From the University of York:
A new study led by a University of York scientist addresses an important question in climate science: how accurate are climate model projections?
Climate models are used to estimate future global warming, and their accuracy can be checked against the actual global warming observed so far. Most comparisons suggest that the world is warming a little more slowly than the model projections indicate. Scientists have wondered whether this difference is meaningful, or just a chance fluctuation.
Dr Kevin Cowtan, of the Department of Chemistry at York, led an international study into this question and its findings are published in Geophysical Research Letters. The research team found that the way global temperatures were calculated in the models failed to reflect real-world measurements. The climate models use air temperature for the whole globe, whereas the real-world data used by scientists are a combination of air and sea surface temperature readings.
Dr Cowtan said: “When comparing models with observations, you need to compare apples with apples.”
The team determined the effect of this mismatch in 36 different climate models. They calculated the temperature of each model earth in the same way as in the real world. A third of the difference between the models and reality disappeared, along with all of the difference before the last decade. Any remaining differences may be explained by the recent temporary fluctuation in the rate of global warming.
Dr Cowtan added: “Recent studies suggest that the so-called ‘hiatus’ in warming is in part due to challenges in assembling the data. I think that the divergence between models and observations may turn out to be equally fragile.”
Dr Cowtan’s primary field of research is X-ray crystallography and he is based in the York Structural Biology Laboratory in the University’s Department of Chemistry. His interest in climate science has developed from an interest in science communication. This is his second major climate science paper. For this project, he led a diverse team of international researchers, including some of the world’s top climate scientists.
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http://c3headlines.typepad.com/.a/6a010536b58035970c017ee88df70e970d-pi
The model projections describe the takeoff flight path of a 747 packed with climateers, AR5 was the last chance to abort, red lights are flashing on the control panel alarms are sounding and the captain is reassuring the passengers that everything is under control.
Just love those ‘Anomally’ graphs where no one states the value of zero.
Can anyone tell us what it is.
The value of zero is define as how much this article is worth.
Apologies all around to the authors who are present, but I gots to calls ’em like I sees ’em.
Why don’t we want nicer weather and more abundant crops again?
Again?
This is very confusing, because crop yields have been on a steady march upwards for a very long time now.
Both as an absolute number, and in terms of Calories per person on Earth, yields per unit area, and about any other metric one may care to choose.
And hey, the weather is fine!
Are you sore because you moved to a desert or something, and are disappointed to find out there are long droughts in deserts?
Oh, I know…maybe you are a snowman, and anxiously await the return of full on ice age conditions?
Please, do tell.
Cryptic comments make me so dang curious!
The models diverge because the physics that underpins them is wrong. Until scientists acknowledge that we have an atmospheric effect and not a Greenhouse Effect, there is no hope for any of them!
correct. greenhouses warm by limiting convection. CO2 theory says the surface will warm due to increased radiation, which has nothing to do with greenhouses.
+1 on the “Dog ate my model” list.
Neville July 30, 2015 at 3:22 pm
The versions are 5.6.
Dr Cowtan added:
“Recent studies suggest that the so-called ‘hiatus’ in warming is in part due to challenges in assembling the data. I think that the divergence between models and observations may turn out to be equally fragile.”
____
the challenges in assembling data for DrCowtans team could lessen with experience in spread sheets.
but
‘the so-called divergence between models and observations may turn out to be equally fragile’ –
as long as real world resists to equal models.
– watch DrCowtans teams next paper ‘work on ‘observations”.
Hans
““Recent studies suggest that the so-called ‘hiatus’ in warming is in part due to challenges in assembling the data.”
Gavin and Tom are doing all they can to address that challenge. 😉
The entire AGW argument is based on assumption, and when we assume…
https://youtu.be/KEP1acj29-Y
[Fake email address. ~mod.]
Steven Mosher July 30, 2015 at 2:31 pm
Nothing new, surface measure liquid expansion or contraction, just the conversion is more complex with brightness and needs a model to do it. The point earlier is once the conversion has occurred it is not data from brightness or liquid expansion, it is data that corresponds to temperatures. (therefore it is now temperature data) The satellite has been measured to be more accurate than the thermometer, matches with balloon data and covers far more coverage of the planet then surface temperatures ever will even if they were 50 x more.
Its going to be HILARIOUS watching the antics of the climate bletheren and salesmen when the current drop in solar activity starts to kick in.
Even a small downward trend in REAL temperatures will cause massive panic amongst them. (Even more than now)
Popcorn time , for sure 🙂
Robert Way left a stinker of a comment on my blog about the fact that other’s model vs temp comparisons aren’t done right and if it is models do agree with observations.
I suppose if you crank models down a third, crank sea temps up a third and then have a 1/3 difference in your CI, models are magically in agreement!
Eureka! Good science finally!
Well, after reading this article and all the comments, I am saddened that this new paper has not ended all disagreement and settled the entire matter of global warming, like it was intended and supposed to do.
I am going to hold out the hope that the NEXT paper which attempts to explain and reconcile everything will finally do the job.
It seems we are ever so close to having everyone converge on the same point of view and reading of the “facts”.
*rolls the eyes*
So close!
So close, and yet…
*
“Any remaining differences may be explained by the recent temporary fluctuation in the rate of global warming.”
In other words, any remaining differences between the models and actual observations, may be explained by the actual observations being different than the models?
Which remaining differences are only temporary because the models say so.
Zeke,
‘compare
from the models to the observations. Otherwise you end up with a bias because models have air temperature over oceans warming a bit faster than sea surface temperature.’
____
compare, what for?
You’re already biased showing models leading reality, CO2 ahead of temperature.
____
compare FROM MODELS
TO OBSERVATIONS ?
____
from Hollywood Matrix
to pay the rent in LA ?
We can make the models look better by adding even more bad sea temperature data to the real world record.
If, over the past 10-15 years, we have not known “how to assemble the data” of the past 10-15 years, how can we have been correctly “assembling the data” of the past 100-150 years?
Cowtan et al have made an astounding admission. That the wrong data set has been used to evaluate the models.
This is important because it established that the wrong data set was used to train the models. As since the models were trained incorrectly, the cannot be expected to perform correctly.
The models report atmospheric temperatures. As Dr Cowtan says:”you need to compare apples with apples”. This applies to model training as well as model evaluation.
As such, if Cowtan et al are correct, then the model training must also be faulty. And with faulty training, no model can hope to deliver the correct answer, except by accident.
This then explains the divergence. FAULTY TRAINING. This is a HUGE ISSUE.
Yes, I think you may be right Mr. Berple.
Either that or this whole thing is a bunch of made up gobbledygook and means nothing, and the models are just wrong because they fail to model the atmosphere accurately, have way to grainy of a resolution, use faulty reasoning in their conception, overestimate some feedbacks, fail to account for others, leave clouds and solar variability out, and all the other stuff that has been discussed up to now.
Or what you said…
Either way.
Even if the models are 100% correct on all the other possible issues. You cannot train a dog to be a duck and expect it to fly.
Dear colleagues:
Predictions are products of the scientific method of investigation. Projections are products of a pseudoscientific method of investigation. Thus, “prediction” and “projection” should not be used as synonyms.
The only thing that they can project in the pseudo-chaotic climate system is their own egos. !
“Dr Cowtan’s primary field of research is X-ray crystallography …”
But he felt he needed to get a piece of the climate band wagon and after all, it is not as if any relevant qualifications are necessary.
I’m guessing Dr. Cowtan has figured out that a scientist, and especially an “X-ray crystallographer”, can’t a research grant to study squirrels in the park (or their crystals) unless that scientist somehow ties it to AGW. Man’s gotta’ EAT!
Wow, what a crock of cow dung. The alarmists models predict too far over the observed temperatures to be some chance fluctuation. The models are simply wrong because they’re wrong about the CO2/water vapour positive feedback relationship, the lack of a mid tropospheric hotspot proves that.
