Climate models outperformed by random walks

Climate Models: If a frog had wings,……….

Heh — the magenta random walk produced a pretty decent hockey stick…

This surprises me – and probably most people here – not one bit.
As long as the input assumptions are flawed, all that the climatologists will manage with larger and more expensive supercomputers (as demanded by the UK MET) will be to compound their errors.

the models are screwed up…we have been saying that for a long time…there are too many factors…many poorly understood…co2 is already absorbing all the infrared it can…adding more will not make any difference…these climatologist guys dont seem to understand basic physics

LevelGaze: “This surprises me – and probably most people here – not one bit.”
Yeh no. I’m not simply surprised but mortified. It’s one thing to hold that simply gluing together words ‘climate’ and ‘science’ doesn’t mean there’s much of either in ‘climate science’. It’s quite another to state that the “consensus science” is so stupidly distant from a random walk, on the wrong side of things, that we’re beyond Jonestown and that we’ve gone beyond Ludicrous Speed.
Each of these models, which the article refreshingly notes, are a scientific theory about the climate. And they are when empirically tested make errors more than twice that achieved by a monkey throwing darts. Under any notion of science every one of those models is a miraculously falsified theory.
And of course it serves to note that the average stock broker does slightly worse than an actual monkey throwing darts at a dartboard — for those that remember the MonkeyDex. Slightly worse. And it’s a felony for stock brokers to make too much of their own prognostications without a disclaimer to the effect that they are not Delphic Oracles, Palm Readers, or merely reliable. And that’s just for ‘slightly worse’.
So while I’m unsurprised that the models are terrible, I’m utterly aghast that they are so grotesquely biased as to make the static on an empty television channel the acme of rigorous statistical predictions in climate.

Oops. Brain now switched on.

Doesn’t surprise me in the least.
All the climate models do is project the modellers belief in the net forcing of various factors, primarily CO2, forward into the future. That the models do worse than random chance is proof that some or all of the believed forcings are wrong, and specifically the believed CO2 forcing is wrong.
No other conclusion is possible, except perhaps serious programming incompetence by the modellers.

This is the best ever article on climate modelling (although Ross McKitrick’s article just published comes a close second).
Please read it carefully – it’s worth it –
http://climateaudit.org/2011/05/15/willis-on-giss-model-e/

Roger Longstaff says @ June 14, 2012 at 1:03 am
Hi Roger,
Pretty certain that what RB has in mind is that (A) short term random noise (white noise) prevents models from making accurate forecasts over weeks and months and (B) the white noise is averaged out over climate time scales (decades) and hence the models should end up providing reasonable average behaviour over the longer time scales.
The reason that (B) is wrong is simply that the noise in the system does not average out over the decadal time scales. Over these time scales, the pink noise mentioned in the “Is Global Temperature a Random Walk?” link begins to dominate. Models are not currently able to simulate this behaviour, which means that their use in decadal projections is unlikely to be useful globally, let alone regionally.

The UK. Met. Office have yet to learn that when you have dug yourself into a hole you stop digging.
Their reply to Roger Longstaff is typical of QUANGOs which the Met Office has become. At least when it was officially under RAF control it did apologize for errors it made.
For non UK readers a QUANGO is a Quasi Non Governmental Organization that is formed to advise the government and in doing so get paid money. The Met Office has dreams of operating the worlds biggest climate computer so needs to scare the government into paying more money. It can’t be bothered with members of the public, who actually provide the money through taxation, asking what it considers stupid questions.
Keep trying Roger.

Random walk? What random walk? The yellow line and the black line OBVIOUSLY prove my **ULTIMATE THEORY!**
If you people just can’t see that, well, I hope Schroedinger’s Cat bites you!
– MJM

Yes,, this is exactly how they are supposed to work.
If you have two climate models, one says ‘rains and floods will increase’ and another that uses the same data says ‘droughts and hot weather will increase’ then depending on the weather at the time you just refer to the model that got it correct and blame it on climate change. I thought you all knew the science behind AGW ? /sarc

Thanks Philip & Letmethink.
My problem is that any filtering (between calculation steps), or “re-setting” of variables to preserve stability, inevitably leads to loss of information on the system.
Filtering is used to increase signal to noise ratio when there is “a priori” knowledge of the signal (for example in a radio receiver that uses a narrowband filter tuned to a specific frequency). With climate models there is only an assumption that there will be a GHG signal. Furthermore, any low pass filtering will remove information generated by the model itself, as signals must be sampled at twice their highest frequency component (Nyquist theory) in order to preserve information.
If one were to equate accurate information to Shannon entropy, it seems inevitable that climate models will deviate from reality exponentially with respect to time, as a consequence of the logarithmic nature of information theory.
Is there a flaw in my argument?

Suppose, we have an exam test consisting of 30 two-choice items. If you do not know anything about the subject, your expected number of correct answers is 15. Sometimes it may happen that someone answers all items correctly but also that someone does everything wrong. This may happen in a large population of subjects without knowledge. Usually, we interpret 30 items correct as a sign of knowledge. Therefore, all items false may also mean that someone did everything deliberately wrong.

a “random walk” alternative, consisting simply of using the last period’s value in each location as the forecast for the next period’s value in that location.

That’s not a random walk, that’s a persistence model, a.k.a a straight line.

The test measures the sum of errors relative to the random walk.

Assuming we’re talking about the persistence model above, all the test is doing is scoring the amount of change from the baseline. This says nothing about what is right or wrong. This is at best misleading to try and judge the performance of climate models using a test like this.

