In my last post I created a reconstruction of global temperature over 350 years which I show below:

And as I said in the article, I was confused why the UAH 6 (red) had not warmed as much as the Central England Temperature (CET). I thought about it and it seemed to me that there was some factor affecting CET which in some way was being averaged out by the far more global UAH, which implied that some parts of the world were more like CET and had been warmer and some must have been a lot cooler so that the average overall was lower. At first I assumed this might be a northern-southern hemisphere. So, I played around with a few graphs on WoodForTrees.org and eventually produced this:

Here we can see that the actual global temperature from UAH is the lowest of the bunch, which is not that different from HADCRUT global south which is mostly ocean, next is Hadcrut north which is more land and the highest is CRUTEM north which is land only from the northern hemisphere. Continue reading →

Recently there’s been a couple of times I needed a global temperature but for obvious reasons I do not think it would be ethical to use reconstructions that I feel are fraudulent. This has left only UAH6 (which is the best impartial temperature we have in terms of the modern era from ~1980. Before that we now have a host of literally fabricated graphs that consistently add warming.
However, back in 1981, the guys at NASA were still in two minds about whether the world was warming or cooling. As such, whilst the data may have inherent biases like urban heating, it doesn’t necessarily have cherry picking of stations and processing techniques to manufacture an additional trend.
So, I started with a copy of Hansen et al 1981. Allegedly this is a 5 year running mean, but when I tried to match it to a similar running mean of UAH there was clearly far more variation in UAH. So, eventually I used a 20year smoothing on UAH 6.0
However, unfortunately, UAH starts in 1980 and Hansen et al finishes then, so they do not overlap. So in order to judge the the vertical offset I grabbed a copy of the Central England Temperature series. I’m not sure of the smoothing on this.
Hansen et al 1981 fits reasonable well – showing that CET is a good proxy for global temperature, but then the UAH 6.0 section shows a good start and then England is about 1C warmer between 1990 and 2009. I’ve checked several times and I can’t see anything wrong with the scaling so it appears to be genuine. It still needs work, but I’ll post it anyway in case anyone spots any obvious mistakes or has any ideas about vertical alignment.

Comment

Is it just me or does that “catastrophic global warming” scam look pretty daft when you see it shown thus. In particular – I’m struggling to reconcile is the feeling that there ought to be more warming in the last section … whilst knowing that on CET even the higher warming shown is totally unremarkable. So global temperature change shown red+black is less remarkable than not remarkable.

What’s all the fuss about?

Back in December 2014 I wrote an article entitled: “Inhofe: finally the big guns are firing!”

I guess those shells have finally landed. Today I heard this:

Climate scientist Michael Mann’s new book says climate denial is ‘driving us crazy’“This is what happens when ExxonMobil and the Koch Brothers run the show,” said Michael Mann,
….“It’s an all-out assault on Earth,”

Notice how the simple expediency of removing funding from bloated and corrupt government departments who waste their time on bogus hockey sticks, hiding the decline and fabricated graphs like this:
… becomes an assault on the whole planet. Bring out the straight jackets!
Or to use another of my Metaphors the kick from Climategate has finally reached Michael Mann’s tiny brain and it’s clear his eyes are watering:

At this point I want to again thank all the individuals both sceptic bloggers and those who supported us and made this possible. I can’t repeat this sentiment often enough:

Never before have so few achieved so much with so little … except that is the whole of science and mother nature on our side.

As expected the cuts are beginning. We’ve already heard that NASA climate is effectively closing, now we have more details at NOAA and EPA.

Trump administration to cut climate change science agency NOAA’s budget by almost 20 percent claims new report

Read more: http://www.dailymail.co.uk/news/article-4280688/NOAA-budget-cut-Trump-administration-memo-says.html

I had to share this tweet:

#Brexit & negative UKGov #renewables policies have killed industry causing mass redundancies in #Scotland. #indyref2 https://t.co/QtuAnx0ONv

— Chris Cathrine (@ChrisCathrine) March 4, 2017

Of course I will not stop trying to end the unreliables scam until every one of these bird/bat mincers has been torn down and those who pushed the scam have their names blackened as the scum of history.

The theory of developmental pathways is that technology develops not due to some spontaneous event which is often referred to in the media as a “discovery”, but because technology and learning has reached a stage where it is inevitable that it will be discovered. So, for example, whilst someone today claimed that CERN invented the internet … the fact was that other technology required a form of communication, and whilst the exact form of that communication may be down to one particular group or individual, whether or not they made their contribution it is certain that something very similar and arguably also quite possibly better would have been “invented”.
The importance of this theory is that it makes “invention” or “discovery” subject to scientific investigation. This is because we do not attribute discovery to chance, but instead to inevitability. Therefore, the theory states that in the same conditions with enough people working on it, given a reasonable time, the same invention would always have come about.
Thus if we know what the technological environment was like before a change in technology, we can determine the likely “developmental pathway” – as the most likely route by which the technology changed step by step from one state to another.
This allows us to make a science out of understanding technological development. Because rather than accepting the vague and often unsupportable claims of “inventors” (like Al Gore invented the internet), we can create hypothesis as to what conditions led to changes and use experiment to determine the most enerconic pathway

An ensemble model to sustain variability power.

