatiales [Toulouse] (CNES)-Observatoire Midi-Pyrénées (OMP), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS), Incheon National University, Alfred Wegener Institute [Potsdam], Alfred-Wegener-Institut, Helmholtz-Zentrum für Polar- und Meeresforschung (AWI), University of Gothenburg (GU), Pacific Centre for Environment and Sustainable Development, University of the South Pacific (USP), Euro-Mediterranean Center on Climate Change (CMCC), Department of Biology, University of Zagreb, Hellenic Centre for Marine Research (HCMR), Laboratoire d'Acoustique de l'Université du Mans (LAUM), Centre National de la Recherche Scientifique (CNRS)-Le Mans Université (UM), Inst Ciencias Mar, Collecte Localisation Satellites (CLS), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National d'Études Spatiales [Toulouse] (CNES), Marine Ecosystems Modeling and Monitoring by Satellites (MEMMS), CLS, Departament d'Ecologia, Universitat de Barcelona (UB), Loughborough University, AZTI-Tecnalia (Marine Research Division), AZTI-Tecnalia, Analytic and Computational Research, Inc. - Earth Sciences (ACRI-ST), Aarhus University [Aarhus], Departament d'Oceanografía Física, Institut de Ciències del Mar, Istituto Nazionale di Geofisica e di Oceanografia Sperimentale (OGS), Sistema d’observació i predicció costaner de les Illes Balears (.) (SOCIB), Institut Mediterrani d'Estudis Avancats (IMEDEA), Consejo Superior de Investigaciones Científicas [Spain] (CSIC)-Universidad de las Islas Baleares (UIB), Institute of Biogeochemistry and Pollutant Dynamics [ETH Zürich] (IBP), Department of Environmental Systems Science [ETH Zürich] (D-USYS), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich)-Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology in Zürich [Zürich] (ETH Zürich), Processus de Transfert et d'Echanges dans l'Environnement - EA 3819 (PROTEE), Université de Toulon (UTLN), Plymouth Marine Laboratory (PML), Plymouth Marine Laboratory, Danish Meteorological Institute (DMI), Catalytic Spectroscopy Laboratory (CSIC), Institute of Catalysis and Petroleum Chemistry, Tallinn University of Technology (TalTech), Royal Belgian Institute of Natural Sciences (RBINS), Department of Animal Biology (Institute for Research on Biodiversity (IRBio)), University of Barcelona, Hellenic Center for Marine Research (HCMR), UMS 3343 (OSU), Observatoire des Sciences de l'Univers de Rennes (OSUR), European Centre for Medium-Range Weather Forecasts (ECMWF), Observatoire océanologique de Villefranche-sur-mer (OOVM), Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Pierre et Marie Curie - Paris 6 (UPMC)-Centre National de la Recherche Scientifique (CNRS), Norwegian Meteorological Institute [Oslo] (MET), Institut National des Sciences et Technologies de la Mer [Salammbô] (INSTM), Institut Français de Recherche pour l'Exploitation de la Mer - Brest (IFREMER Centre de Bretagne), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Deltares [The Netherlands], Institut Pierre-Simon-Laplace (IPSL (FR_636)), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU)-Institut Pierre-Simon-Laplace (IPSL (FR_636)), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-École polytechnique (X)-Centre National d'Études Spatiales [Toulouse] (CNES)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Paris (UP)-Institut de Recherche pour le Développement (IRD)-Muséum national d'Histoire naturelle (MNHN)-Centre National de la Recherche Scientifique (CNRS)-Sorbonne Université (SU), Institut de Recherche pour le Développement (IRD)-Aix Marseille Université (AMU)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Toulon (UTLN)-Centre National de la Recherche Scientifique (CNRS), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), Department of Biology [Zagreb], Faculty of Science [Zagreb], University of Zagreb-University of Zagreb, Consejo Superior de Investigaciones Científicas [Madrid] (CSIC)-Universidad de las Islas Baleares (UIB), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich)- Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), and Tallinn University of Technology (TTÜ)

