Guest Post by Willis Eschenbach
I love thought experiments. They allow us to understand complex systems that don’t fit into the laboratory. They have been an invaluable tool in the scientific inventory for centuries.
Here’s my thought experiment for today. Imagine a room. In any room dirt collects, as you might imagine. In my household I’m somewhat responsible for keeping the dirt down, so I get out the vacuum cleaner to clean up.
But suppose I had a magic way to handle that dirt. Suppose there was a class of beings such that whenever there was a concentration of dirt in some small part of the room, one of these beings would pop into existence, clean up the dirt, and then disappear. We’ll say this being is a rare relative of the Tasmanian Devil, call it a “Tasmanian Dirt Devil” (TDD).
Figure 1. One of the few authenticated photos of the elusive Tasmanian Dirt Devil (TDD) cleaning a floor in its natural habitat. PHOTO SOURCE: Annals of Cryptozoology, Vol. 6, 1954
After observing the room for a while, we realize that the TDD only appears when there is some small area of the floor with more than a certain concentration of dirt. However, we see that the TDD does not limit itself to that small concentration of dirt. It moves around and cleans out any other smaller concentrations of dirt around it as well. Once the area is cleaned to a certain level, the TDD vanishes, leaving the room somewhat cleaner. We also see that on days when traffic is heavy, often there are a number of Tasmanian Dirt Devils working on the room at once. No single TDD cleans the whole floor, but the floor is never dirty anywhere for long.
Now, here’s the question for our thought experiment: can we use a computer to model the effect of the TDDs, for example in order to calculate the rate at which dirt is being added to the floor, based on the average amount of dirt on the floor?
I say we cannot model it adequately if our only input to the model is the average level of dirt on the floor. Here are two circumstances to explain part of why the problem is ugly.
1. Someone spills a very small bit of dirt on one corner of the floor. Because the dirt is concentrated in one area, a TDD materializes, cleans up the dirt and the surrounding area, and vanishes.
2. Four people simultaneously spill a very small bit of dirt in all four corners of the floor. Four TDDs materialize, clean up the dirt and the surrounding areas, and vanish.
If all we have is the average dirtiness of the floor, a few bits of dirt which are rapidly cleaned up will make little difference in a daily average of floor conditions. Despite those small fluctuations, in one case there is four times as much dirt being added to the system as in the other case.
So I think we can agree that in our thought experiment, the average dirt level of the floor is not linearly related to the amount of dirt being added to the system. If we want to model what’s going on, it is very difficult to do it based on the average dirt level. We need much more detailed information in both time and space.
Here’s an illustration of a different problem. Again, two conditions.
1. Someone spills a very small bit of dirt on one corner of the floor. Because the dirt is concentrated in one area, a TDD materializes, cleans up the dirt and the surrounding area, and vanishes. Average dirt level on the floor ends up slightly below where it started.
2. Someone spills a very small bit of dirt evenly all over the floor. There is no concentration of dirt above the threshold level, so no TTD appears. The average dirt level on the floor ends up slightly above where it started.
Again, as you can see, average dirt levels and amount of dirt added show no correlation, even as to sign.
So what do Tasmanian Dirt Devils have to do with the climate? If we saw something like a TDD in our kitchens, we’d be amazed. However, something just as amazing exists in the climate. We’re not astounded by it all purely because are so familiar with it. However, let me take a small digression on the way to explaining the relationship between climate and Tasmanian Dirt Devils.
Emergent phenomena are a special class of things. They can be recognized by certain traits that they have in common. In general, emergent phenomena arise spontaneously at a certain time and place. Typically they exist for a definite duration and eventually dissipate at another time and place. Their appearance is often associated with some natural variable exceeding a threshold. Many times they involve a change of state of a variable (e.g. condensation of water vapor). Often they can move about somewhat independently. If so, although they have general tendencies, their specific movements are usually very difficult to predict.
One clear characteristic of emergent phenomena is that the properties of emergent phenomena are not apparent in the underlying stratum from which they arise.