The bottom line is there’s no need to panic according to the evidence and that is not the reality the globalists looking to use CO2 as a means of economic world governance want to promote.
sabretruthtiger:
That’s incorrect. The alarmist’s models project. They don’t predict.
Terry Oldberg:
That’s incorrect. The alarmist’s models predict.
Richard
Debate over usage of “predict” and “project” dates back to a post to the blog of “Nature” by Kevin Trenberth circa 2008. In the post, Trenberth insisted that the general circulation models did not make predictions. Instead, he asserted, they made projections.
Green and Armstrong reacted by polling professional climatologists on their use of the two terms. As I recall from reading Green and Armstrong’s subsequent journal article, most climatologists used “prediction” in reference to the outputs from the models.
Green and Armstrong concluded from this evidence that the models made “predictions.” Thereupon they compared the values of features of the models to those of “scientific” models. They found that the match was poor. Thus, they concluded that the models were not “scientific.” Later, I reached the same conclusion but by a different argument.
In separate articles, Vincent Gray and I demonstrated that climatologists were repeatedly guilty of applications of the equivocation fallacy. According to Gray this practice had been so successful in deceiving people as to have led to the “triumph of doublespeak.” “Doublespeak” is a synonym for “equivocation.”
Applications of the equivocation fallacy are prevented when a monosemic language is used in making arguments. In a paper ( http://wmbriggs.com/post/7923/ ) I present a list of terms which, if used, would halt applications of this fallacy. The suggested terminology follows Trenberth’s suggestion of distinguishing between “predict” and “project.” Any set of terms that distinguishes between models having underlying statistical populations and models lacking underlying statistical populations would have the same effect. The models of the alarmists are of the latter type. They are unscientific and otherwise unsuited to the task of regulating Earth’s climate but are being applied to this task as a result of the phenomenon that Vincent Gray calls the “triumph of doublespeak.”
Terry, do you, or do you not utilize the projections made by weather forecasting models when planning outdoor activities such as picnics, travel, etc.?
What’s the relevancy?
[Comment deleted. commenter using fake identity, deleted per WUWT policy –mod]
Terry Oldberg:
I repeat, the alarmist’s models predict.
Equivocation about the matter is silly. But I have met it before.
Long ago, in 2000, I gave a presentation on climate model performance at the US Congress, Washington, DC.
There were questions after my presentation and one of the questioners asserted that the IPCC doesn’t provide predictions. The assertion is only true if one accepts that the IPCC reporting climate model predictions is not a provision of predictions.
So, I replied saying,
“Sir, there is much you say that I agree, but not all.
For example, you say the IPCC doesn’t provide predictions.
The IPCC says it is going to warm.
I call that a prediction.”
The questioner was not sufficiently stupid for him to dispute the fact that climate models predict warming.
Richard
richardscourtney:
The argument that you have repeated remains an application of the fallacy of argument by assertion.
Predictions, projections, shoot ’em all; let Gaia assert ’em out.
===================
What say?
Terry Oldberg:
The climate models predict warming: they all do.
That is NOT “argument by assertion”: it is a statement of empirical observation.
You took the trouble to repeat your silly and untrue assertion that climate models don’t predict but you forgot to provide the long-awaited explanation of what you mean by an “event”.
Please correct your oversight.
Richard
richardscourtney:
Let’s try this. An argument with a true conclusion aka syllogism has three lines. The form is:
Major premise
Minor premise
Conclusion
The argument that “climate models predict” lacks a major premise and a minor premise. As this argument is not of the form of a syllogism there is not a logical reason for belief in the truth of the conclusion of this argument, namely that “climate models predict.”
Terry Oldberg:
No your “try” did not work because it was meaningless gobbledygook.
Try this.
You need to accept the reality that – as every body knows and can see – the models predict and their predictions have proven to be wrong to date.
Also, you have still failed to provide the long-awaited explanation of what you mean by an “event”.
Richard
richardscourtney:
In the English vernacular, “predict” has a number of different meanings. For use in scientific research one needs a single, precise meaning that is drawn from the field of probability theory and statistics.
A model that “predicts” under this scientific use of the term leaves telltale signs that include: a sample space, frequencies, relative frequencies, relative frequency values, probabilities, probability values, sampling units, unit events and validation or invalidation of the model in a test of it. In AR4 the report of Working Group I exhibits none of these signs. The signs that it exhibits are possessed by the entities that Dr. Trenberth calls “projections.” Projections can neither be validated nor invalidated. However they can be “evaluated.” AR4 exhibits “evaluation” but not “validation” of its general circulation models. It is validation of a model that makes of it a scientific theory. Thus none of the general circulation models are scientific.
In its early assessment reports, the IPCC claimed its general circulation models to be “validated.” In the paper entitled “Spinning the Climate” Dr. Vincent Gray reports informing IPCC management that: a) the models were not validated and b) were insusceptible to being validated. The management responded by replacing the word “validate” with the similar-sounding word “evaluate.” People with weak to nonexistent grasps of probability theory and statistics such as yourself failed to note the difference. Some, including yourself, persistently exhibited their ignorance by insisting upon calling a projection a “prediction.”
To call a projection a prediction has the downside of creating arguments of the type that is called an “equivocation.” It is an argument that looks to a person as ignorant as yourself to be a syllogism. However, while the conclusion of a syllogism is true, the conclusion of an equivocation is false or unproved.
A model that “predicts” under this scientific use of the term leaves telltale signs that include: a sample space, frequencies, relative frequencies, relative frequency values, probabilities, probability values, sampling units, unit events and validation or invalidation of the model in a test of it.
Not at all. A prediction made by application of known [or assumed] physical laws [most scientific predictions] does not need any of those ‘signs’. Some typical examples:
The prediction of the return of Halley’s comet
The prediction of the position of the the planet Neptune
The prediction of the deflection of light by mass in General Relativity
The prediction of the size of Solar cycle 24
The prediction of the total solar eclipse in 2017
The notion of an ‘event’ does not enter in those scientific predictions; there is no ‘sample space’, etc.
You seem to have no idea about what scientific predictions are.
lsvalgaard:
Thank you for giving me the opportunity to clarify.
As you use the word “prediction” it may or may not be the product of a conditional prediction aka “predictive inference.” A predictive inference has the properties of being falsifiable and conveying information to the user of the associated model. Absent a predictive inference, there is an absence of falsifiability and information.
The entities that Dr. Trenberth calls “projections” are not the product of a predictive inference. Thus the associated model lacks falsifiability and conveys no information. To call them “predictions” is to make of “prediction” a word with dual meanings. In one of these a “prediction” is the product of a model that is non-falsifiable and conveys no information. In the other it is the product of a model that is falsifiable and conveys information. When this dual meaning “prediction” is used in making an argument, this argument is an example of an equivocation. One cannot draw a logically draw a conclusion from an equivocation. To draw one is the “equivocation fallacy.” Applications of this fallacy are common in making climatological arguments ( http://wmbriggs.com/post/7923/ ).
Applications of the equivocation fallacy can be avoided through maintenance of the distinction made by Trenberth between a “prediction” and a “projection.” Maintenance of it has no downside unless one’s purpose is deception.
As you use the word “prediction” it may or may not be the product of a conditional prediction aka “predictive inference.” A predictive inference has the properties of being falsifiable
All my examples [and almost all scientific predictions] are eminently falsifiable [otherwise they would not be scientific].
Again: you have no idea what you are talking about. I know you think you have, but you are as wrong as one can be. What Trenberth may have said or meant is irrelevant. The climate models are supposed [and claimed] to be based on the physics of the situation, not on statistics. This is not a word-game, but hard-nosed physical science. And it is not about ‘my use of the word “prediction”‘, it is about the generally accepted use of that word in science.
lsvalgaard:
In my message to you I asserted that your use of “prediction” makes no distinction between whether this “prediction” is or is not the product of a “conditional prediction” aka “predictive inference.” Thus this usage sets up an application of the equivocation fallacy. Though this was at the heart of my argument you ignored it thus reaching the false conclusion that “you have no idea what you are talking about.”
