Estimating temperature and salinity profiles using empirical orthogonal functions and clustering on historical measurements

Oceanographic climatology is normally estimated by dividing the world's oceans into geographical boxes of fixed shape and size, where each box is represented by a climatological salinity and temperature profile. The climatological profile is typically an average of historical measurements from that region. Since an arbitrarily chosen box may contain different types of water masses both in space and time, an averaged profile may be a statistically improbable or even non-physical representation. This paper proposes a new approach that employs empirical orthogonal functions in combination with a clustering technique to divide the world's oceans into climatological regions. Each region is represented by a cluster that is determined by minimising the variance of the state variables within each cluster. All profiles contained in a cluster are statistically similar to each other and statistically different from profiles in other clusters. Each cluster is then represented by mean temperature and salinity profiles and a mean position. Methods for estimating climatological profiles from the cluster information are examined, and their performances are compared to a conventional method of estimating climatology. The comparisons show that the new methods outperform conventional methods and are particularly effective in areas where oceanographic fronts are present.