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Estimating Salinity between 25° and 45°S in the Atlantic Ocean Using Local Regression

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  • 1 NOAA/Atlantic Oceanographic and Meteorological Laboratory, Miami, Florida
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Abstract

The empirical relationship between salinity and temperature in the South Atlantic is quantified with the aid of local regression. To capture the spatial character of the TS relationship, models are fitted to data for each point on a three-dimensional grid with spacing of 1° in latitude, 2° in longitude, and 25 dbar in the vertical. To ensure sufficient data for statistical reliability each fit is to data from a region extending over several grid points weighted so that more remote data exert less influence than those closer to the target grid point. Both temperature and its square are used as regressors to capture the curvature seen in TS plots, and latitude and longitude are used to capture systematic spatial variations over the fitting regions. In addition to using statistics of residuals to characterize how well the models fit the data, errors for data not used in fitting are examined to verify the models’ abilities to simulate independent data. The best model overall for the entire region at all depths is quadratic in temperature and linear in longitude and latitude.

Corresponding author address: W. C. Thacker, NOAA/Atlantic Oceanographic and Meteorological Laboratory, 4301 Rickenbacker Causeway, Miami, FL 33149. Email: carlisle.thacker@noaa.gov

Abstract

The empirical relationship between salinity and temperature in the South Atlantic is quantified with the aid of local regression. To capture the spatial character of the TS relationship, models are fitted to data for each point on a three-dimensional grid with spacing of 1° in latitude, 2° in longitude, and 25 dbar in the vertical. To ensure sufficient data for statistical reliability each fit is to data from a region extending over several grid points weighted so that more remote data exert less influence than those closer to the target grid point. Both temperature and its square are used as regressors to capture the curvature seen in TS plots, and latitude and longitude are used to capture systematic spatial variations over the fitting regions. In addition to using statistics of residuals to characterize how well the models fit the data, errors for data not used in fitting are examined to verify the models’ abilities to simulate independent data. The best model overall for the entire region at all depths is quadratic in temperature and linear in longitude and latitude.

Corresponding author address: W. C. Thacker, NOAA/Atlantic Oceanographic and Meteorological Laboratory, 4301 Rickenbacker Causeway, Miami, FL 33149. Email: carlisle.thacker@noaa.gov

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