ALBERT

Description: The Copernicus Marine Environment Monitoring Service (CMEMS) Ocean State Report (OSR) provides an annual report of the state of the global ocean and European regional seas for policy and decision-makers with the additional aim of increasing general public awareness about the status of, and changes in, the marine environment. The CMEMS OSR draws on expert analysis and provides a 3-D view (through reanalysis systems), a view from above (through remote-sensing data) and a direct view of the interior (through in situ measurements) of the global ocean and the European regional seas. The report is based on the unique CMEMS monitoring capabilities of the blue (hydrography, currents), white (sea ice) and green (e.g. Chlorophyll) marine environment. This first issue of the CMEMS OSR provides guidance on Essential Variables, large-scale changes and specific events related to the physical ocean state over the period 1993–2015. Principal findings of this first CMEMS OSR show a significant increase in global and regional sea levels, thermosteric expansion, ocean heat content, sea surface temperature and Antarctic sea ice extent and conversely a decrease in Arctic sea ice extent during the 1993–2015 period. During the year 2015 exceptionally strong large-scale changes were monitored such as, for example, a strong El Niño Southern Oscillation, a high frequency of extreme storms and sea level events in specific regions in addition to areas of high sea level and harmful algae blooms. At the same time, some areas in the Arctic Ocean experienced exceptionally low sea ice extent and temperatures below average were observed in the North Atlantic Ocean.

Description: Published

Description: s235–s320

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: The Colour and Light in the Ocean (CLEO) Workshop, organized by the European Space Agency (ESA) and the Plymouth Marine Laboratory (PML) was held on the ESRIN, the ESA Centre for Earth Observations, at Frascati, Italy on 6-8 September 2016. The workshop is sponsored through selected SEOM (Scientific Exploitation of Operational Missions) projects, including: Pools of Carbon in the Ocean (POCO), Photosynthetically Active Radiation and Primary Production (PPP), Synergistic Exploitation of Hyper- and Multispectral Sentinel-Measurements to Determine Phytoplankton Functional Types (PFT) (SynSenPFT), and Extreme Case-2 Waters (C2X). Additional partner projects of ESA are: Marine Photosynthesis Parameters from Space (MAPPS), a Pathfinder STSE (Support to Science Element) project; and Ocean Colour Climate Change Initiative (OC-CCI) through the CCI (Climate Change Initiative). The objectives of the workshop were to: Evaluate state-of-art Exchange information with other relevant projects and activities Bring together remote sensing community, in situ data providers, modellers and other users Explore applications in marine ecosystem models Plan for the future: Identify challenge areas and research priorities for future EO data exploitation activities Discuss key science issues and make recommendations to strengthen community engagement Shape ideas for potential new ocean-colour products to be developed in the era of the Sentinel-3 mission The workshop was organized in five themes, developed around the activities of the sponsoring projects. Each t heme had oral, poster and discussion sessions. The workshop attracted some 160 registered participants. The workshop served an important need to connect the community, to provide a forum for lively exchange of ideas, and to recommend priorities for future activities in a collective manner. The workshop brought together scientists working on development of novel products from ocean-colour data and the user community, including, notably, the modeling community. One of the key outputs of the workshop is this report, which provides the Scientific Roadmap for future activities. Another planned outcome is a Special Issue on Colour and Light in the Oceans, to be published in the Journal, which will highlight the major scientific results presented at the workshop. Each section of the report, dealing with one of the themes of the workshop, is self-contained, but cross-references to other sections are provided where appropriate. Some recommendations found common resonance across sections, such as the need for continuous, consistent, ocean-colour data streams from satellites for long-term monitoring of the marine ecosystem; the need for an integrated approach, bringing together the remote-sensing community, the in situ data providers and the modeling community; the need to promote development of novel products and advanced sensors; and the importance of providing high-quality and uninterrupted support to the user community, through easy and free access to data and products. Each section discusses the current state of the art, identifies user requirements and gaps, and priorities for research in the short and medium terms. The workshop served the important function of sounding the community’s aspirations, and presenting them in a concise manner for ESA, through this Scientific Roadmap. One of the recommendations from the participants was that CLEO workshops be organized on a regular basis in the future, to develop the ocean-colour community , to promote exchange of new results and ideas, and to plan future activities. We thank all workshop participants, keynote speakers, authors of the oral presentations and the posters, the Scientific Committee and the Organising Committee, and the Session Chairs for all their contributions to the workshop. For the logistical support and local organization and hospitality, we thank the ESRIN Graphics Bureau, Administration, Catering Service and the Events Office, especially Irene Renis, Anne Lisa Pichler and Giulia Vinicola.

