Bishop Hill You call this progress?
Jun 13, 2013 Climate: Parliament The House of Commons Environmental Audit Committee held a hearing yesterday on the UK's progress on its carbon budgets.
As is normal on these things the politicians took evidence from people who could be expected to tell them what they wanted to hear:
- Aubrey Meyer of the Global Commons Institute
- Andrew Shepherd, University of Leeds
- Myles Allen, University of Oxford
- Julia Slingo and Jason Lowe, Met Office
I've started watching and it seems quite interesting so far, in a "we're all going to fry" kind of way.
I enjoyed Aubrey Meyer's asking us to worry about viral attacks on carbon-hungry organisms in the oceans. He's a fun guy.
Myles Allen makes some pretty amazing claims about the temperature standstill:
The assertion that temperatures haven't risen as fast as predicted is simply wrong. There was a range of predictions made and in fact the temperatures of the last decade have been pretty much exactly as was predicted for the decade back in the 1990s...
I guess this is a reference back to this argument.
The issue about what these new data imply for the future. There have been a number of papers published recently which suggest that the highest responses of the current climate models - that's the models right at the extreme top of the range of behaviour we're getting at the moment - look less likely, but the bulk of the models are still within the range of uncertainty consistent with the observations.
If he's saying what I think he's saying then I think this is extremely misleading.
I gather there is some discussion of David Rose's article on climate sensitivity, but I haven't got to that bit yet.
Reader Comments (71)
Damn. Cant watch this and the Iraq war debate in the Commons at the same time. Is this available later?
Same old sphincters, same old ...
It is dangerous to deliberately mislead a HoC committee; but if it's all in a good cause....
"The assertion that temperatures haven't risen as fast as predicted is simply wrong. "
Look out your window and marvel at the work of one of the finest brains in the land.
RB This is later! It's a recording from yesterday.
And I thought Professor Allen had opened his eyes to reality.
Cognitive dissonance.
Hopefully he can get word to Kevin out in his boat and tell him to head back in before one on those frankenstorms whips up - no heat is missing after all!! What a relief!
Cognative onanism.
My blood pressure pills can only cope with so much and this looks about 10 doses beyond the limit.
I know it's been said before, but why-oh-why-oh-why is the House of Commons using a Microsoft-proprietary viewer when there are plenty of better alternatives?
As a result, I cannot watch the hearing.
Managed 5 minutes of the Julia & Jason love-in and then lost the will to live...
Myles "11 degrees" Allen must surely be referring to the predictions that were, apocryphally, '...on display in the bottom of a locked filing cabinet stuck in a disused lavatory with a sign on the door saying "Beware of The Leopard"'.
Stevetta;
Technology to the HoC is like observation to a climatologist - incomprehensible.
Better just follow along with the big boys and thinking becomes unecessary.
I those words, I sense a fellow pilgrim, like me Andrew - you don't know whether to bark in sarcastic laughter, or to scream in anguish at the blinkered, bog standard, amateurish and wholly prejudiced - pseudo science exhibition.
It may be a recording, but there is no way that I can install that obsolescent piece of shite, Silverlite, on Linux to watch it.
This is, of course, the same Myles Allen who's prognostications regularly appear in The Guardian.
...That being a newspaper which routinely deletes comments and rescinds commenting privileges from those who are not sufficiently in tune with their "community". A newspaper which now shelters activists from an Australian website which is one of the most infamous in the entire blogsphere.
I am rather annoyed with these frauds.They are making use of the apparent independence of scientists to push a self-serving and commercial agenda.
Once it becomes obvious to everyone that what they are claiming simply isn't going to happen, they will retire with their money, and the credibility of science will be badly damaged....
Damn the thermometers, Full Speed Ahead!
==================
For Myles:
http://rankexploits.com/musings/2013/summer-is-cold-proves-global-warming/
(Stephen Richards - might be best to leave until tomorrow or the weekend)
Aargh! I failed to close the blockquote tag after "Jun 13, 2013 at 6:02 PM | Unregistered CommenterLeo Smith"
Where's the edit button?
