Guest Post by Willis Eschenbach
Like anyone else, I’m not fond of being wrong, particularly very publicly wrong. However, that’s the price of science, and sometimes you have to go through being wrong to get to being right. Case in point? My last post. In that post I looked at what is known as “net cloud radiative forcing”, and how it changed with surface temperature. Net cloud forcing is defined as the amount of downwelling upwelling longwave radiation (ULR, or “greenhouse radiation”) produced by the cloud, minus the amount of solar energy reflected by the cloud (upwelling shortwave radiation, or USR). If net cloud forcing is negative, it cools the earth below.
I found out that indeed, as temperature goes up, the net cloud radiation goes down, meaning the clouds have a greater cooling effect. I posted it, and asked for people to poke holes in it.
What could be wrong with that? Well, I forgot a very simple thing, and none of the commenters noticed either. The error was this. Net cloud forcing is cloud DLR ULR minus shortwave reflected by that same cloud. But what I forgot is that reflected shortwave is the cloud albedo times the total insolation (downwelling solar shortwave radiation).
The catch, as you probably have noticed, is this. If the cloud doesn’t change at all and the total insolation rises, the net cloud forcing will become more and more negative. The upwelling reflected solar is the cloud albedo times the insolation. As insolation rises, more and more sunshine is reflected, so the net cloud forcing goes down. That’s just math.
The problem is that as insolation rises, temperatures also rise. So by showing net cloud forcing goes down with increasing temperature, all I have done is to show that net cloud forcing goes down with increasing insolation … and duh, the math proves that.
However, recognizing that as the problem also gave me the solution. This is to express the net cloud forcing, not as a number of watts per square metre, but as a percentage of the insolation. That way, I could cancel out the effect of the insolation, and extract the information about the clouds themselves. Figure 1 shows the results of that analysis.
Figure 1. Net Cloud Forcing (W/m2) as a percentage of gridcell insolation (W/m2), monthly averages from 1985-1989. Average percentage results shown above each map are area-averaged. Missing data shown in gray. Cloud forcing data from ERBE, insolation data from NASA.
This is an interesting result, for a variety of reasons.
First, it is quite detailed, which gives me confidence in the geographical accuracy of my calculations. For example, the cooling effect of the thunderstorms in the Inter-Tropical Convergence Zone (ITCZ) is clearly visible in the Pacific as a horizontal blue line slightly above the equator, and can be seen in the Atlantic Ocean as well. The ITCZ is the great band of equatorial thunderstorms around the planet that drive the Hadley circulation. Remember that the majority of the energy entering the climate system is doing so in the Tropics. Because of that, a few percent change in the equatorial net cloud forcing represents lots and lots of watts per square meter.
Second, the differing responses of the clouds over the land versus clouds over the ocean are also clearly displayed. In general, land clouds warm more/cool less than ocean clouds. In addition, you can see that while the clouds rarely warm the NH ocean, they have a large warming effect on the SH ocean.
Third, and most significant, look at the timing of the seasonal changes. Take December as an example. In the Northern Hemisphere this is winter, the coldest time of year, and the clouds are having a net warming effect. In the Southern Hemisphere summer, on the other hand, clouds are cooling the surface. But by June, the situation is reversed, with the clouds having a strong cooling effect in the warm North, while warming up the winter in the South. (Note that the NH warming effect is somewhat masked by the fact that there are large areas of missing data over the land in the NH winter, shown as gray areas. The effect of this on the global average is unknown. However, by using a combination of gridcells which are adjacent temporally and gridcells which are adjacent spatially, it should be possible to do an intelligent infill of the missing areas and at least come to a more accurate estimate of the net effect. So many paths to investigate … so little time.)
I have hypothesized elsewhere that the earth has a governor which works to maintain a constant temperature. One of the features of a governor is that it cannot be simple fixed linear feedback. By that, I mean it must act in two directions—it must act to warm the earth when it is cold, and to cool the earth when it is warm. This is different from linear negative feedback, which only works to cool things down, or linear positive feedback, which only works to warm things up. A governor has to swing both ways.
Figure 1 clearly shows that, as I have been saying for some time, including both the longwave and shortwave effects clouds act strongly to warm the earth when it is cold (red areas in Figure 1) and to cool the earth when it is warm (blue areas in Figure 1). In addition, as I have also said (without much evidence until now to substantiate my claim), the ITCZ has a large net cooling effect.