Examples of natural emergent phenomena with which we are familiar include sand dunes, the behavior of flocks of birds, vortexes of all kinds, termite mounds, consciousness, and indeed, life itself.
Regarding climate, there is one particularly important class of natural emergent phenomena. These are the natural “heat engines”. Heat engines are able to turn heat into work. Examples of these natural emergent heat engines include hurricanes, thunderstorms, dust devils, tornadoes, and the Hadley Circulation itself.
The most common and most important of these heat engines are thunderstorms. Thunderstorms do two kinds of mechanical work. First, they power the deep tropical convection that is the driving force for the circulation of the entire ocean and atmosphere.
Second, thunderstorms drive what can be thought of as a sophisticated air conditioner, using a variation of the standard refrigeration method. This method, used in your home air conditioner, uses ambient heat to evaporate a liquid. This removes the heat from the area where the evaporation is taking place.
Then you move the evaporated liquid (and the latent heat it contains) to another location. In the new location, you condense the liquid, releasing the latent heat of condensation. The heat is then transferred to the surroundings, and the condensed liquid is returned to start the cycle over.
In the natural Hadley air conditioner that we call a thunderstorm, the same process takes place. Water is evaporated at the surface, cooling the surface. The water vapor rises to the clouds. There it is condensed. The latent heat it contains is released, rises, and is radiated out to space.
Meanwhile, in addition to losing latent heat through evaporation, the surface is further cooled by the fall of cold rain from the thunderstorm. This is accompanied by an entrained cold wind, which assists in the cooling.
In both cases (Hadley circulation and refrigeration) the net effect of a thunderstorm is to remove energy from the surface and move it up into the troposphere.
Having digressed, I return to what climate has to do with Tasmanian Dirt Devils.
Consider our thought experiment. If you replace TDDs with thunderstorms, replace the room with a climate model gridcell of the tropical ocean, and replace dirt with energy, you have an excellent description of the action of the climate system at the hot end of the climate heat engine, the Tropics.
Whenever there is a “hot spot” on the tropical ocean or land, if it is hot enough, a thunderstorm springs up and starts pushing huge amounts of energy vertically. As the thunderstorm moves across the surface, it moves towards the warmest area in its path. This preferentially cools the warmest areas. In addition, it continues to do so until the local surface temperature is a few degrees below the initiation temperature.
There are some conclusions that we can draw from this thought experiment:
1. In our thought experiment, increasing the rate at which dirt is added does not commensurately increase the average dirtiness of the floor. Similarly, increasing the rate at which energy is added to the Tropics does not commensurately increase the surface temperature.
2. Attempting to model our thought experiment using room-wide averages won’t work because Tasmanian Dirt Devils are driven by local conditions, not average conditions. Similarly, attempting to model our climate using gridcell-based averages won’t work because thunderstorms are driven by local conditions, not average conditions.
3. Modeling a system that contains simple linear feedback is not too difficult. In that case, average changes in the response variable are linearly related to changes in the forcings. Modeling a system with an active governor, like TDDs or thunderstorms, requires a much different type of model. As I showed above, in that case the response variable is not linearly related to the forcing.
4. Thunderstorms preferentially cool the warmest areas. Although the average temperatures might be the same, this has a different effect than a gridcell-wide uniform cooling. Again, this makes the modeling of the system more complex.
Let me be clear about what I am saying about models. I’m not saying that we can’t model the climate. I think we can, although it won’t be easy. But we have to model it the way it really is.
It is not a system with a linear relationship between forcing and temperature as conventional theory claims. It is a dynamic governed system with a complex, nuanced, non-linear response to forcing. Yes, we can model that. But as I show above, we can’t do it under the assumptions made by the climate models.
Could we model it parametrically, without having to model individual thunderstorms? Perhaps … but the model has to be designed to do that. And the current climate models either are not designed to do it or are not doing it successfully.
How do I know that they are not doing it successfully? Drift. Consider the room with the Tasmanian Dirt Devils. If there is no change in the amount of dirt being added per day, the system will rapidly take up a steady-state condition.
