The replication of the highly influential Rahmstorf 2007 A Semi-Empirical Approach to Sea Level Rise, one of the main sources of projected sea level rise, was reported in the previous post.

In a now discredited (and disowned) Rahmstorf et al 2007 publication, Steve McIntyre showed that Rahmstorf had pulled an elaborate stunt on the community by dressing up a simple triangular filter with “singular spectrum analysis” with “embedding dimensions”, I can now report another, perhaps even more spectacular stunt.

His Figure 2 is crucial, as it is where the correlation between the rate of sea level increase, deltaSL, and the global temperature, Temp, is established. If these were not correlated, then there would be no basis for his claims of a significant “acceleration” in the increase in sea level when temperature increases, and his estimates of sea level rise by 2100 would not be nearly so high.

It is well known that smoothing introduces spurious autocorrelations into data that can artificially inflate correlations, and one of the comments on his paper (attached to the first link above) picked up on this. Rahmstorf’s procedure introduces no less than 5 different types of smoothing to produce his Figure 2:

1. singular spectrum analysis – the first EOF

2. he then pads the end of the series with a linear extrapolation of 15 points

3. convolution, (or 15 point filtering)

4. calculates the linear trend from 15 points (on the sea level data only)

5. binning of size 5

I replicated his procedure in the previous post in the series. Here, the entire procedure is substituted with a single binning (averaging each successive M data points). The figure below compares the Rahmstorf procedure at parameters m=13:16 (red line), and the result of binning the same data into bins of size m=13:16 (black line). The sea level data is differenced after binning to get a delta SL.

Continue reading Smooth Operator