So that’s where I am up to right now in my investigation of the ERBE data. Always more to learn, I’ll continue to report my results as they happen, the story of the ERBE data is far from over. I’ll be in and out of contact for a bit, I’m around today but I’m hitchhiking up to Oregon tomorrow for a friend’s bachelor party, so don’t think I’m ignoring you if I don’t answer for a bit.
w.
PS – there are some interesting results that I’ll post when I have time. These involve looking at the phase diagrams for cloud forcing, temperature, and insolation. Having the insolation available allows the phase of both the temperature and the forcing to be compared to what is actually the underlying driving mechanism, the insolation.
Regarding temperature and insolation, the ERBE data shows what is well known, that the temperature changes lag the insolation changes by about two months in the Southern Hemisphere, and by one month in the Northern Hemisphere. This is because of the thermal inertia of the planet (it takes time to warm or cool), along with the greater thermal inertia of the greater percentage of ocean in the south.
The interesting part is this: the phase diagram shows that there is no lag at all for the changes in the clouds. They change right in step with the insolation, in both the Northern and Southern Hemispheres.
This means, of course, that the clouds move first, and the temperature follows.
I’ll post those phase diagrams when I have some time.
[UPDATE: The phase diagrams, as mentioned. First, Figure 2 shows the temperature versus the insolation:
Figure 2. Insolation vs absolute temperature, from the equator to 65 N/S. The poles are not included because the ERBE cloud data only covers 65 N/S. This does not affect the phase diagrams. Black line shows no lag, gold line shows one month lag, red line shows two months lag between maximum insolation and maximum temperature. Numbers after month names show months of lag.
Since the driving signal (insolation) peaks in June and December, those months will be in the corners when the two cycles are aligned. In the Northern Hemisphere (upper panel), December is in the lower left corner with a lag of 1 month (gold line).
The Southern Hemisphere is half a cycle out of phase, so December is maximum insolation in the upper right corner. This occurs with a lag of two months (red line).
This verifies that temperatures lag insolation by a month in the Northern Hemisphere (the warmest time is not end June, when the insolation peaks) and two month in the southern hemisphere.
However, the situation is different with the clouds, as Figure 3 shows.
Figure 2. Insolation vs cloud forcing %, from the equator to 65 N/S. The poles are not included because the ERBE cloud data only covers 65 N/S. I suspect that the odd shape is a consequence of the missing gridcell data in the ERBE dataset, but that is a guess.
For the cloud forcing in both Hemispheres, there is no lag with regards to the insolation.
w.
Willis: This is “on topic” believe it or not. When you talk about Op Amps, I remember a friend of mine, going to De-Vry Tech in Chicago. (I was going to a liberal arts school also in the area. He eventually got a BS EE and MS EE from U of MN, me BS ChemE, BS Metallurgy, MS Mech.) He showed me one time a set of Op Amps, a Triangle Wave generator, a Linear Resistor, and an Oscilliscope. Straight signal and you could easily use the linear resistor to compensate the triangle wave and keep the “spot” centered on the scope.
One Op Amp (one integrator, first order..) after about 1/2 hour to an hour you could learn to keep the “ball” centered.
Two Op Amps and it was IMPOSSIBLE.
Translation: In terms of the “bare” human mind, we can work with: 1. Directly linear effects, 2. Something of the “first order” response effects…with a LOT of practice and effort. BUT when we come to 3rd order or above, we can’t handle it. (With our “observational/coordination” skills.)
Therefore if the responses of the “climate” to various “forcings” are actually 3rd order or above (I suppect they are) the ONLY way to understand them will be with EXTENSIVE data analysis. And sometimes very involved.
This is the way you are heading, and I just caution you, because of that very “human” 1st and 2nd order effects “limit”, when you get to 3rd and 4th order, you’ll get a lot of people who will “tune out”.
Max
PS: I took my P.E. Exam in Electrical, and passed it. Due to some other educational background and work that I got into after graduating. So Op amps, Bode Plots, Nyquist Criteria, Z transforms, Laplace Transforms, etc. are all part of my mix.
I am hardly even classified as a layman in the realm of Climate Science. I have however been involved and interested virtually all forms of science and research for over 35 years. Well all but statistics, sorry guys your work is wonderful and needed but its like reading an accounting text.
Preface aside I have seen amateurs in almost every field contribute to an advancement. Why is it that amateurs in the field of climatology are publicly humiliated for even trying. When an amateur Astronomer makes a finding he/she is raised on the shoulders of the professionals and heralded as a innovator. Many times even when an amateur may be wrong they open doors to the truth by forcing the teachers to look down a path they traditional would not.
