Albedic Meanderings

Guest Post by Willis Eschenbach

I’ve been considering the nature of the relationship between the albedo and temperature. I have hypothesized elsewhere that variations in tropical cloud albedo are one of the main mechanisms that maintain the global surface temperature within a fairly narrow range (e.g. within ± 0.3°C during the entire 20th Century). To provide observational support for the hypothesis, I’ve been looking at the relationship between temperature and albedo, both globally and more particularly in the tropics.

To start with, the “albedo” of an object is a number from 0.0 to 1.0 that measures the fraction of solar radiation that is reflected from the surface of the object. It’s often given as a fraction, although I prefer it as a percentage. The albedo of the earth is about 0.29, meaning 29% of the sunlight is reflected back to space. Figure 1 shows the average albedo around the planet.CERES total albedo

 

Figure 1. Average total albedo, including surface and cloud albedo. Calculations in the heading are for the northern and southern hemispheres (NH, SH) the tropics (Trop), the Arctic (Arc), the Antarctic (Ant), and the land and ocean.

Figure 1 shows some salient features. One is the inter-tropical convergence zone (ITCZ), which is the light green area just north of the Equator. It marks (as the name suggests) the average boundary between the northern and southern hemispheric air masses. The ITCZ is the area of deep tropical convection, the area increased clouds just above the Equator. To verify that the oceanic variations we are looking at are a result of cloud albedo rather than ocean surface albedo, we can compare Figure 1 with Figure 2, which shows the surface albedo.

 

CERES surface albedoFigure 2. Average surface albedo only.

As you can see, the average albedo of the ocean varies little, other than increasing slightly from equator to pole. The combination of the two figures highlights an albedic mystery—the total albedo of the northern and southern hemispheres are identical to three significant digits. This is clearly the result of the clouds, as the surface albedos of the two hemispheres are quite different. However, the mechanisms involved in the rebalancing are unclear. It does emphasize the responsive nature of the cloud albedo.

Now, as I said above, I wanted to look at the relationship between temperature and albedo. I started by looking at how the relationship breaks out spatially. Figure 3 shows the correlation between average temperature and average albedo. “Correlation” is a number that can vary between -1.0 and +1.0. A correlation of plus one indicates perfect positive correlation (both either go up or go down together). A correlation of minus one indicates perfect negative correlation (when one goes up the other goes down, but still in step with each other).

 

CERES correlation albedo temperature 2014Figure 3. Correlation between temperature and albedo. The area outlined in red is analyzed separately below in Figures 5 & 6.

As you can see, the northern hemisphere land towards the poles is strongly negatively correlated with temperature. This is because as the northern land warms, the ice and snow melts and the plants grow. Both of these changes lower the solar reflectivity (albedo). In the tropics, on the other hand, there are a number of large areas that are positively correlated with temperature.

Next, I took a look at the general relationship between the temperature and the albedo. I wanted to look in particular at what is happening in the ocean. Figure 4 shows that relationship.

 

scatterplot ocean temperature vs albedoFigure 4. Gridcell by gridcell comparison of average albedo and average ocean temperature. Temperatures below freezing are of ice-covered ocean.

Now, this is most interesting. The warmer the ocean gets, the lower the albedo goes, a negative correlation … except when the temperature gets over about 26°. Above that, the warmer it gets, the higher the albedo goes. This is the tropical area shown in Figure 3 where there is positive correlation between the albedo and the temperature. This is exactly the mechanism that I have proposed, that increasing tropical temperatures cause increasing albedo and thus help to regulate the global temperature. I say that this is due to a combination of both earlier and stronger daily emergence of the cumulus, thunderstorm, and squall line regimes.

However, it could be fairly argued that in Figure 4 we’re not looking at temperature and albedo changes in one location. Instead, we’re looking at average values in a host of different locations. So it might be that the “hook” at the high temperatures doesn’t reflect what is happening as the temperature changes in each individual location.

To see if this is so, I’ve invented a kind of plot that I call a “Lissajous scatterplot”. Or maybe I didn’t invent it, but I’ve never seen one before. It is a combination of Lissajous figures and a scatterplot. Instead of displaying the average for each gridcell, I display the Lissajous figure for that gridcell. And what is a Lissajous figure when it’s just sitting at home by the fire?

A Lissajous figure is a display of two cyclical values, with one shown on the horizontal axis and the other on the vertical axis. As usual there’s a good description at Wolfram Mathworld,  and Wolfram also has an interesting interactive demonstration of the Lissajous figures here.