Today, I discussed the fiddler dilemma in which I discussed the issues for the data fiddler as they constantly cherry pick random data leaving little randomness left. I then explained how a similar problem besets ensemble forecasting of climate. Because climate models inject variation as initial conditions – and the “variability power” diminishes over time meaning that much of the “variability space” remains unexplored by their ensemble forecasts.
However, I now realise I may have been a bit of a hypocrite. Because in the theory of developmental pathways, I have constantly suggested that there is an optimum developmental route. In part this is the reverse of the fiddler’s dilemma – physics/engineering and practicality of the technological environment means that only certain pathways are feasible. And since the physics/engineering does not change, if we can reproduce the technological environment we should be able to find an optimum route.
However, what about “natural variation”. What about (using the example I gave in the fiddler’s dilemma of navigating a city) … what about the individual who by chance goes down a technological back alley?
Hence this note … which is really saying that rather than trying to find the one single “developmental pathway”, I should consider development as a group of possibilities from an ensemble of pathways … and that I shouldn’t just consider optimums but as I suggested re climate models, I should continually allow for the continuous vibrant natural variation in the way technology develops.
In other words – yes the optimum might be one way – but sometimes the suboptimum fluke variations like micro$oft wins out.

Given a large number of meteorological stations some will be warming and some not. Thus by simply excluding cooling stations it is possible to manufacture a warming trend. Likewise, given a large number of possible ways to process data, some of those methods will create warming and some will create cooling. It is therefore also possible to manufacture a warming trend from random data by selecting the most advantageous methods to process the data.
But there is a catch – which I will call the “Fiddler’s dilemma” and this is that in order to fiddle the data this way, you need “natural variation” (ironic isn’t it – as they deny it exists) – but the more you filter the stations, or the data, or the ways to process the data, the more you lose this necessary natural variation and therefore your ability to apparently “honestly” fiddle the data.
When I started going on about the pause, I knew that sooner or later this “Fiddler’s dilemma” would strike. Because given a large set random set of meteorological stations, you could cherry pick stations to create a warming trend by constantly removing those that didn’t suit the kind of graph you wished to create.
However, as time went by, and the number of stations still left in your cherry picked set would grow smaller and smaller – but what is also very important, is that those left would be more and more like each other. In evolutionary terms, the population diversity would diminish down to almost nothing, so that like animal populations that lose genetic diversity the fiddler’s population of potential fiddles loses variability and becomes susceptible to chance adverse conditions.
So, whilst a huge population of available stations and techniques to process the data allows many different ways to fiddle a warming trend, if you use this approach, sooner or later the very act of weeding out those that don’t fit your preconceived ideas means that sooner or later it becomes impossible to keep the fiction going because the cherry picking data fiddler gets rid of the very variability they need for their scam.

An Example

Continue reading →

For fun, I decided to see how well an inverted ENSO graph fitted with itself and find out whether this fit was optimised at any specific lag. One pattern (writing shown correctly) is dragged back in time to find whether the current period is matched by previous years.
If there is a match between a current El Nino and a previous La Nina it will be shown as a match in the upper half of the graph. If there is a match between a current La Nina and a previous El nino, it will be shown by a match in the bottom half of the graph at the lag.
I think there is a match in the lower half at around 1.2 years – and to highlight this I’ve paused the graphic temporarily. Around 70% of strong El Ninos are a match for a corresponding La Nina 1.2 years laters. This lag suggests a 70% chance a La Nina will follow 1.2 years after a (strong) El Nino.
There is also a match in the top half at around 2 years suggesting an El Nino tends to follow about 2years after a (strong) La Nina. Mayby 80% of strong La Ninas are followed by a El Nino 2 years later. Finally I tried to match the current pattern of El Ninos with previous ones. There might be a very weak match at 5.5years.
Given the El Nina peaked around December 2016, this suggests that there’s around a 70% chance of another La Nina peaking around February 2018. Note, this may sound high, but if La Nino and La Nina are equally probable, there’s a 50% (1 in 2) chance of having a La Nina at any point. So, the predictability of 2 in 3 – or that La Nina is twice as likely as El Nino – is only fractionally better than pure chance. The pattern is too chaotic to predict with any confidence over longer periods.