Guest Essay by Willis Eschenbach
Abstract
The Thermostat Hypothesis is that tropical clouds and thunderstorms, along with other emergent phenomena like dust devils, tornadoes, and the El Nino/La Nina alteration, actively regulate the temperature of the earth. This keeps the earth at an equilibrium temperature.
Several kinds of evidence are presented to establish and elucidate the Thermostat Hypothesis – historical temperature stability of the Earth, theoretical considerations, satellite photos, and a description of the equilibrium mechanism.
Historical Stability
The stability of the earth’s temperature over time has been a long-standing climatological puzzle. The globe has maintained a temperature of ± ~ 3% (including ice ages) for at least the last half a billion years during which we can estimate the temperature. During the Holocene, temperatures have not varied by ±1%. And during the glaciation periods, the temperature was generally similarly stable as well.
In contrast to Earth’s temperature stability, solar physics has long indicated (Gough, 1981; Bahcall et al., 2001) that 4 billion years ago the total solar irradiance was about three-quarters of the current value. In early geological times, however, the earth was not correspondingly cooler. Temperature proxies such as deuterium/hydrogen ratios and 16O/18O ratios show no sign of a corresponding warming of the earth over this time. Why didn’t the earth warm as the sun warmed?
This is called the “Faint Early Sun Paradox” (Sagan and Mullen, 1972), and is usually explained by positing an early atmosphere much richer in greenhouse gases than the current atmosphere.
However, this would imply a gradual decrease in GHG forcing which exactly matched the incremental billion-year increase in solar forcing to the present value. This seems highly unlikely.
A much more likely candidate is some natural mechanism that has regulated the earth’s temperature over geological time.
Theoretical Considerations
Bejan (Bejan 2005) has shown that the climate can be robustly modeled as a heat engine, with the ocean and the atmosphere being the working fluids. The tropics are the hot end of the heat engine. Some of that tropical heat is radiated back into space. Work is performed by the working fluids in the course of transporting the rest of that tropical heat to the Poles. There, at the cold end of the heat engine, the heat is radiated into space. Bejan showed that the existence and areal coverage of the Hadley cells is a derivable result of the Constructal Law. He also showed how the temperatures of the flow system are determined.
“We pursue this from the constructal point of view, which is that the [global] circulation itself represents a flow geometry that is the result of the maximization of global performance subject to global constraints.”
“The most power that the composite system could produce is associated with the reversible operation of the power plant. The power output in this limit is proportional to
where q is the total energy flow through the system (tropics to poles), and TH and TL are the high and low temperatures (tropical and polar temperatures in Kelvins).
The system works ceaselessly to maximize that power output. Here is a view of the entire system that transports heat from the tropics to the poles.
Figure 1. The Earth as a Heat Engine. The equatorial Hadley Cells provide the power for the system. Over the tropics, the sun (orange arrows) is strongest because it hits the earth most squarely. The length of the orange arrows shows relative sun strength. Warm dry air descends at about 30N and 30S, forming the great desert belts that circle the globe. Heat is transported by a combination of the ocean and the atmosphere to the poles. At the poles, the heat is radiated to space.
In other words, flow systems such as the Earth’s climate do not assume a stable temperature willy-nilly. They reshape their own flow in such a way as to maximize the energy produced and consumed. It is this dynamic process, and not a simple linear transformation of the details of the atmospheric gas composition, which sets the overall working temperature range of the planet.
Note that the Constructal Law says that any flow system will “quasi-stabilize” in orbit around (but never achieve) some ideal state. In the case of the climate, this is the state of maximum total power production and consumption. And this in turn implies that any watery planet will oscillate around some equilibrium temperature, which is actively maintained by the flow system. See the paper by Ou listed below for further information on the process.
Climate Governing Mechanism
Every heat engine has a throttle. The throttle is the part of the engine that controls how much energy enters the heat engine. A motorcycle has a hand throttle. In an automobile, the throttle is called the gas pedal. It controls incoming energy.
The stability of the earth’s temperature over time (including alternating bi-stable glacial/interglacial periods), as well as theoretical considerations, indicates that this heat engine we call climate must have some kind of governor controlling the throttle.
While all heat engines have a throttle, not all of them have a governor. In a car, a governor is called “Cruise Control”. Cruise control is a governor that controls the throttle (gas pedal). A governor adjusts the energy going to the car engine to maintain a constant speed regardless of changes in internal and external forcing (e.g. hills, winds, engine efficiency, and losses).
We can narrow the candidates for this climate governing mechanism by noting first that a governor controls the throttle (which in turn controls the energy supplied to a heat engine). Second, we note that a successful governor must be able to drive the system beyond the desired result (overshoot).
(Note that a governor, which contains a hysteresis loop capable of producing overshoot, is different from a simple negative feedback of the type generally described by the IPCC. A simple negative feedback can only reduce an increase. It cannot maintain a steady state despite differing forcings, variable loads, and changing losses. Only a governor can do that.)
The majority of the earth’s absorption of heat from the sun takes place in the tropics. The tropics, like the rest of the world, are mostly ocean; and the land that is there is wet. The steamy tropics, in a word. There is little ice there, so the clouds control how much energy enters the climate heat engine.
I propose that two interrelated but separate mechanisms act directly to regulate the earth’s temperature — tropical cumulus and cumulonimbus clouds. Cumulus clouds are the thermally-driven fluffy “cotton ball” clouds that abound near the surface on warm afternoons. Cumulonimbus clouds are thunderstorm clouds, which start life as simple cumulus clouds. Both types of clouds are part of the throttle control, reducing incoming energy. In addition, the cumulonimbus clouds are active refrigeration-cycle heat engines, which provide the necessary overshoot to act as a governor on the system.
A pleasant thought experiment shows how this cloud governor works. It’s called “A Day In the Tropics”.
I live in the deep, moist tropics, at 9°S, with a view of the South Pacific Ocean from my windows. Here’s what a typical day looks like. In fact, it’s a typical summer day everywhere in the Tropics. The weather report goes like this:
Clear and calm at dawn. Light morning winds, clouding up towards noon. In the afternoon, increasing clouds and wind with showers and thundershowers developing as the temperature rises. Thunderstorms continuing after dark, and clearing some time between sunset and early hours of the morning, with progressive clearing and calming until dawn.
That’s the most common daily cycle of tropical weather, common enough to be a cliché around the world.
It is driven by the day/night variations in the strength of the sun’s energy. Before dawn, the atmosphere is typically calm and clear. As the ocean (or moist land) heats up, air temperature and evaporation increase. Warm moist air starts to rise. Soon the rising moist air cools and condenses into clouds. The clouds reflect the sunlight. That’s the first step of climate regulation. Increased temperature leads to clouds. The clouds close the throttle slightly, reducing the energy entering the system. They start cooling things down. This is the negative feedback part of the cloud climate control.
The tropical sun is strong, and despite the negative feedback from the cumulus clouds, the day continues to heat up. The more the sun hits the ocean, the more warm, moist air is formed, and the more cumulus clouds form. This, of course, reflects more sun, and the throttle closes a bit more. But the day continues to warm.
The full development of the cumulus clouds sets the stage for the second part of temperature regulation. This is not simply negative feedback. It is the climate governing system. As the temperature continues to rise, as the evaporation climbs, some of the fluffy cumulus clouds suddenly transform themselves. They rapidly extend skywards, quickly thrusting up to form cloud pillars thousands of meters high. In this way, cumulus clouds are transformed into cumulonimbus or thunderstorm clouds.
