Guest essay by Pat Frank
This essay expands on a point made in a previous post here at WUWT, that climate models do not produce a unique solution to the energy state of the climate. Unique solutions are the source of physical meaning in science, and make a physical theory both predictive and falsifiable.
Predictive because a unique solution is a derived and highly specific statement about how physical reality behaves. It allows that only one possibility, among an infinite number of possibilities, will occur. A unique solution asserts an extreme improbability; making it vulnerable to disproof by observation.
Falsifiable because if the prediction is wrong, the physical theory is refuted.
Figure 1 in the previous post showed that the huge uncertainty limits in projections of future global air temperatures make them predictively useless. In other words, they have no physical meaning. See also here (528 kB pdf), and see Figure 1 here, a paper just now out in Energy & Environment on the pervasive negligence that infects consensus climatology. [1]
This post will show that hindcasts of historically recent global air temperature trends also have no physical meaning.
The Figure below shows data from Figure SPM.5 of the IPCC 4AR. [2] The dark red line in the top panel shows the multi-model average simulation of the 20th century global surface air temperature. The blue points are the 1999 version of the GISS land+sea global average air temperature record. [3] The correspondence between the simulated and observed temperatures is good (correlation R = 0.85; p<0.0001). The inset at the top of the panel shows the SPM.5 multi-model average as published in the 4AR. The grey IPCC uncertainty envelope about the 20th century simulation is ± one standard deviation about the multi-model mean.
The IPCC’s relatively narrow uncertainty envelope implies that the hindcast simulation merits considerable confidence. The good correspondence between the observed and simulated 20th century temperatures is well within the correlation = causation norm of consensus climatology.
The bottom panel of Figure 1 also shows uncertainty bars about the 20th century multi-model hindcast. These represent the CMIP5 average ±4 Wm-2 systematic cloud forcing error propagated through the simulation. The propagation is carried out by inserting the cloud error into the previously published linear equation that accurately emulates GCM air temperature projections; also see here (2.9 MB pdf). Systematic error propagates as the root-sum-square.
Figure 1. Top panel (red line), the multi-model simulation of the 20th century global air temperature (IPCC AR4 Figure SPM.5). Inset: SPM.5 multi-model average 20th century hindcast, as published. Blue points: the GISS 1999 land+sea global surface air temperature record. Bottom panel: the SPM.5 multi-model 20th century simulation with uncertainty bars propagated from the root-sum-square CMIP5 average ±4 Wm-2 cloud forcing error.
The consensus sensibility will now ask: how is it possible for the lower panel uncertainty bars to be so large, when the simulated temperatures are so obviously close to the observed temperatures?
Here’s how: the multi-model average simulated 20th century hindcast is physically meaningless. Uncertainty bars are an ignorance width. Systematic error ensures that the further out in time the climate is projected, the less is known about the correspondence between the simulation and the true physical state of the future climate. The next part of this post demonstrates the truth of that diagnosis.
Figure 1 from Rowlands [4], below, shows “perturbed physics” projections from the HadCM3L climate model. In perturbed physics projections, “a single model structure is used and perturbations are made to uncertain physical parameters within that structure…” [5] That is, a perturbed physics experiment shows the variation in climate projections as model parameters are varied step-wise across their physical uncertainty.
Figure 2. Original Legend: “Evolution of uncertainties in reconstructed global-mean temperature projections under SRES A1B in the HadCM3L ensemble.” The embedded black line is the observed surface air temperature record. The horizontal black lines at 1 C and 3 C, and the vertical red line at year 2055, are PF-added.
The HADCML model is representative of the behavior of all climate models, including the advanced CMIP3 and CMIP5 versions. Different sets of parameters produce a spread of projections of increasing deviation with simulation time.
Under the SRES A1B scenario, atmospheric CO2 increases annually. This means the energy state of the simulated climate increases systematically across the years.
The horizontal black lines show that the HADCM3L will produce the same temperature change for multiple (thousands of) climate energy states. That is, different sets of parameters project a constant 1 C temperature increase for every single annual climate energy state between 1995-2050. The scientific question is, which of the thousands of 1 C projections is the physically correct one?
Likewise, depending on parameter sets, a constant 3 C increase in temperature can result from every single annual climate energy state between 2030-2080. Which one of those is correct?
None of the different sets of parameters is known to be any more physically correct than any other. There is no way, therefore, to choose which temperature projection is physically preferable among all the alternatives.
Which one is correct? No one knows.
The identical logic applies to the vertical red line. This line shows that the HADCM3L will produce multiple (thousands of) temperature changes for a single climate energy state (the 2055 state). Every single Rowlands, et al., annual climate energy state between 1976-2080 has dozens of simulated air temperatures associated with it.
Again, none of the different parameter sets producing these simulated temperatures is known to be any more physically correct than any other set. There is again no way to decide which, among all the different choices of projected annual air temperature, is physically correct.
This set of examples shows that the HADCM3L cannot produce a unique solution to the problem of the climate energy state. No set of model parameters is known to be any more valid than any other set of model parameters. No projection is known to be any more physically correct (or incorrect) than any other projection.
This means, for any given projection, the internal state of the model is not known to reveal anything about the underlying physical state of the true terrestrial climate. More simply, the model cannot tell us anything at all about the physically real climate, at the level of resolution of greenhouse gas forcing.
The same is necessarily true for any modeled climate energy state, including the modeled energy states of the past climate.
Now let’s look back at the multi-model average 20th century hindcast in the top panel of post Figure 1. Analogize the multiple temperature projections in Rowlands, et al., Figure 1, that represent the ignorance widths of the parameter sets, onto the single hindcast line of SPM.5. Doing so brings the realization that there must be an equally large set of equally valid but divergent hindcasts.
Each of the multiple models that produced that hindcast has a large number of alternative parameter sets. Those alternative sets are not known to be any less physically valid than whatever set produced each individual model hindcast.
There must exist a perturbed physics spread, analogous to Rowlands Figure 1, for the 20th century hindcast projection. The alternative parameter sets, all equally valid, would produce a set of hindcasts that would diverge with time. Starting from 1900, the individual perturbed physics hindcasts would diverge ever further from the known air temperature record through to 2000. But they have all been left out of Figure SPM.5.
The model states that produced the SPM.5 20th century hindcast, then, do not reveal anything at all about the true physical state of the 20th century terrestrial climate, within the resolution of 20th century forcing.