The models are subjected to a very similar test. In this test, called a “control run”, every one of the forcings of the model is held exactly steady. Then the models are run for a number of model years. Figure 2 shows the results from the Coupled Model Intercomparison Project (CMIP) control runs. We would expect the models to rapidly take up a steady-state condition.
Figure 2. Results of control runs for 16 coupled atmosphere-ocean climate models. SOURCE
Notice the drift in the surface air temperature in a number of runs over the 80-year simulation. The CERFACS model is the worst, but even a mainstream model like the NASA GISS model of James Hansen and Gavin Schmidt shows drift over the 80 years.
How much drift? Well, the trend in the NASA GISS model control run is a warming of about 0.7°C per century. This is about the same as the IPCC estimate of the warming over the last century, which is 0.6°C.
Now, you could look at that GISS model 0.7°C per century inherent warming drift with no forcing change as a bug. I prefer to think of it as a feature. After all, it lets Hansen and Schmidt simulate the warming of the 20th century without the slightest change in the forcings at all, and how many models can do that?
However, that drift does strongly suggest that they are not modeling the climate correctly …
As always, the quest for understanding continues. My best regards to all,
w.
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Dave F says:
December 15, 2010 at 2:52 pm
“How many licks does it take to get to the center of absolute zero? :-)…I would think that it wouldn’t take a long time for Earth to lose the energy it holds.”
Jesus Haploid Christ, what kind of eclipse is it that we’re talking about?! You do realize that a huge portion of the Earth would still be receiving sunlight, right?
My question referred to calculating the time taken for the climate to re-equlibrate to suddenly having a shadow cast upon it 24/7, such as the one linked below.
http://apod.nasa.gov/apod/ap990830.html
Oops, haha, I went with the idea we were blocking out all of the Sun, not just one part of the Earth from receiving Sun. Sorry for the misunderstanding.
If the shadow were to stay in one spot, I have no idea.
Still, CO2 isn’t really analogous. You have huge changes in the amount of insolation in one location over a day, to which the atmosphere responds everyday. I don’t think it is reasonable to suspect that CO2 is going to destabilize the equilibrium when you look at the Δs for WM^2 on a daily basis and then look at the Δ expected for doubling CO2.
Paul Birch
Please tell us how you can have a positive feedback that is bounded?
Paul Birch :
December 15, 2010 at 1:28 am
No thunderstorms without humidity, you are right.
Humidity depends on the absolute temperature of the water. The hotter the water the more steam, which you can observe in your kitchen. This disproves your statement that absolute temperatures are irrelevant to the creations of thunderstorms.
Therefore hunderstorms depend to first order on the absolute temperature of the water, and to second order to the difference in temperatures between surface and stratosphere.
oldseadog says:
December 15, 2010 at 10:24 am
Thanx for the heads up sea dog. I have no doubt you are correct.
I guess my erraneous analogy proved my earlier statement (“Just because an analogy sounds good, doesn’t mean it applies to the intended target”) to be true.
I just felt comparing the massive size of a tanker to that of a jet ski to be off the mark, I still maintain that.
Leaving analogies aside, having read the comments, I’m still not convinced about the ‘decades” claim.
A large volcano goes off near the equator, it doesn’t take decades for T’s to fall. A strong El Nino develops in the Eastern Pacific, it only takes few months for T’s to rise.
But I may at some stage read a convincing argument and change my mind, until then….
Steve says:
December 15, 2010 at 2:17 pm
Paul Birch says: If you can plot the overall amount of dirt against the amount of dirt being added, then find a correlation between the two, then you have a working model. Even if the dust devils themselves are a complete mystery to you.
“Well that’s the whole point – you won’t find a statistically sound correlation between the two!”
How do you know? Have you tried it? I suspect that if you did you would find quite a strong correlation on at least some timescales. Certainly some control systems of this type do show such correlations – though sometimes the dependency can be opposite in sign to what you might expect (ie, the more dirt added, the lower the average dirt level).
