Guest Post by Willis Eschenbach
Yeah, I know Nature doesn’t have human emotions, give me a break. I’m aware it is unscientific and dare I call it atavistic and perhaps even socially unseemly to say Nature “hates” straight lines, but hey, it’s a headline, cut me some poetic slack.
My point is, everyone is aware that nature doesn’t deal in straight lines. Natural things move in fits and starts along complex paths, not straight from point to point. Phenomena have thresholds and edges, not slow linear changes at the perimeter. Tree branches and coastlines are jagged and bent. Things move in arcs and circles, relationships are complex and cyclical. Very little in nature is linear, particularly in complex systems.
Forcing is generally taken to mean downward radiation measured at the TOA (top of atmosphere). The IPCC says that when TOA forcing changes, the surface temperature changes linearly with that TOA forcing change. If there is twice the forcing change (twice the change in solar radiation, for example), the IPCC says we’ll see twice the temperature change. The proportionality constant (not a variable but a constant) that the IPCC says linearly relates temperature and TOA forcing is called the “climate sensitivity”.
Figure 1. Photo of impending change in climate sensitivity.
Today I stumbled across the IPCC justification of this linearity assumption. This is the basis of their claim of the existence of a constant called “climate sensitivity”. I quote it below.
I’ve removed the references and broken it into paragraphs it for easy reading. The references are in the original cited above. I reproduce all of the text on the web page. This is their entire justification for the linearity assumption. Having solved linearity in a few sentences, they then proceed to other matters. Here is their entire scientific justification for the assumption of linearity between forcing and temperature change (emphasis mine):
Linearity of the Forcing-Response Relationship
Reporting findings from several studies, the TAR [IPCC Third Assessment Report] concluded that responses to individual RFs [Radiative Forcings] could be linearly added to gauge the global mean response, but not necessarily the regional response.
Since then, studies with several equilibrium and/or transient integrations of several different GCMs [Global Climate Models] have found no evidence of any nonlinearity for changes in greenhouse gases and sulphate aerosol. Two of these studies also examined realistic changes in many other forcing agents without finding evidence of a nonlinear response.
In all four studies, even the regional changes typically added linearly. However, Meehl et al observed that neither precipitation changes nor all regional temperature changes were linearly additive. This linear relationship also breaks down for global mean temperatures when aerosol-cloud interactions beyond the cloud albedo RF are included in GCMs. Studies that include these effects modify clouds in their models, producing an additional radiative imbalance.
Rotstayn and Penner (2001) found that if these aerosol-cloud effects are accounted for as additional forcing terms, the inference of linearity can be restored. Studies also find nonlinearities for large negative RFs, where static stability changes in the upper troposphere affect the climate feedback (e.g., Hansen et al., 2005).
For the magnitude and range of realistic RFs discussed in this chapter, and excluding cloud-aerosol interaction effects, there is high confidence in a linear relationship between global mean RF [radiative forcing] and global mean surface temperature response.
Now, what strikes you as odd about that explanation of the scientific basis for their claim of linearity?
Before I discuss the oddity of that IPCC explanation, a short recap regarding climate sensitivity. I have held elsewhere that climate sensitivity changes with temperature. I will repeat the example I used to show how climate sensitivity goes down as temperature rises. This can be seen clearly in the tropics.
In the morning the tropical ocean and land is cool, and the skies are clear. As a result, the surface warms rapidly with increasing solar radiation. Climate sensitivity (which is the amount of temperature change for a given change in forcing) is high. High sensitivity, in other words, means that small changes in solar forcing make large changes in surface temperature.
By late morning, the surface has warmed significantly. As a result of the rising temperature, cumulus clouds start to form. They block some of the sun. After that, despite increasing solar forcing, the surface does not warm as fast as before. In other words, climate sensitivity is lower.
In the afternoon, with continued surface warming, thunderstorms start to form. These bring cool air and cool rain from aloft, and move warm air from the surface aloft. They cool the surface in those and a number of other ways. Since thunderstorms are generated in response to rising temperatures, further temperature increases are quickly countered by increasing numbers of thunderstorms. This brings climate sensitivity near to zero.