From the advice to witnesses: "Knowingly to mislead a Committee is a contempt of the House."
Will anyone have the wit to ask for evidence of this phony prediction? Even stating it hasn't warmed when countless scientists are actively discussing the reasons for the selfsame unexpected warming standstill is pretty stupid. These guys clearly think they can walk on water.
Also funny how the slippery eels switch between the words projection and prediction according to the gullibilty and/or laziness of their audience..
What really puzzles me, is that people like Myles Allen don't seem to get that they are just digging the hole bigger. If they're wrong about CO2 driving climate change, which seems increasingly likely, then to keep pushing the issue is nuts. Sooner or later they are going to be found out. Why don't they cut their losses? I know that the admission of being wrong is very difficult for "experts", but really - it's all going to hit the fan sooner or later.
If he's saying what I think he's saying then I think this is extremely misleading.
Yes that his 'job' , like the rest of them its science be dammed in the name of 'the cause ' all lies are justified.
JamesG:
"Even stating it hasn't warmed when countless scientists are actively discussing the reasons for the selfsame unexpected warming standstill is pretty stupid. These guys clearly think they can walk on water."
Yep - in their own way they are just as disgusting and corrupt as Yeo. There will be grant and funding applications which will be explicitly recognising issues such as stalled temp rises, uncertainties in models, uncertainties in clouds, aerosols etc. as being essential for investigation yet these will not be mentioned when pushing the policy line.
Just like Yeo - what they say in private is "quite another matter."
so nthey ask Aubrey Meyer rather than some scientist...
it is rather like the way Hodge runs the PAC...expertise is banned!
Where's the edit button?
Jun 13, 2013 at 7:50 PM | Unregistered CommenterGary Turner
Become a registered commenter and you will have up to 14 minutes to edit stuff after you have posted it.
The blind leading the blind. I skipped through the video, it was a "time warp" back to the late 80's early 90's, out of touch with reality and in denial of the empirical evidence, as said by others, just keep digging, the hole will eventually collapse.
Yep - in their own way they are just as disgusting and corrupt as Yeo.(...)
Jun 13, 2013 at 9:55 PM not banned yet
Even when the Great Delusion is well and truly over, its legacy of dishonesty and deceit will still be with us.
Not exactly the same thing, but analogous - the repeal of prohibition did not end the gangster organisations that had been made possible by its existence.
I watched the first session where Aubrey Meyer was questioned. He brought to mind some fading actor who , half conscious that as time passes his performance is having less and less effect, tries to hold the audiences attention by means of sweeping gestures and outlandish poses.
What practical conclusions could the committee draw from all of this? I would be intrigued to see what the
Secretary recorded.
Is it my imagination .See 15:52:00 and Myles Allen body language (disbelief I think) directly after Julia Slingo responds to a question about pipeline heating.
Jun 13, 2013 at 9:55 PM | not banned yet
IIRC Phil Jones (post Climategate) had funding for a paper on the 'divergence problem'.
Has anyone seen that paper?
Billy - no, but he had US Dept of Energy support for his part in this one:
http://www.met.reading.ac.uk/~ed/home/hawkins_jones_2013_Callendar.pdf
Prof. Allen should access this link at WUWT, http://wattsupwiththat.com/2013/06/13/no-significant-warming-for-17-years-4-months/ , and then scroll down to the comment (more like a book) rgbatduke says: June 13, 2013 at 7:20 am ... have a read of that my good man ... feel a little silly now, eh ?
Got to agree with StreetCred above. This is a must read from rgb. Bishop please delete if I've crossed a boundary by pasting from Anthony's blog without permission by pasting the entire text of rgb's response
rgbatduke says:
June 13, 2013 at 7:20 am
Saying that we need to wait for a certain interval in order to conclude that “the models are wrong” is dangerous and incorrect for two reasons. First — and this is a point that is stunningly ignored — there are a lot of different models out there, all supposedly built on top of physics, and yet no two of them give anywhere near the same results!