I use the monthly average values of two cyclical variables to make a Lissajous figure. Here, for example, is the Lissajous figure for temperature and albedo for the gridcell located at 45N 80W:

 

CERES lissajous figure 45N 80W temp albedoFigure 5. Lissajous figure, monthly average temperature versus monthly average albedo. The location is near the Great Lakes in North America.

As you can see, in that particular location, as the temperature goes up, the albedo goes down.

So with that as Lissajous prologue, Figure 6 shows a Lissajous scatterplot of the temperature and albedo of an area of the tropical Pacific. This is the area of the Pacific outlined in red in Figure 3. In essence Figure 6 shows the lower right end of the graph shown in Figure 4, but with Lissajous figures for each gridcell rather than dots representing the gridcell averages.

 

CERES lissajous scatterplot temp vs albedoFigure 6. Lissajous scatterplot, showing the monthly changes in tropical Pacific temperatures and albedo. The area of the analysis is outlined in red in Figure 3. Each gridcell is represented by a Lissajous figure showing how monthly average albedo varies with monthly average temperature 

Recall that I am using this method to see if the “hook” in the high-temperature region of Figure 3 was actually reflected in the temperature and albedo changes in each individual location over time. And indeed, the change in the direction of the relationship with the rising temperature shown in Figure 3 is totally borne out by Figure 6. Albedo is dropping as temperatures rise, but only up to about 26°C. As temperatures start rising above 26°C the albedo just goes through the roof.

Finally, how much more sunlight is reflected by this increase in albedo? Figure 7 shows a Lissajous scatterplot of the reflected sunlight versus temperature:

 

CERES lissajous scatterplot temp vs reflectionsFigure 7. Lissajous scatterplot, as in Figure 6 but showing the monthly changes in tropical Pacific temperatures and reflected sunlight. The area of the analysis is outlined in red in Figure 3.

Figure 7 makes it clear just how much difference the change in albedo makes. The white dashed line shows the approximate trend of the high-temperature section of the graph. The slope of that line is no less than 60W/m2 per °C. In other words, in the warmest tropical regions, for each degree that the temperature warms, the albedo cuts down the incoming sunlight by about 60 W/m2.

My conclusion? The “hook” in the high temperature end of the temperature/albedo graph is evidence that cloud albedo is part of the system that places a limit on how warm the tropical ocean is able to get. When the temperature gets above a certain point, increased clouds cut way back on the incoming energy. The “hook” also provides evidence of some kind of “set point” around 26° – 27°C, with temperatures warmer than that being cooled and temperatures cooler than that being warmed by the variation in albedo.

The albedo data is thus strong support for my hypothesis that the timing and strength of the daily onset of the tropical cumulus and cumulonimbus regimes exercises a strong control on the amount of incoming energy. The presence of large areas of tropical ocean with a positive correlation between temperature and albedo lead to a naturally stable system … which will likely be the subject of my next post, unless I’m once again distracted by … oooh, shiny!

Regards to all, keep the pedal to the metal …

w.

My Customary Request: If you disagree with someone, please QUOTE THEIR EXACT WORDS that you disagree with so we can all understand your precise objections.

Data: Once again I’ve used the CERES EBAF satellite-based radiation dataset.

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oppti
June 3, 2015 10:08 am

I am amazed!

Pittzer
June 3, 2015 10:11 am

Thanks, Willis. Good read as always.

June 3, 2015 10:14 am

Willis, this is just gorgeous. Your finest work yet, I think.

Bob Weber
Reply to  daveburton
June 3, 2015 11:07 am

Agreed.