Finally, thunderstorms have a unique ability. They can drive the surface temperature underneath them below the temperature at which the thunderstorm formed. In this case, we have local areas of negative climate sensitivity – the solar forcing can be increasing while the surface is cooling.
As you can see, in the real world the temperature cannot be calculated as some mythical constant “climate sensitivity” times the forcing change. Sensitivity goes down as temperature goes up in the tropics, the area where the majority of solar energy enters our climate system.
So the IPCC claim of linearity, of the imagined slavish response of surface temperature to a given change in TOA forcing, goes against our daily experience.
Let me now return to the question I posed earlier. I asked above what struck you as odd about the IPCC explanation of their claim of linearity regarding forcing and temperature. It’s not the fact that they think it is linear and I disagree. That is not noteworthy.
Here’s what made me stand back and genuflect in awe of their claims. Perhaps I missed it, but I didn’t see a single word about real world observations in that entire (and most important) justification for one of their core positions.
I didn’t see anyone referenced who said something like ‘We measured solar radiation and downwelling longwave radiation and temperature at this location, and guess what? Temperatures changed linearly with the changes in radiation.’ I didn’t see anything at all like that, you know, actual scientific observations that support linearity.
Instead, their claim seems to rest on the studies showing that scientists looked at four different climate models, and in each and every one of the models the temperature change was linearly related to forcing changes. And in addition, another model found the same thing, so the issue is settled to a “high confidence” …
I gotta confess, that wasn’t the first time I’ve walked away from the IPCC Report shaking my head, but that one deserves some kind of prize or award for sheer audacity of their logic. Not a prize for the fact that they think the relationship is linear when Nature nature hates straight lines, that’s understandable, it’s the IPCC after all.
It is the logic of their argument that left me stammering.
Of course the model results are linear. The models are linear. They don’t contain non-linear mechanisms. And of course, if you look at the results of linear models, you will conclude with “high confidence” that there is a linear relationship between forcing and temperature. They looked into five of them, and case closed.
I mean, you really gotta admire these guys. They are so far into their models that they actually are using the linearity of the model results to justify the assumption of linearity embodied in those same models … breathtaking.
I mean, I approve of people pulling themselves up by their own bootstraps, but that was too twisted for me. The circularity of their logic made my neck ache. I kept looking over my shoulder to see if the other end of their syllogism was circling behind to strike me again. That’s why I genuflected in awe. I was overcome by the sheer beauty of using a circular argument to claim that Nature moves in straight lines … those guys are artists.
Meanwhile, back in the real world, almost no such linear relationships exist. Nature constantly runs at the edge of turbulence, with no linearity in sight. As my example above shows, the climate sensitivity changes with the temperature.
And even that change in tropical climate sensitivity with temperature is not linear. It has two distinct thresholds. One is at the temperature where the cumulus start to form. The other is at the slightly higher temperature where the thunderstorms start to form. At each of these thresholds there is an abrupt change in the climate sensitivity. It is nowhere near linear.
Like other natural flow systems, the climate is constantly restructuring to run “as fast as it can.” In other words, it runs at the edge of turbulence, “up against the stops” for any given combination of conditions. In the case of the tropics, the “stops” that prevents overheating is the rapid proliferation of thunderstorms. These form rapidly in response to only a slight temperature rise above the temperature threshold where the first thunderstorm forms. Above that threshold, most of any increase in the incoming energy is being evaporated and used to pump massive amounts of warm air through protected tubes to the upper troposphere, cooling the surface. Above the thunderstorm threshold temperature, little additional radiation energy goes into warming the surface. It goes into evaporation and vertical movement. This means that the climate sensitivity is near zero.
Now it is tempting to argue that the IPCC linearity claim is true because it only applies to a global average temperature. The IPCC only formally say that there is “a linear relationship between global mean RF [radiative forcing] and global mean surface temperature response.” So it might be argued that the relationship is linear for the global average situation.