Perhaps you have read the late Ed Jaynes’s book “Probability theory: the logic of science.” I stand with Jaynes. To eliminate probability theory and statistics from science is to divorce it from logic. If you disagree with me and with Jaynes this could be a fruitful topic for discussion.
To eliminate probability theory and statistics from science is to divorce it from logic
Most scientific predictions based on physical models are not about probability [although the may predict a probability] and certainly not about statistics, and even less [if possible] about logic. The examples I gave illustrate that abundantly. From experiment we derive physical laws [with very few exceptions] or relationships expressible as mathematical equations. From some initial conditions we predict events [future or past]. If the prediction fails, the laws or the relationship or the assumed initial conditions are falsified. If the prediction is successful, we gain confidence in what went into it. As simple as that. No verbal or philosophical gymnastics needed. And we never, ever do ‘projections’. There is little need to discuss any of this with non-scientists who have never made a scientific prediction.
You appear to overlook the fact that circumstances arise in practice in which the values of probabilities are limited to 0 and 1. This produces the classical logic. If the equations that you reference express logical relationships then they conform to the classical logic or the more general probabilistic logic. Mathematical relations conform to the classical logic.
The classical logic applies to situations for which information needed for a deductive conclusion from an argument is not missing. In the environment in which a scientific researcher usually works, information is missing. Thus, the classical logic is inapplicable.
Global warming climatology is one of the many fields of research for which information is missing. Thus, mathematical reasoning is of limited usefulness for it.
Global warming climatologists proceed as though information were not missing. This is expressed by their expectation of success from an approach in which solution of coupled differential equations produces a set of projections. This has led them into the costly blunder of creating models that convey no information to a policy maker and using scare tactics to induce politicians to use these models in policy making.
The only way to respond to such a question is “I do” or “I don’t”
What about “F*ck off you crap strawman builder?”
That’s now four ways and counting
philincalifornia:
Its better if we avoid swearing at each other.
Terry Oldberg:
In contrast to the meaningless twaddle you spout, the word ‘prediction’ has a clear and unequivocal meaning. Clearly, you do not know the meaning of ‘prediction’ and it is obvious that use of a dictionary is beyond you so I will help.
All dictionaries agree on the meaning of ‘prediction’ and this is the definition in the OED:
Climate models PREDICT (i.e. make forecasts).
Richard
Terry Oldberg:
PS You have still failed to provide the long-awaited explanation of what you mean by an “event”.
Richard
Terry Oldberg is correct. His discussion of the meaning of prediction in science is exactly right. In particular, here, where he distinguishes between prediction and projection.
Prediction implies falsifiability. Unfalsifiable statements about the future, no matter how precise, are not predictions. The reason is that such statements convey no causal information. I.e., a guess can be arbitrarily precise, but its falsification by subsequent observation does not increase our knowledge content. Guesses are not deductions from a set of logically rigorous statements about physical causality.
Climate model projections are not physically unique and have no physical meaning, as I noted here. No physical meaning means their output is not a rigorously specific deductive inference regarding the future behavior of the terrestrial climate. No deductive inference is identical with no prediction.
While it is true that climate models include hard physics, the centrally pertinent question is whether that physics represents a complete, or even adequate, physical model of the terrestrial climate. The magnitude of cloud errors alone produced by advanced climate models clearly indicates that the physics is either wrong or incomplete. See also more CMIP5 error. Model errors are so large that their projections must quickly diverge from the future trajectory of the physically real climate. The divergence reflects physical error, not dynamical chaos. Uncertainty due to error becomes so large so quickly, that the projections lose any physical meaning and therefore have no predictive power. In that event, whatever the climate is eventually observed to do can neither verify nor falsifiy the model.
Terry Oldberg’s distinction between prediction and projection in terms of falsifiability is exactly the standard of science. So, insistent statements to the contrary notwithstanding, he appears to know exactly what he’s talking about. And from my own experience of his posts, that seems always the case.
Prediction implies falsifiability. Unfalsifiable statements about the future, no matter how precise, are not predictions
Climate model predictions are eminently falsifiable [one might argue that they have already been falsified].
Terry and you, it seems, have no idea what you are talking about. Have you ever made a scientific prediction?
I’ll answer your question to Mr. Frank for myself. Over a period of 13 years I held the lead role in the design and management of a succession of scientific studies on behalf of the electric utilities of the U.S. I specialized in building falsifiable predictive models. This work resulted in some of the first applications of information theory in the construction of a model. While the IPCC general circulation models convey no information to a policy maker these models conveyed the maximum possible information to him or her.
applications of information theory in the construction of a model
You don’t build scientific models on that basis, but on the physics and the engineering constraints of the subject matter. Otherwise you are just making curve fitting, with limited predictive capability.
Actually, what we do using information theory is build models that are statistically validated and reflect all of the available information but no more. Contrary to your assumption, curve fitting is not involved. Among the well known products of this method of construction for a model are thermodynamics and the modern theory of telecommunications. When you sit down to watch your HDTV you are the beneficiary of this method of construction for a model.
Leif, with respect to Terry Oldberg’s categories and your example of Halley’s Comet:
Sample space: the solar system
Frequency: comet periodicity
Relative frequency: periodicity with respect to long times
Relative frequency values: periodicity magnitudes over time
Probabilities: likelihood of continued observed and/or predicted periodicities
Probability values: specification of likelihood magnitudes of periodicity variation over time
Sampling units: dimensional units (time, distance, orbital ellipticity, etc.)
Unit events: specified instances of future appearance.
Your other examples can be similarly parsed.
Terry uses generalized terms that take some thought to understand in the specific context of any field of science. Anyone in a serious conversation owes it to him to make the effort to figure out what he’s saying.
Your other examples can be similarly parsed.
Nonsense.
Leif, climate models are not falsifiable in the scientific sense. I included a link to that in my previous post.
The fact that model projections do not conform to the observed trajectory of the evolving climate merely tells us that their guesses are wrong. That’s naïve falsifiability, not scientific falsifiability.
Do you understand the difference between naïve and scientific falsifiability, Lief?
If you do, then you’ll know my point stands.
If you don’t then you’ll argue on.
Arguing on will demonstrate no understanding that merely being shown wrong is not necessarily identical with scientific falsifiability.
Every prediction is qualified. Often we don’t know ALL of the physics and will have to parameterize our unknowns, often the physics is only approximately right [e.g. Newtonian gravity], always we never know all of the initial conditions with enough precision, etc. None of that matters: we make predictions based on what we know, surmise, and guess. In the absurd limit you and Terry are advocating, there can be NO predictions ever. As a working scientist I have no problems with calling a spade a spade and a prediction a prediction. As I said, you guys have no idea.
You have no problem with applying the equivocation fallacy evidently.
equivocation fallacy
Does not apply in this context. What I described is how science works, whether you understand it or not.
To the contrary, the equivocation fallacy applies to any situation in which a term changes meaning in the midst of an argument. Under your version of reality, “scientific” changes meaning at the whim of a “scientist.”
You contradict yourself, Leif. You wrote, “Every prediction is qualified.” followed by , “In the absurd limit you and Terry are advocating, there can be NO predictions ever.”
My prior post included a linked analysis that was all about qualifying climate model projections in terms of their physical uncertainty.
Likewise, here’s what Terry wrote, “your use of “prediction” makes no distinction between whether this “prediction” is or is not the product of a “conditional prediction” aka “predictive inference.””
Both Terry and I are clearly talking about qualified predictions. We all know there can be no prediction in science without some qualification concerning its physical uncertainty bound. That concept is in obvious evidence in our posts.
But you’ve ignored it.
At best, you’re not doing either Terry or I the courtesy of a careful reading. At worst, you can’t brook contradiction. Whatever the source, the content of your riposte does not rise above a straw man argument.
qualifying climate model projections
Projections by definition are not predictions and are not science, but a statement of belief. By limiting yourself to projections you divorce yourself from discussing scientific predictions. ‘Projections’ is the straw man. Climate models are concerned with predictions, not projections, e.g. https://www.e-education.psu.edu/earth501/content/p5_p10.html
Now, part of the model may be projections, e.g. of future CO2 emissions, but that is OK as we here can play a ‘what-if’ game. With the assumed [projected] input the model predicts a falsifiable outcome.