No MT that is not what is assumed. With a simple random walk the last observation represents the instantaneous mean of a probability distribution (at the simplest a normal distribution) of known variance from which the next observation will be randomly drawn.

Letmethink says:
June 14, 2012 at 2:25 am
@ Roger Longstaff
“Is there anybody here who understands how numerical models can be “low skill” over short timescales but could possibly be accurate enough to justify massive intervention over multi-decadal timescales?”
No, because this is not remotely feasible. The climate is a chaotic system so a tiny variation in an initial condition will result in entirely unpredictable (or random) outcomes.

The argument is:::
A model could show it would not rain in UK in June on the 12th to 15th but on all other days it would – whereas in reality it did not rain on the 22nd to the 25th but on all other days it did. There was no ‘skill’ in the forecast of the 12th-15th or 22nd-25th – but for the overall month of June the model said it rained 26 days out of 30 and that could be claimed to be high skill in aggregate.
However, the longer a model runs without showing any skill the less likely it is to be skillful in aggregate and at some point it can be said that there is no chance of recovery. Effectively, this is the weather vs climate argument in different clothes.

I wonder if anyone can take this further and show that the random walk data put through Michael Mann’s analysis will always show a ‘hockey stick’?

@mt,
Oh, if you’d like to do your own random walk graph, and have Excel, try this:
in cell A1, put =randbetween (-1,1)
in cell A2, put =randbetween(-1,1)+A1
use the fill option and fill the A2 cell down a few hundred cells.
select the cells and make a line graph.
press f9 to refresh, and you’ll get quite a few graphs that look a lot like temperature graphs.

Tamsin, please let me offer brief comment that relates both to your weblog’s post “All Models are Wrong: Limitless Possibilities” and to Anthony Watt’s present headline post on WUWT titled “WUWT: Climate Models Outperformed by Random Walks”.
As a warm-up, let’s consider a non-controversial subject: models of turbulent flow over airframes. As we improve the spatial and temporal resolution of airflow simulations, we find that our ability to predict microscopic details of the flow does *not* improve. The reason is simple: the microscopic dynamics are chaotic, such that no dynamical model (however sophisticated) can predict their future evolution with microscopic accuracy.
None-the-less, experience shows that fluid dynamical simulations DO successfully predict (typically within errors of order one percent) the flight characteristics that we mainly care about, including (for example) fuel efficiency, stall-speeds, and g-limits.
How does this happen? It happens because the microscopic dynamics is governed by global conservation laws and thermodynamic constraints, chief among them being strict conservation of mass and energy and global increase in entropy. So instant-by-instant, we don’t know whether a Karman vortex will spin-off an extended wing-flap, and yet minute-by-minute, we can predict the lift, drag, and glide-path of an airliner with considerable accuracy and confidence.
As with fluid dynamics, so with climate dynamics. Chaotic fluctuations on continental spatial scales and decadal time scales are difficult to predict with confidence. Yet global climate changes are constrained by strict conservation of mass and energy and global increase in entropy, and thus *CAN* be predicted. So year-by-year, we don’t know whether the local weather will be hot or cold, and yet decade-by-decade, we can predict the warming of the earth, and the rise of the sea, with considerable accuracy and confidence.
Appreciating this, James Hansen and his colleagues have focussed their predictions on the global energy balance, and in particular, upon sea-level rise as an integrative proxy for that global energy balance. In 2011 they confidently predicted an acceleration in sea-level rise for the coming decade. Hansen’s prediction required a certain measure of scientific boldness, since at the time satellites were showing a pronounced decrease in the sea-level rise-rate.
In coming years we will see whether Hansen’s prediction is correct. Supposing that the prediction *is* proved correct, then the concerns of rational climate-change skeptics will be largely addressed.
More broadly, it is global conservation of mass and energy and global increase in entropy that explain why simulations of both airflow and climate can be inaccurate on individual space-time gridpoints, yet accurate globally.
—————————
LOL … for fun, I’ll post this essay to Judith Curry’s Climate Etc. and to Tamsin Edwards’ All Models are Wrong too.
It will be fun to see which forums are mainly interested in rational arguments accompanied by scientific links, versus mainly interested in compiling an ideology-first “enemy list.”   🙂

mt says:
June 14, 2012 at 4:47 am

…
Assuming we’re talking about the persistence model above, all the test is doing is scoring the amount of change from the baseline. This says nothing about what is right or wrong. This is at best misleading to try and judge the performance of climate models using a test like this.

Ah, the tussle between “right” and “wrong”.
If you read closer, the “RW” (for Random Walk) models had less error (E) in predicting what actually happened (“measured”) than ideology-driven “Climate Models”. So ERM < ECM.
This does indeed say which is (more) right and which is (more) wrong. That is, if your primary concern when building models in the first place is to determine which actually MODELS.
But for those still unwilling to grasp the concepts or have hockey sticks for spines, continue singing (to any tune you like):
“Don’t bother me with reality; what I’m looking for is a good fantasy.”

A fan of *MORE* discourse says:
June 14, 2012 at 6:22 am
As with fluid dynamics, so with climate dynamics.
False analogy.
Chaotic fluctuations on continental spatial scales and decadal time scales are difficult to predict with confidence. Yet global climate changes are constrained by strict conservation of mass and energy and global increase in entropy, and thus *CAN* be predicted.
You just need a program sophisticated enough to account for all the variables — which hasn’t been written yet — and a computer powerful enough to run it — which hasn’t been built yet.
So year-by-year, we don’t know whether the local weather will be hot or cold, and yet decade-by-decade, we can predict the warming of the earth, and the rise of the sea, with considerable accuracy and confidence.
Sooooo, how do you explain the divergence between predicted and observed this past decade?