This is reflected in the graphs Monckton publishes above, where the AR5 trend line is the average over all of these models and in spite of the number of contributors the variance of the models is huge. It is also clearly evident if one publishes a “spaghetti graph” of the individual model projections (as Roy Spencer recently did in another thread) — it looks like the frayed end of a rope, not like a coherent spread around some physics supported result.
Note the implicit swindle in this graph — by forming a mean and standard deviation over model projections and then using the mean as a “most likely” projection and the variance as representative of the range of the error, one is treating the differences between the models as if they are uncorrelated random variates causing >deviation around a true mean!.
Say what?
This is such a horrendous abuse of statistics that it is difficult to know how to begin to address it. One simply wishes to bitch-slap whoever it was that assembled the graph and ensure that they never work or publish in the field of science or statistics ever again. One cannot generate an ensemble of independent and identically distributed models that have different code. One might, possibly, generate a single model that generates an ensemble of predictions by using uniform deviates (random numbers) to seed
“noise” (representing uncertainty) in the inputs.
What I’m trying to say is that the variance and mean of the “ensemble” of models is completely meaningless, statistically because the inputs do not possess the most basic properties required for a meaningful interpretation. They are not independent, their differences are not based on a random distribution of errors, there is no reason whatsoever to believe that the errors or differences are unbiased (given that the only way humans can generate unbiased anything is through the use of e.g. dice or other objectively random instruments).
So why buy into this nonsense by doing linear fits to a function — global temperature — that has never in its entire history been linear, although of course it has always been approximately smooth so one can always do a Taylor series expansion in some sufficiently small interval and get a linear term that — by the nature of Taylor series fits to nonlinear functions — is guaranteed to fail if extrapolated as higher order nonlinear terms kick in and ultimately dominate? Why even pay lip service to the notion that or for a linear fit, or for a Kolmogorov-Smirnov comparison of the real temperature record and the extrapolated model prediction, has some meaning? It has none.
Let me repeat this. It has no meaning! It is indefensible within the theory and practice of statistical analysis. You might as well use a ouija board as the basis of claims about the future climate history as the ensemble average of different computational physical models that do not differ by truly random variations and are subject to all sorts of omitted variable, selected variable, implementation, and initialization bias. The board might give you the right answer, might not, but good luck justifying the answer it gives on some sort of rational basis.
Let’s invert this process and actually apply statistical analysis to the distribution of model results Re: the claim that they all correctly implement well-known physics. For example, if I attempt to do an a priori computation of the quantum structure of, say, a carbon atom, I might begin by solving a single electron model, treating the electron-electron interaction using the probability distribution from the single electron model to generate a spherically symmetric “density” of electrons around the nucleus, and then performing a self-consistent field theory iteration (resolving the single electron model for the new potential) until it converges. (This is known as the Hartree approximation.)
Somebody else could say “Wait, this ignore the Pauli exclusion principle” and the requirement that the electron wavefunction be fully antisymmetric. One could then make the (still single electron) model more complicated and construct a Slater determinant to use as a fully antisymmetric representation of the electron wavefunctions, generate the density, perform the self-consistent field computation to convergence. (This is Hartree-Fock.)
A third party could then note that this still underestimates what is called the “correlation energy” of the system, because treating the electron cloud as a continuous distribution through when electrons move ignores the fact that individual electrons strongly repel and hence do not like to get near one another. Both of the former approaches underestimate the size of the electron hole, and hence they make the atom “too small” and “too tightly bound”. A variety of schema are proposed to overcome this problem — using a semi-empirical local density functional being probably the most successful.
A fourth party might then observe that the Universe is really relativistic, and that by ignoring relativity theory and doing a classical computation we introduce an error into all of the above (although it might be included in the semi-empirical LDF approach heuristically).
In the end, one might well have an “ensemble” of models, all of which are based on physics. In fact, the differences are also based on physics — the physics omitted from one try to another, or the means used to approximate and try to include physics we cannot include in a first-principles computation (note how I sneaked a semi-empirical note in with the LDF, although one can derive some density functionals from first principles (e.g. Thomas-Fermi approximation), they usually don’t do particularly well because they aren’t valid across the full range of densities observed in actual atoms). Note well, doing the precise computation is not an option. We cannot solve the many body atomic state problem in quantum theory exactly any more than we can solve the many body problem exactly in classical theory or the set of open, nonlinear, coupled, damped, driven chaotic Navier-Stokes equations in a non-inertial reference frame that represent the climate system.
