A Modest Proposal—Forget About Tomorrow

The AGW modelers are just a money sink. Money sinks are crucial for Keynesian economics to work. Without money sinks, the money wouldn’t know where to go.

Dear Mr. Eschenbach
A slight typo:
“So it looks like John Von Neumann was right, you can fit an elephant with three parameters, and with four parameters, make him wiggle his trunk.”
however the PDF states:
“you can fit an elephant with four parameters and with five, make him wiggle his trunk.”.
Appreciate the article and the PDF!
John :-#)#

“I would think that after the unbroken string of totally incorrect prognostications from Paul Ehrlich and John Holdren and James Hansen and other failed serial doomcasters, the alarmists would welcome such a hiatus from having to dream up the newer, better future catastrophe.”
Nope Mann is hard at it but this time Manns appearance in Minneapolis gets occupied by protesters!
http://www.msnbc.msn.com/id/45107032/ns/us_news-environment/#.Tq8DE_SP5LI

That is a great find! many thanks.

Even the inventor of the computer ‘had problems’….
“On two occasions, I have been asked [by members of Parliament],
‘Pray, Mr. Babbage, if you put into the machine wrong figures, will the right answers come out?’
I am not able to rightly apprehend the kind of confusion of ideas that could provoke such a question.”
– Charles Babbage (1791-1871)

When their sound physics models start doing something useful, like winning the lotto…
…I’ll start paying attention
..or even predicting tomorrow’s weather….or hurricanes

Willis:
The models are tuned to match past climate, without a strong understanding of the factors involved and the complex relationships between those factors. There is quite a bit of fudge factoring and outright guessing in the models. For example, Hansen recently talked about the failures of the models and their overestimation of ocean heating. The problem was, they just made up that ocean heating based upon their theories, not any facts. It was the only way they could think of to make the models work.
It’s sort of like how GISSTemp works — the models assume the poles will warm faster, and since there aren’t any surface stations at the poles, just project the temps based upon the models. Viola! Global warming!
Climate models should best be considered as hypothesis. As we’ve seen time and time again, they fail to properly predict the real climate. This should take the modelers back to the drawing board, but their is too much time, money, prestige and ego invested, and too much at stake, to admit they are wrong and start over.

Leif Svalgaard says:
October 31, 2011 at 1:46 pm
If the ‘model’ is plain curve fitting it may indeed have no predictive capability. If the model is based on sound physics it usually will have predictive capability, unless it is too simple.

And then there’s Pioneer 10… off course with no explanation. Sound physics, prediction wrong.

I’d like to add another Larry’s Law. (I’ve lost count of what the number should be.)
No clothes does not necessarily imply no emperors.

Willis, all these doom casters are good for media sales of newspapers and TV shows so they get plenty of encouragement for what they do. If you don’t know how deeply corrupt the media is then you’re not paying attention.

Surely the term ‘calibrated model’ a bit of an oxymoron insofar as it is limited to the data for the calibration and the extension of said data via the output/prediction is only dependent on the available data? So it’s a bit of a chicken and egg situation in respect of output reliability – of course, when one adds in the chaotic non-linear relationships of something say, like ‘the climate’ – well, the world is your funding oyster…..
I agree with Leif – a sound physics based model is the only likely useable/useful one. (in terms of predictive capabilities).

Leif Svalgaard says:
October 31, 2011 at 2:37 pm
All physics must be included. For the spin-stabilized Pioneers thermal emission from the spacecraft are likely the cause. For the attitude stabilized Voyagers there is no anomaly.

So, to apply this to the previous topic then, are we sure all physics in their first and second order influences are included in any model of earths climate? It took how many decades to arrive at RTG radiation imbalance (still not universally accepted as the explanation, btw), and with fewer decades for climate models (a much more complicated mathematical problem) we’re expected to believe there is predictive value.
Newtons laws are simple. Einsteins improvements on that can be taught to high-schoolers. Yet still the best and the brightest failed to predict what happened with something they built. How much confidence can anyone place in predicting the direction of that which was built by entropy?

Willis – don’t you forget : they’re still giving prizes out to Ehrlich. Being right was never their business, being gloomy it was.

Tinkertoy … now that’s something I hadn’t seen in a long time. Used to have so much fun building things with ’em. Let the climate scientists build their models out of tinkertoys … that’ll keep ’em busy for awhile.

“Leif Svalgaard
If the ‘model’ is plain curve fitting it may indeed have no predictive capability. If the model is based on sound physics it usually will have predictive capability, unless it is too simple.”
This is what the enzymologists believed, then they found out that even if you knew the rate constants of even simple enzymatic pathways, classical descriptions didn’t work. The led to metabolic control theory, then control theory. The biochemists were of course re-inventing the wheel to a large extent, economists had already made the leap some 50 years earlier. The ides of control coefficients and elasticity, along with a switch to the use steady state kinetic analysis, rather than using near-equilibrium thermodynamics to explain non-equilibrium states,
Hard that you may find it to believe Leif, complex model that are trained are in fact fits and are about as useful as a one legged man at an arse kicking party.
This has been shown time and again.

