Chemical characterisation and modelling of water masses in the Northeast Atlantic

31 pages, 11 figures, 4 tables We develop a simple model to provide information about the thermohaline and biological control of nutrients and inorganic carbon in the Northeast Atlantic (39–48°N, 11–27°W). Using data collected during the Vivaldi survey (Charles Darwin cruise 58) in May, 1991 togethe...

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Bibliographic Details
Published in:Progress in Oceanography
Main Authors: Castro, Carmen G., Pérez, Fiz F., Holley, S., Ríos, Aida F.
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 1998
Subjects:
Online Access:http://hdl.handle.net/10261/297009
https://doi.org/10.1016/S0079-6611(98)00021-4
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Summary:31 pages, 11 figures, 4 tables We develop a simple model to provide information about the thermohaline and biological control of nutrients and inorganic carbon in the Northeast Atlantic (39–48°N, 11–27°W). Using data collected during the Vivaldi survey (Charles Darwin cruise 58) in May, 1991 together with published thermohaline characteristics for the source waters of the Northeast Atlantic, we have deduced triangular mixing percentages for every sample collected during the Vivaldi survey. Subsequently, we devised an inverse method correlated to the mixing percentages to model nutrient, oxygen and inorganic carbon concentrations. This enables us to explain more than 95% of the nutrient variability on the basis of correlation with thermohaline properties. The difference between `real' nutrient and `modelled' nutrient represents, in part, the biological activity not correlated to the thermohaline properties. From the modelled nutrient it is possible to gain information about preformed nutrient and an idea of the biological aging undergone by water masses. There is a covariance between oxygen consumption and nutrient and inorganic carbon anomalies which is related to areas of greater remineralization and ventilation. Such areas have been identified. The subsequent application of our model to TTO data explained about 90% of the nutrient variability. The modelling work was supported by EC projects MAST-CT900017 and MAS3-CT96-0060. Peer reviewed