Guest Post by Willis Eschenbach
[UPDATE: An alert commenter, Ken Gregory, has pointed out that in addition to the temperature affecting the CRE, it is also affected by the changing solar radiation. He is correct that I did not control for this. SO … I need to go off and re-think and then re-do the entire analysis. In the meantime, in the immortal words of RMN, my analysis below is no longer operative. Bad Willis, no cookies … but that’s the nature of science. Thanks, Ken, for pointing out my error. -w.]
[UPDATE: See the subsequent post here. -w.]
Figuring that it was about time I did some more scientific shovel-work, I downloaded the full ten-year CERES monthly satellite 1° x 1° radiation dataset (link below). I also got the Reynolds monthly Sea Surface Temperature 1° x 1° dataset, and the GHCN monthly 1° x 1° land dataset. This gave me nominally complete ten-year gridded data for the ten-year period from March 2000 through February 2010 for both the temperature and the radiation.
Among the CERES datasets are the shortwave-, longwave-, and net- cloud radiation effect (CRE). Clouds affect the radiation in a couple of ways. First, clouds reflect sunlight so they have a big cooling effect by cutting the downwelling shortwave radiation. In addition, however, they are basically perfect blackbodies for longwave radiation, so at the same time, they warm the surface by increasing the downwelling longwave radiation. And of course, at any instant, you have the net of the two, which is either a net cooling effect (minus) or a warming effect (plus). All of these are measured in watts per square metre (“W/m2”).
So without further ado, Figure 1 shows the net cloud radiative effect (CRE) from the ten years of CERES data. It shows, for each area of the earth, what happens when there are clouds.
Figure 1. Net cloud radiative effect (CRE). Red and orange areas show where clouds warm the earth, while yellow, green, and blue show areas where clouds cool the earth. The map shows that if there is a cloud at a certain area, how much it will affect the net annual radiation on average.
Note that in some areas, particularly over the land, the net effect of the clouds is positive. Overall, however, as our common experience suggests, the clouds generally cool the earth. But this doesn’t answer the interesting question—what happens to the clouds when the earth warms up? Will the warming cloud feedback predominate, or will the clouds cool the earth? It turns out that the CERES data plus the earth temperature data is enough to answer that question.
What I’ve done in Figure 2 below is to calculate the trend for each gridcell. The meaning of the trend value is, if the surface temperature goes up by a degree, what do the clouds do to the radiation? I used standard linear regression for the analysis,. It’s a first cut, more sophisticated methods would likely show more. As is always true in the best kind of science, there were a number of surprises to me in the chart.
Figure 2. Slope of the trend line of the net cloud radiative effect as a function of temperature. This give us the nature of the cloud response to surface warming in different areas of the world. This is what is commonly known as “cloud feedback”, although it is actually an active thermoregulatory effect rather than a simple linear feedback.
The first surprise to me is the size of the variation in cloud response. In some areas, a 1° rise in temperature causes 20 extra W/m2 of downwelling energy, a strong warming effect … and in other areas for each 1° fall in temperatures, you get the same 20 extra watts of downwelling energy. I didn’t expect that much difference.
The second surprise was the difference in the polar regions. Antarctica itself is cooled slightly by clouds. But when temperatures rise in the Southern Ocean around Antarctica, the clouds cut down the incoming radiation by a large amount. And conversely, when the temperatures in the Southern Ocean fall, the clouds provide lots of extra warmth. This may be why the Antarctic and Arctic areas have responded so differently to the overall slight warming of the globe over the last century.
The third surprise was the existence of fairly small areas where the cloud response is strongly positive. It is surely not coincidental that one of these is in the area of the generation of the El Nino/La Nina events, near the Equator on the west side of South America.
One thing that did not surprise me is that the reaction of the clouds in the area of the Inter-Tropical Convergence Zone (ITCZ) in the Pacific. This is the greenish band about 10° North of the Equator across the Pacific and across the Atlantic. In this area, as I’ve shown in a variety of ways, the cumulus clouds strongly oppose the rising temperature.
Finally, there’s one more oddity. This is the fact that overall, as an area-weighted average trend, for every degree the globe warms, the warming is strongly opposed by the cloud radiation effect. The action of the clouds reduces the downwelling radiation by 3 W/m2 for every degree the planet warms … in IPCC terminology, this is not only a negative feedback, but a strong negative feedback.
