Image Credit: Climate Data Blog
By Richard Linsley Hood – Edited by Just The Facts
The goal of this crowdsourcing thread is to present a 12 month/365 day Cascaded Triple Running Mean (CTRM) filter, inform readers of its basis and value, and gather your input on how I can improve and develop it. A 12 month/365 day CTRM filter completely removes the annual ‘cycle’, as the CTRM is a near Gaussian low pass filter. In fact it is slightly better than Gaussian in that it completely removes the 12 month ‘cycle’ whereas true Gaussian leaves a small residual of that still in the data. This new tool is an attempt to produce a more accurate treatment of climate data and see what new perspectives, if any, it uncovers. This tool builds on the good work by Greg Goodman, with Vaughan Pratt’s valuable input, on this thread on Climate Etc.
Before we get too far into this, let me explain some of the terminology that will be used in this article:
“In signal processing, a filter is a device or process that removes from a signal some unwanted component or feature. Filtering is a class of signal processing, the defining feature of filters being the complete or partial suppression of some aspect of the signal. Most often, this means removing some frequencies and not others in order to suppress interfering signals and reduce background noise.” Wikipedia.
A Gaussian Filter is probably the ideal filter in time domain terms. That is, if you consider the graphs you are looking at are like the ones displayed on an oscilloscope, then a Gaussian filter is the one that adds the least amount of distortions to the signal.
Full Kernel Filter:
Indicates that the output of the filter will not change when new data is added (except to extend the existing plot). It does not extend up to the ends of the data available, because the output is in the centre of the input range. This is its biggest limitation.
Low Pass Filter:
A low pass filter is one which removes the high frequency components in a signal. One of its most common usages is in anti-aliasing filters for conditioning signals prior to analog-to-digital conversion. Daily, Monthly and Annual averages are low pass filters also.
A cascade is where you feed the output of the first stage into the input of the next stage and so on. In a spreadsheet implementation of a CTRM you can produce a single average column in the normal way and then use that column as an input to create the next output column and so on. The value of the inter-stage multiplier/divider is very important. It should be set to 1.2067. This is the precise value that makes the CTRM into a near Gaussian filter. It gives values of 12, 10 and 8 months for the three stages in an Annual filter for example.
Triple Running Mean:
The simplest method to remove high frequencies or smooth data is to use moving averages, also referred to as running means. A running mean filter is the standard ‘average’ that is most commonly used in Climate work. On its own it is a very bad form of filter and produces a lot of arithmetic artefacts. Adding three of those ‘back to back’ in a cascade, however, allows for a much higher quality filter that is also very easy to implement. It just needs two more stages than are normally used.
With all of this in mind, a CTRM filter, used either at 365 days (if we have that resolution of data available) or 12 months in length with the most common data sets, will completely remove the Annual cycle while retaining the underlying monthly sampling frequency in the output. In fact it is even better than that, as it does not matter if the data used has been normalised already or not. A CTRM filter will produce the same output on either raw or normalised data, with only a small offset in order to address whatever the ‘Normal’ period chosen by the data provider. There are no added distortions of any sort from the filter.
Let’s take a look at at what this generates in practice.The following are UAH Anomalies from 1979 to Present with an Annual CTRM applied:
Fig 1: UAH data with an Annual CTRM filter
Note that I have just plotted the data points. The CTRM filter has removed the ‘visual noise’ that a month to month variability causes. This is very similar to the 12 or 13 month single running mean that is often used, however it is more accurate as the mathematical errors produced by those simple running means are removed. Additionally, the higher frequencies are completely removed while all the lower frequencies are left completely intact.
The following are HadCRUT4 Anomalies from 1850 to Present with an Annual CTRM applied:
Fig 2: HadCRUT4 data with an Annual CTRM filter
Note again that all the higher frequencies have been removed and the lower frequencies are all displayed without distortions or noise.
There is a small issue with these CTRM filters in that CTRMs are ‘full kernel’ filters as mentioned above, meaning their outputs will not change when new data is added (except to extend the existing plot). However, because the output is in the middle of the input data, they do not extend up to the ends of the data available as can be seen above. In order to overcome this issue, some additional work will be required.
