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.
Mike Jonas says:
October 3, 2013 at 11:27 pm
(and clivebest)
Darn Empiricists, always demanding that I get up from my supercomputer console and go outdoors. Will you never rest and never let me work? /sarc off
It is time that all of us learn that a natural phenomenon such as cloud formation has its own integrity that must be studied in its own right through empirical methods rather than in accordance with assumptions used in Alarmist model-toys.
Of course Willis understands this point and has no budget for empirical research. We can thank him for showing us what can be learned about clouds given Alarmist model-toys assumptions.
Opps Greg:
Make that matrix:
var(x) cov(x,y)
cov(y,x) var(y)
Typed the matrix to solve the linear system – and wrongly at that.
Willis used the “CERES net- cloud radiation effect” dataset. That dataset gives numbers that are presumed to be information about the influence of clouds on radiation. Notice that it contains no information about clouds at all. Let me put it this way: using this dataset, we are treating clouds as a kind of featureless stuff, cosmic mayonnaise, that fill a void somewhere between Earth’s surface and the top of the atmosphere. If climate science follows this approach then we will never learn anything about clouds or their effects on climate. Our data must take into account the different conditions for formation of clouds, the different kinds of clouds and their effects, and how these things change over time. Svensmark’s work goes to the fundamentals. He looks at cloud formation and how various kinds of aerosols impact cloud formation. That is genuine science that is attentive to the full range of facts about clouds. His work might not answer all questions about AGW but it will have a place in the archives of science.
You are planning a paper on this, right?
“at the same time, they warm the surface by increasing the downwelling longwave radiation.”
Willis
This has been commented on by Kristian (October 4, 2013 at 2:12 am) but suggesting clouds warm the surface means that a ground surface can rise in temperature if there is enough down welling radiation from clouds? Clouds are also the resultant of air temperature and it’s moisture content. Therefore, if it is warmer when there is overcast it might also be because that airmass is warmer and the clouds are produced by the prevailing conditions. I have experienced cold as well as warm nights with and without cloud cover and the temperature seems more to do with whether I am in a tropical or polar air mass than whether it is cloudy.
Out of interest I came across this NOAA article but sadly can’t get the link as the site is currently closed.
“Arctic summer time the puzzling summer of 2003”
“The onset of melting usually occurs in early June, when the temperature reaches 0°C and the surface layer turns into a constant-temperature ice bath. In 2002, the temperature record shows an abrupt warming to about 0°C, on 24 May, suggesting an early arrival of the melt season. The warming event coincides with about a week of low short-wave (250 Wm-2) and high long-wave (300 Wm-2) down-welling radiation, which are typical of low overcast conditions. The web cam pictures of that period confirm the overcast. Both radiation and temperature values remained in the normal range for the rest of the summer, and freeze-up occurred as usual in the last week of August. Based on the early warming event in May, one may have expected an early onset of surface melting.
Contrary to that expectation, the web cams show that it was not until late July 2002 when the snow cover took on a soggy appearance and isolated melt ponds appeared on the surface.
I hope your UK visit is not a too distant memory.
If you watch ESRL daily plot of the radiation at various locations and over a long enough period (through multiple seasons) of time you start to really understand what all of this is “saying”. On an overcast night the net LW drops to zero, or very close. So one way to properly view the 20 W/m² to one degree Celsius is if on the average you have more/less night cloud cover which is usually right at 66 W/m² and you increase/decrease the cloud cover by a third you have the 20 W/m² that the 1°C brought if 1°C changes the cloud cover that much. But it’s not really a change in radiation as you would at first assume, it is a mere change in how many nights are cloudy and not so cold.
What gets me is LOCALLY the people would think this is great improvement in their environment because it affects daytime maximums so little but IPCC will wrap that increase into a “Global Temperature” and try to scare the world as if it is bad. Clouds are formed usually (if you are not concentrating only on the ITCZ) when temperature drop after sunset and humidity is adequate.
This is such a farce, this “Global Temperature”. If every single place in this world had many less frigid nights all the populace would say it is GOOD but the “Global Temperature” would soar since it doesn’t diurnally differentiate and I can just hear what IPCC would be saying. Danger where there is no danger, in fact it is an improvement.
THAT kind of temperature increase, at night with cloud cover, does not increase evaporation, it prevents it, plants love it. Humidity is very high, no net LW radiation at the surface and both warmth and water in the soil and vegitation is preserved. But that kind of talk is heresy isn’t it? A rise in temperature without fear attached.
Nice work, Willis.
Stunning, if true, that w/billions of bucks supposedly toward climate research, this hasn’t been done before. Maybe it has and was caught/smothered by the gatekeepers.
