by Indur M. Goklany
Phil Jones famously said:
Kevin and I will keep them out somehow – even if we have to redefine what the peer-review literature is!” – Phil Jones 8/7/2004
Today, we have an example:
“[T]his is also the way science works: someone makes a scientific claim and others test it. If it holds up to scrutiny, it become part of the scientific literature and knowledge, safe until someone can put forward a more compelling theory that satisfies all of the observations, agrees with physical theory, and fits the models.” – Peter Gleick at Forbes; emphasis added. 9/2/2011
Last time I checked it was necessary and sufficient to fit the observations, but “fits the models”!?!?
So let’s ponder a few questions.
- 1. Do any AOGCMs satisfy all the observations? Are all or any, for example, able to reproduce El Ninos and La Ninas, or PDOs and AMOs? How about the spatial and temporal distribution of precipitation for any given year? In fact, according to both the IPCC and the US Climate Change Science Program, they don’t. Consider, for example, the following excerpts:
“Nevertheless, models still show significant errors. Although these are generally greater at smaller scales, important large scale problems also remain. For example, deficiencies remain in the simulation of tropical precipitation, the El Niño-Southern Oscillation and the Madden-Julian Oscillation (an observed variation in tropical winds and rainfall with a time scale of 30 to 90 days).” (IPCC, AR4WG1: 601; emphasis added).
“Climate model simulation of precipitation has improved over time but is still problematic. Correlation between models and observations is 50 to 60% for seasonal means on scales of a few hundred kilometers.” (CCSP 2008:3).
“In summary, modern AOGCMs generally simulate continental and larger-scale mean surface temperature and precipitation with considerable accuracy, but the models often are not reliable for smaller regions, particularly for precipitation.” (CCSP 2008: 52).
This, of course, raises the question: Are AOGCMs, to quote Gleick, “part of the scientific literature and knowledge”? Should they be?
- 2. What if one model’s results don’t fit the results of another? And they don’t—if they did, why use more than one model and why are over 20 models used in the AR4? Which models should be retained and which ones thrown out? On what basis?
- 3. What if a model fits other models but not observations (see Item 1)? Should we retain those models?
I offer these rhetorical questions to start a discussion, but since I’m on the move these holidays, I’ll be unable to participate actively.
Reference:
CCSP (2008). Climate Models: An Assessment of Strengths and Limitations. A Report by the U.S. Climate Change Science Program and the Subcommittee on Global Change Research [Bader D.C., C. Covey, W.J. Gutowski Jr., I.M. Held, K.E. Kunkel, R.L. Miller, R.T. Tokmakian and M.H. Zhang (Authors)]. Department of Energy, Office of Biological and Environmental Research, Washington, D.C., USA, 124 pp.
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Why models should never be a part of scientific literature when the variables and processes are not well understood;
http://lorenzo-thinkingoutaloud.blogspot.com/2009/03/computer-models-and-cognitive-failure.html#0
Heritic! /sarc
Stating the obvious but models depend on the observed or recorded data being input. The whole basis of warmist science is that because the act of observing, biases the data, then they can manipulate the data however they like to make the models right.
What they do not account for is the statistical testing.
They still use statistics but only to confirm their desired outcome.
Gleick’s comments are especially remarkable when one considers the observation of Brian Hoskins that the models are “lousy“.
At the Bishop Hill blog in the comments. Professor Jonathan Jones (Physics Oxford) had this to say about it.
http://www.bishop-hill.net/blog/2011/9/2/journal-editor-resigns.html?lastPage=true#comment14976500
Professor J Jones:
“This is truly bizarre, and just shows how profoundly warped the climate science community has become. I make no judgement here on the correctness of the paper, but editors just don’t resign because of things like this.
Nobody resigned at Science when they published that utter drivel about bacteria replacing phosphorus with arsenic; they just published seven comments (IIRC) back to back with a rather desperate defence from the original authors.
Nobody resigned at Phys Rev Lett when I trashed a paper (on the evaluation of Gaussian sums) they had selected as one of the leading papers of the month: indeed nobody has formally ever accepted that I was right, but remarkably all the later papers on this subject follow my line.
