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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“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.”
Hello Willis,
A nice analogy that ties in perfectly with your analysis on the models, but I have to somewhat disagree with the statement above as being too simplistic and sometimes incorrect. A better term than “hot spots” might be “hot corridors” but that still wont remove the oversimplification.
From my long term observational experience in my neck of the woods (Florida), given the absence of a strong synoptic pattern summer thunderstorm activity is governed strictly by daily mesoscale events, i.e. sea breezes. There are no “hot spots” per se, but rather collision corridors created by two air masses traveling in opposite directions with nowhere to go but up when they collide. The position of the “corridors” for these collisions is set by the differential wind speed between the competing sea breezes (mainly the easterly component) and is accurately predicted on a daily basis by local meteorologists. Where the thunderstorms go from there is a result of which sea breeze wins the daily fight with the easterly component winning the large majority of them, so thunderstorms usually move to the west.
In your defense though, this too is kind of simplistic because there are many other variables that come into play. For example, this is a sunrise image made on the West coast of Florida:
http://www.josesuroeditorial.com/Nature/Clouds/Clouds/1228906_ry6rV#1024192053_eXxNC-O-LB
The image depicts an unusual but not rare event when the wind pattern is reversed and storms are born early in the morning, instead of in the afternoon/evening, and they move to the East rather than to the West.
Below are two images showing the more typical east to west summer pattern – evening storms:
http://www.josesuroeditorial.com/Landscapes/Scenics-2007/2348997_u6avH#179117325_6suat-O-LB
http://www.josesuroeditorial.com/Landscapes/Scenics-2008/4113375_y8Sqs#374868986_kbbD5-O-LB
Best,
Jose
Paul Birch says:
December 14, 2010 at 2:01 am
For example, thunderstorms are driven by a temperature difference between surface and stratosphere, not the absolute temperature; and there is evidently a variable latitude, or range of latitudes, by which the tropical mechanism ceases to operate. He’s had this pointed out to him several times before, but prefers to ignore it.
I distrust thought experiments because they are too Aristotelian for my taste. I want real data. But the quote from your post above has a contradiction in real data evaluation.
You say that thunderstorms are driven by temperature differences, not absolute temperatures.
And then “there is a range of latitudes by which the tropical mechanism ceases to operate” !!!
Higher latitudes mean lower SST s , i.e. absolute temperatures because of lower energy inputs from the sun.
Real data contradicts you.
Re: Figure 2 – WOW – look at the range of absolute temperatures! Apparently, they all start with different initial conditions. Oh well, it IS climate “science”…
Chris Edwards says:
December 14, 2010 at 3:49 am
Why do the warmist lot get the high paying jobs and will it stop?
Chris – my own research has show that government employees like Hansen, Schmidt, Karl, Lacis, et al. earn very comfortable six figure salaries – not including benefits. They also received salary increases in 2009 when people in the private sector were being laid off. And I’m sure the notoriety of people like Jim Hansen earn them additional incomes from their “outreach” efforts…
Jose Suro says:
December 14, 2010 at 7:38 am
Fabulous photographs!
/Mr Lynn
Paul Birch wrote:
In replying to Willis assertion that we have to model climate the way it is, you replied:
“No, we really don’t. We can empirically or analytically model those parts of the system and those mathematical relationships that interest us, without bothering with all the complexities that we’re not interested in, or don’t know much about, or can’t predict.”
This doesn’t make much sense to me. If you are saying we can model only those parts that interest us or we understand, then what we are doing is modeling a subset of a system. But I thought the idea is to model the climate as a whole – in other words, to predict the global averaged temperature at some future date. If you are saying that if all we want to do is model, say, the radiative forcing from CO2 levels, and ignore the effect on the climate as a whole, then I agree with you. But if you are implying that we can then go on and model the whole climate even though we have missed out bits of the whole, then I disagree.
Maybe I am misuderstanding your point here.
anna v says:
December 14, 2010 at 7:42 am
“Higher latitudes mean lower SST s , i.e. absolute temperatures because of lower energy inputs from the sun. Real data contradicts you.”