The columnar body of the thunderstorm acts as a huge vertical heat pipe. The thunderstorm sucks up warm, moist air at the surface and shoots it skyward. At altitude the water condenses, transforming the latent heat into sensible heat. The air is rewarmed by this release of sensible heat and continues to rise within the thunderstorm tower.
At the top, the rising much dryer air is released from the cloud up high, way above most of the CO2, water vapor, and other greenhouse gases. In that rarified atmosphere, the air is much freer to radiate to space. By moving inside the thunderstorm heat pipe, the rising air bypasses any interaction with most greenhouse gases and comes out near the top of the troposphere. During the transport aloft, there is no radiative or turbulent interaction between the rising air inside the tower and the surrounding lower and middle troposphere. Inside the thunderstorm, the rising air is tunneled through most of the troposphere to emerge at the top.
In addition to reflecting sunlight from their top surface as cumulus clouds do, and transporting heat to the upper troposphere where it radiates easily to space, thunderstorms cool the surface in a variety of other ways, particularly over the ocean.
1. Wind driven evaporative cooling. Once the thunderstorm starts, it creates its own wind around the base. This self-generated wind increases evaporation in several ways, particularly over the ocean.
a) Evaporation rises linearly with wind speed. At a typical squall wind speed of 10 meters per second (“m/s”, about 20 knots or 17 miles per hour), evaporation is about ten times greater than at “calm” conditions (conventionally taken as 1 m/s).
b) The wind increases evaporation by creating spray and foam, and by blowing water off of trees and leaves. These greatly increase the evaporative surface area, because the total surface area of the millions of droplets is evaporating as well as the actual surface itself.
c) To a lesser extent, the surface area is also increased by wind-created waves (a wavy surface has a larger evaporative area than a flat surface).
d) Wind-created waves in turn greatly increase turbulence in the atmospheric boundary layer. This increases evaporation by mixing dry air down to the surface and moist air upwards.
e) As spray rapidly warms to air temperature, which in the tropics can be warmer than ocean temperature, evaporation also rises above the sea surface evaporation rate.
2. Wind and wave driven albedo increase. The white spray, foam, spindrift, changing angles of incidence, and white breaking wave tops greatly increase the albedo of the sea surface. This reduces the energy absorbed by the ocean.
3. Cold rain and cold wind. As the moist air rises inside the thunderstorm’s heat pipe, water condenses and falls. Since the water is originating from condensing or freezing temperatures aloft, it cools the lower atmosphere it falls through, and it cools the surface when it hits. Also, the droplets are being cooled as they fall by evaporation.
In addition, the falling rain entrains a cold wind. This cold wind blows radially outwards from the center of the falling rain, cooling the surrounding area. This is quite visible in the video below.
4. Increased reflective area. White fluffy cumulus clouds are not very tall, so basically they only reflect from the tops. On the other hand, the vertical pipe of the thunderstorm reflects sunlight along its entire length. This means that thunderstorms reflect sunlight from an area of the ocean out of proportion to their footprint, particularly in the late afternoon.
5. Modification of upper tropospheric ice crystal cloud amounts (Lindzen 2001, Spencer 2007). These clouds form from the tiny ice particles that come out of the smokestack of the thunderstorm heat engines. It appears that the regulation of these clouds has a large effect, as they are thought to warm (through IR absorption) more than they cool (through reflection).
6. Enhanced night-time radiation. Unlike long-lived stratus clouds, cumulus and cumulonimbus often die out and vanish in the early morning hours, leading to the typically clear skies at dawn. This allows greatly increased nighttime surface radiative cooling to space.
7. Delivery of dry air to the surface. The air being sucked from the surface and lifted to altitude is counterbalanced by a descending flow of replacement air emitted from the top of the thunderstorm. This descending air has had the majority of the water vapor stripped out of it inside the thunderstorm, so it is relatively dry. The dryer the air, the more moisture it can pick up for the next trip to the sky. This increases the evaporative cooling of the surface.
8. Increased radiation through descending dry air. The descending dry air mentioned above is far more transparent to surface radiation than normal moist tropical air. This increases overall radiation to space.
In part because they utilize such a wide range of cooling mechanisms, cumulus clouds and thunderstorms are extremely good at cooling the surface of the earth. Together, they form the governing mechanism for the tropical temperature.
But where is that mechanism?
The problem with my thought experiment of describing a typical tropical day is that it is always changing. The temperature goes up and down, the clouds rise and fall, day changes to night, the seasons come and go. Where in all of that unending change is the governing mechanism? If everything is always changing, what keeps it the same month to month and year to year? If conditions are always different, what keeps it from going off the rails?
In order to see the governor at work, we need a different point of view. We need a point of view without time. We need a timeless view without seasons, a point of view with no days and nights. And curiously, in this thought experiment called “A Day In the Tropics”, there is such a timeless point of view, where not only is there no day and night, but where it’s always summer.
The point of view without day or night, the point of view from which we can see the climate governor at work, is the point of view of the sun. Imagine that you are looking at the earth from the sun. From the sun’s point of view, there is no day and night. All parts of the visible face of the earth are always in sunlight—the sun never sees the nighttime. And it’s always summer under the sun.
If we accept the convenience that the north is up, then as we face the earth from the sun, the visible surface of the earth is moving from left to right as the planet rotates. So the left-hand edge of the visible face is always at sunrise, and the right-hand edge is always at sunset. Noon is a vertical line down the middle. From this timeless point of view, morning is always and forever on the left, and afternoon is always on the right. In short, by shifting our point of view, we have traded time coordinates for space coordinates. This shift makes it easy to see how the governor works.
The tropics stretch from left to right across the circular visible face. We see that near the left end of the tropics, after sunrise, there are very few clouds. Clouds increase as you look further to the right. Around the noon line, there are already cumulus. And as we look from left to right across the right side of the visible face of the earth, towards the afternoon, more and more cumulus clouds and increasing numbers of thunderstorms cover a large amount of the tropics.
It is as though there is a graduated mirror shade over the tropics, with the fewest cloud mirrors on the left, slowly increasing to extensive cloud mirrors and thunderstorm coverage on the right.
After coming up with this hypothesis that as seen from the sun, the right-hand side of the deep tropical Pacific Ocean would have more clouds than the left-hand side), I thought “Hey, that’s a testable proposition to support or demolish my hypothesis”. So in order to investigate whether this postulated increase in clouds on the right-hand side of the Pacific actually existed, I took an average of 24 pictures of the Pacific Ocean taken at local noon on the 1st and 15th of each month over an entire year. I then calculated the average change in albedo and thus the average change in forcing at each time. Here is the result:
Figure 2. Average of one year of GOES-West weather satellite images taken at satellite local noon. The Intertropical Convergence Zone is the bright band in the yellow rectangle. Local time on earth is shown by black lines on the image. Time values are shown at the bottom of the attached graph. The red line on the graph is the solar forcing anomaly (in watts per square meter) in the area outlined in yellow. The black line is the albedo value in the area outlined in yellow.
The graph below the image of the earth shows the albedo and solar forcing in the yellow rectangle which contains the Inter-Tropical Convergence Zone. Note the sharp increase in the albedo between 10:00 and 11:30. You are looking at the mechanism that keeps the earth from overheating. It causes a change in insolation of -60 W/m2 between ten and noon.