The main climate model forecasts over the years versus the temperature observations (up to April and May 2012).
http://img441.imageshack.us/img441/5987/climodfrcstsobsmay2012.png

Philip Richens says:
June 14, 2012 at 6:36 am
Hi Philip,
A similar point was made on Tamsin’s blog: “On filtering, you were given a clear answer in the Met Office’s reply: the low-pass filter described has nothing to do with the workings of the model as it is running. It’s a technique used to aid analysis of a specific climatic phenomenon after the model run has completed.”
My reply was that any filtering “to aid analysis” must result in loss of information, given that there is no a priori knowledge of the signal.
Do you agree?

Wonder what these folks would say about the models:
http://statistics.ku.dk/

@RockyRoad read the paper. As far as I can tell, they’re giving scores based on looking at future data (2007-2017 and 2007-2027). How is it possible to say what model is “right” and “wrong” versus data that doesn’t yet exist?

Yeah, but if you take a bunch of them and average them together it’s better, right? Oh, I know what the problem is here; These guys aren’t climate scientists, so it’s just too complex for them to understand. I’ll bet they forgot to turn some of the data upside (you have to do that sometimes).

This post and comments include statements with the terms random and chaotic with regard to weather and climate. I wonder who gets to define the terms, outcomes, and miss or match of the results? For example, if the weather forecaster looks at a computer output and it says the High Temp for tomorrow will be X.23 and then posts X and then the actual High comes as X +2, is that forecast considered wrong? In the strictest sense, if the actual High was measured as X.23 but the forecast was X — is it still wrong? On a different level, if, for 999 times out of 1,000, tomorrow (June 15) will be more like today than if will be like January 15, 2013, is it useful to think of weather as random?
In a similar sense, with climate there seems to be a sense of sameness involved. Sometimes the terms average or normal or climatology enter into the discussions. That a person or model, or tea leaves, cannot say exactly what the high temperature and total precipitation will be for June 15, 2020 may not constitute a FAIL in the sense of climate sameness. Maybe the sameness should be compared to the entire month and not one day. And how close should the numbers be to declare “same/not-same” statements?
If June 2020 will be more like June 2012 than it will be like January 2013, is the climate chaotic? I cannot recall a June when this comparison – June to June, versus June to January – hasn’t produced the expected result. Yes, I know. There have been instances when it was cold and snowed in June (southern hemisphere folks, please don’t take offence at this NH statement) — but this is within the meaning of “sameness” in my use of the term. I don’t consider that every day has to be very close to the averages for the month for June to be “June” and not some surprising anomaly.
Consider the statements in the opening paragraph: “ . . . the path traced by a molecule as it travels in a liquid or a gas, the search path of a foraging animal, . . . ”
One has to define these in a way to make them both “random walks” because the animal is not random in the step-by-step fashion while actually foraging. A horse, say, almost never backs up while foraging in a pasture. They tend to defecate from one end and take in food at the other and they have an aversion to ingesting plant material they have just soiled. To call their movement a “random walk” requires a definition that eliminates the step-by-step movements of the horse. Perhaps, one only plots the position once every half hour. Anyway, a step-by-step movement of a molecule in a gas does not involve the definitional issue as does the foraging horse.
The people who define the path of a molecule and a horse as equivalent must also be the sort of people who can define weather and climate as random and chaotic. The phrase “The devil is in the details” seems appropriate.

“…The mathematical equations describing the evolution of the atmosphere and oceans cannot be solved analytically. Instead they must be solved numerically using computers. This process frequently involves a compromise between accuracy and stability…”
If the models uded by the Met are a compromise between accuracy and stability, then that stability must be the proverbial “immovable object”.
We’ve already seen their accuracy…
And, as a reply to mt (June 14, 2012 at 4:47 am)
“…Assuming we’re talking about the persistence model above, all the test is doing is scoring the amount of change from the baseline. This says nothing about what is right or wrong. This is at best misleading to try and judge the performance of climate models using a test like this…”
And you could say the same about the anomaly charts they pass off as “global temperatures”.
All they do is show the amount of change from their chosen baseline (averaging period). Not all charts use the same baseline, or even use the same reporting stations.
So those charts say nothing about what’s “normal”, or if we’re above or below that. This is at best misleading to try and judge “rise” in GLOBAL temperatures (with values less than a degree for most) using charts like this.

“””””…..Helen Ap-Rhisiart says:
June 14, 2012 at 12:43 am
Surely a diagram on a flat surface is in two dimensions?…..”””””
Not so surely Helen. If you do the exact same random walk yourself out in some safe place so you won’t step off a cliff; how about aschool playing field; you step forward or you step backward, depending on whether the referee calls heads or tails. You can do this till the cows come home, and you will never move sideways. so you either make it to your goal line or you do a wrong way Corrigan score at the enemy’s goal, but you never move sideways.
If Anthony plotted your path exactly as you walked it, you would see all of your steps on top of each other and you couldn’t tell anything, except the length of the line.
Time was invented to preventing everything from happening all at once, so the bottom scale on Anthony’s chart is just to time separate the moves so you can see them all. So it is one dimensional steps. In a two dimensional case, you would need to step sideways sometimes depending on some random signal.