Note well that solving for the exact, fully correlated nonlinear many electron wavefunction of the humble carbon atom — or the far more complex Uranium atom — is trivially simple (in computational terms) compared to the climate problem. We can’t compute either one, but we can come a damn sight closer to consistently approximating the solution to the former compared to the latter.
So, should we take the mean of the ensemble of “physics based” models for the quantum electronic structure of atomic carbon and treat it as the best prediction of carbon’s quantum structure? Only if we are very stupid or insane or want to sell something. If you read what I said carefully (and you may not have — eyes tend to glaze over when one reviews a year or so of graduate quantum theory applied to electronics in a few paragraphs, even though I left out perturbation theory, Feynman diagrams, and ever so much more:-) you will note that I cheated — I run in a semi-empirical method.
Which of these is going to be the winner? LDF, of course. Why? Because the parameters are adjusted to give the best fit to the actual empirical spectrum of Carbon. All of the others are going to underestimate the correlation hole, and their errors will be systematically deviant from the correct spectrum. Their mean will be systematically deviant, and by weighting Hartree (the dumbest reasonable “physics based approach”) the same as LDF in the “ensemble” average, you guarantee that the error in this “mean” will be significant.
Suppose one did not know (as, at one time, we did not know) which of the models gave the best result. Suppose that nobody had actually measured the spectrum of Carbon, so its empirical quantum structure was unknown. Would the ensemble mean be reasonable then? Of course not. I presented the models in the way physics itself predicts improvement — adding back details that ought to be important that are omitted in Hartree. One cannot be certain that adding back these details will actually improve things, by the way, because it is always possible that the corrections are not monotonic (and eventually, at higher orders in perturbation theory, they most certainly are not!) Still, nobody would pretend that the average of a theory with an improved theory is “likely” to be better than the improved theory itself, because that would make no sense. Nor would anyone claim that diagrammatic perturbation theory results (for which there is a clear a priori derived justification) are necessarily going to beat semi-heuristic methods like LDF because in fact they often do not.
What one would do in the real world is measure the spectrum of Carbon, compare it to the predictions of the models, and then hand out the ribbons to the winners! Not the other way around. And since none of the winners is going to be exact — indeed, for decades and decades of work, none of the winners was even particularly close to observed/measured spectra in spite of using supercomputers (admittedly, supercomputers that were slower than your cell phone is today) to do the computations — one would then return to the drawing board and code entry console to try to do better.
Can we apply this sort of thoughtful reasoning the spaghetti snarl of GCMs and their highly divergent results? You bet we can! First of all, we could stop pretending that “ensemble” mean and variance have any meaning whatsoever by not computing them. Why compute a number that has no meaning? Second, we could take the actual climate record from some “epoch starting point” — one that does not matter in the long run, and we’ll have to continue the comparison for the long run because in any short run from any starting point noise of a variety of sorts will obscure systematic errors — and we can just compare reality to the models. We can then sort out the models by putting (say) all but the top five or so into a “failed” bin and stop including them in any sort of analysis or policy decisioning whatsoever unless or until they start to actually agree with reality.
Then real scientists might contemplate sitting down with those five winners and meditate upon what makes them winners — what makes them come out the closest to reality — and see if they could figure out ways of making them work even better. For example, if they are egregiously high and diverging from the empirical data, one might consider adding previously omitted physics, semi-empirical or heuristic corrections, or adjusting input parameters to improve the fit.
Then comes the hard part. Waiting. The climate is not as simple as a Carbon atom. The latter’s spectrum never changes, it is a fixed target. The former is never the same. Either one’s dynamical model is never the same and mirrors the variation of reality or one has to conclude that the problem is unsolved and the implementation of the physics is wrong, however “well-known” that physics is. So one has to wait and see if one’s model, adjusted and improved to better fit the past up to the present, actually has any predictive value.