That means the multi-model average hindcast in SPM.5 has no apparent physical meaning. It is the average of hindcast projections that themselves have no physical meaning. This is the reason for the huge uncertainty bars, despite the fact that the average hindcast temperature trend is close to the observed temperature trend. The model states are not telling us anything about what caused the observed temperatures. Therefore the hindcast air temperatures have no physical connection to the observed air temperatures. The divergence of the perturbed physics hindcasts will increase with simulation time, in a manner exactly portrayed by the increasingly wide uncertainty envelope.
This conclusion remains true even if a given climate model happens to produce a projection that tracks the emergent behavior of observed air temperatures. Such correspondences are accidental, in that the parameter set chosen for that model run must have had offsetting errors. They were inadvertently assigned beneficial values from within their uncertainty margins. Whatever those beneficial values, they are not known to be physically correct. Nor can the accidental correlation with observations imply that the underlying model state corresponds to the true physical state of the climate.
The physical meaning of the recently published study of M. England, et al., [6] exemplified in Figure 3 below, is now apparent. England, et al., reported that some CMIP5 projections approximated the air temperature “hiatus” since 2000. They then claimed that this correspondence proved the, “robust nature of twenty-first century warming projections” and that it, “increase[s] confidence in the recent synthesized projections reported in the Intergovernmental Panel on Climate Change Fifth Assessment Report.”
Compare Figure 1 of England, et al., 2015, below, with Figure 1 of Rowlands, et al., 2012, above. The horizontal black lines and the vertical green line transmit the same diagnosis as the analogous lines in Rowlands, et al., Figure 1.
The England, et al., set of CMIP5 models produced constant air temperatures for multiple climate energy states, and multiple air temperatures for every single annual climate energy state. This, despite the fact that, “all simulations follow identical historical forcings ([6], Supplementary Information).” The divergence of the projections, despite identical forcings, clearly reveals a spread in model parameter values.
Figure 3. Figure 1 from England, et al. 2015. [6] Original Legend: Global average SAT anomalies relative to 1880–1900 in individual and multi-model mean CMIP5 simulations. Blue curves: RCP4.5 scenario; red curves: RCP8.5 scenario. The horizontal black lines at 2 C and 3 C and the vertical green line at 2060, are PF added.
The diagnosis follows directly from Figure 3: CMIP5 climate models are incapable of producing a unique solution to the problem of the climate energy state. They all suffer from internal parameter sets with wide uncertainty bands. The internal states of the models do not reveal anything about the underlying true physical state of the climate, past or future. None of the CMIP5 projections reported by England, et al., has any knowable physical meaning, no matter whether they track over the “hiatus” or not.
This brings us back around to the meaning of the huge uncertainty bars in the bottom panel of the 20th century hindcast in post Figure 1. These arise from the propagated CMIP5 model ±4 Wm-2 average cloud forcing error. [7, 8] Like parameter uncertainty, cloud forcing error also indicates that climate models cannot provide a unique solution to the problem of the climate energy state.
Uncertainty bars are an ignorance width. They indicate how much confidence a prediction merits. Parameter uncertainty means the correct parameter values are not known. Cloud forcing error means the thermal energy flux introduced by cloud feedback into the troposphere is not well-known. Models with internal systematic errors introduce that error into every single step of a climate simulation. The more simulation steps, the less is known about the correspondence between the simulated state and the physically true state.
The more simulation steps, the less knowledge, and the greater the ignorance about the model deviations from the physically true state. This is the message of the increasing width of the uncertainty envelope of propagated error.
Every single projection in England, et al.’s Figure 1 is subject to the ±4 Wm-2 CMIP5 average cloud forcing error. A proper display of their physical meaning should include an uncertainty envelope like that in post Figure 1, bottom. Moreover, the systematic error in the projections of individual models enters a multi-model average as the root-mean-square. [9] England, et al.’s multi-model mean projections — the dark red and blue lines — have even greater uncertainty than any of the individual projections. This is an irony that regularly escapes consensus climatologists.
So, when you see a figure such as Figure 4 top, below, supplied by the US National Academy of Sciences [10], realize that a presentation that fully conformed to scientific standards would look like Figure 4 bottom.
Figure 4. Top: Figure 4 from [10]; original legend: Model simulations of 20th century climate variations more closely match observed temperature when both natural and human influences are included. Black line shows observed temperatures. Bottom, the top left US NAS panel showing the global 20th century air temperature hindcast, but now with uncertainty bars from propagated ±4 Wm-2 CMIP5 average cloud forcing error.
It makes no sense at all to claim that an explanation of later 20th century warming is not possible without including “human influences,” when in fact an explanation of later 20th century warming is not possible, period.
Climate modelers choose parameter sets with offsetting errors in order to successfully hindcast the 20th century air temperature. [11] That means any correspondence between hindcast temperatures and observed temperatures is tendentious — the correspondence is deliberately built-in.
The previous post made the case that their own statements reveal that climate modelers are not trained as physical scientists. It showed that climate modeling itself is a liberal art in the manner of cultural studies, but elaborated with mathematics. In cultural studies, theory just intellectualizes the prejudices of the theorist. This post presents the other side of that coin: the lack of understanding that follows from the lack of professional training.
The fact that England, et al., can claim the “robust nature of twenty-first century warming projections” and ‘increased confidence‘ in IPCC projections, when their models are obviously incapable of resolving the climate energy state, merely shows that they can have no understanding whatever of the source of physical meaning. This is why they exhibit no recognition that their models projections have no physical meaning. Likewise the editors and reviewers of Nature Climate Change, the management of the US National Academy of Sciences, and the entire IPCC top to bottom.
The evidence shows that these people do not know how physical meaning emerges from physical theory. They do not know how to recognize physical meaning, how to present physical meaning, nor how to evaluate physical meaning.
In short, they understand neither prediction nor falsification; conjointly the very foundation of science.
Climate modelers are not scientists. They are not doing science. Their climate model projections have no physical meaning. Their climate model projections have never had any physical meaning.
To this date, there hasn’t been a single GHG emissions climate projection, ever, that had physical meaning. So, all those contentious debates about whether some model, some set of models, or some multi-model mean, tracks the global air temperature record, or not, are completely pointless. It doesn’t matter whether a physically meaningless projection happens to match some observable, or not. The projection is physically meaningless. It has no scientific content. The debate has no substantive content. The debaters may as well be contesting theology.
