Guest Post by Willis Eschenbach
Back in 2007, in a paper published in GRL entitled “Twentieth century climate model response and climate sensitivity” Jeffrey Kiehl noted a curious paradox. All of the various different climate models operated by different groups were able to do a reasonable job of emulating the historical surface temperature record. In fact, much is made of this agreement by people like the IPCC. They claim it shows that the models are valid, physical based representations of reality.
Figure 1. Kiehl results, comparing climate sensitivity (ECS) and total forcing.
The paradox is that the models all report greatly varying climate sensitivities but they all give approximately the same answer … what’s up with that? Here’s how Kiehl described it in his paper:
[4] One curious aspect of this result is that it is also well known [Houghton et al., 2001] that the same models that agree in simulating the anomaly in surface air temperature differ significantly in their predicted climate sensitivity. The cited range in climate sensitivity from a wide collection of models is usually 1.5 to 4.5C for a doubling of CO2, where most global climate models used for climate change studies vary by at least a factor of two in equilibrium sensitivity.
[5] The question is: if climate models differ by a factor of 2 to 3 in their climate sensitivity, how can they all simulate the global temperature record with a reasonable degree of accuracy?
How can that be? The models have widely varying sensitivities … but they all are able to replicate the historical temperatures? How is that possible?
Not to give away the answer, but here’s the answer that Kiehl gives (emphasis mine):
It is found that the total anthropogenic forcing for a wide range of climate models differs by a factor of two and that the total forcing is inversely correlated to climate sensitivity.
This kinda makes sense, because if the total forcing is larger, you’ll have to shrink it more (smaller sensitivity) to end up with a temperature result that fits the historical record. However, Kiehl was not quite correct.
My own research in June of this year, reported in the post Climate Sensitivity Deconstructed, has shown that the critical factor is not the total forcing as Kiehl hypothesized. What I found was that the climate sensitivity of the models is emulated very accurately by a simple trend ratio—the trend of the forcing divided by the trend of the model output.
Figure 2. Lambda compared to the trend ratio. Red shows transient climate sensitivity (TCR) of four individual models plus one 19-model average. Dark blue shows the equilibrium climate sensitivity (ECS) of the same models. Light blue are the results of the forcing datasets applied to actual historical temperature datasets.
Note that Kiehl’s misidentification of the cause of the variations is understandable. First, the output of the models are all fairly similar to the historical temperature. This allowed Kiehl to ignore the model output, which simplifies the question, but it increases the inaccuracy. Second, the total forcing is an anomaly which starts at zero at the start of the historical reconstruction. As a result, the total forcing is somewhat proportional to the trend of the forcing. Again, however, this increases the inaccuracy. But as a first cut at solving the paradox, as well as being the first person to write about it, I give high marks to Dr. Kiehl.
Now, I probably shouldn’t have been surprised by the fact that the sensitivity as calculated by the models is nothing more than the trend ratio. After all, the canonical equation of the prevailing climate paradigm is that forcing is directly related to temperature by the climate sensitivity (lambda). In particular, they say:
Change In Temperature (∆T) = Climate Sensitivity (lambda) times Change In Forcing (∆F), or in short,
∆T = lambda ∆F
But of course, that implies that
lambda = ∆T / ∆F
And the right hand term, on average, is nothing but the ratio of the trends.
So we see that once we’ve decided what forcing dataset the model will use, and decided what historical dataset the output is supposed to match, at that point the climate sensitivity is baked in. We don’t even need the model to calculate it. It will be the trend ratio—the trend of the historical temperature dataset divided by the trend of the forcing dataset. It has to be, by definition.
This completely explains why, after years of better and better computer models, the models are able to hindcast the past in more detail and complexity … but they still don’t agree any better about the climate sensitivity.
The reason is that the climate sensitivity has nothing to do with the models, and everything to do with the trends of the inputs to the models (forcings) and outputs of the models (emulations of historical temperatures).
So to summarize, as Dr. Kiehl suspected, the variations in the climate sensitivity as reported by the models are due entirely to the differences in the trends of the forcings used by the various models as compared to the trends of their outputs.