So you are saying that higher latitudes, with there relatively lower temperatures from surface to stratosphere, prove that thunderstorms are driven by absolute temperatures and not the surface-stratosphere temperature differential? I don’t see even one baby step in your chain of (bad) logic for that. Is there a lower temperature differential, yet more thunderstorms, at higher latitudes?
Thanks, Willis. At the risk of calling down the wrath of some, when I read the first part of your post, I thought you were heading down the path of showing why it is not possible to know exactly how much CO2 is being added to the atmosphere (or removed), simply based on the amount currently measured in the atmosphere. I think that would be a fair point as well and one that follows from your thought experiment.
steven mosher says:
B. Time lag. The response to C02 addition is not instananeous. Its not like a blanket despite stupid metaphors to that effect
Say the atmosphere doubles in CO2 concentration instantly. When do we see the effects? A year? Ten? A hundred? We see the effects of reduced W/M^2 everyday, not instantly sure. Why is CO2 forcing special, needing a lag?
“Now, you could look on 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. ”
In programming, bugs are features. They are features we do not desire, but they are features.
Awww, Willis, now you’ve gone and given the AGWers a clue.
@ur momisugly Paul Birch
“However, my point was that this does not tell you whether or not you have net positive or negative feedback. The Earth could have survived with the former – or suffered catastrophe with the latter.”
Err, no. Net positive feedback means that temperature is unbounded. In the “ball and bowl” analogy, net positive feedback is the inverted bowl. No matter how shallow the curve, the ball will roll off. We have a billion years or so of history to show this hasn’t happened. Therefore we have negative feedback in the system. The challenge to climate scientists should be to elucidate the cause and effect relationships which give rise to the negative feeedbacks both in the heating and cooling phases.
@Paul Birch:
“No, we really don’t. We can empirically or analytically model those parts of the system and those mathematical relationships that interest us, without bothering with all the complexities that we’re not interested in, or don’t know much about, or can’t predict.”
Huh? Are you sure you didn’t mean ‘can’t’? If not, why wouldn’t the exact opposite also be true? Isn’t this akin to arguing that being able to say with 100% certainty ‘the ball will stay within the wheel and roll around a bit’ means you can accurately model the outcome of a roulette game? The stock Market operates within defined boundaries that can be modelled, however if it were possible to dismiss inconvenient complexity with a hand wave, there would be some very rich software engineers out there.
It seems to me you are determined to set up a straw man in implying that Willis is arguing that because it is impossible to model a certain climatic system with any useful accuracy given the crudity of current models, it is impossible to construct any kind of model. The ‘useful accuracy’ part is vital to justify however if you are going to make prognostications of doom based on flimsy virtual evidence and circular reasoning. Hey, my climate model’s just told me it’s going to rain sometime. It works!
Aye – the Hadley Heat Pump.
There is a reason why heat pumps use a phase change, too – because it carries a whole lot more heat (without even requiring an actual change in temperature!) than is possible otherwise. No heat pump uses CO2…lol
Please note, also, that H2O gas requires no convection- it will rise because it is considerably less dense than the other gasses – addition of heat only makes it rise faster than fast.
I don’t see this as any kind of ’emergent’ phenomenon, though.
Paul Birch says:
December 14, 2010 at 6:52 am
“Sorry, but this does not follow. If adding dirt in a particular distribution increases or decreases the overall amount of dirt in approximate proportion to the added amount – or even leaves it the same – that is a linear relationship.”
Willis is stating that if the exact current distribution of dirt is known and the exact future deposit of dirt is known (all the devils), then yes we can model the kitchen floor’s “dirt climate”. You just need to know where all the dirt is now and where all the new dirt is going. From that, you know where TDD’s will appear and perform their clean up (after some experimental testing).
But if all you know are averages (the average depth of dirt on the floor now, and the average deposit of dirt coming in the future) you will not know where TDD’s will appear, so you cannot model the future “dirt climate”. Depending on how the dirt falls, future dirt could go up or down. There is no simple linear relationship between average dirt deposit and future average dirt level. You could certainly create a model, and over time it may give you average predictions that match average results! But for the particular result of any point in time, odds are high that the model will be wrong.
