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.
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.”
Alan Wilkinson says:
September 3, 2011 at 1:20 am
Clearly a ridiculously stupid claim. A model is a scientific theory. Changes to the theory change the model.
AND:
Paul M says:
September 3, 2011 at 1:27 am
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!
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AGREED
John Marshall says:
September 3, 2011 at 1:57 am
If your theory fits observations in climate science it will certainly NOT fit the models.
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YES. But equally true and compelling is that: if your theory fits the model, it sure as hell will not fit empirical observations!!!
A model is nothing more than software code.
It’s functions are written to perform operations on input data. They cannot do anything more than what the logic has told them to do, however right, wrong, or out of sequence.
Whatever means are used to mimic chaos (random numbers generators) they can never accuractely model such apparent chaos until all known and unknown cyclic variations are isolated from Climate. They will eventurally fail, and do fail, at some point, no matter how well the program has been adjusted to match the wiggles of a pre-selected run of data. They must fail at some point, because the functions and logic are not the actual forces operating in climate, and too much has not been accurately defined.
A weather forecast model begins to turn off the track at around 3 days, and the range of actual error will not be constant 3 days out. They become useless past 10-14 days. Climate models cannot be any better than those confines, being more complicated. They should not be used for prediction.
Climate Forecasters would be better suited, at this level of understanding, to using pattern matching skills and seat of the pants decision making and observation of animanl and plant behavior. Why? Because nature itself has embedded the necessary instincts into species to cope with climate change and seasonal extremes. That includes man. Over centuries, millenia and millions of years. You cannot model that because the entire Planet is the computer.
Truthseeker reiterates something which I have muttered about on these blogs for some time now, and its this.
The IPCC stands for the Intergovernmental Panel for Climate CHANGE…. not RESEARCH; Change. The presumption, as required by the scientific funding, is that CHANGE is what the scientists must be looking for.
Sustitute ‘statistics’ for ‘models’ and the old truism applies:
‘They use statistics as a drunk uses a lamppost; more for support than illumination…’
Indur
Last time I checked it was necessary and sufficient to fit the observations, but “fits the models”!?!?
Last time I checked, and that was in population health delivering population level programs that resulted in real outcomes for humans, we had to satisfy these criteria:
# necessary
# sufficient
# lack of temporal ambiguity.
I am not clear why you invoke only two criteria: ergoneccessary & sufficient in the deluded AGW ‘science’ debate,
@Gleick
> satisfies all of the observations, agrees with physical theory,
> and fits the models.
“model” is a fuzzy word. If Gleick meant that all future scientific theories must be validated through today’s GCMs, then yes that is a very silly statement. Sadly it seems to be embraced by the current regime of peer reviewers.
Science is not science without observations.
Having said that, I would like to point out that scientific observations are not possible without models for measuring physical entities. Perhaps there are gods who can perfectly perceive temperature, pressure and space/time directly, but we humans utterly depend on model-based transducers to perceive the universe around us.
We perceive using proxy devices whose symbolic output must be interpreted and are subject to noise and measurement error. A model must be created and applied to render these interpretations.
For example, there is no way to observe temperature without using some kind of non-trivial model based on the thermal properties of proxies such as alcohol/mercury expanding or contracting in a thin capillary or current flowing through a thermocouple.
Even our subjective sense of time and space cannot be trusted for scienfiic purposes. We must employ rulers (with modeled markings) and watches (with modeled electro-mechanical escapements) which operate using standard models (metric) that require interpretation and are thus subject to two kinds of errors: using a wrong model and making errors interpreting the model output. (Of course, “all models are wrong, some are useful” – Geo. Box)
The real problem is the ‘engineering fallacy’ that lets us forget that these instruments are really models of reality. But we pretend that we are gods who can perceive matter and energy precisely by merely “observing”.
Just saying.
Peter Wilson says @ September 3, 2011 at 1:47 am “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.”
It is not the CO2 emissions that make the models yield well-fitting hindcasts. It is the the input of aerosols. And it is not the “actual forcings” of aerosols; rather it is estimated and conveniently chosen values for aerosols that make the hindcast fit well.
There are various choices on how to measure solar variations — and the scientific communitiy is not in agreement on which ones have primary impact on climate. The GCM models used by the IPCC use models that choose solar measures with little variation.
Barry WoodsBarry Woods says:
September 3, 2011 at 1:07 am
Barry,
The simple answer is politics driving subjective research. “I’ll pay you well to find evidence to support my agenda.”
The elegance of lying.
I guess Al Gore took a direct hit and his investors in his scam are coming for him.
I just read Gleick’s article. I knew his bias before, but I’m disturbed at his embrace of the political aspects of the story.
I don’t have an account at Forbes, and won’t bother to make one, but I was tempted to ask about the “fits all the models” phrase. If the observations do fit models, I’d argue that would be nearly proof that we completely understand the science. Clearly observations (poor as they are) don’t fit the models (poor as they are) and there’s a lot of science left to be done.
If Gleick seriously considered his statement that scientific progress requires that it fits the model, then his view of the scientific method forces observers in a single direction and puts models upon a pedestal that I think is reserved for good observations.
Piffle
As an AeroSp Engr student, I was taught to develop and use models to simulate flight dynamics. We were also taught that if the output of the model(s) did not match observations, you needed to change the model, not tweak the observational data. Maybe climate scientists should bring a couple of engineers into their groups?
Bill
Let me try my idea of WHY warmaholics trust models. When the IPCC was given a mandate to prove that CO2 caused CAGW, they had a problem. They could show that adding CO2 to the atmosphere changed the “radiative balance”, but they could not show, quantitatively, how much this change made to global surface temperatures. There was no way to do any actual experiments on the atmosphere, so the only recourse to provide the needed proof was to use models. If models do not provide the proof, as most scientists would agree, then this approach was simply nonsense. So the IPCC had to maintain, and still must maintain, that models provide actual proof, otherwise they must admit that CAGW is built on quicksand.
And there is still no science that enables anyone to estimate change in surface temperature from a change in radiaitve balance. Can anyone answer my simple question. If CAGW occurs, does the lapse rate change?
It’s simple: Climate science is an oxymoron.
jonjermey says:
September 3, 2011 at 2:04 am
You’re right–there are two universes: Models and Reality. The two seldom coincide.
“and fits the models”.
There are models and then there are models. More precisely there are theoretical models and there are practical models.
A theoretical model would be derived from the theory, but may be impractical in ordinary life (i.e. a theoretical model of an atom).
On the other hand, the quest for practical models (i.e. computer programs) for large chaotic systems such as weather and climate are easily arguable as “crude.” Some of these same techniques are used daily in an attempt to model the stock market, and some are actually used to “bet” money, but it is just that, a “bet” on a “gamble.” Often they work just fine, but when an new (or forgotten) scenario comes up, they, more often than not, will lose the farm.
Weather and climate practical models are easily arguable as “crude” because they have so little “training data.” Yes, it may be terabytes, but who can say they contain enough data points, let alone cover all possible scenarios for weather and climate. We all know that data does not exist.
Adam says:
September 3, 2011 at 1:12 am
“Why do people trust models.”
=============================================================
Adam, the answer is a lot simpler than that…..
…”it’s all they’ve got”
Parameters in the models can be tweaked small percentages to get the answers they want. If the model doesn’t match observations, go back and tweak it again until it does….
Climate science morphed into computer programming a long time ago. They can’t even talk about the weather any more without saying “our most reliable model”
…it’s really disgusting
EDIT: The comments by Gleick, that is
Well its not uncommon for people to put up their personal definition of science and scientific method and get it wrong. People here do that all the time.
We are talking about a Forbes article after all.
The models are a perfectly good part of science.
In fact as a way of capturing understanding of climate they are much better than the enumerate hand waving that is popular here. Ordinary language is poor at describing the complexities of climate. In short if you can’t calculate something chances are you don’t properly understand it.
Theories must agree with the observations. The observations don’t have to agree with any theory. By George, the blasted theory may be WRONG.
This is equally, if not more so, true for Models.
Someone should hit Mr. Gleick with these truths. As if it would do any good with these High Priest of The Church of Global Warming,
@Peter Wilson
Peter hits the nail squarely on the head. Since the models don’t agree with each other predicting future climate, any claimed skill at hindcasting can be ignored. In fact, it can be concluded that any claimed skill at hindcasting is the result of scientific malfeasance, not blind luck.
@Gleick
> satisfies all of the observations, agrees with physical theory,
> and fits the models.
Gleick’s intellectual forbears said much the same thing at Galileo’s trial, with regard to the Ptolemaic model.
Nothing’s changed, except the claimed focal point of the worship. The *true* focal point, then as now, of course, is always the men who are running the system and who will do and say anything to keep it from being upended.
“In short if you can’t calculate something chances are you don’t properly understand it.”
Well, you got that right anyway.
Alan Wilkinson says:
September 3, 2011 at 1:20 am
“Clearly a ridiculously stupid claim. A model is a scientific theory. Changes to the theory change the model.”
This claim shows total incomprehension of the matter. If one had physical hypotheses that collectively make up a theory, one would not need models. Models are purely analytic tools that cannot do the synthetic work of hypotheses.
LazyTeenager says:
September 3, 2011 at 6:32 am
In short if you can’t calculate something chances are you don’t properly understand it.
===========================================================
Thank you……
..obviously then, no one properly understands climate
Peter Wilson says:
September 3, 2011 at 1:47 am
“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?”
Brilliant post. The entire post is a must read.
And because they are using models, any particular “idea” about reality makes sense only within the context of the model. It is impossible to take from the model the “ideas” about CO2 and independently use them for prediction and possible confirmation. For that reason alone, these “ideas” are not physical hypotheses and do not belong to science.
omnologos says:
September 3, 2011 at 3:04 am
Excellent. Gavin reveals the deepest sort of sheer prejudice.
LazyTeenager: In short if you can’t calculate something chances are you don’t properly understand it.
Hmmm. I’ve got 13-20 models with 13-20 different calculated outcomes. Chances are….
“The models are a perfectly good part of science”
Agreed.. and what is the falsification test for the climate models? I think the current lack of warming has already falsifiied ALL of the IPCC models.
Any model that does not have a method of falsification is religion, not science.
LazyTeenager says:
September 3, 2011 at 6:32 am
“Well its not uncommon for people to put up their personal definition of science and scientific method and get it wrong. People here do that all the time.
Ordinary language is poor at describing the complexities of climate. In short if you can’t calculate something chances are you don’t properly understand it.”