Given all of that, I actually laughed out loud when I was perusing the latest United Nations Inter-Governmental Panel on Climate Change’s farrago of science, non-science, anti-science, and pseudo-science called the Fifth Assessment Report (AR5). Bear in mind that as the name implies, this is from a panel of governments, not a panel of scientists:
The model spread in equilibrium climate sensitivity ranges from 2.1°C to 4.7°C and is very similar to the assessment in the AR4. There is very high confidence that the primary factor contributing to the spread in equilibrium climate sensitivity continues to be the cloud feedback. This applies to both the modern climate and the last glacial maximum.
I laughed because crying is too depressing … they truly, truly don’t understand what they are doing. How can they have “very high confidence” (95%) that the cause is “cloud feedback”, when they admit they don’t even understand the effects of the clouds? Here’s what they say about the observations of clouds and their effects, much less the models of those observations:
• Substantial ambiguity and therefore low confidence remains in the observations of global-scale cloud variability and trends. {2.5.7}
• There is low confidence in an observed global-scale trend in drought or dryness (lack of rainfall), due to lack of direct observations, methodological uncertainties and choice and geographical inconsistencies in the trends. {2.6.2}
• There is low confidence that any reported long-term (centennial) changes in tropical cyclone characteristics are robust, after accounting for past changes in observing capabilities. {2.6.3}
I’ll tell you, I have “very low” confidence in their analysis of the confidence levels throughout the documents …
But in any case, no, dear Inter-Governmental folks, the spread in model sensitivity is not due to the admittedly poorly modeled effects of the clouds. In fact it has nothing to do with any of the inner workings of the models. Climate sensitivity is a function of the choice of forcings and desired output (historical temperature dataset), and not a lot else.
Given that level of lack of understanding on the part of the Inter-Governments, it’s gonna be a long uphill fight … but I got nothing better to do.
w.
PS—me, I think the whole concept of “climate sensitivity” is meaningless in the context of a naturally thermoregulated system such as the climate. In such a system, an increase in one area is counteracted by a decrease in another area or time frame. See my posts It’s Not About Feedback and Emergent Climate Phenomena for a discussion of these issues.
Stever,
“The clue is that folks like Hansen and others think that models are poor source of information about sensitivity. Paleo and observations are better. Models.. dont really give you any “new” information about sensitivity.”
But isn’t the problem here that the only way to determine sensitivity in the paleo and observational data is to model it? Snake eats tail.
Steven Mosher says:
check the relationship between aerosol forcing ( a free knob) and the sensitivity of models.
The clue is that folks like Hansen and others think that models are poor source of information about sensitivity. Paleo and observations are better.
————————————
Paleo also has a free knob – Albedo.
In my estimation, Albedo on planet Earth can vary between 24% to 50%.
24% as in Pangea / Cretaceous hothouses (zero ice, continents weighted toward the equator, extensive shallow oceans), 33% as in Last Glacial Maximum (glaciers down to Chicago, sea ice down to 50 N/S) to 50% as in Snowball Earth (glaciers and sea ice to 30 N/S).
Hansen and paleo-climate-sensitivity scientists have never been objective in determining these values but have just played around with the numbers / ignored the changes completely so that they can assign more sensitivity to GHGs/CO2.
Technically, the paleoclimate indicates a GHG/CO2 sensitivity somewhere between 0.0C (as in None) to 1.5C per doubling.
Willis: “…in the context of a naturally thermoregulated system such as the climate. In such a system, an increase in one area is counteracted by a decrease in another area or time frame.”
When you write ‘another area or time frame’, what scale do you mean? Do you mean 10s of miles and hours or much larger scales? I can see how your naturally thermoregulated system works in the moist tropics to keep temperatures nearly constant, but how does that regulate most temperate and especially polar zones?
JKnapp: “… it is clear that they are “tuned” and are not really derived from physical principals as claimed.”
Excellent point. With the models all matching history so well, all having different sensitivities, and all failing at predicting the pause over the last 15 years, that’s a rather indisputable point at this juncture.
Steve Reynolds says:
Willis: “…in the context of a naturally thermoregulated system such as the climate. In such a system, an increase in one area is counteracted by a decrease in another area or time frame.”
When you write ‘another area or time frame’, what scale do you mean? Do you mean 10s of miles and hours or much larger scales? I can see how your naturally thermoregulated system works in the moist tropics to keep temperatures nearly constant, but how does that regulate most temperate and especially polar zones?
