Brewin
Robert J. W.
Brewin
Robert J. W.
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ArticleTowards an end-to-end analysis and prediction system for weather, climate, and marine applications in the Red Sea(American Meteorological Society, 2021-01-01) Hoteit, Ibrahim ; Abualnaja, Yasser ; Afzal, Shehzad ; Ait-El-Fquih, Boujemaa ; Akylas, Triantaphyllos ; Antony, Charls ; Dawson, Clint N. ; Asfahani, Khaled ; Brewin, Robert J. W. ; Cavaleri, Luigi ; Cerovecki, Ivana ; Cornuelle, Bruce D. ; Desamsetti, Srinivas ; Attada, Raju ; Dasari, Hari ; Sanchez-Garrido, Jose ; Genevier, Lily ; El Gharamti, Mohamad ; Gittings, John A. ; Gokul, Elamurugu ; Gopalakrishnan, Ganesh ; Guo, Daquan ; Hadri, Bilel ; Hadwiger, Markus ; Hammoud, Mohammed Abed ; Hendershott, Myrl ; Hittawe, Mohamad ; Karumuri, Ashok ; Knio, Omar ; Kohl, Armin ; Kortas, Samuel ; Krokos, George ; Kunchala, Ravi ; Issa, Leila ; Lakkis, Issam ; Langodan, Sabique ; Lermusiaux, Pierre F. J. ; Luong, Thang ; Ma, Jingyi ; Le Maitre, Olivier ; Mazloff, Matthew R. ; El Mohtar, Samah ; Papadopoulos, Vassilis P. ; Platt, Trevor ; Pratt, Lawrence J. ; Raboudi, Naila ; Racault, Marie-Fanny ; Raitsos, Dionysios E. ; Razak, Shanas ; Sanikommu, Sivareddy ; Sathyendranath, Shubha ; Sofianos, Sarantis S. ; Subramanian, Aneesh C. ; Sun, Rui ; Titi, Edriss ; Toye, Habib ; Triantafyllou, George ; Tsiaras, Kostas ; Vasou, Panagiotis ; Viswanadhapalli, Yesubabu ; Wang, Yixin ; Yao, Fengchao ; Zhan, Peng ; Zodiatis, GeorgeThe 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.
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ArticlePhytoplankton phenology indices in coral reef ecosystems : application to ocean-color observations in the Red Sea(Elsevier, 2015-02-18) Racault, Marie-Fanny ; Raitsos, Dionysios E. ; Berumen, Michael L. ; Brewin, Robert J. W. ; Platt, Trevor ; Sathyendranath, Shubha ; Hoteit, IbrahimPhytoplankton, 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.
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ArticleAn ocean-colour time series for use in climate studies: The experience of the ocean-colour climate change initiative (OC-CCI)(MDPI, 2019-10-03) Sathyendranath, Shubha ; Brewin, Robert J. W. ; Brockmann, Carsten ; Brotas, Vanda ; Calton, Ben ; Chuprin, Andrei ; Cipollini, Paolo ; Couto, André B. ; Dingle, James ; Doerffer, Roland ; Donlon, Craig ; Dowell, Mark ; Farman, Alex ; Grant, Michael ; Groom, Steven ; Horseman, Andrew ; Jackson, Thomas ; Krasemann, Hajo ; Lavender, Samantha ; Martinez-Vicente, Victor ; Mazeran, Constant ; Melin, Frederic ; Moore, Timothy S. ; Müller, Dagmar ; Regner, Peter ; Roy, Shovonlal ; Steele, Chris J. ; Steinmetz, François ; Swinton, John ; Taberner, Malcolm ; Thompson, Adam ; Valente, André ; Zühlke, Marco ; Brando, Vittorio ; Feng, Hui ; Feldman, Gene ; Franz, Bryan A. ; Frouin, Robert ; Gould, Richard ; Hooker, Stanford B. ; Kahru, Mati ; Kratzer, Susanne ; Mitchell, B. Greg ; Muller-Karger, Frank E. ; Sosik, Heidi M. ; Voss, Kenneth ; Werdell, Jeremy ; Platt, TrevorOcean 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.