I think most people here understand science and certainly scientific method more than you give them credit for. If by not understanding science and scientific method means that we hide data and obfuscate data that we do show others, then yes I would agree we do not understand science and scientific method.
The inabilty to calculate something does not mean that it cannot be understood. I do not reduce to mathematical equations everything I need to understand. If I did I would not be able to reply to your comments. As for the complexities of the climate, personally I don’t think there is a computer that has been built that can predict climate to any degree of accuracy. Especially when it has been fed a stream of supposition and bigotry masking itself as data.
Jim Cripwell says:
September 3, 2011 at 5:58 am
“There was no way to do any actual experiments on the atmosphere, so the only recourse to provide the needed proof was to use models.”
Excellent point. However, since that time, Svensmark and Kirkby have shown us how to do experiments on the atmosphere. Of course, what they have shown us was obvious to all scientists.
The Warmista have always found themselves arguing the truly childish position that they cannot experiment because they would need a second Earth to experiment on.
LazyTeenager says:
September 3, 2011 at 6:32 am
Well its not uncommon for people to put up their personal definition of science and scientific method and get it wrong. People here do that all the time. And climate modelers are the most proficient of all in “putting their personal definition of science and ignoring the scientific method.
We are talking about a Forbes article after all. Are you saing the publisher controls the content? I say where it’s published doesn’t matter; truth is truth.
The models are a perfectly good part of science. I refute your description of “perfectly good”. They’ve done a horrible job so far. They can’t even hindcast without significant adjustments so how, not knowing the future adjustments needed in the model, can they ever forecast?
In fact as a way of capturing understanding of climate they are much better than the enumerate hand waving that is popular here. Ordinary language is poor at describing the complexities of climate. In short if you can’t calculate something chances are you don’t properly understand it. Your concept of “capturing understanding” needs refutation–a model is simply a pile of code with a prior objective in mind; it only fortituously matches the real world.
Have you ever personally run computer models? I have–thousands of them. They do a huge number of mathematical calculations and spit out an approximation based on your basic assumptions and pre-programmed algorithms. Very seldom is there a surprising conclusion–you pretty much know the outcome beforehand. If not, you simply adjust the model and/or the data to arrive at a preconceived notion. And climate models are no different from the type of models I’m familiar with.
You put much more stock in models that I ever would. This article complains that some would require that “models” be part of the objective evaluation called “science”. I would completely disagree (waves hand).
If you study the claims made by climate change advocates, the political power levers they employ, you will learn much about how regime change is effected on decadal scales by the US power elites.
Whether that’s a comfortable thing for US Republicans to hear, I don’t know.
But there are significant parallels……..
Parametrized models as are used in climate science very consistently predict what model builders believe will happen, with very high accuracy.
Thus, it we require scientific theory to fit the models, then we are requiring theory to be consistent with bias. That is not science, it is superstition.
This has been shown time and time again. Parametrized models predict the bias of the model builders that set the parameters. They do not predict future climate, any more than they can predict the future price of gold.
The average person knows that gold is likely to go up in price over time, because this has been the pattern over time. We also know that temperature has been going up since 1750, so it is likely to go higher in future. Thus, it doesn’t take a computer model to predict this.
What we don’t know and can’t predict are the fluctuations. The increase in gold prices is not constant, thus there is money to be made if one can predict the local hills and valleys.
The same is true in climate. Global warming has been going on for 350 years. Any moron can predict that it will continue. However, within those 350 years there have been local peaks and valleys. Over the past 150 years they have occurred on a cycle of about 60 years. The question for a skilled prediction is whether we will get another 60 year cycle. None of the climate models have predicted is that this 60 cycle will repeat going forward. Current observations suggest otherwise, which suggests that the climate models lack skill.
a model is just a theory written in a programming language.
sharp point – nice post!
” In short if you can’t calculate something chances are you don’t properly understand it”
If you can calculate it you made assumptions!
Jim Cripwell:
Turn your question around, and you have the key: If the lapse rate does not change, can CAGW occur?
I have given the definitive answer to that with my Venus/Earth comparison. The lapse rate does not change, it is THE long-term governing effect, and CO2 AGW does not occur. Those who introduced the adiabatic lapse rate into the debate, as clear and convincing evidence against the greenhouse effect promulgated by the consensus, were right, and my Venus/Earth analysis makes that obvious and definitive for any real progress in a true climate science.
LazyTeenager says:
September 3, 2011 at 6:32 am
I have to agree, there is a lot of hand-waving here, as in most blogs. There is also a lot of good solid science, much of which admittedly goes over my head. That is uncommon.
I do have to say that al lot of your comments could and are interpreted as hand-waving, as this one is. Kind of ironic, really!
But what we are discussing is the actual science (non-hand-waving) of Spencer’s paper, and the very hand-waving excuse of the editor for resigning. More irony perhaps, or is that what you meant?
Peter Wilson says:
September 3, 2011 at 1:47 am
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.
Bold mine.
So, what if one of the other variables: solar variations, volcanoes, aerosols, etc. is removed? Do the models also fail to show the warming that actually occured, thus proving that that specific variable was responsible for the increase in temperatures?
Just askin’.
Gives a whole new meaning to the term “special needs science”.
Dear LazyTeenager,
There’s an old Irish saying: “Not everything that can be counted, counts – and not everything that counts can be counted.” Don’t confuse “calculation” with “understanding”, or “wisdom” with “intelligence”.
David, UK says:
September 3, 2011 at 1:32 am
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].
Snip? You can’t say “bullshit” here anymore? Not even in context to an intelligent crowd? I mean, who got hurt?
I’m beginning to think the biggest mistake is arguing against ‘the models’, it can certainly be entertaining, but it does nothing to inform the general public still unaware of just how duped they are.
Which ‘model’?
http://theresilientearth.com/?q=content/seven-climate-models-seven-different-answers
What’s really going on: http://hockeyschtick.blogspot.com/2010/04/nasas-changing-facts.html
It’s a complete and utter scam. That’s the only Model they have and the only one they use, all other ‘models’ are irrelevant except for continuing distraction from this ClimateScamModel.
That NASA is shown here to be without any scientific integrity whatsoever is what is important, because one of all the institutions using their historic science credibility to con us.
They are morally, and fiscally, responsible for the misery being imposed on the general population through their support of this ClimateScamModel. As is the IPCC.
LazyTeenager says: September 3, 2011 at 6:32 am
[…]
In short if you can’t calculate something chances are you don’t properly understand it.
You have described climate science quite aptly.
The overconfidence in climate models probably stems from the folks getting overexcited with the real beauty of simple models which are able to predict future outcomes.
An example of a successful model would be one which can predict how near to earth a comet may pass. While orbital mechanics is complex, the variables are relatively simple in comparison to those affecting planetary climate.
It seems that certain folks at NASA have gone a bridge too far with their models, and are having trouble crossing back over.
Peter Gleick contends there are three criteria for a new theory to replace the existing theory (of climate change?) It will be accepted when it “……..satisfies all of the observations, agrees with physical theory, and fits the models.”
It seems to me that a model could not be any more scientifically valid than the level of scientific understanding (LOSU) that provides its foundation. If one or more components of the scientific understanding are being questioned, one would expect there to be disagreement with the models that are founded on the science … or assumptions… in question.
There are several important areas in the climate science today that the IPCC admits the LOSU is low. Should not the assumptions arising from these imperfect understandings be subject to scientific inquiry without subjecting the inquiries to such a circular litmus test?
Hey, Ric Werme. Thanks for the link! I knew there was a reason I keep haunting this site. I have been properly re-educated. Oil ain’t abiotic, after all.
“Never mind if something works in reality. What is important is whether or not it works in theory.” That seems to be the attitude of some “scientists” who seem to be abysmally ignorant of the history of science.
The models don’t fit each other in climate science; not even one model run fits another model run, so Gleick is clearly talking about something he knows nothing about.
Fred Singer says in this talk that two model runs of the same model (over 20 or more years) produce global temperature trends that can be as far apart as an order of magnitude.
http://notrickszone.com/2011/09/03/fred-singer-at-suppressed-seii-presentation-1976-to-2000-warming-thats-fake-it-doesnt-exist/#comments
Taking Gleick by his words, we would have to discard the climate models first as they don’t fit each other; personally, I would welcome such a move. The broke western nations could use the spare money well.
Reminds me of the current administrations economic team. They model. They get it badly wrong as judged by apparent result. Make up data, based upon another model, to prove they were right. Exactly the same mind set as Warmists.
LazyTeenager says:
September 3, 2011 at 6:32 am
“In fact as a way of capturing understanding of climate they are much better than the enumerate hand waving that is popular here.”
As others have observed, your next sentence is a prime example of handwaving.
“Ordinary language is poor at describing the complexities of climate.”
I am sure the term “negative feedback” is not part of “ordinary language”. Also, I seldomly debate the Stefan-Boltzmann Law with “ordinary people”; they happen to not know it.
“In short if you can’t calculate something chances are you don’t properly understand it.”
Perfect description of climate modelers. The models have to show predictive skill. They haven’t by now, and they will not in the future, I say, because climate is chaotic (no reference to “complexities of climate” required; the word chaotic is well-defined in a branch of science called mathematics; look it up.) and they will, as a matter of simple logical principles, never be able to improve on the forecasting horizon of a meteorological forecasting model. No, running the model a few times more doesn’t help you very much; see the definition of chaotic.
Has any GCM ever consistently predicted temperature, precipitation, cloud cover, humidity, etc even one year out?
Peter Wilson says:
September 3, 2011 at 1:47 am
I think you have summarized the issue very well, and I don’t think you’re missing anything. But before agreeing with you completely, I need to determine what my model says..
If NASA has any input to the models, I sure hope they don’t mix English units with metric units.
When I was deep into economic modeling in the late ’60s / early 70’s, our computer models were certainly useful in helping to understand the dynamics of what were then called “complex non-deterministic systems”. But If anyone back then were to have asked if the theory fitted the model output, they’d have been quickly referred to the college psychiatric department. Models were used to better understand the theory, with a view to polishing up the theory. That is, the models were nothing more than an extension to the theory aimed at enabling us run simulations of the theory such that the model output could be checked against reality. I don’t see any changes in the past 40 years that would make switching models from the theory side to the reality side anything other than insanity plain and simple.
Classic science. Adam is totally on track. Look up “The map is not the terrain.”
Models are over-simplifications that help us understand things and phenomena, and help us predict how some natural thing or process will act. Models are over-simplifications.
When a very strong, accurate model is developed, it gives the impresssion that we have discovered a “law,” and that we now know how the physical universe operates. Sure, we can be very close. But never accurate.