The basic principles of filters work over all timescales, thus we do not need to constrain ourselves to an Annual filter. We are, after all, trying to determine how this complex load that is the Earth reacts to the constantly varying surface input and surface reflection/absorption with very long timescale storage and release systems including phase change, mass transport and the like. If this were some giant mechanical structure slowly vibrating away we would run low pass filters with much longer time constants to see what was down in the sub-harmonics. So let’s do just that for Climate.
When I applied a standard time/energy low pass filter sweep against the data I noticed that there is a sweet spot around 12-20 years where the output changes very little. This looks like it may well be a good stop/pass band binary chop point. So I choose 15 years as the roll off point to see what happens. Remember this is a standard low pass/band-pass filter, similar to the one that splits telephone from broadband to connect to the Internet. Using this approach, all frequencies of any period above 15 years are fully preserved in the output and all frequencies below that point are completely removed.
The following are HadCRUT4 Anomalies from 1850 to Present with a 15 CTRM and a 75 year single mean applied:
Fig 3: HadCRUT4 with additional greater than 15 year low pass. Greater than 75 year low pass filter included to remove the red trace discovered by the first pass.
Now, when reviewing the plot above some have claimed that this is a curve fitting or a ‘cycle mania’ exercise. However, the data hasn’t been fit to anything, I just applied a filter. Then out pops some wriggle in that plot which the data draws all on its own at around ~60 years. It’s the data what done it – not me! If you see any ‘cycle’ in graph, then that’s your perception. What you can’t do is say the wriggle is not there. That’s what the DATA says is there.
Note that the extra ‘greater than 75 years’ single running mean is included to remove the discovered ~60 year line, as one would normally do to get whatever residual is left. Only a single stage running mean can be used as the data available is too short for a full triple cascaded set. The UAH and RSS data series are too short to run a full greater than 15 year triple cascade pass on them, but it is possible to do a greater than 7.5 year which I’ll leave for a future exercise.
And that Full Kernel problem? We can add a Savitzky-Golay filter to the set, which is the Engineering equivalent of LOWESS in Statistics, so should not meet too much resistance from statisticians (want to bet?).
Fig 4: HadCRUT4 with additional S-G projections to observe near term future trends
We can verify that the parameters chosen are correct because the line closely follows the full kernel filter if that is used as a training/verification guide. The latest part of the line should not be considered an absolute guide to the future. Like LOWESS, S-G will ‘whip’ around on new data like a caterpillar searching for a new leaf. However, it tends to follow a similar trajectory, at least until it runs into a tree. While this only a basic predictive tool, which estimates that the future will be like the recent past, the tool estimates that we are over a local peak and headed downwards…
And there we have it. A simple data treatment for the various temperature data sets, a high quality filter that removes the noise and helps us to see the bigger picture. Something to test the various claims made as to how the climate system works. Want to compare it against CO2. Go for it. Want to check SO2. Again fine. Volcanoes? Be my guest. Here is a spreadsheet containing UAH and a Annual CTRM and R code for a simple RSS graph. Please just don’t complain if the results from the data don’t meet your expectations. This is just data and summaries of the data. Occam’s Razor for a temperature series. Very simple, but it should be very revealing.
Now the question is how I can improve it. Do you see any flaws in the methodology or tool I’ve developed? Do you know how I can make it more accurate, more effective or more accessible? What other data sets do you think might be good candidates for a CTRM filter? Are there any particular combinations of data sets that you would like to see? You may have noted the 15 year CTRM combining UAH, RSS, HadCRUT and GISS at the head of this article. I have been developing various options at my new Climate Data Blog and based upon your input on this thread, I am planning a follow up article that will delve into some combinations of data sets, some of their similarities and some of their differences.
About the Author: Richard Linsley Hood holds an MSc in System Design and has been working as a ‘Practicing Logician’ (aka Computer Geek) to look at signals, images and the modelling of things in general inside computers for over 40 years now. This is his first venture into Climate Science and temperature analysis.