Worst of all, one cannot easily use statistics to determine when or if one’s predictions are failing, because damn, climate is nonlinear, non-Markovian, chaotic, and is apparently influenced in nontrivial ways by a world-sized bucket of competing, occasionally cancelling, poorly understood factors. Soot. Aerosols. GHGs. Clouds. Ice. Decadal oscillations. Defects spun off from the chaotic process that cause global, persistent changes in atmospheric circulation on a local basis (e.g. blocking highs that sit out on the Atlantic for half a year) that have a huge impact on annual or monthly temperatures and rainfall and so on. Orbital factors. Solar factors. Changes in the composition of the troposphere, the stratosphere, the thermosphere. Volcanoes. Land use changes. Algae blooms.
And somewhere, that damn butterfly. Somebody needs to squash the damn thing, because trying to ensemble average a small sample from a chaotic system is so stupid that I cannot begin to describe it. Everything works just fine as long as you average over an interval short enough that you are bound to a given attractor, oscillating away, things look predictable and then — damn, you change attractors. Everything changes! All the precious parameters you empirically tuned to balance out this and that for the old attractor suddenly require new values to work.
This is why it is actually wrong-headed to acquiesce in the notion that any sort of p-value or Rsquared derived from an AR5 mean has any meaning. It gives up the high ground (even though one is using it for a good purpose, trying to argue that this “ensemble” fails elementary statistical tests. But statistical testing is a shaky enough theory as it is, open to data dredging and horrendous error alike, and that’s when it really is governed by underlying IID processes (see “Green Jelly Beans Cause Acne”). One cannot naively apply a criterion like rejection if p < 0.05, and all that means under the best of circumstances is that the current observations are improbable given the null hypothesis at 19 to 1. People win and lose bets at this level all the time. One time in 20, in fact. We make a lot of bets!
So I would recommend — modestly — that skeptics try very hard not to buy into this and redirect all such discussions to questions such as why the models are in such terrible disagreement with each other, even when applied to identical toy problems that are far simpler than the actual Earth, and why we aren’t using empirical evidence (as it accumulates) to reject failing models and concentrate on the ones that come closest to working, while also not using the models that are obviously not working in any sort of “average” claim for future warming. Maybe they could hire themselves a Bayesian or two and get them to recompute the AR curves, I dunno.
It would take me, in my comparative ignorance, around five minutes to throw out all but the best 10% of the GCMs (which are still diverging from the empirical data, but arguably are well within the expected fluctuation range on the DATA side), sort the remainder into top-half models that should probably be kept around and possibly improved, and bottom half models whose continued use I would defund as a waste of time. That wouldn’t make them actually disappear, of course, only mothball them. If the future climate ever magically popped back up to agree with them, it is a matter of a few seconds to retrieve them from the archives and put them back into use.
Of course if one does this, the GCM predicted climate sensitivity plunges from the totally statistically fraudulent 2.5 C/century to a far more plausible and still possibly wrong ~1 C/century, which — surprise — more or less continues the post-LIA warming trend with a small possible anthropogenic contribution. This large a change would bring out pitchforks and torches as people realize just how badly they’ve been used by a small group of scientists and politicians, how much they are the victims of indefensible abuse of statistics to average in the terrible with the merely poor as if they are all equally likely to be true with randomly distributed differences.
rgb
rgb's comments never dissapoint. I usually skip any comment over two screens long. rgb gave us twelve screenfuls, and I read them all! (Don't ask how many I understood 100%.) The majority of the comment is crystal clear:
"...Of course if one does this, the GCM predicted climate sensitivity plunges from the totally statistically fraudulent 2.5 C/century to a far more plausible and still possibly wrong ~1 C/century, which — surprise — more or less continues the post-LIA warming trend with a small possible anthropogenic contribution." --rgb
Chicken Little is dead!