But the average of non-linear data is almost always non-linear. Since daily forcing and temperature vary non-linearly, there is no reason to think that real-world global averages vary linearly. The real-world global average is an average of days during which climate sensitivity varies with temperature. And the average of such temperature-sensitive records is perforce temperature sensitive itself. No way around it.
The IPCC argument, that temperature is linearly related to forcing, is at the heart of their claims and their models. I have shown elsewhere that in other complex systems, such an assumed linearity of forcing and response does not exist.
Given the centrality of the claim to their results and to the very models themselves, I think that something more than ‘we found linearity in every model we examined” is necessary to substantiate this most important claim of linearity. And given the general lack of linearity in complex natural systems, I would say that their claim of linearity is an extraordinary claim that requires extraordinary evidence.
At a minimum, I think we can say with “high confidence” that it is a claim that requires something more weighty than ‘the models told me so’ ...

ooops, the world doesn’t warm should be the second one. More coffee please.
Archonix says
“Nature doesn’t work like that. You can look at the fluid motion of milk in a cup of tea and the motion of a massive gas cloud nebula in space and describe both using the same basic rules, yet the cup of tea is an event measured in seconds and the motion of that nebula is measured in millions of years.”
Nature has a lot of non-linear responses. Non-Newtonian fluids for example. The reponse of the system in that case is very dependant on the speed of application.
I suggest that daily responses to external forcings may partially mask long term changes to forcings.
I restate my question to Willis. Does your example match parameter for parameter with the models? Especially the temporal impulse and response?
From the first paragraph, I was sure you were writing about the magazine.
Tom says:
October 25, 2010 at 2:49 am
The laws you cite have all made spectacular predictions with astoundking accuracy over an extended period of time.
The GCM’s have also made spectacular predictions, none of which have come to pass and some of which turned out to be diametrically opposed to actual observation over the past 30 years:
increasing Hurricanes,
sea-level rise,
raising temps,
etc,
etc,
etc…
Tom says:
October 25, 2010 at 2:49 am
“The average law works quite well. Same with the kinetic theory of gasses; PV=NkT”
The gas law works well because it is only used for gasses.
The sustance which most affects the heat and temperature of the atmosphere is H2O. H2O changes phase in the troposphere. It does not respond in a linear way. Vapor, clouds and storms have different physics. Clouds reflect, H2O vapor doesnt. Change of phase involves latent heat. There is no average law for H2O in the atmospere.
The IPCC theory of linear radiative forcing is wrong.
The classic example of non-linearity:
The sun takes a typical path relative to the observer over a years time, and generates a sine.
The temperature lags the actual path and creates a different sine from the path.
The actual storm fronts and pressure cells that pass on by the observer create infinitely complex patterns superimposed upon the 2 sines.
The result of this non-linearity is that it is virtually impossible for the observer to live long enough to see a carbon-copy ( : of any given year.
And we have not included any other superimpostitions that nature provides to the weather.
So, what have Global Climate modelers actually achieved?
Global Climate modelers have proven that AI is impossible without non-linear equations.
They have also proven that supercomputer clusters are fundamentally linear and without AI.
> It’s those dam models again!
Let me play devil’s advocate one more time and correct a common misconception that I see over and over again:
There are no ‘model free’ _measurements_ of Nature!!!
What ‘model’ do you ‘consult’ to get your local time and temperature?
“I don’t use a model, I just look at my watch and the thermometer in my patio!”
Ha! You’ve just consulted two ‘models’!
Your watch doesn’t show you ‘Time’, rather it is a model based on the integration of tiny ‘ticks’ or escapements, which model the flow of time. Accuracy dependent on a number of factors, escapement rate, calibration and your visual acuity.
Likewise, your thermometer doesn’t show you ‘Temperature’. It’s another model which exploits the thermal coefficient of expansion for alcohol, mercury, or the resistive behavior of currents in a thermocouple, all non-linear approximations to this phantom concept of Temperature, which ideally only exists in your minds.
