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.
I think the inverse relation would be interesting too: what is the change in temperature for a given change in clouds?
lsvalgaard says:
October 3, 2013 at 9:42 pm
Good question … that and many other questions will likely get answered when I have more time. Scientific shovel-work is a slow process, particularly since I have to make it up as I go along. Or perhaps someone will download the files and beat on them and find a host of new things.
w.
Willis, you are a glutton for punishment!!!!!!!!
Paging the IPCC…
According to the lead author on clouds for AR5: “IPCC hasn’t done a good job on clouds before,” says clouds lead author Piers Forster of the University of Leeds, UK. “They were a big unknown in modelling warming.” In 2007, it was uncertain even whether clouds cooled or warmed the planet overall. “But we now believe that they are a positive feedback on temperature,” he says. “Their warming effect will intensify with global warming.”
http://www.newscientist.com/article/dn24295-climate-report-how-the-science-has-moved-on.html#.UkmgPdLkuE9
Wow amazing work, should try and get funding and turn it into a proper paper. Would heads turn if it passes Peer-review
Well, this could be the start of something big. Great going, Willis. 🙂
areas under clouds should be cooler than otherwise during the day…and warmer at night…but since cloud cover changes all the time….good luck with the calculations
Very interesting Willis, although I’m maybe not so much surprised by the results, because I already tryied to incorporate the data into my 1° gridded global insolation semiempiric model I’m working on (although I still didn’t make the nice visualizations you have), it is very nice piece of work. My preliminary findings are simmilar, and I also find big differences between ocean and landmass regions (especially the high albedo ones) and between southern and nothern hemisphere, but I never got the idea to try determine any cloud feedback – very seminal! Thank you.
Assuming you are correct, this makes the IPCC totally redundant – negative cloud feedback buries the AGW concept and highly negative cloud feedback makes it a laughable concept.
The trolls will not like this.
Supposedly, lower altitude clouds are a negative forcing (increasing the albedo of the Earth), while high altitude clouds are a positive forcing, helping to trap IR radiation. I’m not sure that I buy the latter, particularly if they are high-altitude ice crystals (i.e. noctilucent clouds, jet trails etc.).
Dr. Joel Norris of the Scripps Institution of Oceanography made a presentation at the Fermilab Colloquium on May 12, 2010 titled “Cloud Feedbacks on Climate: A Challenging Scientific Problem” It is very interesting, among the points he made were:
a) it is very difficult to differentiate between cloud cover and snow-ground cover by satellite;
b) many factors are involved, including cloud height and depth, and
c) nobody really knows how clouds impact climate, and the climatologists have done a lousy job of factoring in the effects of clouds.
The presentation is archived at http://www-ppd.fnal.gov/EPPOffice-w/colloq/colloq.html
By all means, mine this storehouse of information! Prof. Richard Lindzen gave a real barn-burner on Feb 10, 2010!
Nice work Willis. The dark blue in the southern seas around Antarctica seems to explain much. Strong negative feed back here verified by a cooling south polar area. Since both the southern continent and its surrounding seas are cooling this is a very interesting result ignored, or only rarely mentioned, by the warmists. Meanwhile, they focus on the Arctic where the results appear mixed with warming at the highest latitude changing to cooling a distance south. Fascinating results and the wonderful insight and hard work producing them is wonderful. Thank you.
Henrik Svensmark has had some thoughts about cosmic rays and clouds. It would be interesting to know if a relation to cosmic ray impingement can be quantified into a temperature or net energy quantity from the clouds created.
Willis,
I have been married for 30 years (to 3 separate women) but I can honestly say “I LOVE YOU”!!!!
I love to send your posts to my tree-hugging friends here in Seattle. It irritates them to no end. One day they are going to wake up and realize they can no longer justify their existence based on the horrors of climate change..
Keep up the good work!!
Willis – I think your study suffers from what may be a completely invalid assumption: You assume that clouds change in reaction to temperature. Reality may be that clouds change for other reasons. I read Leif’s comment as saying this, if rather obliquely.
There is a parallel in this between your study and the IPCC report: The IPCC assume that temperature is driven by CO2, but when they try to evaluate the climate’s sensitivity to CO2 they get a wide range of values, and that range even changes over time. The reason for the high range is that temperature is driven mainly by other factors. In other words, when they take the ratio of two factors to determine sensitivity they are using two factors that are at best only weakly related. You report “The first surprise to me is the size of the variation in cloud response“. I suspect that the reason for the size of the variation is simple: clouds are not in fact driven by temperature, so you too are using two factors that are at best only weakly related.
“..as our common experience suggests, the clouds generally cool the earth. ”
When a cloud passes in front of the sun, the temperature falls. However clear nights are always colder than cloudy nights. The explanations for these effects are fairly obvious.
How do clouds cause warming during the day ?
I recommend you this video. A lecture from Graeme Stephens about the clouds. Don’t miss the end, questions and answers (from min. 50)
– Is the planet characterized by a sensitivity to a forcing?
– I don’t think one number characterizes the system as a whole. I think you would find the sensitivity is different in the tropics and somewhere else.
– I don’t know what the real world would do (as a response to a forcing)
– I understand the convenience of this metrics, but we tend to get lot to much into it.
From http://scienceofdoom.com/2013/02/07/ceres-airs-outgoing-longwave-radiation-el-nino/
“OLR has – over the globe – decreased over 10 years. This is a result of the El-Nino phase – at the start of the measurement period we were coming out of a large El-Nino event, and at the end of the measurement period we were in a La Nina event.”
@ 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.
Willis, this is one of your most interesting posts ever. This is an area in which I have seen very few people doing any work. For this amazing effort, you deserve to receive massive Big-Oil funding!
I wonder, if you turned it into a paper whether it would pass Peer (Pal)-reviw?
Getting a lot of cloud lately. As I don’t sleep much these days I distract myself on my night time walk abouts by looking at the thermometer stuck on the outside of my kitchen window. I HAVE seen night time temperatures increase, but only when there was a clear sky the previous afternoon and evening. In other words temperature had dropped well below that daytime maximum. When a layer of 10/10 cloud then arrives overhead, the temperature climbs. I have NEVER observed cloud covered night time temperatures exceed the previous days sunshine temperature level. I have observed a SMALL night time increase if previous day was also cloud covered, but this was usually when wind speed and direction changed.
I make no claims as to the accuracy of my kitchen window thermometer. I check it against this site which is about 400 yards from my house.
I have great difficulty in understanding how clouds can make the ground warmer. Where did the extra energy come from? I have recorded small temperature increases from scattered light through the cloud during the day. That increase could on occasion remain through the following night. But that increase was never as much as a clear sky sunshine temperature.
Evidence of positive feedback is probably not what most people here want to hear.
Willis,
We think alike, but you are well ahead on translating into words. One of my early blogs, about 2005, asked if it is warmer or cooler if a cloud comes between you and the sun when you are on the Antarctic ice shelf. Nobody ventured an answer and I have not been there.