Atomic theory is good enough to predict an aweful lot of stuff to a pretty good degree. The drug companies take a recognized molecule that has some effect, then strive to develop molecules with similar structure, then trial these to see if they can develop an even-better drug. That is impressive. But it is still trial-and-error. Because the models of the molecule, down to the atomic level, and the models of the body, down to the receptor level, are not totally accurate. Close enough to develop effective drugs, with trial-and-error, but not “exact.”
Some phenomena really can only be conceptialized by complex models. Climate is an example. when you get a model that fits, you are tempted to be impressed by your model, and believe you have it.
Even if Mann 98 were properly done and properly predictive, that model, as all other climate models, would eventually be shown to be limited in some way.
THere is a fundamental challenge in a climate model. A climate model can either be developed to ultimately include limiting factors that will keep the output within some physical bound, from running away far beyond what might happen naturally, or some factor of the model, given any input, will eventually run away to extreme values.
An example is: if a climate model can predict runaway temps, with no calculation to bound that parameter, then the model could be run far enough into the future that the output, predicted temp would physically be impossible – a temp at which the earth would be hotter than the sun, for example.
if the model is bounded, i.e., hey, we better put in some feedback loop, some rate-limiting loop, so that a set of parameters does not ultimately run to the bottom of the kelvin scale or to the temp of the sun, then the boundary of the model might be one of three things: either spot-on, or too high, or too low. Spot-on is so incredibly unlikely that it is obvious that an awesome model, given current knowledge, could only run into the future a limited amt and yield valuable output – i.e., a fair estimate of global temps 100 yrs from now, given a couple of scenarios (i..e., co2 steady, or rising, or falling).
So, get the most awesome temp model, and ask the originator: doe it have some rate-limiter, or not? Could it mathematically run to yield temps higher than the sun’s temp, if predicting far enough into the future?
That obvious factor shows that a model is just a model. The physical universe does not zig then zag because our models changed, or got updated. The tail does not wag the dog.
The model is always at risk of being tossed aside. It is always just a model. Scientific theories or observations are not supported because they fit some model. Models must always serve the data. Of course, this is a sticky wicket, since data depend upon measurement and resolution, etc. – this is why it takes a lot of training to become a scientist, and why it takes a lot of work to develop a model and test it against real-world measurement (which, also, are inherently limited – but that is for another post).
The bottom-line issue is: can we develop predictive models well enough, and figure out all of the variables well enough, and measure them well enough, to predict a future catastrophe that we could then avoid?
My lowly opinion is that we can predict the path of asteroids and space junk well enough to know pretty clearly whether a piece would hit earth or not, so answering the decision abt whether to send a nuke out to space to blow up an asteroid well enough that it will veer away or result in pieces small enough to burn up on hitting the atmosphere.
Can we send a rocket to the moon? Obviously. Course corrections have been needed on all moon shots – they never just launched, like a catapult might throw a stone, and sit back and watch the rocket approach the moon – ridiculous.
Sure, I want to know whether exhaust in the atmosphere will lead to a disastrous green house effect. A model is one way to test this hypothesis. There are others. I myself believe that the confidence in models is overblown, and that it is circular – the models seem to have taken on great authority and power over mother nature, for the true believers. They have a hard time listening to us skecptics, and entertaiing other lines of investigation (soot, other temp proxies, gamma rays, etc.).
There is nothing sacred about a model. Models do not drive nature. A model is always suspect. The science is never “settled.” That is 100% science for ya.
LazyTeenager says:
September 3, 2011 at 6:32 am
In fact as a way of capturing understanding of climate they are much better than the enumerate hand waving that is popular here. Ordinary language is poor at describing the complexities of climate. In short if you can’t calculate something chances are you don’t properly understand it.
—————
O Teenager of apparently eternal youth (and all the self-assured certainty of others’ stupidity that this entails): In fisheries science in the late 1970s a new method for calculating the size of commercial fish populations was developed, a kind of hind-casting called Virtual Population Analysis. By fitting models to past population catches and estimates of the size of fish populations, fisheries biologists then felt confident to predict future populations and catches. Some Canadian fisheries managers -the ones in charge of the Northwest Atlantic fishieres- felt so confident about these models that they saw no need to develop robust data sets independently to monitor the state of the fish stocks. Rather, they relied on the data provided by corporate fishing boats (which were, incidentally, using sonar to detect the remaining fish concentrations). They were so confident of these models that they cast as ‘deniers’ those independent fishermen who were actually out in boats (unlike most of the scientists) and who saw abundant evidence that the groundfish stocks were in serious decline. Guess who had egg on their face when the fish stocks collapsed in 1992?
Unfortunately, as entire society had to lose its way of life because the models were wrong. The people of Newfoundland deserved better.
Spencer uses a simple model, albeit poorly. People in fields from biology to engineering use models all the time to create a means to test what if scenarios and do design work. One can mathematically describe elements of a system and how they interact and set the model to work on a complex problem that can’t be hand-calculated. Sure, the IPCC admits that on small scales, the models won’t be able to predict what day it is going to rain at you house, but there is strong support that models can do well at the continental and global scale. If they can hindcast and predict effects in advance, they seem to be on the right track for predicting the magnitude of this century’s temp rise based on varying emissions scenarios.
http://www.skepticalscience.com/climate-models.htm
Here lies the rub, though. The models predict that rising CO2 emissions will also contribute to raise the temperature, a human impact that scares some people due to the threat of the regulatory boogey man (let’s be honest, folks that is where the denial comes from). The paleoclimate evidence provides an even more compelling estimate of Earth’s eventual temperature rise due to the geologically instantaneous jump in greenhouse gas levels.
So yeah, that’s the rub. Any takers on creating a model that can perform better and show that CO2 is not one of the primary drivers of climate? Get peer review approval from the Journal of Geophysical Research? If not, step aside and let the people brave enough to address the issue using the best evidence we have figure out what to do.
Heads up to all, from the Guardian:
Should be an interesting read.
“…safe until someone can put forward a more compelling theory that satisfies all of the observations”
Even that part is not true. There is no requirement in science to put forward a more compelling theory before you’re allowed to debunk an existing theory. Providing solid evidence that a theory is incorrect is enough to remove the theory from its “safe” status. Debunking a faulty theory is a valid service to science whether you can come up with a valid replacement theory or not.
All models are wrong, a few are useful.
I would like to offer a partial defense of good models vs bad data.
in the late 80s and early 90s I worked for about a decade for a consulting company that provided highly sophisticated, non-linear, fundamental science and kinetic chemistry-based computerized models to our world-wide clientele. The experience obtained there is, I believe, instructive because it is analogous to the present-day climate modeling issue.
In the early days of our simulation efforts, we had a serious problem of having the model match the client’s data. When there was a mismatch, we had to determine if the data was wrong, or the model was wrong. That’s a tough question, but was finally wrestled down and actually was an iterative process. We would question the data, the measurements, instrument calibration, laboratory analysis, fundamental errors in measurements (is the flow accurate to plus-or-minus 3 percent, or 10 percent, or something better?). We also had issues with fundamentals such as conservation of mass, and conservation of energy. The data for our purposes had several input streams and even more output streams. Everyone agreed that the total mass input had to match the total mass output, because there was no inventory change in the system. The same was true for the energy balance, several heat inputs had to be summed, then had to match the heat flows out plus heats of reaction. Once the data was finally hammered on and straightened out, we turned our attention to the model to tune it to match the data. Sometimes we found errors in the model and corrected them. Finally, we had what we considered a robust, accurate, and useful simulation. I’d like to note that we had one model, not a dozen or more.
Then another client came along with a slightly different situation and our model had to be modified to match not only the first client’s data but the second one’s also. That also was finally accomplished, again by ferreting out errors in the new client’s data and tuning the model to match once his data was properly vetted.
After several years of this, we had confidence that our model was indeed accurate and robust. When new clients came along, they were understandably proud of their data and would at first argue with us that the discrepancies between model runs and their actual data was due to a fault with the model. We then explained how the model had been improved over the years and suggested they review their data. Invariably, the client found problems with their data and would collect a new set of data after instrument calibration and laboratory fine-tuning.
The point of this rather long narrative is that this method of model development and data acquisition is not new, is not unique, and has occurred in many applications for decades.
Where the problem in climate modeling lies is in the premature declaration that the models are accurate, are valid, have been vetted, and therefore any new data that does not match must be discarded. Quite simply, the state of this art is nowhere close to that point. As I mentioned, we had one truly robust and battle-tested model. The model was accepted as state-of-the-art by the majority of the world’s approximately 2000 industrial sites with that process. Climate science has multiple models, some say 13 and I’ve heard as many as 20. At their meetings, sessions are referred to as “spaghetti charts” due to all the lines on a slide to show each model’s results. It is in no way correct to say that the models are accurate, when there are multiple and inconsistent models.
There may indeed be errors in the new climate data sets that are collected, and those should of course be carefully evaluated and vetted so that the data is as accurate as possible. Only then can the models be improved.
Nonsense. As with any politicized movement, there will be some basing their positions purely on outcomes (desired or otherwise,) but the “denial” has nothing to do with the outcome in general. In fact, very few actually deny anything other than the claims of accuracy and magnituded. Perhaps you get all of your talking points from RC? For many, the threat of the regulatory boogey man is a concern whether there is warming or not, and whether it is human caused or not. They are unrelated. So, let’s be honest, you really don’t understand anything about those you so vehemently oppose.
Uh, you’re letting your ignorance show here. The paleo record – the last 800k years – indicates the opposite sign, i.e., CO2 changes as a result of temperature changes. Over longer terms, the record indicates that there is no connection between CO2 and temperature. Get your facts straight.
Your assumption is a logical fallacy. You are presupposing that a) current models actually perform well (in any measurable context, they do not at all) and b) it is possible for a model (any model) to perform well. Sorry, but epic fail.
Brave enough? You have got to be joking…
Mark
I’ve not read all of the comments yet, but surely a more pertinent point regarding the scientific method versus the statement
” 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.”
would be that we do not need a new theory to dismiss the existing state of the art, merely find some observation or other which disagrees with the current theory, at which point the theory becomes extant.
“agrees with physical theory, and fits the models.” – Peter Gleick at Forbes; emphasis added. 9/2/2011”
Seems to me that no more science need be done. A set of observations is taken, a theory is created, a model based on that theory is created and we’re done. Since the models and the theory are all then the subject is closed and all climate funding can be dropped. It’s done. Scientists losing their jobs as a result can move into some other Gov. funded scientific field.