Reply to  daveburton
June 3, 2015 11:21 am

With my background in Systems Science, I see feedback mechanisms everywhere. One develops a nose for them, and, in the natural world, as in engineering, most of them seem to be negative (stabilizing) mechanisms, which attenuate forcings.
You seem to have identified a non-obvious but doubtless very important negative (stabilizing) feedback mechanism, Willis.
There are many other negative feedback mechanisms at work, too. Some of them are obvious, others less so.
1. Most basically, of course, the warmer things get, the faster they lose heat. But, also:
2. As temperatures go up, evaporation at the surface increases. That removes “heat of evaporation” from the surface. Moist air is lighter than dry air (contrary to intuition), so the moist air rises until the moisture condenses into water droplets or ice flecks, as clouds, releasing the latent heat which was absorbed at the surface. Thus the water cycle is a classic phase-change refrigeration cycle, removing heat from the surface, and releasing it aloft, just as the Freon phase-change refrigeration cycle cools your refrigerator.
Anything which increases surface evaporation (such as warmer water, or reduced ice cover) increases water-cycle cooling.
Because increase surface temperatures make the cycle run faster, it is a negative feedback mechanism, cooling the surface faster, as temperatures go up.
3. As levels of The Precious Air Fertilizer (CO2) levels go up, so do plant growth rates. That removes more CO2 from the air, attenuating the increase in CO2 — another negative feedback mechanism.
4. At extreme latitudes, warmer water reduces ice cover. That increases evaporation, making the water cycle run faster, and cooling the water (#2, above). It also allows wave action to create turbulence beneath the surface, allowing faster exchange between water at the surface (cooled by evaporation) and water beneath the surface, thereby helping to cool the water.
5. The additional evaporation due to more open water also apparently causes additional cloud cover, increasing albedo at altitude, and additional lake-effect/ocean-effect snowfall downwind. Some of that snow falls on the ice-sheets, increasing ice accumulation, and some of it falls on land, increasing albedo, decreasing land temperatures, and prolonging winter — another negative (stabilizing) feedback mechanism, by which additional warmth causes additional cooling.
Note: that snow accumulation is a big deal. The magnitude of ice accretion from snowfall on ice sheets was illustrated by the team which salvaged Glacier Girl from under 268 feet(!!) of accumulated ice, 50 years after she landed on the Greenland ice sheet.
But the negative feedback mechanism you’ve identified, Willis, is new to me. Very excellent work!

Reply to  daveburton
June 3, 2015 11:22 am

(Sorry for the italics — I botched a </i> tag.)
[Fixed. ~mod.]

Erik Magnuson
Reply to  daveburton
June 3, 2015 11:40 am

The albedo feedback for sea surface is positive for SST’s less than 26C at which point it rapidly becomes negative. This corresponds somewhat to a positive feedback excursion on an amplifier running into a power supply rail. The climate over the last million years has acted like a bistable circuit slamming into the positive and negative rails.
The bad news is that there is no apparent short term feedback mechanism for downward temperature excursions, which would explain why glacial periods are typically much longer than interglacial periods. The long term albedo negative feedback probably comes from the extreme dryness during the glacial period causing dust to be blown around and decreasing the albedo of the ice and snow.

Reply to  Erik Magnuson
June 3, 2015 11:47 am

The long term albedo negative feedback probably comes from the extreme dryness during the glacial period causing dust to be blown around and decreasing the albedo of the ice and snow.

I suspect Co2 plays a big part in this, volcano’s load the atm, ice cuts it off from cold water and rocks to erode, allowing a build up. But I think that’s also a long term feedback.

donaitkin
Reply to  daveburton
June 3, 2015 5:50 pm

A nice summary, for which many thanks. I might even build an essay around it. And you can read about Glacier Girl here as well:
http://donaitkin.com/melting-the-greenland-icecap/

Reply to  daveburton
June 3, 2015 10:10 pm

One small correction/clarification: “additional cloud cover, increasing albedo at altitude,” isn’t necessarily a negative feedback mechanism (which reduces warming). At night and when the sun is near the horizon, additional cloud cover helps warm the surface. So if additional cloud cover results from reduced ice cover which results from warmer water, that means it’s a positive (rather than negative) feedback mechanism, at least when the sun is near or beneath the horizon.

Reply to  daveburton
June 3, 2015 10:15 pm

Clarifying my clarification: I’m correcting/clarifying myself, not Willis. Or trying to, anyhow.
I’d better go to bed, before I make an even bigger mess of it.

Erik Magnuson
Reply to  daveburton
June 3, 2015 10:26 pm

One reason that 26C ay be the magic number for the inflection point is that is about where a degree rise in dew point causes more of reduction in air density than what is caused by raising the air temp one degree with no change in absolute humidity. Since the vapor pressure of water doubles for roughly every 10C increase (at least around 26C), it doesn’t take much of a dew point rise to have a large effect on convection and dew point will roughly be the sea surface temperature.
Willis’s findings make perfect sense to me.

johnmarshall
Reply to  daveburton
June 4, 2015 3:48 am

Do not forget that the convective clouds are formed from condensed water vapour which removes a lot of surface heat through latent heat during evapouration. So latent heat, convective cloud and increased albedo all add to that negative feedback.

