The Cloud Radiative Effect (CRE)

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.