“Finding the correlation requires knowing the details, not the averages.”
It really doesn’t. Empirical correlations are found by plotting the empirical data.
“So I agree that you can create an empirical model, but I do not agree that it will have any predictive power, with power being the key word.”
If it predicts either of the variables from the other at even slightly better than chance, then it has predictive power. And if what you are wanting is a model not of the immediate local “weather” but the long-term overall kitchen “climate” (which surely is the point of this analogy) then the model is likely to be quite good.
Smokey says:
December 15, 2010 at 3:03 pm
“Paul Birch doesn’t understand what “empirical” means in this thread. … There is empirical data, and there are models. The first is evidence, the second is not.”
An empirical model is a model based on empirical data. As distinct from an analytic model, which is based upon physical or mathematical analysis from first principles. Or a semi-empirical model (like the highly useful empirical mass formula for atomic weights) which uses a bit of both. Or, I suppose, an ideological model (like AGW), based upon what you want the results to be.
James Macdonald says:
December 15, 2010 at 6:33 pm
“Paul Birch
Please tell us how you can have a positive feedback that is bounded?”
OK. Consider a simple amplifier circuit with two additive inputs and unity gain on each. Apply a signal S. The output signal O=S. Then feed half of the output back to the other input. This is positive feedback. The output signal is now S+S/2=3S/2. But this means that 3S/4 is now fed back. So the output increases again to S+3S/4=7S/4. Then again to S+7S/8=15S/8. Then 31S/16, 63S/32 … to the limiting value of 2S. Positive feedback has doubled the initial signal. In general, if the feedback fraction is f, the output is multiplied by 1/(1-f). This remains finite (stable) so long as f is less than 1.
anna v says:
December 15, 2010 at 10:37 pm
“No thunderstorms without humidity, you are right. Humidity depends on the absolute temperature of the water. The hotter the water the more steam, which you can observe in your kitchen. This disproves your statement that absolute temperatures are irrelevant to the creations of thunderstorms.”
I made no such claim. On the contrary, what I said was this: “One can only have a tropical thunderstorm if there is a sufficient temperature difference between surface and stratosphere and a high enough absolute humidity. If the overall temperature is too low, or the air too dry, you don’t get them. If the temperature is high, but there is insufficient temperature difference to drive them, you don’t get them … The absolute temperature helps determine the strength of the effect, but it is the temperature difference that the governor is regulating.”
As heat engines, thunderstorms are fundamentally driven by the surface-stratosphere temperature difference. With a sufficient temperature gradient, they can even arise in polar and mountain regions at quite low absolute temperatures, albeit less powerfully than the tropical thunderstorms we are talking about here. But once the thunderstorm has hammered down that temperature difference it switches off. The thermostat switch is not working off the absolute temperature, but off the temperature difference. So changes in the absolute temperature are not regulated (ie, held within fixed bounds), only ameliorated by the negative feedback from the thunderstorms. The system dynamics are quite different.
JJB MKI says:
December 15, 2010 at 5:01 am
“Could you elaborate on why, in your opinion, the global circulation models currently employed are not good?”
The fundamental reason is that they’re not scientific models at all – they’re propaganda tools intended to “justify” an essentially irrational dogma, endlessly fudged and refudged to give the desired results.
More specifically, if you create a model supposedly based upon the underlying physics, then you have to include all the relevant phenomena. Not every last detail, but all the stuff capable of producing effects within the range of interest. In a climate model, you will, for example, have to include the feedback from variable cloud cover, tropical thunderstorms, vegetation, etc., or your model will be garbage. The AGW models leave out much of the crucial material, or fudge it with invalid approximations or worse. Much of the information that would be necessary for an adequate analytic climate model is simply unknown or not yet understood.
Alternatively, you can base your model on what you actually observe; then you don’t need to address all the individual phenomena explicitly, because they are built into the empirical data automatically by nature. However, the AGW models never had any real correspondance with observation; they couldn’t even retrodict the climate of the previous century. Reluctantly recognising this (if never openly admitting it), the AGW ideologists found ways of “adjusting” the models and the data so that the failure of the theory was less obvious.
