Guest Post by Willis Eschenbach
This is an extension of the ideas I laid out as the Thunderstorm Thermostat Hypothesis on WUWT. For those who have not read it, I’ll wait here while you go there and read it … (dum de dum de dum) … (makes himself a cup of coffee) … OK, welcome back. Onwards.
The hypothesis in that paper is that clouds and thunderstorms, particularly in the tropics, control the earth’s temperature. In that paper, I showed that a falsifiable prediction of greater increase in clouds in the Eastern Pacific was supported by the satellite data. I got to thinking a couple of days ago about what other kinds of falsifiable predictions would flow from that hypothesis. I realized that one thing that should be true if my hypothesis were correct is that the climate sensitivity should be very low in the tropics.
I also figured out how I could calculate that sensitivity, by using the change in incoming solar energy (insolation) between summer and winter. The daily average top of atmosphere (TOA) insolation is shown in Figure 1.
Figure 1. Daily TOA insolation by latitude and day of the year. Phi (Φ) is the Latitude, and theta (Θ) is the day of the year expressed as an angle from zero to 360. Insolation is expressed in watts per square metre. SOURCE.
(As a side note, one thing that is not generally recognized is that the poles during summer get the highest daily average insolation of anywhere on earth. This is because, although they don’t get a lot of insolation even during the summer, they are getting it for 24 hours a day. This makes their daily average insolation much higher than other areas. But I digress …)
Now, the “climate sensitivity” is the relationship between an increase in what is called the “forcing” (the energy that heats the earth, in watts per square metre of earth surface) and the temperature of the earth in degrees Celsius. This is generally expressed as the amount of heating that would result from the forcing increase due to a doubling of CO2. A doubling of CO2 is estimated by the IPCC to increase the TOA forcing by 3.7 watts per metre squared (W/m2). The IPCC claims that the climate sensitivity is on the order of 3°C per doubling of CO2, with an error band from 2°C to 4.5°C.
My insight was that I could compare the winter insolation with the summer insolation. From that I could calculate how much the solar forcing increased from winter to summer. Then I could compare that with the change in temperature from winter to summer, and that would give me the climate sensitivity for each latitude band.
My new falsifiable predictions from my Thunderstorm Thermostat Hypothesis were as follows:
1 The climate sensitivity would be less near the equator than near the poles. This is because the almost-daily afternoon emergence of cumulus and thunderstorms is primarily a tropical phenomenon (although it also occurs in some temperate regions).
2 The sensitivity would be less in latitude bands which are mostly ocean. This is for three reasons. The first is because the ocean warms more slowly than the land, so a change in forcing will heat the land more. The second reason is that the presence of water reduces the effect of increasing forcing, due to energy going into evaporation rather than temperature change. Finally, where there is surface water more clouds and thunderstorms can form more easily.
3 Due to the temperature damping effect of the thunderstorms as explained in my Thunderstorm Thermostat Hypothesis, as well as the increase in cloud albedo from increasing temperatures, the climate sensitivity would be much, much lower than the canonical IPCC climate sensitivity of 3°C from a doubling of CO2.
4 Given the stability of the earth’s climate, the sensitivity would be quite small, with a global average not far from zero.
So those were my predictions. Figure 2 shows my results:
Figure 2. Climate sensitivity by latitude, in 20° bands. Blue bars show the sensitivity in each band. Yellow lines show the standard error in the measurement.
Note that all of my predictions based on my hypothesis have been confirmed. The sensitivity is greatest at the poles. The areas with the most ocean have lower sensitivity than the areas with lots of land. The sensitivity is much smaller than the IPCC value. And finally, the global average is not far from zero.
DISCUSSION
While my results are far below the canonical IPCC values, they are not without precedent in the scientific literature. In CO2-induced global warming: a skeptic’s view of potential climate change, Sherwood Idso gives the results of eight “natural experiments”. These are measurements of changes in temperature and corresponding forcing in various areas of the earth’s surface. The results of his experiments was a sensitivity of 0.3°C per doubling. This is still larger than my result of 0.05°C per doubling, but is much smaller than the IPCC results.
Kerr et al. argued that Idso’s results were incorrect because they failed to allow for the time that it takes the ocean to warm, viz:
A major failing, they say, is the omission of the ocean from Idso’s natural experiments, as he calls them. Those experiments extend over only a few months, while the surface layer of the ocean requires 6 to 8 years to respond significantly to a change in radiation.
I have always found this argument to be specious, for several reasons:
1 The only part of the ocean that is interacting with the atmosphere is the surface skin layer. The temperature of the lower layers is immaterial, as the evaporation, conduction and radiation from the ocean to the atmosphere are solely dependent on the skin layer.
2 The skin layer of the ocean, as well as the top ten metres or so of the ocean, responds quite quickly to increased forcing. It is much warmer in the summer than in the winter. More significantly, it is much warmer in the day than in the night, and in the afternoon than in the morning. It can heat and cool quite rapidly.
