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