Given the way the components of the surface temperature record extracted from the SSA (singular spectrum analysis) line up with various potential causes of climate change in the previous post here, the temptation is to latch onto series 2, and say, aha, there is the forcing due to increase in CO2. It’s the right shape, exponential. Its the right size, about 0.6C.
But looking into fractal data is like seeing pictures in clouds. Be suspicious of magic methods that pull explanations out of the air. Below I have plotted SSA decompositions of the the monthly global temperature anomaly from the HadCRU dataset from 1976 to the present, the period of most recent rise, and attributed largely to GHGs. Kind of zooming in.
The similarities to the previous post are there in the initial series, although the year ranges are different. The thing I noticed was the first two series, the main ones, have very noticeable downturns at the end. Clearly, neither of series 1 or 2 could represent a signal due to steadily increasing GHG’s, with a hook in the end like that. Perhaps the downturn in the last few years is significant, and the exponential seen in the previous decomposition from 1900 on is due to something else, or nothing, an endogeneous trendiness!
I think I need to lie down. I need to understand a lot more about the limitations of SSA before jumping to conclusions. Can SSA reliably recover exponential signals anyway? Here is were you need to start running SSA on simulated data.
For reference I plotted the first series (red line) in the figure above onto the actual temperature data (black line). The hook down really only starts in the last few years.