I have always thought that the mix of frequencies provides hints as to what is going on.
Incidentally, I ran another wavelet scalogram and the amount of agreement between model and data looks on the surface quite remarkable. There are no breaks in the model, and so did not have to invoke a "climate shift" event to any point in the time series, like I did earlier at 1980
A wavelet scalogram is like a diffraction pattern in that it is a concise way of showing all the contributing frequencies. But it does take time and practice before one can start to intuitively interpret the meaning at a glance.
I still need to find a metric when applied to the scalogram quantifies this agreement.