Leif asked my question before I could cut in.
Are you confident using GHCN land temperatures? I wonder what your nice graphic would look like if you could grid the raw values for T.7.
High pressure means less clouds, low pressure more clouds.
High pressure in summer gives warmer temps, high pressure in winter gives colder temps.
The opposite for winter.
So clouds can give both higher and lower temps, mainly depending on season, thereby to a large degree cancelling each other out during the course of a year.
Perhaps the picture would become clearer if you showed NH winter with SH summer, and vice versa…
Don’t tell too many alarmists, they will start ‘adjusting’ the data.
Negative feedback! Cool!!
Willis, have got into it yet, but there are two Fig 1’s.
[Thanks, Keith, fixed. -w.]
Roy Spencer has just posted a similar piece of work.
Simon:
Your post at October 4, 2013 at 12:22 am says in total
Willis concludes the CERES data indicates a net strong negative cloud feedback.
What you or others may think people “want to hear” is not relevant.
Richard
Richard111 says:
October 4, 2013 at 12:18 am
———————————————
Clouds contain condensed water and are formed within air masses saturated with the most important radiative gas in our atmosphere, water vapour. They emit LWIR. Incident LWIR can slow the cooling rate of most materials. Land under cloud cools more slowly and this effect is most notable at night.
The oceans however are an entirely different issue. To judge the effect of clouds over the ocean you would need a high resolution plot of down welling LWIR with a simultaneous plot of sea surface temperature below the skin evaporation layer at 5 min increments for night time only. I have not seen data of this type.
Hi Willis
What I like about your excellent articles is the clear writing, easy to follow, backed with solid science and solid reasoning.
England is for significant part of the year is a cloudy country. This moderates average daily maximum-minimum temperature difference to about 5C in the winter months and for the rest of the year the difference is mostly below or just above 10C, as shown here:
http://www.vukcevic.talktalk.net/CET-Dmax.htm
If the CERES data is monthly samples, How are those samples generated?
I think the real issue with the whole Global Warming/CO2 thing is that it is easy to prove that Clouds are by far the major influence of earth temperatures – including ENSO – that we have.
It’s simple: More clouds = higher albedo = lower temperatures.
So in order to model the temperature you HAVE to accurately model the clouds. The IPCC try to apply a delicate radiative equation to a situation where they have no idea what the albedo will be from hour to hour, week to week, year to year.
Modelling a system with variable albedo without modelling the cause of the abedo change is futile. Every time you see a cloud move the IPCC models are invalidated.
Very interesting post, Willis
However, the general assumption is that changes to clouds are a feedback to warming, and all GCM models assume this. What if changes to cloud cover are also caused by natural processes independent of CO2 ? For example ENSO is known to change cloud cover. This is the elephant in the room IMHO, which can also explain the observed hiatus in warming.
The global average (cooling) radiative forcing of clouds is about -21 W/m2 and the Earth has about 68% cloud cover. So if cloud cover reduces by just 2% the radiative forcing increases by 0.45 W/m2. For comparison the increase in radiative forcing from CO2 since 1950 is about the same size.
Has no-one done such basic figure bashing before? What are tehy doing with their grant money?
Willis
Good work. Two comments you said;
‘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.”
Surely this is a very well studied area, after all the IPCC have been doing assessments for many years and clouds have always been a bone of contention. Difficult to believe that there aren’t a pile of papers out there on this. If not it seems an extreme dereliction of science.
Point two. 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. More sun generally equals more warmth. More sun and changes in cloud levels (and types?) surely account for much of the temperature changes we can observe?
tonyb
What I can say for sure about clouds is that they screwed up every grand engineering attempt to replace power plant cooling towers with “spray ponds” with which I was ever associated.
It was relatively easy to predict the heat rejection performance of either forced or natural draft cooling towers using only ambient temperature and humidity as the major external variables. I never saw anyone get it right for spray ponds.
The problem with spray ponds was that radiative heat transfer at night was such a huge portion of their overall heat rejection performance. On cloudless nights they dumped heat like a house afire. On cloudy nights the awful things just sat there grinning at you.
They were on the same track with ERBE:
http://itg1.meteor.wisc.edu/wxwise/museum/a2/a2cloudforce.html
Willis, please stop saying things like this: “In addition, however, they [the clouds] are basically perfect blackbodies for longwave radiation, so at the same time, they warm the surface by increasing the downwelling longwave radiation.” No. Their presence reduces the steepness of the temperature profile from the surface up by being warmer than the sky and thus disrupts the rate of heat loss from the surface. And this surely does not include just radiative heat loss. They intercept some of the outgoing thermal radiation from the surface heading for space by absorbing it and warming slightly from it, thus reducing the emission to space. This is the LWIR effect of clouds. From the link above: “In the longwave, clouds generally reduce the radiation emission to space (…)” You make it sound like it’s the downwelling radiation from the cooler clouds/atmosphere that do the actual (direct) warming of the already warmer surface. You know this isn’t possible. No, it’s the cloud temperature compared to the sky temperature that matters. Basic heat transfer.
Suggestions in this thread that variations in cloud cover have little effect on temperature are ill founded. Pinker et al. (2005) found a radiative forcing of 3 W/m2 from a naturally-occurring decrease in cloud cover from 1983-2001, intriguingly coincident with the positive phase of the PDO, and inadvertently indicating that the cloud feedback may be negative, and perhaps strongly so. 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.
Determining the influence of cloud variability on climate is not easy. The radiative effect of clouds is strongly altitude-dependent and the CERES data are poorly resolved vertically. The causes of cloud-cover variability are unknown. The cosmic-ray effect may have an underlying long-term influence. There may even be some influence from our minuscule alteration in the atmospheric composition. And the climate object behaves chaotically. It may, therefore, be impossible to determine even the sign of the cloud feedback, let alone its magnitude: but without determining it one can only guess (poorly) at climate sensitivity.
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).
In the absence of those ‘papers’ and press releases I would put money on it that you are on the right track.
How can satellites possibly measure downwelling thermal radiation flux?
BTW, clouds are only part of the story. There is also the so called “water vapor feedback”, which is supposed to be strong positive and operates even under clear skies, when relative humidity never gets saturated at any height.
However, I am not sure at all it is as simple as that. Water vapor, unlike carbon dioxide is not a well mixed greenhouse gas, its atmospheric distribution is fractal-like with huge differences in absolute humidity in adjacent air parcels at any scale. Now, the so called “greenhouse effect” is not generated by average concentration of GHGs, but by Planck weighted average optical depth in the thermal IR frequency range. The latter quantity is a monotonic function of the former in case of well mixed absorbers, but it is not the case for not well mixed ones.
The most one can tell about average optical thickness in water vapor absorption bands is it either increases or decreases with increasing average total column water vapor content, depending on higher moments of its distribution. A thin metal plate may be opaque while a wire fence, containing the same amount of metal per unit surface area is almost completely transparent.