===
Willis’ hypothesis seems good for tropics (it’s a tropical phenomenon). It does not apply to extra-tropics, though they probably benefit by some ocean mixing through the main ocean gyres which helps stabilise SST.
http://climategrog.wordpress.com/?attachment_id=310
Some of what Willis’ ‘governor’ dumps to troposphere will radiate to space some will end up moving polewards. Poles have their own feedbacks, which the evidence shows are not positive ‘tipping points’.
http://climategrog.wordpress.com/?attachment_id=503
Explained here:
http://climategrog.wordpress.com/2013/09/16/on-identifying-inter-decadal-variation-in-nh-sea-ice/
Willis: I enjoyed this post and the two posts on climate sensitivity/emergent phenomena linked at the end. A couple of comments on the latter:
1) The onset of convection and cloud formation can only serve as a negative feedback for part of the planet – rising air must come down somewhere. No matter how hot it may become, there won’t be any clouds where the air is descending. I’m sure you are aware of all of the deserts below the descending loop of the Hadley cell. (Roy Spenser once answered a question from me by saying: Look at any satellite picture of the planet. Where there are clouds, the air is ascending. Where it’s clear, the air is descending.) As radiative forcing from GHGs increases, can the fraction of the planet covered by clouds increase enough to compensate? Or is the proportion of cloudy and clear areas determined by the relative rates of ascent and descent, not surface temperature?
2) The daily emergent phenomena you describe in the tropics are driven by massive change in radiation – from a low of about 400 W/m2 of DLR at night to a maximum of about 1500 W/m2 of combined DLR and SWR at noon. It’s not surprising that new phenomena emerge in response to such massive changes in radiation. Knowledge that a daily 1000 W/m2 radiative forcing produces unquantified negative feedbacks associated with clouds and wind (and positive feedback from water vapor) gives me no confidence about how the planet will respond to a forcing from GHGs <1% as big lasting for at least a century.
3) The emergent phenomena you convincingly describe apply mostly to the tropical oceans and your earlier post has demonstrated that tropical SSTs rarely exceed 30 degC. (You could add hurricanes to the phenomena that emerge – fortunately rarely – when SSTs exceed 26.5 degC threshold.) In addition to exporting excess heat to the upper atmosphere, however, increased radiative forcing in the tropics will driven poleward transport of heat away from the tropics by convection – which will warm the rest of the planet. A "local climate sensitivity" of 0.5 at the equator due to negative cloud feedback could gradually increase to a "local climate sensitivity" of 5 at higher latitudes. In the temperate zones, thunderstorms are phenomena that usually occur mostly in the summer, but not daily. Upward convection of heat mostly occurs at moving "fronts" between moving warmer and colder air masses and at lows that follow that follow Rossby waves in the jet stream. (The most violent thunderstorms that produce tornados are associated with such clashing air masses in the spring in the Central US, not the warmest temperature.) These phenomena exist all year in the temperate zone; they don't emerge only when the surface temperature exceeds a certain threshold.
4) The increase in evaporative cooling with wind speed may deserve more attention. When air is saturated with water vapor, water molecules are returning to the ocean as often as they escape the ocean's surface – no matter what the ocean's surface temperature is. Evaporative cooling only occurs when the water molecules that escape the surface move far enough away from the surface that they usually return as precipitation. Molecular diffusion is an extremely slow process compared with convection (wind). The change from laminar flow to turbulent flow produces the planetary boundary layer. The transport of water vapor through the boundary layer precedes the development of thunderstorms and cloud formation at the top of the boundary layer. Warm boundary layer clouds are effective at reflecting SWR with minimal reduction in outgoing LWR.
When answering a question its always important to check that you are answering the right question:
In climate science, climate sensitivity is the WRONG question. If one frames the issue of climate, CO2, whether climate change is possible without the effect of humans, the history of climate – has there been any change of climate prior to 1850, etc. around “climate sensitivity” then one has failed before you have even started.