IPCC TAR 3 ;
“In climate research and modeling, we should recognize that we are dealing with a coupled non-linear chaotic system, and therefore the long-term prediction of future climate states is not possible”
http://www.realclimategate.org/2010/11/are-computer-models-reliable-can-they-predict-climate/

Leif Svalgaard says:
If the ‘model’ is plain curve fitting it may indeed have no predictive capability. If the model is based on sound physics it usually will have predictive capability, unless it is too simple.

Considering what we now know about the climate, such things as the variability of solar UV radiation, the effects of biology on climate, variability of cosmic rays and their possible effect on clouds, and especially the fact that clouds appear to have much more complex effects than the models show, how can current models not be too simple? Some of these factors appear to dwarf the effects of CO2. There is also the fact that we have large data holes and areas where the data is very suspect. And those are just some of the known unknowns…
In addition, I can make a model with perfect physics, but if you allow me to hide my data and my code, I can get you any prediction you like, highest bidder gets it.
Finally, when it comes to CO2 and it’s effects, what we are primarily concerned with, the increase of downward longwave radiation due to increasing CO2 over time has never actually been directly measured. While the physics seems perfect for the models, until we measure it out there, in the real world, and see whether it is actually increasing or whether some other effects (such as evaporation and clouds, for instance) are overwhelming it, we really don’t have any way to tell if the models output is correct. After all, this idea, increasing downward radiation, is the very central idea of AGW. If we could somehow directly observe this, the central idea of AGW, and see if it is in line with the model predictions, then at least we would have something to verify or falsify the central idea behind the models. Right now, we are merely imperfectly and partially measuring what we believe are secondary effects from that. We are merely making grand assumptions that it will work out there the way it works in our models.
Note that one of the big problems is that, indeed, the models were specifically made to show that increasing CO2 has a catastrophic effect on climate. What we need are models that are there simply to understand the climate, not to deliberately try to show the catastrophic effect of just one variable among a great many that effect the climate. We need to stop deliberately trying to prove something and start to do the spadework of understanding the climate enough to be able to prove anything.
In short, the models are, in fact, too simple.

It actually worked as well as economic forecasts normally work.

I have seen this headline many times economists were suprised today when…”. Predicting the economy is similar to predicting future climate, especially when the climate predicters are not trying to predict what will happen, but what they wish to happen. Basically, when you try to predict the economy, you ae trying to predict the actions of a very great many unpredictable people, and one of the chief unpredictabilities are your won biases. There are simply too many variables, and you are one of them, and your variables (biases and wishes) can overwelm all the others.
Mnay times, the economists are simply telling the government types what they want to hear. The universities, paid for by the government, are teaching them how to.

Zac says:
October 31, 2011 at 2:37 pm
Ref the use of RN deck log books and ‘met obs’
I asked a long time ago why the 100s of years of Merchant Navy deck log books weren’t being used as climate data source; along with ‘Met Obs’ (which I think started not long after Radio).
I was trampled on – no quality control – bad readings – unknown errors – the anemometers were wrong; the chronometers were wrong (so the synoptic hours were wrong); the barometers were wrong and not calibrated (ha bloody ha !); the water temperature was wrong ‘cos the bucket wasn’t deep enough (SURFACE temp); temperature was wrong ‘cos of the engine cooling water…. .
I think the responses I got from the AGW advocates that I asked (initially in all innocence) where the final nail in the personal journey of becoming an AGW denier.
I spent too many hours as a ship’s Radio Officer (helping to prepare and) sending OBS to accept being told so many people had spent so much time and effort for nothing – especially as what they claimed was patently wrong. ( I remember being complained at by a 3rd mate about ‘bloody sparkies’ always want the OBS done properly; not fudged … MET OBS took about 10 minutes to tabulate – 3 minutes to code and anything up to 30 minutes to send – 3 minutes on the key to actually send after 25 minutes of calling & queuing…))
And of course; following Anthony’s surface station expose; I would bet that ship’s OBS are a hell of a lot more accurate and reliable than many land based weather stations – even after some of the lousy morse operators had managed to mangle it all !

You say projections,
I say predictions,
Let’s call the whole thing off..

Willis, in case you haven’t noticed, most model studies are aimed at gaining understanding. But part of that understanding comes from comparing model predictions of what we should be seeing with data collected in the real world.

If models were so very good there would be no role for test pilots

People don’t expect that : “The model may have no predictive capability despite being a perfect model. The model may represent the physics of the situation perfectly and exactly in each and every relevant detail. But if that perfect model is tuned to a dataset, even a perfect dataset, it may have no predictive capability at all.”
Let’s have an example: the lottery machine. The physics and data are known but it is very difficult to forecast the lottery numbers. The runs are not repeatable – you get different results. That is what chaos theory is talking about.
Is this what the paper is preferring to?

Leif do you believe the various climate models to be reasonably physics based mathematical simulations or are they highly trained fits that contain little physical information?
Pick one or the other Leif.