Thanks Jun 13, 2013 at 10:26 PM | Registered CommenterMartin A
Seems I was already a registered member of the congregation. I just didn't know/remember it. :shrug:
The real object is international Marxism trough a Copenhagen climate treaty look alike thing. CAGW is just one of the means to get there. So for some everything is legal to get there?
In synopsis, Roy says the [computer] models are 8h*&e.
But I could have told him that, moreover all Politicians wouldn't be able to differentiate between a 'good' climate model[ there isn't one] and a bad one [see GISS, HadCRUt et al] - so they just go with the massive runny green mucus flow.......................... and we all end up - up sh*& creek.
Everybody that is, except the bankers and green hedge funds - of course they larf loud and larf longest.
..................
To summarize Roys argument: how is it possible to meaningfully derive a mean from meaningless models? Its as though, in modelling air traffic, one model didn't leave the ground, the other flew a mile high at 700 mph, so the scientist proudly announces that the most likely future of air travel is 350 mph at altitudes of half a mile. (and as that is not too far off observations, he can prate on about the robustness of his method!)
"I googled Aubrey Meyer after watching a few minutes of his performance, as I couldn't believe they were all taking him so seriously. this is how the site described him -
"Aubrey Meyer was a South-African born musician and composer. He became involved in environmental activism while researching the topic for a musical, and eventually left his musical career to found the Global Commons Institute (GCI)"............. enough said!!
So who do I believe? Dr Robert G Brown, physicist, of Duke University and Dr Murry Salby, atmospheric physicist and chair of climate at Maquarie, and Professor Judith Curry,physicist and climatologist, Chair of Earth and atmospheric Sciences at Georgia. Or:
Professor Julia Slingo, physics and meteorology at Bristol and Reading and Professor Myles R. Allen, head of the Climate Dynamics group at the University of Oxford's Atmospheric, Oceanic and Planetary Physics Department.
If one just looks at professional qualifications there is no way to choose that I can see. However if one looks at the spaghetti (frayed rope..nice!) graph from AR5 and the black dots of reality along the bottom you do not have to make that decision. It is the simplest thing in the world to look at that and call BS on Slingo and Allen. Is this a correct approach? I don't know, but as a lay person it is all I have.
Aubrey Meyer was here a while ago, repeatedly on one thread. I can say without exaggeration that I have never encountered such an intellect.
Rhoda/Alison - Aubrey Meyer being called as an expert witness sums up the futility of trying to put the debate onto a rational footing.
Hey Disko don't forget the activist musician Aubrey (slightly unhinged if the video is anything to go by) when weighing up the protagonists!
Seriously, who decides who goes before these committees and how did dear Aubrey get there? Anyone? In some respects you can't blame the panel if they only get to see alarmists and not a broad spectrum of views. The bias seems worse than even the Question Time audience!
Oh and Disko you seem to have cut through the forest of info pretty well. Comparing the forecasts with the "black dots of realtity" is the best way to see if those who would alarm us know what they're talking about in sufficient detail to inform policy. They don't.
Dosen't seem to bother Aubrey unduly though.
I like the rgb comment, though I can't pretend to understand all of it. It always seemed odd to me to include in the ensemble models from every Tom, Dick and Harry - surely the mean and median could be distorted by 'rubbish' ones. I'd have though it would be much more sensible to discard the ones which are furthest from reality (I think they already chuck out ones which lead to logarithmically runaway scenarios) to at least give a semblance of checking their results. I seem to remember a few years back that some models constructed by Russian climate scientists did have flat spots etc, but if anything they seem to have been disappeared, or defunded.
All this, notwithstanding rgb's contention that ANY ensemble of differently-coded models is by definition worthless.
Jun 14, 2013 at 1:20 AM | not banned yet
Thanks for the link to the Callendar paper. I look forward to the divergence problem paper when (if?) he gets around to it.
Whatever Rhoda (another anonymous undecipherable name) not interested as to whether you think he is a 'nice bloke' or not !!
Here is the website:
http://www.gci.org.uk/
There's over twenty years of an argument right there for you if you are up to it mate
I am over and out
I suppose Aubrey Meyer's daughter makes a change from Phil Jones' Mum.