“Climate sensitivity” is loaded with unproved assumptions. It is the WRONG QUESTION. Asking the question shows that you are a dishonest person, that you have a political agenda more important than the curiosity to know what is really happening in climate. This is why the question of “climate sensitivity” is completely irrelevant to the true issues of climate science. Climate sensitivity pre-assumes that CO2 changes drive climate temperature changes which is a weak and unproven hypothesis at odds with the palaeo history of climate taken as a whole. If there is a sensitivity to CO2 then this figure:
http://imageshack.us/photo/my-images/34/7ty.png/
strongly indicates that climate sensitivity (to CO2) is not distinguishable from zero.
Real climate science does not start from inductive hypotheses about backradiation etc. but instead starts with the DATA about how climate has changed in the past, how ALL the possible influences on climate have changed or not changes in parallel with climate temperature, and from the most complete possible body of data, start to frame and test hypotheses. Climate science done this way would be unlikely to involve CO2 except as a minor footnote.
“2) The daily emergent phenomena you describe in the tropics are driven by massive change in radiation ”
Thunderstorms are triggered by temperature not radiation. Your argument falls apart there.
There is probably a need a local, spacial temp gradient too, but that variability is always available.
See my previous post for “unquantified”.
http://climategrog.wordpress.com/?attachment_id=310
Gail Combs on October 2, 2013 at 6:19 am:
Thank you Gail. I have a copy of John Kehr’s book. I very much like his explanation of the hemispherical heating cycles of our planet. Makes more sense than most. Especially the time scales as confirmed by the performance of glaciers.
I like KevinK’s comment. I used to work in electronics and wondered about those points he makes.
—————————————–
kadaka (KD Knoebel) on October 1, 2013 at 11:31 pm:
KD kindly posted a series of links for me to study at my request. Unfortunately I have fallen at the first link.
If I may point out where my confusion starts…
http://wattsupwiththat.com/2011/02/20/visualizing-the-greenhouse-effect-a-physical-analogy/
Guest post by Ira Glickstein
“”Since, at this stage of my physical analogy, there are no GHG in the Atmosphere, the purple balls go off into Space where they are not heard from again. You can assume the balls simply “bounce” off like reflected light in a mirror, but, in the actual case, the energy in the visible and near-visible light from the Sun is absorbed and warms the Earth and then the Earth emits infrared radiation out towards Space. Although “bounce” is different from “absorb and re-emit” the net effect is the same in terms of energy transfer.If we assume the balls and traytop are perfectly elastic, and if the well-damped scale does not move once the springs are compressed and equilibrium is reached, there is no work done to the weight scale. Therefore, Energy IN = Energy OUT. The purple balls going out to Space have the same amount of energy as the yellow balls that impacted the Earth.””
My understanding is the atmosphere in contact with the surface WILL WARM via conduction and convection. Only when ALL the atmosphere reaches thermal equilibrium will the outgoing energy balance incoming energy. The atmosphere is transparent. The atmosphere cannot lose energy to space. The atmosphere without GHGs is still an INSULATOR.
Why is the ‘greenhouse effect’ needed? Our planet’s atmosphere is nothing like a greenhouse.
@Willis Eschenbach
You have challenged me on several occasions to produce an analysis, You asked if anyone was prepared to help you when you said you wanted to produce a more complex model.
On the first occasion, I wrote a very simple piece on signal processing in response to your article that contained a concept that was completely wrong.
The second occasion you challenged me to put up or shut up, I wrote a piece on some of the pitfalls of modelling and sent it to WUWT. This did not see the light of day.
Your response to my offer to help you was extremely rude and you told me that I was an amateur on the basis of attributing complete rubbish to me, which I certainly had never said.
The problem is that:
1) You have editorial control over what is posted.
2) You are excessively sensitive to criticism.and you attempt to stifle dissenting views from your own.
3) You seem either unwilling or incapable of responding objectively to valid criticisms your “maths”.
4) When you produce maths it is not set out so that it easy to understand what you have done.
Since you are for ever challenging me to say what is wrong with your models, I am prepared to do this but I will write it in a way that the majority of people who read this blog can understand. However, I am only prepared to do so on the understanding that my response will be published – I see no reason why I should do a significant amount of work simply to have it binned.