And the cooling effect of the clouds is even stronger in the ITCZ. There, for every degree it warms, the downwelling radiation drops by ten W/m2 or so …
I think, although I’m by no means sure, that this is the first global observational analysis of the size of the so-called “cloud feedback”. It shows that the cloud feedback is strongly negative overall, -3 W/m2 for each degree of warming. In addition, in the critical control areas such as the ITCZ, the cooling effect is much larger, 10 W/m2 or so. Finally, it shows a very strong negative cloud feedback, 20 W/m2 or more, in the area of the Southern Ocean
Like I said … lots of surprises. All comment welcome, and please remember, this is a first cut at the data.
w.
DATA
Land Temperature Data: From KNMI, in the “Land” temperature section, identified as the “CPC GHCN/CAMS t2m analysis 1.0°”.
Sea Temperature Data: Again from KNMI, in the “SST” temperature section, identified as the “1° Reynolds OI v2 SST, v1”.
Once you click on the observations you want, at the bottom of the succeeding page is a link to a NetCDF (.nc) file containing all of the data.
CERES Data: From NASA (offline now, likely the Gov’t shutdown), identified as “CERES_EBAF-TOA-Terra_Ed2.5_Subset_200003-201002.nc”
If you don’t want to mess with the underlying datasets, I have collated the CERES and the temperature datasets into a series of arrays in R, that are 180 row x 360 column x 120 layers (months) in size. They are available here, along with the corresponding arrays for the surface temperatures, and a landmask and a seamask file. WARNING—Be aware that this is a large file (168 Mb).
The file is an R “Save()” file named “CERES long”, so it is loaded as follows:
> mytest=load("CERES long")
> mytest
[1] "toa_sw_clr" "toa_sw_all" "toa_lw_clr" "toa_lw_all" "toa_net_clr" "toa_net_all" "cre_sw" "cre_lw" "cre_net" "solar" "landmaskarr" "seamaskarr" "allt"<
In the naming, “toa” is Top Of Atmosphere, “sw” is shortwave, and “lw” is long-wave; “all” is all-sky, “clr” is clearsky; “cre” is cloud radiative effect, “solar” is downwelling solar”, and “allt” is all the temperature records (land and sea).
The R program I used is here … but I must warn you that far from being user-friendly, it is actively user-aggressive. Plus it has lots of dead code. Also, none of my programs ever run start to finish, they are run in chunks as needed. However, the functions work, and the mapping section (search for “MAPSTART”) works.
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Old England says:
October 4, 2013 at 2:41 am
Willis,
If you were wrong and the effect of clouds was to amplify warming you can bet that there would already have been funded papers finding this and trumpeted with big press releases. I can’t believe that well funded climate scientists haven’t played around with this data for some time looking for a way to show it proves positive feedbacks from water vapour (clouds).
Establishment climate scientists show a pathologically complacent absence of curiosity.
We are often told that the keys to effective activity in many creative and intellectual spheres are:
-curiosity
-self criticism
-flexibility/agility/creativity
these are all conspicuously absent in the pronouncements from the climate establishment. These read much more like doctrinal pronouncements from a religious order, reacting to heresies rather than doing anything creative, curious or self-critical. Political fear and/or ideological/talebanic zeal no doubt strangle natural curiosity and creativity in salaried climate scientists.
I hope Willis and others read this from Roy Spencer.
http://www.drroyspencer.com/2013/10/oceanic-cloud-decrease-since-1987-explains-13-of-ocean-heating/#comments
Willis,
I wouldn’t make much of that blue around Antarctica in Fig 2. It’s a regression against T, but for much of the year, T = -1.8°C – sea ice temp. A regression with varying CRE, const T won’t work. I would have expected trouble in the Arctic too; doesn’t seem to happen there.
Interesting plots though.
Willis
All looks good.
But just a point on experimental setup. You are obviously mixing two different data sources temperature (instrumental) vs cloud cover (satellite). How do you account for changes in spatial coverage of temperature. I suspect the temperature data has been interpolated does this not open an entire can of warms as regard its usefulness. Why didn’t you use the UAH data for example?
Berényi Péter says, October 4, 2013 at 2:43 am:
“How can satellites possibly measure downwelling thermal radiation flux?”
They can’t. And they don’t. They measure outgoing thermal radiation flux from the ToA.
Fascinating work.
“This may be why the Antarctic and Arctic areas have responded so differently to the overall slight warming of the globe over the last century. …”
Perhaps. But surely the biggest reason is that we have mostly open water at the northern pole and a land mass covering the southern pole. Ice cannot get as thick over water as it can over land. The Arctic would surely would wax and wane to a greater degree and be generally more susceptible to changes, while Antarctica would persist as a giant cold sink.
Konrad says:
October 4, 2013 at 1:23 am
——————————–
We seem to agree. I understand the emissivity of cloud water droplets is better than 0.9.
