Guest Post by Willis Eschenbach
I got to thinking about how I could gain more understanding of the daily air temperature cycles in the tropics. I decided to look at what happens when the early morning (midnight to 5:00 AM) of a given day is cooler than usual, versus what happens when the early morning is warmer than usual. So what was I expecting to find?
Well, my hypothesis is that due to the emergence of clouds and thunderstorms, when the morning is cooler than usual, there will be less clouds and thunderstorms. As a result the day will tend to warm up, and by the following midnight it will end up warmer than where it started. And when the morning is warmer than usual, increased clouds and thunderstorms will cool the day down, and by the following midnight it will end up cooler than when it started. In other words, the emergent thermoregulatory phenomena will cause the temperature to tend to revert to some mean, not over months or years, but on a daily basis.
Now, this is the third post in a series discussing the effects of albedo and thunderstorms on the tropical temperatures. In order they were Albedic Meanderings, An Inherently Stable System, and The Daily Albedo Cycle. This post will make more sense if you’ve read those three first and seen the Figures.
So to investigate warm and cold days what I did was to take the air temperature data from some sixty-seven TAO buoys. I sorted them by average temperature, and I started to look at them. Figure 1 shows the temperature data from one of the coolest TAO buoys, where the mean temperature is 24°C. I split the data into “warm” and “cool” days, based on the average early morning temperature from midnight to 5 AM, and then took an hourly average of the warm and cool datasets individually.
Figure 1.Cool TAO buoy, averages of the days with warmer early mornings (Midnight-5AM) and the days with cooler early mornings. Straight lines connect the temperature at midnight at the start of the day with the midnight temperature 24 hours later. “Mean” is the mean temperature of all days. “Recovery” is how much the following midnight averages have moved towards the mean compared to the opening midnight averages. “Recovery Percentage” is the same as “Recovery”, expressed as a percentage of the distance from the beginning temperature to the mean.“Warm Recovery” is how much the warm temperatures have moved towards the mean, and “Cool Recovery” is how much the cool temperatures have moved to the mean. Horizontal black line shows the mean (average) temperature of all midnights. Red and blue straight lines connect the starting and ending midnight temperatures.
My hypothesis says that the temperatures should move towards the mean. That is to say, the temperatures at midnight of the end of the day (hour twenty-four in Figure 1) should be closer to each other than the temperatures at midnight at the start of the day (hour zero in Figure 1). So I have measured the difference between the opening distance (warm-to-cool temperature difference at opening midnight), and the closing distance (warm-to-cool temperature difference at closing midnight ). This I have called the “recovery” in Figure 1. This movement towards the mean is reported both in °C and as a percentage of the opening warm-to-cool difference. I’ve also noted how much the ending midnight temperatures of the warm and cool days separately have moved towards the mean midnight temperature.
However, there’s not a lot happening in Figure 1. The temperatures are barely moving towards the mean. When the day starts out cold it seems that it stays cold, and when it starts out warm, it stays warm. There is very little difference over the 25 hour period shown (0-24). Looking at other buoys I found that at the coolest end of the TAO buoy locations, there is little indication of my hypothesized thermoregulatory mechanisms. None of the TAO buoys in the cooler locations show any significant thermoregulated recovery to the mean.
But then I looked at the records from a TAO buoy at one of the warmest locations, where the mean temperature is over 28°C. There, the situation is totally different.
Figure 2. Warm TAO buoy, averages of the days with warmer early mornings (Midnight-5AM) and the days with cooler early mornings. Straight lines connect midnight at the start of the day with midnight 24 hours later. “Mean” is the mean temperature of all days. “Recovery” is how much the following midnight averages have moved towards the mean. “Warm Recovery” is how much the warm temperatures have moved towards the mean, and the same for “Cool Recovery”.
Now, this is quite different. At the warm end of the TAO buoy locations, the warm days end up cooler, and the cool days end up warmer, exactly as my hypothesis predicts.
One of the most interesting things about Figure 2 is how rapidly the restorative forces are able to move the temperature back towards the mean. In only one day, on average the temperature at midnight moves sixty percent of the way back to the mean midnight temperature … that’s a very rapid and rigid temperature control compared to what is happening in the cooler TAO buoy locations.