@RC Saumarez
I think the problem here is that everyone is making assumptions about what each model is doing – or rather an assumption that they all are doing the same thing. But even if they deal with one part of the system slightly differently the outputs can be drastically different. For example, the models have a voluminous component therefore differences in warming between one part of the globe will cause, even if poorly modelled, convective transfer, which is not likely to be expressed in an initial temperature change. I’m sure each modelling team will have their own unique method of implementing this and the result must also be a function of resolution. In short, the models have spatial and temporal responses – emergent phenomenon during run-time. So unless we know line for line, each algorithm used during simulation, and what the experimental setup is, I can’t see how one can answer the apparent paradox.
On a general point your posts do seem a little adversarial. Furthermore, you do – only sometimes – appear to dismiss some of Willis’ arguments on weight of qualifications rather than the strength/weakness of the arguments.
RC Saumarez:
At October 3, 2013 at 1:56 am you say to Willis
If you truly do know something “wrong with {Willis’} models then please “say what is wrong”. I and others would like to know of it.
However, you refuse to post such criticism but, instead, you keep writing snark and insults, and you feign offence when Willis replies in kind. That is not helpful to anybody, and it is certainly not informative of Willis’ models.
I can only think of two reasons for your suggestion that your “work” may be “binned”.
You may think the mods. will censor a post or post(s) in this thread. Well, that is implausible, but you could test it by posting a criticism of Willis’ work in this thread and seeing what happens.
Alternatively, you are demanding the right to provide a head post article which our host will guarantee to publish on WUWT in an unabridged form whatever the contents and quality of the article. That demand is unreasonable by any standards. Also, you could submit your article for publication on WUWT and if it is rejected then post it on a blog of your own creation and post a link to it on WUWT.
If you have a criticism of Willis’ work then please provide it. And please stop pretending you could provide such a criticism but you won’t because the “bin” may eat your homework.
Richard
@richardscourtney
I think it’s implicit in what RC has said. I don’t wish to put words into his mouth, but in order for Willis to be correct he has to make a sweeping assumption about how the models work – he could be right. And his “radiation-balance” approach is fine if the models follow the logic expressed above.
But the models, as far as I understand them, are not just expressing a simple physical model where temperature responds directly to forcing and from this we have a series of mathematical constructs. Again, the models are likely to have local physical models that govern how the system responds locally and these will effect the rate of warming.
For example 2 local physical models:
Model 1:
warmer air => conduction => warmer ocean
Model 2:
warmer air => lower vapour pressure => more evaporation => cooler ocean (ultimately convective cell)
Model 1 and Model 2 will use the extra energy (express as net change in terms of forcing if you wish) in the system to different types of work and on different time scales. Has Willis looked into what physical models the different modelling groups try to emulate and how they implement these approaches? I guess that is what RC is asking.
I’m not saying this is what is going on, but I agree with RC that the above post, while good and engaging, needs important caveats and RC shouldn’t be shouted down for merely questioning the simplicity presented – which one could see as a plus.
@richardscourtney
Correction:
Model 2:
warmer air => reduced atmospheric pressure => more evaporation => cooler ocean (ultimately convective cell)
Back-to-front.
cd:
Thankyou for your post to me at October 3, 2013 at 2:52 am.
You say to me
Sorry, but “questioning the simplicity presented” is not valid criticism (especially when accompanied by snark and insults as provided by RC Saumarez).
An explanation of why “the simplicity presented” is inadequate or misleading would be valid criticism.
All models are simplified representations of reality. Indeed, they are constructed to provide a simplified framework which aids understanding of some aspect of reality. For example, a cow may be modeled as a sphere with similar surface area to a real cow. This model may be useful for understanding of how a cow’s metabolic rate affects its surface temperature. If the ‘spherical cow’ model is only used for the metabolism and surface temperature considerations then is not a valid criticism of the ‘spherical cow’ merely to say it is too simple because it does not have legs. But study of a cow’s movements requires a model which includes legs and, therefore, if the model is to assess the cow’s movements then it would be a valid criticism to say the model does not include legs.
In other words, a good model is as simple as possible for its purpose but not too simple for that purpose. Saying a model is simple is a truism of no value and is no a valid criticism. But it is a valid criticism to say a model is too simple because it omits a factor which is significant to whatever the model attempts to emulate.
Please note that you did not quote a valid criticism from RC Saumarez but said what you think is “implicit in what RC has said”.