So what we have now is a mish-mash that is neither empirically nor analytically sound nor complete. Garbage, in fact. However, like garbage, it is not totally useless; if you hunt about in the muck long enough you can find grains of recyclable truth amid the lies. Perhaps, though, it’s not worth the bother; might as well chuck it all away.
Moderator: my post of
December 15, 2010 at circa 3 am ,
directly responding to
[Willis Eschenbach says:
December 14, 2010 at 6:14 pm]
seems to have gone missing.
This was perhaps the most important of my replies yesterday, so I’d be grateful if you could recover it.
PB.
******
steven mosher says:
December 14, 2010 at 10:31 pm
Now double the C02 and you’d see an effect of 1-3C at equillbrium. takes decades.
******
Nonsense. The GHG effect is essentially instantaneous. Some effects lag, of course, maybe up to 1000 yrs (time for oceans bottom-currents to complete a “cycle”), but unless one assumes some fantastically huge positive feedback, then the immediate effect is by far the largest.
beng December 16, 2010 at 5:54 am said: Nonsense. The GHG effect is essentially instantaneous. Some effects lag, of course, maybe up to 1000 yrs (time for oceans bottom-currents to complete a “cycle”), but unless one assumes some fantastically huge positive feedback, then the immediate effect is by far the largest.
Beng, believe me, it’s absolutely no use debating with Steven Mosher.
This all started as a result of my original comment here on December 14, 2010 at 1:54 am to which he replied on December 14, 2010 at 4:39 am to which I replied on December 14, 2010 at 2:15 pm.
Originally I had suggested that the hydrological cycle stabilizes the Earth’s temperature because the fixed physical environment of the Earth involves some very strong negative feedbacks that (in the absence of any other perturbations) cause the temperature to remain broadly constant. But, in contrast, adding an extra slug of CO2 is an open-ended perturbation possessing no feedback compensation mechanism of its own.
The result? The hydrological cycle does what it always does when presented with any upward (or downward) uncompensated perturbation, it simply adjusts its own strong negative feedbacks so as to exactly compensate for the CO2 perturbation.
To illustrate the significance of this, I drew an analogy between the Earth’s hydrological cycle and my 40kW house central heating boiler system which similarly keeps my house at a more-or-less fixed 21degC due to the fixed physics of the boiler and the very simple negative feedback loop involving its house thermostat.
The house analogy to adding CO2 to the atmosphere is that I turn on a 3kW electric fan heater (which has no thermostat) which just continues indefinitely to add heat to the interior of the house.
Does the house temperature go up? Does it heck! Why? Because the central heating system’s thermostat exactly compensates for the added heat from the 3kW heater by closing down for proportionately longer periods.
So, presented with this powerful analogy as to why we haven’t seen any sign of a warming effect due to the sharp upturn in post-WW2 man-made CO2 emissions, instead of addressing it head on he chooses to go off at a complete tangent by suggesting that:
(1) The effect of the post-WW2 upturn in emissions will take decades to show up in the temperature record. This is unsubstantiated rubbish, as you and others here have quickly pointed out (why, for example, doesn’t water vapor take that long?)
(2) Negative forcings from volcanoes etc. might have masked the positive forcing of the added CO2 for 40 years (and why not earlier? why not in the future?). This simply shows he didn’t understand my proposed mechanism which exactly compensates for all forcings whether positive or negative.
The bottom line is that Steven is a convinced warmist, which is absolutely his right and good luck to him. But if he really wants to take part in a useful debate with the highly intelligent skeptics that inhabit this blog, he should engage with their arguments just as they try to engage with his.
But this he always fails to do, so nobody learns anything.
It’s true, Mosh is like Gore in this respect. He just delivers his pronouncement, and ignores logical argument against it.
