Synergy among ocean scalars usign their turbulent singularity structure

Abstract:

Despite of the differences in the equations governing the dynamics of different ocean scalars (as SSH, SST, SSS, chlorophyll concentration, or sea roughness), visual inspection of remote sensing maps of these different ocean scalars reveals the existence of many common features. They always correspond with dynamic structures of the oceanic flow, such as eddies, filaments or upwelling areas. That consistent correspondence may be explained by the action of flow advection, which stretches and shears scalars along the most energetic streamlines.

Horizontal advection does not only shape structures in scalars. Eddy transport and filament advection carry long away water masses with relatively homogeneous scalar properties, especially at areas of large Eddy Kinetic Energy. The signature of those water masses can be recognised as specific functional relations among the variables when joint histograms are computed. The repeated observation of particular arrangements of values of scalars allows identifying those water masses, similarly to what oceanographers do with T-S diagrams of vertical profiles. But processes at surface are more energetic than in the water column; hence, the value of scalars evolve very fast and there is not a permanent rule linking them, although for a given period one such relation may hold for a specific region.

The introduction of singularity analysis has meant a significant advancement in the description and understanding of the dynamic relations at ocean surface. Using singularity analysis, the Singularity Exponents (SE) of any scalar field can be calculated. SE are dimensionless variables linked to the structure of the underlying surface flow and not to the specific properties of the scalar from which they are derived. It has been shown that SE from different remote sensing scalars have very close values. This correspondence can be used to increase the signal to noise ratio of a given scalar, and even to extrapolate it into data gaps, using a different variable as a template of SE.

In this presentation we will show that SE are linked to the dynamics of ocean quasi-2D turbulence. We will also discuss on the dynamic interpretation of SE and how much we can know from currents, horizontal dispersion, etc using SE derived from remote sensing scalars. Finally, some examples of application are illustrated.