As I said, you guys don’t know what you are talking about.
There is a disconnect between you and the government of the United States. You say the models make predictions. The government says the models make projections ( http://www.epa.gov/climatechange/science/future.html ). Who is right and why?
The models make predictions based on projections of future emissions. Try to understand the difference. Future emission is a free parameter and cannot be predicted and the models do not concern themselves with that, but take the future emissions as input. As simple as that.
lsvalgaard:
Your argument has a shortcoming that is independent of the magnitudes of future CO2 emissions. It is that your “predictions” are not the product of a predictive inference. “Predictions” matching this description lack falsifiability and convey no information to a policy maker about the outcomes from his/her policy decisions. If you are skeptical about my allegations I’d be pleased to provide a detailed argument for your review.
It is that your “predictions” are not the product of a predictive inference.
They are the product of solving the equations of motions of the atmosphere for given scenarios. That is good enough for me [and ought to be good enough for you too]. No ‘inference’ involved, just plain old physics.
lsvalgaard:
I disagree. A model is a procedure for making inferences. The builder of a model is repeatedly faced with selection of the inferences that will be made from among the many candidates. Builders of global warming models make this selection ineptly with the result that their models are non-falsifiable and convey no information to the users of them. They are unscientific and worthless for the intended purpose.
The dictionary definition of ‘projection’ is
“an estimate or forecast of a future situation or trend based on a study of present ones”
This is predicated on the belief that the present one and its trend are good predictors of the future [that is certainly often the case: I live in California and based on the present situation I can with confidence project that it will not rain tomorrow], but it is not a scientific prediction as it does not follow from solution of the equations with appropriate input that govern the evolution of the weather.
To the contrary, you cannot project with confidence that it will not rain tomorrow because “confidence” is a statistical concept but “project” is not.
Confidence can be measured by how much one would wager on the outcome. I would wager quite a lot based on experience. No statistics needed (the future is not part of the sample space).
Though that is not the “confidence” of statistics it sounds as though you are “confident” of something. Are you confident of the outcome of an event? This can’t be the case as you have dismissed the import of probability and statistics. What is it that you are confident of if this is not the outcome of an event?
if this is not the outcome of an event?
Since you have not defined what an ‘event’ is [see your discussion with Richard] it is unclear what you mean. To perhaps clarity I would say that the event is the outcome.
lsvalgaard
Thank you for revealing your thinking. To think as you do that “the event is the outcome” is a mistake that sometimes arises among people who are confused about probability theory and statistics. Actually rather than being an event an outcome is a description of an event aka state of nature. Folks who think an event is an outcome are prone to confusing an IPCC-style “evaluation” with a statistical “validation” for though there are not the events that are required for validation it seems to these folks as though there are. Thinking that a pseudoscientific theory has been validated these folks mistake it for a scientific theory.
outcome is a description of an event
Nonsense, the ‘outcome’ is what actually happens. If I flip a coin and get ‘heads’, the outcome of the flip is ‘heads’. The ‘event’ [if you wish to mislead] is that a flip takes place, the outcome is ‘heads’ [what actually happens].
You stated (incorrectly) that “the event is the outcome.” Do you mean to revise this statement?
Since you have not defined ‘event’ I sought to clarify what you might have meant [as it comes across]. I have already explained what I think. I’ll repeat it here for your convenience:
the ‘outcome’ is what actually happens. If I flip a coin and get ‘heads’, the outcome of the flip is ‘heads’. The ‘event’ [if you wish to mislead] is that a flip takes place, the outcome is ‘heads’ [what actually happens].
lsvalgaard:
Actually, the definition of “event” in probability theory was defined before my birth by mathematicians thus needing no definition by me. Thus Mr. Courtney’s persistent demand for me to define the term is nonsensical and cranky.
Regarding your contention that an outcome is an event is this still your contention? Is ‘heads’ an example of an event? Is ‘tails’? Or is it the coin flip?
For this particular case, the event is the flip, and it can have two outcomes, ‘heads’ and ‘tails’.
Remember that Science is not statistics and probability theory. Those are handy tools that can be used [and misused] as the situation calls for. In your case it seems that if you are a hammer everything looks like a nail.
But you have gotten in so deep now that you have lost sight of the issue and have stooped to irrelevancies.
You may benefit from studying this paper by Frisch:
http://citations.springer.com/item?doi=10.1007/s13194-015-0110-4
http://adsabs.harvard.edu/abs/2014AGUFMGC43G..04F
“Model tuning is unavoidable in climate models. This raises the question whether data used in tuning or calibration can also be used in evaluating a model’s performance or skill. In the philosophical literature this question is discussed as the problem of old evidence: is a model more highly confirmed by novel evidence predicted by the model or is evidence that is accommodated by the model during model construction equally as confirmatory of the model? In this paper I present several conditions under which a weak predictivism holds—conditions under which predictive success is more highly confirmatory of a model’s empirical performance than mere accommodation—and argue that these conditions are met in the case of climate modeling. In particular, I argue that predictive success can be evidence that a model has certain ‘good-making’ features that are ‘epistemically opaque’—that is, the presence of which is difficult to detect otherwise. I also propose a Bayesian formulation of the predictivist thesis.”
lsvalgaard:
Thus, an outcome is not an event though you claimed the opposite a few hours ago. Interestingly, for a statistical ignoramous to think an outcome is an event can lead him/her to thinking that an “evaluation” of a model is a “validation” of this model though validation is impossible because the underlying statistical population does not exist. Isn’t this what we have in modern global warming climatology: an error in thinking among people with a dim grasp of statistical ideas that has gotten so out of hand as to be about to cost us trillions of dollars in expenditures for replacing fossil fuels by renewables?
an outcome is an event
I explained to you that the event was the flip, the outcome was either heads or tails. Try to grasp that.
You should study:
http://adsabs.harvard.edu/abs/2014AGUFMGC43G..04F
“Model tuning is unavoidable in climate models. This raises the question whether data used in tuning or calibration can also be used in evaluating a model’s performance or skill. In the philosophical literature this question is discussed as the problem of old evidence: is a model more highly confirmed by novel evidence predicted by the model or is evidence that is accommodated by the model during model construction equally as confirmatory of the model? In this paper I present several conditions under which a weak predictivism holds—conditions under which predictive success is more highly confirmatory of a model’s empirical performance than mere accommodation—and argue that these conditions are met in the case of climate modeling. In particular, I argue that predictive success can be evidence that a model has certain ‘good-making’ features that are ‘epistemically opaque’—that is, the presence of which is difficult to detect otherwise. I also propose a Bayesian formulation of the predictivist thesis.”
In English, “an outcome is an event” implies the equivalence of an outcome to an event. Perhaps you meant to say “an event has an outcome.”
lsvalgaard
I am glad to see that you have reversed your position and now agree with me that the event is the coin flip while heads and tails are the outcomes. Mr. Courtney appears to think heads and tails are the events. This is why, I believe, he harangues me to supply “my” definition of “event” and why I continue to refer him to the literature. Now that we agree on the respective roles of events and outcomes, please cease joining Mr. Courtney in his harangue.
Regarding a possible role for statistical ideas in global warming climatology, climatologists already exhibit fondness for statistical ideas when these take the form of parameterized models and Bayesian parameter estimation. This fondness has led them to create models that make non-falsifiable claims and convey no information to their users. Models with these characteristics should be described as making “projections” according to Dr. Trenberth but should be described as making “predictions” according to Mr. Courtney and yourself. This choice of language makes of “prediction” a polysemic term. When used in making a climatological argument it makes of this argument an equivocation. People, thinking it to be a syllogism, draw conclusions from this argument. However unlike a syllogism, an equivocation does not have a true conclusion. By this mechanism people draw conclusions from climatological arguments that are false or unproved thinking they are true. You can help us to avoid this phenomenon by heeding Dr. Trenberth’s advice.