So, when someone says about AGW that, “The science is settled!,” one can truthfully respond that it is indeed settled: there is no science in AGW.
References:
1. Frank, P., Negligence, Non-Science, and Consensus Climatology. Energy & Environment, 2015. 26(3): p. 391-416.
2. IPCC, Climate Change 2007: The Physical Science Basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, S. Solomon, et al., Editors. 2007, Cambridge University: Cambridge.
3. Hansen, J., et al., GISS analysis of surface temperature change. J. Geophys. Res., 1999. 104(D24): p. 30997–31022.
4. Rowlands, D.J., et al., Broad range of 2050 warming from an observationally constrained large climate model ensemble. Nature Geosci, 2012. 5(4): p. 256-260.
5. Collins, M., et al., Climate model errors, feedbacks and forcings: a comparison of perturbed physics and multi-model ensembles. Climate Dynamics, 2011. 36(9-10): p. 1737-1766.
6. England, M.H., J.B. Kajtar, and N. Maher, Robust warming projections despite the recent hiatus. Nature Clim. Change, 2015. 5(5): p. 394-396.
7. Lauer, A. and K. Hamilton, Simulating Clouds with Global Climate Models: A Comparison of CMIP5 Results with CMIP3 and Satellite Data. J. Climate, 2013. 26(11): p. 3823-3845.
8. Frank, P., Propagation of Error and the Reliability of Global Air Temperature Projections; Invited Poster, in American Geophysical Union Fall Meeting. 2013: San Francisco, CA; Available from: http://meteo.lcd.lu/globalwarming/Frank/propagation_of_error_poster_AGU2013.pdf (2.9 MB pdf).
9. Taylor, B.N. and C.E. Kuyatt., Guidelines for Evaluating and Expressing the Uncertainty of NIST Measurement Results. 1994, National Institute of Standards and Technology: Washington, DC. p. 20.
10. Staudt, A., N. Huddleston, and I. Kraucunas, Understanding and Responding to Climate Change 2008, The National Academy of Sciences USA: Washington, D.C.
11. Kiehl, J.T., Twentieth century climate model response and climate sensitivity. Geophys. Res. Lett., 2007. 34(22): p. L22710.
Do mathematical projections have any real meaning?
This answer to this is nothing new. Huxley said it in 1869.
“Mathematics may be compared to a mill of exquisite workmanship, which grinds you stuff of any degree of fineness; but, nevertheless, what you get out depends upon what you put in; and as the grandest mill in the world will not extract wheat-flour from peascod, so pages of formulae will not get a definite result out of loose data. ” -Thomas Henry Huxley
A perfect insight.
Except that TH Huxley had no ability to do math, so that his opinion was self serving. Also, he argued vehemently for a theory which was based purely on abduction, and is not falsifiable.
And yet, where is Huxley wrong?
In science, inference is to best predictive (falsifiable by observation) hypothesis, not to best explanation. The difference is thoroughly fundamental.
Evolutionary theory is indeed falsifiable. Darwin knew nothing of genes or genetics, and yet predicted a material mechanism of heredity (see gemmule), to account for the action of natural selection upon organismal variation.
Evolutionary theory would be falsified were there no evidence for the physical mechanism of inherited traits. And yet Mendelian genetics was found, independently of Darwin and his prediction.
If 100 climate models give 100 different climate scenarios, aren’t 99 wrong by definition? There is only one right answer, and if you know which model is right, why bother with the others? Using an average of 99 incorrect models and one perhaps correct model is meaningless. If 2 guys say they are Jesus, at least one is lying.
The warmist claim the science is settled and that CAGW is based upon sound principles and understanding of the physics.
If the science was settled and understood, why are there approximately 100 GCMs and not jsut one?
Doesn’t the fact that there are about 100 GCMs demonstrate initself that the science is not settled and understood?
I frequently ask warmists to identify which of the about 100 GCMs is the one that is based upon setlled science and the correct physics of the Earth’s climate system and its response to CO2?
So far, no one has been able to answer that simple question.
Dr. Brown highlights this issue repeatedly.
I believe the primary issue is the political nature of the IPCC. All countries (i.e. models) are equal, we must not discriminate. I am old enough to remember when “discriminating” meant something different.
Craig,
If the weather report says that there is a 75% chance of rain, will you consider bringing your umbrella? This has falsified your hypothesis. How do you think they came up with the 75% figure? They ran many different simulations, and 75% of them showed rain.
“So, when someone says about AGW that, “The science is settled!,” one can truthfully respond that it is indeed settled: there is no science in AGW.”.
LOL
Thanks, Pat Frank.
Models are devoid of physical meaning, but loaded with political and emotional meaning.
Yes! They do, indeed, provide meaningful “information”…
WELL DONE, Dr. Pat Frank!
Bottom line for those in a hurry:
“… the correspondence is deliberately built-in.
Exactly right, Andrés. The whole enterprise runs on that.
Let me add that one of the most pernicious assumptions of the modelers is that the actual climate parameter space is adequately both explored and mapped by the variations in both the models and the parameters used in the model runs …
This incorrect assumption is implicit in e.g. the equally incorrect idea that the average of an “ensemble” of untested, uncalibrated, unvalidated, and unverified models has some kind of statistical value.
w.
Climate model means – 117 wrongs make a right.
The average of wrong is wrong.
The averaging is to reduce the width of the error in the wrongness of it all, not to make the output correct. it does nothing to aid the reasonablenss and reliability of the projection/prediction.
I totally agree with you, Willis.
The implicit assumption underlying all those multi-model projections and averages is that the model physical theory itself is physically complete and would yield a physically true representation of the climate if only the parameters were exactly known, along with the initial conditions.
Somewhat like Newtonians [though I hate to glorify them as being that competent] locked into the orbit of Mercury and going round and round getting nowhere.
Wait, are you saying that it’s impossible to create a model that is complete enough to predict the climate to a reasonable accuracy?
I pride myself on being the most skeptical, but you people are starting to froth at the mouth with anti-science rhetoric.
>> The average of wrong is wrong.
Reality check: the models are off by .1%.
VikingExplorer May 21, 2015 at 10:50 am
Recognizing our own limitations is now “anti-science”?