There’s no more need to do science since either the new observations/data will agree with the models and are, therefore, pointless (since they agree); or they observations/data will disagree and are, therefore, wrong.
The only remaining discussion is why the models don’t agree with each other, which is odd, since they’re perfect and must, therefore, be correct. The discussion can be adequately carried out in the blogs, including why Hansen’s Model C seems to be the closest of the models to what’s observed in the real world.
/end_snark
It’s similar to the BBC redefining Balance, to mean what we used to call bias.
http://www.bbc.co.uk/bbctrust/our_work/other/science_impartiality.shtml
As the acadaemic elite hijack & redefine the language, to fit their sociological models & reinforce their cosy view of the world.
“and fits the models”
Hmmm, that’s model bias at its best. So, if the new work satisfies everything else and does not fit the models, it’s bogus, right? Just checking.
First, for their post/comments/links, kudos to: Indur M. Goklany, Truthseeker, Steve Keohane, Josualdo Silva, and Jim Cripwell. Too many more to list.
Next, special thanks to Lazy Teenager for revealing his ignorance of the meaning of enumerate, giving a certain specialness to his comment, which provided many easy shots and much amusement here.
Like Peter Gleick’s statement, a model is a good way to bring your ignorance to the forefront. Models, no matter how complex and expensive, are only tools. In themselves, they are no more Science than is a ruler or a magnifying glass. As one who has attempted to encapsulate behavior of complex systems within mathematical constructs, I can state that AGW’s faith in models is dangerous, hubristic pseudo-science.
It is time to DROP THE CHARADE and admit that TRUE progress in “science and technology” DOES NOT COME FROM “PEER REVIEWED RESULTS”.
Now, this is a bold statement. Let me give some researchable examples and contrasts to the “so called” – climate “scientists” and their “modis operandi”, which will illustrate my point quite completely.
I am a fan of Dr. Kwabena Boahen (http://www.stanford.edu/group/brainsinsilicon/) . I would HIGHLY encourage all us “skeptics” to look over his work and his group. Several points come to mind when evaluating his work and his graduate student’s work.
Number 1. is, THEY HAVE NO PEERS !!! There are, right now, no major groups doing their approach. SO HOW DO THEY GET A PEER REVIEW?
Number 2. ALL THEIR WORK IS COMPLETELY PUBLISHED AND AVAILABLE. If you want to duplicate their circuits, you download their programs for designing them. If you want to understand the technology, you look at the posting of 10+ years of the “intro” course Dr. Boahen has posted. If you have a question, you Email them. (They are pretty good at answering questions, even from dilitants as myself.)
Number 3. They were running entirely on Stanford Department funding, up until 4 years ago, when they got a $5 Million dollar grant from the NIH. Dr. Boahen thinks they may have a “brain in a shoe box” in about 3 years. They already have duplicated Choclea function and Retina functionality.
SO the key question is: WHY DO THEY NOT WITH-HOLD information, and WHY don’t they have “critics”? (Or need for “peer review”.) The answer is .. they are doing REAL work, with REAL results, which are all testable. The second answer is that Dr. Boahen comes from Ghana and he really believes in the “common good”.
My only complaint about this, is that if he “played the game”, maybe he’d have 50 Million or 500 Million. But then, like the money squandered by the Human Genome project (10 Billion in 10 years) compared to the mere 300,000,000 in 3 years, and cracking the genome by Salara corp, privatedly funded..it might be a detriment to get TOO MUCH MONEY.
Again, another example of “real science”, and proper exposition of work, with little peers to review. Dr. Stephan Hell: http://www.mpibpc.mpg.de/groups/hell/ Dr. Hell NOW has some peers, doing similar work In 1998 he was almost ALONE in doing work to break the “Abbe Limit” with regard to microscopic resolution. His papers are amazing, in that the SPECIMEN preparation for samples shown using their various methods, are usually COMPLETELY OUTLINED as an appendix at the end of the paper. (Particularily the last 5 to 7 years.) This is because people doing parallel work CANNOT SET UP TO DUPLICATE WELL unless they know how to prepare the specimens. Dr. Hell WANTS his work to be duplicated and to be clear. NOTHING IS WITH-HELD!!!
Real SCIENCE, REAL RESULTS, totally transparent. It’s time we pointed out that the POINTY HEADS doing the “CLIMATE SCIENCE” are for the most part “speculators” and “snake oil salesmen”. They deserve NO GLORY, SCANT ATTENTION and general denigration for their behavior, arrogance and bluster.
Maybe, some day, we’ll get a group that starts with the premise: DATA FIRST, ANALYSIS SECOND, PREDICTION THIRD, OBSERVED DATA AGAIN, COMPARISON WITH PREDICTION, AND “YES OR NO” on DATA (OBSERVATIONS) FITTING PREDICTION and the humble attitude to not publish in an obscure journal an admission of failed theory and prediciton, but to POST IT ON THE INTERNET PUBLICALLY and indicate what lesson was learned from the failure.
That’s a LOT of “humility” to have. Judging by what I’ve seen of the HOLYIER THAN THOU, STIPEND POSITIONED, ACADEMIC SNOBS in the “climate science” realm, I doubt that will happen any time in the future.
Mean time, when your Brains in Silicon robot is tending to you in old age, or your custom drug is being made, after the elucidation of your bio-problem, using advanced molecular imaging…all developed by REAL scientists, with NO PEERs, just “kiss your robot” thanks for the REAL SCIENTISTS making progress, who have no peers.
Did any of you in your youth ever come across one of those sure fire promotions for a horse race betting system? They may still be around for all I know. The main selling point was a very long list of of past winners, with prolific detail of names, courses, dates, starting prices etc. which (after payment) would be demonstrated to have been predicted by this wonder system together with a calculation of how much you would have won if only you had been privy to the system. Too often the gullible parted with their money while the more skeptical gave the matter some thought and came to appreciate that given a list of determined events, one can come up with umpteen different methods (models) that could absolutely predict those outcomes in hind-cast. A similar scam aimed at a different market did the same with stock prices. Almost any fool can come up with a system which will hind-cast the outcome of past events but foreseeing the future is a different proposition. Ask Mr. Greenspan or Mr. Bernanke.
otter17 says:
September 3, 2011 at 10:22 am
“Sure, the IPCC admits that on small scales, the models won’t be able to predict what day it is going to rain at you house, but there is strong support that models can do well at the continental and global scale.”
Models don’t do well at all; please refute me by providing a link to a paper that SHOWS that a climate model has been proven to have predictive skill; a link to “skepticalscience” won’t do. Models fail to get ENSO right (they’re at a complete loss there), they fail to get cloudiness by latitude right (that’s on a continental or global scale), they fail to get large convective fronts right (they’re too big to be described by statistical properties, and the models are incapable of simulating the physical processes).
Or does “skepticalscience” mean with “can do well” that they get it right once in a blue moon? Yeah, that’s surely worth billions. ;-P
LazyTeenager says:
September 3, 2011 at 6:32 am
“In fact as a way of capturing understanding of climate they are much better than the enumerate hand waving that is popular here.”
Oscar Wilde once wrote, “In America, the young are always ready to give to those who are older than themselves the full benefits of their inexperience.”
Never a truer word.
So Lazy Teenager, what you have done is direct your initial comment at the readership of this Blog, not at the issue under discussion.
So to rephrase your comment, and to ask a question, “In fact as a way of capturing understanding of climate, models are much better than using observational data.”. Is this what you really think?
Please give us the benefit of all of your experience.
I posted this elsewhere prior to reading this priceless little tidbit from Gleick:
“I find it hilarious that it’s even suggested that modelers should be consulted about real world measurements that their models will be judged upon. It’d be like proponents of String Theory being given an editorial veto over what CERN can publish so they don’t hurt their feelings.
It’s up to the modellers to reconcile where their theory went sideways when compared with reality…”
Well, I think models are great. If they don’t work, then you clearly don’t understand something, and you can fix it. The problem stems from how things get fixed. If they fix it by simply tweaking it in some way, that is not going to work except by accident. Any tweak needs to based on a principle that wasn’t previously understood that accounts for increased understanding of the whole system. You can’t simply “adjust” the data. The model is your friend if you aren’t trying to cheat. It tells you when your ideas don’t work so you can go back and figure out what it is you don’t yet understand.
I think debates would be good so that people can see why skeptics are skeptics. It isn’t hard to pull a few important variables out that affect climate which are not accounted for in the models, like the cosmic radiation cloud seeding, or Willis’ thunderstorm thermostat theory. When you have major variables and forcings that are not accounted for, I think it is a huge mistake to base policy on incomplete theories.
I think the real problem is that climate is being used for political purposes, which is very bad for science. Government likes to control people. It looks for any reason to do so. If an area such as climate is useful, government will use it. And you have the apparatchiks like Schmidt, Mann, Hansen who claim the mantle of science to do it. Their power depends on coming up with a particular outcome in the debate, not on truth. I believe that explains an awful lot of the behavior we see out of the Team.
I haven’t read all of the above entries but hasn’t anyone pointed out that observations do NOT mirror the model’s predictions ? Several of the posts I have read take as a given that the GCM’s correctly predicted the last 13 years of non warming when they did not !
The AR4 models predicted that there should have been around .3 ° C warming from 2000 to present.
There hasn’t been any measurable warming at all in that time period.
http://www.cgd.ucar.edu/ccr/strandwg/CCSM3_AR4_Experiments.html
Hansen’s model has long since Jumped the shark.
As of the present we are way below the scenario “C” which was with stringent CO2 reduction which never took place. [In effect it is the control]
http://sppiblog.org/news/the-hansen-model-another-very-simple-disproof-of-anthropogenic-global-warming
otter17 says:
September 3, 2011 ” Here lies the rub, though. The models predict that rising CO2 emissions will also contribute to raise the temperature, a human impact that scares some people due to the threat of the regulatory boogey man (let’s be honest, folks that is where the denial comes from).
“The paleoclimate evidence provides an even more compelling estimate of Earth’s eventual temperature rise due to the geologically instantaneous jump in greenhouse gas levels.”
—————————————————————————————————————————
Hogwash I got interested in this in 1972 when I was in elementary school and my teachers said we were all going to die because pollution was going to cause an ice age. Then by 1980 it was global warming. I wanted to know what had happend to the ice age. I didn’t give a fig about regulations. I have followed it ever since. If there ever appears any data that shows I am wrong I will change my mine, so far I haven’t seen any. Which gets me to my question.
Where is the peer reviewed studies that support this statement “The paleoclimate evidence provides an even more compelling estimate of Earth’s eventual temperature rise due to the geologically instantaneous jump in greenhouse gas levels.”
otter17 says:
September 3, 2011 at 10:22 am
Any takers on creating a model that can perform better and show that CO2 is not one of the primary drivers of climate?