“””””…..ferd berple says:
June 14, 2012 at 7:07 am
Philip Richens says:
June 14, 2012 at 2:27 am
Pretty certain that what RB has in mind is that (A) short term random noise (white noise) prevents models from making accurate forecasts over weeks and months and (B) the white noise is averaged out over climate time scales (decades)
=========
Correct, the climate models make the assumption that climate is normally distributed (constant mean and deviation), that the errors plus and minus average out over time……”””””
Well white noise may average out to zero after long time scales; but only in mathematical models, which have a Gaussian distribution. Physical systems don’t have such an ultimate error distribution. Most of them eventally show the presence of 1/f noise, where the amplitude of an error or step, can grow without limit, but at an occurrence frequency that is inversely proportional to the size of the error or step. One can make the argument that 1/f noise is a consequence of Heisenberg’s Principle of Uncertainty. Maybe the Big Bang, was simply the bottom end of the 1/f noise spectrum.
So in real physical systems, noise does not average out to zero no matter how long you wait, because there is always the chance of a step much greater than anything previously seen. And no; 1/f noise does not violate any thermodynamic limits, such as the total power or energy growing without limit, which it would in a white noise system. It is simple mathematics, to show that with 1/f noise each octave of frequency range contains the same amount of power as any other, no matter how large the amplitude gets.

Not to be too pedantic, but Random Walks do not model the motion of foraging animals very well.

The climate models are absolute bullshit. And they have failed, all of them. And the scare-mongers predictions of doom, going back over 4 decades, have failed, all of them. And the foundation of trumped up AGW theory, the CO2 / temperature correlation, has been debunked (see algor repeat the lie: http://www.youtube.com/watch?v=WK_WyvfcJyg). The hockey has been shown to be a fabrication. There is nothing wrong with the climate.
What amazes me is just a few short years ago nearly everybody seemed to be going along with the scam, even conservatives. Now skeptic Senator Inhofe’s position on climate change is the “new normal,” and he is leading the way: http://epw.senate.gov/public/index.cfm?FuseAction=Minority.Blogs&ContentRecord_id=eb26d140-802a-23ad-4a8f-cc2a20647095.
But, amazing yet so sad quote from the link, Inhofe said: “I was all alone starting in 2001. When you’re an army of one you don’t get much attention.” How was it that the leftist econuts and their liberal political allies duped conservatives, virtually across the board?? Someone needs to tell the story.

John Ray quote from 2009 on the longest temperature record ever, that of Central England:
“It is clearly a random walk and any trend up or down is a statistical creation rather than anything real.”
http://www.stoptheaclu.com/2009/11/28/30129/
His entire series of news digest blogs presents a calmly welcome break from the statistics bickerfest that skeptics risk addiction to, starting with http://antigreen.blogspot.com and branching out to academic politics and medical report skepticism that oft ridicules epidemiology as statistical dredging.

Mark Bofill says:
June 14, 2012 at 10:06 am
Pielke Sr. has been pointing this (more or less) out for as long as I’ve been reading his blog – climate models show no skill, over and over again. Unfortunately, if they aren’t listening to him, they aren’t going to listen to anybody. It’s pretty sad.”
It doesn’t benefit them one bit to listen to an alternative explanation. That doesn’t pay their bills.

Jim Clarke says: “Your analogy of fluid dynamics over a wing is not appropriate for predicting climate. To make it more compatible, you would have to have a wing that changed shape in response to the chaotic flow, so that the next calculation of the model was not just applying a new flow, but a new flow over the new shape predicted in the previous calculation.”

Jim, your assertion is just plain wrong-on-the-facts. Existing computational fluid dynamics (CFD) simulations *DO* take into account the nonlinear deformation, both static and dynamic, of wings under aerodynamic loading … this aeroelastic forcing is the CFD analog of climatological forcing.
As documented on Judith Curry’s weblog, aeroelastic forcing can be exceedingly large and grossly nonlinear, and none-the-less be accurately simulated.

A fan of *MORE* discourse . You write “Yet global climate changes are constrained by strict conservation of mass and energy and global increase in entropy, and thus *CAN* be predicted.”
I see others have been ahead of me in pointing out that this is utter garbage. It will be interesting to see whether you try and respond to them, here on WUWT, as you did with me on Climate Etc. I suspect on this forum, you will simply disappear.

johnwdarcher,
Click on the link to the wikipedia article for Random Walk: http://en.wikipedia.org/wiki/Random_walk
By the way, while walking into my building this morning, I passed a car with a the tag number of FTH 336. What are the odds of that?

mt says:
June 14, 2012 at 7:59 am

@RockyRoad read the paper. As far as I can tell, they’re giving scores based on looking at future data (2007-2017 and 2007-2027). How is it possible to say what model is “right” and “wrong” versus data that doesn’t yet exist?

Last time I looked at my calendar, “2007” is a number less than “2012”. By 5, to be exact.

