Inverse modeling of salinity-temperature-depth relationships: Application to the upper eastern North Atlantic subtropical gyre

• Published in: Journal of Marine Systems
• Main Authors: Machin, F., Herraiz, L., Pelegrí Llopart,José Luis, Marrero-Diaz, A., Font, J., Rodriguez-Santana, A.
• Other Authors: Herraiz Borreguero, Laura, Marrero-Diaz, Angeles, Rodriguez-Santana, Angel, Pelegri, Josep, Font, Jordi, Machin, Francisco, 6602804374, 35558597700, 7003869003, 6507074043, 7201620951, 6506514177, 1451397, 3085594, 358123, 1846069, 202513, 1821092
• Language: English
• Published: 0924-7963 2010
• Subjects: 251007 Oceanografía física; (10.5–5.5 °W/33.5–37.5 °N); Inverse model; Upper ocean; Expandable bathythermograph; North Atlantic
• Online Access: http://hdl.handle.net/10553/48189; https://doi.org/10.1016/j.jmarsys.2009.10.005

We test the skill of a polynomial fit to reproduce the upper ocean (down to 750 m) salinity in the eastern North Atlantic (from the Canary Islands to the Iberian Peninsula, approximately 12° × 12°) as a function of temperature and depth. A historical database, constructed by merging several regional datasets, is used. An ANOVA test is performed to determine the optimum degree of temperature and depth in the polynomial fit. The polynomial coefficients are estimated by solving an inverse model where we control the size of both coefficients and residuals. We divide the basin in 21 zones (2° × 2°) and four regions (each comprising several zones), and run the inversion for the whole basin, as well as for each individual region and zone. This allows us to assess the sensitivity of the model to changes in the spatial domain, and to investigate the spatial variability of the polynomial coefficients. Regions are defined by applying a cluster analysis to objectively group those zones with similar oceanographic properties. The seasonality of the coefficients is addressed with data from the whole basin and individual regions. We find that, for either the whole basin or individual regions, seasonal coefficients predict salinity more accurately than annual ones, but annual coefficients per zone yet provide the best results. The depth-averaged error estimating salinity is less than 0.086 psu.