Characterization of Ocean Turbulence Regimes from Satellite Observations of Sea Surface Temperatures
Isern-Fonatnet, Jordi¹; Turiel, Antonio²; Portabella, Marcos^2
1Institut Català de Ciències del Clima, SPAIN; ²Institut de Ciències del Mar (CSIC), SPAIN

Satellite infrared radiometers have unveiled the existence of distinct turbulence regimes in the upper ocean. These regimes have associated different vertical transport of nutrients, heat, and climatically important gases, between the oceanic upper layers and the ocean interior, contain information about the dynamics in the mixed layer and determine whether the Surface Quasi-Geostrophic framework can be used to diagnose sea surface velocities from Sea Surface Temperatures (SST). Besides, the existence of long time series (> 20 years) of global SST observations on the ocean enables us to investigate potential changes in upper ocean dynamics related to global warming. The main difficulty is to define descriptors able to univocally identify the various turbulence regimes and to quantify the dynamical characteristics of the upper ocean. Furthermore, the huge amount of infrared observations requires the use of descriptors to allow automated classification of observations retrieved from databases.

In this study, we have investigated the capability of different descriptors to characterize turbulence regimes from the observations of the AATSR (Envisat). First, the data have been divided into granules adapted to the observed cloud coverage with a maximum size of 512 by 512 pixels. Then, for each granule we have explored some classical statistical and geometrical properties of SST, as the study of Probability Density Functions (PDF), spectral slopes and the curvature of SST fronts. This classical approach has been completed with the analysis of the multifractal properties of SST images obtained with the singularity analysis technique, forming a complete, robust set of descriptors. It is shown that, those descriptors lead to a comprehensive classification of satellite SST imagery. Moreover, this work opens the door for operationally deriving informative variables about ocean state, which are commonly not available with remote sensing platforms such as upper ocean velocities or mixed layer properties.