Now, consider what happens if for some reason the surface of the tropics is a bit cool. The sun takes longer to heat up the surface. Evaporation doesn’t rise until later in the day. Clouds are slow to appear. The first thunderstorms form later, fewer thunderstorms form, and if it’s not warm enough those giant surface-cooling heat engines don’t form at all.
And from the point of view of the sun, the entire mirrored shade shifts to the right, letting more sunshine through for longer. The 60 W/m2 reduction in solar forcing doesn’t take place until later in the day, increasing the local insolation.
When the tropical surface gets a bit warmer than usual, the mirrored shade gets pulled to the left, and clouds form earlier. Hot afternoons drive thunderstorm formation, which cools and air conditions the surface. In this fashion, a self-adjusting cooling shade of thunderstorms and clouds keeps the afternoon temperature within a narrow range.
Now, some scientists have claimed that clouds have a positive feedback. Because of this, the areas where there are more clouds will end up warmer than areas with fewer clouds. This positive feedback is seen as the reason that clouds and warmth are correlated.
I and others take the opposite view of that correlation. I hold that the clouds are caused by the warmth, not that the warmth is caused by the clouds.
Fortunately, we have way to determine whether changes in the reflective tropical umbrella of clouds and thunderstorms are caused by (and thus limiting) overall temperature rise, or whether an increase in clouds is causing the overall temperature rise. This is to look at the change in albedo with the change in temperature. Here are two views of the tropical albedo, taken six months apart. August is the warmest month in the Northern Hemisphere. As indicated, the sun is in the North. Note the high albedo (areas of light blue) in all of North Africa, China, and the northern part of South America and Central America. By contrast, there is low albedo in Brazil, Southern Africa, and Indonesia/Australia.
Figure 3. Monthly Average Albedo. Timing is half a year apart. August is the height of summer in the Northern Hemisphere. February is the height of summer in the Southern Hemisphere. Light blue areas are the most reflective (greatest albedo)
In February, on the other hand, the sun is in the South. The albedo situation is reversed. Brazil and Southern Africa and Australasia are warm under the sun. In response to the heat, clouds form, and those areas now have a high albedo. By contrast, the north now has a low albedo, with the exception of the reflective Sahara and Rub Al Khali Deserts.
Clearly, the cloud albedo (from cumulus and cumulonimbus) follows the sun north and south, keeping the earth from overheating. This shows quite definitively that rather than the warmth being caused by the clouds, the clouds are caused by the warmth.
Quite separately, these images show in a different way that warmth drives cloud formation. We know that during the summer, the land warms more than the ocean. If temperature is driving the cloud formation, we would expect to see a greater change in the albedo over land than over the ocean. And this is clearly the case. We see in the North Pacific and the Indian Ocean that the sun increases the albedo over the ocean, particularly where the ocean is shallow. But the changes in the land are in general much larger than the changes over the ocean. Again this shows that the clouds are forming in response to, and are therefore limiting, increasing warmth.
How the Governor Works
Tropical cumulus production and thunderstorm production are driven by air density. Air density is a function of temperature (affecting density directly) and evaporation (water vapor is lighter than air).
A thunderstorm is both a self-generating and self-sustaining heat engine. The working fluids are moisture-laden warm air and liquid water. Self-generating means that whenever it gets hot enough over the tropical ocean, which is almost every day, at a certain level of temperature and humidity, some of the fluffy cumulus clouds suddenly start changing. The tops of the clouds streak upwards, showing the rising progress of the warm surface air. At altitude, the rising air exits the cloud, replace by more air from below. Suddenly, in place of a placid cloud, there is an active thunderstorm.
“Self-generating” means that thunderstorms arise spontaneously as a function of temperature and evaporation. They are what is called an “emergent” phenomenon, meaning that they emerge from th background when certain conditions are met. In the case of thunderstorms, this generally comes down to the passing of a temperature threshold.
Above the temperature threshold necessary to create the first thunderstorm, the number of thunderstorms rises rapidly. This rapid increase in thunderstorms limits the amount of temperature rise possible.
“Self-sustaining” means that once a thunderstorm gets going, it no longer requires the full initiation temperature necessary to get it started. This is because the self-generated wind at the base, plus dry air falling from above, combine to drive the evaporation rate way up. The thunderstorm is driven by air density. It requires a source of light air. The density of the air is determined by both temperature and moisture content (because curiously, water vapor at molecular weight 16 is only a bit more than half as heavy as air, which has a weight of about 29). So moist air is light air.
Evaporation is not a function of temperature alone. It is governed a complex mix of wind speed, water temperature, and vapor pressure. Evaporation is calculated by what is called a “bulk formula”, which means a formula based on experience rather than theory. One commonly used formula is:
E = VK(es – ea)
where
E = evaporation
V= wind speed (function of temperature difference [∆T])
K = coefficient constant
es = vapor pressure at evaporating surface (function of water temperature in degrees K to the fourth power)
ea = vapor pressure of overlying air (function of relative humidity and air temperature in degrees K to the fourth power)
The critical thing to notice in the formula is that evaporation varies linearly with wind speed. This means that evaporation near a thunderstorm can be an order of magnitude greater than evaporation a short distance away.
In addition to the changes in evaporation, there at least one other mechanism increasing cloud formation as wind increases. This is the wind-driven production of airborne salt crystals. The breaking of wind-driven waves produces these microscopic crystals of salt. The connection to the clouds is that these crystals are the main condensation nuclei for clouds that form over the ocean. The production of additional condensation nuclei, coupled with increased evaporation, leads to larger and faster changes in cloud production with increasing temperature.
Increased wind-driven evaporation means that to get the same air density, the surface temperature can be lower than the temperature required to initiate the thunderstorm. This means that the thunderstorm will still survive and continue cooling the surface to well below the starting temperature.
This ability to drive the temperature lower than the starting point is what distinguishes a governor from a negative feedback. A thunderstorm can do more than just reduce the amount of surface warming. It can actually mechanically cool the surface to below the required initiation temperature. This allows it to actively maintain a fixed temperature in the region surrounding the thunderstorm.
A key feature of this method of control (changing incoming power levels, performing work, and increasing thermal losses to quelch rising temperatures) is that the equilibrium temperature is not governed by changes in the amount of losses or changes in the forcings in the system. The equilibrium temperature is set by the response of wind and water and cloud to increasing temperature, not by the inherent efficiency of or the inputs to the system.
In addition, the equilibrium temperature is not affected much by changes in the strength of the solar irradiation. If the sun gets weaker, evaporation decreases, which decreases clouds, which increases the available sun. This is the likely answer the long-standing question of how the earth’s temperature has stayed stable over geological times, during which time the strength of the sun has increased markedly.
Gradual Equilibrium Variation and Drift
If the Thermostat Hypothesis is correct and the earth does have an actively maintained equilibrium temperature, what causes the slow drifts and other changes in the equilibrium temperature seen in both historical and geological times?
As shown by Bejan, one determinant of running temperature is how efficient the whole global heat engine is in moving the terawatts of energy from the tropics to the poles. On a geological time scale, the location, orientation, and elevation of the continental land masses is obviously a huge determinant in this regard. That’s what makes Antarctica different from the Arctic today. The lack of a land mass in the Arctic means warm water circulates under the ice. In Antarctica, the cold goes to the bone …
In addition, the oceanic geography which shapes the currents carrying warm tropical water to the poles and returning cold water (eventually) to the tropics is also a very large determinant of the running temperature of the global climate heat engine.
