I’m sure that sooner or later the legal profession is going to realise that with some $trillion in value being made from this scam – that there’s an awful lot of ways they can make a huge amount of money by arguing for the “rights” and “compensations” deserved by all the many groups and companies involved.
And because it was just one enormous “snouts in the trough” rush to make money, not many cared or even knew about the massive personal liability that they have taken on. There was not careful consideration of the risks they took on – most had no idea of their own personal liability.
For example, Phil Jones – who was ruled to have broken the law in Climategate – has been personally vouching that we can all rely on him (the bloke who can’t use excel) and that based on his many years of perfect forecasting (none) that we should all spend billions on bird killers & other environmental destroyers – when even a casual look at the figures will see would be far far far far far far better spent on hospitals or other facilities EVEN IF EVERYTHING HE WAS RESPONSIBLE FOR WAS RIGHT (which it is not – kids won’t know what snow is …).
So, the last and perhaps the most ugly and vicious phase of this scam was always likely to involved the lawyers (the copy-n-paste media no longer having the balls to do investigative reporting).
And now it seems that dam has burst:
Germany: City Council Members Approving Wind Parks May Face Personal Liability For Damage To Health!
The legal winds are shifting! Many city councils and wind park planners are going to have to clean up their acts when pushing their pet wind park projects.
Germany’s Fundamental Law specifically expresses that the State is obligated to protect the life and physical body of the individual, foremost from illegal attacks by others.
Consequently, according to German legal experts Prof. Michael Elicker and Andreas Langenbahn here, city councils approving the installation of wind parks may be held personally liable to damage to health of persons who live close to them.
– See more at: http://notrickszone.com/2016/02/08/germany-city-council-members-approving-wind-parks-may-face-personal-liability-for-damage-to-health/#sthash.gDcwHFbs.dpuf
The significance is not this individual case – it is that it marks a change in the attitude of the legal profession. And those are the ones the scamsters really need to fear!
How to put this … let me put it this way: every engineer knows it is relatively easy to defend your calculations when the proverbial bridge is standing. But what is difficult is to defend your calculations when the bridge has not only fallen down – but someone has died – and some corrupt material supplier is now hiring a very expensive lawyer to pin the blame on you – and all you have left in your defence is your hastily prepared calculations written a decade ago before you even knew it was going to be built.
Likewise, it was all too easy for Phil Jones to claim the world was falling apart when he had fooled himself – and could still count of the support of every other academic to back him in saying that … by a bit of bad data that the world was falling apart. What however isn’t going to look quite so compelling in court, is defending statements that the world is going to fall and a lot of people MUST put a LOT OF MONEY into projects based on his “expertise” … when the world clearly did not fall apart, when his predictions look totally stupid if not corrupt … when he has refused to come clean, when he personally has made a lot of money off the scam … it doesn’t take a very good lawyer to spin that into a very nasty perhaps even politically inspired fraud.
This is why I keep saying academics are like lambs being taken to the slaughter – and it is why most sceptics are engineers. Because no engineer ever says “the bridge will stand up” (or to put it in climatic terms – it must fall down). Instead they will only say: based on our calculations assuming the materials are as specified, our best judgement is …”. Caution, Caution — data-defendable caution.
And what do acedemics engage in … crying wolf, time and time and time again and never stopping not even after the evidence clearly contradicts then.
And likewise – just as an engineer needs to be able to defend themselves when the bridge falls down – to put that in a legal context. The law on liability for the penalty for pushing the “global warming scandal” will not be made when everything looks rosy in “Phil Jone’s Garden”. Instead it will be made when the public mood has completely changed, when the evidence is pretty obviously contradicting Phil Jones … at a time no doubt when the public are baying for blood – baying for someone to pay back the billions of wasted money.
And for engineers who are always the first people to blame when something goes wrong – their culture impels to caution as a defence against the inevitable law suits when (usually not for the engineer’s fault) things do go wrong.
But academia is a culture which has no real concept of caution, or quality – where it is believed getting a buddy to give a nod and wink to a paper is all that is needed to make something “true” – where in short they think the worst think that can possibly happen is to have a paper withdrawn and a bit of ribbing from their in-the-same boat colleagues. So, these poor lambs, who haven’t any involvement outside academia – who thought that being an academic made this somehow immune to prosectuation – there will be a very unwelcome shock waiting around the corner in the form of those all too vicious lawyers who will make mince-meat out of them.
The importance of this latest article
OK, this latest article is starting at the edges – it’s changing the perception of liability. but it is a very important event because it shows:
- People are seriously beginning to think about suing
- That means the tide has turned and people are beginning to believe they can win a court battle for compensation.
- It can only get worse – because once one set of lawyers realise that there’s a bountiful harvest in this area of law – the idea will quickly spread.
Now the only questions left are these: how quickly will the lawyer-sharks smell blood? Will they spot the easy prey floundering in the sea and at what point will they move in for the kill? And last but not least – exactly who will they attack – because like this case – it is not necessarily the obvious victims who are the first ones to be attacked – nor in the obvious places.
Long -Term Climate Change: What Is A Reasonable Sample Size?
And the answer is fairly simple.
For a reasonable degree of certainty (~75% – but see end) One needs around 10x the length of time of data of the length of time in which we are taking a trend – and all the data must be from one homogeneous source. So, e.g. in order to assess whether the last century was abnormal, we need around a millennium of data. In order to assess whether the 1970-2000 warming was abnormal, we must compare the 1970-2000 trend in CET with the last 300-350 years in CET. And the longest period in the raw instrumental dataset of ~160years that can be assessed for abnormality would be around 16years.