The second definition is pretty much the definition of chaos : you can know everything about a system and still can not predict the future. Chaos theory was developed to decribe the impossibility of accurate long term weather forecasting [longer than 3 days!]
With the climate change models it is equivalent to measuring a sine wave from the trough to the steepest slope and then projecting that as a trend! As the sine wave flattens out you just ignore it, or claim it is just temporary, or claim there is a counter effect, etc. What you don’t do is admit you are wrong, ever!
Normally when people favour fantasy over reality they are regarded as insane, but when you get huge amounts of money for this behaviour accusations of insanity moves to the funders, governments. But then you realise rich friends of the governmnets can make huge amounts of money ‘fighting climate change’. So there is no insanity – just a gullible public.

Fron p. 197 of the article: “The very large spike, with h roughly 10, corresponds to the truth case. We can also see notable local optima with 0 < h < 8, 30 < h < 38 and 40 < h < 45. The global optimum has a small basin of attraction around it and has proved diffcult to identify in previous
work[2], the easiest optimum to find has been the one with 30 < h < 38. The rather noisy structure of the objective surface is largely an artifact of the of the way that kg is sampled."
Now I'm not a physicist. But I am an experimenter and statistician. When I read this, it sounds to me like the problem here is an artifact of either: (1) the estimation method (a genetic algorithm); (2) the data sampling interval; and/or (c) the nature of the model itself, which gives rise to an irregular objective function in the parameters.
If I were leading a group of grad students, I would be asking them questions like these. Suppose we sample this at a much higher rate; would that help? Suppose we use a different estimation algorithm: Would that help? Suppose we don't worry about estimating one of these parameters, but instead use some outside estimate (or prior information)…Would that help?
Or, put differently. I frequently tell students that 90% of the statistical thought should have happened before you ever started collecting data (or running an experiment if you are lucky enough to be able to). You should already have simulated your model and asked: What is a good sampling plan? How many observations do I need, and at what interval? If I have a lot of confidence in some prior information, should I incorporate that, and how?
I don't see much of this kind of thinking in the article Willis cites. Perhaps that is irrelevant to the evaluation of climate science business-as-usual. But I would hate for people to take away the wrong message from an example like this one. In this example, the experimental design is given, and the poor message is perhaps as much a result of that as the underlying physics (or whatever). This is, to my mind, a good example of what can happen when you don't think carefully about the design of experiments… at least as much as an example of fundamental uselessness of models.
Just sayin.

RE: Leif Svalgaard
Define “sound physics” in terms of a complete, validated and verified, system model for the climate. Get back to me when you are finished.

I’m betting that F=MA is not strictly speaking perfectly correct.

Jaye Bass says:
October 31, 2011 at 9:21 pm
Define “sound physics” in terms of a complete, validated and verified, system model for the climate. Get back to me when you are finished.
You can prove me wrong by reading and understanding Jacobsen’s text book describing the physics: http://www.stanford.edu/group/efmh/FAMbook/FAMbook.html

Albert D. Kallal says:
October 31, 2011 at 8:26 pm
“The idea that we don’t know where the baseball is going to land is NOT an excuse or proof that we throw out the laws of physics and that the lottery balls don’t follow a set of math and rules here. ”
No is not, but that this not the issue.
Both physics and computer programs are deterministic. Same inputs give same outputs. But models are simplifications of reality. The output of reality and output of the model do not match. Real computer programs have limited precision in data storage and calculations. Real life physics can’t be expressed by simple equations. You can’t have indefinitely precise measurements of the input parameters either. When run your model thousands of steps the errors aggregate.

This post reminds me of high-school math (or was it early college) about interpolation, extrapolation and polynomials. Through any set of n points one can draw a n-1-order polynomial and there’s even an explicit expression for it, the Lagrange interpolation formula (if I remember correctly). It will have no error in this context but NO predictive value, i.e. it will be totally useless for extrapolation. However, if one knows something about the physics behind the data, one can allow interpolation errors, lower the interpolation polynomial and retain some predictive value of the model. Better yet, one can fit the data to a function that has something to do with the physics and have even more predictive power (but that’s another story).

Willis,
I’d say this only shows that their objective functions are not suitable.
I find it rather weird that a calibration on a seven month period (Fig. 2b, Equation 2) gives a better-defined optimum calibration result than a calibration on a 36-month period (Fig. 2a, Equation 1). You’d expect 5 times more data to result in a less noisy objective function landscape, not the other way around.
This point is only made more clear when they add modeling error and the best tuning of their parameters (Fig. 4a) actually gives a completely wrong throw (h).
I don’t think this article says anything about predictive value of models in general.

TimTheToolMan says:
October 31, 2011 at 11:00 pm
Parametisation is fitting not “sound physics”.
It is if the parameter is based on the physics involved.
An example is the Charnock relation:
http://www.termwiki.com/EN:Charnock's_relation

@E.M.Smith
“There are ‘hydraulic computers’ for various uses ”
+++++++++
My father was involved in the early 1960’s in the development of a ‘teaching machine’ that used hydraulic logic circuits that were impressed onto the side of a plastic card the size of a standard business card. These could be removed (as from a countertop PayPoint credit card machine) and swapped for another one, which had the effect of reprogramming the ‘computer’. It had no electronics in it.
Perhaps this was developed from the hydraulic transmission science, or preceeded it. Not sure. It had control gates, amplifiers and controllable ‘current’. The circuits looked like squashed spiders and had common water connection points arranged in a grid so they were interchangeable.