George E. Smith; says:
June 14, 2012 at 9:08 am
“””””…..ferd berple says:
June 14, 2012 at 7:07 am
Philip Richens says:
June 14, 2012 at 2:27 am
Pretty certain that what RB has in mind is that (A) short term random noise (white noise) prevents models from making accurate forecasts over weeks and months and (B) the white noise is averaged out over climate time scales (decades)
=========
Correct, the climate models make the assumption that climate is normally distributed (constant mean and deviation), that the errors plus and minus average out over time……”””””
Well white noise may average out to zero after long time scales; but only in mathematical models, which have a Gaussian distribution. Physical systems don’t have such an ultimate error distribution. Most of them eventally show the presence of 1/f noise, where the amplitude of an error or step, can grow without limit, but at an occurrence frequency that is inversely proportional to the size of the error or step. One can make the argument that 1/f noise is a consequence of Heisenberg’s Principle of Uncertainty. Maybe the Big Bang, was simply the bottom end of the 1/f noise spectrum.
So in real physical systems, noise does not average out to zero no matter how long you wait, because there is always the chance of a step much greater than anything previously seen. And no; 1/f noise does not violate any thermodynamic limits, such as the total power or energy growing without limit, which it would in a white noise system. It is simple mathematics, to show that with 1/f noise each octave of frequency range contains the same amount of power as any other, no matter how large the amplitude gets.

Perhaps then the exercise should be repeated with a ‘Levy Flight’ random walk with fatter tails to the Gaussian distribution?

Phil. says:
June 14, 2012 at 12:29 pm

Jim Clarke says:
June 14, 2012 at 9:41 am
The fact that the models are so much worse than a random walk is proof that one or more of the assumptions (or equations) are incorrect!
This has not been shown, so this ‘proof’ fails!

But nowhere, Phil, have you been able to show that the [climate] models are BETTER!
That’s the crux of this whole issue.

CTL,
“The graph that Anthony included in the post is the example graph from the Wikipedia…” [CTL]
Whoops! Thanks. I missed that. So it was Wiki that cherry picked the graph, not Anthony.
Not that there’s anything wrong in doing that though. One would naturally choose a ‘nice’ example for illustrative purposes so that the general features were brought out clearly.
Earle Williams,
“By the way, while walking into my building this morning, I passed a car with a the tag number of FTH 336. What are the odds of that?” [EW]
Depends. But seeing as it was near your building there’s probably a fair chance that it belongs to one of your colleagues or someone else who works close by so I’d guess the probability was pretty high, and higher if you are habitually late. But it might have been a client’s or customer’s for all I know, so it depends on how often he drops by and what time he likes to arrive. And so on for other possibilities. But you know your environment far better I do.
Monitor it for a week or a fortnight though. That should give you a rough idea. But it strikes me as little odd that you bother with such distractions. Try to focus on things that are more important to you is my advice.
“…that graph in the post is sourced from Wikipedia, to put silly speculation like that from johnwdarcher to rest…” [EW]
“Silly”? Never mind my mistaken attribution, there was nothing silly about my speculation. 100 to 1 says that graph was cherry-picked  by whoever did it. There were only 8 random walks in it. That’s far too low a number to get a nice symmetric fan shape like the one shown just by chance. If you don’t believe me, run a few simulations yourself and see how many runs you need to get a ‘nice’ one like Wiki’s.
Randomness generally produces ugly patterns if left to itself when the number of ‘options’ available is small. For example, if you ever decide to tile a wall with a random pattern made with tiles of, say, only four different colours then you’d be well advised not to actually place them at random but rather to plan ahead and tweak the pattern. If you don’t you’ll end up with noticeably large ugly sub-configurations of the same colour. Again, if you don’t believe me run a simulation yourself on a grid of, say, 20 x 20 cells.
Finally, a question for you: did you bother to look at the image in the link I gave?

old construction worker says:
June 14, 2012 at 12:16 am
Climate Models: If a frog had wings,……….

Would they be pigs?

Why are we referencing Wikipedia? Are we trying to embarrass ourselves? Can we please reference valid sources that cannot be edited by anyone with an Internet connection.

Jim, your assertion is just plain wrong-on-the-facts. Existing computational fluid dynamics (CFD) simulations *DO* take into account the nonlinear deformation, both static and dynamic, of wings under aerodynamic loading … this aeroelastic forcing is the CFD analog of climatological forcing.
As documented on Judith Curry’s weblog, aeroelastic forcing can be exceedingly large and grossly nonlinear, and none-the-less be accurately simulated.

Why are you linking to “comments” at another site? Your video does not look like a simulation but a real world test. Let me know which passenger airliner was cleared safe to fly based only on computer simulations.

This is all anyone needs to know about the computer illiterates programming the climate models,
http://www.nature.com/news/2010/101013/full/467775a.html
“Researchers are spending more and more time writing computer software to model biological structures, simulate the early evolution of the Universe and analyse past climate data, among other topics. But programming experts have little faith that most scientists are up to the task. […]
…as computers and programming tools have grown more complex, scientists have hit a “steep learning curve”, says James Hack, director of the US National Center for Computational Sciences at Oak Ridge National Laboratory in Tennessee. “The level of effort and skills needed to keep up aren’t in the wheelhouse of the average scientist.”
As a general rule, researchers do not test or document their programs rigorously, and they rarely release their codes, making it almost impossible to reproduce and verify published results generated by scientific software, say computer scientists. […]
Greg Wilson, a computer scientist in Toronto, Canada, who heads Software Carpentry — an online course aimed at improving the computing skills of scientists — says that he woke up to the problem in the 1980s, when he was working at a physics supercomputing facility at the University of Edinburgh, UK. After a series of small mishaps, he realized that, without formal training in programming, it was easy for scientists trying to address some of the Universe’s biggest questions to inadvertently introduce errors into their codes, potentially “doing more harm than good”. […]
“There are terrifying statistics showing that almost all of what scientists know about coding is self-taught,” says Wilson. “They just don’t know how bad they are.”
As a result, codes may be riddled with tiny errors that do not cause the program to break down, but may drastically change the scientific results that it spits out.“

This paper [Validation and forecasting accuracy in models of climate change] does a poor job phrasing skeptic arguments,
“From a scientific perspective, disbelief in global warming is found in the work of the Heartland Institute and its publications (Singer and Idso, 2009) and supported by the arguments of a number of eminent scientists, some of whom research in the field (see Lindzen, 2009)”
It is not “disbelief” in “global warming” but skepticism in “anthropogenic global warming alarm”.