Willis,
I am not sure exactly what you have done here but I suspect you may have used seasonal temperature changes to derive the cloud feedback value. There are large regional variations in cloud cover – for example the monsoon seasons. The value of -3 W/m2/deg.C looks to be way too large to me. The Planck response to warming (negative feedback) is only -3.5 W/m2/deg.C.
I also think that clouds must act as a negative feedback. Otherwise how has the earth avoided runaway heating over the last 4 billion years to retain its oceans ?
Lord Monckton is also correct that cloud feedback are the largest uncertainty for all models in determining climate sensitivity.
Dr Burns says:
How do clouds cause warming during the day ?
1. When clouds form, latent heat is released, in all directions.
2. Clouds also slow down convection cooling, hence you can get a build up of energy below the clouds
All this is dependent of cloud level and ‘other stuff’ like: dew points, lapse rate (moisture content) pressure /temperature differences, sideways air currents (wind) etc etc.
We still have a LOT to learn about this stuff.
The science is far from settled.
Simon,
how did you come to this conclusion? Did Willis cherry pick something? Say what. Or did you find Evidence of positive feedback somewhere? Post a link to it. As an engineer I would be very interested to see it because the climate doesn’t behave as if there is some. You know, one may get huge profits out of using positive feedbacks the right way.
What we need to realise is that EVERYTHING in the atmosphere is driven by pressure gradients, (both horizontal and vertical, which in turn can be driven by a variety of factors. Ultimately, the vertical atmospheric pressure gradient is and the fact that the Earth rotates (shh..don’t tell Trenberth) causing continually changing temperature and pressure fluctuations is what drives the climate.
The atmospheric pressure gradient only allows just so much energy to be stored, and as soon as that is reached at any altitude, the atmosphere will try to balance itself.
The atmosphere is actually a “net cooling” mechanism.
There is no blanket around the Earth
Monckton of Brenchley says:
“Determining the influence of cloud variability on climate is not easy. The radiative effect of clouds is strongly altitude-dependent and the CERES data are poorly resolved vertically. ”
Mr. Monckton, just took the words out of my mouth. Not only altitude dependent it is greatly dependent on general humidity of areas. An overview of chart one leads you to the conclusion that where CRE is positive and orange/red generally, is also is areas of low population/agriculture, not always (India) but generally. Australia, Antarctica, western N.A., North Africa/Middle East, Greenland. Why? Just below average humidity? Also of higher altitudes? Seems to be one, or the other, or both.
Increased radiation is not immediately bad, depends on whether it is night or day and where if it affects the general world population. Two graphs of the diurnal differences may show a completely different picture.
Simon says:
“Evidence of positive feedback is probably not what most people here want to hear.”
Its a good thing that this points to rather large negative feedbacks then, isn’t it ! 🙂
By the way, Willis.. Nice start ! 🙂
Willis is there a way to distinguish between cloud cover induced cooling/warming in the data for clouds that are “just there” and those that produce precipitation over their area? From your contributions regarding the tropical thunderstorm cycle we know that the evaporation/precipitation cycle is particular in regulating local heat phenomena.
Rabe says:
Yeah, imagine if you could harness the hypothetical CO2 radiation feedback.
A perpetual motion machine with unlimited capacity so long as we keep burning carbon fuels 🙂
Glancing at figure 1, the map of CRE (cloud radiative effect) I have the impression that the dark blue regions, the regions of largest negative CRE, are also the regions which in recent years seem to consistently show the biggest temperature anomalies – either warmer (e.g. off the north east coast of the USA, North Pacific) or colder (North Pacific, South Pacific, Peruvian coast La Nina upwelling region).
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
@ 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?
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.
Some ask question “How can clouds make it warmer”. This is a matter of how much warming you have in the sun. If you live where it gets cold in winter, you learn it. Clouds make it warmer when sun in not warming much (winter or nights), but colder when sun is warm (summer and day).
— Mats —
Very nice, my only suggestion is tweaking the palette on the map and getting this published! Of course it would be completely invisible to the IPCC just like the Terra Satellite data and study by Spencer but it needs done regardless.
I was going to suggest the same thing that I can’t believe they haven’t had a look. They probably have and got some inconvenient results. Such results cannot and will not be allowed to pass into the IPCC reports.
Without any research on my part whatsoever I conclude that clouds are a net negative feedback. Take a look at 60 millions years ago down to the present day, look at the changes in co2 and “inferred” cloudiness, we should have our goose cooked by now.
One can also see why ‘The IPCC’ have such trouble with Clouds. Not only do they reflect SW and interfere with the ‘orderly exit’ of LW … The LW could be from picked up from anywhere in their path, not just the surface that they are currently over. There is also the element of their initial energy (measured as they coalesce and dump it) having been ‘picked up’ when they just molecules leaving the Sea on the Equator (for example).
Hmm … glad I don’t have to model that one.
.
AntonyIndia says:
October 3, 2013 at 11:35 pm
I’m not finding that … my analysis shows that there is a very tiny decrease, just under half a watt per square metre, over the decade of the data. The decomposition of the CERES OLR data is as follows:
As you can see, the annual average (“Trend”) varies by only about 0.8 W/m2 over the period and is fairly irregular. I wouldn’t put much stock in a purported “decrease” in OLR.
w.
In addition. It would seem that as long as that graph you presented ends at the same (24/0 hour) point each day then the net effect of cloud over 24 hours is … zero?
I am stunned that this has not been done before. It seems to me that this ought to have been done Monday Morning 8:00 am in the 1960s.
I am actually stupefied that this isn’t just something that we know. The fact that Willis can suggest a negative feedback and it bears out in the data is a shock to me. Has this been overlooked?
Or buried?
Cloud altitude, cloud composition, cloud cover thickness, multiple layers of different types of clouds and day vs night clouds, it would seem should all have differing effects upon the resultant temperature.
RC Saumarez says:
October 4, 2013 at 1:31 am
Thanks, RC. The CERES data are monthly averages, not monthly samples. The CERES satellite images a strip around the earth with every revolution. All of these are collated, gridded, and averaged.
w.
Willis,
Forgive me if this is covered, but I do not see a mention of the effect of clouds on downwelling IR.
It seems to me that any IR emitted at high altitude from clouds, water vapor or CO2 would be more likely to penetrate the thin, dry atmosphere above than the thickening, moist atmosphere below. If IR is radiated in random directions, then the net flow would be toward space due to the resistance of the thicker, moister atmosphere below. (simple terms, but trying to illustrate a concept I do not see much).
Willis, do you have a link to your first post on all this where you had sat photos of cloud spreading across tropics in relation to time of day. From memory that was a fairly thin line along ITCZ. That was quite an ingenious demonstration of the effect that I found quite convincing. It would be interesting to compare to these maps.
Great stuff Willis. I really need to learn R.
So this is all 10 year average info? If so, it’s amazing that you still have that much signal with so much smearing going on. I’m sure you’ll delve into the dynamics (it’s something I’ve been dabbling with), and I think you will see truly GIANT cloud effects if you study one cell during the time when you know very large storms happened there.