Jeff D says:
October 31, 2011 at 10:47 pm
Cracks me up. We cant even measure temperature properly. Why would we think that a model could even come close for long term predictions. Is it worth the effort to try? Yes, but don’t confuse this with reality and try and convince me the end of the world is nigh. Just last Tuesday the models said that it was going to be clear and sunny sky’s for the next 5 days. It was raining on Thursday. Most of the time they get pretty close but even short term it is so chaotic as to almost be useless.
Speaking of proper measurements. How accurate are the CO2 readings say for the last 20 years?
>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>>
The first question is WHERE.
One of the first “ASSumption” made is that CO2 is well mixed and uniform. This ASSumption is accepted by most skeptics despite the efforts of a few scientists.
If I get the physics at least sort of correct, the interaction of Long Wave Radiation emitted by land. is much higher at the bottom of the atmosphere then at the top. This is because the density of air follows the Gas Laws.
Therefore the “Green House Gas” interaction that is the center of all this is MORE dependent on the CO2 at the bottom of the atmosphere then at the top. I am sure there is some sort of height above sea level vs interaction math or there should be.
So what is the CO2 concentration at the ground level????
WHEAT:
“…The CO2 concentration at 2m (~6 ft) the crop was found to be fairly constant during the daylight hours on single days or from day-to-day throughout the growing season ranging from about 310 to 320 p.p.m. Nocturnal values were more variable and were between 10 and 200 p.p.m. higher than the daytime values….”
CO2 depletion “…Plant photosynthetic activity can reduce the CO2 within the plant canopy to between 200 and 250 ppm… I observed a ppm drop within a tomato plant canopy just a few minutes after direct sunlight at dawn entered a green house (Harper et al 1979) … photosynthesis can be halted when CO2 concentration approaches 200 ppm… (Morgan 2003) Carbon dioxide is heavier than air and does not easily mix into the greenhouse atmosphere by diffusion… “
From this it is obvious that:
1. Plants can RAPIDLY change the concentration of CO2 from ~400ppm down to as low as 200ppm.
2. The change in CO2 depends on the presents or absence of sunlight.
3. Wind bringing more CO2 to the plants is a bigger factor than diffusion.
4. The amount of sunlight changes every day of the year and through out each day.
5. Wind changes
6. Plant activity changes with temperature. (Hard frost can stop some but not all )
So even if there is an “even distribution of CO2” somewhere higher in the atmosphere, at the lowest level of the atmosphere where a lot of the “action” is, the amount of CO2 is chaotic.
(By the way do not forget all the sources like volcanoes or termites emitting CO2)
Seems to me “Modeling” CO2 is a lot more complicated then is ever admitted. Not surprising since the whole objective from the very start was to show industrialization by man was hurting the environment.
From Mauna Loa’s methodology:
4. In keeping with the requirement that CO2 in background air should be steady, we apply a general “outlier rejection” step…. http://www.esrl.noaa.gov/gmd/ccgg/about/co2_measurements.html
…..They also pointed out that the CO2 measurements at current CO2 observatories use a procedure involving a subjective editing (Keeling et al., 1976) of measured data, only representative of a few tens of percent of the total data. … http://www.co2web.info/esef3.htm
The information presented to the public is Less than one percent of the data??? This is the “cherry picked” data used in the climate models??? Most “Skeptics” with few exceptions agree with “the requirement that CO2 in background air should be steady” despite an ever changing amount of sources and sinks in the real CO2 dynamics Changes mixed only by diffusion and the wind.
Graph of CO2 measurements by different labs: http://img27.imageshack.us/img27/3694/co2manytrends.png
THE POLITICS
Discussion of the politics and science of CO2 measurement : http://www.co2web.info/
For example:
“…The acknowledgement in the paper by Pales & Keeling (1965) describes how the Mauna Loa CO2 monitoring program started:
“The Scripps program to monitor CO2 in the atmosphere and oceans was conceived and initiated by Dr. Roger Revelle who was director of the Scripps Institution of Oceanography while the present work was in progress. Revelle foresaw the geochemical implications of the rise in atmospheric CO2 resulting from fossil fuel combustion, and he sought means to ensure that this ‘large scale geophysical experiment’, as he termed it, would be adequately documented as it occurred. During all stages of the present work Revelle was mentor, consultant, antagonist. He shared with us his broad knowledge of earth science and appreciation for the oceans and atmosphere as they really exist, and he inspired us to keep in sight the objectives which he had originally persuaded us to accept.”…” http://www.co2web.info/ESEF3VO2.pdf
OBJECTIVES???? PERSUADED us to ACCEPT???? What do those words have to do with science?
This is just the CO2, then there is Water in all its forms not to mention the shennanigans with temperature.
In other words the input data is bogus. GIGO

I really hate this dinosaur of a computer.
The quote with out the word drop was: “I observed a 50 ppm drop in within a tomato plant canopy just a few minutes after direct sunlight at dawn entered a green house (Harper et al 1979) “ Source

Leif at 8:04 a.m.
You left out a couple of the most obvious (at least as they relate to lack of confidence):
5) A basic realization of the kinds of issues that need to be treated in the models, which are admitted to not be fully treated well (clouds, for example).
6) The models’ abysmal failure to accurately predict the last decade plus.
7) A general healthy skepticism about complex, multi-paramater models, which to date have not been demonstrated to have predictive value.

