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’ ...
Apologies. I see that Tom Vonk has already said something similar to my above comment. That’ll teach me to jump in without reading to the end of the comments first.[yup]
Tom says:
October 25, 2010 at 2:49 am
There are a lot of scientific laws that claim linearity, where if you look closer things don’t behave anything like what the law describes. Ohm’s law says V=IR; if you look closely, you find that electrons are jumping all over the place. Some will go backwards, some will go sideways, the majority will go in the right direction but with a wide spread of velocities. But when you zoom out a bit, the average law works quite well. Same with the kinetic theory of gasses; PV=NkT at a wide scale, but at a smaller scale you can see that actually atoms are crashing around in entirely random ways and locally concepts like “pressure,” “volume” and “temperature” don’t make a lot of sense. So you can’t point to local weather effects and claim that they disprove the aggregate average theory of linear climate sensitivity; the randomness of small-scale weather processes doesn’t disprove the overall theory of linearity any more than observing individual electron tunnelling in silicon disproves Ohm’s law.
I have been discussing this often, more recently in the Spencer thread.
Physics formulations have regimes where they hold.
Meta levels. An example from everyday life is
a) the alphabet level ( or sounds if you prefer)
b)words
c) sentences with structure
b is a meta level of a and c is a meta level of b. Each is founded on the previous, but the rules are completely different.
You will not argue that a poem has too many letter “e” or needs many “a” to carry meaning.
In the same way the current frameworks of physics, which are developed with rigorous mathematics, start with quantum mechanics, go into quantum statistical mechanics, which goes into thermodynamics. A parallel line was before the 20th century, started with the atoms, goes into statistical mechanics, goes into thermodynamics.
Each level has its own rigorous mathematics There are theorems that connect the quantities from one level to the metalevel, and there are conservation laws that are strict in all levels. The mathematics is different at each level, so one could claim/show that linearity holds in a higher level while not in the lower one, but it is not for the same quantities really. It is like mixing alphabet with words.
The difference with climate modeling is that climate is not shown to be a different framework than weather. Particularly in the infamous GCMs the weather models, that cannot predict the weather reliably for more than a few days, are taken over and used for climate with averages replacing a lot of variables that weather modeling uses. There is no different mathematics, the same equations are solved .
Secondly, whether proving linearity from a model is justified depends on the details of the models and whether they do actually assume linear sensitivity. You would need to follow all those references you deleted to find out. Perhaps the model is based on some low-level empirically-derived theory that allows you to infer whether the aggregate behaviour is linear or not; we can’t tell from the text you cite. Perhaps they actually model all the thunderstorm creation processes in the tropics, and snowfall at the poles, based on observed data, and find that when you add it all up it comes out linear. The kinetic theory of gasses is again a great example, though in reverse; the aggregate gas law was developed empirically and the model of an ideal gas was developed by reasoning about what underlying processes might produce that aggregate behaviour. There was no direct evidence that gasses were made up of tiny particles when the model was developed; that didn’t make it wrong. The model agreed with the empirically-derived law, and was successful in explaining it. It turned out to in fact be pretty much right. If you followed your reasoning in the mid-19th century, you would decry the KTOG as fairy-tales that no sane man would believe and you would later look very foolish.
When one is replacing the variables of an equation by an average value, which is what the climate models are doing, one is really expanding in a perturbation series and taking the first term : constant +a*X +b*X^2+…
a is related to the average of the value of the function.
If the weather predictions are no good after N time iterations because non linearities kick in, since solutions of coupled non linear differential equations are highly non linear, more so for climate projections which have more assumed linearities.
One does not need to go to the references to know GIGO.
Assuming that climate sensitivity is a constant is an assumption of stationarity. That is, the climate sensitivity does not change with time, provided the changes over time are ‘small’, whatever small means. This is the same assumption inherent in all of paleoclimatology. For example, tree rings in special trees are assumed to reflect changes in local temperature, and that the linear relationship between tree ring (width or density) and temperature is the same over time.