In the shorter term, there could be slow changes in the albedo. The albedo is a function of wind speed, evaporation, cloud dynamics, and (to a lesser degree) snow and ice. Evaporation rates are fixed by thermodynamic laws, which leave only wind speed, cloud dynamics, and snow and ice able to affect the equilibrium.
The variation in the equilibrium temperature may, for example, be the result of a change in the worldwide average wind speed. Wind speed is coupled to the ocean through the action of waves, and long-term variations in the coupled ocean-atmospheric momentum occur. These changes in wind speed may vary the equilibrium temperature in a cyclical fashion.
Or it may be related to a general change in color, type, or extent of either the clouds or the snow and ice. The albedo is dependent on the color of the reflecting substance. If reflections are changed for any reason, the equilibrium temperature could be affected. For snow and ice, this could be e.g. increased melting due to black carbon deposition on the surface. For clouds, this could be a color change due to aerosols or dust.
Finally, the equilibrium variations may relate to the sun. The variation in magnetic and charged particle numbers may be large enough to make a difference. There are strong suggestions that cloud cover is influenced by the 22-year solar Hale magnetic cycle, and this 14-year record only covers part of a single Hale cycle. However, I have yet to find any significant evidence of this effect on any surface weather variables, including clouds.
Conclusions and Musings
1. The sun puts out more than enough energy to totally roast the earth. It is kept from doing so by the clouds reflecting about a third of the sun’s energy back to space. As near as we can tell, over billions of years, this system of increasing cloud formation to limit temperature rises has never failed.
2. This reflective shield of clouds forms in the tropics in response to increasing temperature.
3. As tropical temperatures continue to rise, the reflective shield is assisted by the formation of independent heat engines called thunderstorms. These cool the surface in a host of ways, move heat aloft, and convert heat to work.
4. Like cumulus clouds, thunderstorms also form in response to increasing temperature.
5. Because they are temperature driven, as tropical temperatures rise, tropical thunderstorms and cumulus production increase. These combine to regulate and limit the temperature rise. When tropical temperatures are cool, tropical skies clear and the earth rapidly warms. But when the tropics heat up, cumulus and cumulonimbus put a limit on the warming. This system keeps the earth within a fairly narrow band of temperatures (e.g., a change of only 0.7°C over the entire 20th Century).
6. The earth’s temperature regulation system is based on the unchanging physics of wind, water, and cloud.
7. This is a reasonable explanation for how the temperature of the earth has stayed so stable (or more recently, bi-stable as glacial and interglacial) for hundreds of millions of years.
Further Reading
Bejan, A, and Reis, A. H., 2005, Thermodynamic optimization of global circulation and climate, Int. J. Energy Res.; 29:303–316. Available online here.
Richard S. Lindzen, Ming-Dah Chou, and A. Y. Hou, 2001, Does the Earth Have an Adaptive Infrared Iris?, doi: 10.1175/1520-0477(2001)082<0417:DTEHAA>2.3.CO;2, Bulletin of the American Meteorological Society: Vol. 82, No. 3, pp. 417–432. Available online here.
Ou, Hsien-Wang, Possible Bounds on the Earth’s Surface Temperature: From the Perspective of a Conceptual Global-Mean Model, Journal of Climate, Vol. 14, 1 July 2001. Available online here (pdf).
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bill (05:09:38), thanks for your comment. You say
You are 100% correct that there are a few trace gases with big effects, far out of proportion to their size. So you are right, size alone is not the determinant.
CO2, however, is not one of those high-effect gases. If you cut ozone levels in half, since it is the only significant absorber of incoming UV, it will make a big difference in UV levels. According to your most interesting citation (thanks, it went into the library), basically, cut ozone in half and UV doubles.
CO2, on the other hand, is not the major absorber of outgoing IR. Double the CO2 and surface forcing definitely does not double.
Total downwelling radiation at the surface is on the order of 490 W/m2. This is comprised of about 170 W/m2 downwelling longwave radiation from the sun, and about 320 W/m2 from downwelling IR (so-called greenhouse radiation).
If we double the CO2 in the air to 720 ppmv (which is a century away if not more), it will increase the downwelling radiation by something on the order of 3.7 W/m2. This is less than a one percent change in total forcing, far too small to measure.
So I fear your analogy doesn’t fit the situation. Yes, cutting ozone in half will double downwelling UV … but doubling CO2 only changes surface forcing by less than 1%, an undetectable amount.
Often, people don’t realize how much energy runs through the heat engine w call climate. One of the ways of discussing such a system involves the idea of first-order and second-order forcings. First-order forcings for me are ones that can change a system’s forcings by ten percent or more. ote that a doubling of ozone make a 50% change in the UV, so it is a first-order forcing for UV.
Since the total downwelling radiation at the surface is about 500 W/m2, ten percent of that is 50 W/m2. So what are the first-order forcings?
I show above that in the ITCZ, clouds can cause a 60W/m2 change in about an hour and a half. And a cloud covering your head, or your state, can reflect 500 W/m2 or more of sunlight, so certainly clouds have to be a first-order forcing.
Land use and land cover change (“LULC” in the trade) is another first-order forcing. If you cut down a forest, the albedo skyrockets. Going from a fir forest (albedo 0.1) to bare earth (albedo 0.3) is about a seventy W/m2 change, so that is a first order forcing.
Water vapor is another first-order forcing. It absorbs more than three-quarters of the 350 w/m2 of IR intercepted by the atmosphere, call it about 250 W/m2. Cut H2Ovap in half, a swing of 125 W/m2 … yes, water vapor is a first order forcing.
Wind is most definitely a first order forcing. Doubling the wind speed doubles the evaporation, radically changing the evaporation rate and moving huge amounts of energy.
Second-order forcings for me are those that make a change of one to ten percent of the total forcing. For the climate system, this is five to fifty W/m2. By this criteria, at 3.7 W/m2, a doubling of CO2 doesn’t make the grade. It is a third-order forcing.
So like I tried unsuccessfully to say before, if you want to spend your time concerned with third-order forcings, that’s fine by me. You are correct that the issue is not the amount in the atmosphere, my bad. What I was trying to say is that I’m working to understand the first-order forcings …
w.
bill (06:13:27), sorry, it’s taken me a big to take a hard look at your citation, “On the observed near-cancellation between longwave and shortwave cloud forcing in tropical regions”. You had said:
This is an excellent example of the kind of thing that goes on in climate science. Go and take a look at Figure 1 of that citation. Take a hard, close look. Come back and I’ll tell you what I see there.
The first thing that struck me is that the majority of the data points are below the central black like. The second thing was that it tailed off low in the lower right of the graph. The lines fool your eye into thinking that it is more balanced and “cancelled” than it is. The third thing was that the “outliers” were almost all in the bottom of the graph.
So being a very suspicious guy, I did what I do in these cases. I took a screen shot of the graph in the PDF, at 500% scale. I digitized the graph manually, I use a program called “GraphClick” but there’s others.
To my lack of surprise, when I ran the numbers on the actual data I find that the relationship is:
Longwave = Shortwave * 0.88Hmmm … I wouldn’t call that “near cancellation”, that’s 12% difference on data running from 20 to 110 W/m2.