“(Willis) We get the same result if we add three units of dirt and three TDDs clean it up, or if we add one unit of dirt and one TDD cleans it up….(Paul) Not in general. A similar result, very probably. But exactly the same? Almost certainly not.”
Now that’s just damn funny. Willis creates a thought experiment with a fantasy creature called a Tasmanian Dirt Devil, and you tell him that he doesn’t get to say how his fantasy creation works! If the inventor of the thought experiment states that his creations follow a specific algorithm, guess what – you have to follow the thought experiment according to that algorithm!
I mean really, disagreeing on the physics of the Tasmanian Dirt Devil – priceless.
Steve says:
December 14, 2010 at 8:50 am
anna v says:
December 14, 2010 at 7:42 am
“Higher latitudes mean lower SST s , i.e. absolute temperatures because of lower energy inputs from the sun. Real data contradicts you.”
So you are saying that higher latitudes, with there relatively lower temperatures from surface to stratosphere, prove that thunderstorms are driven by absolute temperatures and not the surface-stratosphere temperature differential? I don’t see even one baby step in your chain of (bad) logic for that. Is there a lower temperature differential, yet more thunderstorms, at higher latitudes?
No, you are saying that.
Let me spell out what I am saying:
He (Birch) said that “the tropical mechanism ceases to operate at higher latitudes”. This is an observational fact. Also an observational fact is that the absolute temperature in the tropical region is larger than in the higher latitudes, as the link shows. Data again. The stratosphere has the same temperature, so it is the high absolute seas surface temperature that is responsible for creating the storms and not the large differential, considering that energy goes with T^4, and not with ( energy differential)^4, and it is heat energy that is feeding the tropical thunderstorms. The large differential is due to the absolute sea surface temperatures and is irrelevant to the problem.
I’d like to thank Charles Nelson for his marvelous analogy and thought game on rising air and its limits, or, more likely, lack of them.
anna v says:
December 14, 2010 at 7:42 am
“Let me spell out what I am saying:…The stratosphere has the same temperature, so it is the high absolute seas surface temperature that is responsible for creating the storms and not the large differential … The large differential is due to the absolute sea surface temperatures and is irrelevant to the problem.”
Ahhh, I see. Since it is the troposphere that is receiving the lion’s share of the heat, and increasing in absolute temperature, you have decided that the causal mechanism for thunderstorms can be seen as independent of the troposphere-stratosphere temperature differential.
Since Earth’s climate is relatively stable, over the geologically short term you may be correct (I don’t know). Given the average absolute temperature of the troposphere during any point in time, you are saying someone could accurately predict the average number of thunderstorms at any given latitude. Maybe Andy could speak on that – what do meteorologists do? Yeah, I know it’s weather, not climate, but in predicting thunderstorms do meteorologists compare troposphere-stratosphere temperature differentials, or do they just care about pressure, (absolute) temperature and latitude? If they knew the troposphere-stratosphere temperatures, would their thunderstorm predictions be more accurate (i.e., how relevant is the data)?
Which doesn’t change the fact that thunderstorm’s, causally, do rely on the troposphere/stratosphere temperature differential. The stratosphere does not have an evenly distributed temperature throughout all latitudes, by the way (I assume that’s what you meant by “the stratosphere is the same temperature”).
anna v says:
December 14, 2010 at 10:25 am
Anna – like you, I am amazed at the people who show up here and seem to believe that absolute temperatures have no place in atmospheric physics. Let’s take a look a just basic thermodynamics. There are, for example:
* boiling and freezing points of water
* the ideal gas law
* the Stefan-Boltzmann relation for radiation heat flux, q” = sigma*T^4
* Thermodynamic and transport property variations with temperature (e.g. specific heat, viscosity, thermal conductivity)
* saturation pressure versus temperature
* entropy
This is why comparisons like Figure 2 are interesting. Why is it that climate scientists are apparently OK with GCM numerical solutions where the mean absolute temperature levels between different codes vary by a full 5 deg C!