Description: The concept of phytoplankton functional types has emerged as a useful approach to classifying phytoplankton. It finds many applications in addressing some serious contemporary issues facing science and society. Its use is not without challenges, however. As noted earlier, there is no universally-accepted set of functional types, and the types used have to be carefully selected to suit the particular problem being addressed. It is important that the sum total of all functional types matches all phytoplankton under consideration. For example, if in a biogeochemical study, we classify phytoplankton as silicifiers, calcifiers, DMS-producers and nitrogen fix- ers, then there is danger that the study may neglect phytoplankton that do not contribute in any significant way to those functions, but may nevertheless be a significant contributor to, say primary production. Such considerations often lead to the adoption of a category of “other phytoplankton” in models, with no clear defining traits assigned them, but that are nevertheless necessary to close budgets on phytoplankton processes. Since this group is a collection of all phytoplankton that defy classification according to a set of traits, it is difficult to model their physi- ological processes. Our understanding of the diverse functions of phytoplankton is still growing, and as we recognize more functions, there will be a need to balance the desire to incorporate the increasing number of functional types in models against observational challenges of identifying and mapping them adequately. Modelling approaches to dealing with increasing functional diversity have been proposed, for example, using the complex adaptive systems theory and system of infinite diversity, as in the work of Bruggemann and Kooijman (2007). But it is unlikely that remote-sensing approaches might be able to deal with anything but a few prominent functional types. As long as these challenges are explicitly addressed, the functional- type concept should continue to fill a real need to capture, in an economic fashion, the diversity in phytoplankton, and remote sensing should continue to be a useful tool to map them. Remote sensing of phytoplankton functional types is an emerging field, whose potential is not fully realised, nor its limitations clearly established. In this report, we provide an overview of progress to date, examine the advantages and limitations of various methods, and outline suggestions for further development. The overview provided in this chapter is intended to set the stage for detailed considerations of remote-sensing applications in later chapters. In the next chapter, we examine various in situ methods that exist for observing phytoplankton functional types, and how they relate to remote-sensing techniques. In the subsequent chapters, we review the theoretical and empirical bases for the existing and emerging remote-sensing approaches; assess knowledge about the limitations, assumptions, and likely accuracy or predictive skill of the approaches; provide some preliminary comparative analyses; and look towards future prospects with respect to algorithm development, validation studies, and new satellite mis- sions.

Description: © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Remote Sensing of Environment 160 (2015): 222-234, doi:10.1016/j.rse.2015.01.019.

Description: Phytoplankton, at the base of the marine food web, represent a fundamental food source in coral reef ecosystems. The timing (phenology) and magnitude of the phytoplankton biomass are major determinants of trophic interactions. The Red Sea is one of the warmest and most saline basins in the world, characterized by an arid tropical climate regulated by the monsoon. These extreme conditions are particularly challenging for marine life. Phytoplankton phenological indices provide objective and quantitative metrics to characterize phytoplankton seasonality. The indices i.e. timings of initiation, peak, termination and duration are estimated here using 15 years (1997–2012) of remote sensing ocean-color data from the European Space Agency (ESA) Climate Change Initiative project (OC-CCI) in the entire Red Sea basin. The OC-CCI product, comprising merged and bias-corrected observations from three independent ocean-color sensors (SeaWiFS, MODIS and MERIS), and processed using the POLYMER algorithm (MERIS period), shows a significant increase in chlorophyll data coverage, especially in the southern Red Sea during the months of summer NW monsoon. In open and reef-bound coastal waters, the performance of OC-CCI chlorophyll data is shown to be comparable with the performance of other standard chlorophyll products for the global oceans. These features have permitted us to investigate phytoplankton phenology in the entire Red Sea basin, and during both winter SE monsoon and summer NW monsoon periods. The phenological indices are estimated in the four open water provinces of the basin, and further examined at six coral reef complexes of particular socio-economic importance in the Red Sea, including Siyal Islands, Sharm El Sheikh, Al Wajh bank, Thuwal reefs, Al Lith reefs and Farasan Islands. Most of the open and deeper waters of the basin show an apparent higher chlorophyll concentration and longer duration of phytoplankton growth during the winter period (relative to the summer phytoplankton growth period). In contrast, most of the reef-bound coastal waters display equal or higher peak chlorophyll concentrations and equal or longer duration of phytoplankton growth during the summer period (relative to the winter phytoplankton growth period). The ecological and biological significance of the phytoplankton seasonal characteristics are discussed in context of ecosystem state assessment, and particularly to support further understanding of the structure and functioning of coral reef ecosystems in the Red Sea.