For example, we know that strong rain at 1″ per hour will give up about 15,924 W/m^2 due to latent heat of vaporization. (distributed somewhere in the column above that 1m^2). So 1″ of rain is like leaving 10 space heaters in 1 square meter on full blast for an hour. So the effects happening are truly huge. Warmed air is carried aloft, and it stops when the lift equals the surrounding buoyancy. And it spreads out. The next cool thing is that the water droplets on the top side of the cloud (if clear above) have now been transported high enough that there simply isn’t much GHG above to scatter IR, so you’ve effectively shunted surface warmth to space with very little GHG interaction. You simply bypassed the vast majority of the GHG effect. As you have pointed out, the stronger the storm, the more +feedback in strength it has (until it exhausts its fuel), the higher it punches through the atmosphere, and the stronger the ability to radiate more directly to space is. So this outer layer is going to radiate away its energy very quickly to space, and even more quickly the higher the altitude (with the offsetting change in radiation rate due to temperature/altitude effects). Subsequent layers of water droplets are also going to radiate, but at warmer temperatures because there are water droplets above that which are also radiating in all directions. These lower layers have a reduced ability to reject heat to space, but you’ve also just created a thermal differential (cooler top loses energy at a higher rate than layer below) so you also now have a convective engine to keep bringing warmer droplets to the top where they radiate out faster. Obviously, the water droplets can’t radiate at 15,924 W/m^2, but to the extent that they do radiate (SB, T^4), the ratio will largely be a factor in determining the area that this top layer (the anvil) will occupy, which also determines the (also huge) effect of reflected sunlight. The layer thickness at which radiative energy transfer gets cut in half inside the cloud is also an important metric in determining the temperature differential, propensity to form thermals, and resulting strength of convective processes to help transfer the heat to the outer radiating surface.
I think you’ll find short term effects in these datasets that will confirm what you’ve been saying all along, that the ability to reject heat on demand is so staggeringly huge, that whatever small effect that CO2 may produce is simply caught up in the dragnet and rejected with all of the other huge swings we see on a daily basis. 15,924 W/m^2 > 2 W/m^2. Of course, the grid size is still way too coarse to uncover the true scale of these effects. The true magnitudes of these factors will be far higher than can be seen in a 1°x1° grid since thunderstorms almost never get that large.
This sounds like such a blast to work on. I’m convinced you are working on the very issue that deals with why climate models fail (and always run hot). Heat doesn’t collect here (and go “missing”), it gets actively rejected on demand by an enormously powerful heat shunting engine.
I can even imagine a cartoon with the first image of earth from space. Willis comments from his spaceship “Huh, Look! Textbook example of a vapor phase change thermostatically governed planet!”
Where I live cloud in winter means warming while cloud in summer means cooling. To avoid paradoxical (in the statistical sense) misinterpretation of misleadingly aggregated stats (pooling across key conditional lurking variables), better next extend the exploration to seasons. Also recommend (to anyone exploring further) constructing a battery of diagnostic multivariate scatterplot matrices to look for things like residual kinks & heteroskedasticity changes near 0 degrees C (due to phase change of water) and sensitive regional dependence of regression coefficients on start & end dates due to spatiotemporally nonrandom interannual variation (since the record length is only 10 years). Map animations (by time of year) of the sensitivity of regression coefficients could point to regions where standard mainstream assumptions about equator-pole transports fail catastrophically. Deepening insight via due diagnostics — i.e. learning from systematic patterns of regression model assumption failures by looking carefully at nonrandom residual patterns in every which way possible, always remaining vigilantly, lucidly, & consciously aware that regression coefficients depend conditionally on the absence of omitted key conditioning variables. Based on the looks I’ve had at these multivariate relations, I would not recommend naively trying to derive a single coefficient that applies globally at all times — careful diagnostics strongly counsel against this. Those who have the time & resources can explore every path to everywhere, but for those short on luxuries there are universally-constrained paths that afford more efficient & decisively-conclusive exploration.
“ 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. ”
*choke* *choke* … cough cough … can’t take a bite of that w/o a reaction … if this was meant as an IQ test (‘trap’) on the general readership here, you succeeded; I don’t think the graphic depicts quite what has been expressed …
.
Willis,
Interesting post; I especially like the graphics. One doubt comes to mind: The correlation is very strong, but does that necessarily imply causation? For example, in the eastern tropical pacific, it is clear that warmer water (El Nino) is associated with greater cloudiness (higher ocean surface temperature, more water vapor, more clouds), so your graphic shows strong ‘positive cloud feedback’ (suggesting more clouds warm the water in the eastern tropical Pacific), when it is pretty clear based on ENSO that the causation is the other way around (warming eastern tropical Pacific causes more clouds). I suspect that the same cause/effect uncertainty is associated with much of the correlation your graphic shows. Similar analyses have appeared in the literature (Lindzen, and then Lindzen and Choi, along with papers claiming to show the Lindzen & Lindzen and Choi correlation is not causal). The key seems to be an adequate lead/lag analysis of the correlation, but IIRC, even that is not always enough to give a clear answer.
Willis is over reaching with his research once again,(as was done with his volcanic study).
Therefore I say interesting but take it with a grain of salt.
And, as ever, we are waiting for you to be SPECIFIC. What EXACTLY is Willis ‘over reaching# with – could you clarify.
Or are you yet another idiot who comes here in order to demonstrate their unique ability to pollute a post with crap. Seriously, you follow Willis like a rash, PUT UP OR SHUT UP FFS.
(BE SPECIFIC. What EXACTLY..,)
tonyb says:
October 4, 2013 at 1:59 am
Upon further research, I find there are a couple of papers out there. One uses MODIS data, the other CERES. Both are paywalled, one here and the other here.
Both find different answers (one positive, one negative). They are much smaller than what I find. I suspect that this is because they’ve (wrongly in my estimation) used yearly averages rather than actual data. Don’t know why. I’ve checked my results, I stand by them (until errors are found, of course).
w.
clivebest says:
October 4, 2013 at 2:49 am
Of course I’ve used the seasonal data. Why would you want to average it and lose all of the actual changes?
What you call “seasonal” changes are changes due to the ground warming and cooling. This gives us the perfect way to see what actually happens to the clouds when the earth actually warms and cools.
And what we find is that as a global average, for every degree warmer that the earth gets, the downwelling radiation drops by about 3 W/m2. Now, that’s just facts. How you want to interpret that is up to you … but as the earth warms and cools, that’s what happens.
w.
I am a bit confused. I would have guessed that the areas positively correlated temperatures with cloudiness would be dominated by nighttime cloudiness, and areas of negatively correlated temperatures in areas dominated by daytime cloudiness (such as the tropics)
cd says:
October 4, 2013 at 8:42 am
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?
###############
See the appendix to the methods paper. Posting the spatial uncertainty in grids is on the to do list. For every update we generate over 300K files. plowing through those is quite a job, but
once I’m sure that is operating correctly we’ll move on to added more data.
