Guest Post by Willis Eschenbach
I have put forth the idea for some time now that one of the main climate thermoregulatory mechanisms is a temperature-controlled sharp increase in albedo in the tropical regions. I have explained that this occurs in a stepwise fashion when cumulus clouds first emerge, and that the albedo is further increased when some of the cumulus clouds evolve into thunderstorms.
I’ve demonstrated this with actual observations in a couple of ways. I first showed it by means of average photographs of the “view from the sun” here. I’ve also shown this occurring on a daily basis in the TAO data. So I thought, I should look in the CERES data for evidence of this putative phenomenon that I claim occurs, whereby the albedo is actively controlling the thermal input to the climate system.
Mostly, this thermoregulation appears to be happening over the ocean. And I generally dislike averages, I avoid them when I can. So … I had the idea of making a scatterplot of the total amount of reflected solar energy, versus the sea surface temperature, on a gridcell-by-gridcell basis. No averaging required. I thought well, if I’m correct, I should see the increased reflection of solar energy required by my hypothesis in the scatterplots. Figure 1 shows those results for four individual months in one meteorological year. (The year-to-year variations are surprisingly small, so these months are quite representative.)
Figure 1. Scatterplots showing the relationship between sea surface temperature (horizontal axis, in °C) and total energy reflected by each gridcell (in terawatts). I have used this measurement in preference to watts/square metre because each point on the scatterplot represents a different area. This approach effectively area-averages the data. Colors indicate latitude of the gridcell. Light gray is south pole, shading to black at the equator. Blue is north pole, shading to red at the equator. Click to enlarge
So … what are we looking at here, and what does it mean?
This analysis uses a one-degree by one-degree gridcell size. So each month of data contains 180 rows (latitude) by 360 rows (longitude) of data. Each point in each graph above is one gridcell.That’s 64,800 data points in each of the graphs. Each point is located on the horizontal axis by its temperature, and on the vertical axis by the total energy reflected from that gridcell.
The main feature I want to highlight is what happens when the ocean gets warm. From about 20°C to maybe 26°C, the amount of solar energy reflected by the system is generally dropping. You can see it most clearly in Figure 1’s March and September panels. But from about 26° up to the general oceanic maximum of just above 30°C, the amount of solar energy that is reflected goes through the roof. Reflected energy more than doubles in that short interval.
Note that as the ocean warms, the total energy being reflected first drops, and then reverses direction and increases. This will tend to keep ocean temperatures constant—decreasing reflections allow more energy in. But only up to a certain temperature. Above that temperature, the system rapidly increases the amount reflected to cut down any further warming.
Overall, I’d say that this is some of the strongest evidence that my proposed thermoregulatory system exists. Not only does it exist, but it appears to be a main mechanism governing the total amount of energy that enters the climate system.
It’s very late … my best regards to everyone, hasta luego …
w.
[UPDATE] A commenter asked that I show the northern and southern hemispheres separately. Here is the Southern Hemisphere
And the Northern. The vertical lines are at 30.75°C, nothing magical about that number, I wanted to see the temperature shift over the year and that worked.
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graphics of the northern and southern hemispheres
In which case Southern December should be compared with Northern June for similar solar Zenith Angles.
Likewise,
SH March compares to NH September,
SH June compares to NH December
SH September compares to NH March.
Stephen Rasey:
They compare, but don’t forget that the whole Earth is closer to the sun in December than in June, so the comparison isn’t quite perfect. If the southern hemisphere is reflecting more light in December than the northern hemisphere does in June, it may just be that there is more sunlight to reflect.
@Owen in GA
No reason to expect perfect comparison. The shape and amount of the landmasses in the NH and SH are different. I mearly wanted to make clear that NH December shouldn’t be compared to SH December
The regulation appears to me as currently confined to the tropics, given greater variability of temperature elsewhere. Paleoclimate climate reconstruction suggests that past global temperature regulation against warming was generally at 24 degrees C. Also, a major change in received insolation appears to me as capable of moving the temperature of the tropical tropopause. So far, that has been protecting the convective subset of the tropics from greenhouse gas warming. However, since tropopause-level air over the Intertropical Convergence Zone appears to me as having a net gain of heat by radiation (it is cooler than most tropopause level air elsewhere, and apparently warms as it moves elsewhere), I suspect that increasing greenhouse gases will cause that to warm (or move to a higher altitude) – despite greenhouse gas increase causing cooling of much of the uppermost troposphere elsewhere and in most of the lower stratosphere.
Willis:
What an amazing post. The non-linearity of the feedback mechanism is breathtaking to behold in the actual data – a super-sharp hockey stick. It clearly supports negative feedback, but because of temperature dependence it is quite difficult to model as an average, thus all the confusion.
Although the main point is the sharp cutoff at 30C, it might be clearer if you analyze the data at lower temperatures in a way that produces a more “collapsed” description. Have you tried plotting the data versus angle of the sun at noon instead of latitude? This might properly account for the month of the year and northern/southern latitudes, and allow you to describe the phenomenon with only one graph.