Very statesmanlike…
😉
Paul Birch says:
December 16, 2010 at 1:37 am
“How do you know? Have you tried it? I suspect that if you did you would find quite a strong correlation on at least some timescales. Certainly some control systems of this type do show such correlations – though sometimes the dependency can be opposite in sign to what you might expect (ie, the more dirt added, the lower the average dirt level).”
Per your own words, “I suspect” is an ideological model, not an empirical model. You will not find a strong correlation between future average dirt levels and average dirt level added because there is no strong correlation! The strong correlation is between future average dirt levels and specific dirt level added per unit area. If you add a lot of dirt to a small area, odds are high that future average dirt levels will go down a little. If you add the same amount of dirt to a wide area, future average dirt levels may go up or down. If you add a huge amount of dirt to the entire area, odds are extremely high that future average dirt levels will go down.
“(Steve)Finding the correlation requires knowing the details, not the averages….(Paul)It really doesn’t. Empirical correlations are found by plotting the empirical data.”
Well, a correlation can always be found, however weak it is. If your empirical data shows a weak correlation between two variables, you can create a weak model.
“(Steve) So I agree that you can create an empirical model, but I do not agree that it will have any predictive power, with power being the key word….(Paul) If it predicts either of the variables from the other at even slightly better than chance, then it has predictive power.”
Well then you have given us your definition of an predicatively powerful model – anything slightly better than chance (over any time scale, I assume). That is not my definition of a powerful model.
tallbloke says: December 16, 2010 at 9:15 am It’s true, Mosh is like Gore in this respect. He just delivers his pronouncement, and ignores logical argument against it. Very statesmanlike… 😉
Tallbloke, You’ve just given me a hell of an idea. Perhaps Steven Mosher is Al Gore.
Paul Birch says:
December 15, 2010 at 2:08 am
Paul, let me go over this again. I have said, very explicitly, that I do think it is possible to model the thunderstorm situation parametrically.
You have made this identical ludicrous claim before. I have pointed out before that your claim was nonsense, viz:
Seems pretty clear to me. I said it in the head post, and I repeated it in response to you the first time you made your current asinine claim. In response, you simply make the claim again.
This why I said that you will have to apply elsewhere for your further education. Here, you not only pay no attention to what I say. It appears you also pay no attention to what you have said.
Paul Birch says:
December 15, 2010 at 2:54 am
Paul, see my immediately previous post. I am tired of repeating myself, particularly to an unpleasant inquisitor who seems to not notice that he is asking the same question over and over DESPITE IT BEING ANSWERED!
So no, Paul, I will not answer your questions. You want answers from someone? Fine. Find someone who knows something, treat them decently and LISTEN WHEN THEY ANSWER!
Paul Birch says:
December 16, 2010 at 1:37 am
Ummmm … err … Paul, it’s a THOUGHT EXPERIMENT. How would someone “try it”???!??
Steve says:
December 16, 2010 at 9:22 am
“Per your own words, “I suspect” is an ideological model, not an empirical model.”
It is neither. It is a suspicion.
“You will not find a strong correlation between future average dirt levels and average dirt level added because there is no strong correlation!”
Prove it. My experience as a scientist and systems engineer leads me to suspect that there would in fact be quite a strong correlation for most viable algorithms and realistic deposition patterns. This is because of the lags that arise before the deposition is detected and the time it takes for the devils to work. If you believe (ideologically?!) that there is no such correlation, then you will have to show mathematically how your chosen algorithms produce no correlations for any reasonable deposition pattern. Neither Willis nor you has done this. Alternatively, go out and buy some actual dust devils (they do exist, though I’m not sure whether they’re on the consumer market outside Japan yet) and measure what happens empirically.
“The strong correlation is between future average dirt levels and specific dirt level added per unit area…”
These are not global “kitchen climate” correlations; they are the specific programmed responses of the devils’ algorithm locally. Quite different.
“Well, a correlation can always be found, however weak it is. If your empirical data shows a weak correlation between two variables, you can create a weak model.”