Leif Svalgaard,
I agree that models are worth some investment. But given all the diagnostic issues that have been reported: correlated surface albedo bias, under representation of precipitation, under representation of solar cycle signatures, poor regional performance, questionable cloud feedback, etc., they shouldn’t be being used for attribution and projection yet, and that is before the issue of local vs global optima raised by the paper is considered. There is a good chance that climate might be predictable, given that even a 1960s geography textbook will probably only be off by one to two hundred miles in climate zone locations in the year 2100, even in worst case scenarios.
If you have come this far, you have to agree that the IPCC conclusions and confidence were premature, and reporting model projections without error range estimates due to the diagnostic issues was unscientific.

Leif Svalgaard,
The fluid and mass balance equations are just a small part of the climate models. Most of the rest of the physics is parameterized. BTW, I believe the times steps are much longer than 5 minutes, which is why they use “white sky albedo” another parameterization. The time steps are in the range of two to four hours, at least for the AR4 models. Maybe they are doing shorter time steps now.

Leif Svalgaard says:
October 31, 2011 at 1:46 pm
If the ‘model’ is plain curve fitting it may indeed have no predictive capability. If the model is based on sound physics it usually will have predictive capability, unless it is too simple.

The problem is that even if we perfectly understood the physics, the computer to perfectly calculate the climate would have to be the size of the known universe. 😉
Surely you would agree that cosmic rays influence the climate. That means that, not only do we have to predict the sun’s activity (because that modulates the cosmic rays), we have to predict the activity of the heavenly bodies supplying the cosmic rays in the first place.
What we need is not a knowledge of the underlying physical principles, it is a knowledge of how the climate works in general.
An example would be Ohm’s law. It is empirical. You don’t have to know anything about physics to use Ohm’s law and get a useful result. With a knowledge of Ohm’s law, you can model a linear circuit with great accuracy.
A similar case is Piers Corbyn. He seems to get accurate (more than average for weather forecasters) climate predictions without resorting to complicated physical models. His main thing seems to be a correlation between solar activity and the climate. As far as I can tell, it is empirical.
It seems likely to me that it impossible to accurately model a complex chaotic system based on physical properties alone unless the computer is as complex as the system itself. For the climate, that would seem to imply that each element in the model would have to be quite small in order to be strictly deterministic rather than acting chaotic. A wild ass guess would be each cubic meter of the atmosphere, ocean and land. OTOH, perhaps we are dealing with butterfly sized elements.

Leif Svalgaard,
The time step depends on the numerical method, implicit methods that allow longer time steps are being sought, and different physics is stepped at different intervals. Even at the AR4, parameterizations, averaging and smoothing must be tuned not just for physics but for numberical requirements over different time scales. However, great our faith is in physics, simulating physics on a global scale is far from that ideal:
“Coupling frequency is an important issue, because fluxes are
averaged during a coupling interval. Typically, most AOGCMs
evaluated here pass fluxes and other variables between the
component parts once per day. The K-Profi le Parametrization
ocean vertical scheme (Large et al., 1994), used in several
models, is very sensitive to the wind energy available for
mixing. If the models are coupled at a frequency lower than
once per ocean time step, nonlinear quantities such as wind
mixing power (which depends on the cube of the wind speed)
must be accumulated over every time step before passing to
the ocean. Improper averaging therefore could lead to too
little mixing energy and hence shallower mixed-layer depths,
assuming the parametrization is not re-tuned. However, high
coupling frequency can bring new technical issues. In the
MIROC model, the coupling interval is three hours, and in this
case, a poorly resolved internal gravity wave is excited in the
ocean so some smoothing is necessary to damp this numerical
problem. It should also be noted that the AOGCMs used here
have relatively thick top oceanic grid boxes (typically 10 m or
more), limiting the sea surface temperature (SST) response to
frequent coupling (Bernie et al., 2005).”
http://www.ipcc.ch/pdf/assessment-report/ar4/wg1/ar4-wg1-chapter8.pdf

Martin Lewitt says:
November 1, 2011 at 11:12 am
The time step depends on the numerical method, implicit methods that allow longer time steps are being sought, and different physics is stepped at different intervals. Even at the AR4, parameterizations, averaging and smoothing must be tuned not just for physics but for numerical requirements over different time scales. However, great our faith is in physics, simulating physics on a global scale is far from that ideal:”
Your comment goes to stability of the numerical algorithm used in the climate model. Unfortunately, it is difficult or impossible to prove anything about the stability of systems of partial differential equations, unless they are linearized model equations. The “optimal” time step is therefore usually found by trial and error (i.e. the largest value that doesn’t cause the solution to “blow up”).
This also gets us into fuzzy area of why the numerical algorithms for climate model atmospheric dynamics are any different from numerical weather prediction models…
“For time step check slide 7 of http://uscid.us/08gcc/Brekke%201.PDF”
You’ve got to be kidding?! Where is the stability analysis to prove the values asserted? There aren’t even any equations in the entire presentation!