If RC Saumarez has an explicit criticism he needs to make it and not provide silly excuses for throwing insults instead of stating the explicit criticism. Nobody has “shouted down” his arguments and explanations. Willis has rightly “shouted down” the insults and disparaging remarks which RC Saumarez has provided instead of arguments and explanations of what he claims would be a valid criticism if he were to explicitly state it.
My post you have answered is at
http://wattsupwiththat.com/2013/10/01/dr-kiehls-paradox/#comment-1434438
It was addressed to RC Saumarez and concluded
I stand by that.
Richard
richardscourtney
I agree with, and like your cow – moovement (sorry!) – analogy. Nicely put.
I think the issue is, and it is only a suggestion as to why Willis’ approach might be wrong, is that until you know exactly what each model is doing and how it deals with things like conduction and convection, then he’s second guessing why the paradox exists. In short, and why I agree with RC point – “naive” argument, I prefer simple/elegant – is that you’re starting with the end point, drawing a conclusion without trying to back-engineer what assumptions/processes the models are making, all the while assuming that the additional energy in the models does the same work irrespective of model.
It’s like trying to work out why two cars (of equal weight) travel different distances on the same volume of petrol, without knowing anything about the engine and only having the volume of petrol and distance traveled available.
@richard Courtney.
I have made a number of criticisms of Willis’s models.
1) They do not give the correct autocorrelation functions
2) There is no consideration of non-linearity
3) The statistical characterisations are incorrect.
This is generally met with abuse and is not addressed.
I was asked to put up or shut up on a number of occasions and asked by you and others to justify my criticisms. I am perfectly happy to do so. However, since this involves some graphs and maths it is not easy to do on a simple response here.
I was urged to justify myself. It is a matter of record that I wrote a respose on the pitfalls of modelling and sent it WUWT. This was not published.
If someone posts what he claims to be an intellectual construct on this blog, has editorial control, preferential ability to post answers but does not address the issues made by myself and others, this is not “science” as claimed by WE.
You will note that WE produces models on a weekly basis and claims that they work as well as anything else in the field. Does this not seem to be a rather extreme claim?
phlogiston says: October 3, 2013 at 12:46 am
A very good post, and I enjoyed both the video AND the figure at
http://imageshack.us/photo/my-images/34/7ty.png/
Your comment and figure are consistent with my hypo and comment from above:
So atmospheric CO2 LAGS temperature at all measured time scales.*
So “climate sensitivity”, as used in the climate models cited by the IPCC, assumes that atmospheric CO2 primarily drives temperature, and thus assumes that the future is causing the past. I suggest that this assumption is highly improbable.
Regards, Allan
* This does not preclude the possibility that humankind is causing much of the observed increase in atmospheric CO2, not does it preclude the possibility that CO2 is a greenhouse gas that causes some global warming. It does suggest that neither of these phenomenon are catastrophic or even problematic for humanity or the environment..
RC Saumarez:
Thankyou for your post at October 3, 2013 at 4:28 am. This link jumps to it for those wanting to refer to its full text.
http://wattsupwiththat.com/2013/10/01/dr-kiehls-paradox/#comment-1434487
In this reply I quote then address address each of your points and if I have missed any then that is not intentional.
You say you have made a number of criticisms of Willis’s models which you number 1 to 3.
That is an assertion and not a criticism.
Why is the determination of Willis wrong?
What would be the correct autocorrelation functions and why?
That is also an assertion and not a criticism.
Why not assume linearity?
What form should be considered and why if not linearity?
That is merely another assertion and not a criticism.
Why are they incorrect and how?
What would be correct statistical characterisations.
After listing those spurious assertions of criticisms you have made although you have not, your post says
No, you have been repeatedly abusive and demeaning of Willis. Repeatedly, you have made the assertions that you have pretended are criticisms of Willis work (n.b. they are assertions and not criticisms), and then claimed your assertions prove Willis is incompetent. Typically you have accompanied that with an appeal to authority and compounded the offense by citing the authority as being yourself!
And you complain when Willis replies to your barrage of abuse with the contemptuous put-down it deserves. Contemptible behaviour deserves to be treated with contempt.
Please note that Willis is not one of your students. He has no need to cower in fear of what grades you award. Hence, he has no reason to soak-up your abuse.
I accept your “graphs and maths” difficulty. However, I am not a computer buff and I can think of ways to overcome that difficulty, so I am surprised it is beyond your capabilities. Perhaps a conversation with one of your students would overcome that difficulty?
