The Global Climate Model clique feedback loop

Well said that man.
Even the most basic (sounding) computer models very soon become more complex. I am in no way described as a computer programmer, but I did a course many years ago in VB. Part of it involved writing a computer ‘model’ or simulation of a scenario that went something like this.
In your city you have 10,000 parking machines, on average 500 break down every day. it takes on average 30 minutes to fix each one and 20 minutes to drive to the location. How many engineers do you need?
Simple? Not on your nelly !

” Charles Nelson says:
May 7, 2014 at 3:41 am
Warmists often ask me just how such a giant conspiracy could exist amongst so many scientists…this is one very good particular example of the kind of structures that sustain CAGW.
Thanks for the insight!
”
I think that it is not that there is a conspiracy, it is more that they do not understand basic scientific principles and ‘believe’ that what they are doing is for the good of mankind (and their pockets)
.

Are they designed to actually be predictive in the hard science tradition or are these models actually just artefacts illustrating a social science theory of changing practices in political structures and social governance and economic planning and human behavior? My reading of USGCRP, especially the explicit declaration to use K-12 education to force belief in the models and the inculcation of new values to act on the models is that this is social science or the related system science of the type Ervin Laszlo has pushed for decades.
It’s no different from the Club of Rome Limits to Growth or World Order Models Projects of the 70s that admitted in fine print that they were not modelling physical reality. They were simply trying to gain the political power and taxpayer money to alter physical reality. That is also my reading of the Chapter in yesterday’s report on Adaptation.
I keep hyping this same point because by keeping the focus just on the lack of congruence with the hard science facts, we are missing where the real threats from these reports is coming from. They justify government action to remake social rules without any kind of vote, notice, or citizen consent. It makes us the governed in a despot’s dream utopia that will not end well.

The real battle is over energy. Note how almost all our wars are against fossil fuel energy exporting nations. Today’s target is Russia.
Unlike previous victims, Russia has a large nuclear armed force that can destroy the US!
The US is pushing for a pipeline for ‘dirty oil’ from Canada while at the same time telling citizens that oil is evil and the US has lots of coal and saying coal is evil.
The rulers want to do this because they know these things are LIMITED. Yes, we are at the Hubbert Oil Peak which isn’t one year or ten years but it is real. Energy is harder to find and more expensive to process.
To preserve this bounty of fossil fuels, the government has to price it out of the reach of the peasants which are the vast bulk of the population. This is why our media is all amazed and loving tiny houses while the elites build bigger and bigger palaces.

Very good exposé, it tallies nicely with the statement that GCM’s are complex re-packed opinions.

Man Bearpig says:
May 7, 2014 at 4:00 am
“In your city you have 10,000 parking machines, on average 500 break down every day. it takes on average 30 minutes to fix each one and 20 minutes to drive to the location. How many engineers do you need?”
It doesn’t matter how many you NEED, it’s government work so figure how many they hire.
Don’t forget that each engineer needs a driver (union rules apply) plus you will need dispatchers, administrative personnel and auditors. Each of those sections would need supervisors who will need supervisors themselves. The agency would end up costing more than the income from the parking meters so they will then need to be removed, most likely at a cost that is higher than the installation cost, most likely from the same contractor who installed them and who is no doubt related to someone in the administration. All in all just a typical government operation.
But back to the subject matter by Dr Brown. Most of the people to whom I speak about this haven’t the faintest clue that models are even involved. They believe that it is all based on proven science with hard data. When models are discussed the answer is very often “they must know what they are doing or the government wouldn’t fund them”.
It’s still going to be a long, uphill battle against a very good propaganda machine.

From Man Bearpig on May 7, 2014 at 4:00 am:

In your city you have 10,000 parking machines, on average 500 break down every day. it takes on average 30 minutes to fix each one and 20 minutes to drive to the location. How many engineers do you need?

None. Engineers don’t fix parking meters, repair techs do.
And at that failure rate, it’s moronic to field repair. Have about an extra thousand working units on hand, drive around in sweeps and swap out the nonworking heads, then repair the broken ones centrally in batches in steps (empty all coin boxes, then open all cases, then check all…). Which throws out your initial assumptions on times.
However at an average 10,000 broken units every 20 days, you might need an engineer or three. As expert witnesses when you sue the supplier and/or manufacturer who stuck you with such obviously mal-engineered inherently inadequate equipment. With an average failure rate of eighteen times a year per unit, anyone selling those deserves to be sued.
Isn’t it wonderful how models and assumptions fall apart from a simple introductory reality check?

It’s good to hear Roy Spencer mention Chris Essex’s critique of climate models, as I don’t think anyone has uncovered the reality of the GCM challenge – technical and political – for me as much as Robert Brown, since I read Essex and McKitrick’s Taken by Storm way back.
Just one word of warning to commenters like dearieme: the ‘insanely stupid’ referred to something in what we tend to call the architecture of a software system. The current GCMs don’t have the ability to scale the grid size and that’s insane. The people chosen to program the next variant of such a flawed base system are, as Brown says, taken from “the tiny handful of people broadly enough competent in the list”. They aren’t stupid by any conventional measure but the overall system, IPCC and all, most certainly is. Thanks Dr Brown – this seems to me one of the most important posts I’ve ever seen on WUWT.

Anybody who knows anything about modeling complex, dynamic, multivariate, non-linear systems knows full well that John von Neumann was absolutely right when he said:
Give me four parameters, and I can fit an elephant. Give me five, and I can wiggle its trunk.

By the way: “However, the Bray and von Storch survey also reveals that very few of these scientists trust climate models — which form the basis of claims that human activity could have a dangerous effect on the global climate. Fewer than 3 or 4 percent said they “strongly agree” that computer models produce reliable predictions of future temperatures, precipitation, or other weather events. More scientists rated climate models “very poor” than “very good” on a long list of important matters, including the ability to model temperatures, precipitation, sea level, and extreme weather events.” http://pjmedia.com/blog/bast-understanding-the-global-warming-delusion-pjm-exclusive/
That seems to be the scientific consensus about climate models.

If the hypothetical “parking machines” were as reliable as the climate models, you’d have 9,700 broken machines and 300 working ones.
The term “good enough for government work” seems apt.

Roy Spencer referred above to models being doomed, but the real reasons they are doomed has its foundation in incorrect understanding of physics. There is no understanding among climatologists that, wheras Pierrehumbert wrote about “convective equilibrium,” there can only be one state of equilibrium according to the Second Law of Thermodynamics, and that state is thermodynamic equilibrium. The two are the same.
Think about it, Dr Spencer in relation to your incorrect assumption of isothermal conditions which is also inherent in the models. Likewise the models incorrectly apply Kirchhoff’s Law of Radiation to thin atmospheric layers. The absorbed solar radiation in the troposphere does not have a Planck function distribution, so Kirchhoff’s Law is inapplicable.
Also, because the models completely overlook the fact that the state of thermodynamic equilibrium (that is the same as “convective equilibrium”) has no net energy flows, and is isentropic without unbalanced energy potentials (as physics tells us) then they don’t recognize the fact that the resultant thermal profile has a non zero gradient. Water vapor and other radiating gases like carbon dioxide do not make that gradient steeper, as we know. It is already too steep for comfort, and so fortunately we have water vapor to lower the gradient, thus reducing the surface temperature.
:

Dr. Robert Brown
This is a brilliant piece and complements a talk I heard by Dr Essex on the same issue. He argued like you, that to model energy transfer properly you need to solve Navier-Stokes equations, and this would require a cellular grid with a resolution of c. 1 cubic mm. He also added that due to numerical instabilities solutions tend to diverge rather than converge. Now I’m not pretending to be an expert on these issues, and I may even sound confused, but I appreciate the issues and why they are such fundamental problems for those who claim GCM are based on fundamental physics. But even if we assume this claim to be completely true, the implementation of those fundamental physics in code is far from perfect.
…run at a finer resolution without basically rewriting the whole thing…
This is news to me. I heard a talk where the speaker, and perhaps I picked her up wrong, suggested that they can run their models at higher voluminous resolutions but at the expense of temporal scale (shorter time periods) due to limits in computer power; so that if she were correct then your statement doesn’t hold. My experience in this type of modelling would suggest, that if you’re correct, they’ve made a serious design error.
Dr Essex’s talk:

With the terrestrial climate system we have a single run of unique physical instance which is way too large to fit into the lab, so no one can perform controlled experiments on it. However, it is a member of a wide class, that of irreproducible* quasi stationary non equilibrium thermodynamic systems, which do have members one could perform experiments on with as many experimental runs as one wishes.
Therefore we should abandon climate modelling for a while and construct computational models of experimental setups of this kind, which can be validated using standard lab procedures.
Trouble is, of course, there is no general theory available for said class, which makes the endeavor risky, but also interesting at the same time. By the way, it also shows how fatuous it is to venture into climate with no proper preparations.
* A thermodynamic system is irreproducible if microstates belonging to the same macrostate can evolve into different macrostates in a short time, which is certainly the case with chaotic systems. It makes the very definition of entropy difficult, let alone rate of entropy production. And that’s the (theoretical) challenge.

Nick Stokes:

It’s not insane. It’s a hard physical limitation. A Courant condition on the speed of sound. Basically, sound waves are how dynamic pressure is transmitted. Timestepping programs relate properties in a cell to the properties in that and neighboring cells in the previous timestep. If pressure can cross more than one cell in a timestep (at speed of sound), there is total instability. So contracting grid size means contracting timestep in proportion, which blows up computing times.

I don’t think you’ve understood what I was driving at – and what I took Robert Brown to be driving at. I fully understand the blowing up of compute times as grid size and thus timesteps are reduced. But as Moore’s Law continues to hold (or something close) one might well wish to reduce both in a new generation of models and model runs. Not to be able to do this – and do it easily – is, in Brown’s words, insanely stupid. That’s why I characterised it as a software architecture problem.
I’m open to correction on what Dr Brown meant but that’s I took from his words.

Nick Stokes says:
May 7, 2014 at 5:58 am
Richard Drake says: May 7, 2014 at 5:01 am
“The current GCMs don’t have the ability to scale the grid size and that’s insane.”
It’s not insane. It’s a hard physical limitation. A Courant condition on the speed of sound. Basically, sound waves are how dynamic pressure is transmitted. Timestepping programs relate properties in a cell to the properties in that and neighboring cells in the previous timestep. If pressure can cross more than one cell in a timestep (at speed of sound), there is total instability. So contracting grid size means contracting timestep in proportion, which blows up computing times.
—
“Basically, sound waves are how dynamic pressure is transmitted”
Really? Dynamic pressure = 1/2*rho*V^2. Please explain this.
” Timestepping programs relate properties in a cell to the properties in that and neighboring cells in the previous timestep. If pressure can cross more than one cell in a timestep (at speed of sound), there is total instability.”
Really?? So the GCMs are limited to Courant < 1. That's NOT what I've read – many use multistep methods to promote temporal stability. However, given the near total lack of documentation on these and other important issues, it wouldn't surprise me if they were stability limited to Courant < 1.
"So contracting grid size means contracting timestep in proportion, which blows up computing times."
Yes this is true – time step goes with cell size. The problem goes beyond this simplisitic analysis, since most GCMs have very strong source terms and extensive model coupling (e.g. ocean models coupled with atmosphere dynamics models coupled with radiation physics). This likely imposes even more stringent constraints on stability (and ultimately accuracy).

We don’t even have the data needed to intelligently initialize the models we have got, and those models almost certainly have a completely inadequate spatiotemporal resolution on an insanely stupid, non-rescalable gridding of a sphere. So the programs literally cannot be made to run at a finer resolution without basically rewriting the whole thing,
—
This is really a bit surprising. One of the first things that one would try to ascertain if the spatial resolution is fine enough, is to use finer or coarser grids and see whether or not this has a major effect on the trajectories produced by the model. Hard-coding a single resolution seems really amateurish.
It might be a quixotic undertaking, but it seems to me that here on WUWT there would be enough senior gents with some time on their hands and the required skills to collaboratively write a better climate model with a proper, modular structure, one that could be adapted and extended in a sane way as new empirical knowledge about the climate system arrives.

A different non modeling approach must be used for forecasting . Forecasts of the timing and amount of a possible coming cooling based on the 60 and 1000 year natural quasi-periodicities in the temperature and using the neutron count and 10Be record as the best proxy for solar activity are presented in several posts at
http://climatesense-norpag.blogspot.com
During the last eighteen months I have laid out an analysis of the basic climate data and of methods used in climate prediction and from these have developed a simple, rational and transparent forecast of the likely coming cooling.
For details see the pertinent posts listed below.
10/30/12. Hurricane Sandy-Extreme Events and Global Cooling
11/18/12 Global Cooling Climate and Weather Forecasting
1/22/13 Global Cooling Timing and Amount
2/18/13 Its the Sun Stupid – the Minor Significance of CO2
4/2/13 Global Cooling Methods and Testable Decadal Predictions.
5/14/13 Climate Forecasting for Britain’s Seven Alarmist Scientists and for UK Politicians.
7/30/13 Skillful (so far) Thirty year Climate Forecast- 3 year update and Latest Cooling Estimate.
10/9/13 Commonsense Climate Science and Forecasting after AR5 and the Coming Cooling.
The capacity of the establishment IPCC contributing modelers and the academic science community in general to avoid the blindingly obvious natural periodicities in the temperature record is truly mind blowing.
It is very obvious- simply by eye balling the last 150 years of temperature data that there is a 60 year natural quasi periodicity at work. Sophisticated statistical analysis actually doesn’t add much to eyeballing the time series. The underlying trend can easily be attributed to the 1000 year quasi periodicity. See Figs 3 and 4 at
http://climatesense-norpag.blogspot.com/2013/10/commonsense-climate-science-and.html
The 1000 year period looks pretty good at 10000,9000,8000,7000, 2000.1000. and 0
This would look interesting I’m sure on a wavelet analysis with the peak fading out from 7000- 3000.
The same link also provides an estimate of the timing and extent of possible future cooling using the recent peak as a synchronous peak in both the 60 and 1000 year cycles and the neutron count as supporting evidence of a coming cooling trend as it appears the best proxy for solar “activity” while remaining agnostic as to the processes involved.
I suppose the problem for the academic establishment is that this method really only requires a handful of people with some insight ,understanding and the necessary background of knowledge and experience as opposed to the army of computer supported modelers who have dominated the forecasting process until now.
There has been no net warming for 16 years and the earth entered a cooling trend in about 2003 which will last for another 20 years and perhaps for hundreds of years beyond that. see
ftp://ftp.ncdc.noaa.gov/pub/data/anomalies/annual.ocean.90S.90N.df_1901-2000mean.dat
The current weather patterns in the UK and USA are typical of those developed by the more meridional path of the jet stream on a cooling earth. The Fagan book “The Little Ice Age ” is a useful guide from the past to the future. The frequency of these weather patterns, e.g. for the USA the PDO related drought in California and the Polar Vortex excursions to the South will increase as cooling continues
The views of the establishment scientists in the USA and of the UK’s CSA and Met office’s leaders in this matter post AR5 reveals their continued refusal to recognize and admit the total failure of the climate models in the face of the empirical data of the last 16 years. It is past time for the climate community to move to another approach based on pattern recognition in the temperature and driver data and also on the recognition of the different frequencies of different regional weather patterns on a cooling ( more meridional jet stream ) and warming (more latitudinal jet stream ) world.
All of the warming since the LIA can easily be accommodated within the 1000 year natural cycle without any significant contribution from anthropogenic CO2.
The whole UNFCCC travelling circus has no empirical basis for its operations and indeed for its existence depending as it does on the predictions of the inherently useless climate models.. The climate is much too complex to model but can be predicted by simply knowing where we are in the natural quasi -cycles

It takes the most sophisticated models running on supercomputers to “model” a modern aircraft. Those presumably actually work.
Now try modeling “global” climate. It’s comparing modeling a DNA molecule with a whale.