Perhaps your use of ‘projection’ is of the nature described here;
http://www.urbandictionary.com/define.php?term=Projection
” An unconscious self-defence mechanism characterised by a person unconsciously attributing their own issues onto someone or something else as a form of delusion and denial”
etc
Leif, you wrote, “With the assumed [projected] input the model predicts a falsifiable outcome.” (your bold).
You’re wrong, Leif.
If you actually do think a 3±15 C climate model air temperature expectation value is falsifiable, or amounts to a physical prediction, then you, in demonstrated fact, don’t know what you’re talking about.
If you actually do think a 3±15 C climate model air temperature expectation value is falsifiable
Your error estimate is junk, but apart from that, such a model is certainly falsifiable in principle, even if not in actuality, and thus qualifies as [very poor] science. Your belief, on the other hand, that it is not, just betrays your bias.
That claims made by a model are “falsifiable” implies that the associated propositions have truth-values. What are these propositions?
What are these propositions
You are thrashing around. The result of the prediction is what it is. It will have an uncertainty, and if the observed data are too far outside the uncertainty, the model is falsified. As easy as that.
“The result of the prediction is what it is” is circular and unresponsive.
No, it is a statement of fact. If you feel it is not responsive to your comments, perhaps you should review and revise those offending comments.
Leif, you wrote, “Projections by definition are not predictions and are not science, but a statement of belief. ”
Terry’s entire point, e.g., here right at the start, and mine, e.g., here, right at the start, has been that projections are not predictions.
The fact that you finally elaborate this idea in your post just means you’ve come to agree with us without having to directly admit it.
You wrote, “I live in California and based on the present situation I can with confidence project that it will not rain tomorrow]” It’s presently raining in Alturas, CA, with a 50% chance of extending into tomorrow. Oh, well, Leif.
You link to Penn State as evidence that climate models make predictions. But that’s just an argument from authority. Perusal of the site shows that climate models, in fact, do not make predictions in the scientific sense, and in fact are incapable of making such predictions.
For example, Penn State Figures 1 and 3 show that climate models will project the same air temperature for a large number of different climate energy states, and different temperatures for the same climate energy state. The Figures they present as predictions — a diagnosis with which you agreed — instead show that climate models are incapable of producing unique solutions to the problem of the climate energy state.
When their physical error is propagated through their air temperature projections, the uncertainty limits are huge. That means climate models are not even capable of producing usefully constrained solutions to the problem of the climate energy state. I have demonstrated that fact, here; an argument you have not yet seen fit to dispute.
Figures 1 and 3 at the Penn State site also show that the writers of that essay think that model precision is a measure of accuracy. It’s not. This mistake on their part yet again demonstrates the banal truth that climate modelers are not scientists. They have no idea of the meaning of prediction in the scientific sense.
The fact that you think such pictures constitute predictions makes me wonder whether you apply critical thinking outside your own discipline.
In short, Leif, you’re wrong. Climate models don’t make predictions in the scientific sense. They are incapable of making predictions in the scientific sense. Terry Oldberg has been correct all along.
Obviously I meant rain where I live.
But to the main point: climate models attempt to predict based on physics, not on curve fitting to the current trend, and are thus predictions, not projections. That they are not any good is another matter.
About your +/-15 C: that is completely unsupported, no climate model asserts that.
Scientific is what scientists [like me] say it is. That your bias makes you believe otherwise is your problem and can simply be dismissed [herewith done].
That “Scientific is what scientists [like me] say it is” is an oxymoron.
An oxymoron is juxtaposing elements that appear to be contradictory. What are those elements here. Please be as specific as you can in order not to appear moronic.
“Scientific is what scientists [like me] say it is” makes “scientific” polysemic thus being a perfect vehicle for applications of the equivocation fallacy.
Leif, you wrote, “Your error estimate is junk,….
Your unsupported word is worthless, Leif. Let’s see you demonstrate your point.
And the Wichita Lineman is still on the line by the time you get to Phoenix rain will be just fine.
=================
We’re in the midst of a serious conversation. If you would omit the doggerel for the time being I would appreciate same.
While I agree that Kim’s comment is noisy, useless, and unnecessary chatter. I disagree that your comments are in any way serious. As I said you don’t know what you are talking about. As Mark Twain said: “it is not what you know that gets you in trouble, it is what you know that ain’t”
Leif, on the contrary, climate models are curve fit to past observables as a way to choose their parameter sets. Then they are extrapolated to future climate using the parameters derived from the fits.
This curve fitting approach is called model tuning. It’s very well known that various climate models are able to reproduce past observables, despite factor of 2-3 differences in so-called climate sensitivity, because their chosen parameter sets have off-setting errors.
So, by your definition, namely that, “climate models attempt to predict based on physics, not on curve fitting to the current trend, and are thus predictions, not projections.“, climate models produce projections not predictions.
climate models are curve fit to past observables as a way to choose their parameter sets
This is not a correct statement. Some of the microphysics are parameterized, but the resulting relationships are used in concert with solving the equations, time-step for time-step. This is no different from using measured values for macroscopic physical constants [such as Enthalpy of vaporization] instead of calculating them from first principles.
A projection is using the present state and trends [and nothing else] to extrapolate into the future. It is important that you understand the difference.
Your definition of “projection” differs from the IPCC’s and from Trenberth’s. In both cases, a “projection” is the result of the computations that are made by a general circulation model.
Especially noting the chatter is temporally disorganized, which makes the line hard to follow. Who is serious about that static?
Heh, seriously static, like this serious convocation.
===================
‘Tain’t worth beans for policy, and there goes the pea under the thimble. Isn’t that what you’re betting on?
Mebbe you aren’t very serious.
===========
Pat Frank:
A person presenting an argument has a responsibility to provide it to the target audience by explaining it in language the audience can comprehend.
A recipient of the argument has no responsibility to translate it into comprehensible language and any such translation may include errors.
Hence, although I usually agree with you, I strongly disagree when you write
NO!
Terry uses “generalised terms” that he cannot – at least, he refuses to – explain when asked to define them. In other words, Terry only writes meaningless nonsense because the meanings of his words are not known by their recipients.
Richard
richardscourtney
My guess is that the words are meaningless to you because you are ignorant on topics that one needs to master to perform competently on the theoretical side of scientific research. I climbed the learning curve on these topics in earning between 9 years and 10 years worth of university credits and three degrees then working over a period of 20 years in scientific institutions. Based on competency attained in this work I rose to a level at which I designed and managed a long sequence of scientific studies. By listening to experts I learned how to design a study and to build the associated model in a manner that was logically flawless.
So far as I can determine you are close to square 1 in attaining mastery over most of the topics that one needs to master to achieve competency. If I try to steer you in the right direction I find that you respond only with complaints about the quality of the instruction I’ve tried to supply. Thus, I’ve abandoned attempts at tutoring you and left you to your own devices.
Leif and Pat:
I write in hope of helping by clarifying the issue of ‘prediction’ and ‘projection’.
A prediction is a forecast.
It is a statement concerning what a future event(s) will be.
Scientific predictions were made by scientists long, long before scientists started to use much statistical analysis.
A scientific prediction is a forecast that can be falsified by comparison with later reality. So, for example, an assertion that a horse will win the Derby is a scientific prediction. It does not matter how or why that forecast was obtained and/or used: it is a scientific prediction because it can be falsified by comparison with later reality.
The condition of falsifiability being the requirement of scientific predictions is uncomfortable for many scientists because it means that some very unscientific activities (e.g. astrology) make scientific predictions. However, falsifiability being THE requirement is why predictive skill is important for scientific predictions.
Importantly, merely being scientific does not mean a prediction is useful .
Pure chance provides a probability of some predictions being right. But a series of predictions made using a particular method can be assessed to determine if the method makes forecasts which have higher statistical probability of being right than would be provided by pure chance. And this probability is the predictive skill of the method.