Anyway, if you are unable to express your point in words then I suggest that inability perhaps indicates you lack sufficient understanding of your point; e.g. Robert Brown provides cogent posts critical of of models without using “graphs and maths”.
Simply, if you cannot justify your assertions then you may want to consider the wisdom of making the assertions. In this context, I note your saying
I take your word for that but it is not clear what you are saying.
If you are saying a post was censored then I am extremely surprised. Mods often snip a post but always show where and when it was snipped together with a brief explanation of why. I have been given ‘time out’ from WUWT as punishment (most recently in the last month) for ‘overstepping the mark’, but I have never been censored.
If you are saying you submitted an article that was rejected for publication on WUWT then ‘tough luck’. Try to do better and you may have more success in the competition for publication of an essay on WUWT.
On the basis of your posts on this and a previous thread I suspect your attempt “to justify {your}self” may have been an essay which was an abusive tirade directed at Willis. If my suspicion is anywhere near correct, then your essay did not warrant publication on WUWT.
Willis does address issues put to him. Indeed, you claim to be offended that he replies to your abuse in the manner it deserves. In light of his replies to substantial points, I am certain he would address substantial points if you were to make any instead of your assertions.
You conclude by asking me
Ouch my formatting went wrong again, Sorry. Richard
@ur momisugly Richard Courtney
1) Autocorrelation functions. These do not conform to an ARMA 1 model. I have posted this on Judith Curry;s blog following the controversy between Richard Tol and Ludeke et al.
http://judithcurry.com/2012/02/19/autocorrelation-and-trends/
I have pointed this out several times, the last after being told to grab some data and do some calculations.
The model proposed by Eschenbach cannot possibly be right in the basis of the well known auto-correlation properties of the temperature signal.
2) Non-linearity.
The structure of the ACF points to either a multicompartment model or a Hurst process which is non-linear. There are many non-linear process in the climate system, water condensation, ice formation for example or turbulent mixing in the atmosphere or oceans. One would expect the system to be non-linear a priori and, if one were going to apply a linear model to data, one has to show that it is indistinguishable from data.
3) Statistics.
I have pointed out on several occasions that R^2 is an insufficient test of a model. This is well known. Although WE claims to have validated his models on this basis, Based on my experience, I am highly suspicious of whether he has done it correctly. Specimen calculations show that the errors involved are very large, although WE claims that this is not the case. I would agree that the statistical characterisation of model results are a specialised field that requires training and experience.
I, and others, have commented that WE’s mathematical development is often unintelliglible and you end up trying to guess what what he is trying to do. When he publishes the R code, one can read between the lines. This is hardly “science”.
I have made serious, objective criticisms of an intellectual approach. I have made them on a number of occasions but they have never been addressed. This is certainly not being a troll as you called me, If I were teaching students I would not give them bad grades or abuse them, but would hope that they would approach the problems above rigorously.
I have tried to make my criticisms known after being challenged to write a post, which I did. Ok, it wasn’t published, you win some and you some. If you read my post in response to WE’s challenge after I criticised his conclusions on correlation and filtering
http://wattsupwiththat.com/?s=saumarez
I do not think many people would regard the tone as offensive and the response that I sent recently was in the same vein. Frankly I don’t really care.about this any longer, I have better things to do.
RC Saumarez says:
October 3, 2013 at 9:27 am
I say again, Richard, what the temperature signal does is meaningless. My model is not a model of the temperature signal. It is a model of the GCM climate models temperature output, and NOT A MODEL OF THE TEMPERATURE SIGNAL. Sorry to shout, but I’ve said this several times.
Following your raising the issue of the ACF in a previous thread, I showed the ACF of both the model outputs and my emulation of those outputs here. Actual data. As far as I know, you haven’t commented on that data at all. Here it is again:
Again, please note that my model is NOT a model of the climate system, so “non-linear processes in the climate system” mean nothing to my model. It is a model of the GCMs, not a model of the climate.
Would a multi-compartment model give a better fit to the GCMs? Yes, they do … just not much better. I’ve run those kinds of models as well, and typically there is a marginal improvement in the emulation. However, the match with a single-compartment model is so good that the increase in complexity is not warranted.