The vile comments I have seen focused on Willis for even trying is truly a sad statement for those who participate in this field.
If at all possible please check your ego’s at the door. It is blatantly clear to me that science of Climatology is probably the most misunderstood science of today. Views not indoctrinated by formal teachings may just be what is needed. (A new look at a question that has yet to be answered). Anyone who is really trying to learn doesn’t have a problem with critiques bring them one and all. If you have another view state it! This is how we all learn but do so in a way that wont make you look like an ass.
I love that a number of commenters are stuck on the IPCC paradigm. If clouds respond to insolation rather than temperature, things look different – IPCC only allows for temperature to influence clouds and not well at that. The data may support one view, the other, or some combination. We will never know until the ideas are explored. Science can not be shut down by one appeal to authority paradigm. In short, no matter how much an authority Aristotle was, barnacles NEVER become geese, yet the appeal to authority claimed it must be so. We are now at a point where the IPCC is the equivalent of Aristotelian scientists defending the indefensible.
“So, let’s be specific: clouds can slow down cooling but cannot ‘warm’. ”
Hmm. Lets think about that for a second. Air, clouds, the earth, water have thermal mass. They can absorb heat. This heat is radiated from the body.. this is a universal principle.
So if an area had been without cloudcover on a cool night, and a cloud moves over an area, did that not just have the effect of adding a heating source to the area that had not previously been present? It’s not hot like the sun, but it has more themal mass than space.
So the cloud radiates heat downward, and the earth radiates heat upwards. The net effect is the change in heat content for the land mass. If the surface temperature is cooler than the cloud temperature (fast wind bringing a cloud from over the ocean), then it seems obvious that the cloud would actually warm the area up.
Am I wrong in my thinking? You seem to be coming from the assumption that the surface temperature is always higher, but I don’t think that’s neccessarily true in winter.
Phase shift of water may be one of the factors to explain what happend in Your statement: “clouds act strongly to warm the earth when it is cold and to cool the earth when it is warm”
Some of the explanation may be “Cloud forming act strongly to warm the earth when it is low insolation and to cool the earth when it is high insolation”
Cloud forming release latent heat and the process to load water vapor into the atmosphere use energy. When the temperature going down due to lack of insolation will latent heat be released,
During high insolation conditions are latent heat used to vaporise water into the atmopshere.
Focus are on cloud but maybe it is partly cloud and partly cloud forming.
That is a dynamic process which explain the different sign of the Net Cloud Forcing (W/m2) as a percentage of gridcell insolation (W/m2)
Willis!
I’m an idiot!
Earlier in the thread I asked about a physical mechanism that would result in cloud forcing having no lag compared to insolation. Of course there’s no lag! THERE SHOULDN’T BE ONE!
When the sun peaks over the horizon, there’s cloud, or there’s no cloud. If there is cloud, whatever effect that cloud has, is instantaneous. The CLOUD doesn’t change, the INSOLATION changes. The cloud is ALREADY THERE. Or isn’t. As the case may be.
So there are clearly mechanisms that drive cloud levels. But whatever the cloud level at any given time is, any change in insolation would be reflected by a change in cloud forcing compared to that insolation that would occurr (literaly) at the speed of light. If there WAS a lag, we’d have a conundrum.
The fact that there ISN’T a lag suggests you are on the right track.
Willis:
Tell interested parties to go to http://www.nengo.ca and download the Nengo Neural Network Simulator.
Takes about 15 minutes to download and install. About 15 minutes to learn the basics. Use the “simple integrator” *.py example.
Plot the input and the output, use the slider to control the input. Keep the output a “Zero”.
I was wrong, took me about 2 minutes to master it!
Then put in #2 integrator. HA! Good luck. (Actually NOT quite the same as the demo my friend did 30 years ago. As his system had a Triangle Wave driver. In the Nengo case you ARE the driver.
Max
Dave Springer: The ocean on the other hand has an albedo close to 0 so when a cloud covers that it deprives the surface of a tremendous amount of shortwave energy.
It depends on the angle of incidence, hence on season and latitude.
What Willis has done is not a “mistake”, it is a step forward. Now that he has shown his work, others (including you) can take the next steps along with him.
Rob: For starters, clouds are warming the surface during the night, and cooling the surface during the day. Neither of which tells anything about cloud forcing (which talks about cooling to space rather than warming the surface), but either way, why would you think that taking the monthly average (as you did) is all fine but taking the annual average is “snare and a delusion” ? Does radiation have some magical property that shows up only at monthly averages, but not at daily or annual average ?