A projection is an extrapolation from existing trend(s).
It is an assumption that existing trend(s) will continue for some amount of future time.
Projections are probably the most used method for obtaining predictions. For example, every sportsman running to catch a ball is projecting the trajectory of the ball as a method for predicting where he will be able to catch the ball. This method has a high predictive skill. Indeed, its predictive skill is so high that the military uses it to compute where to shoot moving targets.
Climate models use projections to make predictions with no demonstrated predictive skill.
A climate model is provided with a future scenario such as projection of atmospheric CO2 concentration then calculates a prediction of the future climate if that projection were to be correct. The prediction can be compared to future reality.
Please note that the use of a projection to obtain the prediction does NOT convert the prediction into a projection. Similarly, an aim at an enemy aircraft projects when the aircraft will be when a shell reaches altitude, and the aim is a prediction of where to send the shell whether or not the aircraft alters course.
Richard
richardscourtney:
An assertion that a horse will win the Derby is insusceptible to being falsified unless it makes a predictive inference. My guess is that you lack experience with falsification thus failing to understand that this is true.
A model is falsified when it is tested on “out of sample” data and it is found that the predicted values of the probabilities of the outcomes fail to match the observed relative frequencies. For a model that makes projections, this cannot happen as the underlying statistical population does not exist.
When tested in this way and not falsified, a model is said to be “validated.” Thus, models that make projections cannot be validated. However, they can be “evaluated.” “Evaluation” is a term that was introduced into IPCC assessment reports after Vincent Gray informed IPCC management that models which the IPCC was claiming in its assessment reports to be “validated” were neither validated nor even susceptible to validation. It kinda sounds as though a model has been “validated” when the IPCC says it has been “evaluated” as the two words are similar sounding. This may have been IPCC management’s intent. Validation is a requirement for a model to be “scientific” and while the models do not satisfy this requirement the tricky wording makes it sound as though they do.
Predict this
Project that;
All the fire
Is in the fat.
It seems that sometime during the night Terry has edged over to sabretruthtiger’s original point. Leif’s still dancing pinheadedly.
The models are crap for policy guidance; can we get on with that understanding?
===============
By the time we get to Paris, she’ll be dying.
=====================
Terry Oldberg:
I suggest you don’t take out a bet with an organisation fronting for the mob if you think an assertion that a horse will win the Derby is not a prediction which can be falsified. They would give you the education most people want to.
And your words are meaningless because you refuse to say what you mean when you use them. Furthermore, when Lief did ascribe a very sensible meaning to the word “event” then you claimed he was wrong!
It is long, long past the time when you should have defined what you mean when you use the word “event”.
Richard
Brian G Valentine
To provide one’s CV does not make one guilty of argument from authority.
Leif, you wrote that, my observation that, “climate models are curve fit to past observables as a way to choose their parameter sets” “is not a correct statement.”
The following examples are far from exhaustive.
From Eisenman, I., N. et al., On the reliability of simulated Arctic sea ice in global climate models Geophys. Res. Lett., 2007. 34(10) L10501 “A frequently used approach in GCM sea ice components is to tune the parameters associated with the ice surface albedo.”
From Hargreaves and Annan Using ensemble prediction methods to examine regional climate variation under global warming scenarios Ocean Modelling, 2006. 11(1-2), 174-192. ““Recent developments in parameter estimation have now opened up the possibility of performing ensemble integrations of models which have been objectively tuned to climate observations, and which therefore have the potential to generate more meaningful probabilistic estimates of future climate.”
From Kiehl, J.T. Twentieth century climate model response and climate sensitivity Geophys. Res. Lett., 2007. 34(22), L22710. ““Note that the range in total anthropogenic forcing is slightly over a factor of 2, which is the same order as the uncertainty in climate sensitivity. 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.”
From Knutti, R., et al., Challenges in Combining Projections from Multiple Climate Models J. Climate, 2010. 23(10), 2739-2758 ““One of the difficulties [of evaluating model predictions] is that the observations often have been used in the modeling process before, to derive parameterizations, or to tune earlier versions of models. Therefore, there is a risk of double-counting information, overconfidence, or circular logic if model evaluation and weighting is done on the same datasets that were used to develop the models.”
From Lauer and Hamilton Simulating Clouds with Global Climate Models: A Comparison of CMIP5 Results with CMIP3 and Satellite Data J. Climate, 2013. 26(11), 3823-3845 “There is generally only very modest improvement in the simulated cloud climatology in CMIP5 compared with CMIP3. The better performance of the models in reproducing observed annual mean SCF and LCF therefore suggests that this good agreement is mainly a result of careful model tuning rather than an accurate fundamental representation of cloud processes in the models.”
Climate model parameter sets are tuned so that model output matches observations. They achieve that goal using parameter sets with offsetting errors.
Tuned models with internal offsetting errors are then used to project future climate. The method exactly matches your definition of curve fitting.
Their method is not at all like, “using measured values for macroscopic physical constants [such as Enthalpy of vaporization] instead of calculating them from first principles..
It’s important that you know this.
Richard Courtney, falsification of a declarative statement by observation is necessary but not sufficient to be scientific. The falsification criterion in science necessarily extends all the way back to the theory.
Scientific statements about future observables — predictions, in other words — are necesarily deduced from a causally specific physical theory by way of an unbroken chain of analytical logic; typically stated in mathematical language so as to be completely unabmbiguous.
Falsification of deductive predictions not only disprove the prediction itself, but also disprove the causal theory from which the prediction was derived. Disproof of theory is a consequence of the unbroken chain of logic between the theory and the prediction. Inference travels down the chain into a prediction, falsification travels back up (as does verification).
Therefore, a statement such as “a [certain] horse will win the Derby” is not a scientific prediction because it was not derived from an analytically causal and falsifiable theory of horse racing. That is, the statement is not a rigorously deductive inference. It is just a declaration of belief. Its falsification by way of the victory of another horse does not travel back up an unbroken chain of rigorous logic to falsify any analytically causal theory.
So, the criterion for a scientific statement is two-fold: the prediction itself must be falsifiable, as you suggested, which requires that the prediction be very constrained (tightly bounded or, ideally, unique) and the prediction must have been deduced from an analytical theory of causality by way of an unbroken chain of rigorous logic.
The large uncertainty limits manifested when error is propagated through climate model air temperature projections shows that such projections do not meet the criterion of falsification. The reason is that any concivable change in the true physical air temperature will fall well within the uncertainty bars. Falsification is become impossible. So, the models are incapable of predictions because their outputs are not sufficiently constrained.
This is apart from the problem that models are adjusted to observables, which method produces parameter sets with offsetting errors. Their projections are hardly more than extrapolations from semi-empirical curve fitting.
Pat Frank:
Sorry, but you are making one of the same mistakes as Terry Oldberg promotes when you say
NO!
1. There may not exist any theory but only an hypothesis.
2. The hypothesis may have no “unbroken chain of analytical logic” but merely represent a deduction from observations.
3. And the use of mathematics is only one of many tools that scientists may or may not choose to use.
Often a prediction is made as a test of the hypothesis when there is insufficient data to provide a theory. Lief provided an example of this when he cited the prediction that Halley’s Comet would return in a specific year. A comet did appear in that year and no statistical analysis was required to discern that this was a correct prediction although there was no evidence that it was the same comet that ‘returned’. This apparent ‘reappearance’ was supporting evidence for the hypothesis that comets were satellites of the Sun that had much more elliptical orbits than planets. Accumulation of subsequent evidence enabled construction of a theory of comets.
Most seminal science has no “Scientific statements about future observables — predictions, in other words — are necessarily deduced from a causally specific physical theory by way of an unbroken chain of analytical logic; typically stated in mathematical language so as to be completely unambiguous.”
Almost all seminal science uses predictions based on observations of past behaviour to explain a system. (This is exactly the same method as predicting the winner of a horse race on the basis of the past records of the racers’ performances.) Subsequent scientific studies then attempt to generate theories which explain the predictive skill of ascribing the observed system behaviours to future system b ehaviours. The method operates as follows:
A scientist discovers e.g. a new species.