If you built a new climate model (after using large parts of the code from other models obviously) and you ran it and it kept coming back with just 1.0C temperature increase by 2100, …
… what would then happen.
Well, you can’t get invited to all the great global warming parties with your new 1.0C climate model. You are not only uninvited, you are drummed out of the business. Obviously, the model gets a tweak or two or hundreds until it is in the nice safe range of 2.2C to 4.0C (exactly the range of acceptable models prescribed by the IPCC AR4 team – you couldn’t submit a forecast unless the model had an underlying sensitivity within that range).

Nick Stokes says:
May 7, 2014 at 5:02 am
“GCMs are traditionally run with a long wind-back period. That is to ensure that there is indeed no dependence on initial conditions. The reason is that these are not only imperfectly known, especially way back, but are likely (because of that) to cause strange initial behaviour, which needs time to work its way out of the system.”
So the initial condition which is finally reached after the arbitrary “wind up” period is a unique and accurate initial condition for the start time of the simulation? Please explain in more detail how this is achieved.
All systems of PDEs are dependent on their initial/boundary conditions. There has to be an initial condition to begin any analysis, and the solution will depend on this. You really can’t have “no dependence”…

This is all a very intelligent discussion of an extremely technical nature, but can I return to Middle School for a moment and ask how we can call these models “Science” in the first place? If the experiment is “let’s build a predictive model and see if we can predict the future climate or temperature of the earth” then I think it clear that the experiment has failed miserably many times over. We certainly can’t expect the projections to improve over time if they diverge from reality so early in the experiment. The models may be based upon scientific laws and theories, but that doesn’t make the models science. If it did, then every time I get in my car and drive it, I am doing science. There’s lots of science under the hood, but I am just running the car, not doing an experiment. Also, if my husband (a Food Scientist) makes me some toast, is that science?
We need to stop referring to these models in the same sentence as the word science.

Many thanks to Dr, Robert G. Brown for these insights into the GCM’s. For me,the final analysis is in the differing results of the various models. It seems a truism that the model does what the modeler wants it to do.

Robert G. Brown,
A stimulating read. Thank you.
I think the modelers will just shrug their shoulders at their model’s non-real behavior and exclaim with non-scientific self-righteousness something like => Hey, our climate models are a work in progress and to be on the safe side for humanity (and our grandchildren) we purposely made them dangerously warm until (sometime in the far future) we get our models working right.
I see no other position they can take except they warmed the models for the precautionary principle. They aren’t doing science.
The question is, is what they did lying?
John

Sun et al. (2012) found that
“.in global climate models, [t]he radiation sampling error due to infrequent radiation calculations is investigated …. It is found that.. errors are
very large, exceeding 800Wm2 at many non-radiation time steps due to ignoring the effects of clouds..”
===================================
Watts a silly little watt between friends.

Dr Brown, you have good company.
“If you live with models for 10 to 20 years, you start to believe in them…A model is such a fascinating toy that you fall in love with your creation.”
– Freeman Dyson (At the age of five he calculated the number of atoms in the sun.)

@RGB

It’s just that unless you have a Ph.D. in (say) [a bunch of degrees in many independent, interdisciplinary, and complementary science, mathematics, programming fields] you don’t know enough to intelligently comment on the code itself. You can only comment on it as a black box, or comment on one tiny fragment of the code, or physics, or initialization, or methods, or the ode solvers, or the dynamical engines, or the averaging, or the spatiotemporal resolution, or…

Dr. Brown, your post at top was delicious. But I make a small issue with the blockquote above. You make it sound that if you are not some post-doc octopus, you are not worthy to make comments on the GCM code. Quite the contrary, only a post-doc octopus may be capable of writing such a model, but normal one-fisted experts ought to be able to point out flaws in any part of the process.
Building a house requires the skills of a soils and foundation engineering, masonry, structural engineering, carpentry, HVAC, electrical, plumbing, roofing, insulation, cabinetry… etc. Any single element of that endeavor should be and usually is reviewed and critiqued by independent masters of the craft. That a plumbing inspector might not know roofing doesn’t disqualify him for red tagging a drain pipe with insufficient gradient.
You point is that GCMs are horribly complex dynamic processes where ALL the skills are necessary to write a good one. All true. But since all these processes must work correctly in isolation and in dynamic cooperation, EACH process can and should be able to withstand criticism by any expert, indeed anyone with knowledge, in that field. The burden of complexity is on the authors of the GCM, not the peer reviewers.
The human body is horribly complex, but you don’t need the skills of a neurosurgeon to recognize the patient has a broken leg, dislocated shoulder, or is dehydrated.
I am sure you agree with this in principle because you close with the hope that a some ordinary

people doing “statistics” on the output of the GCMs come to their senses and stop treating each GCM as if it is an independent and identically distributed sample drawn from a distribution of perfectly written GCM codes plus unknown but unbiased internal errors….

For here our ordinary statisticians need to make inferences and question not just one GCM, but on the entire genus of GCMs.

@David L. Hagen at 8:23 am
I am familiar with Type 1 and Type 2 error. I’m familiar with false positives and false negatives.
Thanks to Lord Monckton, I am familiar with a dozen styles of invalid reasoning.
But I am not familiar with “Type B Error”, at least by that name. What is it? And what is Type A Error?
(Google wasn’t much help: http://www.west.net/~ger/math_errors.html, not bad, but not on point.)