TYoke
Reply to  daveburton
June 3, 2015 2:41 pm

Also agree. This is Willis finest work. The hook suggests a true stabilizing process. Low temps let in more heat. Hi temps reduce the amount of heat allowed in. The graphs are spectacular.
It would be swell if the IPCC would pay some attention to this fine idea, given that the cloud albedo model, whatever that model turns out to be, is always going to be absolutely central to the GCM predictions.

Reply to  TYoke
June 3, 2015 7:13 pm

A prediction of Dr Richard Lindzen’s Iris hypothesis in action.

Brett Keane
Reply to  TYoke
June 4, 2015 1:40 am

Attention! Hockey stick alert!! (sarc off)

Alan Robertson
June 3, 2015 10:15 am

Willis, your continued presentations about the emergent cloud/albedo phenomena acting as temperature buffers has been most instructive- thanks much.
Speaking of albedo, the Northern Hemisphere snow cover remains relatively high for this time of year.
http://home.comcast.net/~ewerme/wuwt/cryo_compare.jpg

Reply to  Alan Robertson
June 3, 2015 11:11 am

I’m afraid not, Cryosphere Today is having problems with the source of the snow cover data and as they say on their website:
“Note: snow cover data not updating … we hope to have a new data source by July, 2015.”

Reply to  Alan Robertson
June 3, 2015 11:15 am

Rutgers snow lab appears to show below average snow cover.
http://climate.rutgers.edu/snowcover/chart_vis.php?ui_year=2015&ui_month=5&ui_set=2

ARW
Reply to  Alan Robertson
June 3, 2015 11:17 am

That has to be wrong for 2105. it looks like the great lakes is still surrounded by snow. Looking out my window all I see is a sea of green. Perhaps my geography skills are weak?

Reply to  ARW
June 3, 2015 11:48 am

Green here (N41 W81).

Alan Robertson
Reply to  ARW
June 4, 2015 4:29 am

Good grief. Sorry about the misinformation.
The Great Lakes link (nested within Sea Ice links Within Reference pages at top) has a lot of info about that region.
http://wattsupwiththat.com/reference-pages/sea-ice-page/great-lakes-ice-page/

Reply to  Alan Robertson
June 3, 2015 2:25 pm

Alan
Try NIC instead.

Curt
Reply to  Alan Robertson
June 3, 2015 6:59 pm

Alan:
They’ve screwed up the land snow cover this spring. It’s still showing the snow cover from a date this winter. They’ve acknowledged the problem, but haven’t fixed it yet.

oebele bruinsma
June 3, 2015 10:15 am

Wiilis,
Brilliant, the albedo as an emergent phenomenon. Thanks.

AnonyMoose
June 3, 2015 10:20 am

Has anyone seen how climate models behave in tropical regions? How does their albedo behave?

TGBrown
June 3, 2015 10:22 am

Hi Willis,
Interesting observations. This seems generally connected to Lindzen’s iris hypothesis (see http://judithcurry.com/2015/05/26/observational-support-for-lindzens-iris-hypothesis/#more-18748 for a discussion). Although that discussion seems to focus on the difference in short wave versus long wave albedo. I’m assuming your data corresponds to short wave (which would represent the bulk of the incident solar energy).

ossqss
June 3, 2015 10:30 am

Interesting, 26C is also the baseline for tropical cyclogenesis support.
Related Yin Yang?
Somehow that doesn’t come across like I meant it 🙂

Bill Illis
June 3, 2015 10:38 am

Let’s say for the tropics then, when CO2 doubling occurs and there is +3.7 W/m2 more in GHG forcing causing the temperature to rise by 1.0C, …
… your numbers indicate that Albedo would increase substantially resulting in a negative feedback of something like -35 W/m2 of sunlight reflected (with an offsetting OLR reduction caused by the increased clouds of about half that amount or +17.5 W/m2 which is what increased clouds do) so the net negative cloud forcing would be -17.5 W/m2 for the +3.7 W/m2 of CO2 doubled forcing.
The climate models and the theory assume there is +0.7/W/m2 of cloud feedback forcing per +3.7 W/m2 of increased GHG forcing while these numbers for the tropics say it should really be -17.5 W/m2 per +3.7 W/m2 of GHG forcing.