? We have real data, such as Vostok and countless other studies, which show conclusively that CO2 lags behind temperature by c800 years – a real modeller disciplined in real science wouldn’t bother including it as a driver, let alone a ‘primary’ driver..
Get peer review approval from the Journal of Geophysical Research? If not, step aside and let the people brave enough to address the issue using the best evidence we have figure out what to do.
Brave enough to address a non-issue? With no evidence that CO2 is even a driver? What can you possibly imagine they will be able to figure out other than garbage out? That their ‘peers’ think this is science is our loss, even yours.
You’re arguing that magic, illusion, is science because you’re really beginning with a premise that Carbon Dioxide drives climate 800 years after climate temperatures have changed. In other words, your ‘driver’ does nothing for 800 years while great changes take place. Can’t you see how illogical that is? That’s magic, ‘CO2 without doing anything directs great temperature changes for 800 years and then decides to come out and see his handiwork’.
And you really think we should take any of the models seriously? Peer reviewed by like-minded irrationalists peddling mumbo-jumbo?
Friends:
I have posted the following on WUWT before (when some appreciated it) and it seems appropriate to post it again in response to the silly comment by Peter Gleick that is quoted in the above article.
Any model should be validated against empirical observations. This is especially the case for climate models because they do not emulate the climate system of the Earth: I explain this as follows.
None of the climate models – not one of them – could match the change in mean global temperature over the past century if it did not utilise a unique value of assumed cooling from aerosols. So, inputting actual values of a cooling effect (such as a negative feedback from clouds) would make every climate model provide a mismatch of the global warming it hindcasts and the observed global warming for the twentieth century.
This mismatch would occur because all the global climate models and energy balance models are known to provide indications which are based on
1.
the assumed degree of forcings resulting from human activity that produce warming
and
2.
the assumed degree of anthropogenic aerosol cooling input to each model as a ‘fiddle factor’ to obtain agreement between past average global temperature and the model’s indications of average global temperature.
More than a decade ago I published a peer-reviewed paper that showed the UK’s Hadley Centre general circulation model (GCM) could not model climate and only obtained agreement between past average global temperature and the model’s indications of average global temperature by forcing the agreement with an input of assumed anthropogenic aerosol cooling.
And my paper demonstrated that the assumption of aerosol effects being responsible for the model’s failure was incorrect.
(ref. Courtney RS An assessment of validation experiments conducted on computer models of global climate using the general circulation model of the UK’s Hadley Centre Energy & Environment, Volume 10, Number 5, pp. 491-502, September 1999).
More recently, in 2007, Kiehle published a paper that assessed 9 GCMs and two energy balance models.
(ref. Kiehl JT,Twentieth century climate model response and climate sensitivity. GRL vol.. 34, L22710, doi:10.1029/2007GL031383, 2007).
Kiehl found the same as my paper except that each model he assessed used a different aerosol ‘fix’ from every other model.
Kiehl says in his paper:
”One curious aspect of this result is that it is also well known [Houghton et al., 2001] that the same models that agree in simulating the anomaly in surface air temperature differ significantly in their predicted climate sensitivity. The cited range in climate sensitivity from a wide collection of models is usually 1.5 to 4.5 deg C for a doubling of CO2, where most global climate models used for climate change studies vary by at least a factor of two in equilibrium sensitivity.
The question is: if climate models differ by a factor of 2 to 3 in their climate sensitivity, how can they all simulate the global temperature record with a reasonable degree of accuracy?
Kerr [2007] and S. E. Schwartz et al. (Quantifying climate change–too rosy a picture?, available at http://www.nature.com/reports/climatechange, 2007) recently pointed out the importance of understanding the answer to this question. Indeed, Kerr [2007] referred to the present work and the current paper provides the ‘‘widely circulated analysis’’ referred to by Kerr [2007]. This report investigates the most probable explanation for such an agreement. It uses published results from a wide variety of model simulations to understand this apparent paradox between model climate responses for the 20th century, but diverse climate model sensitivity.”
And, importantly, Kiehl’s paper says:
”These results explain to a large degree why models with such diverse climate sensitivities can all simulate the global anomaly in surface temperature. The magnitude of applied anthropogenic total forcing compensates for the model sensitivity.”
And the “magnitude of applied anthropogenic total forcing” is fixed in each model by the input value of aerosol forcing.
Thanks to Bill Illis, Kiehl’s Figure 2 can be seen at
http://img36.imageshack.us/img36/8167/kiehl2007figure2.png
Please note that the Figure is for 9 GCMs and 2 energy balance models, and its title is:
”Figure 2. Total anthropogenic forcing (Wm2) versus aerosol forcing (Wm2) from nine fully coupled climate models and two energy balance models used to simulate the 20th century.”
It shows that
(a) each model uses a different value for “Total anthropogenic forcing” that is in the range 0.80 W/m^-2 to 2.02 W/m^-2
but
(b) each model is forced to agree with the rate of past warming by using a different value for “Aerosol forcing” that is in the range -1.42 W/m^-2 to -0.60 W/m^-2.
In other words the models use values of “Total anthropogenic forcing” that differ by a factor of more than 2.5 and they are ‘adjusted’ by using values of assumed “Aerosol forcing” that differ by a factor of 2.4.
Therefore, each climate model emulates a different climate system. But the Earth has only one climate system. Therefore, at most only one of the models emulates the climate system which exists, and it is probable that none of them do.
Averaging their outputs does not solve this problem because average wrong is wrong.
Hence, it is ridiculous for Peter Gleick or anybody else to imply that doubt is provided to an empirical result by the disagreement of that result with behaviours of the climate models.
Richard
otter17 says:
Any takers on creating a model that can perform better and show that CO2 is not one of the primary drivers of climate?
*************************
There is a better theory which has been proposed in the peer reviewed literature.
Give me $!0,000,000 and a staff of programers and I will create the model. [On second thought make it $ 100 Million. ]
1) There is a 1/2 ° C per century warming which started when records began [at the end of the little ice age] and continues today. Feedback can continue for hundreds of years according to Dr Hansen.
Increased solar activity [and effects like ionization creating clouds and feedbacks .] account for the increase in temperature
2) Superimposed on this there is a 60 year /cooling sine wave going nowhere. Its only effect is to scare the alarmists into saying we are going into an ice age [1978] or going to bake to death [1998]. both are wrong.
http://www.woodfortrees.org/plot/hadcrut3vgl/from:1860/to:2012/plot/hadcrut3vgl/from:1910/to:1940/trend/plot/hadcrut3vgl/from:1860/to:1880/trend/plot/hadcrut3vgl/from:1978/to:1998/trend
1860 to 1880 = Least squares trend line; slope = 0.0104956 per year
[total = .208]
1910 to 1940 = Least squares trend line; slope = 0.0152788 per year
[total = .456]
1978 to 1998 = #Least squares trend line; slope = 0.0122255 per year
[Total = .244]
The last one 1978 to 1998 is the only warming which MIGHT be caused by CO2.
Lazy teenager, people who have problems with math(s) are innumerate not enumerate. Very sloppy, must try harder.
Our friend otter17 believes that Spencer is part of a Big Conspiracy inspired by Big Tobacco’s ways, and that the Remote Sensing paper was published with the intent of getting mud into the water and providing the creationist climate denialist hordes with a straw to clutch as the Merchants of Doubt prevent the betterment of the planet.
Hence any taker of otter17’s challenges will be quickly classified as another member of the Conspiracy, and models and peer-reviewed papers dismissed at once as 100% flawed even when 100% impossible to be retracted.
All arguments with otter17 are therefore pointless. It’s like talking to a chemtrailer or Moon hoax believer.
While computer models have their drawbacks, in fairness I have to point out that computers do have the advantage of getting the wrong answer faster.
The GCM oracles must be master thespians as well.
Though strongly against the direction of Jerome Ravetz’s ‘Post-Normal science’ (beloved by many climate scientists), his observations are usually very perceptive. He reminds us that scientific models are metaphors. So whoever heard of a theory that had to fit a metaphor? With regard to climate models, Ravetz has this to say:
“…climate change models are a form of “seduction”…advocates of the models…recruit possible supporters, and then keep them on board when the inadequacy of the models becomes apparent. This is what is understood as “seduction”; but it should be observed that the process may well be directed even more to the modelers themselves, to maintain their own sense of worth in the face of disillusioning experience.
…but if they are not predictors, then what on earth are they? The models can be rescued only by being explained as having a metaphorical function, designed to teach us about ourselves and our perspectives under the guise of describing or predicting the future states of the planet…A general recognition of models as metaphors will not come easily. As metaphors, computer models are too subtle…for easy detection. And those who created them may well have been prevented…from being aware of their essential character.”
If the observations fit ALL the models, then the observations would all disagree, and the uncertaintly would be enormous….which describes the models predictions perfectly.
Models could be (and almost certainly are) giving the right answer for the wrong reasons. Science has lost its way.
Gary Hladik says:
September 3, 2011 at 2:54 pm
While computer models have their drawbacks, in fairness I have to point out that computers do have the advantage of getting the wrong answer faster.
_________________________________________________________________________
Computers ALWAYS get the RIGHT answer.
The problem is whether they are being asked to do the correct sums….
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.” I haven’t gotten an answer, and after two years of searching that’s a little disappointing to say the least.
———-
Well I would conclude then that you have been looking in the wrong place. Blogs, any blogs, but especially blogs whose agenda is the make AGW go away no matter what, will not tell you anything about climate models.
You need to go to the local university library and read the papers describing the models.
Jack says:
September 3, 2011 at 12:49 am
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.
———–
Please tell us that you have a deep insider knowledge of how climate science works.
Or is this really a concoction of stuff you made up after reading other peoples dubious Internet opinioons.
Before bloviating about an ill chosen choice of words I always take time to consider how much weight the person saying those words really placed on them, because it is the easiest thing in the world to misspeak oneself. Were those considered words with thought behind them that he intended to stand behind. Somehow I doubt it.
In particular note that this is the third thing in a list of three. Never put much weight on those. It looks to me like he got caught in a “three things” trap, by which I mean that the sentence structure and flow of prose he was using needed three things to complete itself in a satisfying way, so he put “agreement with models” in there as an ill considered third choice simply to complete the sentence.
I would classify this as a Lapsus Linguae – a slip of the tongue. I won’t hang a man for that because I trip over my tongue all the time.
Gary Mount says:
September 3, 2011 at 3:18 am
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.