It is clear from looking at Figure 1 that taking annual averages would obliterate the signal. In other analyses (TAO/TRITON data) Willis has looked at hour-by-hour data, so he has never asserted that there is anything blessed or magical about monthly averages.
I predict that this work of Willis will have impact. It can be critiqued and refined, but I think that it can not be ignored.
Dave Springer;
No, the major mistake you make and that you continue to make is that you treat all radiation as generic power sources measured in Watts/m2 and all surfaces as generic absorbers. The fact of the matter is that when a cloud covers a snowfield the shortwave that is blocked has no effect because it would be been reflected in any case.>>>
Willis is doing a simply energy balance analysis Dave. If you’re read through the comments, you would note that he is proposing no driving physical mechanism to support his observations, he’s just reporting his observations. His observations are that cloud forcing varies in tandem with insolation. There’s no need to take into account ANY other factors to substantiate that the correlation exists and is significant.
It is like weighing a bucket of rocks. You can argue all day long that the bucket is filled with rocks of different sizes, shapes, and densities, and you would be right. Doesn’t change the weight of the bucket of rocks by a single gram.
@dp
“This is making me crazy – clouds don’t warm squat any more than a blanket warms you”
I’m with you. There is a difference between adding heat and slowing the process of losing heat. The precisely correct terminology is that clouds can, when they appear at night, slow the cooling that would otherwise happen, resulting in warmer temperatures than would have existed without them.
But that’s VERY long. So we need something shorter that doesn’t imply that the clouds somehow add heat.
Dave Springer;
These are the facts and they are, being observed facts not theoretical predictions, beyond dispute.>>>
Ah, I see. A “the science is settled” variant but from a coolist perspective instead of warmist perspective. Sigh. Was only a matter of time….
Willis,
Another recommendation: In figure 2, it would be informative to do both plots with the same vertical axis. We axis readers can spot the message, but the impact of the graph would be more immediate, something that graphs are good at.
kcrucible: So if an area had been without cloudcover on a cool night, and a cloud moves over an area, did that not just have the effect of adding a heating source to the area that had not previously been present? It’s not hot like the sun, but it has more themal mass than space.
So the cloud radiates heat downward, and the earth radiates heat upwards. The net effect is the change in heat content for the land mass. If the surface temperature is cooler than the cloud temperature (fast wind bringing a cloud from over the ocean), then it seems obvious that the cloud would actually warm the area up.
Well said.
It is astonishing how many people there are who post regularly and who do not grasp this.
@kcrucible: Well, perhaps in certain exceptional circumstances you may be right about clouds being warmer than the surface. I have no specific information on that, but I would certainly be VERY surprised if it were a common occurrence. Certainly not sufficiently so to be relevant in the context of a general hypothesis? But people much more knowledgeable than me seem to be quite happy to accept the “warming cloud” idea. I stand bemused, with my technical instincts still itchy on this little issue. Otherwise I do find the post very intruiging in its illustration of the immense complexity of the entire subject… and the settled evidence that the science is FAR from settled.. 🙂
@Septic Matthew: Hmmm… do you have some special knowledge about this? In my ignorance I would strongly suspect that any cloud warmer than the surface (ok maybe not over snow or ice surface) would most likely evaporate and disappear? I think that the idea that clouds may warm the earth because they are warmer than the earth is a REALLY suspect idea. A non-starter. But I am happy to be expertly informed otherwise…
@The Monster: Ah! Another voice of sanity! Thank you thank you… I felt so alone (ok, other than for DP)
GabrielHBay: In my ignorance I would strongly suspect that any cloud warmer than the surface (ok maybe not over snow or ice surface) would most likely evaporate and disappear?
Everything takes time. As the unfrozen water vapor in the cloud cools by radiation, it warms the earth, and settles down as dew. Of course, it’s more complicated than that. But “evaporate and disappear” is a brief and slightly misleading phrase for a time-consuming process.
Very nice work. In a previous post you discussed the daily cycles of clouds using the daily temperature cycle measure their effect. http://wattsupwiththat.com/2011/08/25/taotriton-take-two/
This post shows that clouds are cooling on a monthly average and only have a regional warming effect where there is little insolation around the winter solstices. If you could find direct measurements of clouds during the daily cycle across representative areas of the globe you would would have even more evidence to confirm the Thermostat Hypothesis. Clouds could only be warming on a daily basis if there were more of them at night than during the day. It would be good if a hourly average of cloud cover over the daily cycle could be teased out of the ERBE data.