I have been up to my neck for over a year in a huge row with Iannis Kominis about the underlying quantum mechanics of spin sensing chemical reactions, and either his papers or mine (or just possibly both) are complete nonsense: but nobody has resigned over Koniminis’s paper in Phys Rev B or mine in Chem Phys Lett.
Sure, my two controversies above never hit the popular press, but the arsenic stuff was discussed all over the place, far more than Spencer and Braswell.
What sort of weird warped world to climate scientists inhabit?
How have they allowed themselves to move so far from comon sense?
What is wrong with these guys?”
————————————————–
I wonder if any other scientists are as bemused by what has happened here, as Professor Jones apparently is?
Professor J Jones recently finally won his FOI case with CRU at the University of East Anglia:
http://www.guardian.co.uk/environment/2011/jul/01/climate-data-uea
Guardian- “An Oxford academic has won the right to read previously secret data on climate change held by the University of East Anglia (UEA).”
http://www.physics.ox.ac.uk/al/people/jones.htm
Here’s a discussion starter. I’ve been reading climate blogs for two years and actively seeking an answer go the question, “Why do people trust models.” I haven’t gotten an answer, and after two years of searching that’s a little disappointing to say the least.
The best I’ve gotten is something along the lines of “Well, they agree with observations and are based on sound science, so there’s no reason not to trust them.” Okay, that puts them somewhere akin to a hypothesis. A hypothesis, at least a valid one, has to agree with observations and with known science. It also should be verifiable, which a model may or may not be.
In fact it’s a little worse than that. A model is really just a bunch of equations: some we ‘know’ are true (as far as a scientist can know something), others we are pretty sure or at least think are true, we hope everything we left out is too small to make a difference, and we know our method is going to get error terms, but we solve the equation, hang it on the wall and praise it like some magic, divine oracle. And this is called SCIENCE?!?
But this is not the kicker. The real kicker is that I cannot be the only one who noticed that. Okay granted that we have a whole community of skeptics here who have probably also noticed that- but that isn’t enough. Every scientist, every grad student, everybody who even took an telemetry school class where they taught the scientific method should be able to see that there’s something not right here. Something is going on. Some how somebody’s got their wires crossed about what constitutes scientific evidence- and they’ve infected a whole generation of people. Who’s done this? How did they do this? And how are we going to sit them down, and make them take a second look at what they’ve held true for so long- and what will probably humiliate them to change their minds upon?
I’m serious, guys- how?
Clearly a ridiculously stupid claim. A model is a scientific theory. Changes to the theory change the model.
I baulked at that too. Firstly it’s a massive contradiction since no model (and certainly not all models) satisfy ALL observations so the stated criteria will never be met. Did he really mean what he just wrote? Baffling!
Due to the sheer complexity of these models and the sorts of numbers being crunched by them, it is easy for some to forget that they are still wholly dependent on the variables input by the programmer. Because the real climate has so many unknowns, there are myriad assumptions and missing variables in the models. That’s why they have such utterly [snip . . useless] predictive power.
And yet apparently intelligent scientists try to convince us and themselves that as long as you have lots and lots of these inaccurate models, all the inaccuracies will somehow more-or-less just cancel out. To borrow from Al Gore: BULL[snip]! If all the models individually are crap, then 20 models together are a whole heap of steaming BULL[snip]. And all the continual tweaking is nothing more than desperate measures to keep the models in line and to keep telling the correct story. There’s some serious pathology behind some of these pro-AGW scientists/propagandists/programmers.
My understanding of the IPCC case for having confidence in the predictions of the GCMs can be summarised thus:
All the GCM models (13 I believe) are able to hindcast the climate of the twentieth century with acceptable accuracy, once the actual forcings (CO2, solar variations, volcanoes, aerosols etc) are programmed in. What is more, if the human CO2 emmisions are removed, the models fail to show the warming that actually occurred, thus proving that we are responsible for the increase in temperatures. The success of the GCM’s in hindcasting is powerful evidence that the forward projections of the same GCMs are valid and reliable.
