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About this site

This site is produced by Mike Haseler AKA Scottish Sceptic

About the "forecast"

If we examines records of temperature like that of the Central England Temperature series (shown right), we will find that climate varies naturally and that the variation present is what is known as "flicker noise" in reference to the similarity of the light intensity of a candle which tends to grow and diminish over time.

This kind of natural variation has the characteristic that long-term changes are larger than shorter-term and for typical flicker noise the relationship is that the size of the variation increases proportionally to the periodicity of the change. Or inverting, the size is equal to 1/f. This is why it is also referred to has 1/f type noise, although more accurately as 1/fn noise where m varies between 0 to 2.

The primary purpose of this site (so far) is to show what pure 1/f noise looks like and so it has been presented humorously on the front page as a "97% accurate" prediction. Indeed like all climate academics I am so confident in these predictions that if they aren't all entirely correct at the end of 100 years then I will gladly run a marathon naked, swim the Forth wearing a pink tutu and then climb Ben Nevis singing "I'm a teapot".


1/f climate "forecast"

Technical details

There are 1024 data points in the plot but as the plot is only shown 500pixels wide there are around 4-500 discrete points. The 1/f noise is produced using 10 random number generators (giving integers -100 to 100) whereby the the first changes every 2nd point, the second every 4th, the third every 8th, etc. The random number generators are then summed and scaled to give a value from -2 to +2 - although the number seldom go above 1.

The decadal graph is produced by a rolling average equal to 0.05 x datan + .05 x previous. If α is 0.05, this equates to a moving average of (2/α)-1 points. If we take the graph as being a century, with around 400 points across, then the filter parameters used correspond roughly to a moving average filter of 10 years.

You should be able to spot the following typical characteristics of 1/f noise:


Probability distribution of "forecast"

Probability distributions

I've plotted tow simple distributions of the values of the "forecast" split in equal buckets. The first shows the first half of the series of data points and the second is the second half.

This is the main purpose of the site! Which is to try to understand the statistics of 1/f type noise. In theory, the second distribution should be more likely to be offset higher or lower. This shows how this 1/f noise can appear to have "trends" when in fact all that is happening is natural variation.

However the bigger questions are these:


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