Alan Robertson
Reply to  Bill Illis
June 3, 2015 10:54 am

Mankind still understands very little about clouds, or their effects on climate and bless their pointy heads, the modellers seem to know as much as the rest of us, or willfully less as it suits their purpose. (Sorry, I can no longer give modelers too much benefit of the doubt, but I’m just some guy with an opinion.)

Reply to  Bill Illis
June 3, 2015 11:29 am

Negative feedback mechanisms can only attenuate a forcing, Bill (or cause oscillations if delayed). They can’t reverse the sign of a forcing.

Reply to  daveburton
June 3, 2015 2:52 pm

It’s still a feedback mechanism, Willis, it’s just not a linear feedback mechanism.
A governor is a feedback mechanism. When engine speed goes up, the governor notices the increase, and cuts the fuel flow (or fuel & air) supply, to bring the engine speed back down. When engine speed goes down, the governor increases the fuel supply, to compensate.
If your governor is controlled by engine speed, then it cannot reverse the sign of the effect of increasing or decreasing drag on the engine or any other factor which affects engine speed. A governor can erase most of the effect, so that the engine speed under heavy drag is not much less than the engine speed with no load, but it can’t make the average engine speed be faster under load than with no load.
(Caveat: if there’s a delay between the engine speed change and the governor responding to it — and there always is some delay — that can cause instability, if the feedback amplification is too high [i.e., if the governor is too sensitive]. That can cause transient “overshoots” or even persistent oscillations in engine speed.)
The same is true for climate systems. If higher temperature causes an effect (such as increased cloud cover) which causes lower temperature, that can reduce some or even most of a forcing which increases temperature. But it cannot reverse the sign.
Think of your kitchen. Your thermostat may turn on the air conditioner to keep the kitchen temperature comfortable while you’re cooking, but It can’t make turning the oven on cause the kitchen to get colder.
Of course, if you had a governor which was controlled directly by engine drag, rather than by (or in addition to) engine speed, then you could, indeed, make a system in which engine speed was higher under load than under no load. Likewise, if you had a thermostat system which was tied to your oven controls, so that it could turn on the air conditioner in anticipation of the warming from the oven, you could make turning on the oven cause the kitchen to get colder. But that’s not the result of the warming from the oven, that’s just adding another function to your oven controls, so that turning on the oven turns on the air conditioner, too.
For there to be something like that going on in a climate system would require separate feedback mechanisms coupled to each of the many forcings which affect temperature, rather than driven by temperatures. So, for clouds, there would have to be a mechanism by which CO2 directly increases cloud cover (rather than by the intermediate agency of warming), a mechanism by which volcanic aerosols directly decrease cloud cover, a mechanism by which increased solar irradiance directly increases cloud cover, etc. That might be plausible for one or two exceptional forcings, but it is not plausible in the general case.

stevefitzpatrick
Reply to  daveburton
June 3, 2015 6:24 pm

Willis,
Dave is right about this. Negative feedback most certainly can ‘move the throttle’ in either direction; if the controlled variable is above the ‘set point’ then negative feedback will reduce the throttle. If it is below the set point, then negative feedback will increase the throttle. It is just proportional control, and proportional control (unless it is too sensitive/aggressive and the system oscillates) will always act to reduce, but not eliminate, the response to a perturbation. In other words, there will always remain a residual ‘offset’ which is proportional to the strength of the perturbing force, and inversely proportional to ‘sensitivity’ of the negative feedback. BTW, there are lots of kinds of governors, the most common of which is just a proportional negative feedback. More complicated governors include addition of an integration of the residual offset to the control signal, to gradually reduce that offset towards zero (PI control) A more sophisticated control scheme adds both integration of the offset and the first derivative of the offset (both multiplied by suitable constants) to the output signal (PID control). This reduces tendency to oscillate and so allows more ‘aggressive’ control, with smaller deviations from set point.

