General Circulation Models are the driving force behind human caused climate change, yet climate modellers don’t even understand the flaws and limits of their own systems.
One of the greatest advances in computers has been in simulations. We have flight simulators, sea vessel simulators, and even computer games that are very realistic. So why wouldn’t climate simulations be just as good?
Just about everything we model with computers are, well, simple. They follow the simple known laws of physics. The problem for climate models is the nature of the climate isn’t simple. It isn’t linear. In fact, it is governed by Chaos Theory.
“Unpredictability: Because we can never know all the initial conditions of a complex system in sufficient (i.e. perfect) detail, we cannot hope to predict the ultimate fate of a complex system. Even slight errors in measuring the state of a system will be amplified dramatically, rendering any prediction useless. Since it is impossible to measure the effects of all the butterflies (etc) in the World, accurate long-range weather prediction will always remain impossible.”
In the film Jurassic Park, Malcolm (played by Jeff Goldblum) talked about chaos theory when he let water run down Dr Sattler’s hand.
Essentially, small unpredictable initial conditions can radically alter the outcome of the simulation. That in turn means computer simulations of the climate system simply are wrong with any predictions they make for the simple reason we cannot model the chaos. Part of the reason is we can never know all conditions, all the components, which make up the climate system (for example this) and every day we find new forcings in the climate which are not modelled (for example here and here).
Watch the Weather Channel’s predictions a week in advance and take note of the weather. Then watch as that specific day gets closer and take note how their predictions change. Not even on that day can they get it right, even though you see that day’s prediction change as it gets closer.
What the climate modellers are doing is updating their simulations as new data, they didn’t predict would happen, happens.
Climate modellers know this, yet they are so confident their predictions 100 years in the future will happen. Like this:
How Western Canada glaciers will melt away
B.C., Alberta glaciers will shrink 70% by 2100
This was based on a ten year long study where they modelled the climate of the past, then used that to predict the future. Note the confidence of these researchers in their models to use the word “will” as opposed to “might”. It is frustrating that taxpayer money funds this kind of complete nonsense.
What is interesting is these guys predict things so far into the future that no one living today will be around to take them to task for getting their predictions so very wrong.
There have been a number of studies done on the accuracy of climate models. For example this:
A new paper published in Global and Planetary Change finds that IPCC climate models are unable to reproduce either the El Nino Southern Oscillation [ENSO] or the Indian summer monsoon, the two most influential natural weather patterns on Earth, both of which have large effects upon global climate. The authors therefore caution that, given these large uncertainties of natural variation, current models cannot be relied upon to project future global warming from greenhouse gases. According to the authors, “More research in improving the current day simulations, improving model capacity to simulate better by improving the greenhouse gases (GHG) and aerosols in the models are some of the important and immediate steps that are necessary.”
Climate modelling of CO2’s affects on the climate is referred to the sensitivity of the climate to CO2. There is a wide range of what that sensitivity is in these models. Some make the claim that it could be as much as 4C with a doubling of CO2. The worse case, and least likely, are almost always used by the alarmists when they want to make their case.
Yet many studies claim that the sensitivity is very low. See here, here and here.
Robert G. Brown at the Duke University Physics Department said this in a post on the WattsUpWithThat blog:
It would take me, in my comparative ignorance, around five minutes to throw out all but the best 10% of the GCMs (which are still diverging from the empirical data, but arguably are well within the expected fluctuation range on the DATA side), sort the remainder into top-half models that should probably be kept around and possibly improved, and bottom half models whose continued use I would defund as a waste of time. That wouldn’t make them actually disappear, of course, only mothball them. If the future climate ever magically popped back up to agree with them, it is a matter of a few seconds to retrieve them from the archives and put them back into use.
Of course if one does this, the GCM predicted climate sensitivity plunges from the totally statistically fraudulent 2.5 C/century to a far more plausible and stillpossibly wrong ~1 C/century, which — surprise — more or less continues the post-LIA warming trend with a small possible anthropogenic contribution. This large a change would bring out pitchforks and torches as people realize just how badly they’ve been used by a small group of scientists and politicians, how much they are the victims of indefensible abuse of statistics to average in the terrible with the merely poor as if they are all equally likely to be true with randomly distributed differences.
A more recent post on WUWT went even further. Pat Frank has been trying for two years to get a science paper published that tears climate models apart. Reviewers of his paper kept rejecting it, no surprise, not because Frank’s arguments were wrong, but because the climate modellers who reviewed his paper don’t understand their own climate model flaws and limitations.
Frank’s argument is mostly around the propagation of errors:
Error propagation is a standard way to assess the reliability of an experimental result or a model prediction. However, climate models are never assessed this way.
Panel A shows what the numerous climate models predict the future “will” be. Panel B shows what the error of those predictions would be once it was allowed to propagate. Even though the climate alarmists are claiming up to 4C in 100 year increase, Frank says:
Typical error bars for CMIP5 climate model projections are about ±14 C after 100 years and ±18 C after 150 years.
He notes that:
All of the physical sciences hew to these standards. Physical scientists are bound by them.
Climate modelers do not and by their lights are not.
I will give examples of all of the following concerning climate modelers:
- They neither respect nor understand the distinction between accuracy and precision.
- They understand nothing of the meaning or method of propagated error.
- They think physical error bars mean the model itself is oscillating between the uncertainty extremes. (I kid you not.)
- They don’t understand the meaning of physical error.
- They don’t understand the importance of a unique result.
Bottom line? Climate modellers are not scientists. Climate modelling is not a branch of physical science. Climate modellers are unequipped to evaluate the physical reliability of their own models.
Basically, this means the BILLIONS of dollars spent on ever more powerful super computers has been an effort in futility. Chris Essex at the University of Western Ontario noted in his book TAKEN BY STORM, that computers have a fundamental limitation in how they compute. Part of the problem is they must round their numbers. This rounding error can have a profound impact on the computer program’s ability to give predictions. The same computer code run on different super computers can give different results, thus exposing the limitations of computer systems.
This rounding error is in addition to the errors noted by Frank above. See here.
Climate models from the 1980’s definitely did not predict the current pause in global average temperature so far into its 19th year.
Frank’s comment in his post is worth ending on:
It’s immediately clear that climate models are unable to resolve any thermal effect of greenhouse gas emissions or tell us anything about future air temperatures. It’s impossible that climate models can ever have resolved an anthropogenic greenhouse signal; not now nor at any time in the past.
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