Viking, the real answer is that we do not know whether such a climate model is possible, even in theory. As even the IPCC has noted,
Climate is far and away the most complex system that we have ever tried to model. It has at least six major subsystems, none of which are completely understood and some of which are hardly investigated—atmosphere, cryosphere, lithosphere, biosphere, ocean, and electrosphere. Modeling even one of these to the required level of detail is currently beyond our abilities, because they are all inter-related by feedbacks and chains of effect and non-linear couplings and individual and multi-system resonances, both known and unknown.
So no, Viking, I’m afraid that today in 2015, we truly do not know if it is possible to “predict the climate to a reasonable accuracy” even in theory … however, we can confidently say that to date, we have completely failed to be able to do so in practice.
w.
>> even in theory …
Willis, I think your comment is plausible and perhaps likely correct up to the above words.
All of the areas that you list may be problematic but with enough time, they are knowable. The problem is certainly not inherently impossible. Not understanding something is an issue for 2015, but the rest of what you wrote is just a matter of analysis and computer horsepower.
With all due respect to your incredible writing skill and remarkable citizen scientific efforts, when someone with your background says it’s impossible even in theory, it rings hollow.
My goal is to create a climate model someday. Although I don’t currently have a PhD (like my father – PhD EE), but I’ve got a BSEE (as well as my wife and brother). Yesterday, my two oldest kids (in school for Physics and Chem Engineering) were here calculating the behavior of a proton gas inside a metal container. So, from my point of view, it looks different.
VikingExplorer May 22, 2015 at 8:12 am
So you believe that all systems are inherently modelable and computable? Let me introduce you to my little friend … chaos. The future evolution of some chaotic systems seem to be uncomputable with anything less complex than a model which is essentially a totally identical parallel universe.
That is to say, in some chaotic systems you literally need to know the location and velocity of every particle in the system to compute which way it will evolve.
Which sounds doable, at least in theory … but as Heisenberg observed, simultaneously knowing both the location and velocity of even a single particle is not possible, even in theory.
Best regards,
w.
… which leads to the following mathematician’s joke.
A policeman pulled Werner Heisenberg’s car over because he was speeding. The cop asked him, “Do you know how fast you were going?”
“No,” said Heisenberg, “… but I know where I was!”
>> my little friend … chaos
That’s the great thing about climatology: it’s much easier than Weather, because the timescales are much greater. In my pervious example, the study of how a river changes it’s course over time is much, much easier than determining the course of a leaf thrown into the river. The latter is all about chaos, while the former is much more predictable.
You should be aware that we’ve done it before. At the molecular level, it’s totally chaotic. However, this didn’t stop humanity from creating Thermodynamics and Circuit Theory. These sciences remain completely valid, although they summarize the net effects of a tremendous amount of chaotic behavior.
But in the end, V = I*R.
VikingExplorer May 22, 2015 at 9:55 am
Thanks, Viking. That could be true … but only if the weather is chaotic and the climate is not. However, Mandelbrot himself investigated the question and found as follows (emphasis mine):
Mandelbrot and Wallis, 1969. Global dependence in geophysical records, Water Resources Research 5, 321-340.
So on the one hand, you claim without proof that there is a statistical difference between weather and climate such that although you agree that weather is chaotically unpredictable, you say climate is predictable.
Mandelbrot, on the other hand, offers investigative observational proof that your claim is wrong. He looked at the statistics of 9 rainfall series, 12 varve series, 11 river series, 27 tree ring series, 1 earthquake occurrence series, and 3 Paleozoic sediment series. He found no evidence for your claim of distinctions between weather and climate. This means that if weather is chaotic, the climate is as well … and we know the weather is chaotic.
There’s a good discussion of this from a decade ago over at Climate Audit.
Best regards,
w.
Viking, you might also enjoy (emphasis mine):
The paper is here. Koutsoyiannis is always good, detailed, well cited, and persuasive.
w.
>> This means that if weather is chaotic, the climate is as well … and we know the weather is chaotic.
I don’t believe Mandelbrot is correct in the general case. Thermodynamics and Circuit Theory falsify his idea.
>> weather is chaotically unpredictable, you say climate is predictable.
1) When you said “without proof”, you missed a subtle point of logic. I’m not one of those people who believe that given enough time, man will be able to do anything, like redesign our own DNA and travel at warp speeds. I think we agree that it may be impossible. However, if someone says “it’s impossible”, that seems clearly wrong.
2) I also think some people should be concerned about arguing themselves into a corner. If the climate were in fact chaotic, then the idea of a tipping point becomes more plausible. The long history of earth would seem to falsify this idea. This is also the pattern we see with other chaotic phenomena. No one can easily predict a path of a leaf, but we can say with some certainty that it will go downstream.
3) We also have to consider that chaotic doesn’t mean totally unpredictable. Weather predictions are better today than they were 20 years ago.
4) Thanks for the link to the CA discussion. I note that there was no significant argument in favor of a chaotic climate. One commenter pointed out that if climate is that which is caused by external forcing, then solar weather would introduce some chaos into the climate.
5) Another point is that what we normally think of when we say weather actually only involves .07% of the thermal mass of the water/air system. Although there is still some chaos in the oceans, it’s far less than the atmosphere.
6) The issue with the Koutsoyiannis reference is one of logic. “Thus local model projections cannot be credible”. Clearly, climate is an average over the whole globe (land/sea/air), with the minimum timescale being a decade. Koutsoyiannis compares model results to a certain 8 places around the globe. This is weather by definition. My plan for a climate model would be to have N number of weather systems randomly traversing the globe, with no attempt to try to predict the where or when of any particular real life weather system. A climate model should NOT be considered an extension of a weather model.
VikingExplorer May 23, 2015 at 7:28 am
Mandelbrot said nothing about a general case. He, just like us, is discussing whether there is a difference between weather and climate such that one is chaotic and one is not. He said no. He said that both are chaotic.
Let me review the bidding. My statement that you seemed to find incorrect was:
You said the first part was correct, up to “even in theory”.
I said we don’t know if the climate is predictable in theory. So your claim is that we DO know that modeling the climate is possible in theory … perhaps you’d be so kind as to point to the study that in your mind shows that we can model chaotic systems over long time spans? Serious question, Viking. I see you believe we can model anything. I used to believe that. I’ve been writing computer programs of all types, including complex models of a variety of physical systems, for fifty-two years now. I no longer believe that anything can be modeled.
