Guest Post by Willis Eschenbach
David Rose has posted this , from the unreleased IPCC Fifth Assessment Report (AR5):
‘ECS is likely in the range 1.5C to 4.5C… The lower limit of the assessed likely range is thus less than the 2C in the [2007 report], reflecting the evidence from new studies.’ SOURCE
I cracked up when I read that … despite the IPCC’s claim of even greater certainty, it’s a step backwards.
You see, back around 1980, about 33 years ago, we got the first estimate from the computer models of the “equilibrium climate sensitivity” (ECS). This is the estimate of how much the world will warm if CO2 doubles. At that time, the range was said to be from 1.5° to 4.5°.
However, that was reduced in the Fourth Assessment Report, to a narrower, presumably more accurate range of from 2°C to 4.5°C. Now, however, they’ve backed away from that, and retreated to their previous estimate.
Now consider: the first estimate was done in 1980, using a simple computer and a simple model. Since then, there has been a huge, almost unimaginable increase in computer power. There has been a correspondingly huge increase in computer speed. The number of gridcells in the models has gone up by a couple orders of magnitude. Separate ocean and atmosphere models have been combined into one to reduce errors. And the size of the models has gone from a few thousand lines of code to millions of lines of code.
And the estimates of climate sensitivity have not gotten even the slightest bit more accurate.
Can anyone name any other scientific field that has made so little progress in the last third of a century? Anyone? Because I can’t.
So … what is the most plausible explanation for this ludicrous, abysmal failure to improve a simple estimate in a third of a century?
I can give you my answer. The models are on the wrong path. And when you’re on the wrong path, it doesn’t matter how big you are or how complex you are or how fast you are—you won’t get the right answer.
And what is the wrong path?
The wrong path is the ludicrous idea that the change in global temperature is a simple function of the change in the “forcings”, which is climatespeak for the amount of downward radiation at the top of the atmosphere. The canonical (incorrect) equation is:
∆T = lambda ∆F
where T is temperature, F is forcing, lambda is the climate sensitivity, and ∆ means “the change in”.
I have shown, in a variety of posts, that the temperature of the earth is not a function of the change in forcings. Instead, the climate is a governed system. As an example of another governed system, consider a car. In general, other things being equal, we can say that the change in speed of a car is a linear function of the change in the amount of gas. Mathematically, this would be:
∆S = lambda ∆G
where S is speed, G is gas, and lambda is the coefficient relating the two.
But suppose we turn on the governor, which in a car is called the cruise control. At that point, the relationship between speed and gas consumption disappears entirely—gas consumption goes up and down, but the speed basically doesn’t change.
Note that this is NOT a feedback, which would just change the coefficient “lambda” giving the linear relationship between the change in speed ∆S and the change in gas ∆G. The addition of a governor completely wipes out that linear relationship, de-coupling the changes in gas consumption from the speed changes entirely.
The exact same thing is going on with the climate. It is governed by a variety of emergent climate phenomena such as thunderstorms, the El Nino/La Nina warm water pump, and the PDO. And as a result, the change in global temperature is totally decoupled from the changes in forcings. This is why it is so hard to find traces of e.g. solar and volcano forcings in the temperature record. We know that both of those change the forcings … but the temperatures do not change correspondingly.
To me, that’s the Occam’s Razor explanation of why, after thirty years, millions of dollars, millions of man-hours, and millions of lines of code, the computer models have not improved the estimation of “climate sensitivity” in the slightest. They do not contain or model any of the emergent phenomena that govern the climate, the phenomena that decouple the temperature from the forcing and render the entire idea of “climate sensitivity” meaningless.
w.
PS—I have also shown that despite their huge complexity, the global temperature output of the models can be emulated to a 98% accuracy by a simple one-line equation. This means that their estimate of the “climate sensitivity” is entirely a function of their choice of forcings … meaning, of course, that even on a good day with a following wind they can tell us nothing about the climate sensitivity.
Discover more from Watts Up With That?
Subscribe to get the latest posts sent to your email.
Beware of the “climate automatons” and their latest hope, ocean acidification:
“Or have you ALL morphed into climate automatons?”
http://seattletimes.com/html/localnews/2021826582_westneat15xml.html
The wrong path is the ludicrous idea that the change in global temperature is a simple function of the change in the “forcings”, which is climatespeak for the amount of downward radiation at the top of the atmosphere
As far as I know many statistical model make that assumption. But GCMs are little more complicated.
