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  • 1
    Publication Date: 2023-03-02
    Keywords: Date/Time of event; DEPTH, water; Event label; Expendable bathythermograph; Latitude of event; Longitude of event; MEDOC-2010; Salinity; Sarmiento de Gamboa; Sound velocity in water; T5_00008; T5_00010; T5_00012; T5_00014; T5_00017; T5_00019; T5_00021; T5_00023; T5_00025; T5_00027; T5_00029; T5_00031; T5_00033; T5_00035; T5_00037; T5_00040; T5_00042; T5_00044; T5_00046; T5_00048; T5_00050; T5_00052; T5_00054; T5_00056; T5_00058; T5_00060; T5_00062; T5_00064; T5_00067; T5_00069; T5_00071; T5_00074; T5_00076; T5_00078; T5_00080; T5_00082; T5_00084; T5_00086; T5_00088; T5_00090; T5_00092; T5_00094; T5_00096; T5_00098; T5_00100; T5_00102; T5_00104; T5_00107; T5_00109; T5_00111; T5_00113; T5_00115; T5_00117; T5_00119; T5_00121; T5_00123; T5_00125; T5_00127; T5_00129; T5_00131; T5_00133; T5_00135; T5_00137; T5_00139; T5_00141; T5_00143; T5_00145; T5_00147; T5_00149; T5_00151; T5_00153; T5_00155; T5_00157; T5_00159; T5_00161; T5_00163; T5_00165; T5_00167; T5_00169; T5_00172; T5_00174; T5_00176; T5_00178; T5_00180; T5_00182; T5_00184; T5_00186; T5_00188; T5_00190; T5_00192; T5_00194; T5_00196; T5_00198; T5_00200; T5_00202; T5_00204; T5_00206; T5_00208; T5_00210; T5_00212; T5_00214; T5_00216; T5_00218; T5_00220; T5_00222; T5_00224; T5_00226; T5_00228; T5_00230; T5_00232; T5_00234; T5_00237; T5_00239; T5_00241; T5_00243; T5_00245; T5_00247; T5_00249; T5_00251; T5_00253; T5_00255; T5_00257; T5_00259; T5_00261; T5_00263; T5_00265; T5_00267; T5_00269; T5_00271; T5_00273; T5_00275; T5_00277; T5_00279; T5_00281; T5_00283; T5_00285; T5_00287; T5_00289; T5_00291; T5_00293; T5_00295; T5_00297; T5_00299; T5_00301; T5_00303; T5_00305; T5_00307; T5_00309; T5_00311; T5_00313; T5_00315; T5_00317; T5_00319; T5_00321; T5_00323; T5_00325; T5_00327; T5_00330; T5_00332; T5_00334; T5_00336; T5_00338; T5_00340; T5_00342; T5_00344; T5_00346; T5_00347; T5_00349; T5_00351; T5_00353; T5_00355; T5_00357; T5_00358; T5_00362; T5_00364; T5_00366; T5_00368; T5_00370; T5_00372; T5_00374; T5_00376; T5_00378; T5_00380; T5_00382; T5_00384; Temperature, water; XBT
    Type: Dataset
    Format: text/tab-separated-values, 1783957 data points
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  • 2
  • 3
    Publication Date: 2020-06-29
    Description: We use seismic oceanography to document and analyze oceanic thermohaline finestructure across the Tyrrhenian Sea. Multichannel seismic (MCS) reflection data were acquired during the MEDiterranean OCcidental survey in April-May 2010. We deployed along-track expendable bathythermograph probes simultaneous with MCS acquisition. At nearby locations we gathered conductivity-temperature-depth data. An autonomous glider survey added in-situ measurements of oceanic properties. The seismic reflectivity clearly delineates thermohaline finestructure in the upper 2,000 m of the water column, indicating the interfaces between Atlantic Water/Winter Intermediate Water, Levantine Intermediate Water, and Tyrrhenian Deep Water. We observe the Northern Tyrrhenian Anticyclone, a near-surface meso-scale eddy, plus laterally and vertically extensive thermohaline staircases. Using MCS we are able to fully image the anticyclone to a depth of 800 m and to confirm the horizontal continuity of the thermohaline staircases of more than 200 km. The staircases show the clearest step-like gradients in the center of the basin while they become more diffuse towards the periphery and bottom, where impedance gradients become too small to be detected by MCS. We quantify the internal wave field and find it to be weak in the region of the eddy and in the center of the staircases, while it is stronger near the coastlines. Our results indicate this is because of the influence of the boundary currents, which disrupt the formation of staircases by preventing diffusive convection. In the interior of the basin the staircases are clearer and the internal wave field weaker, suggesting that other mixing processes such as double-diffusion prevail. Synopsis We studied the internal temperature and salinity structure of the Tyrrhenian Sea (Mediterranean) using the multichannel seismic reflection method (the same used in the hydrocarbon industry). Low frequency sound (seismic) waves are produced at the surface with an explosive air source and recorded by a towed cable containing hydrophones (underwater microphones). The data are processed to reveal 'stratigraphy' that result from contrasts in density that are themselves caused by changes in temperature and salinity. In this way we can map ocean circulation in two-dimensions. We also deployed in situ oceanographic probes to measure temperature and salinity in order to corroborate and optimize the processing of the seismic data. We then quantified the internal gravity wave field by tracking the peaks of seismic trace wavelets. Our results show that the interior of the Tyrrhenian Sea is largely isolated from internal waves that are generated by a large cyclonic boundary current that contains waters from the Atlantic ocean and other parts of the Mediterranean. This isolation allows the thermohaline finestructure to form, where small scale vertical mixing processes are at play. Understanding these mixing processes will aid researchers study global ocean circulation and to add constraints that can help improve climate models.
    Type: Article , PeerReviewed
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  • 4
    Publication Date: 2017-03-22
    Description: Seismic oceanography is based on the passage of a regularly repeating acoustic impulsive source and an acquisition streamer along the surface of the ocean, and on summing together all signals reflected from temperature and salinity interfaces in the ocean (where there are acoustic impedance contrasts). Due to the inherent redundancy of the method, random noise is attenuated, while signal is preserved; however, if the original signal-to-noise ratio is large enough, one need not use data from the entire streamer to create a 2D profile. A processing scheme is here devised to obtain consecutive images, known as stacks, of the structure of the water column. The scheme, named Seismic Offset Groups (SOG), consists in splitting the data from the whole streamer at a given geographical position into data produced by different streamer subsets. The method is illustrated by partitioning data from a 5-km long streamer into 7 offset groups separated by 3.5 min in time, thereby imaging the same seafloor-referenced location over a period of 21 min. As the streamer passes over a fixed geographical point, motions within the water column are observed. Each stack, created with a subset of the complete streamer, can therefore be considered an image of the water column at a particular time step (animation frame). In this way each image shows a different thermohaline fabric and the animation allows us to visualize internal ocean motions.
    Type: Article , PeerReviewed
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