########################################
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.
hmm. you dont understand the process. The temperature at any given location is expressed
as a deterministic function and a random component. read the appendix to the methods paper
and then read around in the kriging literature. the approach is standard cookbook. There are some splining approaches that could also be substituted.
Willis –
I can tell you right now that the map is wrong for where I live – the dead center of Mexico, in Mexico’s Central Valley. That area on your map is shown orange, indicating that clouds warm.
That is not true at all. Whatever you’ve done to derive that map, it doesn’t hold true here.
When clouds come the area gets about FIVE degrees C cooler.
Our climate here is VERY stable, throughout the year – maybe the best and most consistently flat climate in the world. The hottest to coolest high temps only range about 20°F(85°F to 65°F), summer to winter. And the reality is that when clouds come, it INVARIABLY cools off.
Speaking of clouds…
Decades ago, for reasons and purposes I really can’t discuss, I was looking into the vertical tranmissivity of the atmosphere with respect to a certain band of the infrared spectrum. In our problem, clear skies were okay, but clouds were regarded as complete blockage. We assembled the statistics for cloud cover and were quite surprised to discover that most of the Earth, on a time-averaged basis, had prevailing cloud cover. We were seeking cloud-free lines of sight from stations on the ground, and were narrowly constrained. The problem was so bad, we generally concluded that a system solution using that approach was the last resort.
I can’t recall whether the subject of worldwide cloud coverage statistics has been discussed, but if it hasn’t, it would be worthwhile.
People interested in this topic should take a good long look at the work of Roy Spencer, starting with his blog at http://www.drroyspencer.com. From there you can go on to his technical papers and/or his book “The Great Global Warming Blunder”.
Most of the mainstream climate models (at least at the time of AR4) took note of observations (made quite a while ago) that showed lessened cloud cover correlated with warmer conditions, then made the assumption that warming conditions caused reduced cloud cover. As a result, virtually all of the models used in AR4 (I have not had time to check AR5) show a positive cloud feedback effect.
Several years ago, NASA GISS scientists led by Andrew Lacis published an article in Science
http://www.sciencemag.org/content/330/6002/356.short
where they used the GISS climate models to simulate what would happen if all CO2 were removed from the atmosphere. The model produced a virtual runaway that led to a 90% reduction in atmospheric water vapor (from it condensing out of the cooling atmosphere) and also a 50% increase in low cloud cover so that over three-quarters of the world would always be clouded over. A desert planet perpetually shrouded in clouds – how plausible!
Anyway…
Since reducing cloud cover due to some other means would lead to warming (direction of causality going the other way), this assumption should be checked very carefully, as Roy has been advocating. I don’t know if Roy’s conclusions or inclinations are correct, but the bigger issue to me is that no one else in the climate establishment has really looked at this issue with the detailed attention it deserves.
“Since reducing cloud cover due to some other means would lead to warming (direction of causality going the other way), this assumption should be checked very carefully, as Roy has been advocating.”
Indeed this seems to have been happening. About half the warming from 1980 to 1999 can be attributed to a reduction in cloud cover during this period. After 1999 cloud cover then stabilized before increasing slightly. Combined with CO2 forcing this can explains both the rapid warming prior to 1999 and the consequent hiatus. As a result CO2 climate sensitivity is reduced. Euan Mearns and myself have a paper describing this effect which is under consideration.
Very interesting post. I’m sure there are many ways to look at this.
Ever since AR4’s admission of lousy understanding of clouds and the blithe statement that they were generated randomly in climate models, I have
a) thought of this as the achilles heel of the catastrophic warming hypothesis
b) wondered what weather conditions (including temperatures in the oceans) lead to consistent biases towards daytime or nighttime clouds.
Perhaps this dataset and some old-fashioned meteorology can answer (b)?
Didn’t know El Ninos/La Ninas originated in the Atlantic. Should this say “…the west side…”?
Just had to stop on this one. I see confusion.
—
Oh come on Willis, you were not setting on that buoy watching the clouds go by as it recorded! 😉 Also you do not feel the “increased LW radiation” from clouds passing overhead during the day as you seem to imply, thinking the total radiation goes up.
or Willis, try this…. the spikes you see are moments that the sun shines through gaps in the clouds. Depends on which radiometer you are looking at doesn’t it.
Or, you didn’t say, that is the ‘Thermal Irradiance’ plot, and even there, it is lowest when the ‘Solar Irradiance’ is at its highest (no clouds) when a cloud comes over the thermal irradiance does go up but solar irradiance drops much more than the thermal goes up. Sum the two isolated readings and the radiation still does drop overall under clouds during the day. No “warming”.
Could that not be the real case? A dattime cloud passes overhead and it immediately gets cooler. And I’ve never felt the warmth at night from a cloud passing overhead but I have noticed that it does immediately stop it from getting any cooler, maintaining any warmth that is left. Net LW drops to zero in that case. See ESRL SURFRAD sites for some real-time examples. Get the NWS radar of those sites and watch the radiation (SW down, LW down, LW up, NET LW at surface) as clouds come and go but it might take days to catch such an event in action.
So when it was said by Dr Burns:
“How do clouds cause warming during the day ?”
and you replied:
“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 ….”
That is false. They do not “warm”, they moderate or attenuate the even larger drop in solar radiation, that’s all, it’s called “cooling” and Dr. Burns was correct to question you there.
Steven Mosher
Thanks for your answer.
The second part of your answer, the deterministic component is what I meant by the “geomodel” where one assumes that the structural component can be expressed in terms of latitude + altitude. This is very primitive to say the least and is in itself a model. As for it being standard practice, it would not see the light of day in industry for the reason I’ve just given – it might be cookbook in this fraternity but sounds more like laziness than good practice.
Anyway thanks Steven for taking the time to answer my question I do appreciate it. Perhaps my lack of confidence in the second part of your answer has to do with the fact that I haven’t read the paper in some time – BTW I did read the supporting literature to the BEST study, unfortunately it left me with more questions than anything else.
Willis says:
“…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.”
You have noticed a difference within the last century that numerous people have also tried to explain. None of the explanations are even close to the mark. Arctic and Antarctic temperatures are controlled by entirely different physical processes. Whereas Antarctic temperature swings are a response to periodic long-term upwellings of warm bottom water, traceable as far back as the Pleistocene, there was no Arctic warming whatsoever until the turn of the twentieth century. Then, suddenly, warming started. It paused in mid-century for thirty years, then resumed, and is apparently still going strong. There was no increase of atmospheric carbon dioxide when the warming started and this rules out the greenhouse effect as a cause. It appears that a rearrangement of the North Atlantic current system around the turn of the century is responsible for this warming. The changed currents started to bring warm Gulf Stream water into the Arctic Ocean and thereby warmed it. This is why the Arctic today is the only place on earth that is still warming.The mid-century pause in warming can be attributed to a temporary return of the former pattern of current flow. It was not simply a cessation of warming but an actual cooling at the rate of 0.3 degrees per decade. And herein lies a warming: what has happened before can happen again. If this should recur it would be highly disruptive to plans for developing Arctic resources. All this, and more, can be found in my paper in E&E, volume 22, issue 8, pp. 1069 to 1093 (2010). As usual, climate scientists are too lazy to do their homework and consequently have no idea of what is happening in the Arctic.