Description: © The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in 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., Melin, F., Moore, T. S., Muller, D., Regner, P., Roy, S., Steele, C. J., Steinmetz, F., Swinton, J., Taberner, M., Thompson, A., Valente, A., Zuhlke, 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., & 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(19), (2019): 4285, doi: 10.3390/s19194285.

Description: 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.

Description: This work was funded by the Ocean Colour Climate Change initiative of the European Space Agency (Grant Number 4000101437/10/I-LG). We acknowledge additional funding support by NERC through the National Centre for Earth Observation (Grant Number PR140015). Additional funding from a Simons Foundation Grant (549947, SS) is also gratefully acknowledged. V.B. also acknowledges funding from the European Union’s Horizon 2020 Research and Innovation Programme grant agreement N_ 810139: Project Portugal Twinning for Innovation and Excellence in Marine Science and Earth Observation – PORTWIMS.

Description: Si listano le singole sezioni in cui S.Simoncelli ha contribuito. Ogni sezione puo' essere citata separatamente dal report 1.1 Ocean temperature and salinity S. Mulet, B. Buongiorno Nardelli, S. Good, A. Pisano, E. Greiner, M. Monier E. Autret, L. Axell, F. Boberg, S. Ciliberti, M. Drévillon, R. Droghei, O. Embury, J. Gourrion, J. Høyer, M. Juza, J. Kennedy, B. Lemieux-Dudon, E. Peneva, R. Reid, S. Simoncelli, A. Storto, J. Tinker, K. von Schuckmann, S. L. Wakelin. 2.1. Ocean heat content ..K. von Schuckmann, A. Storto, S. Simoncelli, R. P. Raj, A.Samuelsen, A. de Pascual Collar, M. Garcia Sotillo, T Szerkely, M. Mayer, K. A. Peterson, H. Zuo, G. Garric, M. Monier. 3.4 Water mass formation processes in the Mediterranean Sea over the past 30 years S. Simoncelli, Nadia Pinardi, C. Fratianni, C. Dubois, G. Notarstefano. 3.5 Ventilation of the Western Mediterranean Deep Water through the Strait of Gibraltar S. Sammartino, J. García Lafuente, C. Naranjo, S. Simoncelli. 4.4 Unusual salinity pattern in the South Adriatic Sea in 2016 Z. Kokkini, G. Notarstefano P-M Poulain, E. Mauri, R. Gerin, S. Simoncelli

Description: The oceans regulate our weather and climate from global to regional scales. They absorb over 90% of accumulated heat in the climate system (IPCC 2013 IPCC. 2013. Climate change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the intergovernmental panel on climate change [Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM, editors]. Cambridge: Cambridge University Press, 1535. doi: 10.1017/CBO9781107415324. [Crossref], , [Google Scholar]) and over a quarter of the anthropogenic carbon dioxide (Le Quéré et al. 2016 Le Quéré C, Andrew RM, Canadell JG, Sitch S, Korsbakken JI, Peters GP, Manning AC, Boden TA, Tans PP, Houghton RA, et al. 2016. Global carbon budget 2016. Earth Syst Sci Data. 8( 2): 605– 649. doi: 10.5194/essd-8-605-2016 [Crossref], [Web of Science ®], , [Google Scholar]). They provide nearly half of the world’s oxygen. Most of our rain and drinking water is ultimately regulated by the sea. The oceans provide food and energy and are an important source of the planet's biodiversity and ecosystem services. They are vital conduits for trade and transportation and many economic activities depend on them (OECD 2016 OECD . 2016. The ocean economy in 2030. Paris : OECD Publishing. doi: 10.1787/9789264251724-en. [Crossref], , [Google Scholar]). Our oceans are, however, under threat due to climate change and other human induced activities and it is vital to develop much better, sustainable and science-based reporting and management approaches (UN 2017 UN . 2017. Report of the United Nations conference to support the implementation of sustainable development goal 14: Conserve and sustainably use the oceans, seas and marine resources for sustainable development (Advance unedited version). https://sustainabledevelopment.un.org/content/documents/15662FINAL_15_June_2017_RepoRe_Goal_14.pdf . [Google Scholar]). Better management of our oceans requires long-term, continuous and state-of-the art monitoring of the oceans from physics to ecosystems and global to local scales. The Copernicus Marine Environment Monitoring Service (CMEMS) has been set up to address these challenges at European level. Mercator Ocean was tasked in 2014 by the European Union under a delegation agreement to implement the operational phase of the service from 2015 to 2021 (CMEMS 2014 CMEMS . 2014. Technical annex to the delegation agreement with Mercator Ocean for the implementation of the Copernicus Marine Environment Monitoring Service (CMEMS). www.copernicus.eu/sites/default/files/library/CMEM_TechnicalAnnex_PUBLIC.docx.pdf . [Google Scholar]). The CMEMS now provides regular and systematic reference information on the physical state, variability and dynamics of the ocean, ice and marine ecosystems for the global ocean and the European regional seas (Figure 0.1; CMEMS 2016 CMEMS . 2016. High level service evolution strategy, a document prepared by Mercator Ocean with the support of the CMEMS STAC. [Google Scholar]). This capacity encompasses the description of the current situation (analysis), the prediction of the situation 10 days ahead (forecast), and the provision of consistent retrospective data records for recent years (reprocessing and reanalysis). CMEMS provides a sustainable response to European user needs in four areas of benefits: (i) maritime safety, (ii) marine resources, (iii) coastal and marine environment and (iv) weather, seasonal forecast and climate.