There are two ways in which a correlation can be “weak”. One is through being statistically insignificant. The other is through being noisy. In a climate model correlations are always going to be noisy, because of the effects of local weather, but that does not make them insignificant or useless.
“(Steve) So I agree that you can create an empirical model, but I do not agree that it will have any predictive power, with power being the key word….(Paul) If it predicts either of the variables from the other at even slightly better than chance, then it has predictive power.
“Well then you have given us your definition of an predicatively powerful model – anything slightly better than chance (over any time scale, I assume). That is not my definition of a powerful model.”
No, I have given you the standard scientific definition of predictive power. Just like physical power, you can have little or lots, pW or GW. How “powerful” a model may be depends upon what you want it for. A model may be next to worthless for predicting day to day local weather, yet powerful at predicting mean global temperature over timescales of a century or more. Or vice versa.
Willis Eschenbach says:
December 17, 2010 at 12:11 am
Paul Birch says: … you can get useful models without having to understand the underlying mechanisms. Indeed, for many purposes, an empirical model, by virtue of its simplicity, may well be superior to more sophisticated ones. Willis seems to be trying to argue that if the model doesn’t explicitly include regulation by tropical thunderstorms, etc., it’s no good. And that simply doesn’t follow. Whether or not deliberately including such thunderstorms would improve the predictive power of a global temperature model is an open question.
“Paul, let me go over this again. I have said, very explicitly, that I do think it is possible to model the thunderstorm situation parametrically. ”
I never said you couldn’t. I said that it’s an open question whether that would improve the predictive power of a global temperature model.
“Could we model it parametrically, without having to model individual thunderstorms? Probably … but the model has to be designed to do that. And the current climate models either are not designed to do it, or are not doing it successfully.”
I did not dispute this either. However, an empirical model does not have to include thunderstorms explicitly (whether “parametrically” or “individually”), and if our physical understanding or detailed knowledge of the phenomena is poor it may be superior to any sort of analytic model.
“You have made this identical ludicrous claim before. ”
What is this claim you consider “ludicrous”? That one can get useful empirical models without understanding all the underlying mechanisms? That’s basic scientific method and practise. It shouldn’t even be contentious. I even gave a specific well-established example: the empirical mass formula.
If it’s something else you think I’ve said that is “ludicrous”, “nonsense” or “asinine”, then please tell me what and why you think so. Simply repeating big chunks of your post doesn’t tell me what your problem is.
“This why I said that you will have to apply elsewhere for your further education. Here, you not only pay no attention to what I say. It appears you also pay no attention to what you have said.”
Pot. Kettle.
Willis Eschenbach says:
December 17, 2010 at 12:26 am
Paul Birch: If you can plot the overall amount of dirt against the amount of dirt being added, then find a correlation between the two, then you have a working model. Even if the dust devils themselves are a complete mystery to you. … Have you tried it?
“Ummmm … err … Paul, it’s a THOUGHT EXPERIMENT. How would someone “try it”???!??”
By putting representative algorithms into a computer model of the kitchen and running simulations. Then plotting the results. Alternatively, by performing a real experiment on the same lines.
Willis Eschenbach says:
December 17, 2010 at 12:16 am
Paul Birch says: Then kindly answer the questions and points I have raised in response to your previous posts, which at the time you discourteously ignored. The excuse of repeating yourself won’t wash. No one in those threads had raised those points previously.
“Paul, see my immediately previous post. I am tired of repeating myself, particularly to an unpleasant inquisitor who seems to not notice that he is asking the same question over and over DESPITE IT BEING ANSWERED!”
You did not respond to those points or the comments in which they were made at all. You completely ignored them. If you deny this, please direct me to the specific comment in which you claim to have answered my demonstration that even a single shell can in principle provide an arbitrarily large greenhouse warming factor.
“So no, Paul, I will not answer your questions. You want answers from someone? Fine. Find someone who knows something, treat them decently and LISTEN WHEN THEY ANSWER!”
You haven’t answered! You have responded – with a blatant misreading of what I actually said – to only a single one of the numerous distinct points I raised on December 14, 2010 at 6:52 am.
