TAO Buoys Go Hot And Cold

Can I nominate Willis for a Nobel prize?

Willis:

… 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.

A better and much simpler hypothesis is gut simple. Don’t use climatologist divide by four of the irradiance entering and leaving the Earth system since Earth does have a day and a night side. The TOA energy balance in and out during actual daytime is not 237.5 W/m² but 237.5 W/m² times two, or 475 W/m². Pass that through Stefan-Boltzmann with adequate assumptions as emissivity one and you get the 30°C that you see near the equator. That is assuming also 0.30 albedo but you can adjust if that is not correct for a certain region of limited time when it can go over or under that 30°C but I do agree with you Willis that this 30°C is the approximate mean.
So I do not see the need for some “Super-greenhouse” or even some “Iris Effect”. it is 30°C because the irradience says it should be 30°C.

Nice work Willis. But shouldn’t you be using “adjusted” ship intake temperature data. LOL

There are TAO sensors measuring downwelling shortwave radiation, which is related to cloudiness, if it is defined as the attenuation factor relative to incoming shortwave flux at ToA. The latter quantity is independent of weather and only depends on time and location.
More importantly, they also measure wind speed. Your albedo-related regulatory process clearly depends on rate of evaporation, which is indeed a strong monotonic function of sea surface temperature. However, it also proportional to area of water-air interface, which is very small over calm seas. However, as soon as wind speed exceeds the limit when sea spray starts to form, it suddenly increases by many orders of magnitude.
Therefore thermoregulation is expected to kick in at a lower temperature if wind speed is high enough. Is that so?
It would also explain the apparently explosive nature of storminess. As soon as the first deep convective cell form over an area, wind speed exceeds this threshold, which in turn speeds up evaporation tremendously. A strong positive feedback loop, a sure recipe for an explosion.

We live in a water world, most of it is covered in water, we live on a blue planet, not a green one. Without water, and the amount that is on earth and its unique properties the world would not be a place where life could flourish. I live most of my life in a cold climate, and when I learned in high school that water freeze on the surface because it get lighter at 4C I realized if not for that simple property, earth would be a giant snow ball, since without it lakes and oceans would freeze from the bottom up, and once froze not likely to ever thaw out, but since water gets lighter a 4 C and ice forms on the surface and ice itself is and insulator lake and oceans do not often if ever freeze solid. Water keeps things from getting to hot or to cold, add on the heat conveyor, that the thunder storms present water even more wondrous. Water is the control knob of climate, not CO2, to bad the morons of the world have not figured that out. Even worst some of the same moron are trying to set limits on CO2 production thinking their fools errand might make a difference, I only have contempt for such morons. Oh as a disclaimer those who are born with limited intelligence please do not take offense, my derogatory comments about morons are only for the self made morons.

The variable ‘control’ at the higher t locations suggests something from control theory. You need two things for control: forward response with gain (energy), and neg feedback for stability. Most systems rely on constant gain. In this case, gain is a variable because energy in system is increasing before onset of violent convection. So it’s a control system that can’t work until gain passes some threshold

Frank. One key error you have is that your rand series is the full range of the TOA data , not the range for one site.
If you look at Willis’ fig 1 etc the range of data are much narrower. You need to scale your integrated random walk to fit the data. Ultimately this needs to be done by matching the statistical model to the actual data, but your idea is a good first hit.