3 Heat does not mix downwards in the ocean very well. Warmer water rises to the surface, and cooler water sinks into the depths until it reaches a layer of equal temperature. As a result, waiting a while will not increase the warmth in the lower levels by much.
As a result, I would say that the difference between a year-long experiment such as the one I have done, and a six-year experiment, would be small. Perhaps it might as much as double my climate sensitivity values for the areas that are mostly ocean, or even triple them … but that makes no difference. Even tripled, the average global climate sensitivity would still be only on the order of 0.15°C per CO2 doubling, which is very, very small.
So, those are my results. I hold that they are derivable from my hypothesis that clouds and thunderstorms keep the earth’s temperature within a very narrow level. And I say that these results strongly support my hypothesis. Clouds, thunderstorms, and likely other as-yet unrecognized mechanisms hold the climate sensitivity to a value very near zero. And a corollary of that is that a doubling of CO2 would make a change in global temperature that is so small as to be unmeasurable.
In the Northern Hemisphere, for example, the hemispheric average temperature change winter to summer is about 5°C. This five degree change in temperature results from a winter to summer forcing change of no less than 155 watts/metre squared … and we’re supposed to worry about a forcing change of 3.7 W/m2 from a doubling of CO2???
The Southern Hemisphere shows the IPCC claim to be even more ridiculous. There, a winter to summer change in forcing of 182 W/m2 leads to a 2°C change in temperature … and we’re supposed to believe that a 3.7 W/m2 change in forcing will cause a 3° change in temperature? Even if my results were off by a factor of three, that’s still a cruel joke.
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We can see the effects of water vapour rising but could CO2 also rise in the same way?
Very interesting. You should even be able to make quantifiable predictions for rising CO2 levels with your theory, Willis.
For those who might be interested in seeing Willis’s tropical thunderstorms in action, I recommend a visit to the Typhoon Watch site http://agora.ex.nii.ac.jp/digital-typhoon/index.html.en.
This site, maintained in Japan, compiles daily images into mpeg and wmv files for each month as far back as 1979. Recent observations are available in 120 hr and 240 hr packages. The view is of the western Pacific and east Asia, from pole to pole, centred on the equator just north of Papua New Guinea and provides particularly good impressions of the growth and development of storms in the tropical zone.
Eschenbach: Figure 2 shows my results
Willis, what are you showing?
The chart states: climate sensitivity
Nowhere here or in your other post do you provide a formula for that.
If we take the standard definition
climate sensitivity = change in Radiative Forcing / change in Temperature
I guessing (?) from your wiki insolation chart, you are calculating dRF as the change in daily incoming solar insolation and dividing by dT to calculate a climate sensitivity? Aggregated monthly? Seasonly? If this is roughly correct, what temperature series are you using?
I would really like to understand what you did, but you have included no discussion of the methods or equations used. If the description is too hard, could you provide the code instead?
Thanks
What Willis has not addressed is the fact in some captive environments there are no thunderstorms or atmospheric electrical phenomena at all. None. These are artificial environments never-the-less in which some people exist. Giving rise to the need for a wholly new set of analytical criterion.
Bah: Inverted the formula!
climate sensitivity = change in Temperature / change in Radiative Forcing
It’s a very interesting hypothesis. I must admit, I am not convinced at present, but I am open to persuasion on it.
The area where I would need more evidence is the claim that change from one season to the next is the same (for purposes of measuring sensitivity) as the change from one decade to the next. It does seem to me that some accumulation of heat is possible, or there could be other long-term factors which are not included in seasonal comparisons. However, you could of course be correct.
Willis,
You clearly have the wrong answer. You have not employed a multi-gazillion dollar super-computer, and you have not needed representatives of nearly 200 countries to fly all the way across the world to talk about it.
So you see, there is no way anyone can give your work any credence at all.
Good article. I must read it again to digest. But I can hear the responses now, “not peer-reviewed”
AAAYYYUP!!
Interesting approach Willis. Would the sensitivity be lower yet if the source information was over estimated. The reason I ask is that your source for the thermal/latitude/time graph has a PV map of the US. I have a PV system, and get an average of 4.2 KW/day (exactly what the system is spec’ed to) where the map says I should get 5.5-6KW. In other words, the color of upper Michigan should be in western Colorado. I don’t know what to think of the source data. I always thought PVs would be a great way to look at insolation changes.
Map here: http://en.wikipedia.org/wiki/File:Us_pv_annual_may2004.jpg
Good work Willis, nothing like a simple direct analysis to understand a physical problem. I guess we won’t die after all.
This reminds me of an analysis a friend of mine and his postdoc did of a problem in plasma physics. They came up with an analytic solution by hand that they could solve on a pocket calculator. They absolutely crushed a team of ten physicists at Livermore that used a supercomputer to model the problem. It just goes to show that supercomputers make you stupid, or is it vice versa?
On the ‘SOURCE’ page, under the US map it says “US annual average solar energy received by a latitude tilt photovoltaic cell (modeled).”