RACookPE1978 says:
October 31, 2011 at 10:54 pm
That’s a good contribution. You provide lots of examples of how climate models are, in Leif Svalgaard’s words, “too simple”.

“We have been unable to find ways of identifying which calibrated models will have some predictive capacity and those which will not.”
As noted here: GLOBAL WARMING: FORECASTS BY SCIENTISTS
VERSUS SCIENTIFIC FORECASTS
by
Kesten C. Green and J. Scott Armstrong

“The IPCC WG1 Report was regarded as providing the most credible long-term
forecasts of global average temperatures by 31 of the 51 scientists and others involved
in forecasting climate change who responded to our survey. We found no references
in the 1056-page Report to the primary sources of information on forecasting methods
despite the fact these are conveniently available in books, articles, and websites. We
audited the forecasting processes described in Chapter 8 of the IPCC’s WG1 Report
to assess the extent to which they complied with forecasting principles. We found
enough information to make judgments on 89 out of a total of 140 forecasting
principles. The forecasting procedures that were described violated 72 principles.
Many of the violations were, by themselves, critical.
The forecasts in the Report were not the outcome of scientific procedures. In
effect, they were the opinions of scientists transformed by mathematics and
obscured by complex writing. Research on forecasting has shown that experts’
predictions are not useful in situations involving uncertainly and complexity. We
have been unable to identify any scientific forecasts of global warming. Claims that
the Earth will get warmer have no more credence than saying that it will get colder.

Isn’t that the problem here? No knowledge of the subject.

Leif Svalgaard says:
November 1, 2011 at 10:01 am
“Willis Eschenbach says:
November 1, 2011 at 9:43 am
Then why did they start by mentioning “climate models” in the very first paragraph of their work?
They and you and most others go wrong by assuming that climate models are curve fitting as in the paper. They are not, they are solving differential equations forwards with a small time step [every ~5 minutes]. That is why the models do not compare. This is a qualitative difference.”
Leif, don’t confuse the genetic algorithm that does the curve fitting by selecting a model variation with the individual model runs. They say their genertic algorithm runs a total of 7,000 individual model runs. It is these model runs that they compare to GCM runs.
There is no qualitative difference to a Gavin Schmidt running a GCM, and tunes its parameters and runs it again to see whether it matches historical data better now.
And we KNOW that they do this – they constantly publish papers in which they argue that they now have a better estimate for climate sensitivity to a doubling of CO2 because they tried out some values…
So the human climate modelers in their entirety correspond to a huge curve-fitting algorithm that runs GCMs over and over again and modifies them.

Leif Svalgaard says:
November 1, 2011 at 2:02 pm
“I didn’t say that they were accurately captured. Each is an item of ongoing research and the results will be incorporated at their appropriate time. My view is that this will make the models better with time. What I complained about is the notion that this cannot be done, in principle.”
Their conclusion is that you cannot determine IN ADVANCE whether your model has predictive skill. You can, of course, wait and see and improve your model when you see it failed.
That’s exactly the situation the climate modelers are in. Skepticalscience has defended Hansens wrong forecasts, saying “if he had assumed a climate sensitivity of 3.4 deg C per doubling of CO2 instead of 4.5 he would have been right” – and that’s what they say NOW; they might have to revise it AGAIN in a few years (and again… and again… and again. And during all those years climate models will slowly get better.)

Leif Svalgaard says:
October 31, 2011 at 8:22 pm
“Physical systems are controlled by a [sometimes large] set of coupled differential equations. Given the equations and an initial set of values, the equations can be integrated forward in time. A good example is [the simple] physical system consisting of 568670 asteroids, 3113 comets, 171 planetary satellites, 8 planets and a star that make up our solar system. This is where you are wrong.”
You are referencing our existing physical theory, are you not? In that case, I agree with you completely but your example is no counter example to my argument. I heartily embrace your differential equations. You are allowed to calculate. But if those differential equations do not introduce additonal primitive predicates then they cannot be treated as statements that are true or false. Do you really want to dispense with truth? Many do.
“If not all of the boundary conditions are known or the processes are poorly understood, the model will not perform as well as JPL’s calculation of orbits in the solar system. This is a condition for the climate system that might [and probably will] change as time goes on. The principle is clear, though, and must be understood.”
The only principle that is clear in your statement is that climate science, so-called, could someday become a genuine science. I certainly agree with that principle. My criticism is that climate science is not a science at this time and no one should be asserting that it is. Paraphrasing your words, “because not all of the boundary conditions are known and the processes are poorly understood, the model will not perform as well as JPL’s calculations of orbits in the solar system.” Well, right, that is exactly what I am saying. At this time you cannot claim to have a decent model of the solar system. So, why are you, in the analogous case of so-called climate science, claiming that you have a decent model there? You do not and you should lead the way among your colleagues in explaining that you do not. I wish you the greatest and quickest success but at this time you have neither model nor science.