So, climatologists have to use models to measure time and temperature, just like we all do, all of the time. The only difference is the uncertainty associated with the measurements. Our clocks and thermometers are generally pretty good models, so we forget they’re models and pretend they represent Something Real.
Climate models have greater uncertainty, so that’s when all the fun begins in these climate blogs, skeptic or otherwise.
Personally, I think a lot of these AGW discussions are like trying to predict heads or tails while the coin is still in the air, but I’m sure someone has a ‘model’ for that too!
😐
“and excluding cloud-aerosol interaction effects”
Actually I found that phrase to be the most striking when I read the explanation. What they seem to be doing is making the statement, “we want to say that the relationship is linear”, then they proceed to eliminate whatever process does not provide linearity and voila, they have a linear relationship.
Would we accept an accounting statement that a company was ‘profitable’ if the accounting firm stated up front that they ‘excluded any negative transactions’ therefore the company is profitable?
It’s one reason I abandoned feedback analysis. Every ‘model’ I studied started out with the statement, “we eliminated the following processes to make the model mathematically tractable”. I could see no value in producing a model of a physical system that didn’t actually model the system.
Jerry says:
October 25, 2010 at 4:19 am
C’mon Jerry,
You’re not seriously going to take issue by insisting that the real world example Willis gives match ‘parameter for parameter’ with the model are you?
Don’t you think that’s putting the ‘horse before the cart’ just a little bit?
If the model predictions have not come to pass, any of the numerous (enlighten us if you can) then they are summarily falsified as having no predictive value. The linear assumption made by the IPCC and others may well be the reason, as Willis points out.
You can’t refute cicular logic by using circular logic regardless of what ‘the temporal impulse and response’ is. We just don’t know enough about this “non-linear” and chaotic system to make meaningful predictions about the climate process outside of 72 hour timeframes. I think this is what Willis was implying when he said, “…those guys are artists.”
Haha Steven, very good. May I add that apart from politicians who majored in Philosophy (and hence have some familiarity with the argumentum ad verecundiam), most politicians are completely clueless about how science works and what the limitations of it are. Even when they are familiar, they tend to be in awe of it. Richard Dawkins has done much to promote this kind-of awe. I’m not criticising him for promoting reason over superstition, but his kind of idealism assumes each scientist has no Human frailty and the ethics of a Saint. Reading The Hockey Stick Illusion as I am at present, nothing could be further from the truth!
I could spend al day picking apart to the dubious assumptions in this post. There are just so many of them.
Just for starters:
1. it is assumed that the linearity result is based purely on models. The excerpt provided does not say that. That should be checked.
2. The entire argument is based on verbal hand waving. A lesson that was learn’t a very long time ago is that language us not powerful enough to describe complex processes. That’s why science and math trumps philosophy and gurus contemplating their navels.
3. There is an obvious mismatch between the idealised process described for a day in the tropics and the process of global climate.
As usual I have good reason for being extremely skeptical.
In the first quoted paragraph ” …could be linearly added to gauge the global mean response,but not necessarily the regional response.” Does that help,Willis?
Tom says:
October 25, 2010 at 2:49 am
I don’t like the assumption of linearity. But I don’t find the reasoning in this post very persuasive. I’m not arguing for climate linearity here; I’m arguing that sceptics need to present a well-reasoned position and that this post isn’t it.
No – the argument from the article is simple: The IPCC should base its understanding of the atmosphere on OBSERVATIONS and not on the IPCC assumptions faithfully implemented in software
The climate does not have ONE sensitivity. As Willis says there are huge step jumps in sensitivity dependent on LOCAL conditions. There is no such thing as an average global weather which can then be summed into an average climate. As the temperature increases at the equator are forced higher – the step jump to negative forcing from severe storms will occur earlier and over more degrees of latitude. This is why hurricane season is in summer time.
But this is all shown by recent satellite metrics that are providing this information and were paid for by the same government agencies that fund IPCC . As the real world information is there and paid for one wonders why the IPCC stays in the computer room and studies their own software. Perhaps because unlike the satellites, their software programs provide the expected result?
its more non linear than we thought.