1. He/she names it (e.g. he/she calls it a gazelle) and describes it (e.g. a gazelle has a leg in each corner).
2. He/she observes that gazelles leap. (n.b. the muscles, ligaments etc. that enable gazelles to leap are not known, do not need to be discovered, and do not need to be theorised and/or modelled to observe that gazelles leap. The observation is evidence.)
3. Gazelles are observed to always leap when a predator is near. (This observation is also evidence.)
4. From (3) it can be deduced that gazelles leap in response to the presence of a predator and this deduction is a scientific prediction of future actions of gazelles.
5. n.b. The gazelle’s internal body structure and central nervous system do not need to be studied, known, theorised or modeled for the conclusion in (4) that “gazelles leap when a predator is near” to be a valid scientific conclusion. Indeed, study of a gazelle’s internal body structure and central nervous system may never reveal that, and such a model may take decades to construct following achievement of the conclusion from the evidence because theories need to be developed for each part of the gazelle’s gazelle’s internal body structure and central nervous system before such a model can be constructed.
This has direct relevance to the present discussion of ‘predictions’ and ‘projections’ of climate models. In the illustration of typical seminal science the prediction of future gazelle behaviour has no “causally specific physical theory by way of an unbroken chain of analytical logic”. It is prediction based on projection of observed past system behaviour.
The global climate system is more complex than the central nervous system of a gazelle and an incomplete model of a gazelle’s central nervous system could be expected to provide incorrect indications of gazelle behaviour. For the same reasons, the numerical climate models can be expected to provide incorrect indications of gazelle behaviour.
Mathematics is one tool used by science and some of the best science uses almost none of it (e.g. Darwin C ‘On the Origen of Species’, 1859)
Richard
Ouch! I wrote:
For the same reasons, the numerical climate models can be expected to provide incorrect indications of gazelle behaviour.
I intended to write:
For the same reasons, the numerical climate models can be expected to provide incorrect indications of climate behaviour.
Sorry.
Richard
richardscourney:
Your arguments continue to suffer from the shortcoming of incorporating polysemic terms such as “predict” and “science” into arguments. It would be well if you were to switch to monosemic terms.
Terry Oldberg:
It is very annoying that you repeatedly ask me to use a dictionary for you when you use words in ways that are unique to you, which you change over time, and that you refuse to define.
I still await your definition of what you mean by an “event”.
You say you don’t know the clear and unambiguous words “predict” and “science”.
Please see my above post which informed you that all dictionaries state a prediction is a forecast and quoted the OED as example. To predict is to make a forecast.
The OED also says
Please learn to use a dictionary for yourself so avoiding the need for me to repeatedly do it for you.
And you need to use an English dictionary because my posts are written in English while yours are written in gobbledegook.
Richard
richardscourtney:
Your post is loaded with inaccuracies as usual. This habit of yours makes attempts at logical discourse with you uniformly unproductive.
I have never asked you to use a dictionary. I have repeatedly referred you to the literature of probability theory for a definition of “event”; this word is given an unambiguous definition there. Contrary to your innuendo I do not use “event” in an unusual way.
In the language of global warming climatology, “predict” and “science” are among sever al terms that are polysemic. That they are polysemic and used in making climatological arguments yields frequent applications of the equivocation fallacy. I draw this conclusion from the argument that I make in the peer-reviewed article at http://wmbriggs.com/post/7923/. If you don’t like this conclusion you could try composing a refutation and seek peer-reviewed publication for it.
You may be unaware of the fact that many of the words in the English vernacular are polysemic. There are those of us who wish to avoid leading people to draw logically illicit conclusions from equivocations when we make arguments. We can head off the possibility of doing so by making equivocations impossible. This can be accomplished by replacing the polysemic words of the dictionary by monosemic ones. For example we can give “predict” one of is two definitions and “project” the other. Over the past few days, you have battled like a tiger to preserve your ability to give “predict” two meanings. I guess you must like the result.
Richard Courtney it is almost a truism in science that a hypothesis rises to the level of theory when it is strongly and repeatedly verified by observation and/or experiment.
This rising to the level of theory does not of necessity include a modification of the structure of the hypothesis. The rising only requires repeated successful verification. Therefore, a given hypothesis merits the label “hypothesis” in science only if it is falsifiable by virtue of the predictions deduced from it.
This means a scientific hypothesis has the same structure as a scientific theory. That structure includes internal logical consistency, rigor of expression, and capable of deductive inferences that are connected to the hypothesis by an unbroken chain of causal logic.
In your number 2, how is it possible to deduce from observations absent a chain of logic? Does not “deduction” itself require a reasoned process? How does reasoning proceed successfully absent unbroken logic?
Your description of the progress concerning Halley’s comet and gazelles reflects the use of observation to generate limited-scope semi-empirical analytical models. This is one of the ways science proceeds to full theories. There’s nothing wrong with that. Nevertheless, such models must be internally self-consistent, i.e., logically coherent, and must make predictions according to the rules of logical deduction. That is, they operate by the same rules as does a full theory.
Contradictory observations must have the capacity to falsify your semi-empirical models, as such falsification is the only way these models can be improved. The same is true of full theories. Causal theories are improved by falsification of less correct prior versions.
Just to add, Darwin began the Origin of Species with a long discussion of the effects of breeding upon pigeons. The causal validity of this discussion required an implied physical and systematic relation between external morphology and internal heritable trait. The mathematics describing this relation was necessarily latent in Darwin’s elaboration of his theory; necessary, that is, to justify Darwin’s hypothesis as a part of science. The fact that the mathematical elaboration became explicit only later does not mean it was not intrinsic then.
And we must further agree that the ruthlessness of science is that if such an analytical theory of evolution had later proved impossible by virtue of disconfirming evidence, Darwin’s hypothesis would have been disproved. That is, even Darwin’s hypothesis as expressed using words, was sufficiently monosemous to be logically connected to its deduced implications — its predictions — so as to be falsifiable by disconfirmatory evidence. If no heritable traits (genes) had been found, Evolutionary Theory would have been disproven.
Those internal structural traits are what made Darwin’s work a hypothesis in the scientific sense. Since then, it has been repeatedly verified and strongly elaborated and has risen to the status of Evolutionary Theory. In so doing, Darwin’s Evolutionary Hypothesis has not changed its logical structure.
Leif, you also wrote, About your +/-15 C: that is completely unsupported, no climate model asserts that.”
Now, I’m really worried. What is it you suppose that ±15 C indicates? Really interested in your answer to that.
It is your value. You explain how you got that, and what you think it means.
Leif, I’ve explained that ±15 C in detail here, here, and especially here (2.9 MB pdf).
Now it’s your turn. You wrote my analysis is “junk” and that “no climate model asserts that [±15 C].”
So, what’s it mean, that ±15 C? In your view.
Richard Courtney, I understand your point. Nevertheless, when the conversation is serious, I don’t mind taking the additional step of parsing unfamiliar terms to understand what someone means to communicate.
As you can see here, it was not so difficult to convert Terry Oldberg’s general criteria into more specific scientific delineations. Terry Oldberg has not corrected my indications, and I’ll take his continued silence on this as an assent. So, I don’t agree that his terms are meaningless, although sometimes they take an effort. But that sort of effort is not unusual during professional conversations between differently trained persons.
On the other hand, Terry Oldberg’s response to you was not very helpful either.
I do believe that speculations on another’s professional failings don’t bring much light to a debate.
Pat Frank:
You are arguing for being “logically coherent” while being inconsistent.
You said
But that is NOT true because scientific predictions are NOT necessarily deduced from a causally specific physical theory. So I replied saying
And I explained it with the gazelle illustration.
I also pointed out that Halley’ comet prediction was made as a test of an hypothesis that later developed into a theory. There must be very many similar predictions made as tests of hypotheses which were rejected because they failed the test(s) and, therefore, did not become theories.