Wish I could make a constructive comment, but you can see what the scatter plots say as well as I can. The only way you could do better is by adding dimensions using more data, for example if the buoys were equipped with a simple full-spectrum (all the way down into the LWIR so that it includes “back radiation”, even better a split that records separately SWV and LWIR split at the midpoint between incoming solar and downwelling LWIR) photocell that gave you “top of buoy” insolation as a function of time. This is the thing that would really help you, because it would let you connect what is actually happening with incoming radiation as a function of time/temperature and vice versa. Humidity/precip as well, as I’m guessing getting actually rained on might affect temperatures, or else the peak in temperature might correspond to achieving saturated humidity near the surface.
In the meantime, ascribing causes is going to be basically impossible, because all of the causes discussed are in hidden dimensions and because the regression effect you record is a fraction of a degree and hence is associated with some comparatively weak (on average) phenomenon.
To some extent you may just be seeing the emergence of a seasonal split (it would be interesting to correlate e.g. location of the cool buoys with their interaction with e.g. a jet stream, or the dates over which they return cool behavior vs warm behavior in a scatter plot as your “split” may be purely seasonal). The warm buoys might regress to a mean because there is only one stable mean with little seasonal behavior. The cool buoys clearly have a variable mean with a temporal persistence greater than a day, which suggests seasonal or at least weather cycle persistence.
I’ve read elsewhere about the apparent clipping of open ocean temperatures around 30C. The one thing that is immediately apparent is that that energy doesn’t stop coming down at the TOA just because SSTs reach 30C. It must therefore be a critical point of some sort for a mechanism that either blocks incoming radiation, increases sensible loss via e.g. latent heat, or increases sensible loss via some sort of active transport (or all of these together). It also has to be one that has a rather sharp onset because your scatter plot is clearly the flipping of some sort of climate “switch” (or switches) in higher dimensions. It is reminiscent of the clipping of temperature in water at the boiling point until you add enough energy to break latent heat bonds and start a boil. The energy isn’t going into temperature any more, it is going into something else (but obviously not Hansen’s “boiling oceans”:-) or going somewhere else.
I like going somewhere else, but where? Does this temperature correspond to one that triggers advective rolls in the salinity downward transport cycle, so that any extra heat just speeds up the transport? That could even be the missing mechanism for the missing heat — an entire equatorial band that simply speeds up downward transport of high density warmed water directly proportional to any increase in net downward flux to maintain a constant surface temperature of 30C. More heat equals faster, not warmer.
This sort of thing wouldn’t function alone. It would have to regulate the rate of surface evaporation to match, cope with the extra water vapor produced and increase sensible heat transport laterally in that form poleward, and might actually alter albedo several thousand miles away, several weeks later to further implement your feedback mechanism. That is, the mechanism doesn’t need to be local to affect global average temperatures — increasing water vapor production in the tropics and transporting it poleward could push temperatures down (on average) in the temperature zone three weeks later by increasing the cloud albedo there by a tiny amount.
This is why climate models suck. One would love to be able to build a model for this. But how can one build a model for this? It’s not even a hypothesis. One is postulating an alteration of a probability connected to a purely turbulent phenomenon in the coupled Earth-Ocean system, one involving density not of water but of highly saline ocean water in a way that depends on the local details of its density/depth/thermal/saline profile. The turbulent rolls involved are going to be completely invisible on a scale of 100km square cells. They might have a characteristic length scale of 50 meters! The entire phenomenon I described above might involve them growing to 51 meters when the surface is receiving more energy than it loses to the atmosphere! There might be a whole spectrum of these rolls that varies with surface wind speed and direction and past history, and the entire regulatory mechanism could be a tiny shift in the spectrum.
The only way to build it into climate models is by fiat. Assert that it exists, make up some semi-empirical function that affect tropical 100 km square cells, implement it in a model, and then run the model. At which point voila! The model will produce what you built in as that function! But does this reflect reality? It is literally Impossible with caps, boldface, and italics all intended, to tell. Climate models produce an insensible shotgun blast of future climates. To even detect the phenomenon might require real time detectors in an array the same order of magnitude as the advective rolls themselves place in the open ocean, and one would expect that the existence/presence of the detector might be more than enough to disrupt the phenomenon so that they are literally not observable except with extremely sophisticated techniques, perhaps a doppler sonar, perhaps collecting signals from a few million tiny (oversized bobber sized, too small to affect the evolution of the rolls) research buoys that ride the local currents and return some sort of picture of the local flow of energy in the ocean where temperatures appear to be clipped.