Forewarned, I took a close look at Figure 2 and found the same thing. While the two datasets have a similar shape, longwave peaks out at about 70 W/m2. Shortwave, on the other hand, is often near 100, with a lot of data above 70. I can see why they didn’t use a standard scatterplot …
So there’s chartmanship going on, don’t like that. But in any case, the take-home message I get from the data is that shortwave (reflected solar) on average runs about 10% higher than longwave. That is to say, net cooling from the clouds.
However, there’s a huge problem with averages. It’s like say trying to determine the “average humidity” of some square miles of ocean containing say three thunderstorms … you can get the average, but it is meaningless regarding what’s actually happening in that volume of sea and air. Nature is a piecemean creature, full of boundaries, discontinuities, self-organizing criticalities, and non-linear phenomena.
In such a situation, averages are often less than useful or representative of what is happening on the ground …
The main lesson from this is, don’t trust peer-reviewed science. Until someone (perhaps you) takes the trouble to run the numbers themselves, and determine if the claims are actually true, they are just anecdote. Peer reviewed anecdote, to be sure … but without replication and reanalysis, anecdote just the same.
My best to everyone,
w.
son of mulder (16:00:56), you raise an interesting possibility, viz:
I don’t know how you would answer that question. I mean, how much would we expect it to cool without clouds doing what they do?
There is an interesting paper on a closely related question at . Read it and let me know what you think.
w.
Ooops, it lost my URL
http://www.weatherquestions.com/Spencer_07GRL.pdf
w.
But clouds are a 2 edged sword. They trap LW radiation from the earth, and reflect sw radiation ito space. This document suggests there is little net forcing
On the observed neanr cancellation between LW and SW cloud forcing in tropical regions
http://ams.allenpress.com/archive/1520-0442/7/4/pdf/i1520-0442-7-4-559.pdf
Willis,
I’m thinking of referring to your hypothesis in a future article at climaterealists.com
It is a useful supplement to my global climate description as regards the air side of things.
To discuss that possibility you may email me if you wish on wilde.co@btconnect.com
Stephen Wilde (13:06:21) :
“The situation is analogous to an open pan of boiling water on a stove. If extra energy is added from any source the temperature of the boiling water will not increase. All that happens is that the rate of evaporation increases.
So it is with the Earth. Extra energy in the air alone just accelerates the hydrological cycle and the air gets no warmer from that cause.”
I’ve been following these comments with Stephen’s exact analogy in mind. The diathermic system that this pan of water represents is about the first thing seen in an undergrad thermodynamics course.
I’d like to extend Stephen’s analogy slightly to consider a constant heat source and a pan with insulated sides. The insulation, be it the pan’s sides or atmospheric CO2, will increase the rate of evaporation, but not the temperature of the boiling water.
Of course the properties of water are what govern this closed system. The quantity of water available to drive the heat engine is infinite for all practical purposes. The “setpoint” cannot change, because the properties of water and its vapor do not change at a constant atmospheric pressure.
Stephen Wilde (08:18:03), you say:
First, note that I changed your email address to something you can recognize but less chance for a machine. It’s generally not a good idea to hang your email out on the web for the spambots to gather.
Regarding using my work, I have no objection to someone quoting my work and then discussing it. That’s great. I do not like it when someone says “Well, Willis said …” and launches into their interpretation of what I said.
So to quote and attribute, and only then discuss my ideas is great. That is what science is about.
But when it gets to “Willis says there is no global warming”, well no, I never said that. My position (I think) is nuanced and subtle and complex. It doesn’t fit well into sound bytes, except maybe “Thunderstorms Roolz OK”.
For me, science is (or should be) the public marketplace of ideas based on facts and observations. I claim no copyright on this post, it was designed to stir discussion and foment ideas. You or anyone else is free to quote and/or reproduce any and all of it. The more it gets reproduced and quoted and referred to, the happier I am.
I have the ultimate luxury. I am an amateur scientist. My livelihood doesn’t depend on what I publish or get credit for. I don’t have to guard it. My analyses, my data, the graphs and charts and graphics that I do, that’s all yours. I made it for you. I create it and toss it all to the electronic winds to blow around the world, use it as you like.
w.
Stephen Wilde (13:06:21) raised an excellent analogy
JRP (08:37:23) replied:
Both of you are quite correct. It is an analogy that I have examined myself. This, of course, leads to the question … why is water boiling like thunderstorm formation?
The answer, of course, is that they are both examples of self-organized criticality involving phase transformations. The atmospheric example is much more complex, of course, and the cloud temperature regulation is not as rigid.
The crazy truth is, the earth is a giant steam engine. Like any steam engine, it evaporates water. Like any steam engine, it condenses the water someplace cooler than the hot end. Like any steam engine, the water is then recirculated to start the cycle again. This is the engine that drive the atmospheric from equator to poles, and powers the ocean currents on the same journey. Steam driven climate, go figure … but I digress.
What the analogy with boiling misses is the particular emergent phenomena we call thunderstorms. We grew up with them, so we don’t find them odd. But imagine that you’d never seen a thunderstorm. You’re in the tropics, for some reason it’s a little cool. Cumulus form every afternoon, but no thunderstorms.
After a days of watching the clouds, one afternoon you watch a cumulus cloud, only one of many, start boiling skywards. As it heads skyward, it darkens. Within an hour, as it builds and drifts over your head, there’s rain and lightning and thunder all around you. You are shivering wet in the cold downwash of chilly air entrained with the rain.
Like clouds, thunderstorms are another emergent phenomenon. They arise spontaneously when conditions exceed some threshold, and their properties and abilities are not predictable from the properties of the materials that generated them. If all you ever knew was air and cumuls clouds and ocean, you would not predict that it could deafen, blind, or even kill you where you stand as a thunderstorm can.
There is a further distinction. Clouds and thunderstorms are both emergent phenomena. They are self-generating. If you take cool ocean and start to warm it, there is a threshold above which clouds emerge. They are created without any intervention.
Thunderstorms, on the other hand, are also heat engines. They turn heat into work. But the most important distinction is that thunderstorms are self-sustaining heat engines. The thunderstorm generates wind at the base. Evaporation is proportional to windspeed, so evaporation under the thunderstorm is greatly increased. This lowers the air density under the thunderstorm, so it rises and the thunderstorm continues to run. This lets the thunderstorm continue to cool the surface until the surface temperature is lower than the temperature necessary to ignite the thunderstorm.
I stress this because it is an important point. Thunderstorms are not a negative feedback mechanism. They are an active heat engine, that is born in response to rising temperature, lives of temperature and wind, and eventually dissipates and dies. Negative feedbacks can only slow things down. Clouds are a negative feedback. They cannot drive the temperature below the starting point. Thunderstorms can.
w.
JRP, I wanted to comment on one thing. You say:
In the initial post I listed a variety of ways that the “setpoint” could vary. These included cosmic rays, anything affecting wind speed, and anything affecting cloud cover or color.
For example, if you cut down a forest, you often cut down the clouds with it. There is no longer enough available moisture at the surface for the clouds to form. That’s what happened to Kilimanjaro, they logged the lower slopes and cut down the clouds. Less snow, less glacier.
In the long run, however, you are right in that some parts of the governor mechanism are set in stone. They are governed by unchanging physical laws.
My regards to you,
w.
Willis:
I understand your arguments and agree with them, but I write to correct an assertion you made which is incorrect and could be used against you by opponents of your hypothesis.