To close out this part, here’s a typical record from an intermediate temperature TAO buoy, with average temperatures of 27°C:
Figure 3. Intermediate TAO buoy, averages of the days with warmer early mornings (Midnight-5AM) and the days with cooler early mornings. Straight lines connect midnight at the start of the day with midnight 24 hours later. “Mean” is the mean temperature of all days. “Recovery” is how much the following midnight averages have moved towards the mean. “Warm Recovery” is how much the warm temperatures have moved towards the mean, and the same for “Cool Recovery”.
As you can see, the recovery towards the mean in this medium-temperature TAO buoy is somewhere in between the coolest and warmest buoys. In a single day the midnight temperature moves about a quarter of the way back to the mean.
One oddity that I noted was that although in absolute terms (°C) the recovery was different between the cold and warm days, in percentage terms (for the buoys shown above at least) the recovery is about the same.
This led me to ask, what is the overall dependence of the restorative thermoregulatory forces on the temperature? To see this, I took a scatterplot. Since I wanted to also see if the warm/cold recovery percentages were different, I used a scatterplot of the warm recovery percentages and the cool recovery percentages separately as a function of temperature. Figure 4 shows how the recovery percentage is related to temperature. I have again used the average temperature from midnight to 5 AM as the dividing factor for warm and cool days.
Figure 4. Scatterplot, daily thermoregulatory response to warmer (red) and cooler cooler (blue) days versus annual mean temperature. “Recovery Percentage” is how much closer to the mean the temperature of the midnight at the end of the day is, compared to midnight at the start of the day. If it moved all the way back to the mean it would be 100%.
First, it’s clear that the strength of the thermoregulatory response is a function of temperature. There is almost no thermoregulation at the low end of the temperature scale, while at the high end the midnight temperature moves halfway back to the mean or more in the course of a single day.
Next, it’s kind of hard to see the red and the blue because there is so little difference between them. I’ve printed them transparent so when they overlap they make purple … but in no case is there any significant difference between the warm and cold recoveries when expressed as percentages. This is despite the fact that often they are different in absolute terms (°C), as is shown in Figure 5 below. I have no explanation of why this should be so. Always more puzzles …
Figure 5. Scatterplot, absolute daily thermoregulatory response to warmer (red) and cooler (blue) days versus annual mean temperature in degrees C. “Recovery Amount” is how much closer to the mean the midnight temperature at the end of the day is, in degrees C, compared to the midnight temperature at the start of the day.
Here, we see that the thermal regulatory mechanisms at the upper end of the ocean temperature range can warm or cool a single day by a third to half of a degree C, midnight to midnight …
CONCLUSIONS: Well, I can say that this result is certainly consistent with my hypothesis that there are emergent thermoregulatory mechanisms that warm up the cool days and cool down the warm days in the wet tropics.
Now, scientists have previously proposed temperature mechanisms which they thought were involved in holding the temperature down in the Pacific Warm Pool (PWP), where we find the warmest of the TAO buoys. Sea temperatures in that area are the warmest in the open ocean … but despite that, the sea temperatures rarely exceed 30°C. Ramanathan proposed a “Super-greenhouse” effect to explain this temperature limit, and Lindzen proposed an “Iris Effect”, in order to explain the strong downward pressure on the temperature in the PWP. And those proposed mechanisms may indeed exist, they are not in opposition to my hypothesis.
What I have not seen mentioned previously, however, is that in addition to there being the strong downward pressure on the temperature of the warm days in the PWP noted by previous researchers, there is also a strong upward pressure on the cool days in the PWP … and as far as I know, mine is the only one of those three hypotheses that predicts such an effect.
However, it’s a big world out there, and I certainly could have either missed or misinterpreted previous art …
Finally, my hypothesis is that the temperatures displayed above are regulated by the emergence of cumulus, thunderstorms, and organized squall lines. HOWEVER, this analysis can say little about whether my hypothesis is the actual reason for the remarkably strong daily recovery towards the mean of warm tropical ocean temperatures. All it can say is that such a powerful thermoregulative effect certainly exists, that it operates on both the cool and the warm days, and it is consistent with my hypothesis.
It does not provide evidence that the mechanism is cloud-based. That’s hard to establish with the TAO buoys because they don’t contain information on the cloud coverage. But I think there’s a way to do it, which will be the subject of an upcoming post.
w.
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Interesting as allways.