———–
You raise a good point. So ask another question. Why is Boeing using computer models at all? If they were completely useless they would not be in use.
The climate models can be thought of in some ways as engineering rather than science. Each of the model components by themselves represents some component of the climate system. So for the atmosphere the equations arising from the science and engineering of aerodynamics is used.
It would be utterly clueless to suggest that we should not trust aerodynamics because it gives an answer we dont like.
All of the other components of the climate models are used because they have been studied separately from the climate models and found to be correct.
So a climate model is like a house made out of bricks. The house may have weaknesses because some of the bricks are not as strong as the others. But generally the house will stand and function like a house.
If you want to panic about models then you you need to consider these engineering models are used all over the place, you just have not heard about them. So the panic is selective.
LazyTeenager says:
September 3, 2011 at 4:14 pm
“You need to go to the local university library and read the papers describing the models.”
============
Ah yes, the transparency theory.
Makes one wonder why there are FOI laws.
Peter Plail says:
September 3, 2011 at 2:41 pm
Lazy teenager, people who have problems with math(s) are innumerate not enumerate. Very sloppy, must try harder.
——–
Thanks, I will.
Roy of UK says
So to rephrase your comment, and to ask a question, “In fact as a way of capturing understanding of climate, models are much better than using observational data.”. Is this what you really think?
————
Roy, observational data does not give you “understanding”. For example observing rain is not enough. You have to have a theory that rain comes from evaporated water and that the amount of rain is related to the humidity.
If you assemble a bunch of these theories you have a climate model. If the theories by themselves are correct, and the way they interact is correct, then this catalogue of things can be said to represent some degree of understanding.
So an understanding of climate is a catalogue of theories about:
Air
Clouds
Aerosols
Oceans
Heat transfer
Solar radiation
Water chemistry
Etc..
A verbal description of all this is completely inadequate. In the past this understanding would have been captured in a book or encyclopedia. Now its captured in a computer model.
The accuracy of understanding can now be tested by comparing the observations with the outcome of the computer model.
u.k.(us) says:
September 3, 2011 at 4:48 pm
LazyTeenager says:
September 3, 2011 at 4:14 pm
============
Ah yes, the transparency theory.
Makes one wonder why there are FOI laws.
——————
Makes me wonder why you would rather not go to a library and but instead write a letter to a university, put other people to a whole lot of trouble and then rummage through a whole lot of personal correspondence to gain some understanding of computer models.
Myrrh says
——–
? We have real data, such as Vostok and countless other studies, which show conclusively that CO2 lags behind temperature by c800 years – a real modeller disciplined in real science wouldn’t bother including it as a driver, let alone a ‘primary’ driver..
———–
Myrrh, the thing you are missing here is the concept of a feedback loop. Solar radiation uptake by the earth and CO2 in the oceans are likely part of a feedback loop.
From a modelling perspective it should be possible for the models to reproduce the behavior where an increase in solar energy retention by the earth causes an increase in CO2 800 years later..
Don’t know if there are any published papers on this. Maybe you could download some model code and run it if you are interested. Probably too big job for an amateur though.
Ian H concludes
I would classify this as a Lapsus Linguae – a slip of the tongue. I won’t hang a man for that because I trip over my tongue all the time.
———
I would second that
Bill Yarber says:
September 3, 2011 at 5:49 am
As an AeroSp Engr student, I was taught to develop and use models to simulate flight dynamics. We were also taught that if the output of the model(s) did not match observations, you needed to change the model, not tweak the observational data. Maybe climate scientists should bring a couple of engineers into their groups?
————–
The climate modelers do for climate exactly what you do for airplanes. Climate modelling resembles engineering more than science.
Engineers should get over the professional arrogance thing. Its become something of a joke.
ScientistForTruth says: “Though [I am] strongly against the direction of Jerome Ravetz’s ‘Post-Normal science’ …his observations are usually very perceptive. He reminds us that scientific models are metaphors. …With regard to climate models, Ravetz has this to say:
“…climate change models are a form of “seduction”…but it should be observed that the process may well be directed even more to the modelers themselves…As metaphors, computer models are too subtle…for easy detection. And those who created them may well have been prevented…from being aware of their essential character.”
I agree about Ravetz; he often puts his finger precisely on the troublesome spot. Here, his words hark back to Feynman’s “The most important person not to fool is yourself,” as well as the classic error of mistaking the map for the territory. Beyond some point (which varies with what is being modeled), added complexity doesn’t improve the model as much as it obscures its nature. The subtleties of stochastic and empirical routines with ensemble runs, on top of ad hoc fudge factors, further prevent a realistic grasp of what the model is. As with the famed PhD, the modeler eventually “knows everything about…nothing.”
LazyTeenager says: “Myrrh, the thing you are missing here is the concept of a feedback loop….From a modelling perspective it should be possible for the models to reproduce the behavior where an increase in solar energy retention by the earth causes an increase in CO2 800 years later..”
So the earth’s energy retention goes up without benefit of CO2 greenhouse effect, THEN the temperature rises, and, FINALLY, 800 or so years later, the CO2 goes up. If so, this proves…what? The existence of a chronokinetic greenhouse effect? Most amusing. Did you intend it to be?
To LazyTeenager.
If you believe you are qualified, peer review these two presentations (with statistical models) for me.
http://www.kidswincom.net/climate.pdf and http://www.kidswincom.net/CO2OLR.pdf. You can find my contact information on http://www.kidswincom.net. Others are welcomed.
Fred
Retired environmental scientist
LazyTeenager says:
“Engineers should get over the professional arrogance thing. Its become something of a joke.”
You have it backward:
http://img94.imageshack.us/img94/6774/modelsh.jpg
LazyTeenager says:
September 3, 2011 at 5:27 pm
“Makes me wonder why you would rather not go to a library and but instead write a letter to a university, put other people to a whole lot of trouble and then rummage through a whole lot of personal correspondence to gain some understanding of computer models.”
=========
I’m lazy.
Sometimes, I wonder why you all bother. I appreciate that you do; apparently you are educating some.
I’m a climate scientist since I’m a computer scientist. I have yet to see a single argument that persuades me that AGW is either a threat or real in any sense.
The IPCC is owned and run by the UN, an organization which wants to be the government for the entire inhabited world, by definition. Why is anyone surprised that every production they produce has the intention of misleading and the consequence of enslaving anyone who will open their ears to them? A liar despises those to whom he lies. When they open their mouths, they lie. They cannot cease from sin. Everyone that works for them is like unto them.
Exactly which analysis of the past and present, or prediction of the future has the UN produced that doesn’t elicit calls for further subjugation of some part or all of mankind? Any theory of the past, present, or future that elicits such calls is already suspect, and likely demonstrably wrong. That’s my theory based on all historical evidence.
A scientist worth his salt should be laughing at someone asserting the plausibility that the UN and its followers produce any kind of actual science.
squareheaded says:
September 3, 2011 at 8:
The IPCC is owned and run by the UN, an organization which wants to be the government for the entire inhabited world, by definition.
———–
To point out the obvious. Being a computer scientist does not give you special insight into either climate modelling or the politics of the UN as it relates to the IPCC.
Your IPCC beliefs derive from what you have read on your own personal favorite Internet blogs. The stories you read are made up by people just as knowledgable as yourself.
Just because you like what you are told does not make it the truth.
models are any human’s desire to ‘play’ god…
that’s why doctors get a corpse to learn from…. no model can ever be everything (God).
hence the term. model.
unless slavers find a way to equate God with a human creation
Smokey says:
September 3, 2011 at 6:43 pm
LazyTeenager says:
“Engineers should get over the professional arrogance thing. Its become something of a joke.”
You have it backward:
http://img94.imageshack.us/img94/6774/modelsh.jpg
—————
Well the graphic attached to the link is an ingenious insult to climate modelers. Should I conclude that it was devised by an engineer? If so it does prove my point about professional arrogance.
Or did you not understand that “it” my last sentence referred to professional arrogance.
u.k.(us) says:
September 3, 2011 at 6:49 pm
LazyTeenager says:
September 3, 2011 at 5:27 pm
“Makes me wonder why you would rather not go to a library and but instead write a letter to a university, put other people to a whole lot of trouble and then rummage through a whole lot of personal correspondence to gain some understanding of computer models.”
=========
I’m lazy.
=========
I would not have thought so. It takes a lot of motivation and a lot of work to dredge through people’s emails looking for some snippet that can be used to discredit them.
It’s not just that the climatologists have their models too simple. The fact is that engineers expect their model to withstand reality.
Imagine if Boeing were to build a series of airplanes each based on a different computer model and then try to sell them to an airline saying, “these haven’t been field tested but we believe the average of the models might be correct”.
As a science teacher in a suburban public school, I catch grief all the time about how American high school students apparently stack up against their international peers in science, but I can guarantee my students walk out of my classroom with a better understanding of the scientific method than this.
RockyRoad says
Have you ever personally run computer models? I have–thousands of them.
———–
Well you are a brave man to admit to running models here. Many here hold in utter contempt both models and modellers.
Supposedly its not possible to get a useful answer out of models and if models are improved in any way it’s called cheating.
I assume that you think that your modelling exercises are useful!!!!
VigilantFish says
——–
ulations and catches. Some Canadian fisheries managers -the ones in charge of the Northwest Atlantic fishieres- felt so confident about these models that they saw no need to develop robust data sets independently to monitor the state of the fish stocks.
————
As it happens I agree that models need to be backed up by robust data sets.
However as a practical matter robust data sets can never cover the the whole range of interest. So in practice data allows the models to be spot checked. There is good reason to assume that the model can fill in the gaps between observations or less reliably outside observations.
jorgekafkazar says:
September 3, 2011 at 6:21 pm
LazyTeenager says: “Myrrh, the thing you are missing here is the concept of a feedback loop….From a modelling perspective it should be possible for the models to reproduce the behavior where an increase in solar energy retention by the earth causes an increase in CO2 800 years later..”
So the earth’s energy retention goes up without benefit of CO2 greenhouse effect, THEN the temperature rises, and, FINALLY, 800 or so years later, the CO2 goes up. If so, this proves…what? The existence of a chronokinetic greenhouse effect? Most amusing. Did you intend it to be?
————
Well I don’t think we need to invoke exotic processes like the chromoly Eric effect.
Here’s how I understand the feedback processs involved. Start off with an ice age earth. The known conditions are:
High ice albedo
Zero atmospheric water vapour
Relatively low CO2.
The wobbles on thorough space and starts to pick up more solar energy. But the amount of extra energy is not enough to melt the ice. The effect needs to be amplified somehow.