Poptech,
“This is all anyone needs to know about the computer illiterates programming the climate models…” [Poptech]
That problem with scientists and programming is decades old as you probably know anyway, but it’s taken Nature that long to find out!? — assuming that’s their first piece on it (but I wouldn’t know).
I’ve seen this from both sides as for a short while I did a stint as a analyst/programmer in industry when I was very young. (It wasn’t called IT in those days.) You’d get a good bollocking if you broke documentation, coding or annotation standards. Rightly too. And sacked quickly if you showed any hint of persistence at it. But you found out PDQ why they are an absolute necessity. Yes, I learnt that the hard(ish) way, but very quickly. “Programming is easy; anyone can do it.” Well, certainly much of it is but that isn’t the point. The point is can someone else pick up from where you left off (can you yourself?), i.e. without having to read through a load of spaghetti code you wouldn’t feed to a dog trying to work out what the hell is going on and at what stage things are at, or worse, going right back and starting from scratch again, which is the only sensible thing to do under those circumstances. Subsequent changes in specification and the resulting updates are inevitable so the thing needs to be designed with those in mind. Blah blah blah…  And your time is someone else’s MONEY!
Then jump back into a science environment where everyone is his own god when it comes to systems and programming. What a f..king mess! The worst kind are the prima-donna cleverdicks — they’re the real dummies, and can be very secretive too! Precious, eh? And fun to work with? But it’s impossible to convey to any of them why things need to change. You can explain it all you like but it makes no difference. What they’re doing at the moment is far more important than “mere housekeeping”. If someone leaves and someone else has to pick up the work? “It’s just programming isn’t it?” Right. No. Wrong. In fact, you couldn’t be wronger.
Get out of that environment as fast as you can before you kill someone. I did.

Until the “scientists” can input the “butterfly wing beat” and about a million other data points, no climate/weather model, will work further than a week out……

LOL … Poptech, in aircraft design, computational simulation has replaced “only” 90% of wind-tunnel testing.

That is not what I asked, strawman. Nice video though of an action experiment not done on a computer simulation.

It’s pilot training that is now done 100% on simulators. 🙂
Yes, it’s completely possible — even common nowadays — for a commercial pilot’s first “real” 787 flight to have paying customers aboard.
The reason makes perfect sense — simulation training is superior in detail-and-depth to flying the real thing.

This is a red herring to my actual question. So someone never actually flies a real aircraft before becoming a pilot and flying real passengers for the first time? Or are you just talking about pilots already certified to fly other aircraft? There is a big difference.

Poptech says:
June 14, 2012 at 11:34 pm
This is a red herring to my actual question. So someone never actually flies a real aircraft before becoming a pilot and flying real passengers for the first time? Or are you just talking about pilots already certified to fly other aircraft? There is a big difference.
There *is* a big difference.
In student pilot training, both sims and static flight training devices (FTD) are great tools to teach the fledgling proper engine starting procedures and flight control application and correlation. In rated pilot transitions, they knock a couple of hours off cockpit familiarization drills for pilots transitioning into an advanced aircraft, which allows the instructor pilot to concentrate on maneuver and emergency procedures training.
Sims have three huge advantages over an actual aircraft — they’re cheaper to operate, they eliminate the “fear factor” of dying if the trainee performs emergency procedures incorrectly or too slowly, and they’re perfect for instrument flight training.

In their paper, Fildes and Kourentzes commit the error of conflating projections with forecasts. The climate models make projections and not forecasts.

Bill Tuttle says:
June 15, 2012 at 2:26 am
I have sim time in B757 and B767. They are easy to ‘fly’ on instruments (with both engines turning). The emergency procedures manual for a seven five contains more information than did the entire flight manual for the high performance single engine retract I once owned. I have far more confidence in a flight simulator than I do in any climate model program. Far more.

Babsy says:
June 15, 2012 at 8:47 am
I have sim time in B757 and B767. They are easy to ‘fly’ on instruments (with both engines turning). The emergency procedures manual for a seven five contains more information than did the entire flight manual for the high performance single engine retract I once owned. I have far more confidence in a flight simulator than I do in any climate model program. Far more.
I used to troubleshoot sim glitches in the old Singer-Link mechanical “Blue Canoe” for First US Army, and the basics for writing a good sim program all date from the days when somebody decided that seat-of-the-pants flying in the clouds was suicidal. A well-written flight sim program is a dream to work with — the one for the Mi-8MTV-1 I’m running now is *not* a well-written program.
And you’re right — even a poorly-written flight sim program will consistently give you acceptable results.

Phil. says:
June 15, 2012 at 10:48 am
They certainly are better than a random walk, which they haven’t been compared with. In his intro Anthony correctly describes a random walk, however the paper doesn’t compare against a random walk.
The paper *does* say that plotting random numbers — “using the last period’s value in each location as the forecast for the next period’s value in that location” — resulted in a more accurate forecast than those produced by the models.