Reply to  daveburton
June 3, 2015 9:20 pm

Willis wrote (more or less), “[A governor is not a feedback mechanism.] In the current context, a governor is a mechanism that controls the throttle of a heat engine in order to set it to some specified operating condition. For example, the “cruise control” in a car is an example of a governor. It is… not a simple feedback. The governor can push the throttle either way. Simple negative feedback can’t do that.
Actually, a cruise control is very commonly used in engineering classes and texts as an example of a feedback system. Here are some illustrations (mostly block diagrams) showing how it works:
https://www.google.com/search?q=%22cruise+control%22+feedback+system&tbm=isch
Negative feedback does, indeed, “push the throttle either way.” For example, as CO2 levels go up, plants grow faster and use more CO2, thus attenuating the increase in CO2. But as CO2 levels go down, plants grow more slowly, and use less CO2, thus attenuating the decrease in CO2. That’s a classic negative feedback loop, moving “the throttle” in either direction.
Your body is chock full of negative feedback systems. For example, consider what is involved if you’re just standing in one place. If you start to tilt to the right your body senses it, and tweaks your muscles slightly, lifting your left foot a tiny bit, to shift a bit of weight from the left foot to the right, to make you lean a little more to the left. If you start to tilt to the left, your body will shift your weight to the left foot, to tilt you more to the right. (And it works the same way in the front-back axis: if you start to lean forward your body will tense the muscles in your feet, to shift weight from your heels to the balls of your feet, but if you start to lean back it’ll do the opposite.) That’s negative feedback, moving “the throttle” in whatever way is needed, to keep you upright.
Note that feedback in a feedback system doesn’t need to be linear. Linear feedback systems are the easiest to analyze, but many feedback systems, both natural and human-engineered, are non-linear. For a simple example, consider the thermostat in your house. It is highly non-linear. Most home thermostats are binary or trinary (with a bit of hysteresis to avoid excess cycling, which is inefficient and hard on motors and compressors). They just turn your heat or a/c on or off, as necessary, to keep temperatures within a relatively narrow range. (They sometimes also have an auxiliary heater which they can trigger, on particularly cold days, if the main system is not keeping up with the load.)
In engineering, “overshoot” is usually undesirable, though a little bit of overshoot may be tolerable. What usually happens is that if there’s a delay in the feedback look, as you dial up the feedback gain, the system becomes increasingly responsive to, but also tends to become increasingly unstable, resulting in overshoot or even oscillation. If you can’t reduce the delay in the feedback loop, then simplest remedy may be to simply to dial back the amount of proportional negative feedback. But a better solution is often to augment the negative proportional feedback with a bit of negative derivative feedback (the “D” in “PID” which stevefitzpatrick mentioned). That’s a great way to improve system stability and reduce overshoot, without compromising system responsiveness.

Reply to  daveburton
June 3, 2015 9:25 pm

Sorry, it’s past my bedtime, I’m getting sloppy.
“What usually happens is that if there’s a delay in the feedback look, as you dial up the feedback gain, the system becomes increasingly responsive to, but also tends to become increasingly unstable, resulting in overshoot or…”
should be:
“What usually happens is that if there’s a delay in the feedback loop, as you dial up the feedback gain, the system becomes increasingly responsive, but also tends to become increasingly unstable, resulting in overshoot or…”

stevefitzpatrick
Reply to  daveburton
June 4, 2015 5:54 am

Willis,
I was agreeing with Dave when he wrote this: “Negative feedback mechanisms can only attenuate a forcing, Bill (or cause oscillations if delayed). They can’t reverse the sign of a forcing.”
Control theory is well known and widely used, including your example of an engine governor, which can indeed be based (and often is based!) on simple negative feedback. I think you are mistaken when you suggest that increasing albedo with increasing ocean temperatures (above~25C) is something more than a negative feedback.
BTW, your data show that cloudiness increases when temperatures are lower than 25C, which would indicate an unstable positive feedback if the only impact was reflection of light. Since higher latitudes do not “run away” towards colder temperatures, the system has to be more complicated than one where temperature is regulated by albedo. Clouds have a net cooling effect only where the energy in the sunlight they reflect is more than they reduce infrared energy loss to space. Your graphic of albedo versus temperature (which is very interesting) seems to be showing that clouds have net cooling (negative feedback) at low latitude, where there is lots of sunlight, and net warming (positive feedback) at high latitude, where there is much less sunlight.

Owen in GA
Reply to  Bill Illis
June 3, 2015 7:21 pm

The climate models and the theory assume there is +0.7/W/m2 of cloud feedback forcing per +3.7 W/m2 of increased GHG forcing while these numbers for the tropics say it should really be -17.5 W/m2 per +3.7 W/m2 of GHG forcing.