Willis has provided an example of why climate sensitivity is not stationary. The very existence of the tree ring ‘divergence problem’ proves that the relationship between tree rings and local temperature is not stationary, and that paleoclimatology based on tree rings needs to come to grips with its own ‘UV catastrophe’ moment.
Robinson said:
“… if I make a clock from a dandelion and tell you that it measures time – one hour passes for every petal I pick off – you would be justified in questioning the efficacy of my method, notwithstanding the accuracy of my petal picking. Uncertainty isn’t the only problem with climate models, as you well know.”
Yes, the other major facet of a model is its _correctness_, i.e. can it consistently predict the future (or explain the past)? [We need _consistency_ because a stopped clock is accurate twice a day etc]. A third facet may be _efficiency_, with respect to resources consumed by the modeling.
So, assuming that your dandelion-clock model is correct, i.e. it can consistently measure the passage of time, my only other major concern would be the _uncertainty_ (‘error’) of each time measurement.
So, applying this to AGW, we agree that it is correct that under certain conditions CO2 can exert some positive radiative forcing, our concern should be the calculation of the _certainty_ that this forcing will (or will not) lead to catastrophic global warming.
For the record, I have not seen any convincing proofs of CAGW. If it turns out that some non-catastrophic warming can be traced to man-made activities, we will be enlightened (but not alarmed).
😐
Any linearity will be the result of the physics. Any physical system will obey a least action principle which may lead to a linear or curved trajectory for the system in question so no assumptions of linearity can be allowed without experimental verification. As it is clearly all too complex to integrate lots of partially understood
physics with much missing data over the surface of the earth and through its atmosphere and oceans then the following is the needed experiment.
Use satellites to measure average incoming and outgoing radiation energy flux, just outside the top of atmosphere, to and from the earth over a contiguous 11 then 22 then 33 years and calculate the balance. That will give reasonable and improving 1st order estimations of warming or cooling of the planet over the period after excluding the geothermal contribution of the planet. Do we even know the geothermal contribution?
Is anyone doing such measurements? Are there any results available?
Very interesting. Of course the answer for the IPCC team would be that they admit in the IPCC that clouds are poorly understood. I see no reason to dispute that point with them because it’s an admission of such a large lack. Unhappily few journalists are savvy enough to recognize what a large uncertainty it means for all the sensational warnings. Clouds are poorly understood and it’s not a trivial thing that they are. Stefan-Boltzmann works well in controlled laboratory conditions but why would anyone think that stepping back from earth a few thousand paces and squinting your eyes makes something like global cloud cover a stable average that can be thought of as constant. I believe it’s most likely that global albedo, because of the changeablility of cloud cover, is not anything so stable as the level of the sea.
…
According to what I read, observations of earthshine on the moon from Big Bear Solar Observatory have already established that “Earth’s average albedo is not constant from one year to the next; it also changes over decadal timescales. The computer models currently used to study the climate system do not show such large decadal-scale variability of the albedo.” http://www.universetoday.com/9611/decreasing-earthshine-could-be-tied-to-global-warming/
This result was reported in a May 28, 2004, Science article titled, “Changes in Earth’s Reflectance Over the Past Two Decades.” Interestingly the article I found reporting on this had recast things to fit into the sensational kind of global warming headline we are more familiar with: becoming – “Decreasing Earthshine Could Be Tied to Global Warming.”
I
Another telling post from Willis.
Assume linearity by ignoring the unhelpful bits that they know to be non-linear then spend millions showing the linear models produce linear results. No empirical data to back-up results. Brilliant!
Even ignore data that shows they are wrong – Lindzen/ Choi etc.
Please can our politicians be bombarded with e-mails imploring them to explore the ‘evidence’ for AGW.
This article reads like someone who is trying to use debating tricks to win an argument rather than someone who is trying to discuss the science.
I admire your Thermostat Hypothesis and predict it will turn out to be an important effect.
On the other hand, random sniping and potshots at the IPCC don’t move along the discussion very much. Although everything at some point ends up being non-linear, the simplifying assumption of linearity is a very useful tool when used over limited spans. Assuming a nearly linear relationship between forcing (over some period) and the resulting temperature change is not incompatible with feedback effects like your Thermostat Hypothesis.