You said (at 08:37:23):
“I stress this because it is an important point. Thunderstorms are not a negative feedback mechanism. They are an active heat engine, that is born in response to rising temperature, lives of temperature and wind, and eventually dissipates and dies. Negative feedbacks can only slow things down. Clouds are a negative feedback. They cannot drive the temperature below the starting point. Thunderstorms can.”
However, your “important point” is not entirely correct. I remind that I wrote above (04:27:01):
“
Ramanathan & Collins argued that an effect occurs in the tropics where sea surface temperature is observed to have a maximum value of 305 K. Any additional warming (from any source) increases evapouration, and that evapouration removes the additional heat.
(People have all experienced this effect personally: it is why people sweat when too hot).
But over the oceans the increased evapouration also increases cloud cover over and near the region of maximum temperature. And the clouds shield the surface from the Sun (as every sunbather has noticed). This reduces the heat input to the ocean surface (from the Sun) near the region of maximum temperature.
So, sea surface temperature has a maximum value of 305 K and additional heat input reduces solar heating near the region of the maximum temperature. This reduction to solar heating in the surrounding region provides a net effect that warming the tropical ocean causes its temperature to fall.
n.b. this is an unusual effect whereby any additional heat input causes temperature to fall.
”
So, since 1991 the literature has contained evidence that clouds do “drive the temperature below the starting point” in the tropics.
Please note that this correction does not reduce the effectiveness of your argument. Indeed, it supports it because it says that both clouds and thunderstorms can cause ‘overshoot’ of temperature adjustment.
I hope this helps
Richard
of interest
http://ams.allenpress.com/archive/1520-0450/41/5/pdf/i1520-0450-41-5-473.pdf
Radiative Impacts of Anvil Cloud during the Maritime Continent Thunderstorm Experiment
Another (perhaps more useful) reference:
http://www.arts.monash.edu.au/ges/staff/jberinger/pubs/2001-jd001431.pdf
Surface energy exchanges and interactions with thunderstorms during the Maritime Continent Thunderstorm Experiment (MCTEX)
And another
http://ams.allenpress.com/archive/1520-0493/129/6/pdf/i1520-0493-129-6-1550.pdf
Understanding Hector: The Dynamics of Island Thunderstorms
Thank you for the article.
Can anything be inferred on the influence of biomass on cloudiness ? It would seem that it amplifies the opacity in the shown images.
Richard S Courtney (01:30:46), many thanks for your interesting points. You say:
The simplest example that I can give to show that tropical clouds don’t cause overshoot is the tropical day. This typical day in the tropics, which I referred to above, shows that the clouds are not enough to stop the warming of the day. The day continues to warm. This is proven by the afternoon emergence of thunderstorms. Not only are the clouds unable to drive the temperature below the starting point. They are unable to stem the rise.
One of the unexpected findings in Fig. 2 above is the nature of the change in albedo from early morning to late afternoon. Albedo runs level at 0.30 from eight am to ten-thirty am. In the next hour, it takes a very quick one-hour jump to 0.34. From there it stays level (except for coastal clouds below Central America) until 16:00.
My interpretation of that is that once the cumulus starts to form, it forms in 1-2 hours. At the end of that time, it has covered all possible area. Remember that a cumulus cloud is not a “thing”. It is a flag marking an area of rising air.
Well, what goes up must come down. So these areas of rising air must be, and in fact are, accompanied by areas where the air is descending.
Fairly quickly, it seems, the limit of cumulus growth is reached. Figure 2 shows that cumulus growth ends around 11:30. The growth limit is where cloud cover starts to seriously cut into the area available for descending air. That ratio of cumulus area to area of descending air appears to be maintained for the rest of the day. To me, this is a sign of a system that is maxed out.
I understand that this doesn’t answer the question of whether clouds can drive the temperature below the cloud initiation temperature. I still say no on that one.
It does answer the question of whether on an average day clouds do drive the temperature below the cloud initiation temperature. The answer has to be no.
It’s late, I’ll get to the other question tomorrow.
w.
[If possible, I’d love to do a full guest post fleshing out the below. If not, please post this!]
As Willis Eschenbach and others have noted, the earth seems to be a homeostatic system with respect to global climate. The mathematics of adaptive homeostatic mechanisms are fairly easy to identify and entirely general. Control system mathematics is essentially similar for mechanical, electronic, and biological control systems. The word “homeostasis” was coined in the ‘30s to describe physiological control systems. In order to survive, organisms must be able to react to essentially unpredictable shocks to the system. Like tight rope walkers they must be able to oscillate widely and then return to relatively narrow steady state levels. Given the radical variation in energy from the sun, it would probably be impossible for life to survive on this planet without some overall controls for wide variations in climate. Fortunately, those control mechanisms seem to be in place. In this respect, at least, the earth’s climate system seems to be imitating a living system.
While each mechanism in a homeostatic system can be modeled, adaptive systems as a whole cannot be modeled in a way which would allow us to make reliable predictions. Our models do fairly well predicting what clocks will do. They do badly at predicting what clouds will do. Even in mechanistically-engineered control systems with several control mechanisms in place, models make poor predictors largely because of our inability to predict the particulars of external stimuli, and the necessary time lags in adapting to those stimuli. For example, we may know that there will be accidents on I-5 in the Seattle area, but we cannot know precisely when and precisely where those accidents will occur. If we could know the particulars they would not be accidents. To the extent that the highway control systems fail, we have slow downs, pile-ups, and delays. In biological systems, we know that the system will tend toward a homeostatic mean until and unless the system as a whole fails – in which case the organism dies.
The thermostat is a part of an essential control mechanism in heating systems. We should not be surprised, however, to discover that the homeostatic climate control systems will operate with many different and largely independent “thermostats” and control mechanisms. In a multiparty democracy, one party will damp the other, and, hopefully, scientific blogs such as this one will damp the overwhelming strength of the scientists currently in control of policy. In naturally occurring homeostatic systems there will be many control mechanisms operating largely independently of each other simultaneously. The homeostatic regulation of serum glucose levels involves at least two neural systems and over twenty hormones. Each single control system must be damped or the system will oscillate out of control. Diabetes is a problem at least because a single hormone, insulin, is overwhelmingly important in bringing blood sugar levels down. And, as we would expect, the diabetic’s failure to maintain acceptable steady state levels creates chronic disease. Unless systems are damped by other systems, the organism will tend to spiral into chaotic conditions and the tendency of the system to oscillate widely between diabetic coma and insulin shock may lead to death.
Even in finely controlled engineered systems there are multiple sorts of “thermostats” and governing mechanisms. If there is only one heating system operating off of one thermostat, the temperature in a building could not be controlled unless the external temperature was very steady – which it is not. And so it is actually far more efficient to have furnaces, air conditioning units, automatic light shades and ventilators all operating at the same time. Infant incubators are even more complex.
In such circumstances modeling as we now understand it is at best a method of explaining mechanisms after the fact. It cannot be used to predict the response to an essentially unknown set of shocks to the system as a whole. Clouds are not clocks and people are not simply wind-up toys. The universe doesn’t operate that way. Systems are complex and adaptive systems are both complex and given even very very small changes in initial conditions single control mechanisms are potentially chaotic.
But the above explains why the system, in order to work well, *needs* to be complex, and have many different actors working at cross-purposes. The more independent systems there are at play, the more dynamic the control response as seen from the top, and the better the earth, as a whole, adapts and reacts to different inputs and changes.
