  In the marine environment, many fields have fluctuations over a large range
of different spatial and temporal scales. These quantities can be nonlinear
\red{and} non-stationary, and often interact with each other. A good method to
study the multiple scale dynamics of such time series, and their correlations,
is needed. In this paper an application of an empirical mode decomposition
based time dependent intrinsic correlation, \red{of} two coastal oceanic time
series, temperature and dissolved oxygen (saturation percentage) is presented.
The two time series are recorded every 20 minutes \red{for} 7 years, from 2004
to 2011. The application of the Empirical Mode Decomposition on such time
series is illustrated, and the power spectra of the time series are estimated
using the Hilbert transform (Hilbert spectral analysis). Power-law regimes are
found with slopes of 1.33 for dissolved oxygen and 1.68 for temperature at high
frequencies (between 1.2 and 12 hours) \red{with} both close to 1.9 for lower
frequencies (time scales from 2 to 100 days). Moreover, the time evolution and
scale dependence of cross correlations between both series are considered. The
trends are perfectly anti-correlated. The modes of mean year 3 and 1 year have
also negative correlation, whereas higher frequency modes have a much smaller
correlation. The estimation of time-dependent intrinsic correlations helps to
show patterns of correlations at different scales, for different modes.