An obsession with linearity is essentially political.
I have also noticed that the same modellers who use linear assumptions, as you mention, also tend to also “average out” various datasets, such as proxy temperature reconstructions, and assume the averages of these proxies reveal some kind of meaningful trend. All these averages do is dampen and hide uncertainty in the proxies. Such is the case in Mann et al’s more recent paper about the MWP and LIA going back to about 1200 AD. Thy are using the same linear assumptions projected back into the past, as they do for the future, which is also part of the reason behind the hockey stick’s flatness back to 1000AD. ‘Averaging different uncetainties’ just doesnt work.
There is also another ‘linear- average’ that they assume. They state that if climate senstiviy is high to one variable, than it must be high for all variables. ie if climate is sensitive to eg solar variation, then it must also be just as sensitive to C02. From this they deduce that the common skeptical argument that the MWP was greater than reconstructions such as Mann 1998, only makes things worse, ie it creates more projected warming from human c02. So they have: little warming in the MWP and c02 effect is high, or high warming in the MWP and c02 effect is high. This way they get to have it both ways. A perverse example of their extreme ‘linear’ type of thinking.
Uh Oh! I am linearly extrapolated in a non-linear world.
Bingo. I would say that anything that shows up as linear is immediately suspect and will likely be quickly debunked (well, we’ll need that raw data first). That is my gut instinct and is based on a complex but abstract reason. I still cannot correctly put it into words, but I’ll try anyway …
I can vaguely remember 9th grade algebra, where you have plotting paper and pencils and rulers (there were no calculators yet and no computers). We would write some basic function and draw the graph. You would begin with x = y or x = y + 1 and so on. At this stage the results were of course nice straight lines. Later, you add exponents and degrees. (And it was no fun drawing something like x³ + 4x, of course many years later you just drop whatever function you want into a TI-xx graphing calculator and a nice graph pops on the screen, but I digress).
The point is even then I suspected something very strange and rigid and synthetic about the concept of a straight line linear graph. Studying electricity and electronics provided me the analog perspective. I believe that what has happened is that many become locked into the artificial, neat and pretty, but synthetic world that early math classes guide you into. In a sense it is the purest digital view of our purely analog world. But, I am still not explaining this right …
It is kind of like what the alphabet, vocabulary and language is versus the complete human voice, they are little pieces of the whole. Math, arbitrary numerical representations and algorithms are like the alphabet making words and languages out of pieces of the infinite number space. Linear functions can get us to little pieces of a sine wave without ever seeing it. Trees meet forest. I get a real Matrix (movie) feeling pondering this.
I have the distinct impression that most statisticians (those in the AGW cabal) are locked into this Matrix (a very simple one) where everything can and must be described with the simplicity of a 9th grade math problem. These statisticians are usually decimated once people show up with experience outside of the classroom (and their parents’ basement). I think this is why we see Meteorologists, Geologists, Physicists, Electricians, Programmers, etc, chewing them up for breakfast. (I am also starting to think I was wrong back in the early 80’s when I first used Lotus and Excel. I figured these were just fantastic steps forward, but now wonder if they are making us lazier and dumber. But I digress again, last time :-).
But my point is really a question, Is there anything that is actually linear? Can a linear graph ever represent something real? This post, particularly the title, Nature hates straight lines, really makes sense to me.
PolicyGuy says
——————-
nothing but models that perform as they were written to perform
——————-
You know nothing about the models and you are pretending insight where you have none. You have been fooled by your own sophistry.
The models simulated the climate based on the laws of physics, and a number of empirical relationships. No one knows what is going to come out ahead of time and occaisionally the results are surprising. The initial conditions to the models often have random components to see how that affects the range of outcomes.
No one writes thousands of lines of code and spends millions of dollars just to produce predetermined answers. If that was the intent they could do that in an afternoon with a bunch of print statements.
