  Adaptive time series analysis has been applied to investigate variability of
CO2 concentration data, sampled weekly at Mauna Loa monitoring station. Due to
its ability to mitigate mode mixing, the recent time varying filter Empirical
Mode Decomposition (tvf-EMD) methodology is employed to extract local
narrowband oscillatory modes. In order to perform data analysis, we developed a
Python implementation of the tvf-EMD algorithm, referred to as pytvfemd. The
algorithm allowed to extract the trend and both the six month and the one year
periodicities, without mode mixing, even though the analysed data are noisy.
Furthermore, subtracting such modes the residuals are obtained, which are found
to be described by a normal distribution. Outliers occurrence was also
investigated and it is found that they occur in higher number toward the end of
the dataset, corresponding to solar cycles characterised by smaller sunspot
numbers. A more pronounced oscillation of the residuals is also observed in
this regard, in relation to the solar cycles activity too.