So the positive feedback loop to accomplish needed to accomplish amplification is:
1. Raise temp by solar
2. Outgas CO2 from ocean
3. Raise temp by GHE
4. Outgas more CO2 from ocean
5. Raise temp even more by GHE
6. Outgas more CO2 and evaporate water
After steps 5 and 6 repeat a few times we get to present day temperatures.
The physics based GCMs should have the capabilities to run, starting from the conditions of the last ice age, and be able to reproduce the 800 year delay between temperature rise and CO2 concentration you are so keen on.
jorgekafkazar says:
September 3, 2011 at 11:33 am
Next, special thanks to Lazy Teenager for revealing his ignorance of the meaning of enumerate, giving a certain specialness to his comment, which provided many easy shots and much amusement here.
————-
I am always happy to provide a bit of comic relief.
Oh the boredom of the trite arguments …of course it’s not the models that should be disparaged but their asinine use as ultimative descriptors. Yawn.
LazyTeenager:
At September 3, 2011 at 4:14 pm you assert;
“Blogs, any blogs, but especially blogs whose agenda is the make AGW go away no matter what, will not tell you anything about climate models.
You need to go to the local university library and read the papers describing the models.”
No!
Read my post above at September 3, 2011 at 2:18 pm and if you want to dispute it then “go to the local university library and read the papers describing the models” that are explained in that post.
But your several posts demonstrate you won’t read it because you are too lazy to bother reading anything except support for your deliberate and self-imposed ignorance.
Richard
LazyTeenager says:
September 3, 2011 at 11:09 pm
“Here’s how I understand the feedback processs involved. Start off with an ice age earth. The known conditions are:
High ice albedo
Zero atmospheric water vapour
Relatively low CO2.”
Even in Antarctica in the dead of winter atmospheric water vapor never approaches zero. Those advancing glaciers that are the highlight of ice ages have to come from something.
At the risk of appearing naïve, it seems to me the problem with “models” is the invention of the computer. Once upon the time, the model was something like:
F = ma
or
E = mc**2
Nobody needed to argue about what the model was, who owned it, what it meant, etc. You could go off to your laboratory or garage and try to see if the thing fit observation. With the advent of computers and the “computer model”, the clear, unambiguity of the “model” disappeared into the vastness of 10s of thousands of lines of code. Science now has an epistemological problem it has not had for centuries. Just what constitutes a scientific model or theory in the computer age? How do we distinguish a climate model from Doom? What constitutes falsification? Indeed, how can you even keep tract of versions and refinements of the thing? At the current state of the art, it would seem that computer models are somewhat like rough sketches scientists of a former age might make to motivate a real, rigorous model. But they are not portrayed this way; instead they are advanced as actual scientific theories complete enough to be peer reviewed and serve as the basis of momentous policy decisions. Proponents of computer models should back off, way off, until fundamental problems about the meaning of these things is settled at a much higher level of abstraction than “climate models”. Of course, they won’t. Their meal tickets and those of their political allies require immediate acceptance of whatever code they write as “science” on the same level as that espoused by Newton or Einstein. It clearly is not and it only gives science a bad name by claiming that it is.
LazyTeenager says:
September 3, 2011 at 5:41 pm
Myrrh says
——–
? We have real data, such as Vostok and countless other studies, which show conclusively that CO2 lags behind temperature by c800 years – a real modeller disciplined in real science wouldn’t bother including it as a driver, let alone a ‘primary’ driver..
———–
Myrrh, the thing you are missing here is the concept of a feedback loop. Solar radiation uptake by the earth and CO2 in the oceans are likely part of a feedback loop.
And what you are missing here is that “are likely” doesn’t cut it in real science when referring to something that has no logic in reality because “feedback loop” is not proven re the properties of carbon dioxide and the great changes throughout the ice for the last 800,000 years.
You’re claiming that feedback exists (generic you re the models), but nothing in the data we have, a considerable amount of it, shows any sign of such a thing, CO2 always showing as an effect following great and rapid temperature rises and falls, including staying at highs when temperatures plummet, etc., etc. There is no evidence that CO2 is a driver of temperatures, and you and your ilk never present anything cogent to back up your ‘idea’, the imaginary “most likely feedback loop” irrelevant unless you can show it is relevant.
If you’re going to make such an imaginary claim the basis of your argument that CO2 is the major driver, you should get your generic act together. But you generic quite frankly appear ridiculous because there is no internal coherence in the claims you make – for example, this imaginary “feedback loop” contradicts the other ‘proof’ bandied around, that “CO2 levels haven’t changed for the last 800,000 years and it’s therefore our fault that the current warming is happening because we’ve increased carbon dioxide levels”.
Just to make this really clear. So what you’re actually saying in that ‘soundbite meme’ from the AGWScience fiction meme manufacturing department, is that there is no feedback loop.
And, the second fiction meme is actually saying that all the great changes in and out of interglacials for the last 800,000 years happened without carbon dioxide playing any part at all.
I’m really sorry you take such bull seriously.
LazyTeenager says:
September 3, 2011 at 4:44 pm
“So a climate model is like a house made out of bricks. The house may have weaknesses because some of the bricks are not as strong as the others. But generally the house will stand and function like a house.
If you want to panic about models then you you need to consider these engineering models are used all over the place, you just have not heard about them. So the panic is selective.”
LT are you a bot? You have been repeatedly told that the issue are not models as such but the idea of forgetting about checking models against reality and you keep on posting without trying to understand and digest the answers.
Models are just tools to learn and try to understand how reality works. They are no substitute for reality. Models based on CO2 as drivers fail miserably to describe long term weather oscillations such like El Nino, La Nina so they are even less suited to describe longer term climate. They fail to describe past climate events so are less suited to describe future climate events.
Computer models that would model credibly past events may better describe future events but still are only models only toys in the end and may be proven wrong by reality.
Your argumentation goes at the level of a bot, so why my question. There is no need for sophism here, but of a constructive discussion. Are you able of such?
Re models – http://www.forecastingprinciples.com/files/WarmAudit31.pdf
I think an excellent look at the role of ‘models’ in AGWScience re the thrashed out standards in forecasting.
There’s a lot in this and difficult to choose.., but that AGWScience and its models are being promoted as if they have an intrinsic empirical integrity falls to pieces on closer inspection:
That’s all the models are, opinions expressed in mathematical language, passing themselves off as authoritative.
And in covering up this lack of clear method and relevance to the real world, the above is typical, the constant generation of fudging.
In other words, your guess is as good as mine.. The IPCC is expert in one thing, in presenting an illusion that the base is real science and that proved beyond all reasonable doubt, supporting ‘the deniers are genocidal maniacs for ignoring the models and consensus peer reviewed certainty’, but stripped of the bluff we have the above showing there’s nothing more than opinion hiding in a ‘cloak of science’ at the heart of it, couched in mathematical language to further obfuscate the paucity of any real intelligence, both meanings. And then they have the nerve to cover their posteriors while lying:
My bold.
Magical properties for carbon dioxide, when it’s not being demonised as a toxic, [it’s not a toxic in real physics] and choose whatever parameters want to imput to create elaborate fictional worlds out of models.
Yes, definitely re-defining scientific method. And now we have it on good authority (NASA) that aliens are going to blast us away for daring to produce the dangerous levels of carbon dioxide as proved by these models.
Through force of habit, I have consistently tied my left shoe before the right for the past ten years. I programmed a computer model that correctly hindcasted this ritual with 100% accuracy. Based on the available data from the past ten years, it has also forecast with 100% certainty that I would tie my left shoe before the right for the next ten years.
Today I tied my right shoe first.
LazyTeenager says:
September 3, 2011 at 5:54 pm
Bill Yarber says:
September 3, 2011 at 5:49 am
As an AeroSp Engr student, I was taught to develop and use models to simulate flight dynamics. We were also taught that if the output of the model(s) did not match observations, you needed to change the model, not tweak the observational data. Maybe climate scientists should bring a couple of engineers into their groups?
————–
The climate modelers do for climate exactly what you do for airplanes. Climate modelling resembles engineering more than science.
Engineers should get over the professional arrogance thing. Its become something of a joke.
If engineering resembled climate “science” in any way, mankind would still be in the stone age. What climate modelelers build isn’t based on reality, but on a fantasy, resulting in frankenscience.
Of course, it isn’t engineers who need to set aside “professional arrogance”, but C02-obsessed and delusional climate modelers.
Myrrh says:
September 4, 2011 at 5:51 am
That’s all the models are, opinions expressed in mathematical language, passing themselves off as authoritative.
This is the key. Models are simply a translation of a written language “opinion” (abstract idea) to a computer language. Not really different that translating from English to Russian. The ONLY difference is we can now view how the “opinion” performs in time. That is it. There is NOTHING special about the code that is incorporated in a model. And as such, the models are no better than the “opinion” of the person/s who developed the model.
I actually think lazyteen understands this at some level. His problem is that he BELIEVEs the “opinion” is actually the same as a fact. Or, at least very close to a fact. I think most here think that “opinion” is nothing more than a educated guess. So, it really gets back to whether the “opinion” is valid.
I suspect that most skeptics would accept models based on verifiable facts. Of course, that means observable data. The warmist contingent appears to be willing accept that the verification is unneeded because of the dangers we might face by inaction. But this position is nonsensical because it’s only based on an “opinion”. There are any number of potential problems that could affect humanity. To choose one over many other well known problems (like mass starvation in Africa) and spend billions of dollars on it is almost insane.
The scientific method has been redefined for climate change to include: unconnectable anecdotal information, with the linch pin of global warming theory being woefully inadequate global climate models using dubious initial conditons. Rarely have past projections from these GCMs panned out; they are less reliable than flipping a coin. GWT begins at Mauna Loa starting in 1958. Everything else older than this must be ignored.
CO2 readings of 435 ppm were recorded in 1825, 45 ppm higher than today (Beck 2007).
Temperature data more than 100 years old must be discarded, because it would skew the warming toward normal or even cooling. This is the ‘homogenization’ process being carrried out by Dr. James “thumbs on the temperature scale” Hansen of NASA GISS. (ie: Orland CA)
Pay no attention to the GRIP2 ice core data showing that the CO2 increases occuring about 800 years AFTER the temperature increases.
Ignore the data 6E8 years ago showing CO2 levels were 18 times higher than today (GEOCARB).
Forget the 12C to 22C temperature band that the world has lived in for the last 5.5E8 years (PALEOTEMP) , showing that today’s temperatures of about 14.5C are only 25% off the bottom of the normal range.