I have only one problem Willis: How did the difference between cold and warm days started? Somehow the temperature must have been spreading at some time before it can narrow in. What goes up must come down.
I hope for some explanation.
7.2.1.2 Effects of Clouds on the Earth’s Radiation Budget
The effect of clouds on the Earth’s present-day top of the atmosphere (TOA) radiation budget, or cloud radiative effect (CRE), can be inferred from satellite data by comparing upwelling radiation in cloudy and non-cloudy conditions (Ramanathan et al., 1989). By enhancing the planetary albedo, cloudy conditions exert a global and annual short¬wave cloud radiative effect (SWCRE) of approximately –50 W m–2 and, by contributing to the greenhouse effect, exert a mean longwave effect (LWCRE) of approximately +30 W m–2, with a range of 10% or less between published satellite estimates (Loeb et al., 2009). Some of the apparent LWCRE comes from the enhanced water vapour coinciding with the natural cloud fluctuations used to measure the effect, so the true cloud LWCRE is about 10% smaller (Sohn et al., 2010). The net global mean CRE of approximately –20 W m–2 implies a net cooling.
Clouds -20 W/m^2.
CO2 2 W/m^2
Hmmmmm.
Great article Willis! It also made my mind wander back to a funny detail about K15. Is it still believed that higher CO2 concentrations influence night temperatures disproportionately and pre-dawn temps the most? Might that be one reason why K15 used pre-dawn temperature readings exclusively, to maximize possible temperature increases concurrent with increasing CO2?
“To avoid complications from diurnal heating, only observations taken close to local dawn were
used.” – from Karl et al. 2015 Part 2
It does not provide evidence that the mechanism is cloud-based. That’s hard to establish with the TAO buoys because they don’t contain information on the cloud coverage.
I tried to get that by comparing actual daily measured insolation to maximum measured insolation (per time of day and day of year, per buoy), but I do not think it worked well. Trying to tease out one mechanism in a setting where “regression to the mean” will almost certainly prevail as a result of many mechanisms isn’t a promising course.
In a previous analysis, you showed that lower (higher) early am temperatures produced higher (lower) afternoon maximum temperatures. There, regression to the mean was not a problem to contend with.
Good essay. Thank you again.
It’s a big data set. I am glad that you are sticking with it.
Willis, a letter to the editor of my newspaper in Australia (The Australian)
gives you the thumbs up today.
“Fred Cehak (Letters, 17/6), it would be a grave mistake to assert that people who do not hold formal science degrees (your armchair experts) are not capable of making valuable contributions to the global warming debate. As an example, go to the Watts Up With That? website and read the essays by Willis Eschenbach, a holder of no academic qualifications. I have five science degrees, and would not be able to hold a candle to the way that this chap can analyse data.
G. Gillman, Townsville, Qld”
I used to work with this guy. I only hold one science degree though, and was his Librarian.
Space shuttle view of cloud systems forming by convection of air masses over the Pacific Ocean. Evaporation consumes heat and leads to surface cooling. Higher up condensation of the water
vapour releases heat, which leads to warming of the atmosphere. By convection both heat and water vapour is removed from the surface and are transported up into the atmosphere. The picture covers an horizontal distance of about 40 km from left to right.
Cloud albedo is a measure of the reflectivity of a cloud. High values mean that the cloud can reflect more solar radiation. Cloud albedo varies from less than 10% to more than 90% and depends on drop sizes, liquid water or ice content, thickness of the cloud, and the sun’s zenith angle. The smaller the drops and the greater the liquid water content, the greater the cloud albedo, all other factors the same.
http://www.climate4you.com/index.htm
Willis: Very interesting data, but it tells only part of the story. If a mechanism exists in the warmer part of the Equatorial Pacific that returns temperature to average, then how does the temperature ever move away from average? Your discussion starts at midnight on days when it is colder or warmer than average and follows the return towards average the next day. But what happened on the previous day that led to anomalous temperature at midnight? Why didn’t your mechanism prevent the anomaly from developing in the first place?
One factor that might vary and produce anomalous temperatures is wind speed. The rate of evaporation depends on both surface temperature and surface wind speed.
rgb, can not break away from presenting data when contrasted to other data sources does not hold up as far as showing CO2 is governing the recent temperature trends of the globe from 1850-present..