We are decades away from having the data needed to solve mini-problems like this, and centuries away from having enough compute power to solve the larger problem (the coupled Navier-Stokes equations for the Earth-Ocean-Atmosphere-Sun system) that dictates the evolution of the climate at adequate spatiotemporal resolution to be able to actually model the emergence of emergent negative feedback phenomena.
This is where GCMs utterly fail. 100km square cells basically mean that they can only develop self-regulatory nonlinear phenomena — emergent dissipative structures — at that scale. 5 minute timesteps and enormous amounts of compute time mean that they cannot with any plausibility function well enough to capture (predict) the known, named long period emergent phenomena, the decadal oscillations, that clearly have an enormous impact on global climate. Yet much shorter length scales are almost certainly critical (literally) in the heat transport they are trying to model. Thunderstorms have length scales dictated by their height — a cell with an anvil that reaches the top of the troposphere has a length scale order of 10 km, and there can be a lot of variability on scales down to a km laterally by kms vertically. Thunderstorms represent a huge local transport of heat latent and otherwise straight up through the greenhouse gas barrier.
The ocean no doubt has many similar transport mechanisms that exist on length scales from meters (size of surface waves) to a few hundred meters, pretty much clipped at 700 m if not before at the thermocline. But within the first 700 meters there can be entire encapsulated rivers transporting heat coherently and spinning it off in turbulent chaotic featherings to mix with surrounding water at different temperatures. This process is almost certainly fractal down to very small scales indeed — one can see the featherings in any surface temperature representation of e.g. the gulf stream, but only at a horrendously poor resolution, and of course a lot of what happens is beneath the surface, where the thickening of a layer by a factor of 2 might not affect the surface temperatures at all but which might represent a doubling of the energy being transported at constant temperature.
One hates to plead ignorance as an excuse, but I do so plead on behalf of the human species. We are cosmically ignorant about the climate. We are decades away from enlightenment, decades that are not well-served by a research climate rife with confirmation bias and contaminated by a “religious” desire to save the world from an unconfirmable hypothesis “predicted” only by GCMs that were built for the sole purpose of predicting it.
AGW is a plausible hypothesis that is directly supported by evidence that increased CO_2 is largely anthropogenic and a single, comparatively simple theory of radiative heat transport that, as a mean field theory, predicts a logarithmic warming that is reasonably well-supported by data with a thoroughly unalarming empirical climate sensitivity. Catastrophic AGW is (so far) purely a pipe-dream of a few individual people, amplified beyond all reason and outside of all actual evidence into a trillion-dollar public trough from which the very energy companies that are supposed to be “the bad guys” gobble down billion dollar chunks, money that if otherwise invested might have (by now) brought about an end to global poverty (instead of its perpetuation), a stable and healthy national economy (instead of a fragile energy economy that at this point would collapse if the public trough were to suddenly be emptied), and hey, who knows, World Peace.
Me, I’d be content if we simply invested it in making energy cheap. Energy is the fundamental scarce resource. Given enough, cheap enough, energy, all of the world’s problems can be solved. With energy we can (if necessary) desalinate the oceans and make deserts bloom, we can make fertilizers and run tractors and combines and grow food to feed a hungry world, we can build sewage treatment systems and bring the miracle of flushable toilets and the consequent reduction in misery and disease to the third of the world’s population that uses the nearest open field or the side of a handy street as a bathroom. We could provide energy to heat food without burning charcoal or dried dung and break a cycle of dung-based contamination of food and respiratory illnesses that claim several million lives a year in the third world, most of them children.
Personally, I could care less about where the energy comes from as long as it is cheap. Cheap solar is just fine. Cheap nuclear is peachy. Cheap coal is lovely, as long as coal remains cheap. Cheap fusion is my dream — that is the technology that would allow us to run civilization for so long that we would no longer be recognizably human long before it runs out (if then — by then we can probably mine Jupiter or its moons and never run out in the lifetime of the Sun). Sure, we might die off in the meantime, or be wiped out by an asteroid or gamma ray burst or war or an engineered super-virus built by a crazy person, but at least we wouldn’t do any of the above because of a lack of the fundamental resource.
At heart, all poverty is energy poverty. The units of energy are the units of work, and work, one way or another, is wealth.
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one of the most interesting article series with a very valid set of hypothesis and supported by data sinca a long time. digging this out does not show a lot of other scientists that try to explain these mechanisms.
thanks a lot for this!

I can’t help feeling that plotting this same temperature recovery data, from pole to pole as opposed to equatorially, that the seasonal changes due length of day and sun angle would some how reflect variation in polar sea ice formation/melting from year to year. What could influence the recovery from year to year? The ENSO and other events don’t always jibe with sea ice levels.