I exchanged a few emails with mathematician Chris Essex recently who claimed (I hope I’m translating this correctly) that climate models are doomed to failure because you can’t use finite difference approximations in long-time scale integrations without destroying the underlying physics. Mass and energy don’t get conserved. Then they try to fix the problem with energy “flux adjustments”, which is just a band aid covering up the problem.
We spent many months trying to run the ARPS cloud resolving models in climate mode, and it has precisely these problems.
I’ve spent a fair bit of time solving open stochastic differential equations (or, arguably, integrodifferential equations) — things like coupled Langevin equations — back when I did around five or six years of work on a microscopic statistical simulation of quantum optics, as well as LOTS of work numerically solving systems of coupled (de facto partial) differential equations (the system detailed in my Ph.D. dissertation on an exact single-electron band theory). Even before one hits the level of stiff equations — quantum bound states — where tiny errors are magnified due to the fact that numerically, eigensolutions are always numerically unstable past the classical turning point, problems with drift and normalization and accumulation of error are commonplace in all of these sorts of systems. And yes, one of the consequences of drift is that conservation laws aren’t satisfied by the numerical solution.
A common solution for closed systems is to renormalize per time step (or N time steps) to ensure that the solution is projected back to the conserved subspace instead of being allowed to drift away. This prevents the accumulation of error and ensures that the local dynamics remains MOSTLY on the tangent bundle of the conserved hypersurface/subspace, if you like, with only small/second order deviation that is immediately projected away. This is, however, computationally expensive, and it isn’t the way e.g. stiff systems are solved (they use special backwards ODE solvers).
For open systems doing mass/energy transport, however, there is a real problem — energy ISN’T conserved locally ANYWHERE, and most cells can both gain or lose energy directly out of the whole system. In the case of climate models, in some sense this is “the point” — sunlight can and does warm the entire column from the TOA to the lower bound surface of light transport in the model, outgoing radiation can and does cool the entire column from the TOA to the lower bound surface of light transport for the model, AND each cell can exchange energy not only with nearest neighbors but with neighbors as far away as light can travel from the cell boundaries and remain inside the system laterally and vertically combined. That is, radiation from a ground level atmospheric cell can exchange energy with a cell two levels up (assuming that the model has multiple vertical slabs) and at least one cell over PAST any nearest-neighbor, same level cell. One can model this as a nearest-neighbor interaction/exchange system, but it’s not, and in physics systems with long range interactions often have startlingly different behavior from systems that only have short range interactions. Radiation is a long range coupling and direct source and sink for energy.
The consequence of this is that enforcing energy conservation per se is impossible. All one can do is try to solve a system of cell dynamics that is conservative at the differential level, basically implementing the First Law per cell — the energy crossing the borders of the cell has to balance the change in internal energy of the cell plus the work done by the cell. Errors in the integration of those ODE/PDEs cannot be corrected, they simply accumulate. To the extent that the system has negative feedbacks or dynamical processes that limit growth, this doesn’t mean that the energy will diverge, only that the trajectory of the system will rapidly, irreversibly and uncorrectably diverge from the true trajectory. If the negative feedbacks are not correctly implemented, of course, the system can diverge — the addition of a series of randomly drawn -1, +1 numbers diverges like the square root of the number of steps (a so-called “drunkard’s walk”). In the climate system the issue isn’t so much a divergence as the possibility of bias in the errors. Even if you have some sort of damping/global conservation principle forcing you back to zero, if the selection of +/- 1 steps is NOT random but (say) biased 2 +1’s for ever -1, you will not be driven back to the correct equilibrium energy content. This sort of thing can easily happen in numerical code as a simple artifact of things like rounding rules or truncation rules — setting an integer from a floating point number and then using the integer as if it were the float.
It can also happen as an artifact of the method used to extrapolate when solving differential equations or finite difference equations, or because of using interpolation on an inadequate grid while trying to solve dynamics with significant short term variation. Interpolation basically cuts off extremes, but in a nonlinear model the contribution of the excluded extremes will not be symmetric in the deviation specifically when the deviation is normally distributed around the interpolation. As a trivial, but highly relevant example, if one has a transport process that (say) is proportional to , and use interpolated T’s on an inadequate grid de facto assuming unbiased noise around the interpolation, one will strictly underestimate the transport.
In fact, if one creates a field that has a fixed mean and a purely normal distribution around the mean, and use the mean temperature as an estimate of the (say, radiative) transport, one will strictly underestimate the actual transport because the places where the temperature is warmer than the mean lose energy (relative to the mean) faster than the places where the temperature is cooler, do, relative to the mean temperature. If you like: for any .
Mass transport is even harder to deal with. Well, not really, but it is more expensive to deal with. In the Earth system, one can probably assume that total mass of atmosphere plus ocean plus land (including the highly variable humidity/water/ice distribution that can swing all three ways) is constant. Yes, there is a small exchange across the “boundary” from thermal outgassing and infalling meteor and other matter but it is a very, very small (net) number compared to the total mass in question and probably is irrelevant on less than geological time (in geological time that is not clear!) But GCMs don’t work over geological time so we can assume that the Earth’s mass is basically conserved.
Back a few paragraphs, you’ll note that one has to implement the first law per cell in the model. This is nontrivial, because cells not only receive energy transport across their boundaries in the form of radiation and conduction at the boundary “surfaces”, but they can receive mass across those boundaries and the mass itself carries energy. Worse, the energy carried by the mass isn’t a simple matter of multiplying its temperature by some specific heat and the mass itself; in the case of pesky old water it also carries latent heat, heat that shows up or disappears from the cell relative to its temperature as water in the cell changes phase. Finally, each cell can do work.
This last thing is a real problem. It is left as an exercise to see that a cell model with fixed boundaries cannot directly/internally compute work, because work is a force through a distance and fixed boundaries do not move. If a cell expands into its neighbors it clearly does work (and all things being equal, cools) but “expanding into neighbors” means that the neighbors get smaller and cell boundaries move. One cannot compute the work done at any single boundary from mass transport alone, because no work is done by the uniform motion of mass through a set of cells — a constant wind, as it were. One cannot compute work done by any SIMPLE rule. One has to basically look at net flux of mass into/out of a constant volume cell and instead of evaluating (work) evaluate and try to infer work from this plus a knowledge of the cell temperature, plus a knowledge of the cell’s heat capacity plus fond hopes concerning the rates of phase transitions occurring inside the cell. Yet without this, the model cannot work because ultimately this is the source of convective forces and the cause of things like the wind and global mass-energy transport. Not to worry, this is what the Navier-Stokes equation is all about:
http://en.wikipedia.org/wiki/Navier%E2%80%93Stokes_equations
In fact, the NS equations do even better. They account for the fact that there the motion of the transport is accompanied by the moral equivalent of friction — drag forces that exert shear stress across the fluid, a.k.a. viscosity. They account for the fact that the motion occurs in a gravitational field, so that downward transport of parcels is accompanied by the gain of total energy while upward transport costs total energy (the kind of thing that gives rise to lapse rates). They can be modified to account for “pseudoforces” that appear in a rotating frame, e.g. Coriolis forces, so that mass transported south is deflected spinward (East) in the northern hemisphere, mass transported upwards is deflected antispinward (West) in both hemispheres, by “forces” that depend in detail on where you are on the globe and in which direction you are moving. However, when solving the system (as this article notes) a statement of mass conservation is necessary, for example the continuity equation:

Recall, though, that the continuity equation in climate science is not so simple. The ocean evaporates. Rain falls. Ice melts. Water itself also transports itself nontrivially around the globe according to a separate, coupled NS equation with its own substantial complexity. Not only does this mean mass transport is difficult to enforce as a constraint, it means that one has to account for enormous, nonlinear variations in cell transport dynamics according to the state of a substantial fraction of the mass in any given cell. Where by “substantial” I don’t mean that it is ever a particularly large fraction — most of the dry atmosphere is nitrogen and oxygen and argon — but water averages 0.25% of the atmosphere and locally can be as much as 5%!
This is not negligible in any sense of the word when integrating a long, long time series!
So just as was the case when considering energy, one has to consider mass transport across cell boundaries and the forces that drive this transport, where one is not tracking parcels of mass as they move around and interact with other parcels of mass, but rather tracking what enters and leaves fixed volume, fixed location cells. One is better off because one only has to worry about nearest neighbor cells. One is far worse off in that one has to worry not only about dry air, but about the fact that in any given timestep, an entirely non-negligible fraction of the fluid mass in a cell can “appear” or “disappear” as water in the cell changes state, and worse, does so in perfect consonance with the appearance and disappearance of energy in the cell as measured by things like the “temperature” of the atmosphere in the cell.
Maintaining normalization in circumstances like this in a purely numerical computation is enormously difficult. One could in principle do it by integrating the total mass of the system in each timestep and using the result to renormalize it to a constant value, but this is safe to do only if the system is sufficiently detailed that it can be considered closed. That is, the solution has to track in detail the water in inland lakes, the water locked up as ice and snow, the water that evaporates from the surface of the ocean, the total water content of the ocean (cell by cell, all the way to whatever layer you want to consider an “unchanging boundary”. Otherwise errors in your treatment of water as a contribution of total cell mass will bleed over into errors in the total dry atmospheric mass, and the system will drift slowly away into the nonphysical regime as the integration proceeds.
Finally, one has the same issues one had with energy and granularity, only worse. Considerably worse on a lat/long grid on a sphere. At the poles, one can literally step from one cell to the next — in fact at the north pole itself one can put one’s foot down and have it be in dozens of cells at once. Dynamics based on approximately rectilinear cells at the equator, where cells are hundreds of kilometers across and one might be forgiven for neglecting non-nearest neighbor cell coupling are totally wrong at the poles where a fire in one call can heat somebody’s hands when they are standing four cells away. Mass transport dynamics is also skewed — a tiny storm that would be literally invisible at the equator in the middle of a cell might span many cells at the poles, just as “Antarctica” stretches all the way across a rectangular map with linear latitude and longitude, apparently contributing a 12th or so of the total area of the globe in spite of it being only a modest sized continent that is far less than 1/12 of the land area only, where land area is only 30% of the globe in the first place. Siberia, Canada, Greenland are all similarly distorted into appearing comparatively huge. One can of course compensate — in a sense — for this distortion by multiplying by a suitable trig factor in the integrals (the Jacobean) but this doesn’t correct the dynamical algorithms themselves!
This I do have direct experience with. In my band theory problem, I had to perform projective integrals over the surfaces of spheres. Ordinarily, a 2D integral with controllable error would be simple — just use two 1D adaptive quadratures, or without too much effort, write a 2D rectilinear adaptive quadrature routine to use directly. But on a sphere, using a gridding of the spherical polar angles this does not work. Or rather, it works, but enormously inefficiently and with nearly uncontrollable error estimates. By the time one has a grid that integrates the equator accurately, one has a grid that enormously oversamples the poles. Using a differential adaptive routine can help, but it still doesn’t account for the non-rectangular nature of the cells at the poles and hence one’s error estimates there are still sketchy.
Finally, note well my use of the word adaptive. Even when solving simple problems with ordinary quadrature (let alone trying to solve a nonlinear, chaotic, partial differential equation that cannot even be proven to have general solutions) one cannot just say “hey, I’m going to use a fixed grid/step size of x, I’m sure that will be all right”. Errors grow at a rate that directly depends on x. Whether or not the error growth rate for any given problem can be neglected can only rarely be known a priori, and in this problem any such assertion of a priori knowledge would truly be absurd. That is why even ordinary, boring numerical integration routines with controllable error specification do things like:
* Pick a grid size. Do the integral.
* Divide the grid size by some number, say two. Do the integral again, comparing the answers. (Some methods will do the integral a third time, or will avoid factors of two as being too likely to produce a spurious/accidentally accepted result that is still badly wrong.)
* If the two answers agree within the requested tolerance, accept the second (probably more accurate) one.
* If not, throw away the first answer, replace the first by the second, divide the grid size by two again, and repeat until the two answers do agree within the accepted tolerance.
Again, there are more subtle/efficient variants of this — sometimes adapting only over part of the range where the function itself is rapidly varying (e.g. applying the sort of process observed above directly to the subdivisions until they separately converge). All adaptive quadrature routines can, however, be fooled by just the right function into giving precisely the wrong answer, for example a harmonic function with zero integral will give a nonzero result of the initial gridding is some large, even integer multiple of the period as even 2 or three divisions by 2 will of course give you the same nonzero value and the routine will converge without discovering the short period variation.
Solving differential equations has exactly this problem, only — you guessed it — worse. The accurate knowledge of the integral to be done for each step depends on the accuracy of the integral done in the step before. Or, in many methods, the steps before. Even if the error in that step was well within a very small tolerance, integrating over many steps can — drunkard’s walk fashion — cause the cumulated error to grow, even grow rapidly, outside of the requested tolerance for the ODE solution. If there is any sort of bias in the cumulated error, it can grow much faster than the square root of the number of steps, and even if the differential equations themselves have intrinsic analytic conservation laws built in, the numerical solution will not.
Certain differential systems can still be fairly reliably integrated over quite long time periods with controllable errors by a good, adaptive code. These systems are called (generically) “non-stiff” systems, because they have an internal stability such that the solutions one jumps around between due to small uncontrollable errors in the integration tend to move together so that even as you drift away you drift away slowly, and can usually make the step size small enough to make that drift “slow enough” to achieve a given probable tolerance or absolute error requirement.
Others — yes, the ones that are called “stiff” — cannot. These are systems where neighboring trajectories rapidly (usually exponentially or faster) diverge from one another, even when started with very small perturbations in their initial conditions. In these systems, simply using different integration routines from precisely the same initial condition, or changing a single least-significant digit in the initialization will, over time, lead to completely different solutions.
Guess which kind of system the climate is. Not just the climate, but even comparatively simple systems described by the Navier-Stokes equation. One doesn’t even usually describe such systems as being merely stiff, as the methods that will work adequately to integrate stiff systems with a simple exponential divergence will generally not work for them. We call these systems chaotic, and deterministic chaos was discovered in the context of weather prediction. Not only do neighboring solutions diverge, they diverge in ways described by a set of Lyapunov exponents:
http://en.wikipedia.org/wiki/Lyapunov_exponent
Note well that the Maximal Lyapunov Exponent (MLE) is considered to be a direct measure of the predictability of any given chaotic system (where the “phase space compactness” requirement mentioned in the first paragraph is precisely the requirement that e.g. mass-energy be conserved by the dynamics (allowing for the ins and outs of energy in the energy-open system). The differential equations of quantum eigensolutions aren’t chaotic as they diverge “badly” and produce non-normalizable solutions (violating the axioms of quantum theory, which is why only eigensolutions that are normalizable are allowed), they are merely stiff. I’m not certain, but I’m guessing the the MLE for climate dynamics is such that system stability extends only out to pretty much the limits of weather prediction, decades away from any sort of ability to predict climate.
Without an adaptive solution, one literally cannot validate any given solution to the system for a given, a priori determined spatiotemporal gridding. One cannot even say if the solution one obtains is like the actual solution. All one can do is solve the differential system lots of times and hope that the result is e.g. normally distributed with respect to the actual solution, or that the actual solution has some reasonable probability of being “like” one of the solutions obtained. One cannot even estimate that probability, because one cannot verify that the distribution of solutions obtained is stationary as one further subdivides the gridding or improves or merely alters the algorithm used to solve the differential system.
The really sad thing is that we know that there are numerous small scale weather phenomena that easily fit inside of an equatorial cell in any of the current gridding schemes and that we know will have a large, local effect on the cell’s dynamics. For example, thunderstorms. Tornadoes. Mere rainfall. Winds. The weather system is not homogeneous on a scale of 3 degree lat/long blocks.
What this in turn means is that we know that the cell dynamics are fundamentally wrong. If we put a thunderstorm in a cell, it is too big that has one, it is too big. If we don’t put a thunderstorm into a cell that has one, we miss a rapidly varying mass fraction, latent heat exchange, variation of albedo, transport of bulk matter all over the place vertically and horizontally carrying variations in energy density with it. There isn’t the slightest reason to believe that dynamics carried out with thunderstorms that are always a hundred kilometers across or more will in any way have a long term integral that matches the integral of a system with thunderstorms that can be as small as one kilometer across (not a crazy estimate for the lateral size of a summer thunderhead), or that either extreme of assigning thunderstormness plus some interpolatory scheme for “mean effect of a cell thunderstorm” will integrate out to the right result either.
So here’s the conclusion of the rather long article above. In my opinion — which one is obviously free to reject or criticize as you see fit — using a lat/long grid in climate science, as appears to be pretty much universally done, is a critical mistake, one that is preventing a proper, rescalable, dynamically adaptive climate model from being built. There are unbiased, rescalable tessellations of the sphere — triangular ones, or my personal favorite/suggestion, the icosahedral tessellation. There are probably tessellations still undiscovered that can even be rescaled N-fold for some N and still preserve projective cell boundaries (some variation of a triangular tesselation, for example). These tessellations do not treat the poles any different from the equator, and one can (sigh) still use lat/long coordinates to locate the centers, corners and sides of the tessera. Yes, it is a pain in the ass to write a stable, rescalable quadrature routine over the tessera, but one only has to do it once, and that’s the thing federal grant money should be used for — to fund eminently practical applied computationa mathematics to facilitate the accurate, rescalable solution to many problems that involve quadrature on hyperspherical surfaces (which is a nontrivial problem, as I’ve worked on it and written (well, stolen and adapted) an algorithm/routine for it myself. It happens all the time in physics not just NS equations on the globe in climate science.
Given a solid, trivially rescalable grid, many questions concerning the climate models could directly be addressed, at some considerable expense in computer time. For example, the routine could simply be asked to adaptively rescale until the model converged to some fairly demanding tolerance over as long a time series as possible, and then the features it produces could be compared to real weather features to see if it is getting the dynamics right with the many per-cell approximations being made. Many of those approximations can probably be eliminated, as they exist only because cells right now are absurdly large compared to nearly all known local weather features and only manage large scale, broad transport processes while accumulating errors with an unknown bias in each and every cell due to the approximation of everything else. The MLE of the models could be computed, and used to determine the probable predictivity of the model on various time scales. The dynamically adaptive distributions of final trajectories could be computed and compared to see if even this is converging. And finally, one wouldn’t ever again have to completely rewrite a climate model to make it higher resolution. One would simply have to alter a single parameter in the program input to set the scale size of the cell, and a single parameter to set the scale size of the time step, with a third parameter used to control whether or not to let the program itself determine if these initial settings are adequate or need to be globally or locally subdivided to resolve key dynamics.
I’m guessing that predictivity will still suck, because hey, the climate is chaotic and highly nonlinear. But at least such a program might be able to answer metaquestions like “just how chaotic and nonlinear is that, anyway, when one can freely increase resolution or run the model to some sort of adaptive convergence”. Even if solving the model to some reasonable tolerance proved to be impossibly expensive — as I strongly suspect is the case — we would actually know this and would know not to take climate models claiming to predict fifty years out seriously. Hey, there are problems Man was Not Meant to Solve (so far), like building a starship within the bounds of the current knowledge of the laws of physics, solving NP complete problems in P time (oh, wait, that could actually be what this problem is), building a direct recursion relation capable of systematically sieving out out all of the primes, generating a truly random number using an algorithm, and who knows, maybe long term climate modelling.
rgb