Bill, In actuality the 1C would never be realized. As the temperature started to respond to the 3.7 W/m2 of forcing the temperature would begin to rise. As the temperature went up the first 0.01C the feedback would kick in with .6 W/m2 reduction leaving only a 3.1 W/m2 surplus. When the temperature went up .05 C the feedback would kick in with a 3.0 W/m2 reduction leaving only a 0.7 W/m2 imbalance. When the temperature rose a whopping 0.062 C, the feedback will exactly balance the increase from CO2. 0.062C is not a very scary number at all. My body temperature probably varied by more than that while I calculated the balance point!

Dahlquist
June 3, 2015 10:43 am

There’s the thermostat for you, set at normal. Great article Willis.
Would albedo have any effect on a greenhouse scenario caused by GH gasses?

Dahlquist
Reply to  Dahlquist
June 3, 2015 10:46 am

Illis…Your question was much better. I was typing while you posted.

Dodgy Geezer
June 3, 2015 10:46 am

…I have hypothesized elsewhere that variations in tropical cloud albedo are one of the main mechanisms that maintain the global surface temperature within a fairly narrow range …
Sounds likely. Also sounds very reminiscent of the ‘DaisyWorld’ mechanism proposed by Lovelock – http://en.wikipedia.org/wiki/Daisyworld
But, of course, it has a fundamental difference. Lovelock’s hypothesis was that lifeforms themselves provided the feedbacks necessary to maintain an appropriate environment for life – and you are pointing out that purely mechanistic processes seem to do this for us…

Joseph Murphy
June 3, 2015 10:51 am

Good stuff, thank you Willis.

Russ R.
June 3, 2015 10:54 am

Monthly averages are certainly informative, but the proposed mechanism would appear to operate at much smaller timescales (i.e. hourly). I would expect that the relationship would resolve much more clearly with more granular data.
The data challenge will be that, unlike months, it’s not the same hour everywhere at the same time.

Bob Weber
Reply to  ren
June 3, 2015 11:12 am

ren, I’ve observed more evaporation, ie, clouds, water vapor, and IR, in satellite images when solar flux goes high for a few weeks vs when it goes low for a few weeks, as it has over the past several solar rotations. If i can find the time, I’ll make some animations to illustrate. Probably fits in with this post in some way.

ren
Reply to  Bob Weber
June 3, 2015 11:40 am
ren
Reply to  Bob Weber
June 3, 2015 11:43 am

Product shows the incoming solar radiation at the top of the atmosphere. It is derived from the AVHRR instrument. The available solar energy only varies with the solar zenith angle.
http://www.ospo.noaa.gov/data/atmosphere/radbud/as19_prd.gif

ren
Reply to  Bob Weber
June 3, 2015 12:22 pm

You can see that the dry surface quickly gives off heat at night. You can also see that the clouds absorb solar radiation.

ren
Reply to  Bob Weber
June 3, 2015 12:47 pm

How much less energy reaches the surface through 20 years of low solar activity, the TSI fell by 0.2 W / m?
http://climexp.knmi.nl/data/isolarconstant_daily_1995:2010.dat

Reply to  ren
June 3, 2015 11:30 am

Look at all of that reflected Solar in the Southern Hemisphere at mid to high lat’s!
That’s why there will be no Arctic tipping point at the current orbital profile.

June 3, 2015 11:18 am

Willis, which temperature are you using?

June 3, 2015 11:23 am

First, your Great Lakes point is just a little North and East of where I live (thank!, N41W81).
Second point, all of that water is transported poleward, there should be some sign of it showing up as an increase of warm humid air, and maybe a higher albedo. This might not be apparent at the great lakes even though we have two summer climates, Warm humid Gulf air, and cool Canadian air.comment image

June 3, 2015 11:28 am

Isn’t this validation of Dick Lindzen’s Iris effect?

rogerknights
Reply to  Engineer Ron
June 3, 2015 9:44 pm

Only half of it, because it doesn’t deal with cirrus clouds.

June 3, 2015 11:31 am

Kudos for being able to write your science in a non-scientific manner, enabling the non-science geeks (compliment to geeks) to comprehend the subject.

Jquip
June 3, 2015 11:33 am

“The albedo data is thus strong support for my hypothesis that the timing and strength of the daily onset of the tropical cumulus and cumulonimbus regimes exercises a strong control on the amount of incoming energy.” — Willis
Woah there, Sparky. Results are either consistent or inconsistent, full stop. I do not and have never bought into the ‘Best Available Evidence’ arguments in support of underspecified metaphysical models. Undeniably this is consistent with your metaphysic and, indeed, predicted by it.
But more important is that any model or metaphysic that *cannot reproduce* the correlations demonstrated is *incomplete or inconsistent* with observation. Which are two different ways to say that such a model is *wrong.*
The interesting question then is whether or not the GCMs can produce this correlation, as every single one that cannot must be discarded. Or must put some freaking massive error bars on their results.