Very nice Willis. This is truly important work. By simply comparing the effect of clouds to the radiation you have eliminated many variables. If the cloud data and radiation data is accurate the entire AGW alarmism just evaporated as Bill Illis demonstrated.
BTW, the AIRS data set gives more accurate information on clouds. Don’t know if it can be combined with CERES but it might be worth a try to see if the results agree.
From what I’ve seen Willis has now been given about 10 years additional part-time work from our “suggestions”. Where are the Koch brothers when you need them.
Something I’ve noticed when it’s cloudy where I live in the Pacific Northwest (Western Washington). On a sunny summer day, the temperature can vary greatly at places relatively close together. For example, where I work, Mt. Vernon, WA, the temperature can be as much as 20f higher than where I live on Whidbey Island, a mere 13 or so miles away as the crow flies. And on the top of the Deception Pass Bridge, the temp difference can be as much as 30f from Mt Vernon. Some of this is certainly due to UHI. But on a cloudy summer day, the variation is reduced to only 3-4f on average.
Jeff,
it might be due to living in the Convergence zone behind the Olympics? I live in Kirkland so I know our weather forecasters have poor track records. I want a job that gives me a 6-figure salary, i get to be on TV every night, and don’t get fired for being wrong 50% of the time!!!!
I should have added, it’s always cooler on the cloudy days than on the sunny ones here.
cd says: @Greg “Is this not the same as expressing the Eigenvectors (V=PC) of the covaraince matix”
It can be solved by SVD decomposition. Rather heavy if you want to do it for every dot on a map. IIRC there’s built-in function in R to do that, you’d just need work out how to plug it all in.
Again some care is needed with what implicit assumptions the method implies.
Using variance is like assuming the variance is a measure of the _error_. This is a typical econometrics/statistician approach. This is one of big lies about surface temperature. The variability of the data is used estimate sigma bounded uncertainty levels, without any effort being made to assess experimental error.
This process itself makes huge assumptions about the nature of the error. It may (or may not) work on stock values but it sure is not valid for stories about the colour and shape of buckets going over the side and method changes that introduce systematic errors.
In fact short term ‘noise’ variance is used in place of proper experimental error assessment. This is part of the false logic that is used throughout climatology work to make outrageous claims of uncertainty.
For example if there is 0.2K difference in engine-room intake that progressively introduces a bias over 30 years this will not be reflected in variance of the data but could be a significant part of “+0.5K +/- 0.1K over the last 50 years”.
Neither does it include any assessment of the uncertainty in the fudge factors that used to remove _assumed_ biases in the data which themselves add uncertainty to the result.
As Judith Curry has been saying for some time , there is a totally inadequate and dishonest assessment of uncertainty right across climatology.
So, yes, you can solve it by matrix methods but unless you consider the variability the best estimation of the error in each signal, a crude bisection 2-way fit may be as good.
Good question.
What I find incredible is, if this is true and I have no reason to doubt it, why has nobody else tried to analyse CRE in sumilar fashion? Excellent article, and this should get others thinking more about these effects both long and short term.
Haven’t finished reading the comments yet but several questions jumped out as I read yet another insightful article by Willis.
Fig 1 over areas like the Sahara and Gobi deserts et al the net effect of clouds is warming IF clouds are present. What % of the time are clouds present compared to the global ave.? Same for areas like the NW coast of uS and Canada (in the 90’s I frequently traveled between LA to Hong Kong and it always seemed that area was always covered w/ clouds). Do the input data sets include info the length of time clouds are present for each 1°x1° area?
This analysis is based on only 10 yrs of data, 2000 – 2010, when Earth’s temp has not changed significantly and Antarctica has been cooling. Is that why there is such strong negative cloud feedback in the Antarctic? Will that flip some time in the future when the Arctic starts cooling again and Antarctic starts warming?
sadly the dataset you use for the land surface
is not suitable for climate Studies
From the paper published in support of this data
“The readers are advised that the resulting temperature data set to be described in this paper was NOT constructed first and foremost for climate change studies. While the GHCN component of the data has gone through most quality checks one would like to see, the CAMS component of the data (much more numerous than GHCN over the last few years) is less strictly quality controlled.”
This is one reason why we publish a 1 degree grid. The current 1 degree land products
have huge problems.
I dunno though whats Bob say
http://bobtisdale.wordpress.com/2010/03/23/absolute-land-surface-temperature-dataset/
So willis you might want to look at a 1 degree product that doesnt use CAMS
“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 east side of South America”.
Willis, I found your work very interesting, but surely you meant to say WEST side of SA?
[The mods will wait to edit this pending Willis’ concurrence. Mod1]
[Thanks, Mod1. I’ve fixed it, Bob was right, I was thinking East Pacific … w.]