@Poptech — *hat tip*

I find the following statement by the (admittedly articulate) Jim Clarke to be the perfect culmination (to this point) of the most important exchange in this whole comment thread, and one that simply exemplifies again for me the fundamental cognitive deficiencies in those who reject the science showing the hazards of anthropogenic global warming.
Jim Clarke says:
***Are these statements [where Fildes and Kourentzes affirmed the fundamental science of CO2 as a primary driver of climate change and the dangers inherent from it] derived from the results of their research? No! They contradict the research! Authors make these statements because they like their friends, their jobs and their paychecks.***
Jim: How the *hell* do you know that?
What is *really* more likely–that Fildes and Kourentzes are psychically intimidated into denying the conclusions of their research because the “warmist” conspirators will take away their jobs and/or social standing, or that *you* just don’t understand what they are saying? Seriously, which is *really* more likely? I seriously doubt you read their whole article, Jim. (I read as much as I could grasp, which was certainly not all of it, I’ll admit.)
And your conflation of “theory” and “model” is just . . . (what is a polite word?) . . . unsound. Here’s an analogy:
I have a “theory” that the gunpowder in the shell of a firearm is a “primary driver” of the round into the target. But I doubt an adequate “model”–computer generated or otherwise–could ever perfectly *predict* exactly where the round will land. But whether it strikes a human in the ear or the temple does not make them any less dead one way or the other.
It’s the same with climate change: sure enough, it *might* be that prevailing models overestimate climate sensitivity, it ends up being not that big a deal, and increased global temperatures and it’s associated ills are managed fine without any heroic interventions. (Maybe an unforeseen factor such as a gust of wind will take the bullet off target and spare the target’s life.) Great. No one will be happier about it than I.
Maybe. *Or*, our climate models are wrong in the *opposite* direction–that we are taking an even *bigger* chance with our environment than we realize. Maybe–just maybe Jim–this is why Fildes and Kourentzes affirmed the fundamental science of CO2-induced climate change *and* it’s associated risks–and that this is why we should continue to develop and improve climate models.
But you seem to know better, don’t you Jim? You can read between the lines of an article you skimmed (at best) and determine where the authors are telling you what they really think (such as when they seemingly debunk climate modeling–the part you like) and when they’re towing a “party line”. An amazing talent, that–and one that a number of self-described climate “skeptics” seem to have.
And of course the administrator of this site simply (and of course incorrectly) linked to the *Journal of Forecasting*, blithely assuming the 2011 article proved his point, but course it was the wrong journal, showing that AW, whatever his merits as an individual, did not read the original article. He just saw, believed, and linked.
Why did I bother posting this? Not entirely sure, really. It’s just a question of time before AW censors me.

Next up on WUWT: Afo*M*d checks in with an exposition of how the improved graphics in Flight Simulator XXI demonstrate continuing improvement in climate modeling…

snaparooni says:
June 16, 2012 at 3:39 am
rgbatduke at June 14, 2012 at 8:50 am: “To which the physics professor then replied, “Not even you with all of your philosophy and the pen in hand can do that — but at least I understand why.”
Who is it, after all, that drops the pen?”
That’s excellent! I wish I could travel back in time and use that on the philosophy prof.!
Actually, the “why” he drops the pen is the province of philosophy. The “how” he drops the pen is the physics prof’s bailiwick.
Unless, of course, the physics prof had previously observed the philosophy prof eating popcorn and realized his fingers were still coated with butter…

The most obvious problem, is no one should ever reference Wikipedia since it is a completely unreliable resource that can be edited at will by anyone with an Internet connection. Almost every time I read a Wiki page I find something wrong.

“Wattslings”?? Strike two.
Another pejoritive insult like that and your comments will be deleted. -mod]

Skip, we all “care” what the other side says, we just make sure to verify their claims which frequently turn out to be bogus.
I also know you believe yourself to be the great “moderate” one. Someone rational in a sea of irrationality. The one who can see what others are missing. You are so “objective” in your mind it defies imagination. You are so above all of us peons. There are a select few popular commentators here who share your delusions of grandeur and follow the pied pipers no matter where it leads,
http://www.populartechnology.net/2012/05/truth-about-judith-curry.html
http://www.populartechnology.net/2012/06/truth-about-richard-muller.html

Phil. says:
June 16, 2012 at 11:14 am
Bill Tuttle says, June 15, 2012 at 11:33 am: “The paper *does* say that plotting random numbers — ‘using the last period’s value in each location as the forecast for the next period’s value in that location’ — resulted in a more accurate forecast than those produced by the models.”
That isn’t a ‘random walk’, it’s persistence.
I didn’t say that was a random walk, I said “plotting random numbers resulted in a more accurate forecast than those produced by the models.” You haven’t refuted or rebutted that statement, you’ve merely nattered on about the definition of a random walk.

skip says:
June 16, 2012 at 2:36 pm
***You first have to *prove* CO2-induced climate change exists before you can affirm it.***
I confess I do not even really understand what your point is.
The point is that someone who makes an unsubstantiated allegation and then draws a conclusion based on the assumption that the allegation has been substantiated merely because he stated it is indulging in a logical fallacy.
Aside from the pointlessness of the distinction between “proof” and “affirmation” in this context,
You consider it pointless because you have no rebuttal to it.
you’re missing the *key* point that Fildes and Kourentzes are more in *dis*agreement with Anthony Watts than Watts realized when he posted the comment about their article.
You’re introducing an external argument in an attempt to nullify my statement, which is further evidence that you can’t think of an effective rebuttal — because you have none. And Robert E. Phelan (June 16, 2012 at 10:45 pm) effectively eviscerated you on your “key point” —
“Anthony linked to the paper that Dr. McKitrick was apparently referring to in his article, so his readers could judge for themselves, and YOU criticize him for it?”