Reply to  Jquip
June 3, 2015 11:38 am

The interesting question then is whether or not the GCMs can produce this correlation, as every single one that cannot must be discarded.

Bwahahahahahaha, Are you fricking kidding?
GCM’s don’t reproduce anything(while probably not this bad, they sux) with high fidelity at the regional level.

Reply to  micro6500
June 3, 2015 12:19 pm

Unless you mean throwing the CGM’s away, to which I heartily agree.

Peter Foster
June 3, 2015 11:48 am

Willis, have you ever thought of putting all your writings on climate into one book (pdf). I would certainly buy it. It would be really useful if such a document were divided into sections by topic rather than a chronological presentation. I find your explanations and diagrams very clear and easy to understand and above all, your logic is impeccable.
Long may you continue.

Reply to  Peter Foster
June 3, 2015 12:02 pm

http://nsidc.org/greenland-today/files/2014/06/Figure3.png
Albedo has ranged as high as 34 to 35% during recent Ice Ages. As shown through the Greenland Ice Cap albedo values ,any expansion of ice and or snow will have an impact on global albedo, in addition to changes in cloud coverage.
The last century’s feature of stable climate conditions being more the exception then the rule, and is not representative of how stable /unstable the climatic system really is. A very misleading century to show stability in the climate system, in contrast to the Younger Dryas period as an example which would give a much different picture.

ren
Reply to  Salvatore Del Prete
June 3, 2015 12:31 pm

Top: The total daily contribution to the surface mass balance from the entire ice sheet (blue line, Gt/day). Bottom: The accumulated surface mass balance from September 1st to now (blue line, Gt) and the season 2011-12 (red) which had very high summer melt in Greenland. For comparison, the mean curve from the period 1990-2011 is shown (dark grey). The same calendar day in each of the 22 years (in the period 1990-2011) will have its own value. These differences from year to year are illustrated by the light grey band. For each calendar day, however, the lowest and highest values of the 22 years have been left out.
http://www.dmi.dk/uploads/tx_dmidatastore/webservice/b/m/s/d/e/accumulatedsmb.png

June 3, 2015 12:11 pm

Willis: I’ve been doing “back of the envelope” calculations ever since you came up with the “thunderstorm thermostat hypothesis”. I think you are getting close to a “breakthrough paper”. I have this SNEAKING suspicion that the DATA is there, and it just needs to be processed correctly to verify that TS’s are the prima facia negative feedback mechanism. Now this isn’t quite as strong as what I’m hoping you will find, but do you remember Svensmark’s “Forebush Decrease” paper, correlating cloud cover, with a decrease in cosmic rays caused by a large solar flare? I think YOU may be able to find as strong a correlation as that in the existing data, or BETTER. In which case, 400, 500, 600, maybe 1000 to 2000 PPM is “non-sequitur” (of CO2 that is). By 1000 PPM, the Star-ship Enterprise is using Antimatter propulsion and the CO2 argument is MOOT anyway.

Ragnaar
June 3, 2015 12:21 pm

Figure 5 is good. March through September, things slow down. Then it’s cold and change is more rapid. This might suggest as things warm, stability increases.

Ragnaar
Reply to  Ragnaar
June 3, 2015 12:26 pm

Figure 5 suggests a Lorenz butterfly.

Ragnaar
Reply to  Ragnaar
June 3, 2015 6:31 pm

Looking at figure 5 some more, from May to September the system is tightly constrained with small variations. From September to May it goes on a grand excursion of large variations. It is suggested that the system slows before a regime change. The guessed at regime change is the hard negative feedback in the Pacific equatorial region at about 26 C. In figures 6 and 7 there is this apparent slope change. When the slope passes through zero and takes the opposite sign, there’s your daily dragon king.

June 3, 2015 12:29 pm

Excellent, Willis, one of your best. But I think you should have saved it for a journal publication. It builds on the Iris Effect, with lots of new info.
Glad you posted it here, though. Who knows how long climate peer review would take?

Claude Harvey
June 3, 2015 12:42 pm

Willis,
Your curious mind in operation is always a pleasure for me to watch.

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