Steve Mosher:
Sorry:
the structural component can be expressed in terms of latitude + altitude
should have read:
the structural component of a regionalised variable can be expressed in terms of latitude + altitude
As you seemed to have felt the need to suggest that I somehow misunderstood the rather basic principles behind kriging; which isn’t rocket science!
Steve Garcia on October 4, 2013 at 12:49 pm
Our climate here is VERY stable, throughout maybe the best and most consistently flat climate in the world. The hottest to coolest high temps only range about 20°F(85°F to 65°F), summer to winter.a
Most stable climate in the world? That’d be Singapore. Max 32C, min 22C …… a 10C range….. every day … all year round.
And yes, daytime clouds cool here too…. nightime? Not sure.
http://www.drroyspencer.com/wp-content/uploads/UAH_LT_1979_thru_September_2013_v5.6.png
Some of the issues that the analysis must address is what is changing during the time period, what is the normal base for top of the atmosphere radiation in the region in question, and proof of cause; what is the tail and what is the dog.
Lindzen and Choi’s limited their analysis to three month windows when there was a change in ocean temperature. i.e. Their analysis is a series of small intervals. The logic of that analysis methodology is three months is sufficiently short that other large changes in climate drivers will no affect their analysis results. Lindzen and Choi’s paper included a separate analysis using a lead model and then a lag model to provide support for their assertion that the planet’s response to an increase in temperature is to resist the forcing change. The analysis is however complicated as the increase in planetary cloud cover then causes cooling of the same region.
http://www-eaps.mit.edu/faculty/lindzen/236-Lindzen-Choi-2011.pdf
The following is Svensmark analysis of the polar anomaly which provides support for the assertion that the polar anomaly is caused by changes in planetary cloud cover. It is interesting that there are cycles of warming and cooling in the paleo record that have the same regional pattern of warming that was observed in the last 50 years.
http://arxiv.org/abs/physics/0612145v1
The Antarctic climate anomaly and galactic cosmic rays
Borehole temperatures in the ice sheets spanning the past 6000 years show Antarctica repeatedly warming when Greenland cooled, and vice versa (Fig. 1) [13, 14]. North-south oscillations of greater amplitude associated with Dansgaard-Oeschger events are evident in oxygenisotope data from the Wurm-Wisconsin glaciation[15]. The phenomenon has been called the polar see-saw [15, 16], but that implies a north-south symmetry that is absent. Greenland is better coupled to global temperatures than Antarctica is, and the fulcrum of the temperature swings is near the Antarctic Circle. A more apt term for the effect is the Antarctic climate anomaly.
Figure (2a) also shows that the polar warming effect of clouds is not symmetrical, being most pronounced beyond 75◦S. In the Arctic it does no more than offset the cooling effect, despite the fact that the Arctic is much cloudier than the Antarctic (Fig. (2b)). The main reason for the difference seems to be the exceptionally high albedo of Antarctica in the absence of clouds.
My take on clouds. I don’t think we need to know what clouds are doing globally. I think a general outcome measure (a simple metric that predicts complex global functioning) would be sufficient. Since heat going into or out of the ocean is an equatorial phenomenon, what condition the clouds are in related to Sunshine that hits straight on is more important than what clouds do everywhere. For clouds over oceans that get sunlight at quite an angle, you have diminishing returns so who cares. What clouds do between the 45th parallels in the Northern and Southern Hemisphere may be far more important in terms of land temperatures if the case is made (and I think it has been made) that ENSO conditions precede and predict land temperature trends.
So Willis, what would you get if you narrowed the band to just between the 45th parallels and then did a correlation with global land temperatures on a three month running average basis (JFM, FMA, MAM, etc)? And because it is always worthwhile to look where you haven’t, do the same thing with clouds outside that band. My hunch is that 1) clouds outside that band are way noisier and do not correlate well with global temperatures and 2) clouds inside that band correlate very well with global temperatures with some lag.
And to go further out on a limb, cloud feedbacks may be more model-able if you focus on where they are the most important. And to me, that is in the equatorial band.
Willis – Further to my earlier comment http://wattsupwiththat.com/2013/10/03/the-cloud-radiative-effect-cre/#comment-1435073 in which I said “You assume that clouds change in reaction to temperature. Reality may be that clouds change for other reasons.” and suggested this was why you obtained a wide range of values:-
I had a look at the seas around Antarctica where your pattern is strong. Fig.1 shows that clouds here have a cooling effect. Fig.2 shows increased cooling with higher temperature. The SST seasonal variation there is I think about 2 deg C, and the overall trend over your study period 2000-2010 would have been about zero, so to my mind this suggests that you may have found a seasonal effect (there is more cloud or more-cooling cloud there in summer) not a “feedback” in the IPCC sense.
The same might apply to other areas. In any case, (a) I doubt that surface temperature is the principal long term driver of cloud cover, especially over land, (b) I suggest that your “wide range of values” supports my view, and hence (c) I doubt that your study shows cloud feedback as you claim.
Arno Arak
I agree with your comments . I cannot comment on the entire Arctic , but the Canadian winter temperature departures from1961-1990 averages have dramatically showed cooling since 2010. This has been very noticeable in the Canadian High North or Arctic Tundra, Mountains and Fiords. Temperature departures that were 5-6 C above in 2010 were only 1.1C above in 2012/2013 winter. The Mackenzie District has also shown cooling. The winter departures for Canada as a whole dropped from 4.1C IN 2010 to1.6C in 2012/2013 winter. So Canada’s winters are cooling . Matter of fact the winters of the Northern Hemisphere have been cooling since 1998 and more clearly after 2007[using hadcrut3gl data] I see this continuing for the next 20-30 years
This slope of the trend line of net cloud radiative effect versus temperature is NOT the cloud feedback. The slope is due to an unknown combination of cloud feedback and time‐varying radiative forcing. You don’t know how much of the cloud change was due to radiative forcing. Cloud changes are partly a cause of temperature change, and partly an effect of temperature changes. Radiative changes resulting from temperature change (feedback) cannot be easily disentangled from those causing a temperature change (forcing).
The CERES satellite measures the sum of the radiative forcing and the feedback, where the feedback is lambda X dT . Lambda is the feedback parameter. Radiative forcing includes changes in cloud cover that was not caused by temperature changes. ENSO and PDO can change cloud cover.
Willis ignored the radiative forcing and assumed that changes in net CRE = lamba X dT
Willis calculates the global average radiation response as -2.9 W/m2/C.