Subjects
[SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, 010504 meteorology & atmospheric sciences, variability, HF radar, 010505 oceanography, Oceanography, 01 natural sciences, salinity, 12. Responsible consumption, 13. Climate action, Environmental science, 14. Life underwater, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences
Abstract

The fundamental role of the ocean for life and well-being on Earth is more and more recognised at the highest political level. In 2015, the United Nations (UN) Sustainable Development Goals (SDGs) of the 2030 Agenda for Sustainable Development were adopted by world leaders. The SDG 14 ‘Conserve and sustainably use the oceans, seas and marine resources for sustainable development’ is dedicated to the oceans. The mention of the ocean in the Paris Agreement signed in 2016 marked a decisive milestone. In 2018, the UN Decade of Ocean Science for Sustainable Development (2021–2030) has been proclaimed (https://en.unesco.org/ocean-decade): The Intergovernmental Oceanographic Commission of UNESCO will gather ocean stakeholders worldwide behind a common framework to foster evidence-based policy-making. In fall 2019, the IPCC special report on ocean and cryosphere will be published, and will provide an opportunity to increase awareness and action before COP251 (already claimed as ‘Blue COP’).

Published
2019
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11. Primary Production Distribution

Author

S. Sathyendranath, T. Platt, Robert J.W. Brewin, and Thomas Jackson

Subjects
Geography, Primary (astronomy), Ecology, business.industry, Interpretation (philosophy), Regional science, Distribution (economics), Production (economics), business
Abstract

The study of the distribution of primary production in the world oceans preoccupied biological oceanographers for most of the twentieth century. Understanding the distribution remains a problem for which there is no facile answer; it is a many-faceted problem to which the approaches differ depending on the temporal and spatial scales at which understanding is sought. The issues relate to limitations of measurement techniques, interpretation of measurements that are available, and paucity of data.

Published
2019
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12. The Ocean Colour Climate Change Initiative: II. Spatial and temporal homogeneity of satellite data retrieval due to systematic effects in atmospheric correction processors

Author

Dagmar Müller, Hajo Krasemann, Robert J.W. Brewin, Carsten Brockmann, Pierre-Yves Deschamps, Roland Doerffer, Norman Fomferra, Bryan A. Franz, Mike G. Grant, Steve B. Groom, Frédéric Mélin, Trevor Platt, Peter Regner, Shubha Sathyendranath, François Steinmetz, and John Swinton

Subjects
Data processing, geography, geography.geographical_feature_category, Pixel, Computer science, Cloud cover, Atmospheric correction, Soil Science, Climate change, Geology, Data retrieval, Ocean gyre, Temporal resolution, ddc:551, Computers in Earth Sciences, Remote sensing
Abstract

The established procedure to access the quality of atmospheric correction processors and their underlying algorithms is the comparison of satellite data products with related in-situ measurements. Although this approach addresses the accuracy of derived geophysical properties in a straight forward fashion, it is also limited in its ability to catch systematic sensor and processor dependent behaviour of satellite products along the scan-line, which might impair the usefulness of the data in spatial analyses. The Ocean Colour Climate Change Initiative (OC-CCI) aims to create an ocean colour dataset on a global scale to meet the demands of the ecosystem modelling community. The need for products with increasing spatial and temporal resolution that also show as little systematic and random errors as possible, increases. Due to cloud cover, even temporal means can be influenced by along-scanline artefacts if the observations are not balanced and effects cannot be cancelled out mutually. These effects can arise from a multitude of results which are not easily separated, if at all. Among the sources of artefacts, there are some sensor-specific calibration issues which should lead to similar responses in all processors, as well as processor-specific features which correspond with the individual choices in the algorithms. A set of methods is proposed and applied to MERIS data over two regions of interest in the North Atlantic and the South Pacific Gyre. The normalised water leaving reflectance products of four atmospheric correction processors, which have also been evaluated in match-up analysis, is analysed in order to find and interpret systematic effects across track. These results are summed up with a semi-objective ranking and are used as a complement to the match-up analysis in the decision for the best Atmospheric Correction (AC) processor. Although the need for discussion remains concerning the absolutes by which to judge an AC processor, this example demonstrates clearly, that relying on the match-up analysis alone can lead to misjudgement.

Published
2015
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