I see also that you have not commented on the quote I gave from the IPCC:
Heck, my statement was much weaker than that. I merely said we didn’t know if it was possible in theory … which is why your objecting was such a surprise. They flat-out state that it is NOT possible in theory.
Finally, you say:
Weather predictions are incrementally better now than then, but much of that is because of the widespread use of ever more sophisticated satellite data. Better input gives better output. But even with all of the incremental increases, far too often you look at the forecast on Wednesday and plan your weekend barbecue … only to get rained out on Sunday. The problem is simply stated:
In a chaotic system, divergence between model and reality increases with time.
We can predict the weather five minutes from now with good accuracy. We can’t make much more than an educated guess about the weather five years from now. Divergence increases with time. And as Mandelbrot not only claimed but actually measured, this is true up to and including time spans of hundreds of years. The climate is no less chaotic than is the weather.
Viking, the ugly reality is that there are systems for which we simply cannot compute the future evolution with any computer which is less complex than the system itself.
w.
“The climate is no less chaotic than is the weather.”
Not really. The average global temperature in 2024 won’t be more than a few tenths of a degree different than it was in 2014. The high temperature at my location 10 days from now is probably going to be a few degrees different than the high temperature today.
>> Mandelbrot said nothing about a general case
Sorry, I misspoke. I disagree with Mandelbrot about climate being dominated by chaos. I believe this because the long term reconstructions of earth’s climate indicate as much.
>> I said we don’t know if the climate is predictable in theory
Ok, I see how we miscommunicated here. You meant we do not know whether such a climate model is possible (period). I agree with this rewording. Adding “in theory” is superfluous, and confused me, since by definition, a model is theory. It made me think that you meant that the underlying theory was unknowable.
( in theory is also a synonym for unproven, but that would also be redundant)
>> I no longer believe that anything can be modeled.
I agree with you that it the may be impractical to model physical phenomena when the complexity of the model approaches the reality (e.g. at the molecular level). However, impractical is not the same as impossible.
Yes, and if we were trying to predict the weather, you would be 100% correct. However, can you please take a step back, put your abstract thinking cap on, and read this for comprehension:
A climate model should NOT be designed as an extension of a weather model.
One should not ask a climate model if it’s going to rain, or what the temperature is going to be on a certain day or month 10 years from now.
If I asked for a prediction of future beach erosion, a scientist should not start by modeling fluid dynamics at the molecular level. If I asked for a prediction of what current would flow in response to a voltage, a scientist should not start by modeling electrodynamics at the molecular level.
For an example of what I mean:
As for reality, it’s not that ugly, because we’ve discovered that although it is really chaotic at the small scales, yet at the macro scale, it typically follows well defined rules. Close up, the sun is complete and total chaos, and yet some people consider it a constant.
Bill 2 is correct about 2024. The long term history of our climate temperature falls into a fairly small range.
It’s a noob praising a Jedi, but I really liked this essay, just as I have greatly admired Pat Frank’s other essays on climate science. I love the clarity of expression and the force of the logic presented. Here’s my favorite bit: “The England, et al., set of CMIP5 models produced constant air temperatures for multiple climate energy states, and multiple air temperatures for every single annual climate energy state.” It’s indeed “robust,” but not in the way England, et al. think.
lol — nice choice of quote, Eskimo Quinn.
#(:))
We’re all noobs somewhere Quinn. 🙂 And thanks.
re: “The physical meaning of the recently published study of M. England, et al., [6] exemplified in Figure 3 below, is now apparent. ”
I posted this a bit late on a prior page so I’ll pass it on here also. There is a post by England, on the un-skeptical site Skeptical Science where he talks about that paper and says:
http://www.skepticalscience.com/climate-hiatus-doesnt-take-heat-off-global-warming.html
“Until now, however, no evaluation has been made of the possible consequences for long-term projections. Specifically, if the variability controlling the current hiatus is linked to longer-term sequestration of heat into the deep ocean, this might require us to recalibrate future projections.
With this in mind, we decided to test whether 21st century warming projections are altered in any way when considering only simulations that capture a slowdown in global surface warming, as observed since 2001.”
So he thinks that models that weren’t dealing with long-term ocean sequestration of heat, but somehow accidentally predicted the pause, have relevance to claims about future warming if the ocean were involved in a way that wasn’t in the models? Wow is that absurd, as is of course the paper’s claim of the “robust nature of twenty-first century warming projections” when only a minuscule fraction of the model runs matched the pause. The fact that they are all tweaked to meander a bit but wind up around the same high range eventually and some of them accidentally happened to match the pause isn’t indicating anything “robust” about them.
One thing I haven’t come across what were the ‘tweaks’ that produced accurate predictions of the pause?
And I take it that all the other models have now been discarded?
The issue is that merely out of a large number of model runs, due to inbuilt random variations, they accidentally match the pause, just like accidentally you can manage to flip a coin to get heads a few times in a row. Picture a random walk tweaked to have a slight upward bias, but small enough that for some periods in some runs it can cycle up and down for a bit without rising too much and generate a “trend” that is somewhat flat for a number of years, and then the location of that trend accidentally falling in the right years. That isn’t exactly what is going on (the internal structure isn’t directly a random walk even if it essentially maps onto one in its results), but it illustrates the concept. Tuning the model to match the past likely biases it in favor of being a bit more likely to be able to reproduce the pause by chance due to the characteristics of whatever random walk it in essence corresponds to. By analogy with certain sets of data that are actually generated by say a quadratic function or other polynomial, there might be sections where the curve is almost flat and happens to match a linear fit, but that linear fit will then diverge from the more complicated reality.
The very fact that they can claim “oh the ocean must be swallowing the heat in a way we didn’t account for in the models” but then act as though they can still look at those models for guidance merely because they found some runs that randomly matched reality, suggests a lack of awareness on their part that to be credible models can’t merely accidentally curve-fit to a small set of data, that they actually need to credibly claim to model the underlying processes involved. Getting the “right” answer for the wrong reasons doesn’t lend credibility to any future predictions.
As far as I know, the internals of the climate models are unknown to the public. Do these models have some theory behind them that they are trying to demonstrate? If so, where is an English language version of this theory so that we may look over?