Perhaps I’m wrong, but what is suggest here is not how GCM work. The GCMs start out with an initial forcing, probably derived from some radiative transfer model and then modify other parts of the system after this initial perturbation. They make a series of assumptions then run the model which has a voluminous component – the cells. The models are run time and time again with different assumptions in order to determine how much a change in an individual “feedback” is required to reproduce the given historical record all the while being conditioned by physical laws (as it permeates through each cellular element). They can, at least with the coupled ocean-atmospheric GCMs, account for some natural variation as result of ocean thermal inversion.
I think Dr Chris Essex has an excellent video on their limitations and why they don’t work. And I don’t think the current review is nuanced or detailed enough to account for why they’re crap.
cd:
At September 15, 2013 at 12:52 pm you say of GCMs
Please state the review to which you are referring.
Richard
I think the issue here is that some folks here may be interpreting climate models to be a set of mathematical calculations (short or long) that are all a priori calculations, IE they run by the numbers. To the best of my knowledge, and correct me if I misinterpret:
The GCM (global circulation model) code itself cannot be described as a priori calculations of numeric pre-set sequences. They are dynamical GCM’s simulations (all done with mathematical calculations and based on current understanding of natural physics based processes) that include a suite of randomizing intrinsic and extrinsic natural variables (because of weather and oscillation variations) and that are driven with and without a priori numerical input (scenarios) as many times as you have grant money for. It is even possible, I think but don’t know for sure, to input randomizing variables if you believe your scenarios are themselves dynamical.
The results are the multiple spaghetti graphs of all the runs with an average of the runs somewhere in the middle. This is then graphed as angled sets of ever-widening error bands the further out the simulations are allowed to run from starting conditions. Why the ever widening error bands? That’s a phenomenon of natural variation known to exist regarding natural current conditions predicting future climate. The code itself is thus dynamical. The outcomes become scenarios (and then given model version names) when driven with numerical a prior input (IE CO2 concentration increase from baseline at 1% per year, etc).
So I think Richard maybe is partially correct? GCM models used to produce CO2 scenarios are both numerical and “mathematical” if I am using his understanding of the terms correctly. I prefer to use a priori to distingish between a natural run from a forced a prior run. But then I am just an armchair enthusiast watching the game, not playing it. So what do I know.
a prior”i”. Jeesh Pam. Get it right.
I just checked with “the Team” and 97% had to look up the meaning of “following wind.” They thought you were referring to their normal strategy of passing gas.
@Pamela Gray.
I assure you that my goal is not to break hearts!
I have involved in the prediction of sudden cardiac death. This has involved extensive measurements made within patients’ hearts (which is surprisingly safe) and interpretation of the results throgh signal processing and mathematical modelling.
The object is to identify patients who need implantable cardio-verter defibrillators IICDs), the issue being that the majority of these these devices never deliver therapy, so exposing patients to risk of complications at considerable cost and no benefit.
Pamela Gray:
In your post at you write September 15, 2013 at 12:58 pm
What you “think” does not alter the fact that I am completely correct in what I have written in this thread.
The problem is that you clearly do not understand the code is not the model: the code determines how the model will operate.
You need to look up finite difference analysis (FDA) and finite element analysis (FEA).
You may then desist from your mud slinging.
Richard
Richard
Richard, it was actually on the first page of the first link not too far from the first word. I figured you would read it. When someone sends me a link, I read it. But that’s me. Sorry if I suggested something onerous to you.
“Can anyone name any other scientific field that has made so little progress in the last third of a century? ”
—————
As a couple others have mentioned, physics’s String “Theory” fits the bill … but it’s not really a “theory” , “hypothesis” is more accurate. “Supersymmetry” is not far behind. Not a shred of physical evidence for either one (just mathematics), and a lot of evidence against. Many proponents have spent their entire careers on these subjects, and they twist & contort logic such that they’ve made String “Theory” essentially unfalsiable. Little green aliens that disappear whenever you turn around can also be made unfalsiable.
The only reasons the authoritarian leftist & politicans have not jumped on the empty String Theory bandwagon is, they can’t figure out how to use “Strings” to:
— raise taxes by Trillions of $ .
— increase the size, power, and control of central gov’t .
— profit from money-making scams & rent-seeking.
cd:
You have posted a 52 minute video and addressed it to me.
If you have something to say then say it. I have no intention of watching for 52 minutes merely because something has appeared on the web.
Richard
Up through the leg I assume.
Me too. I don’t think it’s a browser problem. I think WordPress has fiddled with something.
Pamela Gray:
You crossed the line with Willis up thread. And you have now crossed it with me with your post at September 15, 2013 at 1:08 pm.
