Guest Essay by Geoffrey H Sherrington
This short note was inspired by Viscount Monckton writing on 13th June on WUWT about the leveling of global temperatures since 1997 or so, and the increasing mismatch with a number of climate models.
This graph by Drs Christy & Spencer of UAH is referenced in Anthony’s article ‘The Ultimate “Sceptical Science Cherry Pick’ of 10th June and will be referred to as ‘the spaghetti graph’.
In the Viscount Monckton article there was a significant contribution by rgbatduke now elevated to a post titled The “ensemble” of models is completely meaningless, statistically whose comments can be read with reference to this spaghetti graph (with thanks to the authors of it).
Basically rgbatduke argued that model methods to create spectra for chemical elements such as carbon had limitations; that there was a history of improvement of models; the average of such successive models was meaningless; they did not succeed without some computational judgment; and even then, they were not as good as the measured result. The same comments should be applied to the various climate model comparisons shown in the spaghetti graph, particularly the meaningless average. Climate models were stated to be far more complex than atomic spectral calculations.
Here is another model graph, one from my files.
The numerical raw data are here, with some early stage statistics derived from the raw data. I make no claims about the number of significant figures carried, the distributions of data, etc. The purpose of this essay is to invite your comments.
The graph has time on the X-axis. For this exercise, the interval does not matter except to note that data are equally spaced in time. The Y-axis has a dimensionless model score and is integral. It can increment by 0, 2 or 4 units at a time (like ‘no change= 0’, ‘some change =2 units’, much change = 4 units.’) The dark dots are the arithmetic average of each time slice.
You will see that the first part only of the graph is shown. The object of the exercise is to use the information content of the shown data, to calculate projections of each of the series out to 23 time spans. The correct answer, as derived from experiment, is known. It is with the WUWT team.
If, as you work, you seek more information, then please ask. No reasonable requests refused.
If, as you work, you feel you know the source of the data, please don’t tell the others.
Finally, what is the purpose of all of this? Answer is, to try to emulate a simple climate model projection. I do not know if all climate models follow the same routines to get from start to finish, whether they calculate year by year, similar to this example, or if the whole lot goes into a series of matrixes that are solved one after another.
However, consider that for this exercise you have 18 models that have each yielded 20 years of data. Let us all see how well you can project to end of year 23. In a week I’ll post the full graph and full set of figures, then I’ll make some comments on your methods of solving this problem.
As will, I hope, a few others.
Repeating: The exercise is to project the data to the end of time period 23.
That’s 3 more slots.