You now say to me
That is very, very different. And, contrary to your earlier claim, it does NOT reject as being unscientific a prediction of a horse winning the Derby. As I said, it “is exactly the same method as predicting the winner of a horse race on the basis of the past records of the racers’ performances”.
You are supporting one of Oldberg’s untrue assertions and – as he does – you are claiming you said other than you did when shown to be wrong. This is not good. (Perhaps you need to also adopt his practice of using words he refuses to define and alters at will).
In order to remind onlookers of why this issue is important, I repeat the conclusion of my post you have answered.
This has direct relevance to the present discussion of ‘predictions’ and ‘projections’ of climate models. In the illustration of typical seminal science the prediction of future gazelle behaviour has no “causally specific physical theory by way of an unbroken chain of analytical logic”. It is prediction based on projection of observed past system behaviour.
The global climate system is more complex than the central nervous system of a gazelle and an incomplete model of a gazelle’s central nervous system could be expected to provide incorrect indications of gazelle behaviour. For the same reasons, the numerical climate models can be expected to provide incorrect indications of climate behaviour.
Richard
Richard, this, your statement, “contrary to your earlier claim, it does NOT reject as being unscientific a prediction of a horse winning the Derby. As I said, it “is exactly the same method as predicting the winner of a horse race on the basis of the past records of the racers’ performances”. ” says that scientific predictions can be theory-free inductive inferences.
But already in the 18th century, David Hume showed such inferences are free of any predictive content.
Sorry to say, Richard, your descriptions of science are foreign, and inconsistent with how I know science to proceed (predictive deduction from falsifiable theory).
We’ll have to agree to disagree. I don’t see any reason to continue arguing.
My best wishes to you . . .
Pat Frank:
I accept that we must agree to disagree.
Your citation as truth of the well-known error of David Hume does not convince me. Contrary to the sophistry of Hume, the premise that the future will resemble the past is rationally founded for the immediate future.
The future will not continue to resemble the past for ever. But if the premise that the future will resemble the past were rationally unfounded then it would be irrational to get out of bed in the morning.
Others can evaluate our views for themselves. I am content that I have described how and why most scientific predictions are made and that I have provided clear examples which support my view.
Richard
Terry Oldberg
My post to you was NOT “loaded with inaccuracies”.
As is my “habit”, my post was clear, accurate, factual, referenced and quoted the reference.
As is your “habit”, your reply is complete bollocks, and it is yet another failure to state what you mean by an “event”.
Richard
So what they are saying is what they were told back in the sixties. Scientists are clueless about doing things in the real world and should leave real world thing to engineers. Lets face it they never were interested in anything but theories and as long as they are elegant and plausible to their peers do not give a stuff about how well they stand up in reality as they are quite happy to change the measurements of reality to fit the theory if it is popular with their cronies.
Engineers test by how well the theory fits reality without constant adjustments made even after claiming the science was beyond question. Either the adjustments are criminal deception or the original claim was at best the sort of over sellling that in the banking world has given rise to thousands of damages claims.
Too bad no legal companies are wiling to stretch their wings and take on the climate scientists and their universities for damages and demand compensation for all the subsidies justified by climate change.
Climatologists relentlessly apply the equivocation fallacy in making arguments (http://wmbriggs.com/post/7923/ ). In this way they draw logically illicit conclusions from these arguments. This is provable in court.
“This is provable in court”
…
Except that courts deal with issues of law, they don’t deal with issues of science.
Actually, the courts do deal with scientific issues. They deal with them when the admissability of scientific testimony arises as an issue. To address this issue a court needs a definition of “scientific.” For the federal courts and most of the state courts of the U.S. this definition is supplied by the Daubert standard.
We agree that admissibility and validity are separate issues. If a plaintiff were to sue a defendant for fraud over a climatological issue the judge or jury would have to judge the validity of the scientific testimony as well as the admisssibility. I don’t know of a barrier to filing such a lawsuit. Do you?
[Comment deleted. commenter using fake identity, deleted per WUWT policy –mod]
Joel D. Jackson (July 31 at 11:09 am):
You’ve defended your hypothetical client from a straw man argument. A plaintiff could not prove a defendant knew the “science” to be false as “science” lacks a truth-value. A plaintiff could possibly prove a defendant knowingly made a deceptive argument for profit thus being guilty of fraud. The deceptive argument that I have in mind is an application of the equivocation fallacy that conflates “prediction” with “projection.”
Joel,
The courts have repeatedly ruled on the validity of scientific issues, as in this case:
http://ncse.com/creationism/legal/intelligent-design-trial-kitzmiller-v-dover
Hang on…
But this means that the new atmosphere-only method heats faster than the combined atmosphere sea surface method. But last week they told us that the missing heat was goign into the sea, and the sea was heating more. So which is it? Is the sea heating more or less? Can they keep their stories straight?
Friends:
The article reports
Ah, yes, “challenges in assembling the data”. Obviously it must be that! sarc off/
The fact is that divergence exists and the models predicted it would not.
The divergence indicates that the models lack predictive ability.
That is reality, and what Cowtan thinks or what I think does not change it.
Richard
In making arguments would be well if you were to make a distinction between “project” and “predict” as failure to make this distinction is the basis for applications of the equivocation fallacy ( http://wmbriggs.com/post/7923/ ) that are widespread among climatologists . When this distinction is made, it is accurate to state that the general circulation models make “projections” and inaccurate to state that they make “predictions.” Models that make projections convey no information to a policy maker about the outcomes from his/her policy decisions thus being unsuitable for making policy on CO2 emissions. The claims that are made by these models are not falsifiable thus being unscientific.
Terry Oldberg:
The models predict.
It would be well if you were to recognise this reality instead of trying to obfuscate the matter.
Richard
PS
I still await your clear statement of whatever it is that you think you mean by an “event”.
The argument that “the models predict” is an application of the fallacy of argument by assertion.
I predict that the model projection causes psychological projection in politicians who feel that they ‘must do something’ to alleviate a projected future. They then take money to accomplish that.
The model projection is an anomaly. The political projection is a forcing.
Thank you. That makes more sense than an IPCC assessment report.
(Comment deleted. commenter using fake identity, deleted per WUWT policy –mod)
In coming to grips with the shortcomings of modern global warming climatology, the example of a hurricane is of limited value because a hurricane is outside of our control. When we succeed at bringing a system under control this is because we have information about the outcomes for this system conditional upon whatever actions are taken in attempting control. The measure of this information is the mutual information and not the skill. For a projection-making model, the mutual information is nil. For a prediction-making model, the mutual information is not nil.
That’s a misrepresentation of what I said!
Terry Oldberg:
Climate models predict; i.e. they make forecasts.
To date all the predictions of climate models have proven to be wrong.
As lsvalgaard explains to you above in this thread
And your posts make no sense so it would be difficult to “misrepresent” them.
Richard
PS You have still failed to say what you mean by an “event”.
Terry Oldberg,
Please respond to the (repeated) question from Richard: it will do us all good to read your answer(s). Thank you in anticipation.
PS: There is no escape from the truth here at WUWT and you will find that there is always someone who can help you, or, simply “blow you out of the water”. This is a friendly word of advice and is politely given with respect and civility.
Regards,
WL
Warren Latham:
To comply with your request I’d have to tutor you in elementary probability theory and statistics without compensation. While I don’t need the money, I’ve found that a climate blog provides a hostile environment for teaching due to the tendency of a significant subset of bloggers to think and act emotionally rather than logically. Thus, I’ve dropped back into a position of limiting my participation to making and refuting arguments.
My summary from this paper is: The models should make projections for the land only (without oceans) and compare it with the measurements.
My summary is that we should be embracing UAH and RSS datasets as the only truly valid global thermometers.
Heh, hammered for short timespan of operation, but ironically the satellite series now have the longest series of unadulterated original data.
We shall see, won’t we, one fine day?
==================
I searched for a working username and password for hours..finally i found..
4-Ever Models Account – http://www2.4evermodels.com/track/MjAwNDU1OjM6NjE/