Disregard the correlation between cosmic ray modulation by varying solar wind on global cooling cloud formation.
And whatever you do, turn your back to planetary mechanics, which is THE sole driver of climate
change. Planetary mechanics is the elephant in the room of climate change. CO2 is the flea on the elephant’s ass, and can only come along for the ride.
In certain undisciplined disciplines, most egregiously Climate Science, Pal Review has so degraded the quality of published literature that internet fora now provide a far more comprehensive and rigourous sampling and challenging environment.
If the bird book and the bird disagree,believe the bird.
I see that several people here answered your question, but generally only in the instance of climate models. Here I try a little broader scope.
1. Emotional investment. Models summarize, in a simple way, something that is complicated, and something we do not understand. We hope they capture the essence of the phenomena. When a model appears to do so by virtue of powerful explanation or agreement with observations or surprisingly accurate predictions, then the proponent(s) of such a model “fall in love”, approximately. The emotional investment that comes with discovery clouds people’s judgement and leads them to think the model is real and not approximate. As the old ballad goes, “They aren’t seeing things too clear and they’re too far gone to hear.”
2. Authority. The computer enters here. The computer makes no trivial errors, has no emotion, and therefore is an objective arbiter of truth. At least this is true for people who do not, themselves, program, and who do not recognize how utterly the computer is at the mercy of those who program it. Anyone who has seen how the IT and accounting groups gain control of any organization ought to be aware of the magic of computers and printed output.
3. We are becoming conditioned, culturally, to look to experts as the authority on all complicated subjects. This conditioning I believe explains some awful trends, such as the steady drift back toward statism, of which reliance on climate models is but a symptom. Models results are the snake oil that experts sell. Thus, people are being conditioned by culture to accept models results over observation.
LazyTeenager says:
September 3, 2011 at 9:17 pm
To point out the obvious. Being a computer scientist does not give you special insight into either climate modelling or the politics of the UN as it relates to the IPCC.
Your IPCC beliefs derive from what you have read on your own personal favorite Internet blogs. The stories you read are made up by people just as knowledgable as yourself.
Just because you like what you are told does not make it the truth.
So you work for the UN, then?
Nothing shows the performance of Global climate models better than the historical archive at the NHC, formerly the Tropical Prediction Center – TPC but they changed their name, and probably with good reason. If you look at the track changes of our latest storm of concern, Irene, you can easily see that the models could not even predict a track 5-days in advance. The movie is here – at the start it was going to hit South Florida:
http://www.nhc.noaa.gov/archive/2011/graphics/al09/loop_5W.shtml
And these climate model people want us to believe that they can model the Earth’s climate years into the future with any kind of certainty? Right….
Best,
J.
LazyTeenager says: September 3, 2011 at 4:44 pm
[So a climate model is like a house made out of bricks.]
Bricks of Fudge!
valadj=[0.,0.,0.,0.,0.,-0.1,-0.25,-0.3,0.,-0.1,0.3,0.8,1.2,1.7,2.5,2.6,2.6,2.6,2.6,2.6]*0.75 ; fudge factor
yearlyadj=interpol(valadj,yrloc,x)
densall=densall+yearlyadj
http://www.thespoof.com/news/spoof.cfm?headline=s5i64103
In the beginning there was data.
data = data;
Hmmm, not so good, lets loose that high bit in the middle.
data = data – medieval warming period;// (,-0.25,-0.3,0);
That’s better; now bring it up above the current decline.
data = data + unobserved warming; // (1.2,1.7)
Better still; how about a wild increase to really stir thing up. To infinity and beyond.
data = data + imagination; // (2.5,2.6,2.6,2.6,2.6,2.6)
No, they will never go for that.
data = data * 0.75;
Yea, with the Teams help I can get this published.
Computer models have a role in science. If you make a model that embodies your theory of a complex system, you can use the computer model to identify things to measure to refute your theory. A scientist should ALWAYS design experiments that will show the flaws in their own theories. We can never gain a better understanding of the universe if we allow an intellectual elite to pronounce “the way it is” and the rest of us stand around yelling “yea, verily”. A model can show where to apply measuring resources. Other than that it is pretty well useless except to produce wall art.
Isn’t “model” just another word for ” theory”?.
I’ve been reading climate blogs for two years and actively seeking an answer go the question, “Why do people trust models.”
Adam,
I worked with computers for 30 years and happen to have a degree in psychology.
I often saw how people have an irrational faith and belief in what computers tell them. To the point the computer was trusted more than individuals. Its a curious phenomena that I have never seen an adequate explanation for.
Isn’t “model” just another word for ” theory”?.
I’d agree with this statement.
However, for a theory to be scientific it must be well articulated. Such that others than the framers of the theory can derived specific predictions from it.
The climate models are not well articulated. That is, there is no clear explanation of how they work, and others can not derive predictions from them (model simulation runs don’t count). And hence they are not scientific, in the sense of scientific theories.
I remember when I first became aware of the GCM’s in the late ’70’s. I gave some fellow grad students information about combustion products, and later learned they used it to feed CO2 estimates into their models. When I asked them about their conclusions, I couldn’t get satisfactory answers to how CO2 actually could cause warming, what had caused warming in the past, and why weren’t they looking at the sun. All I could conclude was the the models were a PR stunt to impress an illiterate and innumerate public with computers, and get them to believe in AGW. This seems to still be their purpose.
The only thing that seems certain is that Climate Science isn’t Rocket Science. If it were, Climate Scientists would understand enough thermodynamics and fluid mechanics to realize that CO2 can’t have the impact they claim.
Models are only provisional. Niels Bohr devised his model of the atom in order to explain observations made by various other scientists investigating the structure of atoms or spectral lines. His model worked very well for hydrogen but not for larger atoms. It was a significant step in the development of quantum theory but, as Bohr himself realised, models and theories had to fit the observations – not the other way round.
Perhaps Dr. Gleick would like to have another recent paper withdrawn where the models and observations do not match:
“…all models may be missing some fundamental climate process such as a nonlinear response to
forcing. As discussed by Santer et al. [2005, 2008] it is not clear what this could be or why models and observations agree on short timescales but potentially differ on long time scales, given the same fundamental physical processes. There may be natural processes that modulate
behavior on decadal timescales that are not captured by any climate models. But with highly uncertain observations it remains most likely that residual observational biases
underlie the disagreements with the models. However, if the models lack a basic process, then it urgently needs to be understood and incorporated”.
Should I notify Dr. Santer of this new standard or would Dr. Gleick like to take care of this personally?
There is a common tactic used by the warmist apologists and attack dogs to equate criticism of the climate models to criticism of all models used in science and engineering. Quite obviously, these are not the same thing.
Lazy Teenager is a classic Cloned Troll for AGW.
In fact they may even be a Computer program set up to repeat the words others use and find some supposedly clever piece of debunking in a database.
I could write a program to do just that.
You know the old saying “Don’t feed the Trolls”.
Richard M says:
September 4, 2011 at 8:11 am
Myrrh says:
September 4, 2011 at 5:51 am
That’s all the models are, opinions expressed in mathematical language, passing themselves off as authoritative.
This is the key. Models are simply a translation of a written language “opinion” (abstract idea) to a computer language. Not really different that translating from English to Russian. The ONLY difference is we can now view how the “opinion” performs in time. That is it. There is NOTHING special about the code that is incorporated in a model. And as such, the models are no better than the “opinion” of the person/s who developed the model.
I actually think lazyteen understands this at some level. His problem is that he BELIEVEs the “opinion” is actually the same as a fact. Or, at least very close to a fact. I think most here think that “opinion” is nothing more than a educated guess. So, it really gets back to whether the “opinion” is valid.
I suspect that most skeptics would accept models based on verifiable facts. Of course, that means observable data. The warmist contingent appears to be willing accept that the verification is unneeded because of the dangers we might face by inaction. But this position is nonsensical because it’s only based on an “opinion”. There are any number of potential problems that could affect humanity. To choose one over many other well known problems (like mass starvation in Africa) and spend billions of dollars on it is almost insane.
And the “dangers of inaction” have been hyped so strongly that unless one has either a grounding in science that works or a mind that questions (I’m not a scientist but got intrigued when I discovered there were arguments about it), it makes it difficult to see that this is part of the ‘sell tactics’ by those promoting AGW.
Kevin Kilty said: “We are becoming conditioned, culturally, to look to experts as the authority on all complicated subjects.”
I don’t think it’s a recent conditioning, but in our nature. I think that comes from a inherent trait to form co-operative units; family, small hunter/gather groups, larger farming communities. We’re not unique in the animal kingdom for our co-operative nature, but it is one of the great defining character traits of mankind. In that, people have roles, things we’re good at, better than others, and one very important was the role of healer. The information still extant on the healing properties of plants peculiar to specific areas and known to the indigenous peoples and the plants world wide that are known for the same properties is testament to a wide-spread knowledge base about these things over a great many centuries. When that becomes a huge amount of information and the social group larger, or groups associating, the role of healer appears, someone who has a natural bent for the subject and can devote time to learning and practicing. We’re naturally accepting of such roles because we can appreciate that others have skills which we can benefit from because we don’t have the same skill or the time for them.
I recall there have been studies on the ‘white coat = authority’, and when the white coat of the healing arts came into general use, in the creation of hospitals, the scientist donning it has naturally stepped into that role, trusted authority. Unless we have good reason to stop trusting, we don’t. We don’t question it, ok, I’m giving myself as an example here, I had no reason to question AGW – mostly because I had other things I was doing I didn’t get involved in any of it. But I think that’s part of our nature, we’re used to delegating whole areas of expertise to others..
Also, we’re willing to co-operate even to our own detriment, the unselfish gene.. Rather a lot of our history appears to be the conflicts created by those manipulating these genes, and I think in great part this is what is happening here. We trust the models, because we’re constantly being told they are trustworthy, by people we take as read are trustworthy. Until we have good reason not to trust them.
Gleick’s “and fits the models” quote reminds me of the Orwell’s famous line in his novel, Animal Farm:
“Four legs good; two legs better.”
LazyTeenager says:
September 3, 2011 at 4:44 pm
All of the other components of the climate models are used because they have been studied separately from the climate models and found to be correct.
Absent any citations that back this assertion, this is utter nonsense, particularly for real-world systems in which interactions otherwise not accounted for can drive outcomes well away from what individual components’ may have predicted.
Climate model are the modern form of divination by reading chicken entrails. Or am I being too hard on the old soothsayers?
I think “Fitting the Models” can only be accepted if that includes reprogramming or invalidating them as indicated by the new theory. One could not expect relativistic theory to match Newtonian models.