. . . yours stands pretty much alone in claiming that your posts are being “censored”.
Great argument, Smokey. Have you considered that maybe this is because most people who study climate think this blog is a joke, and the vast majority of this blog’s participants are people such as yourself—who cannot, for example, even grasp the simplest point of a debate thread? (See below)
Just like you fail to understand the scientific definition of ‘evidence’, you cannot even understand the definition of ‘censorship’. Censorship is the muzzling of free speech by the government.
No Smokey. It need not be only by the government. Consult a dictionary, please. And in any event, even if the word *was* linguistically inappropriate, this blog still engages in its equivalent: exclusion of threatening ideas.
your comment was [snipped] due to violating site Policy.
I have no doubt of that. Unofficial (but preeminent) policy on this blog is that Anthony Watts may never be shown to be in error.
For example, you flatly denied ever appealing to authority – while repeatedly citing computer modelers Fildes and Kourentzes.
Oh . . . my . . .God. You still don’t get it, do you, Smokey. I *never* appealed to their “authority”. They were *Anthony Watts’s* source! Do you still not grasp this? I beseech the administrator of this site: Mr. Watts, do you believe Smokey grasps the dispute on this thread? Please answer that simple yes or no question.
As for your naive belief that RealClimate does not censor . . . .Not one of my comments has ever made it out of moderation.
Smokey, there is no way to say this nicely. Based on my interactions with you on this forum, and your repeatedly demonstrated inability to follow even the simplest chain of argument, I strongly suspect *that* is the reason your comments were declined: they were not perceived as threatening, there were perceived as valueless. This is *not* to say I think you’re a bad person; for all I know you’re a great guy outside scientific discussions; only that your obvious inability to grasp even the point of this thread discussion suggests the quality of your submissions to RC were rejected for similar reasons.
And you will not be ‘censored’ here for expressing a different point of view, no matter how wrong it is.
Except I have. I *dare* Anthony Watts to deny it.
And I know Anthony Watts to be uncommonly honest. He is a straight shooter.
Great. Then I challenge you to ask him to be honest about this. He censored me on the Tsunami thread when all I did was ask him a direct question to which he had no good answer and I called him on it. That’s your straight shooter for you, Smokey.
AGW is an untestable, unquantifiable conjecture. AGW may be true. Or not. But it is un-measurable, non-reproducible, and un-testable. It is an opinion, based vaguely on radiative physics.
And an opinion which is shared by every major scientific organization in the world—but you know better, don’t you Smokey—even though you *can’t even follow the discussion on this thread.*
And on you go with the rest of your post—the tired and foolish talking points of those who cling to pseudo-science to deny reality: the missing hot spot, etc. Incredible.
And of course all this your attempt at a distraction from my key points, which I challenge you to specifically deny:
1. AW [unsuccessfully!] linked to an article *he had not even read*.
2. The authors of said article *don’t even agree with his position on climate change.*
3. The readership of this blog—none of whom had read the article either—accepted AW’s portrayal of the article (as demonstrating that computer models of climate have no value—which is not even exactly what the authors were saying, but that is another point) without question and without investigating the quality and perspective of the source themselves. This says quite a bit about both the administrator and the readership of this blog.
Direct question, Smokey: With of the above assertions do you dispute?

Skip, I don’t think there’s another poster here who’s continually proven himself so devoid of intellectual honesty, logical consistency, or just plain civility as you are. Your continued presence is proof that you’re not being censored.

Bill Tuttle says:
June 15, 2012 at 11:33 am
‘The paper *does* say that plotting random numbers — “using the last period’s value in each location as the forecast for the next period’s value in that location” — resulted in a more accurate forecast than those produced by the models.’
Such a process is called a martingale.
PS to all: Stop bothering with Skippy. He is just a typical thoughtless drone, his comments provide no value added and do not merit the attention.

I would love to comment further on the . . . . “contributions” . . . made so far on this thread. But I want to hear everyone who has commented so far to tell me why they think *Anthony Watts* has remained silent to this point.
It’s just a simple question! Please–enlighten me!

[Note: Here are a few more of skip’s personal attacks and insults on Anthony Watts and the readership of WUWT]:
“Anthony Watts’s rank blunder is …an embarrassment to Mr. Watts and everyone who reads this blog. …so intellectually lazy…another humiliating example of why the mainstream scientific community rejects Anthony Watts and his ilk… The blunt and ugly truth is that they think Anthony Watts is the scientific equivalent of a court jester, and his acolytes, such as yourselves, are easy marks for his nonsense… And your collective unwillingness to acknowledge Mr Watts’s – and your own — blunders is yet further confirmation of it.”
[The rest were snipped, with a fair warning to ‘skip’: any more of the same will get your entire post deleted. Stick to the science and you will have no problem, your comments will be posted no matter what your scientific point of view. And note also that Anthony had nothing to do with this particular comment moderation. ~db stealey, mod.]

[snip. ~dbs, mod.]