Isn’t what you are saying similar to LC09’s estimation of climate sensitivity from the 20-20 tropics, also derives?
Hmmm- a very interesting hypothesis- the planet has its own way of dealing with extra heat. Now, how much is due to man, or nature. This appears to be nature taking care of itself…
The main problem with climate models is that they don’t model cloud cover accurately. All IPCC models use parameters which show clouds as a positive feedback. This is the result of some extraordinary group think.
If you make a 5% change in cloud cover using a radiative transfer model like RRTMG, you see a large change in temperature. GCMs mishandle clouds, which is why most of them produce nonsensically large climate sensitivities.
Hmm.. what do you define as North and Southern hemisphere? Some sort of average latitude from 45 to 90 degrees? Also, how do you compute the winter to summer change? I like your concept, but need some maths, as I suspect it is not trivial to sum an “average” over latitudes and seasons.
RIGHT ON! Several years ago, I did a similar, though much more crude, exercise, looking at winter to summer temperatures for various individual locations. For example, for Phoenix, the radiation goes from 483 Wm-2 in summer to 275 in winter, for a change of 208. Annual temperature change is 22.5. “Sensitivity” is, therefore, 0.1 For Guam, there’s only 62 watts difference and only a 1.5 C change in temperature, so the “sensitivity” is 0.024 (all 30-year averages from: http://rredc.nrel.gov/solar/old_data/nsrdb/redbook/sum2/state.html )
Chaco Canyon.
What they knew, when they knew it and how they used the knowing.
Who are the late ones to a way of knowing?
http://www.exploration.edu/chaco/
Willis,
I really do not think you can derive anything much from this at all.
For example over the oceans the thermal admittance for seasonal changes is far greater than the thermal admittance into space (the part you are trying to characterise).
You need to subtract the oceanic admittance from the total value that you obatin in order to find the admittance into space.
I do not know what you have taken into consideration. Things like that in the equatorial band, you have a pronounced six month cylcle. This faster cycle makes the oceanic admittance even higher.
In general there are good reasons why the amplitude of the seasonal cycle varies from as little a 1C to as much as 30C, between open equatorial ocean and deeply landlocked areas and it is due in the major part to the the thermal admittance of the surface.
Alex
Willis, I think this is common knowledge to alot of people gifted with skills of observation and common sense. Areas of the earth where summer / winter and day / night change are largests are the most sensitive and vise versa, statistics are not needed to support such a claim, its the elephant in the room! A single day would also be a good example of this, it also has the highest change in temperature and greatest change in incoming radiation.
The equator enjoys moderate temperatures with little variation annually and daily due to the high humidity (evapotranspiration) and action of storms / clouds / rainfall. The monsoon belt for example sits over the area of the earth that recives the most energy and here the negative feedback is strongest. In theory, this belt should increase in size to any warming to counter act it. I also have also observed that increased humidity leads to reduced distance of observation (distance objects are whiter), could this change the surface albedo? It seems effects how fast I sunburn and the colour of the sky looking up!
Deserts experiance large diurnal temperature change, some deserts I have experianced can go from -5 at night to 30 or greater in the day – this is apparently due to the lack of water and very low humidity (also long distances of observation and quikcer sun burn as a result). Water vapour helps moderate the temperature as does the sea, hence less diurnal range in coastal zones.
It is a shame that climate scientists miss the elephant in the room and ignore empirical observation and data and opt for some kind of pseudo science based on models and unjustified assumptions over real data. I think you should progress this train of thought as it is something that needs to be brought to the attention of those that think 3.8w/m2 causes large warming in the scale of natural climate variation!
However, they will argue its about extra energy acumulating in the sea causing increased humidity etc… and feedbacks etc… and you need to do your test over 30 years or its just weather!
ouch
http://www.exploratorium.edu/chaco/
Simple, elegant–two hallmarks of an excellent proof.
It’s an interesting theory and I think it deserves more research, but unless I’ve got it wrong, there appears to be one significant issue that it doesn’t explain:
What caused the MWP and LIA?
The theory shows a short term governor. The feedback and control is in days, if not hours. What is the long term system that caused the MWP and LIA? This theory would indicate that they shouldn’t have happened, because the mechanism would mitigate any variation due to TSI fluctuations. What caused this mechanism to be less effective during the MWP and more effective during the LIA?
You can tell by the way that I’ve asked the questions that I’m half-convinced already, but there are definitely things it doesn’t explain.
Ron Broberg (16:38:25) :
Eschenbach: Figure 2 shows my results
Willis, what are you showing?
The chart states: climate sensitivity
Nowhere here or in your other post do you provide a formula for that.
I may have misinterpreted the post but you seem to have answered your own question with your formula
climate sensitivity = change in Temperature / change in Radiative Forcing
plugging in T (summer – winter for a particular band) and R (summer – winter for that band)
Where R is in the range Willis suggests (150/180) there is a lot of latitude in the value of T. So much so that I would doubt that it matters much how T is sourced so long as it is legitimate.
(still digesting the post but he will no doubt be along later to clarify things)