Willis,
“I don’t know how you would answer that question. I mean, how much would we expect it to cool without clouds doing what they do?
There is an interesting paper on a closely related question at . Read it and let me know what you think.”
I’d try to support or contradict your hypothesis by creating a staged model of El Nino and it’s impacts within the context of your hypothesis.
As I understand it I’d consider the upwelling of energy to the surface of the El Nino as an impact on the climate system in equilibrium.
I suggest this would give the following ‘impact effects’ at each El Nino event if your hypothesis is correct varying in the different transition phases as defined below.
1. Increase in observed surface average temperature (to a certain maximum depending on the strength of El Nino). This is known quantity from the temperature histories. The kick to the system.
2. Associated increase in tropical daytime cloud to a maximum. The hypothesis. Known or unknown amount? A surface cooling driver.
3. Decrease in Solar insolation at the surface to a minimum. From 2. Known or unknown amount? A surface cooling driver.
4. Increase in precipitation. A consequence of 1. Known or unknown? A surface cooling driver.
5. Increase in Boltzmann radiation reflecting the surface temperature increase. Known or unknown? A surface cooling driver.
6. Increase in Latent heat transport upwards. Related to subsequent precipitation and a consequence of 1. A Surface cooling driver.
7.A transient increase in absolute humidity. Related to evaporation. A surface warming driver. Known or unknown?
8. Nightime increased cloud? A surface warming driver. Known or unknown? Or do thunderstorms cool and reduce the night time cloud?
9. Increased tropical thunderstorm activity? More ? Stronger?
10. A subsequent fall to minimum surface temperature below the pre El Nino equilibrium temperature. Assumes the upwelling reverts to ‘normal’ but cloud effects lag.
Look at the above list in terms of the most recent El Nino events where appropriate data (hopefully) exists to model 4 stage posts of El Nino with 3 associated transition phases as follows (i)=>(ii)=>(iii)=>(iv). Analyse the measurements at each stagepost and the mid point of each phase.
(i) Equilibrium immediately before El Nino
(ii) Status at maximum surface temperature.
(iii) Status at minimum surface temperature immediately after El Nino.
(iv) Status at return to equilibrium
Then go back to earlier El Ninos (checking other sudden effects eg Volcanoes weren’t corrupting the scenario) and apply known data and estimate other quantities based on your ‘El Nino Model’.) Does the model fit the actuals well? Statistics needed here.
Maybe there’s a certain critical strength of El Nino before enhanced thunderstorm activity. Just looking at the temperature record in 1998-99 it looks consistent.
Maybe increased tropical thunderstorm activity is the real consequence of AGW as it limits global temperature rise.
A whole mass of statistical analysis possible and going forward fairly frequent El Nino’s to measure and compare.
What about La Nina’s? Is a reverse characterisation possible and complementary conclusions possible to be drawn?
Judging from the paper by Spencer et al that you quoted the advent of satellite observation will help on the go forward in measuring this stuff.
If the above programme is happening in the climatology world then when (are) will the results be available?
If the above analysis has not been done then how can the models be of any real value?
Speaking of models…
Climate change cannot be predicted by a single model no matter how complex that model. Today model building is the preferred method of research in fields as otherwise diverse as economics, population control, and climatology. Experts in the various disciplines are convinced of the efficacy of their models. Such reliance on models has proven disastrous in the past and may continue to do so.
At its core, a model consists of a series of equations, a set of variables, and a list of relations between those variables expressed by coefficients of the model. For example, although the original in-put out-put equations for a macro-economic model may have been created by means of observations of a functioning market-based economy, once fixed into the model, empirical and theoretical in-puts became conceptually redundant. For the most part the predictions of the model are in fact merely restatements of the assumptions of the model and the coefficients, more often than not, are derived by means of empirical generalizations from outdated and possibly irrelevant data. According to Freeman Dyson of Princeton’s Institute for Advanced Study, almost all funding in global warming research is now being devoted to model building and relatively little funding has been devoted to actually determining what is happening in the real world. In the hands of the policy makers, the model itself becomes a black-box mechanism for implementing and formulating economic policy. And so, for example, from 1930s to 1970s macro-economists built enormously complex models of economic systems designed to serve as basis for long term economic planning. Psychologically the models were impressive in that they gave people the illusion of knowledge and control. Unfortunately, those models didn’t work except perhaps to provide employment and professional advancement for cadres of economists and to motivate whole populations to accept poor economic policies. From the beginning, motivating the public was essential to the planners. According to developmental economist Michael Todaro such plans provided important psychological benefits in “mobilizing popular sentiment and cutting across tribal factions with the plea to all citizens to ‘work together,’” so that an “enlightened central government, through its economic plan, [could] provide the needed incentive to overcome the inhibiting forces of traditionalism in the quest for widespread material progress.” Reliance on such models by central governments was often disastrous for developing economies and may very well turn out to be even more disastrous in dealing with global climate change.
Despite the solemn assurances of the experts, such models have proved to have very little explanatory power or predictive power. They rely on equations which are themselves time and place dependent. At best these economic models represented snapshots of a particular economy at particular times although they were used to predict the behavior of other economies at other times. Not only were macro-economists unable to predict the state of an economy ten years out, they were rarely able to predict the state of an economy ten weeks out.
With the enormous computing capability of computers and advances in statistical analysis models have become more and more sophisticated although they may in fact be based on very little theory and relatively few well chosen empirical observations. The logic of the underlying model and assumptions is buried in a sea of equations, calculations, and estimates or probabilities all of which are seemingly theoretically and empirically sound. And, psychologically, even the numbers have become largely redundant. Linked to computer graphics, today’s models are as beautiful, as impressive, and as entertaining as Star Wars battle scenes. We seem to quite literally see the world developing before our eyes. Nevertheless, the fundamental logic of model building has not changed. In many respects Google Earth is the finest of models. Its empirical content is many times more solid than the empirical content of global warming models. Nevertheless, while Google Earth can tell us a great deal about Portland Oregon today or even tomorrow it cannot tell us a great deal about Portland Oregon 50 years from now. Such snapshots can give us no very dependable method of predicting the future and thus formulating and implementing rational policy.
Or, to take other examples, population growth models based on data from 1950 -1970 is inapplicable to population growth patterns in 2009. Investment banking models were based on data from 1945 – 2005 during which time single family housing prices in aggregate increased. The models based on these data predicted that single home mortgages in aggregate had less than a 1% chance risk of default. As a result neither the government nor the investment houses required margins for trading in these aggregated mortgages and their derivatives and the derivatives of their derivatives. The unintended consequences of these models led to an enormous multiplication of liquidity and astronomic growth in leverage and thus M3. Financial collapse was not predictable from within these particular models, although any economists with a scrap of paper and a pencil should have been able to predict the likelihood of that collapse.
The results of this reliance in climatology on models rather than on observation, experiment, and theory is as likely to be as disappointing as similar reliance on econometric models was in the mid 20 century and investment banking models which led to our current financial collapse. Believers in these climate change models, in particular, are becoming increasingly dogmatic. No countervailing opinions even from within particular disciplines are allowed to undermine the faith in the model itself. Critics are castigated as heretics. Unlike genuinely scientific theories like Newtonian mechanics, these models cannot be tested. The models create the appearance of precision by the magic of long division, but there is little real precision in their predictive ability more than two or three weeks out. When the model builders encounter facts which seem to contradict the predictions of they invariably tinker with the model and announce that the model simply needed a little adjusting. The proponents of these models will not admit even the possibility of being fundamentally in error. Tautological arguments are very convincing.