Do they adequately consider winds, ocean currents, clouds, convection, conduction, advection, rain, storms, planetary motion, the effect of gravitation upon the mass of the atmosphere, variations is the output of the sun, and at least a dozen other factors I have seen mentioned in various places? Perhaps they do, but how would I know for sure?
Has any group used the mega funds of government to run a model based on a theory other than the prevailing one of CO2 dominated back-radiation warming the surface? What if the present consensus is wrong? The only thing I know for sure is that the current consensus and the current models appear to be giving wrong results. Perhaps looking in another direction is warranted?
“[…] The only thing I know for sure is that the current consensus and the current models appear to be giving wrong results. Perhaps looking in another direction is warranted?“
When it comes to climate models, we would be wise to avert our eyes. Studies have shown that prolonged gazing at a spaghetti graph of climate model ensembles reduces visual acuity by 38% and lowers the I.Q. by 42 full points.
Links? Why should I provide links to the studies? People would just try to find something wrong with them ;o)
🙂
Climate philosophy, including models, is potentially science when constrained to a limited but variable frame of reference (i.e. scientific domain) in both time and space, where phenomena can observed, reproduced, and characterized through deduction.
The innovation of the scientific method, that acknowledges the chaotic (i.e. incompletely or insufficient characterized and unwieldy) nature of the system, was to establish a firm separation of science and other logical domains: philosophy, faith, and fantasy. Theories will be first classified as philosophy until they are evaluated in the scientific domain. Any theory where there does not exist a probable path from philosophy to the scientific domain is either an article of faith or fantasy. The liberal use of inference (aka “post-normal science”), including simulation models in climate “science”, is the creation of knowledge, rather than its observation.
There is much to criticize in climate models, so why come up with this sort of nonsense? With respect to the lower panel of Figure 1, the author asks “how is it possible for the lower panel uncertainty bars to be so large”. It is because they are calculated with the implicit assumption of infinite climate sensitivity. But that is wrong. What happens is that if more radiation reaches the surface, then it warms up and more radiation is emitted to space. As a result, the errors do not add up randomly.
When you here someone blithely claim that measurements have “no physical meaning”, you should suspect that you are listening to a windbag.
Re: You at 2:54pm today: “… measurements have ‘no physical meaning’, … ”
I looked for that quote in Dr. Frank’s post and could not find it (were you quoting someone else?).
I found this in paragraph 4:
“… projections of future global air temperatures make them predictively useless. In other words, they have no physical meaning.” Dr. Pat Frank
Janice,
“This post will show that hindcasts of historically recent global air temperature trends also have no physical meaning”. Oops. I screwed up and somehow conflated that with the people who make all sorts of ridiculous criticisms of the observed record. Pat Frank did not do that. So by misquoting him, I undermined my credibility. I hate it when I do that. Especially when my basic point was right anyway.
Dear Mike M.,
By promptly and completely admitting your error, your credibility is completely intact. And your character was polished up a bit, too!
And if your basic point was that the IPCC’s GCM’s are junk: I agree!
Janice
Mike M, “[the uncertainty bars] are calculated with the implicit assumption of infinite climate sensitivity.”
Rather, they’re calculated using a successful GCM emulator; one that shows GCM air temperature projections are mere linear extrapolations of GHG forcing.
The essay is about the behavior of climate models, not about the climate.
How you conceive of these two sentences, “What happens is that if more radiation reaches the surface, then it warms up and more radiation is emitted to space. As a result, the errors do not add up randomly.” as a logical sequitur is anyone’s guess.
There was nothing “blithe” about my claim. It’s all analytically justified (2.9 MB pdf). And the propagated error is systematic, not random.
Pat Frank,
I gave you too much credit. You don’t even know elementary statistics. Random errors propagate as the root-mean-square.
Random errors decrease as 1/sqrtN.
Reblogged this on Centinel2012 and commented:
And I agree 100% there is no science in any aspect of of the AGW!
The models do not provide a robust measurement system. The variance is too high. I mean high compared to observed variance of historical temperatures.
Obviously such variance means the scientists do not agree with each other. They may claim to agree but their results prove mathematically that in reality they do not agree. Claiming that they agree is just being nice to each other, but means nothing mathematically. Their nice words are logically inconsistent with their theories.
The models are an art form.
A prerequisite of the model projections is that they must all show some warming, after all that’s the raison d’état for the IPCC.
It’s no accident that the projected warming range is from the barely credible without being risible, to the not quite ignorable.
Really enjoyed the story of Parkes Radio Telescope. The Alien signals turned out to be the microwave in the kitchen cooking the odd pie. When will we learn the short sighted thermometer reader breathes on the thermometer bulb. Can’t wait.
Invasion of the Mouth Breathers.
We have models which are a complex circular argument in disguise. That is all.
Well really! Such gratuitous abuse, next you’ll be claiming Astrology isn’t a Science!
Here is the latest definition of science, which seems pretty good:
Here is the latest definition of science, which seems pretty good:
Astrology fails this test. The definition makes no mention of unique solutions or that model simulations are outside of science.
VikingExplorer
You provide a definition of science you like then say
Which demonstrates you don’t have a clue what you are talking about.
Richard
That’s an impressive argument Richard.
The head post makes a demarcation argument, and excludes from science any analyses that don’t provide “unique solutions” or include multiple model simulations.
I’m just pointing out that the head post definition of science contradicts the most widely held ideas about science. In fact, I would speculate that besides you and Pat, almost no one else on earth has this very extremely narrow definition of science.
One can discuss the definitions of science and disagree but Richard’s understanding of science is surely spot-on in one field of science – engineering. Since ultimately the issue is of climate engineering then this strict understanding of science would be applicable. After all, would anyone build a bridge or a house in the same way climatologists build their ‘science’?
Jon,
Yes, but not sure if you understand the narrowness of the head post definition. One, many engineering disciplines use computer model simulations to do their work, and to prevent Tacoma Narrows type situations. I know I did as an aerospace generator engineer.
Two, according to his strict definition, engineering (applied science), along with medicine (applied epidemiology / biology), are not sciences. Engineers do not form hypotheses and perform falsification experiments.
According to the widely held science council definition, engineers and doctors are doing science. According to the head post, they are not. As Peter Ward explained above, the head post definition essentially excludes everything except basic chemistry and physics.