The data sources I subscribe to all show a strong temperature correlation to solar variability with ENSO,PDO-AMO phase and volcanic activity superimposed upon the solar variability independent of CO2 .
It is quite clear that CO2 when taken alone and plotted against the temperature does not correlate. This fact is further evidenced by this recent pause 1998- still going and when reviewing past recent warm periods such as the Minoan, Roman ,Medieval warm periods which were as warm or warmer then today with less CO2 in contrast to present day. This is quite clear when looked at objectively..
If this present pause turns into a decline (which I expect will happen before this decade is out) then maybe finally some reality might come into the climate arena but in the mean time wishful thinking of a CO2/TEMPERATURE correlation with CO2 leading the way is going to persist.
One last note is, one can say CO2 is correlated to temperature but not that temperature is correlated to CO2.
http://icecap.us/images/uploads/Correlation_of_Carbon_Dioxide_with_Temperatures_Negative_Again.pdf
This study as so many others shows CLEARLY the lack of correlation between CO2 and temperature.
http://wattsupwiththat.com/2008/01/25/warming-trend-pdo-and-solar-correlate-better-than-co2/
NOTE THE CO2 TEMPERATURE CORRELATION IS NOT THERE.
This is rank amateur stuff. Where’s the customary statistical hand-waving; the “adjusted, gridded, homogenized, extrapolation” of measured data? Where’s the engine-manifold/ bucket thermometer bias taken into account? Properly analyzed, an alarming temperature hockey-stick must emerge from these buoys. That’s how you’ll know when you’ve “got it right”.
Reblogged this on gottadobetterthanthis and commented:
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Willis’ article is well presented and insightful. The comments, particularly those of RGB, are quite valuable. Some of the comments are good examples of what not to do. Some are educational and valuable.
Willis and RGB contribute greatly to WUWT, and they are among the greatest minds of our time. If you research the site, with the built-in search or your favorite search engine, you will find a wealth of knowledge and insight.
You will understand the global climate better if you read this article and the comments. The time spent reading will prove worthwhile.
While RGB points out that CO2 physically acts to increase global average surface temperature, Willis shows (in this and prior articles) that CO2 is not the only factor, and as RGB points out, more heat doesn’t necessarily mean hotter; it can instead mean faster, or slightly larger dissipative emergent phenomena.
Carbon dioxide is an essential ingredient in life. We must have it, and it has been deficient in the environment throughout human existence. It is likely still deficient. CO2 is no more a pollutant than O2 and H2O. Oxygen is a killer. Water, even more so. We humans suffer more expense and direct tragedy already, directly due to these other two essential ingredients of life than any plausible scenario associated with CO2.
We will burn all of the fossil fuels unless a genius breakthrough occurs. We will run out of all of it before CO2 even begins to become a true concern to the well being of humans and the biosphere.
Mostly, I agree with RGB (and Willis routinely expresses full solidarity with this sentiment) when he says that climate related policies, and even the vast sums spent on climate research are harmful to the least among us. The Pope wants us to respect the poor. That starts not with only small kindnesses, but with cheap energy by every means available.
RGB is correct when he says:
“At heart, all poverty is energy poverty. The units of energy are the units of work, and work, one way or another, is wealth.”
http://www.phy.duke.edu/~rgb/
Using W.E.’s cloud forming mechanism as a governor, if it is too cool to form clouds, the governor has reached a limit of its range to control temperature. Without cloud, the sun has its maximum warming effect. In the case of Milankovic cycles, it would presumably not achieve a cloud-raising temperature at the greatest distance from the sun. On the warm end of the cycle, the formation of clouds to cool is a certain proposition. The governor, therefore is most effective at the warm end of the range and limited at the cool.
Please use the comments to demonstrate your own ignorance, unfamiliarity with empirical data and lack of respect for scientific knowledge. Be sure to create straw men and argue against things I have neither said nor implied. If you could repeat previously discredited memes or steer the conversation into irrelevant, off topic discussions, it would be appreciated. Lastly, kindly forgo all civility in your discourse . . . you are, after all, anonymous. – Barry Ritholtz
http://www.ritholtz.com/blog/2014/02/how-does-the-u-s-power-grid-work/
Emergent excellency! More good work.
There are those who have never held a hammer nor lived a real life who are going to continue to squirm over your posts!
I simply smile! 😉