Steven Mosher says:
October 4, 2013 at 8:16 am
On the other hand, I could point out that this means that this specific database has NOT been corrupted (er, constructed) first and foremost by years of artificial”corrections” and incidental “loss” (erasing) of uncontaminated original data by the various units of the global climate controlled industries. 8<)
Greg Goodman
Sorry, I don’t know if you saw my following post to you.
http://wattsupwiththat.com/2013/10/03/the-cloud-radiative-effect-cre/#comment-1435321
Anyway it’s a moot point now. I think the main point I was making is that there is a vast array of freeware out there that enables best practice.
Using variance is like assuming the variance is a measure of the _error_. This is a typical econometrics/statistician approach. This is one of big lies about surface temperature. The variability of the data is used estimate sigma bounded uncertainty levels, without any effort being made to assess experimental error.
I couldn’t agree more. One thing that annoys me is their use of statistical adjustment to correct for experimental error.
Even when they employ methods such as Kriging, as the BEST team did, they don’t use the full array of outputs such as the Kriging variance (KV) to at least express the likely magnitude of spatial uncertainty with their gridded average. In their defence, I think they used an indirect method to acquire the Kriged values and the KV doesn’t always fallout that way – but then they didn’t use the best method in that case. In truth, they should’ve approached this using an indicator Kriging method (more laborious but probably more robust).
There is so much wrong in this field and interpenetration of this type of data as Willis has done above only gives weight to its inferred robustness by proponents.
Greg I seemed to have lost my html tags in that last post. But I’m sure you can spot the quote from your comment.
Also, you might want to use the raster package. no need for arrays
Willis, with great respect, I have to ask, “Is the data showing you what you think it shows?”
I am a fan of your cloud-based thermostat. I accept it totally. But it works on the scale of an hour or less.
The data you are using are annual averages for each grid cell. Day-night, winter-summer, hot-cold all blended together, isn’t it? Ever since “Decimals of Precision”, I’ve kept Nyquist issues at the top of the check list. Are all three datasets undersampled by 10,000:1 when it comes to time scales associated with clouds?
Granted, the fact that any patterns come out at all is interesting. But whether it is telling you more about clouds affecting temperature or temperature affecting clouds is not obvious and probably conflated.
Perhaps variation in net cloud radiative effect is as important as it’s mean value.
I’m looking for a post you made about a 0.75-1.5 years ago where you did very good analysis at hour time scales for one patch of tropical ocean, where you showed not only did the sea surface temperature seldom exceed 30 deg C, but that afternoon cloudiness correlated with it.
These excellent posts of yours are close to what I remember, but I still haven’t found the jewel that I think I remember.
http://wattsupwiththat.com/2012/02/09/jason-and-the-argo-notes/ (this one is close, but I remember a colored scatter plot done in R.)
http://wattsupwiththat.com/2012/03/05/argo-latitude-day-and-reynolds-interpolation/
http://wattsupwiththat.com/2012/02/29/argo-notes-the-third/ (Another nomination for a Watts’ Best list. It really hammers home the undersampling of ocean temperatures.)
Richard111 says:
October 4, 2013 at 4:02 am
—-
Getting trapped inside a cumulus cloud is not fun. That’s where spin practice is put to good use! 8-))
Steven Mosher
Would you entertain these questions. I’m sure they are misconceived but a short response to each would be appreciated.
Can you tell us why using the Kriging methodology, the BEST team do not provide the Kriging varaince maps. Is this because it is not forthcoming in your implementation; or is it just that it highlights more uncertainty; or do you express this as part of your confidence in the final time series?
As I remember the implementation assumed a “geomodel” of spatial temperature trends in order to derive the residuals. This seemed somewhat contrived to me. Why was this better than say using radial basis functions of the raw unprojected data or B-Spline of the projected raw data to compute your 0 plane.
And if so, would an indicator method for continuous data not be a more robust, if more laborious, way of deriving the uncertainty.
Since it is important to know whether net radiative effects are positive or negative, I think it would be helpful to modify the colors in the graphs so that there is more contrast between the two shades of yellow.
Given both the phase changes involved and where in the column they occur, combined with the more obvious impacts on inbound and outbound energy flux, this result is a good confirmation of some issues I’d often wondered about.
Dr Burns says:
October 3, 2013 at 11:29 pm
In the same way that they do at night, by increasing the downwelling longwave radiation. You can see it quite clearly in the TAO buoy data, viz:
You can see the spots where the clouds come over, they are the spikes in the record.
That graphic is from “Cloud Radiation Forcing in the TAO Dataset“, q.v.
w.