As some have mentioned, I think it is important to not look at the units being used, as this is just used because it is the measurement used by the satellites. Whether or not this paradigm is a correct measurement of the system is really besides the point when discussing subject such as this which take raw data and just show what is happening over a certain time period.
The largest thing so far that has been seen was pointed out by Willis himself.
Namely, that there is a lot of missing data that could actually change the results quite a bit due to the arbitrary timing of said results. This would be interesting to read about how the data changes if you attempt to fill it in. Does this eventually have an effect on say feedbacks and their effects? I would hazzard to guess it could “fine-tune” the results so to speak, but nothing drastically would be changed since the longer-term averaging tends to filter out most (but not all) of the (noise in the data.)
I think Rob for instance misses the point that this is a seaonal (monthly) look at the data that attempts to show the warming and cooling influence (forcings) on a shorter time-scale then feed-backs. This is important because it shows how the water in the atmosphere via clouds is actually moderating the temperature of the Earth differently depending on the seasons. Why does this matter? Well it has been known for awhile, but I don’t think a study was ever done that showed how this works and to what effect.
Water as a moderater has been known for years. I use that term because that is what water is used for in nuclear reactions for instance because it is very good at that. Another term such as Governer of the system works too, as a fine control knob for instance.
It would only stand to reason that the effect of this is less differences between seasons (temperature-wise) which goes to keeping the Earth in balance for life. What could be interesting further study (after some refinements of course) would be to looking at how this changes as the global temperature goes up and down.
@Septic Matthew: “and settles down as dew” Huh? Dew comes from clouds? Now I have heard everything! I learnt in primary school that dew is condensation from oversaturated surface air coming into contact with something cooder…. My teacher was wrong? For 60 years I believed… The travesty!
Sorry, cooler not cooder… the shock was too much for me…
Dew comes from clouds?
That isn’t what I wrote. As the water vapor in the clouds cools (it isn’t all frozen ice), it settles downward. When the frozen ice in the clouds acquires heat and melts, it also settles groundward.
“I have no specific information on that, but I would certainly be VERY surprised if it were a common occurrence. Certainly not sufficiently so to be relevant in the context of a general hypothesis? ”
Snow-covered land should have a temperature right about freezing right? Or else it would be water. If the cloud was at freezing, it would start snowing?
Good stuff Willis! Now you should be able to programme your analogue computer to produce a good weather simulator!
My observation is that clouds form and disappear quite rapidly, in response to quite tiny local atmospheric pressure changes. Apart from the big changes cased by frontal system, highly localised pressure changes can be caused by breezes and thermal upwellings from the surface. One would therefore expect to see more local “The Simpsons”-style fluffy cloudlets forming and disappearing over the land surface, which heats and cools more quickly than over water. Does thsi show up in the data?
Why is it important to get clouds right?
Have a look at the MODTRAN results in Cloudy-Sky conditions versus Clear-Sky conditions.
Almost all the Modtran charts you have seen on the internet are done for Clear-Sky conditions (you never see the results including clouds). As shown here, the Clear-Sky back-radiation looking up from the surface in the Tropics. Radiating at 6.0C.
http://img171.imageshack.us/img171/4308/rad12081141.gif
Now throw in a Low Cloud Layer. A completely different picture. Now it looks like a perfect Blackbody radiating at 20C. That is clearly going to warm / slow-down long-wave radiation escape from the surface (particularly in the atmospheric windows). Low Cloud completely overwhelms the GHG absorption bands.
http://img171.imageshack.us/img171/7268/tropicalsurfacelookingu.gif
Now let’s take the previous Low Cloud Layer and double CO2 levels. There is an imperceptible, unmeasureable change according to Modtran.
http://img832.imageshack.us/img832/295/tropicalsurfupclouds2xc.gif
Now middle level clouds are a mix of blackbody and clear-sky, not as strong as low cloud; CO2/other GHGs are still operating in the amtosphere above the clouds (particularly 10 kms to 20 kms high); different latitudes show the same pattern just at lower energy/radiation levels.
Clouds are present 65% of the time. They make a very large difference. This is just long-wave radiation, the short-wave solar Albedo changes are even larger. If the climate models can’t get clouds right, how are they supposed model radiation changes in the atmosphere accurately – which what they are supposed to be based on.