@ur momisugly Frank K. “government employees like Hansen, Schmidt, Karl, Lacis, et al. earn very comfortable six figure salaries”
OK, maybe they are paid six figures, but are you really saying they earn them ?
I think Paul Birch is a meanie and has poor manners; but I haven’t the slightest idea what he’s talking about.
Steve says:
December 14, 2010 at 11:15 am
Which doesn’t change the fact that thunderstorm’s, causally, do rely on the troposphere/stratosphere temperature differential. The stratosphere does not have an evenly distributed temperature throughout all latitudes, by the way (I assume that’s what you meant by “the stratosphere is the same temperature”).
OK,have a look at the temperatures in the stratosphere.
at 30.000 meters it plays between -43 and -45C
the tropical sea is +28 – +30C difference 75C
higher latitudes +16 – +19C difference 64C, taking upper numers
My claim is that it is not the 9 degree change in the difference, but the 11 degree hotter sea surface temperature in the tropics compared with higher latitudes that starts the process that leads to tropical thunderstorms.
oh dear, that should be 11 not 9 🙁 .
Let us see the details, but closely:
In the beginning of time, man produced heat by friction between two wood sticks. What does it happen at the end of those sticks?, does it happen the same in every phenomenon where heat/energy appears?
We need to get back to the basics to understand, otherwise we just put names….
Temperature is a “necessary” condition, but not “sufficient” by itself to initiate thunderstorm development. Regardless of temperature, you cannot have thunderstorm development with out adequate moisture (humidity), the proper temperature humidity profile with altitude (instability) and some mechanism to initiate lift.
The large temperature differential does provide more convective energy once things get kicked off, but you can have thunderstorms even in much lower temperatures if you have the proper atmospheric temperature/humidity profile and a strong enough trigger to initiate lifting of the unstable air far enough for it to cross the convective barrier and begin rising due to buoyancy.
In the mid latitudes where the Hadley cell circulation is downward, you have a strong bias against lifting and convection because the air mass as a whole is sinking, warming and drying out. You would need a very strong injection of moisture at low levels and a very strong lifting mechanism such as a front passage to over come this general sinking of the atmospheric column.
Larry
Steven Mosher says:
December 14, 2010 at 4:39 am
David Socrates says: December 14, 2010 at 1:54 am: Willis Eschenbach, ably assisted by Charles Nelson (December 13, 2010 at 11:48 pm), have between them most entertainingly put this global warming conundrum to bed. Sensible skeptics start from the twin observations that:
(1) Contrary to uninformed doom mongering over the past 30 years, at only 0.5degC or so per century the Earth’s temperature is steadfastly failing to rise in correspondence with the sharp (~30%) post-WW2 upturn in anthropogenic GHGs.
(2) The straightforward physics of CO2 indicates that an increase in its atmospheric concentration should have a significant and potentially worrying warming effect.
########
A. you’d have to net out negative forcings ( like volcanoes ) to get the isolated impact of GHGs
B. Time lag. The response to C02 addition is not instananeous. Its not like a blanket despite stupid metaphors to that effect
##########
Steven,
What on EARTH are you on about?
I write a considered piece showing how Earth’s thermostat (the hydrological cycle) will always exactly compensate for every relatively minor perturbation including increases in CO2.
And what do you do? You completely ignore the main thrust of my argument (which you would be perfectly entitled to argue with, constructively, if you disagreed) and instead talk obliquely about another minor perturbation, the cooling effect of volcanoes, which (if you had understood what I was saying) you would have realised would also be compensated for exactly by the same mechanism.
Then, just for good measure, you introduce the issue of time lags and “blanket” warming metaphors. Did my analysis mention blankets? No. Is my analysis affected in any way by time lags? No. So why not address my analysis head on if you disagree with it rather than trying to be uber>-clever by mentioning factors that are quite irrelevant?
This is not a complicated issue. Why turn it into one? Obviously you are no engineer!