Um, Nick, explain this then: http://en.wikipedia.org/wiki/Blast_wave. Are you really going to tell me that a pressure wave cannot be propagated faster than a sound wave? What do you think lightning does in a thunderstorm then? Can a numerical weather model do thunderstorms without tricks?

@Steven Ramey, unless it has changed from my student days, Type I errors could also be called false positives or Type A errors. Type II errors could also be called false negatives or Type B errors.

Yes I see.
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Nope. That’s too hard.
There are some very clever people here.

Dr. Brown is too modest. In addition to the “climatist qualifications” boxes he justifiably checks off, he is a superb writer and presumably can check other relevant boxes regarding team-building and project management. Most of all, he has the perspective to see things as they are and the courage to speak about them.
The spectrum of large-scale science projects, as many of us know too well, can range from “unacceptable nonsense” at one end, through “acceptable nonsense”, “plausibly insightful”, all the way to the other end of the spectrum where we may find “robust, repeatable , tested body of knowledge”. Dr. Brown convincingly positions climate models, and by inference much of the other “global warming” work, clearly at the “unacceptable nonsense” end of the spectrum. Not even the redoubtable Dr. Stokes can nick or pick apart much of Dr. Brown’s narrative (though some may be amused by his attempts.)
What the public relations geniuses have managed to do over the past thirty years is to reposition climate science, in the minds of the public and the press, from the unacceptable side of the spectrum to the robust side. Meanwhile the science itself has made no such journey and is arguably not far along the road.
One can understand how awkward this is for honest scientists. As Dr. Brown points out, only a very small number of individuals actually can grasp the full structure and disciplines involved in building these climate models. But, just as one needn’t be a playwright in order to be a competent drama critic, it isn’t necessary to be able to check all the quantum physics, statistics, thermodynamics and other boxes in order to have a pretty good idea of what is actually going on. Are there really so many senior scientific professionals unwilling to spill the beans and admit to the wobbly underpinnings of their “climate science”?

I feel sure that if co2 caused any additional warming then Chilbolton facilities would be able to show the definitive proof.
http://www.stfc.ac.uk/Chilbolton/facilities/24807.aspx
Here is what they have to say about climate models
“Formation of precipitation in boundary-layer clouds is critical to understanding their microphysical and dynamical evolution, and the impact such clouds have on the earth’s radiation balance
and hydrological cycle. Climate models have so far been unable to realistically simulate this “warm-rain” process”
http://www.nerc.ac.uk/research/sites/facilities/list/ar_cfarr.pdf

“Given a solid, trivially rescalable grid, many questions concerning the climate models could directly be addressed, at some considerable expense in computer time. For example, the routine could simply be asked to adaptively rescale until the model converged to some fairly demanding tolerance over as long a time series as possible, and then the features it produces could be compared to real weather features to see if it is getting the dynamics right with the many per-cell approximations being made.”
Nailed it. Comparison between models and “real weather features” is the heart and soul of working with models. It rests on knowledge that is inherently practical and empirical. Imagination is required. Please note that it is not possible to do this kind of work until an organization has one or more “experts” who can provide his/her practical and empirical knowledge and who can make judgments about the future that will be severely criticized. Models cannot substitute for empirical science that does not exist.

I’m watching the Chris Essex video above now, and it is interesting to see that (so far) we are saying exactly the same things. I mean, it’s scary. One by one, he is laying out the points I made the the post just above on the grid. Except (so far) the tessellation problem. I’m waiting for it.
I do have to say, though, that he is much funnier.
I strongly urge people who have a hard time understanding what I’m saying about the numerical complexity of the problem and the absurdity of the claims of predictivity to watch this video, especially Nick Stokes (who I am perfectly happy to agree with, but not today). Or rather, perhaps I DO agree with him today. Yes, GCMs are basically weather prediction programs repurposed to climate prediction, based on fake physics (Chris Essex’s words, but I don’t disagree) that works well enough to predict weather some moderately short distance out and implemented on an absurd grid that arose because some graduate student in the extremely remote past had never heard of tessellation but had heard of spherical polar coordinates or latitude and longitude and use them as “rectangular” grid coordinates in the very first implementation of a weather prediction problem. Everything since then is mere code inheritance. As Chris (and I) point out, however, that gives us absolutely no good reason to believe that they will work for long term climate prediction in a nonlinear complex system whose climate dynamics is known to be strongly governed by what amount to structured, self-organized, named, macroscopic enormously nonlinear quasi-particle dynamics. ENSO. The PDO. Hadley cells. THE NAO. Monsoon. We don’t even really know the sign of the changes in macroscopic climate descriptors likely to be consequential on an increase in atmospheric CO_2 forcing. All we have is fond hopes, belief that the fake physics on an absurd grid at a ludicrous resolution will integrate 50 years into the future in some meaningful way, a hope that the climate models themselves do not support as one examines the full envelope of their integrated solutions.
rgb

The real problem is the vast bulk of the public and the politicians hear “computer model” and they think “Star Trek” and “Iron Man” and think its REAL. They don’t understand that humans have to build them with a lot of guesses, suppositions and windage.

rgb,
Phew, the last time I did computer programming was as a Junior at university in 1970. The university had a CDC 6400 (Control Data Corp) with a whole bunch of IBM 360s as peripherals. I did my programming projects on punch cards using Fortran IV (if I recall correctly).
Humorous memory => There was a terminal in my dormitory basement. There was a huge printer at the terminal that made loud sounds as it printed; I mean it was like 8 ft wide, 5 ft tall and 6 ft deep. Late at night some nerds would run programs that made the printer mimic music as it was printing. One of my friends would make the printer mimic the song ‘Anchors Away’ as it printed gibberish. : ) I also liked another nerd who would make the printer mimic ‘Whiter Shade of Pale’.
John

Man Bearpig says:
May 7, 2014 at 4:00 am
In your city you have 10,000 parking machines, on average 500 break down every day. it takes on average 30 minutes to fix each one and 20 minutes to drive to the location. How many engineers do you need?
========================================================
One good one, to build a better meter.
However, perhaps you point is that the question does not provide near the needed data. (Just like in Climat Science) Much was said already but, among much other missing info, how many hours do you want the repair crews working? Do the take weekends off?
All productions issues come down to the four “P”s of production. If there is a production problem, it is in one of those four categories. They are “Paint” (The job itself), “paint brush”, (the tools to do the job) “Picture” (The blueprint, work order etc showing what needs to be done), and the fourth P = “People” The personnel doing the job.
Your picture neglected to point out start and comnplete times for doing your 400 hours of work.
On every work order the area availble time, and the comlet by time are critical.