Leif Svalgaard says:
October 31, 2011 at 9:47 pm
Jaye Bass says:
October 31, 2011 at 9:21 pm
Define “sound physics” in terms of a complete, validated and verified, system model for the climate. Get back to me when you are finished.
You can prove me wrong by reading and understanding Jacobsen’s text book describing the physics: http://www.stanford.edu/group/efmh/FAMbook/FAMbook.html”
A textbook does not a physical theory make. Back around 1940, Hans Reichenbach published a really wonderful book with the title “Axiomatization of the Theory of Relativity.” I have one here somewhere and you can buy it on Amazon. It is several hundred pages long. It sets out Einstein’s physical theory and does all kinds of wonderful things such as identifying Einstein’s primitive predicates. Those predicates tell you what the theory is about.
As soon as climate science has one of these babies ready to go, I will be the first to buy it and review it. However, no such book is on the horizon for climate science because they have no rigorous formulation of their (part of) physical theory, axiomatized or not. They cannot so much as guess what their primitive predicates will be in ten years. Climate scientists should accept this reality and address the world accordingly.

Leif Svalgaard says:
November 1, 2011 at 2:02 pm
“I’m sure that that was part of the equation as well. Although it is hard to know when the models ‘blow up’ because of numerical instability.”
Well, unless there is a bug somewhere (always a possibility) a “blow up” (which refers to solutions oscillating wildly until a divide by zero or NAN occurs) is usually due to a numerical instability being amplified, hence the importance of stability analysis of the numerical algorithms. This is not hard to know…
By the way, let me know what other considerations are required for determining the appropriate solution time step outside of numerical stability and accuracy…

Leif Svalgaard says:
November 1, 2011 at 12:22 pm
commieBob says:
November 1, 2011 at 12:17 pm
OK so the computer has merely to be the size of the solar system.
“We actually have such a computer, it is called ‘our solar system’ and shows us what is happening.”
Our solar system is a model of the physical theory that describes it. This is a clear and definite use of the word “model” in this scientific context. You are getting clear on the relationship between model and theory.

I’m late to the party, but I shall have my say anyway. You often see such predictions in the healthcare industry. For that I profoundly miss Junkfoodscience.com

Wow! Willis both you and Leif are overly touchy today.
I must comment for the both of you.
There is no model that is 100% all the time.
There are models that are based on sound principles and are quite useful, particularly in reducing the cost of something or estimating a result. For instance, you can run your simulation software and fine tune your design based on those runs. But, in the end you really need to test that nuclear bomb to make sure it goes KaBoom instead of just Boom.
Chill out boys, Don’t be so crabby.

Leif Svalgaard says:
November 1, 2011 at 5:35 pm
Theo Goodwin says:
November 1, 2011 at 5:25 pm
“The first of two problems for your account is that you cannot tell us what that reasonable physics is; that is, you cannot tell us without citing a text book.
It takes a thick text book to tell you.”
So, there is an “Axiomatization of the Theory of Climate Change?” That would not be a text book. Each of the axioms would be shown in its unique context in the overall theory that is the unique true account of climate change. In textbooks, you get principles with some explanation of how they are applied. Big difference.

Willis Eschenbach says:
November 1, 2011 at 5:11 pm
From Environmental Research Letters:
At present, climate models are tuned to achieve agreement with observations. This means that parameter values that are weakly restricted by observations are adjusted to generate good agreement with observations for those parameters that are better restricted, with the TOA radiative balance belonging to the latter category.
“Yep. Sure ’nuff …”
Yep, unlimited rejiggering with nothing to supply context or give meaning to the individual rejiggers.

In addition to whether all the physics are understood and properly included in the models (which is very possibly asking for more than can ever be achieved — but let’s set that aside now), we also have to include in the models precise information about the starting points. That has very little to do with physics calculations, and everything to do with physical facts, such as current temperatures, current aerosols, current vegetation and on and on, nearly ad infinitum. Unless we get the current state input properly, we can’t expect to run our physics calculations to generate a predictive view of a future state. Further, there are plenty of known unknowns, such as future volcanic eruptions, future changes in aerosols, vegetation, solar activity, just to name a few. By definition, it is impossible to include these in a model with precision.
The idea that the climate models will have good long-term predictive value if they can but manage to “get the physics right” is simply wishful thinking.

Eric Anderson says:
November 1, 2011 at 7:42 pm
Further, there are plenty of known unknowns, such as future volcanic eruptions, future changes in aerosols, vegetation, solar activity, just to name a few. By definition, it is impossible to include these in a model with precision.
Those you deal with by running ‘scenarios’, e.g. by assuming a large volcanic at a given time and see what its effect will be. That is where models actually can shine.