Over the ocean this is effectively two ‘black bodies’ radiating at each other. Liquid water being rather more massy than cloud droplets, my guess is the cloud base temperature could approach the water temperature resulting in an almost zero lapse rate. This could account for the occasional sudden fog I’ve noticed. All very interesting stuff to think about.
——————————-
Monckton of Brenchley says:
October 4, 2013 at 2:14 am
“”The radiative effect of clouds is strongly altitude-dependent and the CERES data are poorly resolved vertically.””
What might be the lapse rate through various types of clouds? I once had the misfortune/terror of being trapped above cloud I was not qualified to fly through. As luck would have it, I caught a glimpse of the ground 10,000 feet below me through a small hole in the cloud and commenced a rapid descent. The inside of that cloud was hollow! Like flying in a huge white cathedral. I was able to continue at a more reasonable rate of descent to my exit hole in the bottom. I look at clouds, especially big ones, with much respect.
tonyb says:
October 4, 2013 at 1:59 am
At the start of the 19th century many parts of the world -Britain amongst them- had a fog of pollution-artists came to London to paint the sunrises. Sun levels have notably increased over the last century.
Hi Tony
What I found interesting about the CET is a shift in the difference between daily max and min in the last couple of years in relation to the 20 year average (bottom graph, orange bold and dotted curves).
http://www.vukcevic.talktalk.net/CET-dMm.htm
March to August D(aily)max-D(aily) min is above the 20 year average, while September to February Dmax-Dmin difference has been below average.
Could this mean climate change shift towards two rather than four distinct climate seasons?
“Yet without assuming a strongly positive (i.e. temperature-amplifying) cloud feedback the IPCC cannot maintain the sensitivity interval [1.5, 4.5] K with which its 1990 report began and with which its 2013 report concludes.” — Monckton of Brenchley
That’s the money quote. Without regard to the difficulty involved in nailing the value to the wall, if there are good results out there that can bound the ranges for Willis approach’, and assuming Willis’ approach is otherwise sound, then it puts a boundary condition on cloud feedback parameters in the models.
This at least allows putting a limit condition in the cloud feedbacks based on empirical results. Which is a Good Thing regardless of one’s opinion of the models themselves.
Wait, I thought clouds were actually less white than the antarctic ice cap, so clear skies raises albedo leading to cooling there.
The primary cooling effect of clouds on our water planet is to reduce the proportion of ToA solar shortwave that is able to enter the oceans.
It is the portion of incoming solar shortwave able to enter the oceans that provides the fuel to drive the climate system with a time lag of a decade or so due to oceanic thermal inertia and residual effects for over 1000 years due to the slow overturning of the thermohaline circulation.
The other more mundane effects of clouds which Willis considers here such as insulation of a cooling surface or shading of a warming surface are in approximate balance globally and any local imbalances between those two effects are dealt with on short daily and seasonal time scales by local or regional circulation adjustments. Willis’s own thermostat hypothesis is an example of such adjustments in the tropical regions.
To deal with changes such as MWP to LIA to date one needs to look at cloudiness changes on a millennial timescale and there we see good correlations between the level of solar activity and multi-centennial climate shifts.
Thus it is likely that solar variations on a millennial timescale are the primary driving force for cloudiness changes on such time scales and such changes are not the subject of Willis’s post which is therefore a good start but needs extending.
The Svensmark hypothesis is one proposal but I prefer the much simpler proposal that zonal jets give less clouds and meridional jets give more clouds.
http://www.drroyspencer.com/2013/10/oceanic-cloud-decrease-since-1987-explains-13-of-ocean-heating/
Roy Spencer: “The correlation coefficient is 0.72, and the regression line shown is a 2-way fit.”
This is one of my pet issues with climate science (well not just climate !) . You can’t do a simple regression on a scatter plot, or more generally regress two variables where both have significant uncertainty.
I had a brief email exchange with Roy about this issue and I’m glad to see he’s taken it to heart. His comment is very terse so it’s probably only that understood the comment because it was the first thing I asked myself when I saw his scatter plot.
I would guess that “two way fit” means simple reversing the axes and taking some bisector of the two fitted gradients. There are several ways to do that but the differences are less important that at least doing it in some way.
Linear regression in these circumstances will always under-estimate the slope. How much depends upon the relative error/uncertainty in each dataset. That’s bitch since it means the only way to correct it properly is to start analysing the errors/uncertainties and many times this is just not known well enough to be useful. Then you’re stuffed.
The bisector is crude but better than nothing , which a really bad option. You results should not depend upon which way round you chose to plot graph !!
