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Avila-Alonso D, Baetens JM, Cardenas R, De Baets B. Response of phytoplankton functional types to Hurricane Fabian (2003) in the Sargasso Sea. Mar Environ Res 2023; 190:106079. [PMID: 37473599 DOI: 10.1016/j.marenvres.2023.106079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Revised: 06/16/2023] [Accepted: 07/04/2023] [Indexed: 07/22/2023]
Abstract
Understanding how tropical cyclones affect phytoplankton communities is important for studies on ecological variability. Most studies assessing the post-storm phytoplankton response rely on satellite observations of chlorophyll a concentration, which inform on the ocean surface conditions and the whole phytoplankton community. In this work, we assess the potential of the Massachusetts Institute of Technology marine ecosystem model to account for the response of individual phytoplankton functional types (PFTs, coccolithophores, diatoms, diazotrophs, mixotrophic dinoflagellates, picoeukaryotes, Prochlorococcus and Synechococcus) in the euphotic zone to the passage of Hurricane Fabian (2003) across the tropical and subtropical Sargasso Sea. Fabian induced a significant mean concentration increase (t-test, p < 0.05) of all PFTs in the tropical waters (except for Prochlorococcus), which was driven by the mean nutrient concentration increase and by a limited zooplankton grazing pressure. More specifically, the post-storm nutrient enrichment increased the contribution of fast-growing PFTs (e.g. diatoms and coccolithophores) to the total phytoplankton concentration and decreased the contribution of slow-growing dominant groups (e.g. picoeukaryotes, Prochlorococcus and Synechococcus), which lead to a significant increase (t-test, p < 0.05) of the Shannon diversity index values. Overall, the model captured the causal relationship between nutrient and PFT concentration increases in the tropical waters, although it only reproduced the most pronounced PFT responses such as those in the deep euphotic zone. In contrast, the model did not capture the oceanic perturbations induced by Fabian as observed in satellite imagery in the subtropical waters, probably due to its limited performance in this complex oceanographic area.
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Affiliation(s)
- Dailé Avila-Alonso
- Planetary Science Laboratory, Department of Physics, Universidad Central "Marta Abreu" de Las Villas, 54830, Santa Clara, Villa Clara, Cuba; KERMIT, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, 9000, Ghent, Belgium.
| | - Jan M Baetens
- KERMIT, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, 9000, Ghent, Belgium
| | - Rolando Cardenas
- Planetary Science Laboratory, Department of Physics, Universidad Central "Marta Abreu" de Las Villas, 54830, Santa Clara, Villa Clara, Cuba
| | - Bernard De Baets
- KERMIT, Department of Data Analysis and Mathematical Modelling, Faculty of Bioscience Engineering, Ghent University, 9000, Ghent, Belgium
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Mostafa N, Kotb Y, Al-Arnaout Z, Alabed S, Shdefat AY. Replicating File Segments between Multi-Cloud Nodes in a Smart City: A Machine Learning Approach. Sensors (Basel) 2023; 23:4639. [PMID: 37430552 DOI: 10.3390/s23104639] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Revised: 04/10/2023] [Accepted: 05/08/2023] [Indexed: 07/12/2023]
Abstract
The design and management of smart cities and the IoT is a multidimensional problem. One of those dimensions is cloud and edge computing management. Due to the complexity of the problem, resource sharing is one of the vital and major components that when enhanced, the performance of the whole system is enhanced. Research in data access and storage in multi-clouds and edge servers can broadly be classified to data centers and computational centers. The main aim of data centers is to provide services for accessing, sharing and modifying large databases. On the other hand, the aim of computational centers is to provide services for sharing resources. Present and future distributed applications need to deal with very large multi-petabyte datasets and increasing numbers of associated users and resources. The emergence of IoT-based, multi-cloud systems as a potential solution for large computational and data management problems has initiated significant research activity in the area. Due to the considerable increase in data production and data sharing within scientific communities, the need for improvements in data access and data availability cannot be overlooked. It can be argued that the current approaches of large dataset management do not solve all problems associated with big data and large datasets. The heterogeneity and veracity of big data require careful management. One of the issues for managing big data in a multi-cloud system is the scalability and expendability of the system under consideration. Data replication ensures server load balancing, data availability and improved data access time. The proposed model minimises the cost of data services through minimising a cost function that takes storage cost, host access cost and communication cost into consideration. The relative weights between different components is learned through history and it is different from a cloud to another. The model ensures that data are replicated in a way that increases availability while at the same time decreasing the overall cost of data storage and access time. Using the proposed model avoids the overheads of the traditional full replication techniques. The proposed model is mathematically proven to be sound and valid.