VikingExplorer, guess what “… based on evidence” means in terms of measurement and prediction. That might (might) lead you to discover a conjunction between evidence and accuracy.
Perhaps you will wonder at a connection, if any, between accuracy and answer. You may then explore how one determines limits of accuracy, and whether, to do so, one needs a strictly bounded solution. But then again, you may not.
Reasoning sequentially through is the mechanism necessary to rise above arguing from authority. Which latter is all you’ve done here.
Bravo! One could also add that models assume a “greenhouse gas” as being real, when there is no compelling evidence to confirm it.
wwf, there’s no doubt that the radiation physics of CO2 is correct.
CO2 absorbs the 15 micron radiation from the warm surface, and dumps it off into the kinetic energy of the atmosphere.
The central question is how the climate responds to that kinetic energy. Climate models assume there’s only one response: atmospheric warming. But the real climate has many response channels. If a different one of them dominates (such as convection), there may be no detectible warming at all from extra CO2.
So far, given the completely unremarkable behavior of the climate over the last 50 years, the latter eventuality seems much more likely.
Pat wrote: “The central question is how the climate responds to that kinetic energy.”
CO2 both absorbs and emits outgoing OLR (and DLR). The net result of both processes is that about 3.7 W/m2 less radiation will reach space when CO2 has doubled IF nothing else changed. There is no doubt that the earth MUST respond by warming until it emits an addition 3.7 W/m2 – restoring radiative equilibrium. Radiation is the only way for energy to enter and exit the planet. The central question is how much will the planet need to warm to emit an additional 3.7 W/m2. If the earth behaved like a blackbody, the answer is about 1.2 degC. Satellites show that OLR from clear skies increases less than about 1 W/m2 less than expected per degC of warming from changes in water vapor and lapse rate (two of your response channels). There is no doubt that surface albedo will decrease somewhat due to changes in snow and ice cover. The answer to the central question mostly depends on clouds.
Collisional decay of CO2* is about 10^5 times faster than radiative decay, at 1 atmosphere pressure, Frank. Collisional decay dominates the relaxation of CO2* throughout the entire troposphere. Radiative decay of CO2* is virtually absent. It doesn’t contribute anything to tropospheric warmth, or to loss of energy from the troposphere to space. (It does dominate in the stratosphere, where the gas is too dilute to give collisional decay much probability.)
The “back radiation” everyone talks about is not 15 micron radiation from decay of CO2*. It’s black body radiation. The kinetic energy resulting from CO2* collisional decay is heat, and shows up as slightly increased tropospheric black body radiation, which of course radiates equally up and down. Black body radiation and kinetic energy are different manifestations of the same thing: thermal energy.
Your comment that, “There is no doubt that the earth MUST respond by warming…” is not correct in its insistence on warming.
It’s true that the extra energy (kinetic or black body) must be removed to restore energetic equilibrium. But TOA radiative loss can just as easily be through the latent heat of water vapor condensation in the upper atmosphere. Tropical precipitation need increase by only a couple percent to achieve that effect. There need be no perceptible increase in tropospheric sensible heat at all. If convection dominates the climatic response to the kinetic energy deposited into the troposphere by CO2*, loss of energy through latent heat of condensation is likely the dominant outcome.
The physical theory of climate has nowhere near the resolution to decide which climate response channel will dominate in removing the excess energy. That’s why your insistence on warming has no weight. Earth may indeed warm from CO2. Or it may just as well not. No one knows. So far, there’s zero evidence that it has.
Pat: You and I agree that radiation is not trapped by GHGs. Collisional excitation and relaxation of the vibrational excited states of GHGs is much faster than absorption and emission throughout the troposphere AND most of the stratosphere (local thermodynamic equilibrium). Emission of OLR (to space) and DLR is controlled by local temperature, not local radiation. Climate models and radiative transfer calculations (MODTRAN) are based on this assumption.
Pat wrote: “But TOA radiative loss can just as easily be through the latent heat of water vapor condensation in the upper atmosphere. Tropical precipitation need increase by only a couple percent to achieve that effect. There need be no perceptible increase in tropospheric sensible heat at all. If convection dominates the climatic response to the kinetic energy deposited into the troposphere by CO2*, loss of energy through latent heat of condensation is likely the dominant outcome.”
Latent heat obviously can not escape directly to space, it is first converted to simply heat by condensation and then to radiation (by collisional excitation of CO2 and other GHGs). The altitude where condensation occurs is warmer than it would be without latent heat and therefore emits more OLR and DLR than it would have otherwise.
The surface of the earth would be cooler if latent heat were transferred faster by convection from the surface to the upper troposphere (where most photons escaping to space are emitted). However, latent heat can only escape faster when the upper troposphere is warmer. Spontaneous buoyancy-driven convection develops only when the rate of cooling with altitude (lapse rate) is greater than a critical threshold, so convection shuts down when the upper atmosphere get too warm through convection. Increasing humidity decreases the lapse rate (lapse rate feedback), allowing the upper atmosphere to warm more rapidly than the surface and more OLR to escape for a given rise in surface temperature. Water vapor feedback does the opposite. Instruments in space tell us how much OLR through clear skies varies with surface temperature, i.e. the combined effects of water vapor and lapse rate feedback. Observations agree with climate models that a 1.2 degC rise in surface temperature produces a 2.5 W/m2 increase in OLR, not the 3.7 W/m2 increase expected for a blackbody.
Pat wrote: “The physical theory of climate has nowhere near the resolution to decide which climate response channel will dominate in removing the excess energy. That’s why your insistence on warming has no weight. Earth may indeed warm from CO2. Or it may just as well not. No one knows. So far, there’s zero evidence that it has.”
We do know some things about how “excess energy” will be removed. 1) It will be by radiation to space. 2) Physics tells us that an instantaneous doubling of CO2 will reduce OLR by about 3.7 W/m2. It also tells us that a blackbody emitting 236.3 W/m2 will need to warm 1.2 degC to radiate 240 W/m2 (equal to post-albedo incoming SWR). 3) OBSERVATIONS tell us how our climate responds to surface warming* by increasing OLR through clear skies to space. See Figure 1B in http://www.pnas.org/content/110/19/7568.full.pdf The dotted line shows how a blackbody would behave. Notice the remarkably small error bars for climate science. Climate models are correct about OLR from clear skies. The rest of the paper shows that climate models are horrible at modeling the OLR response to surface warming from cloudy skies and the SWR response from clear (surface albedo) and cloudy skies (cloud albedo). 4) Common sense tells us that warming will reduce surface albedo (ice albedo feedback). 5) We don’t know how clouds will respond.