Flydlbee says:
October 6, 2013 at 11:49 am
The climate scientists should spend less time behind their desks and more time actually within the element they profess to study to see how it actually works.
============
Nothing new. The Virtuoso is a comedy from 1676 that satirizes the scientists of the day. Sir Nicholas, who thought himself an expert on everything and the world’s best swimmer, never swims in water. He simply lies on a table and follows the movements of a frog dangled on a string in front of him.
Today’s scientists forgo the table. Now they swim on the computer, still believing themselves to be expert on everything and the worlds best swimmers.
“So … what are we looking at here, and what does it mean?”
I wish most posters would say that early and often in their verbose and impenetrable treatises! Nice clarity, Willis, as always.
The 30C barrier is too evident to ignore.
What is it about this sea surface temp that cause cloud cover to explode? I note that Water itself has a number of unusual properties compared to other chemicals. It just acts differently than other chemicals. And it only covers 71% of the Earth’s surface down to 6 kms in places so it is something that might be the make or break factor in the climate.
Then there is the thermodynamic energy transfer properties of Water in the Earth’s climate. Everything from energy accumulation, to cloud formation, to energy transfer of evaporation, to energy transfer of simple rain. God decided to make Water the most important chemical. Now throw in that Hydrogen and Oxygen (two of the most abundant elements in the universe) have an almost unbreakable affinity for each other once formed (as in Water is almost an eternal chemical) its hard to imagine that this is not the most important factor in the climate.
The atmospheric system especially cloud is a dissipative system – it sheds heat energy. It is also nonlinear-chaotic, and thus it should not be at all surprising for it to exhibit one or more Lyapunov stable attractors.
Willis Eschenbach
An excellent post, thank you. Looking at them by hemisphere, I think we see that sensitivity varies if you go along with, the slope of apparent trendlines is the sensitivity. As the slope passes through the horizontal we may be going from a positive feedback to a negative one. You realize though, some are predisposed to see hockey sticks. I can’t figure that one out? In this case the blades seem to be hard walls of negative feedback.
“So … what are we looking at here, and what does it mean?”‘
Mrs Dai Bread Two is looking into a crystal ball which she holds in the lap of
her dirty yellow petticoat, hard against her hard dark thighs. …
…
MRS DAI BREAD TWO : I can’t see any more. There’s great clouds blowing again.
MRS DAI BREAD ONE: Ach, the mean old clouds!
(Dylan Thomas, Under Milk Wood )
The data displayed on these charts might make a good test of climate models. Do they replicate these results? If not, back to the drawing board.
Willis,
from the TAO data (currently surrounded by orange federal traffic cones) can you plot a cooling curve for SST measured below the skin evaporation layer against varying downwelling LWIR for night only. Are there flat spots observed in the SST cooling curve that correlate with passing cloud?
Salvatore Del Prete says:
October 6, 2013 at 11:48 am
“Explain how this ties into abrupt climate changes and inter glacial versus glacial climate regimes?”
Kind of a grand unifying theory of the climate? I’ll settle for small steps, thank you.
If you look at some of the charts, you may see the typical Z or S curve lines associated with regime changes. Chaos on all time and size scales?
Despite several negative comments, mostly unwarranted, I think the basic theory here is sound. There are negative feedbacks, and not just one or two of them. I used to work and live near the sea. On the way to work, day after day, I’d be amazed at how many ways the sun can shine off the ocean. Any model that doesn’t take into account variations in ocean reflectance will fail. Although solar zenith angles near zero theoretically give an ocean absorbtivity of 0.997, wind chop can lower that figure significantly.
It should also be noted that wind chop amplitude is related to water viscosity and surface tension, both of which drop as surface temperature increases, particularly the viscosity, which drops 23% over a ten degree (C) range. Thus for a given wind velocity, the hotter the water, the greater the albedo, and the more the surface rejects further heating. I would bet that GCMs don’t adjust for this. Wind chop in itself wil cause more loss of heat by convection because of the higher resultant surface area.
Multi dimensional plots are a fantastic instrument of scientific discovery. Asking “what if we plot a against b?” frequently unlocks new insights. Seeing space and time as linked dimensions was key to Einstein’s discovery of general relativity.
Willis: “This approach effectively area-averages the data.”
I don’t understand what you mean by this and why you are not plotting W/m2 .
The result should not depend up on the coordinate system chosen to divide up the world. The mass of water in each grid cell determines how important each watt is. You must look at power density not total power in some variable sized grid cell.
This would mean dividing by cos(latitude) and would boost polar regions.
This would probably accentuate the minimum around 26 degrees making the (non-linear) negative feedback maintaining a moderate temperature even clearer. In fact it appears from this that it is not simply a negative feedback with increasing temperature but rather a non linear negative feedback on the deviation from 26 deg C
You currently have two minima in most of these plots , one around 26 deg. the other at the poles. That will not lead to a stable system
However, if you plot power density I’m pretty sure you will only have one minimum, that is the indication of a stable system. Or perhaps more correctly that this is a feedback that will act to make the system stable at all latitudes not just as global or regional average.