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Affiliation(s)
- Nour Mostafa
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
| | - Yehia Kotb
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
| | - Zakwan Al-Arnaout
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
| | - Samer Alabed
- Biomedical Engineering Department, School of Applied Medical Sciences, German Jordanian University, Amman 11180, Jordan
| | - Ahmed Younes Shdefat
- College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait
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Delman A, Landerer F. Downscaling Satellite-Based Estimates of Ocean Bottom Pressure for Tracking Deep Ocean Mass Transport. Remote Sensing 2022; 14:1764. [DOI: 10.3390/rs14071764] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
Gravimetry measurements from the GRACE and GRACE-Follow-On satellites provide observations of ocean bottom pressure (OBP), which can be differenced between basin boundaries to infer mass transport variability at a given level in the deep ocean. However, GRACE data products are limited in spatial resolution, and conflate signals from many depth levels along steep continental slopes. To improve estimates of OBP variability near steep bathymetry, ocean bottom pressure observations from a JPL GRACE mascon product are downscaled using an objective analysis procedure, with OBP covariance information from an ocean model with horizontal grid spacing of ∼18 km. In addition, a depth-based adjustment was applied to enhance correlations at similar depths. Downscaled GRACE OBP shows realistic representations of sharp OBP gradients across bathymetry contours and strong currents, albeit with biases in the shallow ocean. In validations at intraannual (3–12 month) timescales, correlations of downscaled GRACE data (with depth adjustment) and in situ bottom pressure recorder time series were improved in ∼79% of sites, compared to correlations that did not involve downscaled GRACE. Correlations tend to be higher at sites where the amplitude of the OBP signal is larger, while locations where surface eddy kinetic energy is high (e.g., Gulf Stream extension) are more likely to have no improvement from the downscaling procedure. The downscaling procedure also increases the amplitude (standard deviation) of OBP variability compared to the non-downscaled GRACE at most sites, resulting in standard deviations that are closer to in situ values. A comparison of hydrography-based transport from RAPID with estimates based on downscaled GRACE data suggests substantial improvement from the downscaling at intraannual timescales, though this improvement does not extend to longer interannual timescales. Possible efforts to improve the downscaling technique through process studies and analysis of alongtrack GRACE/GRACE-FO observations are discussed.
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Lee YJ, Matrai PA, Friedrichs MAM, Saba VS, Aumont O, Babin M, Buitenhuis ET, Chevallier M, de Mora L, Dessert M, Dunne JP, Ellingsen IH, Feldman D, Frouin R, Gehlen M, Gorgues T, Ilyina T, Jin M, John JG, Lawrence J, Manizza M, Menkes CE, Perruche C, Le Fouest V, Popova EE, Romanou A, Samuelsen A, Schwinger J, Séférian R, Stock CA, Tjiputra J, Tremblay LB, Ueyoshi K, Vichi M, Yool A, Zhang J. Net primary productivity estimates and environmental variables in the Arctic Ocean: An assessment of coupled physical-biogeochemical models. J Geophys Res Oceans 2016; 121:8635-8669. [PMID: 32818130 PMCID: PMC7430529 DOI: 10.1002/2016jc011993] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The relative skill of 21 regional and global biogeochemical models was assessed in terms of how well the models reproduced observed net primary productivity (NPP) and environmental variables such as nitrate concentration (NO3), mixed layer depth (MLD), euphotic layer depth (Zeu), and sea ice concentration, by comparing results against a newly updated, quality-controlled in situ NPP database for the Arctic Ocean (1959-2011). The models broadly captured the spatial features of integrated NPP (iNPP) on a pan-Arctic scale. Most models underestimated iNPP by varying degrees in spite of overestimating surface NO3, MLD, and Zeu throughout the regions. Among the models, iNPP exhibited little difference over sea ice condition (ice-free versus ice-influenced) and bottom depth (shelf versus deep ocean). The models performed relatively well for the most recent decade and toward the end of Arctic summer. In the Barents and Greenland Seas, regional model skill of surface NO3 was best associated with how well MLD was reproduced. Regionally, iNPP was relatively well simulated in the Beaufort Sea and the central Arctic Basin, where in situ NPP is low and nutrients are mostly depleted. Models performed less well at simulating iNPP in the Greenland and Chukchi Seas, despite the higher model skill in MLD and sea ice concentration, respectively. iNPP model skill was constrained by different factors in different Arctic Ocean regions. Our study suggests that better parameterization of biological and ecological microbial rates (phytoplankton growth and zooplankton grazing) are needed for improved Arctic Ocean biogeochemical modeling.