The Planck response is -3.3 W/m2/C. This is defined in climate science as the no-feedback response. It means a one degree temperature increase would cause an extra 3.3 W/m2 emitted to space if clouds and water vapor do not change. Since the regression analysis gives only 2.9 W/m2/C cooling, this is positive feedback when compared to the Planck response. But the analysis is WRONG because it does not include the effects of the radiative forcing. The regression result of -2.9 W/m2/C tells us nothing about the cloud feedback.
The papers by Dr. Roy Spencer proves that ” the presence of time varying radiative
forcing in satellite radiative flux measurements corrupts the diagnosis of radiative feedback.”
See Spencer’s paper: http://www.mdpi.com/2072-4292/3/8/1603/pdf
willis,i do not know whether your hypothesis will stand up to scrutiny or not,but it is heartening to see someone so inquisitive as your self make such an undertaking to forward the understanding of the effect of clouds. it is something i would imagine most people would assume the climate science community would have payed special attention to,along with solar effects,yet appears to be a side issue to the outputs of models and the need to make alarming statements.
the owner of the sks site should take note,this is real sceptical science ,unlike the warmist propaganda on mr cooks misnomer.
the discussion ensuing after your submission really does show the level of expertise of contributors to this blog,and a genuine interest in “discovery”.
its just a pity the type of people that frequent this site are not involved in politics, intelligence,common sense and an insatiable appetite to understand is sadly lacking in todays politicians,hence the the global warming scare lingering on long past its sell by date.
Hi Brad,
I’m not familiar with Kirkland, I just know it’s “down there somewhere” 😉 Perhaps you’re not familiar with Oak Harbor. We’re well north and east of the Olympics. The convergence zone tends to be around Everett, as far as I know. But, there are probably many such zones. But my observations about the temp differences between the Skagit Valley and North Whidbey are extremely consistent.
Daughter and family lived in Oak Harbor, Navy.
We have extremes and can also flatline at one temp for days, usually in the mid 50’s.
Kirkland is just north of Bellevue.
Yes, that’s a known very loud dog that didn’t bark in the IPCC night, isn’t it?
AndyG55 says:
October 4, 2013 at 3:07 am
Andy, CO2 radiation is quite real, and has been measured many times. You can see actual measured amounts in the graph above, which is one day’s measurements of downwelling radiation from one of the TAO buoys. .
Unfortunately, to utilize it, you’d need someplace to reject the waste heat. A heat engine needs more than heat to run. It needs a heat difference, a cold place to reject the waste heat at the cold end of the heat engine. And that place has to be colder than where the radiation is coming from …
Unfortunately, where the radiation is coming from up in the atmosphere is almost always cooler than the surface, so you have no place to reject the heat. Result? Heat engine no workee, even though there is radiation. No place to reject the waste heat.
However, it could be done, you can harness the CO2 radiation to run a heat engine … just not on earth. Remember that the “CO2 radiation” goes both up and down. Half goes to space, and half goes back to earth. So you could certainly put a heat engine on a satellite with one side facing the earth (which radiates both up and down at about 240 W/m2 at TOA) and reject the heat on the other side of the satellite to the brutal cold of outer space. Result? Heat engine workee … same radiation, but now it can exhaust the waste heat.
Finally, could this heat engine running off of the thermal radiation from the earth’s atmosphere be “perpetual motion” as you claim?
Nope … like most everything else, it’s solar powered.
Regards,
w.
Nick Stokes says:
October 4, 2013 at 3:28 am
Nick, well spotted, and originally I thought the same. So I took the sea temperature dataset, and I replaced all of the -1.8°C values with NAs … didn’t make a visible difference. The plot above is the one with the ice (-1.8°C) replaced with NA’s.
Also, the Antarctic sea ice doesn’t get much north of about 65-70N … and there’s lots of blue and green north of that. In fact, most of the Southern Ocean has very strong negative feedback. The waters between Cape Horn and the Antarctic Peninsula never ice up, far too much pressure of wind and current … but it’s blue nonetheless.
Thanks,
w.
cd says:
October 4, 2013 at 3:34 am
Good question, cd. Any dataset that you use has its limitations. For this kind of an analysis, i start with complete datasets so I can understand the big picture … so I used interpolated values. I will of course repeat it with other datasets at some point, this is a first cut.
Because the question of so-called “cloud feedback” is defined as a feedback arising from the warming of the surface.
Regards,
w.
Coldish says:
October 4, 2013 at 5:58 am
Good question, Coldish. The colors represent the exact values next to them. So yes, in Figure 1, the zero line is halfway between yellow and orange.
w.
Theo Goodwin says:
October 4, 2013 at 6:41 am
I often start with the very simplest of analyses, in order to do exactly what you are saying. We can all see on my maps above the different conditions for the formation of clouds. We can see the different kinds of clouds. Not only that, but we can see their effects, both on average and when the system is warming/cooling …
My goodness, I’ve just done what you say you want. I’ve differentiated the kinds of clouds and cloud formation processes graphically, look at the maps … and now you are telling me I should do what I just did?
Theo, you’re letting your antipathy to me blind you to what I’ve done.
Best regards,
w.
Hollow clouds! The bind moggles. What might be the cause, and effects, of that?
Does that increased DWIR compensate for the blocked DWSW? All incident light warms, not just LW.
Willis Eschenbach says:
October 4, 2013 at 7:24 pm
I have no ill will of any kind toward you. In fact, I have described you as the hero for our time.
You misunderstood. I am criticizing the data set. I have no criticism of what you have done. I think what you have shown is of great interest to scientists and skeptics.
Steven Mosher says:
October 4, 2013 at 8:16 am
Thanks, Mosh. My next move, as I mentioned above, will be to swap out for the Best 1°x1° data and see what it does different.
Regards,
w.
Since clouds seem to have a significant effect on the Earths temperature, has anyone considered that the magnetic north pole is moving closer to the rotational pole may also be inducing an effect due to where the arriving ionizing radiation is deposited? In other words, is part of the problem the pole is moving away from North America be causing it to warm and other areas cool as the ionizing radiation creates high cloud cover in a different place?
“This is very primitive to say the least and is in itself a model. As for it being standard practice, it would not see the light of day in industry for the reason I’ve just given – it might be cookbook in this fraternity but sounds more like laziness than good practice.”
The test of course is in the data and not in your head. Latitude and altitude, done correctly, explains a large portion of the variance. There are of course other things one can add to the geo model
1. Distance from Coast. This works on small areas but for the entire globe it doesnt really work very well. We are working on a version where the distance from coast is used but it has more to do with the seasonal range.
2. Terrain slope and aspect. After a bunch of work it became clear that this too did not imp[rove the fit. I may revisit it.
3. Areas subject to boundary layer inversion. This is probably the most important thing to
solve as we move to higher resolutions. I have a couple of approaches proven in the literature.
4. The approach is in fact used in industry. The guys who use it say thank you.
The bottom line is that once you account for latitude and altitude there isnt much more you can do. Now for short periods of time when you have data like LST or other surface properties
Then you can improve the fit, but to do a long historical series you actual need surface characteristics that are relatively stable over time. Understand the fitting to latitude is not a simple regression and neither is the altitude fit.