Nick Stokes says:
May 7, 2014 at 6:11 am
Hollywood fake physics – no explanation required. If it was paired up with some satellite data with temperature similarly codified it might have got my attention.

Thank you Dr. Brown. Excellent exposition.
Your posts are always a breath of fresh air, even though one sometimes gets a bit dizzy breathing that fresh air.

Well I was not able to immediately discern the gist of Prof rgb ‘s post; but I gather he says coding and reading other peoples code is very difficult. I would agree with that and I wouldn’t try to unravel someone else’s code.
I’m sure some people can, including WUWT posters. One reason, I don’t try, is that I’m more interested in the specific equations being solved, than I am in the code that might do that. But I understand, that lots of problems, you just can’t churn out a closed form (rigorous) solution for, so some sort of “finite element” like analysis, with small steps of arithmetical solutions need to be done, in some iterative process.
I do that sort of thing myself in a much simplified form, to develop some graphable form of output, in say an excel spread sheet form.
But I always worry about the issue that Dr. Spencer raised, in that you might overstep some bounds of physics , when doing that.
For people who do the same computation repeatedly, with different data, then obviously a coded computer routine simplifies that process (once you have the code, and debugged it) To which one might add; “and properly commented and annotated it so you yourself can follow it.”
Even with the much simpler systems, I deal with, I sometimes find a “neat trick” (well I think so) while I’m working on it.
I’d like a dollar, for each time I have come back, and then asked; “why the hell did I do this step here ?”
Think about that short term memory fog, when contemplating conversations with ET even just a few light years away.
I can only conclude, when I see planet earth refusing to follow all 57 varieties of GCM; or even one of them; that somehow they are ignoring all the butterflies that there are in Brazil.
If in a chaotic system, a small perturbation pops up unannounced; a “butterfly”, or it could be a Pinatubo, how the hell can any model allow for such events, which we know throw the system for a loop ( minor maybe) but it has to move off on a different path, from the one that it was on before the glitch.
I’ve watched those compound coupled pendulum demos, in “science” museums, and it always amazes me that minor perturbations in the start conditions, can morph into such havoc.
So how the hell do we expect to model systems with unpredictable butterflies ??
But I’m glad that Dr. Roy, and Prof Christy are on top of that; and that Prof rgb is advising students on such follies. Izzat Red, Green, Blue ??

” Climate models have a sound physical basis and mature, domain-specific software development processes” (
“Engineering the Software for Understanding Climate Change”, Steve M. Easterbrook Timothy C. Johns )

How far into the future can Global Climate Models predict?

But that is the key point I’ve been making. They are NWP programs working in a mode where they can’t predict weather. They generate random weather. But because all the physics of NWP is in there, the random weather responds correctly to changes in forcing, so you can see by simulation how average climate changes, even though the weather isn’t right.
Except for the fact that when run with no forcings at all, they completely fail to reproduce the actual variability of the climate on any decadal or longer timescale. If you run them with the CO_2 forcing, they only reproduce the observed climate variation within the reference (training) period. This is not impressive evidence that the models are getting the climate right.
One is tempted to claim that it is direct evidence that the models are not getting the split between natural variability and CO_2 forcing right, greatly underestimating the [former] and greatly overestimating the latter. But even that cannot really be said with any real confidence.
rgb

[SNIP – DOUG COTTON, STILL BANNED ]

Even at a pure layman’s level such as myself, this is one of the best posts I have seen on WUWT, particularly the first third of the posts but most decidedly not at all decrying a number of other really excellent and very readable, lay man language and technically orientated highly explanatory posts throughout the whole list.
And the hoi polloi like myself, have kept our heads down and our many inane comments, like this one, right out of it to the immense benefit of a significant increase in our knowledge on just how seriously bad the model driven climate science is that underlies the colossal waste and destruction of now close to trillion dollars worth of global treasure, the winding down of once vigorous and viable national economies and the completely avoidable deaths of tens of thousands through energy deprivation created by the inflated and increasingly unaffordable cost of energy, the heat or eat syndrome.
We see once again that the claims made by the climate modellers are being made in the full knowledge that they are being thoroughly and publicly untruthful in making their quite spurious claims on the supposed unchallengeable veracity and predictive capabilities of these same climate models

Nick Stokes says:
May 7, 2014 at 7:00 am
Frank K. says: May 7, 2014 at 6:35 am
“Really?? So the GCMs are limited to Courant < 1.”
Usually run at less. Here is WMO explaining:
” For example, a model with a 100 km horizontal resolution and 20 vertical levels, would typically use a time-step of 10–20 minutes. A one-year simulation with this configuration would need to process the data for each of the 2.5 million grid points more than 27 000 times – hence the necessity for supercomputers. In fact it can take several months just to complete a 50 year projection.”
At 334m/s, 100km corresponds to 5 minutes. They can’t push too close. Of course, implementation is usually spectral, but the basic limitation is there.
_______________________________________________________________
And how do they handle the format conversion, truncation, and rounding issues in such a number of calculations?

Found it please forget and forgive my 5:39pm post.

Nick Stokes;
The reason he couldn’t give you a number is that it is intended to damp the effect before you can notice. And the amount of energy redistributed is negligible.
>>>>>>>>>>>>>>>>>>>
Read those two sentences again Nick.
Gobsmacked.

Nick Stokes;
OK. You have a wonderful audio amplifier. But sometimes it goes haywire with RF oscillations. You tell your EE – fix it, but don’t change the sound. He does.
>>>>>>>>>>>>>>>>>
1. Negative feedback in an amplifier does change the sound. Transient distortion in particular rather than harmonic. That amplifiers are now good enough that the human ear cannot for the most part detect the distortion by no means suggests that it doesn’t exist, nor that it isn’t significant under certain conditions.
2. Your analogy has nothing to do with the physics at hand. Perhaps I understood the explanation incorrectly, but if I did, the bottom line is pretty simple. The models are calculating a number known to be wrong, and spreading it out across the planet surface on the assumption that the error is insignificant. Since you don’t know WHY the number is wrong (if you did, the code would be corrected to fix it instead of some bizarre method of damping it out) you ALSO don’t know by how much. You don’t know for example if correcting it would reveal still another error that is being masked by the first one. You may even find out that there is a second problem that is bigger than the first but of opposite sign. It gets worse from there. Your damping method might be fine. But since you don’t know WHY the original calc is wrong in the first place, for all you know the fix is introducing cumulative error larger than the error you were trying to fix in the first place.
The models are increasingly running warmer than the reality, even the IPCC has grudgingly admitted that. Give your head a shake. The models are wrong, nobody knows exactly why, and hear you are defending the practice of handling energy balance in a completely unrealistic fashion. No wonder the models don’t work well when they are underpinned by reasoning such as yours.

Argh I did it again. Apologies, Stephen Rasey.

Climate model predicting some 100s of years… It seems there’s no problem, these fellows can model the Universe. All 13 billion years of it. I wonder what does Chris Essex say? As usual, very interesting info is in the Acknowledgements section – all the funding agencies allocating our taxes so wisely.
See: Nature 509, 177–182 (08 May 2014) doi:10.1038/nature13316
Properties of galaxies reproduced by a hydrodynamic simulation
M. Vogelsberger, S. Genel, V. Springel, P. Torrey, D. Sijacki, D. Xu, G. Snyder, S. Bird, D. Nelson & L. Hernquist
Previous simulations of the growth of cosmic structures have broadly reproduced the ‘cosmic web’ of galaxies that we see in the Universe, but failed to create a mixed population of elliptical and spiral galaxies, because of numerical inaccuracies and incomplete physical models. Moreover, they were unable to track the small-scale evolution of gas and stars to the present epoch within a representative portion of the Universe. Here we report a simulation that starts 12
million years after the Big Bang, and traces 13 billion years of cosmic evolution with 12 billion resolution elements in a cube of 106.5 megaparsecs a side. It yields a reasonable population of ellipticals and spirals, reproduces the observed distribution of galaxies in clusters and characteristics of hydrogen on large scales, and at the same time matches the ‘metal’ and hydrogen content of galaxies on small scales.