Whether this would fundamentally change Willis’ map or just sharpen contrast I don’t know. Maybe he should try it.
Willis, agree with a previous poster. You should try to get this published as a formal paper.
willis: youre an interesting dude. I always stop by to see what youve posted.
thanks.
Based upon this –
“The first surprise to me is the size of the variation in cloud response. In some areas, a 1° rise in temperature causes 20 extra W/m2 of downwelling energy, a strong warming effect … and in other areas for each 1° fall in temperatures, you get the same 20 extra watts of downwelling energy. I didn’t expect that much difference.”
The question I have is where exactly is that extra 20 W/m2 manifested? Is that considered an average value? Also, what is it’s wavelength? What is the amount that actually reaches the surface?
Is the energy actually able to do any work?
John West says:October 3, 2013 at 11:55 pm
@ur momisugly Mike Jonas
I find it easier to swallow an increase in temperature increasing evaporation thereby increasing cloud cover in a manner that produces net cooling (think thunderhead formation on a hot summer day) than a slowing of cooling increasing evaporation in a manner that produces more slowing to cooling to the point of catastrophic warming.
That bothered me too. Then I realized it takes less VW to condense at lower temperatures, since the mean free path is smaller. The inverse function of VW capacity and temperature.
@Greg
Is this not the same as expressing the Eigenvectors (V=PC) of the covaraince matix:
1 c(x,y)
c(y,x) 1
and taking V as your expression of relationship. There is quite a lot of freeware out there that will do this and online php pages.
Isn’t it interesting that you never see work like this on skepticalscience. What I mean is that the regulars there never do scientific research. They’re all about measuring how much people believe them.
Wouldn’t water vapor column change and LW radiation emission tell us when clouds form. How do these correlate with CRF and forbush decrease. Does the evaporation of clouds absorbe any particular type of radiation more? Or, does the formation emit a particular type of radiation, and does this decrease during a forbush decrease?
Great Stuff Willis.
IPCC AR5 has the cloud feedback at +0.7 W/m2/C. Figure 7.10 from Chapter 7 here.
http://s24.postimg.org/asc85ozad/IPPC_AR5_Cloud_Feedback.png
I built a calculation model for the feedbacks because it always bugged me that they didn’t use the Stefan-Boltzmann equation to calculate the temperature response but just used shortcuts such as the “change in temp per w/m2 forcing” X “forcing” = 3.0C per doubling. But it turns out the feedbacks on the feedbacks on the feedbacks on the feedbacks do actually get to 3.0C per doubling if one uses the carefully chosen feedback assumptions for the feedbacks by climate science.
So let’s put -3.0 W/m2/C into the calculation model and what do we get.
Just —> 0.78C per doubling.
A little closer to what is really happening on Planet Earth.
You might wonder why it is still a positive 0.78C versus what one might assume from a large negative -3.0 W/m2/C. Well, there is still about +4.2 W/m2 of forcing from doubling CO2 (and the increase that will occur in the other GHGs like methane) so it doesn’t fully offset the GHG forcing.
In IPCC AR5, they also reduced the net impact of the Lapse Rate feedback from -0.3 W/m2/C to -0.9 W/m2/C. Water vapor was raised from 1.75 W/m2/C to 2.0 W/m2/C. Using all the IPCC assumptions for the feedbacks, the doubling sensitivity falls to 2.4C per doubling which is something NOT specifically outlined in the AR5 Report but hinted at in several places including the temperature responses they proposed.
Figure 7.9 Chapter 7 – Water Vapour and Lapse Rate feedbacks.
http://s8.postimg.org/sy5tk3md1/IPCC_AR5_Water_Vapor_LR_Feedback.png
A bit OT. What might be the effects of a quiet sun and dust from WR 104?
Thanks, Willis, for sharing this interesting study. Can you clarify something about the colour shading on your Figs 1 and 2? Do the figures in the bottom panel indicate the mid point of each colour band, or are they the upper/lower boundary of the band? In other words, on your Fig 1, is the net zero point at the boundary between yellow and orange – or somewhere else? (Apologies if you’ve already explained this somewhere – I did look)
@Bill Illis
That’s an excellent comment: October 4, 2013 at 5:49 am
We needed it in Willis’ last post:
http://wattsupwiththat.com/2013/10/01/dr-kiehls-paradox/
You express much better what I was trying to say. In short, the models don’t necessarily express the additional heat as a temperature increase. They are complex physical models where one can make assumptions to hide or express increasing heat in the system as either temperature or physical work (latent heat if you like). It’s all smoke and mirrors.
And on your Fig 2, is the yellow/orange boundary at +5 Wm-2?