Paul Birch says:
December 17, 2010 at 4:10 am
“(Paul) Prove it. My experience as a scientist and systems engineer leads me to suspect that there would in fact be quite a strong correlation for most viable algorithms and realistic deposition patterns. (Steve) The strong correlation is between future average dirt levels and specific dirt level added per unit area…(Paul) These are not global “kitchen climate” correlations; they are the specific programmed responses of the devils’ algorithm locally. Quite different.”
Ding ding ding!
Willis explained that their is no direct causal relationship between average amount of dirt now, average dirt level added and future dirt levels (the “global kitchen climate”). There is no viable algorithm between these variables. The viable algorithm is between the current distribution of dirt, the distribution of dirt added and the future distribution of dirt. That was the entire point of his article – this climate can’t be (usefully) modeled without empirical data on the details.
“(Steve) Well then you have given us your definition of an predicatively powerful model – anything slightly better than chance (over any time scale, I assume). That is not my definition of a powerful model. (Paul) No, I have given you the standard scientific definition of predictive power. Just like physical power, you can have little or lots, pW or GW. How “powerful” a model may be depends upon what you want it for. A model may be next to worthless for predicting day to day local weather, yet powerful at predicting mean global temperature over timescales of a century or more. Or vice versa.”
Well, in your “experience as a scientist and systems engineer”, I suppose you didn’t learn that there actually is a standard scientific definition of predictive power. And your definition isn’t it. http://en.wikipedia.org/wiki/Predictive_power
If I can guess the average dirt level of the floor for a given period of time and be correct 1 time in 1,000 , and a model comes along and correctly predicts the average dirt level correctly 5 times in 1,000, that model is 5 times more powerful than guessing. But it still would not be considered a powerful model, because 95% of the time it’s wrong!
Your comments haven’t clarified or added to the original article in any way. I understand that you strongly believe some “global kitchen climate” correlations could be found if you analyze enough data, and you could use those correlations to create a model that’s wrong most of the time. Wonderful.
Steve says:
December 17, 2010 at 10:01 am
Thanks, Steve. You have pointed out (once again) the flaw in Paul’s argument.
Here’s the thing. Suppose we have positive feedback, such that any change in a variable ends up getting doubled in the output. We can see this and measure it. Then, as Paul suggests, we can use this to estimate the underlying algorithm. This method works great for a system with feedback.
But now let us suppose that we are in a “governed” system, that is to say a system with a governor. The example most people are familiar with is “cruise control” on your car. This is a governed system, as is the Tasmanian Dust Devil system above.
Here’s the problem. We get in a car, and we want to figure out the engine algorithm that relates the fuel use to the speed that the car is travelling. We note that when the fuel use goes up the speed goes up, and that when the fuel use goes down the speed goes down. We use that information to construct an algorithm for what’s happening regarding fuel and speed.
But when we repeat our experiment in a governed system like a car with cruise control, we see something surprising. Fuel use goes up and fuel use goes down, but the average speed doesn’t change at all! (Note that this also happens with the Tasmanian Dirt Devils, where dirt input goes up and down, but the average dirtiness of the floor stays constant.)
Now, if an input variable (fuel or dirt) changes and the output variable (speed or dirtiness) doesn’t change, we’re out of luck. At that point, it is not simply difficult (as Paul claims) to construct an approximation of the function relating the input to the output. Why?
Because they are not related. In a governed system, output (speed, dirtiness) is not a function of input (fuel or dirt added).
That is why the question of whether the earth has a governor should be the very first question we ask. I say yes here, but YMMV …
Finally, once again, hopefully for the last time to put this to bed, do I think we can model a governed system like the climate or the Tasmanian Dust Devils? I wrote my first computer program in 1963, and I have extensive experience with many types of computer models, including writing some. I say yes, of course in theory we can model the climate.
But to model a governed system like the Tasmanian Dirt Devils’ room or the global climate, you need to explicitly model the governor, and I know of no current climate that does that.