This is powerful stuff -and I agree, worth archiving for that long distant posterity that will look back and say ‘daddy, where were you when all this climate modelling was rampant across all science academies, all governments, the EU, the UN, all environmental NGOs…..and the whole of the left-liberal-green coalition, their press and most of the media?’
I can’t follow the code critics – well, not much of it. But I know good critical stuff when I see it. But there is something missing. Here’s a little bit of history that will illustrate the problem – it is about the creation of alternative models (which is a very tricky business) and how to use them. Maybe there is a lesson here that someone can take up.
In the early part of the nuclear industry in Britain and the US, computer simulation was used to assess the environmental impact of reactor accidents – including melt-down of the core. But this was all kept secret. The reactors were licensed – and ‘safety’ standards set without public involvement, parliamentary oversight or needless to say, critical input from outsiders. Toward the end of that decade, the American Physical Society forced the issue, and the first reports were made public on the consequences of melt-downs. In the UK, this prompted a Royal Commission report (1976). Of course, many reactors had already been built by then.
The consequences of aerial releases were however obfuscated in the way the models presented ‘probabilistic’ combinations of failure rates, amount released, radiotoxicity and even wind direction, to arrive at an individual risk component – that was very, very small. The societal risk was not computed – and of course, failure rates were unvalidated because of the complexity of the systems – and did not include such factors as terrorist attack or aircraft impact. Concerned outsiders, such a s myself wanted to know ‘what if’ the event has happened and the wind is in my direction – but the models could not do that (or rather – in the hands of the nuclear labs, they would not do that).
So – we (my colleagues in an environmental science research group and I) petitioned for the release of the models. We succeeded. Next we had to understand them and modify the parameters to make them deterministic. This was during the time of punch cards on computers that you could walk around inside of! I knew nothing myself – but others came to our aid – even from within the nuclear labs – and the programmes were decoded and rerun – all with alternative but justifiable parameters – such as factoring out the probability of failure (and assuming it has happened), fixing the wind direction, but accepting all the downwind consequence part of the analysis in order to limit objections on methodology.
We succeeded in lifting the obscurity and focussing the consequence side of the equation not on personal individual risk of cancer (tiny beyond 10 miles) but the societal impact of contaminated land, relocation, loss of productions, etc. These reports were then fed into the policy process at all levels of government decision making and in the EU (with some small successes in affecting emergency planning procedures).
Later in the 1980s, we had gained the trust of that particular aerial modelling community. At one point, my research group even commissioned a model-run from a UK national laboratory to input to an inquiry.
Could it be done today? That is – take one of the better models and modify the key parameters of, say, the lambda factor in the RF equations or aerosol fudging factors, and add a coming Maunder Minimum in solar activity? I did ask our MetOffice Hadley Centre if I could have access to their more simpler model -the one they used to predict that a coming Maunder Minimum would not significantly slow down global warming – on the assumption that I could find some help to modify and run it. But they ignored the request (Julia Slingo was otherwise polite). In the old days, such requests could not be ignored because we had a battery of big guns on our side from the NGOs, the press, and even the trades unions representing emergency workers (who funded the study we commissioned). Now, of course, we have no friends or allies – well, at least, none we would care to embrace. And actually, it is not much of a ‘we’ over here in the UK.
But could it be done in the USA? The expertise is there, from what this thread shows.
I would be interested to know……. peter.taylor(at)ethos-uk.com
and to participate.

I will add some noise to this conversation with Nick Stokes and davidhmhoffer. For context:
Nick Stokes says: May 7, 2014 at 7:44 pm
OK. You have a wonderful audio amplifier. But sometimes it goes haywire with RF oscillations. You tell your EE – fix it, but don’t change the sound. He does.
“What did you do?”
“O, I sent filtered feedback from output to input. Only RF, no audio”
“Good. How much energy are you sending back?”
“Well, I can’t notice any RF at all?””
“Gobsmacked. How can it work if you feed back no energy?”
“Do you want me to take it out?”
I (*) can tell you how much energy I am feeding back by direct measurement (oscilloscope or spectrum analyzer, the latter probably better for this purpose).
Your argument seems to be that negative feedback removes the RF entirely and thus there is nothing left to measure. This is incorrect. RF energy ALWAYS exists as broadband thermal noise. That’s what starts the oscillator in the first place. Even when damped it is still there; the idea is to reduce it such that at RF frequencies the gain of the amplifier is less than 1. It is still there and can be measured with a spectrum analyser (or, in a pinch, an oscilloscope with no audio input signal).
In a climate model, which is a computer program, noise cannot exist at any frequency unless the model puts it there. The argument seems to be dealing with “noise” through filtering versus simply not adding noise in the first place.
I hope we can all agree that a model with no noise also cannot vary. All runs of a model, in fact all models using the same code and data, must necessarily produce exactly the same output every time. That was my criticism of Michael Mann’s offer of a MatLab model and data — to see if you get the same result.
Duh, of course I will get the same result. It’s a computer program. It does exactly what it was told to do. I pointed this out on the Scientific American website and was banned shortly thereafter 🙂 (Another groupthink echo chamber bites the dust)
I recognize that a model could be extremely sensitive to input conditions. Hashing algorithms are an example; changing a single byte of input to MD5, SHA(1,2) or even CRC produces a completely different output in a way that is supposed to not be predictable. This is not noise, it is still deterministic as is any computer program, but it is hard to PREDICT. That’s okay.

“The process Gavin describes damps this at the outset, automatically. It takes the energy out of the mode. The reason he couldn’t give you a number is that it is intended to damp the effect before you can notice. And the amount of energy redistributed is negligible.”
And yet measurable. If you can mathematically damp it then you already know its magnitude and it can be reported. All such computations can be logged to a file if you wish and know with precision every instance of adjustment and their cumulative effect.

Nick Stokes;
No, the situation is similar to the amplifier.
>>>>>>>>>>>>>>
An amplifier is a physical analog device. A climate model is a completely digital construct whose link to reality is entirely devised by programmers. Drawing an analogy between the two is utterly absurd.
That said, I agree with richardscourtney. The central take away for readers of this thread should be the devastating critiques by RGB.

David A says:
The GCMs are informative! They are extremely informative. They all run wrong, (not very close to the observations) in the SAME direction! (To warm)
This would be less remarkable if there are actually only a very small number of GCMs.
Which is something the OP was claiming.
A bit like the way you can have lots of “brands” of a product in a supermarket, but on close examination you find many have things in common.

Another fine comment.
Thank you Robert Brown.
So climatology by computer model is a faith based racket.
The shaman and High Priest types who fell from civic dominance with the Reformation and acceptance of the scientific method, are back.
And still lusting to lord it over all.
The climate models are a modern substitute for the incantation and gobbley gook of the dominant state religion.
A nastier state religion would be hard to imagine.
Attacks productive citizens.
Rewards parasitic activity.
Attacks the foundations of civil society.
Seeks active destruction of poor brown persons.
Robs the many to reward the criminal few.
Sure to end well?
These schemes and activities have always been social intelligence tests.
But it can be a mistake to pass the test if the fools and bandits have the guns.
I keep running into this thought; Is it a form of insanity to need to feel yourself morally superior to other people and insist you alone are fit to rule?
Of the nature of;” No.. I am Napoleon”
Is this the nature of the persons who gravitate toward the UN?