You set me the task of finding an unspecified difference between what I said and you claimed was in an entire IPCC Chapter. Willis told you why that was misbehaviour up thread at September 15, 2013 at 10:13 am.
And I would not have found anything because what you mistakenly thought was a difference was ONE WORD which YOU DID NOT UNDERSTAND.
And you try to excuse that!?
I have had enough of you. Post something sensible or choose to clear off.
Richard
Joe Dunfee says:
September 15, 2013 at 9:55 am
The video ad started playing automatically. When an ad starts playing audio without you clicking on it, it is REALLY annoying. Please see if there is any way to stop this from happening.
===================================================================
Ad Block, and tip Anthony now and again.
Sorted. And I gather that even IE has Ad Block now.
@richard Courtney.
I decline to be told to clear off by someone as rude as yourself. I would rather make a proper argument.
a) Mathematical models of physical processes involve a set of equations that are based on physical principles. The contain extrinisic variables such as mass, energy temperature, etc. The parameterisation involves assigning constants to tge system involved, actual;mass, surface areas etc. The parameterisation depends on the model in question and how they vary.. Sometimes it can be highly specified, in others, where the model is simplified it will be less accurate
b) If one write an equation using, say conservation of momentum and and advection, one is maling a definite physical statement as to how the system works. This captures processes within the system. For example, ice-atmosphere interactions in GCMs.
c) I do not regard Eschenbach’s model as a model. It simply assumes a first order process through linearisation and is thus a parameter fitting exercise. If it were not, one might have considerable reservations about its parameterisation.
d) The purpose of a GCM is to attempt to answer the question: we know the state of the system at the present, what will happen next. They try to achieve this result mechanistically. This cannot be approach by a model such as Eschenbach’s.
e) Your statement:
“The GCMs use finite difference analysis to iterate to a stable solution. That is a numerical model obtaining a numerical solution. The fact that the models are coded with mathematics does not change that.” makes no sense to me. The models are assembled using mathematical physics. The finite difference equations on the mesh are constructed to conform the equations of the underlying physics.
Apart from being so bloody rude to everyone and writing in mold italics, do ypu actually:
1) Have any expertise?
2) Have a point.
Awaiting moderation? Have I offended?
Richard
I don’t care whether you watch it or not.
Please state the review to which you are referring.
How do you propose I state the review – despite being grammatically incorrect – I never stated he gave a review I said there was a good video where pointed out why GCM’s are limited.
I’ll assume that your rather pompous, terse and indifferent response was due to your – apparent – poor verbal reasoning.
So, if this hypothesis is correct, the real question is what control’s the level(s) of the governor ??
… it could also be that this branch of study hasn’t progressed simply because there is way too much politics being done & not enough science
@ur momisugly Willis Eschenbch.
http://wattsupwiththat.com/?s=saumarez
I rather enjoyed reading your posts about the UK.
However, as you said in a comment on my post above, you are a self-taught mathemetician.
If you put your head above the parapit and start pontificating on subjects that you know very little about, you should not be surprised when individuals who have more experience than yourself call you out.
R
RC Saumarez:
At September 15, 2013 at 1:25 pm you ask if I have any “expertise”. Well, I clearly have much more than you but – as this thread shows – that is not saying much.
Richard
cd:
At September 15, 2013 at 1:26 pm is boorish.
You told there was a review and I asked you what it was.
In another post you provided a 52 minute video and addressed it to me with no other information.
As to my grammar, you provided no grammar because you said nothing.
As I said, if you have a point to make then make it.
Richard
@richard Courtney
Oh really? Please Justify. Degrees? Publications?
RC Saumarez says:
September 15, 2013 at 1:25 pm
And yet you call it “Eschenbach’s model” … go figure.
I don’t care what you call it, RC, call it what you like. It doesn’t matter, because whatever you call it, it is the functional equivalent of the models, meaning that it can emulate the models to 98% accuracy.
What it reveals is that the models are merely lagging and rescaling the forcings, and thus they are useless for calculating the “climate sensitivity”. What happens is that if the models have large forcings they show small sensitivities, and vice versa.
You seem obsessed with what my model is called. To me, that’s useless semantics. Me, I’m only interested in what it reveals … which is plenty, and none of what it reveals burnishes the reputation of the GCMs. Instead, it shows that for all their complexity they are actually doing something quite trivial, merely lagging and scaling the inputs.
There are plenty of things that you could learn from that finding … but not if you continue your obsession with what to call what I’ve done.
w.