You sound smart. get the code and improve it. Or do you own method and compare. I’d love a better method.
Willis,
cool let me know how that works.
Steven Mosher says:
October 4, 2013 at 8:21 pm
Downloading it now … 287 of 817 mb, 1 hr 33 min remaining …
w.
Steven Mosher says:
October 4, 2013 at 8:23 am
Oh, man, that looks great. Of course, I’ll only have to unlearn everything and start over in Rasterville … looks like it would be worth it, though. For the moment I’ll likely muddle through, and learn about raster objects on the side …
Many thanks,
w.
Stephen Rasey says:
October 4, 2013 at 8:31 am
Actually, no … it’s monthly data. Sorry for the confusion, I’ll edit the head post.
w.
Greg Goodman says:
October 4, 2013 at 10:42 am
The Thermostat Hypothesis, 2009.
Michael D Smith says:
October 4, 2013 at 10:45 am
It’s an awesome language. Steve McIntyre pushed me to learn it, and I’ve never regretted it.
Sorry for the confusion. It’s ten years of monthly data.
w.
Michael J. Dunn says:
October 4, 2013 at 12:50 pm
There’s data in the GISSE model paper. They say 69%, with a reference to the ISCCP.
Curiously, their own GISSE model only shows 59% cloud coverage …
w.
wayne says:
October 4, 2013 at 1:32 pm
Didn’t say I was. I said I knew what the record showed.
The total radiation does go up, but generally not enough to notice during the day. It’s much more noticeable at night, particularly a calm night in the winter.
Say what? The record is not of shortwave (solar) radiation. It is of longwave (thermal) radiation. The idea that someone is looking at the wrong radiometer is ludicrous. You’re just throwing mud at the wall and hoping something sticks.
w.
70 % of the planet is covered by water, its heat capacity is ?? how many times the atmosphere ?,
our air temperature apparatus/location has been shown to be questionable even in developed countries.
We need more ocean data.
While the latest cycle plays out.
Ken Gregory says:
October 4, 2013 at 5:40 pm
HAIIEE!! … you’re right. To isolate the effect of temperature, I need to control for the varying solar radiation, which also changes the net CRE. Bad Willis, no cookies … but that’s the nature of science. Thanks, Ken.
Well … live and learn. I’ll go do that, it’ll likely take a bit, I need to re-think this and re-write my functions. In the meantime … I’ll put a note up at the top of the head post.
w.
@ Steve Garcia
Of course, when a cloud passes over during the day it has a cooling effect. Willis is attempting to discern the net total consequence over time of that effect against the effect of cloud cover especially at night having a warming effect. I would expect in central Mexico you have pretty low humidity and therefore normally a pretty weak GHE (greenhouse effect) except when there’s cloud cover in which case you have a strong GHE. As Willis covered, clouds are actual grey bodies so they don’t just absorb and re-emit certain bands of IR but absorb and then emit based on there temperature. So, over the course of let’s say a year which effect would be greater? Were you cooled more by clouds during the day or warmed more by clouds during the night?
If you needed to estimate the GHE you would most likely take clouds and humidity into account but not CO2 concentration.
http://www.asterism.org/tutorials/tut37%20Radiative%20Cooling.pdf
When the modellers get hold of this they will obviously have to code in Cloud Heat Island (CHI). Which will allow for a rising trend in temperatures if they do their job right!
(quoting) October 4, 2013 at 7:02 pm
“Remember that the “CO2 radiation” goes both up and down. Half goes to space, and half goes back to earth.”
Me thinks the above is a false statement. Radiation of thermal (IR) energy from atmospheric gases goes in all direction; up, down, sideways and all which ways. And thus a very small percentage of said radiation is directed vertically (down) toward the earth itself. Non-vertical radiation to the surface is, I believe, “line-of-sight”. An infrared picture of a warm object proves that to be a fact.
Anyway, ……
Clouds, fog and mists are all forms of atmospheric water vapor (humidity) which have collected into larger “droplets” of water and are visible to the naked eye, …. and are the same as humidity which can not be seen with the naked eye. And that is because of the density of the larger “droplets” of water and the fact that any source of visible light that strikes them will be absorbed more readily and/or reflected away from them more easily.
H2O vapor (humidity), clouds, fogs and mists all have an effect on thermal energy transmissions between the earth’s surface and space. And the resulting “effect” they cause is dependent upon the time of day, …… time of season …… and the temperature difference between them and the earth’s surface. Thermal energy always transfers from “hot” to “cold” via radiation, conduction and convection.
Barry Cullen says:
October 4, 2013 at 8:31 am
“”Getting trapped inside a cumulus cloud is not fun. That’s where spin practice is put to good use! 8-))””
Quite! Problem was I did my spin training in a Chipmunk and I was flying a Cherokee 140 at the time. They don’t spin so well. My guardian angel was looking out for me. 🙂
Point I wanted to make was that that cloud was some 8,000 feet deep and it had a hole in it I could fly around at my leisure. How many clouds have holes in them? How is it possible to calculate any radiation effects through such a cloud? Lapse rate? etc. etc.?
I wanted to do some reading about CERES, but found NASA sites closed “due to” the shutdown.
Some good geeky stuff can be found here:
http://www.cgd.ucar.edu/cas/catalog/satellite/ceres/
In particular, there is a FAQ on the ongoing efforts of the CERES team to identify and fix problems in the data.
Cheers,
Chris
Hi All,
I appreciate the expertise and objectivity, here.
Since AR4 admitted a very poor understanding of clouds, I have been curious about them ever since. In particular, what is the significance of day/night bias in cloud cover? Phrased differently…
* do we know what causes a particular part of the globe to have clouds in a predominantly cooling role (day), warming role (night), or neither (balanced)?
* do we know the spatial and seasonal extent of such phenomena?
Thanks,
Chris
credit all round. we have gone from a post that seemed good enough to go forward as a paper to one that has been falsified in just a few hours.
This shows the benefits of submitting ideas to the blogosphere as wills has done then accepting the verdict of his peers that he needs to go back to the drawing board. Would that all science was as quick and ruthless.
tonyb
climatereason says:
October 5, 2013 at 3:00 pm
Thanks for that, climatereason. Indeed, I think in future all of the scientific discussions will move to the web. WUWT is just ahead of the curve. And while I hate like poison to be wrong, that’s the beauty of WUWT—there are no favorites, no sacred cows. Put it out and take your chances. I think it’s great because it keeps me from haring off on blind trails.
In any case, I’ve redone the analysis controlling for solar radiation. It’s online here.
Best to all, special thanks to Ken Gregory for finding my error,
w.
We already know that there is srong negative feedback from clouds to a warming; this is no surprise and can be easily seen in the OLR/Temperature graphs; this can be up to 80W/m2 on 6-month timescales. It would be amazing if it were otherwise. What we need to know is about the overall feedbacks from an increase in CO2 in the atmosphere.