Didu you fail the most important skeptics test: are the words coming out of my mouth utter rubbish.
Tom says
Tom you miss the point of these laws / models; As with Newtonian physics these laws only work within the specified limits of classical physics. They are fine for everyday calculations, for ensuring that you have the right fuse in your consummer unit, etc. They cease to mimic reality at the atomic / electron level. This is where the Quantum mechanics model takes over. Climate models are so far from this level of modeling that they are no better than a playstation game.
Physical models, for that’s what the formulii are that you have quoted, are tested by physical experiments in order to find their limits and are then not used to describe the ‘observed’ events beyond those limit(s).
Climate models have not gone through this final process to my knowledge.
LazyTeenager says:
October 25, 2010 at 4:48 am
You are arm waving.
LazyTeenager says:
October 25, 2010 at 4:48 am
3. There is an obvious mismatch between the idealised process described for a day in the tropics and the process of global climate.
Ok This is your opportunity to stop waving your arms and explain this phrase in detail.
Decide whether ‘discrete or continuous’
then application of [instruments of] specificity or sensitivity?
Politics can use all the above for effect, they are elected for a fraction of the time.
Science should state up front, they are here, to provide their genius for the benefit of humanity, for which without, we would not be enlightened.
Interesting post Willis Eshenbach, thank you.
“Amplification of Global Warming by Carbon-Cycle Feedback Significantly Less Than Thought, Study Suggests”
http://www.sciencedaily.com/releases/2010/01/100127134721.htm
http://www.nature.com/nature/journal/v463/n7280/full/nature08769.html
Although I think Willis is correct that climate sensitivity is unlikely to be linear and independent of temperature there is a flaw in his argument. The question is whether clearly non linear local and short term negative feedbacks can be used challenge the linearity claim. Tom raises V=RI Ohms law but his argument about random electron motion misses the point. The real challenge is that posed by capacitance and impedence which create non linear behaviour when I is not constant. Such transient behaviour does not disprove Ohms law. In the climate system the oceans are a huge capacitor and water generally (through evaporation and cloud formation) a huge impedence. The problem is that clouds are also part of the climate’s “resistance”. The people who create the climate models cannot model deep sea currents or clouds so it is unlikely that any model they generate will contain a relationship which predicts that the overall avearage level of cloud will change permanently given a new average temperature. Thus a quasi linear constant radiative forcing is built into the model. One cannot be surprised if it is one of the outputs!
Thus the strongest argument (as Willis and others have pointed out) is that you cannot postulate any relationship (liner or otherwise) without testing if it holds true in real life. This has not been done.
Finanally I am bemused by the idea that a linear relationship should be postulated at all. The energy radiated by the earth is dependent on its absolute temperature to the fourth power. So if you increase the radiation received by 4% the absolute temperature has to increase by about 1% to compensate. As Tom Vonk points out this might look linear (particularly measured in degrees C) for very small changes but it is highly non linear in reality. Can anyone who knows the models explain why linearity should be considered even remotely likely?
This goes back to logic 101. The IPCC is basically saying that since A implies B, not A implies not B. Going to their argument: linearity is A, evidence is B. The IPCC is saying “GCM have found no evidence of any nonlinearity for changes in greenhouse gases.” Thus, not A is nonlinearity, and not B no evidence. So, since no evidence exists otherwise implies the system is nonlinear, that means evidence implies linearity. Does that makes sense? This example may help too: If I have the flu, that implies I have a virus but I do not have the flu, that implies I do not have a virus. You see how the second statement is the opposite of the first, yet simple logic and real world sense says it is not true.
I think the IPCC statements are either incompetence in action or purposeful obfuscation. Either way, nothing they state can be trusted. Who is really going to examine every sentence for logical fallacies like this?
LazyTeenager – good name. Yes, you are lazy. If you’d actually read the OP carefully, you’d find that the quoted IPCC material does claim the linearity assumption has been tested purely by analysis of models:
The IPCC assertion is based on studies of models. Not studies of the real world.
Your skepticism is well founded but misplaced.