Your argument isn’t that much different than trying to reject the global average temperature time series as being meaningless because it doesn’t reflect either day to day variations or the spatial variations across the globe. The global average temp indeed is not an accurate depiction of the temperature anywhere on the globe, but it is still a useful metric.
Willis,
I think you misunderstood the linearity assumption.
“Reporting findings from several studies, the TAR concluded that responses to individual RFs could be linearly added to gauge the global mean response,”
The are talking about the process of combining various forcings NOT the dynamics of a particular forcing.
For example, the forcing of C02 would be X , the forcing from Volcanoes would be Y.
Question. can you add these forcings LINEARLY. That is the question they are addressing. And the are not looking at the transient response but the equillibrium response. So for example, if you have a positive forcing increase from say TSI, of 32
and you have a negative forcing from say volcanoes of say -16, can you estimate the combined effect of these individual RFs by merely summing them?
Yes. There is no reason to think that the forcing from volcanoes will drive the forcing from the sun down. You can merely add them to get the net forcing: 16. And there is no reason to think that a volcano erruption will impact the core of the sun and drive its forcing higher. you can merely sum them. That is what they mean by summing the contribution of individual RFs to get the total net forcings. Now, if an increase in TSI DROVE more volcanoes, or if increased TSI changed the way particles reflect light, then adding those two forcings would not give you the correct answer.
As an analytical chemist, I have built a career around relating mathematical models to real-world measurements, and the only place I have found the assumption of linearity to be reliable is as an approximation during interpolation between two actual measurements of knowns (standards), and even there one often finds a deviation from linearity that is revealed by careful measurements. The Beer-Lambert Law, the physical law that relates the amount of energy absorbed by a medium to the concentration of the absorbing species (think CO2 in air) is a *logarithmic* law.
Steven Mosher said:
> I think you misunderstood the linearity assumption.
> The are talking about the process of combining various forcings
> NOT the dynamics of a particular forcing.
I agree. The units of forcing are watts per square meter, i.e. energy normalized over time and space. So it must be additive in the sense that energy must always be conserved, i.e. First Law of Thermodynamics.
Similar confusion arises when students are first told that the Fourier Transform is a linear operator. “But it can’t be linear because it’s summing cosine and sine functions which are highly non-linear”.
Yes, sin() and cos() are (highly) non-linear mappings, but their contributions to the Fourier Transform amount to the summations of little pieces of data ‘energy’ at different frequencies.
“So you’re saying the FT is equivalent to performing a single linear matrix multiply on some time-domain signal?”
Yes, and if you’re still in doubt about this, check out MATLAB’s fftmatrix.m command:
http://www.mathworks.com/moler/ncm/fftmatrix.m
LazyTeenager says:
October 25, 2010 at 5:09 am
“No one writes thousands of lines of code and spends millions of dollars just to produce predetermined answers… are the words coming out of my mouth utter rubbish.”
Yes.
“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”
Couple that with the endless & baseless attributions of observations to AGW and we have a thoroughly defective climate science arena.
With more concocting than scientific measuring occurring in climate science we are facing utter chaos for years to come.
So much fabrication has been and is being established as science that it will take many years to undo if at all possible.
How does science purge itself of wrongdoing that is this massive?
Are there any models showing that it can even be done?
@Francisco – another one who says the same as me but draws utterly the wrong conclusion. Ohm’s law works nicely at the scales encountered for general circuit design. Or does no piece of electronic equipment actually work and it’s only me who hasn’t noticed? Biologists can say what they like, I suspect referring to very small scale (intra-cell etc) processes; engineers will keep on using it for circuit design and succeeding.
@anna v – nice to have someone interact seriously with ideas. You refer to “the climate models” as though every climate model was the same, but the IPCC report refers to only two or three papers specifically to make this point. Those papers might describe models that are extremely intricate interactions of well-known, well-understood physical processes, that show that in the aggregate, simulated over a model of the earth’s surface, those processes work out to a linear (or roughly linear, or linear over the range we are likely to care about) climate sensitivity. My point is not that they do or don’t, (I am too busy/lazy to go and check) but that just saying, “OMG, Models!” doesn’t prove it either way.
