Guest Post by Willis Eschenbach
I wrote before of my investigations into the surface air temperature records of the TAO/TRITON buoys in the Pacific Ocean. To refresh your memory, here are the locations of the TAO/TRITON buoys.
Figure 1. Locations of the TAO/TRITON buoys (pink squares). Each buoy is equipped with a sensor array measuring air and sea temperatures and other meteorological variables.
I have hypothesized that there is a thermostatic mechanism involving clouds and thunderstorms that maintains tropical temperature within a certain range. To investigate this mechanism, I decided to look at what happens at a given buoy on days when dawn temperatures are warmer than average, versus what happens at the same buoy on days when dawn temperatures are cooler than average.
My speculation was that when it was warmer at dawn, there would be more cloud and thunderstorm activity during that day. This would tend to drive the temperature down. On the other hand, when it was cooler at dawn, there would be less or no clouds or thunderstorms during that day. As a result, this would tend to drive the temperature upwards. And while I did find this, I was still surprised by the exact patterns.
To begin with, I compared the overall average of all days for each station with the overall average of the warmer days for each station, and the overall average of the cooler days for each station. Here are those results:
Figure 2. Average of all buoy records (heavy black line), as well as averages of the same data divided into days when dawn is warmer than average (heavy red line), and days when dawn is cooler than average (heavy blue line) for each buoy. Light lines show the difference between the previous and the following 1:00 AM temperatures.
First, the black line, showing the average day’s cycle. The onset of cumulus is complete by about 10:00. The afternoon is warmer than the morning. As you would expect with an average, the 1 AM temperatures are equal (thin black line).
The days when the dawn is warmer (red line) show a different pattern. There is less cooling from 1AM to dawn. Cumulus development is stronger when it occurs, driving the temperature down further than on average. In addition, afternoon thunderstorms not only keep the afternoon temperatures down, they also drive evening and night cooling. As a result, when the day is warmer at dawn, the following morning is cooler.
In general, the reverse occurs on the cooler days (blue line). Cooling from 1 AM until dawn is strong. Warming is equally strong. Morning cumulus formation is weak, as is the afternoon thunderstorm foundation. As a result, when the dawn is cooler, temperatures continue to climb during the day, and the following 1AM is warmer than the preceding 1 AM.
So this is the result that we would expect with a thermostat operating on a daily basis. If the dawn is warm, clouds and thunderstorms ensure that the following day starts out cooler. And when the dawn is cool, extra sun and few clouds and thunderstorms warm the day up, with the warmth lasting into the night.
Now … is this just a statistical oddity? One way to determine if we’re looking at a real phenomenon is the “dosage effect”. That is to say, the response should be proportional to the dosage. In this case, the “dosage” is the overall average temperature for that particular buoy. My hypothesis says that the effect seen above in Figure 2 should be greater in those buoys where the average air temperature is warmer, and less in those buoys where the air temperature is lower. And indeed, that proved to be the case, as is shown in Figure 3. This shows the buoys divided into four quarters (quartiles) on the basis of annual average temperature.
Figure 3. Differences between warm days (red line) and cool days (blue line) for the TAO/TRITON buoys divided into quartiles by temperature. Black line is average for all days.
Note that the response systematically grows larger and more exaggerated as we go from the first quartile (the coolest quarter of the buoys) sequentially to the fourth, warmest quarter of the buoys.
I hold these results out as strong support for my hypothesis that the temperature of the tropics is regulated by the combined action of clouds and thunderstorms. The difference in the temperature response of the warm and cool days shows the homeostatic mechanism in action, with warm mornings having cooler afternoons, and vice versa. All of this shows the clouds and thunderstorms at work.
I will ask again that if you disagree with something I’ve said, please quote it so that we both know what we’re discussing.
All the best,
w.
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Hi Willis, this all seems very sensible and reasonable. Just thinking out loud so be gentle with me but why on a graph of averages is the spread of temperature anomalies so much bigger at the end of the day than at the beginning. Surely the end temperature of one day is the start temperature for the next?
If days that start warmer end cooler, and days that start cooler end warmer, then negative is so strong that the only way there isn’t stasis is because of the delay in the feedback.
The strong negative feedback combined with the delay, producing more than 100% feedback after 24 hours, means there should be oscillation.
Can you do an analysis looking for an oscillation with a period of 24 hours? If what you say is correct, there has to be an oscillation, probably with a period of 24 hours, superimposed on the random changes.