So, if you hear anyone say “the pause is ‘abnormal'” – that might supportable (but is not). If however, they say “the last 30 years is abnormal” or even “the last 100 years” – they are either stupid, fraudulent or insane.
However, it all falls apart if we start comparing apples with cheese: tree rings for 1000 years with bogus fraudulent surface data for 30 years. Becasuse, for example, if we look at what kind of change is normal over the last 1000 years in the tree ring data – then we must compare it the same data for the last 100 years. But in contrast – the whole “hide the decline” scandal, was that, not only weren’t they comparing tree ring data with tree rings, but they knew that the tree ring data showed entirely the opposite trend from that they were stating to have been shown to be “abnormal”. With fraudulent behaviour there is no way to make their assertions credible by merely adding to the (bogus) data.
However, as 10x the data is problematic when we need quicker indications, I would suggest that we can get a “more likely than not” indication for 3x the period. But now it is critical that those doing the assessment come from the right background (which means tried-and-tested-engineering and not woolly-pc-panic-stricken-by-any-change academia.
So, e.g. with 160 years of data, we (engineers) can start saying with a modest certainty that if the last 50 years showed warming that had not been seen before in the last 160 years, then something was odd. But, just to show how ridiculous that assertion would be, even using the bogus upjusted data, the 1970-2000 period shows the same warming as 1910-1940. So, there is no indication of any abnormality with the global temperature (despite the known upjusting which in itself tells us just how normal the present period is – that even fraudulent changes can’t change it enough to make it abnormal).
The rational for long periods of data:
Until we know what is normal we cannot know what is abnormal
To take a simple example, we have two flight computers on a space craft – one says “full throttle”, the other “cease throttle”. How do we decide which is correct? The answer is that unless we have additional information there is no way to even guess which is correct.
If however, we have three computers, two say “cease throttle” and one says “full throttle”, then all other things being equal, then if the chance of any computer being wrong is p. Then the chance of two being wrong is p2
So, the chance of two computers being wrong as opposed just one is p2/p. So as p<1, then irrespective of the actual value of p, it’s always more likely than the minority is abnormal.
Likewise, at the very least, we need three centuries/decades of data to even start guessing which decade is “abormal”.
So, why 10x the length of data? The reasons are many:
The rule of 10
In essence, this rule simply means we need an awful lot more data than we think we need by “academic” statistics – because the real world is full of real people who just don’t think in the way needed for “academic” statistics to be valid.
The biggest problem is that we usually start looking at data when something “odd” appears. (Or to put it another way we ignore data where nothing odd appears) And, by pure chance, if we continue to monitor data, for long enough or from enough different sources, sooner of later by pure fluke, we will see an odd “event”.
As such, when we start assessing the risk of something like “climate change” we are not just picking data at random. Instead we have already “cherry picked” a period which appears odd. So, by pure probability, if we monitored 100 metrics, one of of those 100, should have a signal that only occurs 1/100 of the time in that signal. In that case, we would need 100x the data before that 1/100 signal would be within a sample where it was likely to occur. (but even then another such event should have occurred, so there is twice the probability of this event than would occur by pure chance – just because we only focussed on something that appeared a problem!)
But the untrained human factor gets worse
But, even with simple data, there are so many ways to take the same metric and suggest “abnormality”. Taking temperature, we can for example look for “hottest” and “coldest” (2). Also “faster warming” and “fastest cooling” (2). Then we have the possibilities of turning points(2) and cycles(>2)** – all of which can be construed as “odd”. So, even with simple data, there are around 10 different ways to see something “odd”.
So, quite contrary to what the statistics supposedly suggest, it is actually “normal” to see a 1-in-100 year event in a decade of temperature. It is also normal to see a 1-in-1000 year event in a century of data. So, if you are just looking for something “odd”, in around 10 different metrics, the chances are you will see a 1-in-millennium “event” every decade!!
The human factor
So, if we are intent on finding something “odd” in even one dataset, the chances are quite high and that is why we need long time series. If however, we have a host of datasets (floods, droughts, snow, temperature, rain, hurricanes, peak-rain, peak-wind, peak, rain, etc. etc.), then if we are allowed to cherry pick as academics have done, then we are guaranteed to find something abnormal.
This is why one needs to be properly trained in engineering practices to do risk assessments. Because the biggest quality failing of risk assessment is the idiot doing the risk assessment – and particularly if they don’t come from a culture used to doing risk assessments and living the with result of either overstating risk or under-stating it.
So, even with the best of intentions, and even with 10x the data of the period being assessed, the best we might say is that there is “more chance than not” of some data being abnormal, if (as has happened) you have politically motivated groups free to scour the data and worse – free to channel resources – with the intention of finding “something wrong”.
If however, you have people trained in risk assessment from a suitable culture, who know the temptation to cherry pick data and have the training, experience AND culture to resist, then the the certainty with 10x data can rise as high as perhaps 90% confidence. (Note the idiots at the IPCC have stated 95% confidence, about a period equivalent to the length of their whole dataset – so whilst they have no idea & no data to say what is normal – they are 95% sure that what they have is abnormal).
**To explain, if we accept a turning point is simply a variant of a sin-wave (with period twice the sample length), then a trend is a variant of a cos-wave. Then a simple cycle (up-down-up-down) is just twice the frequency. However, if the probability of this is half as high (there being twice as much “info”), then if we sum the total series, the probability of the total is around 1. However, because we are looking for things “happening” … we can often accept cycles that only appear later in the data. So, there is quite a high chance of seeing something “odd” in the form of an apparent cycle.