There are some posts on Watts Up With That (WUWT) that make me realise the merit of the phrase a little knowledge can be a dangerous thing. There is a recent post by someone called R.J. Salvador called Sunspot cycle and the global temperature change anomaly. In this post there are some fairly impressive figures showing a fit to the global temperature anomaly data and a projection into the future suggesting that we are about to enter a phase of cooling.
So, how is this done? Well there is a formula
Each term is calculated by month and added to the prior month’s calculation. The summation stores the history of previous temperature changes and this sum approximates a straight line relationship to the actual Global Temperature Anomaly by month which is correlated by the constants d and e.
Okay, so let’s see. You have a formula that you make up that has 5 unknown constants and has a cosine and power-law dependence on some function of time, SN. You have a data set – the global temperature anomaly. You choose your constants by fitting your function to the known dataset. Okay, I can believe that this has been done correctly. However, it doesn’t mean anything. I could have probably used almost any time series I liked and, with 5 parameters and a suitable choice of function, fitted to the global surface temperature anomaly data. There’s is no physics here whatsoever. It really doesn’t tell us anything at all. It does not mean that the rise in the global temperature anomaly has been driven by the Sun.
The post then goes on to use the Dalton Minimum (a period of low sunspot numbers during the 1800s) to project the analysis forward to 2044 and predict that global temperatures will drop by about 0.6 degrees. Really, there is a suggestion that we may have reduced sunspot numbers in the future, but it is very uncertain. To be fair, the assumptions and analysis have been very clearly defined. I can’t fault the post in that regard. I could, however, probably have done exactly the same analysis using number of cars sold per month as my time series. I could have produced quite a good fit to the global temperature anomaly data and then predicted (based on future car sales) that global temperatures will continue to rise and that it would be due to our increasing use of cars. It would, however, have been equally meaningless. You can’t simply choose some complicated function with a large number of parameters to make predictions about the future evolution of something. You need to base your model on the underlying physics (and chemistry) that is driving the changes in whatever it is that you’re measuring.