An improved knowledge of the statistical distribution of ocean velocities has an important role in a better understanding of the ocean dynamics, and a remarkable impact on ocean models and simulations. The non-Gaussian behavior of the Probability Density Function (PDFs) of ocean velocities was first remarked by Lilly (1969). Since then, other studies have looked at these PDFs, calculated either from Lagrangian floats, altimetry, or current-meter measurements. The principal aim of these studies has been to determine if the ocean velocity PDFs follow a Gaussian distribution: Are the ocean velocities locally Gaussian? And what does local mean? It has been demonstrated that lateral and temporal inhomogenities can lead to non-Gaussian distributions, with exponential-like tails, but can we consider these deviations from Gaussian behavior to arise because of geographical and temporal data integration? Or the exponential tails are indeed an intrinsic property of the ocean velocity PDFs? In this work we address these questions using the ocean velocities as inferred from the Argo floats. We show that Gaussian ocean velocity behavior depends largely on the area considered, both its size and location. To test the hypothesis of local Gaussian behavior beyond the empirical data, we have developed a model that allows reproducing the empirical PDF with reasonably good accuracy; the model considers the ocean velocities to be locally Gaussian, and assumes that non-Gaussian behavior is due to lateral and temporal residual inhomogeneity. On the basis of the work made by Conrad(2004) and Lyon (2013), we launch the hypothesis that the Gaussian ocean velocities distribution arises from the dissipation processes in the ocean, where the gain of entropy is maximized by the Gaussian distribution of the ocean velocities, being the Gaussian PDF the distribution that gives the highest entropy for a finite domain and a given standard deviation

IV Congress of Marine Sciences, Encuentro de la Oceanografía Física Española (EOF 2014), 11-13 June 2014, Las Palmas de Gran Canaria.-- 1 page

Peer Reviewed