The way to go. Check observation and analyse and since there is not a computer model in sight, this will reveal much more about what is going on in the real world, rather than imaginations in the realm of cyber space.
Thanks Willis, I think this is a great analysis, and good support for your thermostat hypothesis.
It’s noticeable that for all buoys, the amount cooler the following dawn is after a warm start, is smaller compared to the amount warmer the following dawn is after a cool start.
Is that due to an overall trend in the data for the period of study?
Thanks
Or two other inferences in a general sense from this–the warmer the earth’s oceans are, the more cooling takes place; the mechanism just happens to be cloud and precipitation formation (and the converse is true–when the earth is cool, it tends to get warmer since the cooling mechanisms don’t form). These relationships and mechanisms aren’t included in any of the computer models, are they?
Willis,
Very interesting results, and certainly the pattern is consistent with the Thunderstorm Thermostat speculation.
I wonder if the data would show a 48 hour cycle? A ‘red-line’ day starts warmer, ends cooler – would that then engender a ‘blue-line’ day? Is there some other cyclic pattern of X ‘red-line’ days followed by X or Y ‘blue-line’ days?
I am not aware of that sort of cycle pattern so would not necessarily expect a 48 hour cycle, but odd things sometimes appear out of a good analysis.
Wax on Wax off.
Willis,
First, the black line, showing the average day’s cycle. The onset of cumulus is complete by about 10:00.
Please give me a link or blog where you have written of the ‘onset of cumulus is complete…’.
I wish to re-read your work. Thank you
Willis – I like the fact you are working in small steps on hour by hour data.
Please can you give some details on the total length of data set you are working with? From this page I’m guessing that you are using the total records from 1994 onwards – is this correct?:
http://www.pmel.noaa.gov/tao/proj_over/taohis.html
I’d also like to know about the geographical clustering/distribution of the quartiles you have identified.
Thank you.
Willis,
You wrote “I was still surprised by the exact patterns.” Yet it seems that the data analysis bears out your expectations. So what part of it did you find surprising?
What surprises me is that the end-of-day temperature seems to deviate further from the average than the start-of-day temperature. [In the opposite direction.] Naively, this would suggest an unstable cycle, which is patently incorrect. Can you comment on that?
Predator – Prey in clouds
As posted at WUWT in July 2011, these guys were focused on aerosols, but certainly their predator-prey discussion would fit with a pattern of ‘red line’ days following (chasing? ::chuckle::) ‘blue line’ days. Perhaps their observed pattern is due to Thunderstorm Thermostat rather than aerosol cycles.
Koren and Feingold “found that equations for modeling prey-predator cycles in the animal world were a handy analogy for cloud-rain cycles: Just as respective predator and prey populations expand and contract at the expense of one another, so too rain depletes clouds, which grow again once the rain runs out. And just as the availability of grass affects herd size, the relative abundance of aerosols – which “feed” the clouds as droplets condense around them – affects the shapes of those clouds. ”
http://wattsupwiththat.com/2011/07/25/prey-and-predator-model-of-clouds/
Carl Chapman said:
“The strong negative feedback combined with the delay, producing more than 100% feedback after 24 hours, means there should be oscillation.”
Yes, I’d like to see similar graphs done on a 48 hour time scale instead of 24.
If one is averaging the diurnal cycles throughout a year, which I believe you are doing but correct me if I am wrong, one needs to center the records on something like sunrise or sunset for each day, because the time of each varies through the year.
Willis,
This is important work. Modelers need to recognize that the tropics are in an unstable zone and in which thunderstorm activity limits temperatures. If enough data are available, you could also plot a time series of lifted index and CAPE (convective available potential energy) to show how the tropical atmosphere bumps against the unstable regions.
Nice Willis, Thanks for your clear-headed work.
Can this work be translated to land based measurements?
Monitoring stations measuring ground temperature, air temperature and cloud cover?
Checking the difference between arable/forest/city curves to see if similar effects can be discovered?
Wills,
I think my Process Excellence (Allied Signal version) trainers would be pleased with your way of looking at processes- I know I am! I have found your use of Systems Theory concepts (“It is a self-organized emergent phenomenon. It is threshold-based, meaning that it emerges spontaneously when a certain threshold is passed.” with the nity gritty way of looking at the data to be enlightening.