Another thing I notice is that there are two traces in NH especially Dec and March. As a quick guess I suppose that this will reflect differences between land and ocean cells. The similarity being due to bleed-over of the oceanic pattern as persistent winds carry cloud over land in Europe and N. America.
There are grossly similar patterns in SH with some spreading but suggestions a clear split are a lot less clear.
Re-plot this in W/m2 and I think you will have a much more powerful argument.
Salvatore Del Prete says: October 6, 2013 at 11:48 am
Explain how this ties into abrupt climate changes and inter glacial versus glacial climate regimes?
Why? Do you expect (or hope) this could explain everything?
Greg says:
October 6, 2013 at 10:50 pm
Thanks, Greg. Indeed, it does boost polar regions. It makes them look much more important than the equatorial regions … but in fact, in global terms the polar regions are unimportant because they are so small and receive so little sunlight. That’s why I plotted them that way. In any case, here’s the other option, in W/m2 as you wished …
Actually, as mentioned in the head post, this is just the ocean gridcells, no land gridcells are shown.
All the best,
w.
@Theo Goodwin.
Unfortunately the CERES website remains blocked.
The point is that if you want to estimate albedo and get an estimate of radiation imbalance, that is a set of measurements designed with a specific goal.
However, if you want to construct a dynamic model of feedback that incorporates rapidly changing variables, such as cloud, that requires a completely different set of measurements that may require a much higher sampling requirement.
What has been done here, as far as I can understand it, is that the CERES data has been decimated into monthly samples, and has been used with monthly surface measurement data, which has major limitations, see:
http://judithcurry.com/2011/10/18/does-the-aliasing-beast-feed-the-uncertainty-monster/
This leads to major problems in determining the magnitude and even the sign of the feedback as is easily shown by simple modelling.
The reason that the type of analysis, performed here, which is a burning question in climate science, hasn’t been performed is a matter for speculation. It has been tackled, rather poorly in my view, by Spencer et al, see:
http://judithcurry.com/2011/10/10/climate-control-theory-feedback-does-it-make-sense/
but I suspect that the reason is that a few minutes of calculation shows that the problem is almost intractable unless one has very good data.
Yet another fascinating post from Willis. Keep it up.
It looks like a hockey stick – but a nice hockey stick!
Unlike Mann’s fraudulent manipulations, this comes from real data and is not the result of statistical trickery.
Chris
@RC Saumarez
Wow, I wouldn’t even have thought about that.
It seems this is further compounded by the issues another commentator raised in relation to satellite sampling.
So are you saying that the CERES data as used, is pretty useless for this purpose, and what is needed is a global array of light sensors and temperature gauges that can be sampled at c. 10 minute intervals.
If Willis were to state that his grid-measurements were just upscalings of cloud cover (only interested in the total signal over a month) vs upscaled temperature (for the entire month) in order to decipher whether a regulator might exist. Would this be reasonable in light of the issues you raised.
Willis
Looking at the colouration of dots it appears that you are showing that equatorial reflectance is greater than polar reflectance – you are not showing that as the ocean temperature rises so the cloud reflectance rises.
Is this what you intended?
Surely you need to limit the month and the latitude to as small a range as possible (limiting only by sufficient results returned).
The month obviously affects the results (your very broad bands show wildly differing results)
The lattitude obviously affects the results (your same coloured points are very bunched)
are you also taking into account the varying distance from the sun? and the changing obliquity? Both these obviously affect solar input and hebnce the reflected levels. Would it not be better to use a vertical scale of percentage reflectance compared to TOA received solar input?
Also, as a side comment., Even if the thunderstorms are cooling the ocean the global cooling will still be limited by how much upward long wave IR can be passed to a point where it will radiate to space. Then one also has to consider that heat from the deeper ocean layers has to be cooled also, and just how long does it take for deep thermal stores and higher lattitude waters to be cooled by equatorial thunderstorms?
@cdI am saying that if you want to characterise a feedback (or any other) system, the rate at which you have to sample its behaviour is governed by its dynamics.
What is worse, is that if you undersample the signals, the will be irretrievably corrupted, known as aliasing and will give completely misleading results.
A good example of aliasing is wagon wheels in movies. As they start to go round, they appear to move in the correct direction, they then speed up and suddenly appear to reverse direction and then slow down until they appear to stop. This is because the frame rate is too slow to capture the movement and the reversal of motion is aliasing (the true frequency gets represented as a negative frequency).
I really do not know how much aliasing is a problem in the CERES system, but given that cloud dynamics are short lived, if you try to work out was is happening in feedback with poorly sampled data, you will get some very peculiar results.
RC Saumarez
Thanks for your response – I know what aliasing is (hence the reference to the 10 mins: Nyqsuit frequency for the proposed cloud changes c. 20mins). What I was asking, was whether the CERES is any use and if not what was its mission statement. I can’t imagine, even with the limitations you mention, you can’t garner anything from it in relation to the above post.