Description: Copernicus Marine Environment Monitoring Service

Description: Published

Description: S1-S142

Description: 4A. Oceanografia e clima

Description: JCR Journal

Description: Author Posting. © American Meteorological Society, 2021. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Bulletin of the American Meteorological Society 102(1), (2021): E99-E122, https://doi.org/10.1175/BAMS-D-19-0005.1.

Description: The Red Sea, home to the second-longest coral reef system in the world, is a vital resource for the Kingdom of Saudi Arabia. The Red Sea provides 90% of the Kingdom’s potable water by desalinization, supporting tourism, shipping, aquaculture, and fishing industries, which together contribute about 10%–20% of the country’s GDP. All these activities, and those elsewhere in the Red Sea region, critically depend on oceanic and atmospheric conditions. At a time of mega-development projects along the Red Sea coast, and global warming, authorities are working on optimizing the harnessing of environmental resources, including renewable energy and rainwater harvesting. All these require high-resolution weather and climate information. Toward this end, we have undertaken a multipronged research and development activity in which we are developing an integrated data-driven regional coupled modeling system. The telescopically nested components include 5-km- to 600-m-resolution atmospheric models to address weather and climate challenges, 4-km- to 50-m-resolution ocean models with regional and coastal configurations to simulate and predict the general and mesoscale circulation, 4-km- to 100-m-resolution ecosystem models to simulate the biogeochemistry, and 1-km- to 50-m-resolution wave models. In addition, a complementary probabilistic transport modeling system predicts dispersion of contaminant plumes, oil spill, and marine ecosystem connectivity. Advanced ensemble data assimilation capabilities have also been implemented for accurate forecasting. Resulting achievements include significant advancement in our understanding of the regional circulation and its connection to the global climate, development, and validation of long-term Red Sea regional atmospheric–oceanic–wave reanalyses and forecasting capacities. These products are being extensively used by academia, government, and industry in various weather and marine studies and operations, environmental policies, renewable energy applications, impact assessment, flood forecasting, and more.

Description: The development of the Red Sea modeling system is being supported by the Virtual Red Sea Initiative and the Competitive Research Grants (CRG) program from the Office of Sponsored Research at KAUST, Saudi Aramco Company through the Saudi ARAMCO Marine Environmental Center at KAUST, and by funds from KAEC, NEOM, and RSP through Beacon Development Company at KAUST.

Description: © The Author(s), 2016. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Earth System Science Data 8 (2016): 235-252, doi:10.5194/essd-8-235-2016.

Description: A compiled set of in situ data is important to evaluate the quality of ocean-colour satellite-data records. Here we describe the data compiled for the validation of the ocean-colour products from the ESA Ocean Colour Climate Change Initiative (OC-CCI). The data were acquired from several sources (MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD, MERMAID, AMT, ICES, HOT, GeP&CO), span between 1997 and 2012, and have a global distribution. Observations of the following variables were compiled: spectral remote-sensing reflectances, concentrations of chlorophyll a, spectral inherent optical properties and spectral diffuse attenuation coefficients. The data were from multi-project archives acquired via the open internet services or from individual projects, acquired directly from data providers. Methodologies were implemented for homogenisation, quality control and merging of all data. No changes were made to the original data, other than averaging of observations that were close in time and space, elimination of some points after quality control and conversion to a standard format. The final result is a merged table designed for validation of satellite-derived ocean-colour products and available in text format. Metadata of each in situ measurement (original source, cruise or experiment, principal investigator) were preserved throughout the work and made available in the final table. Using all the data in a validation exercise increases the number of matchups and enhances the representativeness of different marine regimes. By making available the metadata, it is also possible to analyse each set of data separately. The compiled data are available at doi:10.1594/PANGAEA.854832 (Valente et al., 2015).

Description: We thank NASA for project funding for data collection.