A model is not a theory, although it will contain theoretical elements. And even a genuine scientific theory is a net which addresses, at best, only certain aspects of reality. In its essence a theory is a simplification and so while its powers to explain are high, its ability to predict is limited except in very controlled conditions. What the theory cannot interpret in its terms, it must ignore. A sophisticated theorist will recognize the limitations of any particular theory. Models are most helpful in explaining mechanisms after the fact and even then multiple models must be used to explain events. Today’s model builders, in contrast, believe that they have somehow reproduced reality and in their zeal they are often able to use “enlightened central governments” “to mobilize public sentiment” in their various causes.
I decided to re-read the thread to see what I have missed or had not responded to. I find:
M. Simon (00:49:17), thanks for your crossposting at:
http://powerandcontrol.blogspot.com/2009/06/cloud-cover.html
crosspatch (01:04:01), you say:
CO2 has been higher in the past. That’s not the problem, or problems to be exact. One is the improbability of GHGs decreasing over billions of years, exactly in tune with the increasing sun.
The other is that CO2 is not big enough. It is a third-order forcing. Here’s the math:
Current solar strength “S” = 340 W/m2 (averaged over the planetary surface)
30% reduction in S = 0.3 * 340 = 102 W/m2
Increased forcing per doubling of CO2 concentration = 3.7 W/m2
Number of CO2 doublings required = 113 / 3.7 = 28
Increase from twenty-eight doublings = 134,217,728 times larger
Heck, suppose the change in the sun’s strength was only 10%. That gives 10% change in sun strength = 34 W/m2 change = 9 doublings. That would give a CO2 concentration of 12,160 ppmv … I haven’t seen an estimate of CO2 that high ever.
Data (01:10:11), you say about feedback:
The key is in your last statement, “a governor mechanism uses negative feedback to operate”. Simple negative feedback on its own cannot do what a governor does … that’s why it’s a separate thing called a governor.
M. Simon (04:59:53), you raise an interesting point:
While you are correct in theory, in practice that kind of system takes a delicate balance to keep it working.
In addition, the ocean is not a linear integrator in any sense. Over the weekend I finally got my Rescue diver certification, so I had a chance to experience this first-hand. Once again, I found the curious phenomenon where my body at the surface was in water warmed by the tropical sun, with my hands dipping into much cooler water at the bottom of each swimming stroke. There was a clear division between the warm and cool layers, a very definite line.
This kind of “thermocline” is quite common in the ocean at various levels. As such, it is a very non-linear integrator, with cold currents coming to the surface over here and stratified warm water over there.
So, the ocean integrating the temperature fluctuations does happen. But I doubt greatly whether it governs day-to-day temperature in anything like the manner that thunderstorms do.
Michael D Smith (07:26:19), you ask:
Go to
http://dcdbs.ssec.wisc.edu/inventory/
Fill in the date and time, a list of satellites comes up. I used a free and quite powerful program called ImageJ to do the analysis. The actual images have a URL like:
http://dcdbs.ssec.wisc.edu/inventory/image.php?sat=GOES-11&date=2008-04-16&time=21:00&type=Imager&band=1&thefilename=goes11.2008.107.210013.INDX&coverage=FD&count=1&offsettz=0
so you can hack that to automate downloading the images.
Pamela Gray (13:38:53), you recommend:
I plan to go back to school soon to work on my PhD … take a guess about my intended thesis …
Robin Kool (15:57:29), you ask:
Good question. My best guess is that we are seeing the effect of looking sideways through the clouds. At 4 PM the sun is only 30° above the horizon, so the sides of the tall cumulus clouds may be dominating and increasing the albedo … but that’s just a guess.
George E. Smith (10:22:22), thanks for your very clear description of emissivity in water. You say:
According to my bible, Geiger’s Climate Near The Ground, the emissivity of fresh snow is even greater than water, 0.986. And ice has about the same emissivity as water, around 0.98.
Finally, Chana Cox (09:55:04), among a host of interesting things you include:
Well, yes and no. I would remind you of the old computer modeler’s axiom that “All models are wrong … and some models are useful.” For example, both Bejan’s and Ou’s model referred to in the “Further Reading” at the end of my post are highly simplified models. As such they are assuredly wrong … but they provide a host of insights and surprises.
I have written and use a simplified radiation / reflection / absorption / convection / evapotranspiration climate model which is also wrong, but was very useful in developing the ideas that became the Thermostat Hypothesis.
So you are correct, no model can predict climate change, but they still can be of great value. Unfortunately, the current crop of climate models start from the wrong end and work backwards …
My thanks to everyone for your contributions. Once again I invite people to contribute, in particular any objections anyone can see to the Thermostat Hypothesis, and any ways that it might be tested.
w.
Willis,
Thanks for telling us something that will NEVER get on the news.
The fact Earth spends at roughly 1,000 mph at the equator and essentially zero at the pole; that the equator receives rougly an equal amount of sun and dark every day but the poles’ exposure fluxuate greatly over the year; surely provokes immense climate dynamics that must be taken into account but seem to be largely ignored by the AGWers. Seems they’ve ignored the same dynamics when raising CFCs.
The argument, that a trace source of CO2 can overcome the more pervasive and historical climate enhancements, gets more and more absurd. Obviously, for the Al Gore’s of the world it’s about money and power. I hope they choke on their own greed.
MR. Eschenbach-
Thank you for your patience, as I had to go and dig out my old notes from ages ago, now I can’t decipher whether it was one of three things: my observation at the time, my professors opinion, or a reference. There were only two other people in the class, so I’m going to have to make a call, because this will drive me up the wall.
Wonderful discussion.
Melinda, thank you for your participation. I await your findings.
All the best,
w.
Well, we better tell all the geologists, palaeontologists and palaeoclimatologists that their reconstructions of past climates are completely wrong (you’ve got a model to prove it).
Calculations for ‘super greenhouse’ times like during the Cretaceous indicate low latitude oceans to have topped 310 Kelvin. But more importantly, high latitude waters probably reached temperatures of 300 Kelvin. A flattening of the temperature gradients, coupled with the lack of significant polar ice resulted in very weak oceanic circulations (halothermal is about 2 orders of magnitude weaker than today’s thermohaline). This probably resulted in stratified and stagnant oceans, which were much less productive than our modern ocean. A significant reduction in algal productivity will also reduce the amount of dimethylsulfide that is released as a byproduct of their growth; this chemical eventually becomes the sulfuric acid or methane sulfonic acid, which are very important cloud condensation nuclei. Bingo, a positive feedback from warming oceans, weakening circulation, lowered productivity, reduced cloud cover, higher insolation, warming oceans, etcetc.
Regarding the change in solar strength (S): Most palaeotemperature graphs are only for the past 600 million years or less (the Phanerozoic). S is thought to increase by 10% every billion years, so since the Ediacaran it has increased 6%, leading to a shortfall of 0.06 * 340 W/m2 = 20.4 W/m2 back then. For CO2 alone to make up for this, it would have required 20.4/3.7 = 5.5 doublings = ca 10,000ppmv CO2. CO2 is thought to have been about 7000ppmv at the start of the Phanerozoic, pretty close from where I stand.