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Affiliation(s)
- Younjoo J Lee
- Bigelow Laboratory for Ocean Sciences, East Boothbay, Maine, USA
- Now at Department of Oceanography, Naval Postgraduate School, Monterey, California, USA
| | | | - Marjorie A M Friedrichs
- Virginia Institute of Marine Science, College of William and Mary, Gloucester Point, Virginia, USA
| | - Vincent S Saba
- National Ocean and Atmospheric Administration, National Marine Fisheries Service, Northeast Fisheries Science Center, Geophysical Fluid Dynamics Laboratory, Princeton University, Princeton, New Jersey, USA
| | - Olivier Aumont
- Laboratoire Océan, Climat, Exploitation et Application Numérique/Institut Pierre-Simon Laplace, CNRS/IRD/UPMC, Université Pierre et Marie Curie, Paris, France
| | - Marcel Babin
- Takuvik Joint International Laboratory, CNRS-Université Laval, Québec, Canada
| | - Erik T Buitenhuis
- School of Environmental Sciences, University of East Anglia, Norwich, UK
| | - Matthieu Chevallier
- Centre National de Recherches Météorologiques, Unite mixte de recherche 3589 Météo-France/CNRS, Toulouse, France
| | | | - Morgane Dessert
- Laboratoire d'Océanographie Physique et Spatiale CNRS/IFREMER/IRD/UBO, Institut Universitaire et Européen de la Mer, Plouzané, France
| | - John P Dunne
- NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
| | | | - Doron Feldman
- NASA Goddard Institute for Space Studies, New York, USA
| | - Robert Frouin
- Climate, Atmospheric Science, and Physical Oceanography Division, Scripps Institution of Oceanography, University of California, La Jolla, California, USA
| | - Marion Gehlen
- Laboratoire des Sciences du Climat et de l'Environnement/Institut Pierre-Simon Laplace, Gif-sur-Yvette, France
| | - Thomas Gorgues
- Laboratoire d'Océanographie Physique et Spatiale CNRS/IFREMER/IRD/UBO, Institut Universitaire et Européen de la Mer, Plouzané, France
| | | | - Meibing Jin
- International Arctic Research Center, University of Alaska, Fairbanks, Alaska, USA
- Laboratoty for Regional Oceanography and Numerical Modeling, Qingdao National Laboratory for Marine Science and Technology, Qingdao, China
| | - Jasmin G John
- NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
| | - Jon Lawrence
- National Oceanography Centre, University of Southampton, Southampton, UK
| | - Manfredi Manizza
- Geosciences Research Division, Scripps Institution of Oceanography, University of California, La Jolla, California, USA
| | - Christophe E Menkes
- Laboratoire Océan, Climat, Exploitation et Application Numérique/Institut Pierre-Simon Laplace, CNRS/IRD/UPMC, Université Pierre et Marie Curie, Paris, France
| | | | - Vincent Le Fouest
- LIttoral ENvironnement et Sociétés, Université de La Rochelle, La Rochelle, France
| | - Ekaterina E Popova
- National Oceanography Centre, University of Southampton, Southampton, UK
| | - Anastasia Romanou
- Department of Applied Physics and Applied Mathematics, Columbia University and NASA Goddard Institute for Space Studies, New York, USA
| | - Annette Samuelsen
- Nansen Environmental and Remote Sensing Centre and Hjort Centre for Marine Ecosystem Dynamics, Bergen, Norway
| | - Jörg Schwinger
- Uni Research Climate, Bjerknes Centre for Climate Research, Bergen, Norway
| | - Roland Séférian
- Centre National de Recherches Météorologiques, Unite mixte de recherche 3589 Météo-France/CNRS, Toulouse, France
| | - Charles A Stock
- NOAA/Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
| | - Jerry Tjiputra
- Uni Research Climate, Bjerknes Centre for Climate Research, Bergen, Norway
| | - L Bruno Tremblay
- Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, Canada
| | - Kyozo Ueyoshi
- Climate, Atmospheric Science, and Physical Oceanography Division, Scripps Institution of Oceanography, University of California, La Jolla, California, USA
| | - Marcello Vichi
- Department of Oceanography, University of Cape Town, Cape Town, South Africa
- Marine Research Institute, University of Cape Town, Cape Town, South Africa
| | - Andrew Yool
- National Oceanography Centre, University of Southampton, Southampton, UK
| | - Jinlun Zhang
- Applied Physics Laboratory, University of Washington, Seattle, Washington, USA
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