* In this paper, surface warming is not “global warming”. Surface warming is “seasonal warming” associated with the asymmetric distribution of land (low heat capacity) and ocean (high heat capacity) between the hemispheres. (GMST does increase 3.5 degK every northern summer, but we eliminate this seasonal change when we calculate temperature anomalies.) Seasonal warming has limitations as a model for GW, but climate models should get both right. Seasonal changes in SWR from clear skies have little to do with the surface albedo feedback that will follow global warming (aka ice-albedo feedback).
An excellent post, thank you. The same conclusion is accessible from information theory which forms the basis of our modern communication infrastructure. It is an axiomatic premise of that discipline that information gain can only occur when a contingency is resolved – that is to say, we can only lean something when we are surprised by a result. The contingency created by running a model of some physical system whose parameters are under-constrained is illustrated beautifully by the model output-plots in this article. But the information gain required to increase our knowledge can only be obtained when that contingency is resolved into a particular evolution that matches experimental data. And while a given set of parameters yields a particular model-state evolution, the only information gained in that “experiment” is about the model itself! No information about the physical world can be conveyed by running a computer simulation, no matter how complex. Either the model is perfectly constrained and thus the outcome certain before the operator hits the run button or under-constrained in which case the outcome is determined completely by the parameter values guessed at by the programmer.
Feynman diagnosed the problem long ago:
“There is a computer disease that anybody who works with computers knows about. It’s a very serious disease and it interferes completely with the work. The trouble with computers is that you ‘play’ with them!”
@Patrick Guinness Frank
This is a very interesting analysis and from what little I know about the Monte Carlo methods used in climate modelling, I know enough to ask what type of Monte Carlo method is being used. Is it applying a Monte Carlo method or making a Monte Carlo simulation, for example. Basically you are saying they treat all possible outcomes as valid. I am not suggesting they are equally valid, just that I understand you are saying they are all treated as equally valid possibilities. There are Monte Carlo methods that do not treat all outcomes as equally likely, informed either by past records of variation, or models of the frequency of the input values for that dimension (freedom to vary).
Well, if they are considered equally valid, there are problems because at the limits of any known set of dimensions of the climate, the likelihood of a set of all dimensions being simultaneously ‘at the limits’ becomes increasingly unlikely to the point of being a ‘rare event’ which would normally be ignored. Are they doing that? Ignoring possible ‘rare events’? (which has a definition)
An alarmist will argue that a rare event must be catered for, like a 200 year storm, or a 2000 year storm, or an extinction event. But as Monckton points out, catering to manage a rare event has a cost and the probability is so low it will be cheaper and more progressive for society in general to cope rather than ‘prevent’, as if ‘prevention’ is possible in the first place.
What your article confirms for me is that the average of a bunch of model outputs (a questionable procedure in the first place) is dependent for alarming content on the existence of high-end predictions from the likes of the Canadian simulator in British Columbia which is the second hottest-running of all models. If they eliminated the worst and most off-the-mark models, the central estimate would drop considerably and be un-alarming. So we know where that is coming from.
They need these ‘equal treatment and equally possible’ limits on each dimensions of each simulation and all the manifestly hot-running models to maintain the fiction that the average of an estimated future world is hot, very hot, unless we have over the keys to the global energy economy.
Exposing the math of the models is a good plan. What can’t work, won’t work. What can’t predict, won’t predict.
Finally someone who notices that the emperor has no clothes on. I have always regarded these model results as nonsense. They are made possible by giving these incompetents nice toys like supercomputers for free so they can pretend to do research. Without supercomputers this stupidity could not exist. In the sixties we did not have supercomputers. We did not even have computers for spectrochemical calculations and simply used graph paper. Direct readers were just coming in but they went to large steel or aluminum producers, not aerospace where I worked. The concept of throwing out hundreds or thousands of attempted graphs was foreign to me until I accidentally started doing climate research. I have called for abandonment of the entire climate modeling enterprise for the simple reason that in 27 years of trying they have never produced any meaningful predictions. That goes for everyone including Hansen whose first attempt in 1988 to predict global temperature was a disaster. Close down the operation and fire the operators. It can be done. Nixon fired ten thousand lunar lander workers for nothing when he cancelled the last three moon shots.
“Nixon fired ten thousand lunar lander workers for nothing when he cancelled the last three moon shots.”
To pay for war…
I happened to notice the following comment posted at the prestigious website “skeptical science” a couple days ago:
“The IPCC has been quoted as saying ‘The chaotic nature of weather makes it unpredictable beyond a few days.’ However, they assert that ‘when weather is averaged over space and time, the fact that the globe is warming emerges clearly…’
“This means IPCC climatologists fully understand that predicting weather beyond a few days with a computer model is exactly as effective as predicting it with, say, chicken entrails.
“Knowing this, they go ahead and consult their entrails, examining them carefully to learn what the weather might be like in 50 years, if only entrails had predictive value.
“But since they know this is silly, they don’t stop there. They go on to examine the entrails of a MILLION chickens. They average the results of the million chicken-entrails predictions together and, voila, pronounce the result ‘scientific.’
“It is amazing what nonsense people will allow themselves to believe.”
The scientistical skeptics at “skeptical science” were not about to let this so-called “argument” stand without a withering logical counterattack. No ma’am!
Mustering all the facts and logic and computer models at their disposal, they immediately responded…
… by deleting the post. Because it consists of “…worn-out sloganeering, strawmen, and argumentative language” that their readers should not have to see.
They went on to explain:
“If you are not prepared to read the actual science with view to understanding, then you are in no position to comment on it. Commentators here have attempted to explain the difference between weather and climate what is predictable or not, but apparently to no avail. … Moderating this site is a tiresome chore, particularly when commentators repeatedly submit offensive or off-topic posts.”
http://www.skepticalscience.com/argument.php?p=3&t=116&&a=227#111619
That pretty much nails it. Nothing could be more persuasive.
I share their words of logic and wisdom with all of you WUWT readers so that you will recognize the obvious folly of your beliefs. Repent, you heretics!
SkS is a waste of time. Just stay away. If they are interested in a debate they can come here.