If anyone thinks this does not accurately reflect the view argued in AR4, I would be interested to know where I am in error.
Assuming I am not, the logic employed appears to have 2 very significant logical flaws. The first is the glaring circularity of the argument that removing (actual) human CO2 emmisions should cause the temperature rise to disappear. All the GCM’s have been “trained”, by the selection of large numbers of parameterised variables from among a far larger pool of plausible alternatives, to replicate what actually did happen, given the forcings that actually happened. To then remove a forcing which did occur, and which is obviously believed by the model makers to be critical, and act as if the resultant divergence from observed reality is evidence of human causation, is circular reasoning in the extreme – just what did they expect would happen, no change to the model outputs?
The second flaw is that all the GCM’s are different, and all give divergent results when run into the future, resulting in the IPCC’s very wide range of 1 to 6 degrees Celsius expected warming. Logically this would imply that either all except one is wrong, of that they are all wrong. In either case, at least 12, and possibly 13, erroneous GCM’s have passed the hindcasting test with flying colours. This surely makes a nonsense of the claim that hindcasting ability is any indication of GCMs skill at forecasting the future climate.
Am I missing something? Or are they?
“and fits the models”.
Models, and I’m talking about the good ones, are nothing more than an encapsulation of the model maker’s current understanding of the reality being modelled. Hence the phrase means:
“and conforms to our current understanding”.
The dog chases his own tail. And goes nowhere.
If your theory fits observations in climate science it will certainly NOT fit the models.
Clearly the things that ‘fit the models’ best are the models. And luckily ‘the models’ fit all the observations too — that is, all the observations of the models. So where’s the problem?
Gleick defeats his own argument with a single letter – “s”. If all (or even most) climate mechanisms were clearly understood and quantified, there’d be only ONE model not modelS.
Rule one of climate science, if the models and reality differ in value , its reality that is in error .
Once you understand that you understand why observations have to fit the models , becasue if they didn’t they be wrong .
Adam, to answer your question, it has nothing to do with the science. That is the point. The models makers were funded to find a problem (that man was destroying the climate) so they did. Models that did not find a problem did not get funding. Therefore you only end up with models that find a problem. Why where they funded this way? They were funded this way so that bureaucrats can justify their existence (no crisis, no need for the IPCC). If you have a crisis, then you need to accumulate power to deal with the “crisis”. Power then becomes the goal and far more important than a little thing called scientific method – although you can fix that by re-defining scientific method. Governments in desperate need of a new source of revenue continue building the snowball, because you can now start taxing (in various forms) air (at least a demonised portion of it) which you justify with more models confirming your requirements and so the cycle goes.
So in other words the models transcend observation and observations that don’t fit the models should be discarded. I hope someone will correct me if I am wrong, but as I understand it, climate models are computer based. The data inputted to create a model must therefore be accurate. For the sake of argument a model is produced based on the idea that every x% increase in CO2 produces y% increase in global temperature. This model will be totally wrong if that basic premise is incorrect. If the observations that don’t show an increase in temperature are discarded, then the “evidence” will always point to CO2 causing global warming because the model is trusted and the observations aren’t.
I know that this is an oversimplification, but it would explain the reluctance of the University of East Anglia to release temperature records and that “global warming MUST be happening and it is a travesty that we cannot measure it”.
“and fits the models”
Is this for real? Isn’t it a logical absurdity?
As you point out how can a data set fit ALL the models, becuase if there’s more than one model I’m guessing they’re different.
This guy is a scientist?
Let’s face it, Gleick understands climate science as little as anybody else. So let’s leave him aside from any serious discussion.
As for the fixation with models, Gavin’s been adamant about it for years. This is from 2008
That’s one good reason why things are going so bad for science, in climate science.
Adam says:
I’m serious, guys- how?
—
That was answered in the article Truthseeker linked:
“But it must be science, they used a computer! And mathematics! Together!”
lol…
Imagine if Boeing designed and built airplanes just using computer models and never testing in a wind tunnel. Would the FAA certify such a plane? Yet something as complex as the climate system if certified as a go using models only.