Leif: “Those [known unknowns] you deal with by running ‘scenarios’, e.g. by assuming a large volcanic at a given time and see what its effect will be. That is where models actually can shine.”
Well, I don’t know about “shine.” The ‘scenarios’ may be interesting, perhaps even educational, but that they have any predictive value does not follow. Further, we have seen that in practice (not in the pristine idealized world of how “science” should operate) these ‘scenarios’ get pushed by those with an agenda as predictions or likely outcomes and then used to demand particular political/economic actions.
And we’re still not dealing with the formidable challenge of inputting the original parameters in a comprehensive enough way to generate valuable predictions.
Look, the models may have some value as tools to help us think about how the climate works and may even help us think about climate possibilities. But right now (1) we don’t have all the physics included, (2) we don’t have all the initial physical parameters included, and (3) we don’t know whether the ‘scenarios’ run with unknowns will bear any relation to future reality. I’m happy to let folks keep working on computer scenarios, but let’s acknowledge the very real limitations. They can keep improving things and then get back to us once they have a model that has actually been successful at forecasting a decade’s worth of climate (or was it 17 years that was needed to see an actual trend . . . or perhaps 30 :)). Until then, the onus is squarely and properly on those who market their scenarios/predictions to demonstrate why anyone else should take any of the scenarios/predictions seriously.

Frank said:

Willis,
I’d say this only shows that their objective functions are not suitable.

Willis said:

Unless you can show where they are are wrong instead of just asserting that they must be wrong, I’m going with their conclusions.

Okay, here’s what I understand of it:
For generating the “measurements” / “truth cases”, they take 3 parameters h, kp and kg, plus a grid of porosity and permeability values and feed them into the ECLIPSE simulator, getting a monthly set of 3 production rates during 3 years (36 sets in total, the “history set”) and a yearly set of the 3 production rates during 7 years (7 sets in total, the “future set”. For the “with error measurements”, they only modify the grid porosity and permeability values to within 1% error and run the same simulator.
Then, they use a genetic algorithm to try and find the optimum values for h, kp and kg that match the production rate sets by running the simulation over and over with different trial parameters. Figs 2 and 4 show more or less a likelihood that the tried parameters are correct. The objective functions are used to calculate this likelihood. For Figs. 2a and 4a, they use the history set, for Figs. 2b and 4b, they use the future set.
So far so good. But now comes the part where I don’t agree.
They say the model has no predictive power, but they don’t actually compare any predictions. In this case, what you would want to predict are the production rates. So to see if the model has predictive power, you would expect a comparison of the predicted and the “truth case” production rates. But this is not done! Instead, they argue that, because tuning to the future set gives different parameter values than tuning to the history set, the predictive power must be weak. However, it may well be that a bunch of parameters that’s not entirely correct (like those in Section 3.2), does in fact give a relatively good production rates prediction.
What I see in comparing Fig. 2a and 2b is that the monthly production rates in the history set don’t have enough variability to unequivocally find the best parameters (multiple spikes), whilst in the future set, the production rates do have enough variability (single clean spike). You can see the difference as trying to match a amplitude and frequency of a sine curve to a values near x=0 or to values along its entire up-and-down domain. In other words, the measurement data doesn’t “stretch” the parameter space enough. Similarly, in Fig. 4, you only see that the history set with error definitely doesn’t have enough data, and the future set only marginally has.
So the logic of saying that a model has predictive powers only if you get the same matched parameters when you tune to past and future sets, is basically wrong. Take the extreme case of having very little past data and lots and lots of future data — the matched parameters will almost always be different.

TimTheToolMan said:

I believe Willis is right. When Carter et al say “We conclude that for this model you can only obtain a good prediction from the truth case, and that good matches from the history matching phase have no predictive value.”

I get the idea that they were getting at. Basically, a lack of peak in Figs. 2b and 4b at parameter values where there *is* a peak in Figs. 2a and 4a, means that that parameter set (tuned by the history set) actually has a large objective function delta_f, and with that, a large difference between the predicted and “measured” values — or bad predictive capacity.
However, they don’t specifically calculate the future objective function at *precisely* the predicted parameter values. They just generate another objective function map which *interpolates* values near the predicted parameter values that happened to be visited by the genetic algorithm, possibly missing a sharp peak at precisely the predicted parameter values.
Moreover, the fact that there may be larger peaks in the future objective function map actually also clouds the issue. So yes, there may be parameter values which happen to give a higher peak than the peak at the predicted values, but that doesn’t mean the prediction is bad. It just means that with *that particular* future data set (a mere 21 data points in our case), there are other solutions for the parameters that happen to give a higher peak.
Coming back to the size of the future set; when they say that in the perturbed case, “the spike at h ~ 10 has the wrong values for kp and kg” while in the unperturbed case, they do find the correct values, doesn’t that mean that they just don’t have enough data in the future set rather than that the model lacks predictive power?