I’ve just been looking at time-lapse satellite images of the western tropical Pacific for the past 24 hours (Aust BOM site). From what I could see there appears to be waves or clusters of cloud that migrate westerly, this may be evidence of the hot / cooler days seen in the graphs? As far as a general diurnal cloud cycle, there seems to be little evidence of it over the ocean from just eyeballing the images. I have certainly seen this cycle over land areas in the past but I’m somewhat sceptical whether it happens to any great degree over the ocean.
Willis, just wondering about the characteristic shape of your temperature graphs, particularly the shoulders on the ones over cooler waters. Have you considered the influence of atmospheric tides? In tropical areas there is a daily 2-3 mb increase in pressure between 4am -10am and about the same from 4pm to 10pm.
Willis, always enjoy your posts from the viewpoint of a cowboy fisherman. Have you considered that the push-and-pull, yin-and yang of your hypothesis could be studied through fractal modeling?
Timetochooseagain says “… one needs to center the records on something like sunrise or sunset for each day, because the time of each varies through the year.”
This is quite true, but near the equator — and these buoys are within ten degrees — the length of day varies much less than it does in more temperate climes. Your suggestion would be most valuable in a refined, second-stage analysis.
Willis, these are some pretty remarkable findings. Certainly far more interesting than almost all of the usual climate “papers” found in the literature. I’d suggest you’d get it published, but that’s like pulling teeth. The legit climate scientists need to study this, tho.
One would think this mechanism would negate any few W/m CO2 warming, at least in the tropics.
So far, so beautiful, at least from my rudimentary perspective. And, therefore, I’m still waiting for some CO2 = AGW physicist to explain to me why, within the actual atmospheric system present, the “ghg physics” of CO2 would “make” water vapor do something – such as produce an extra net heating effect, perhaps even up to a “runaway” – which it was either already able to do, or else was already unable to do – because of some countervailing process – completely on the basis of its own “ghg potential”, or at least including its “ghg potential”, that is, without any CO2 even being present.
That “process” which governs water vapor’s naked ‘ghg potential’ and drastically lessens its alleged relevance seems to be Willis’ thermostat mechanism, in which water vapor itself is only[?] a vehicle for heat energy transport and then a substrate for cloud formation and rain.
Therefore, given the presence and working of the thermostat mechanism, I don’t see why CO2 would “set” the surface temperature any higher than the temperature already could have been set according to AGW “ghg physics”, that is, when increased water vapor due to increased temperature, in an upward cycle ending at some point, could have already done the same thing, according to AGW’s “ghg physics” – since both CO2 and water vapor “are ghg gases”, and given the essentially infinite availability of a water vapor source, or at least enough of an availability compared to the availability of the right kind of electromagnetic radiation.
What the AGW proponents I’ve seen around here do in order to try to avoid this inconvenient, imo, problem, is to claim that water vapor both is and is not “a ghg gas”. It’s not a ghg when they say, “the concentration of water vapor is determined solely by atmospheric temperature”; but it is a ghg when CO2 needs it to be in order to further increase the atmospheric temperature!
So why wasn’t water vapor alone already increasing atmopheric temperature, and then what stopped it?
beng;
naively speaking, it would even suggest that a few W/m^2 CO2 “warming” would stimulate an overshoot, resulting in net cooling!
Thank you Willis for your clear, concise, and unambiguous presentation. This article is everything that Climate Science research is not, but should be. You should consider trying to publish this in a mainstream Science journal. We know your chances would be less than those of a snowflake surviving a forest fire in the Amazon in mid summer, but reading the details of the peer review and editorial shenanigans here on WUWT would be highly entertaining to the rest of us. Are you masochist enough?
@ur momisugly Time to choose again
one needs to center the records on something like sunrise or sunset for each day, because the time of each varies through the year.
Sunrise in Honiara, Solomon Islands, varies between 5.50am and 6.10 am, Sunset is between 17.50 and 18.10, at 9* south of the Equator. It is remarkably stable, there is only one high tide each day a well.
Bloke down the pub says:
August 25, 2011 at 3:58 am
This is a result of the type of analysis. For each buoy, what I did was choose entire days (1AM before the dawn to the next 1AM) based on whether the temperature at dawn was higher or lower than average. I then averaged the days. As a result, the end temperature of the day is not the same as the start of the day, nor is there any reason it should be.
Others have commented that the increase (on the cold days) is greater than the decrease (on the warm days), and that this would lead to an overall trend. In fact, the balance is restored by the fact that there are more warmer-than-average days than colder-than-average days, so the net result is general equilibrium.
w.