I also don’t understand you saying that you “expect the system to be non-linear”, when above you said:
In any case, as I said, what the climate system itself does is meaningless to my model.
Fine. Apply your preferred tests to my my results and tell us the answers. I’m always willing to learn.
Your “suspicions” carry absolutely no weight with me, RC. If you think I’ve done it incorrectly, then show us the error.
Hey, provide your “specimen calculations”. I’ve posted up my data and code … how about you?
So … give us the benefit of your training and experience. Whenever I ask you for that, you say it will take two months to get back to me, or six months to understand it …
“I, and others,” ??? You have asserted above, and for the first time as far as I can recall, that you can’t understand my mathematical logic. You also assert that others can’t follow my math either. Note that you haven’t provided a single fact to back up your claim.
Throughout all of my work, I have been quite rigorously transparent as to my data and my code. I have explained and laid out my math and my logic. I have provided spreadsheets and programs in R. And I have explained (or done my best to explain) exactly what I’ve done when people have asked specific questions.
So if you don’t understand my math, if you find it unintelligible, you should ask a specific question. Which part of it can’t you follow?
Here’s a typical one of your serious, objective criticisms of an intellectual approach, your claim that my math is wrong, presented in its entirety so folks can see the weight of your keen, penetrating analysis and clear exposition of your findings:
Sorry, but that says nothing.
RC, I need specifics. Not intellectual handwaving. Not “it seems to me”. If I’ve said something that you disagree with, QUOTE MY WORDS so we can all understand what you are referring to, and then point out exactly what I’ve said that is wrong. If it’s math, quote the equations I used and tell us where I went off the rails.
You repeatedly make vague (and often unpleasant) assertions that I’m wrong … but that is very different from actually demonstrating exactly where and why I’m wrong.
I read your post cited above at the time, and I commented at the time:
Willis Eschenbach says:
April 9, 2013 at 2:51 pm
However, there was nothing in there that was a “criticism” of my work. You never mentioned my work at all, either in the head post or in the comments.
My problem is that you say you wrote it in response to my errors (in April of this year, 2013), to show me where I was wrong … but I can’t find a single comment from you in all of 2012 or in early 2013, so I’m not clear what errors you are referring to. I also don’t know which of my ideas you think was shown wrong by that exposition (and it was a good one) of Fourier Analysis.
As to your allegation above that I censored your recent post, I fear you give me far too much credit. I have absolutely no say as to what Anthony publishes, nor do I want any. He doesn’t discuss his decisions with me either before or after the fact.
You mean you’re leaving? I won’t stand in your way. To date, your contribution to the discussion has been to re-iterate, over and over, endlessly, that you think I’m wrong. OK, got it. I understand you think I’m wrong.
But until you can point to exactly where I’m wrong about some one thing, and quote the wrong thing I said, and explain to us why I’m wrong about that one thing, what you think is of no interest to me.
w.
Willis:
I write to provide clarity for others.
You make a quote from RC Saumarez headed “@ur momisugly Richard Courtney”.
You then refute that quoted statement of RC Saumarez (n.b. not me) but the heading could suggest you were refuting me by replying
Yes, I know. And I have pointed that out myself e.g. in this thread at October 2, 2013 at 1:42 pm where I concluded
Richard
Reply to Greg Goodman: ∆T = lambda ∆F is perfectly fine – if the system has come to equilibrium after a change in forcing. During the modest temperature drop after a volcanic eruption, both forcing and temperature are changing; so this equation isn’t applicable (and one must integrate).
In replying previously to Willis, I also realized that ∆T = lambda ∆F won’t be useful if the relationship between (functional relating) forcing and temperature is poorly behaved (chaotic) or the temperature change is too big. I don’t think it is likely that either of these caveats seriously interferes with applying the concept of climate sensitivity to our current concerns about GHGs, but I wouldn’t deem other opinions wrong. These caveats appear to apply to other situations: 1) Warming might eventually reduce the height of the Greenland ice sheet, warming surface temperature (1 degC/166 m – lapse rate) and decreasing albedo. These positive feedbacks don’t go away the as soon as the GHG forcing disappears. 2) Ice ages appear to develop slowly and end relatively suddenly, even though orbital forcing is a composite of several time-symmetric sine curves.