@Charile A – yes, what I was trying to say, I think.
LazyTeenager says:
October 25, 2010 at 5:09 am
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.
A bunch of print statements would be easy to falsify by skeptics. Is this not too obvious for you to understand? A million or more lines of code is considerably more difficult.
are the words coming out of my mouth utter rubbish.
At least your use of “my” was perceptive..
It seems to me, Willis, you have illustrated one of the great flaws in our mathematical efforts to forecast reality: In reality nothing is linear but compueter models want to predict liniar results. I have been forecasting weather for over 50 years. Hour to hour, day to day, week to week, season to season, year to year, nothing is liniar. I have been playing poker for 30 years. The cards odd are not linear. I have been aging for 76 years. It has not been a linear process. Everything is non-linear.
John Day said at 3:42 am
You’re correct, Willis, the relationship between temperature and forcing is not exactly linear. But all _observed_ processes tend to be non-linear because of process and measurement ‘noise’.
“not exactly”? “tend to be”? Would that be like – your not exactly pregnant but you possibly could tend to be?
But having said that, most non-linear processes can be approximated by integrating enough tiny linear steps.
“approximated”? You mean modeled… don’t you? And I agree – if the non-linear (analogue) process is simple and controlled with fixed and precisely known I/O values – which is why global weather is impossible (at this time) to “approximate” (digitally model).
I don’t see where proving this relationship doesn’t exactly hold on a hourly basis necessarily proves or disproves any climate theory.
Absolute # 1. – Climate does not exist in reality. It is human conceived condition that exists only in the collective human conscious as a human AVERAGED abstract of specific weather conditions over a relatively longer period of time than any specific weather condition exists and impossible to illustrate without the use of specific weather terms.
Absolute #2. – Climate does NOT drive weather. (See Absolute # 1 for the reason why.)
Absolute #3. – When considering all “climate” (globally averaged weather) theories, see Absolutes 1 and 2.
And proof of the above was indirectly provided in your second comment at 4:35 am – all human “measurements” are “models”. And all “models” are concepts, seen and interpretive of what we perceive to be – reality. Although real to our minds, THEY are NOT reality themselves.
The term “Climate model” is a misnomer. You cannot model climate! And it’s not that you cannot model a concept, it’s because modeling an AVERAGE of anything that is constantly changing (much less a massive and highly dynamic analogue system that is 99% unmeasured) is logically impossible. So, I can “model” weather or a part thereof, say with a thermometer (per your example), and with “real” human experience I can predict, to a limited degree of accuracy, that the “model” will show a change over a specific period of time. We can also say with absolute certainty, based on that same “real” human experience, that there is no instantaneous effect on my “model”, by your “model” of exact same type, half way around the globe. Yes, delayed effect may occur. But not by the “model” – by reality. And therein lies your modeling problem. Linking a singular effect of a virtually unmeasured system through time and space. Let me know when you think you have accomplished that task.
In the mean time I give you one more absolute –
Absolute #4. – Human summarization of global weather into the mythical thing called “climate” is totally meaningless to the individual. (See Absolute #2 for the reason why)
Nature abhors a vacuum… that’s why it’s so dusty!
Charlie A says:
October 25, 2010 at 8:27 am
The global average temp indeed is not an accurate depiction of the temperature anywhere on the globe, but it is still a useful metric.
A useful metric for what? Manipulating? Fudging? Make believe?
Or perhaps all these global averages have had to be created and sanctified because that is all the simplistic GIGO Computer Models can use.
Willis: A straight line may be the shortest distance between two points in 2 dimensional geometry. However, in our (at least) 3 dimensional world the great circle route is alway shorter than the rhumb line when navigating routes longer than the distance to the horizon. Straight lines are shorter only when measuring the distances inside the circumference of our planet. Curves are beautiful and reflect perfection in nature.
LazyTeenager says:
October 25, 2010 at 5:09 am
Quite a mouth full. Hardly warrants attention. Except you try to give an impression that is very off-base.
