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.
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.