Adam says: September 3, 2011 at 1:12 am
Here’s a discussion starter. I’ve been reading climate blogs for two years and actively seeking an answer go the question, “Why do people trust models.”
We all trust models, because we all use models, its part of the way the brain works: Models encapsulate our knowledge in an understandable predictable system.
The real question you should ask is: “why do people trust those producing the models”? Why do people trust people who call themselves “scientists”? Are they any less prone to be wrong? I think the answer lies in the systems that used to surround science: lack of partisanship, truth as an ethic, conservatism with assertions, thorough, honest and impartial peer review.
The truth is that science is in a crisis. It doesn’t have a way to stop the polticos that have infested climate science, ecology and all that ilk taking over science and making science work for them. In part I blame big science projects like NASA, & Antarctic ice stations which were really big PR schemes to show the US could get anything it liked into space or to the South Pole, but they then had to pretend there was a purpose, so they just showeredd remote sensing science with money & icecores …. irrespective of the value or credibility, and then you got all the eco-system science looking at remote imagery, all the ecologists, all the climate “scientists” and people gazing at ice cores to wonder what they meant – and what excuse they could get for another grant from the “give em all the money they like – as long as it looks like science” US. In short, whilst I’m sure there are good people, we got an awful lot of duff science which was heavily tainted by eco-politics. And from that base the cancer of politico-science has spread until it seems to pervade the whole subject.
I can’t find the older example where NASA said all the models of the last 25 years were wrong, but every now and then we have such studies showing one aspect or another in the models failing to account for something and there’s a flurry of discussion with sceptics wishful thinking that this will change the AGW paradigm.
http://news.yahoo.com/nasa-data-blow-gaping-hold-global-warming-alarmism-192334971.html
http://science.nasa.gov/science-news/science-at-nasa/2009/29may_noaaprediction/
http://wattsupwiththat.com/2010/12/07/nasa-climate-model-shows-plants-slow-global-warming-by-creating-a-new-negative-feedback-in-response-to-increased-co2/
http://www.dailygalaxy.com/my_weblog/2010/12/nasa-warns-global-warming-models-wrong-dont-account-for-cooling-factors.html
What’s really being said against the models, and this has been the core concept from sceptics from the beginning, is that models are only as good as the information entered, gigo always applies, and the models have always begun with failure written into the programming because gigo is standard modelling practice. That in itself has been tiresome to point out, but inroads were made, example:
When arguments against the models were making inroads in the more subtle area of the null hypothesis, by simple expedient the null hypothesis was dismissed and the claim made that sceptics had to prove the null hypothesis existed …, the null hypothesis arguments against AGWScience fiction modelling have faded away. Now this kind of rebuttal against the gigo of the models is getting to the point of being widespread discussion the goal posts are again moved, by the simple expedient of claiming ‘the models’ are science fact against which real data has to be measured. This point too, has been a recurring argument against models, that models have been elevated to the status of ‘data’, but now we have the actual ‘official’ claim that models are ‘data’ and real world data has to fit in with them, which is gobbledegook.
I think this is what Wolfgang Wagner was drawing attention to in his resignation statement: http://wattsupwiththat.com/2011/09/02/breaking-editor-in-chief-of-remote-sensing-resigns-over-spencer-braswell-paper/#more-46549
There was no effective rebuttal against the audacity of destroying the null hypothesis in science, because it took a certain amount of refined thinking to understand the point and this is not easily conveyed in sound bites, however, we do have an effective rebuttal against the claim that ‘models are data’, not only that they have been proved to be gigo and which version of gigo is this ‘science data’, but that anyone claiming to be a scientist while claiming models are data is clearly shown to not be a scientist, because science is built on real data. Still not an easy concept for soundbites, but one worth stressing in arguments to bring thinking back to what is the scientific method, if the models do not fit real data, the models are junk as are any predictions made on them. And any scientists defending his junk models isn’t fit for purpose.
The models are simply CGI they give climate-like randomness just as Hollywood CGI strives for natural looking randomness in hair movement or sea.
“[…] a more compelling theory that satisfies all of the observations, agrees with physical theory, and fits Aristoteles and the Summa Theologica.”