The development and appropriate use of models is well known. Before reliance, there is testing and tuning to determine their limits. These have never been properly tested against the correct empirical relationships and the correct databases. The purpose of the original post was to demonstrate that IPCC relied upon a circular correlation between models, not data, to confirm an important linear relationship that has yet to be demonstrated.
My characterization of these models was brief, but so was the comment. I stand by it.
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. In two parts:
There are a lot of scientific laws that claim linearity, where if you look closer things don’t behave anything like what the law describes. Ohm’s law says V=IR; if you look closely, you find that electrons are jumping all over the place. Some will go backwards, some will go sideways, the majority will go in the right direction but with a wide spread of velocities. But when you zoom out a bit, the average law works quite well. Same with the kinetic theory of gasses; PV=NkT at a wide scale, but at a smaller scale you can see that actually atoms are crashing around in entirely random ways and locally concepts like “pressure,” “volume” and “temperature” don’t make a lot of sense. So you can’t point to local weather effects and claim that they disprove the aggregate average theory of linear climate sensitivity; the randomness of small-scale weather processes doesn’t disprove the overall theory of linearity any more than observing individual electron tunnelling in silicon disproves Ohm’s law.”
Actually you have it backwards. Ohms law and the ideal gas law are linear in small ranges, not as the entire pciture. It’s when you go to extremes that the models break down. PV=nRT around STP (Standard temp and pressure) but does a poor job at low pressures or very high pressuresorks. The reason it doesn’t work is because of significant interaction terms which exist in the real world but are mostly always disregarged when scientists create models. Others have tried to come up with gas laws that take into account compressibility factors or interactions of molecules but these mostly fall to empirically derived factors.
The climate models are sorely lacking in any understanding of the important interactions between factors which I believe is a main point of Willis’ excellent article.
Um, crystals have perfectly straight lines, and absolutely precise angles.
Are not thunderstorms in the tropics a continual occurrence as local afternoon is always happening somewhere? Where it’s hot sweaty and time for a cool tall one. And the yellow sun forever gazes down.
So how does the IPCC protestation of a LINEAR relationship jibe with the climatists claim of a LOGARITHMIC relationship ?
The simple fact is they can’t prove either one is true; but whenever you have small inconsequential changes in any noisy chaotic system; simple liean fits can seem to be appropriate; but it takes a lot of gall to claim a logarithmic relationship when a scatter plot of the actual real observed data fitted to such a curve results in a 3:1 range of error in the SLOPE of that relational curve; and exactly the same would happen if you made a straight line fit; the gradiient would stille exhibit a 3:1 range of plausible values.
Remember we have about 52 data points for actual annual averaged valuse for CO2 from Mauna Loa; and about the same number of annual mean values for mean globnal surface Temperature form GISS and similar studies; and those two sets can’t be fitted any better to a logarithmic curve than they can to a linear one; or any arbitrary function with a 3:! range of its most important parameter.
And good luck on deriving a theoretical basic physics model to support either one.
And they should make up their minds whether climate sensitivity includes H2O or just CO2. How can you talk about the doubling of CO2; without discussing the effect of doubling the H2O instead
We know that more warming causes more H2O (evaporation) and we also know that more warming causes more CO2 (Henry’s law and ocean take up/outgassing); and notice I said CAUSES, and not “IS CONSISTENT WITH”
So CO2 could cause ocean warming and more evaporation, hence more H2O; but also more CO2. Then H2O causes more ocean warming, and hence more evaporation; so more H2O; but also more outgassing so more CO2.
So both CO2 and H2O can cause ocean warming and cause more H2O and more CO2.
So how do you justify calling one a greenhouse gas; but calling the other a feedback factor. ? Both are PERMANENT RESIDENTS of the atmosphere; and in the case of H2O permanent residence in ALL THREE PHASES.
Also both are injected into the atmosphere in copious quantities by human activities; but far more H2O than CO2; so human activities have a role (maybe small) in putting both of those greenhouse warming/feedback causing gases into the atmosphere; and processes like rain etc result in the continuous removal of both H2O and CO2 (which is soluble in H2O) from the atmosphere..